Special Issue on Multidisciplinary Sciences and Engineering 2020-21

Together with the intensity of development in the field of technology of unmanned platforms and their effective use for solving various tasks of peacetime and war, the requirements for the training of the operator managing the platform also increase. This fully applies to personnel providing the flight of manned means. Nevertheless, there are significant differences in the requirement of operator preparedness for an unmanned platform. This is, first of all, control in conditions of remoteness from an unmanned platform, orientation on the display of the control panel, delays in the passage of information, and possibly a complete loss of communication. In such conditions, the requirements for the reaction time of the operator increase, he must also have the ability to anticipate the development of the situation, be able to work with available equipment for a long time. These and several other criteria determine the general criterion of operator productivity, which is introduced in the work. The productivity criterion is a linear convolution function of particular criteria with some weighting factors, the exact values of which are unknown. A detailed analysis of particular criteria made it possible to establish their inconsistency and heterogeneity. The article proposes an approach that allows us to eliminate the inconsistency of local criteria by separately calculating weight coefficients for each part based on the hierarchy analysis method. The basic properties of the proposed approach are also given; modeling confirms the correctness of the solutions. This approach can be useful in the certification of operators of various fields of activity.

Insect robots are always amazed by humans due to their ability to fly using a wing flapping mechanism. The butterfly robot was designed in this research based on aerodynamics and aeroelastic especially for designing a flapping mechanism due to its complexity. A butterfly wing structure was designed by considering aerodynamics forces based on assumptions. Aerodynamic equations were derived in order to obtain lift and thrust forces that acted on a small wing section. The wing was assumed to be in the Quasi-steady state when it was analyzed based on the thin airfoil theorem. Airflow was simulated in order to obtain air pressure and vertexes acting on the wing surface when it swings in the still air. By integrating the wing section’s lift force for a flapping cycle motion trajectory, the average lift force was obtained. The robot wing structure was designed based on the average lift. The real butterfly wing was used as the guideline for designing the robot wing. Each wing was fabricated from a laminar plastic sheet. Carbon fiber robs were used as an internal reinforced support structures for wing frames. The reinforced wing achieved the wing’s rigidity and was considered as a thin airfoil. The flapping mechanism was designed by using two separated servo motors because of its flexibility and performance. This mechanism enables the robot’s rotation without an extra actuator. The butterfly robot body was manufactured from the 3D printer using PLA material. The experiments were conducted to identify the robot performance. The designed butterfly robot can take off from the support platform and fly up to a certain height.

The effects of Covid-19 severely affected the Thai higher education model. Therefore, there are three significant objectives in this research: (1) to cluster the mindsets and attitudes toward self-regulated learning styles of undergraduate students at the University of Phayao. (2) to construct a predictive model for recommending an appropriate student learning clusters. (3) to evaluate the predictive model that has been constructed. Samples collected a compilation of 472 student satisfaction with questionnaires from three schools, with seven disciplines at the University of Phayao, Thailand. Research tools consisted of statistical and machine learning techniques as follows: frequency, percentage, average, standard deviation, k-means clustering, decision tree techniques, cross-validation methods, confusion matrix performance, accuracy, precision, and recall measurement. Researcher found that the k-means model with the highest accuracy is the decision tree model that was classified into three clusters by dividing the model testing into the leave-one-out cross-validation method with a depth of seven levels of the decision tree model and an accuracy of 98.73%. From the results and studies, it can be concluded that the developed model is effective and reasonable to be further developed as an application for further organizational development.

Topic classification is a crucial task where knowledge categories exist within hierarchical information systems designed to facilitate knowledge search and discovery. An application of topic classification is article (e.g., journal/conference paper) classification which is very useful for online submission systems. In fact, numerous online journals/magazine submission systems usually receive thousands of article submissions or even more for each month. This leads to a huge amount of time-consumption of editors to process and categorize the submissions aiming to look for and assign appropriate reviewers to the submitted articles. In this study, we propose an approach based on natural language processing techniques and machine learning algorithms (both classic machine learning and deep learning) to automatic classify the topics of articles in an online submission system. We show by promising performance collected from prediction tasks to present that the proposed method is a potential approach for applying to the real system.

Cybersecurity is growing exponentially day by day in both the public and private sectors. This growth also comes with a new and dynamic cyber-threats risk that causes both sectors’ performance to halt. These sectors must update their cybersecurity measures and must understand the capability and maturity of their organization’s cybersecurity preparedness. Cybersecurity maturity models are widely used to measure how ready an organization is when it comes to cybersecurity. The main aim of this article is to conduct a comprehensive review of the current cybersecurity capability maturity models using a systematic review of published articles from 2011 to 2019. A comparative study was conducted based on Hal- vorsen and Conradi’s taxonomy. The review indicated almost all the cybersecurity maturity model consists of similar elements like maturity levels and processes but significantly lacks the validation process, it was observed each of the models were predominantly designed for a specific purpose and also for different organization size and application domain.

The difficulty of learning the underlying structure between processes is a common task found throughout the sciences, however not much work is dedicated towards this problem. In this paper, we attempt to use the language of structure learning to address learning the dynamic influence network between partially observable processes represented as dynamic Bayesian networks. The significance of learning an influence network is to promote knowledge discovery and improve on density estimation in the temporal space. We learn the influence network, defined by this paper, by learning the optimal structure for each process first, and thereafter apply redefined structure learning algorithms for temporal models. Our procedure builds on the language of probabilistic graphical model representation and learning. This paper provides the following contributions: we (a) provide a definition of influence between stochastic processes represented by dynamic Bayesian networks; (b) expand on the conventional structure learning literature by providing a structure score and learning procedure for temporal models; and (c) introduce the notion of a structural assemble which is used to associate two stochastic processes represented by dynamic Bayesian networks.

Dengue fever (DF) is a tropical disease spread by mosquitoes of the Aedes type. Therefore, a DF outbreak needs to be predicted to minimize the spread and death caused by it. The spread of dengue fever is a spatial problem. In this paper, we adopted the Multi Linear Perceptron (MLP) to solve the spatial problem, and we called it a spatial multi-layer perceptron model (Spatial MLP). In this proposed model, we consider two types of input neurons in the Spatial MLP, a region and the neighbourhood of that region. The spatial inputs dynamically change to the region. Additionally, the neighbourhood numbers of a region are also varied. So, the spatial inputs are changed in terms of the number of inputs and the neighbourhoods. As a result, the proposed model is outperformed the traditional MLP since it can adapt to the neighbourhoods. We can conclude the spatial MLP model can manage the information and predict the dengue fever outbreak in Surabaya

Machine learning classifiers provide many promising solutions for data classification in different disciplines. However, data classification at run time is still a very challenging task for real-time applications. Acceleration of machine-learning hardware solutions is needed to meet the requirements of real-time applications. This paper proposes a new implementation of a machine learning classifier on Field Programmable Gate Arrays (FPGA). The proposed implementation utilizes the MicroBlaze soft-core processor on FPGA and uses the Advanced eXtensible Interface (AXI) bus to integrate the MicroBlaze with hardware peripherals. Experimental results shows that hardware-software co-design is a promising solution as it saves silicon area and provides a flexible configuration of decision tree algorithms at run time.

In this paper we design a large scale reconfigurable digital bio-inspired computing model. We consider the reconfigurable and event driven parameters in the developed field-programmable neuromorphic computing system. The various Intellectual Property (IP) cores are developed for the modules such as Block RAM, Differential Clock, Floating Point, and First In First Out (FIFO) for the design of the neuron model in Xilinx ISE, with exploration of register transfer logic (RTL) and hardware synthesis using Verilog code. The architecture for design at device level offers the best possible design tradeoff for specific processor architectures and development choices. In this paper we perform algorithmic design of a large scale reconfigurable logical bio-inspired computing model. The proposed algorithm is implemented on Field Programmable Gate Array (FPGA) to develop a neuron model to be utilized in neuromorphic computing system.

Machine learning has been dramatically advanced over several decades, from theory context to a general business and technology implementation. Especially in healthcare research, it is obvious to perceive the scrutinizing implementation of machine learning to warranty the rewarded benefits in early disease detection and service recommendation. Many practitioners and researchers have eventually recognized no absolute winner approach to all kinds of data. Even when implicit, the learning algorithms rely on learning parameters, hyperparameters tuning to find the best values for these coefficients that optimize a particular evaluation metric. Consequently, machine learning is complicated and should not rely on one single model since the correct diagnosis can be controversial in a particular circumstance. Hence, an effective workflow should effortlessly incorporate a diversity of learning models and select the best candidate for a particular input data. In addressing the mentioned problem, the authors present processes that interpret the most appropriate learning models for each of the different clinical datasets as the foundation of developing and recommending diagnostic procedures. The whole process works as (i) automatic hyperparameters tuning for picking the most appropriate learning approach, and (ii) mobile application is developed to support clinical practices. A high F1-measurement has been achieved up to 1.0. Numerous experiments have been investigated on eight real-world datasets, applying several machine learning models, including a non-parameter approach, parameter model, bagging, and boosting techniques.

A distribution company in Mexico covers the travel expenses for 21 sales representatives. Currently, the routes they follow are not established clearly, which can lead to high costs in this subject. A reduction of such cost is sought after, by optimizing the routes for each one of them. The following research finds an improvement on the routes for the sales representative of a distribution company in Mexico. It was done by using the Traveling Salesman Problem with Hotel Selection or Base selections via a Simulated Annealing algorithm. The results show an improvement in a reasonable timeframe by using the Simulated Annealing. It also shows that the maximum process time was of 156.63 minutes, and the least amount of improvement was 24.44% over the current route selection. Applying this model will be beneficial for the company as the company is trying to reduce costs related to the sales representatives such as; fuel cost, hotel cost, and travel expenses.

ECG analysis is commonly used by medical practitioners and cardiologists for monitoring cardiac health. A high-performance automatic ECG classification system is a challenging area because there is difficulty in detecting and clustering various waveforms in the signal, especially in the manual analysis of electrocardiogram (ECG) signals. In this paper, an accurate (ECG) classification and monitoring system are proposed using the implementation of 1D Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM). The learned features are captured from the CNN model, and then fed to the LSTM model. No handcraft features are required for the model for the ECG classification. The result of the CNN-LSTM model has demonstrated superior performance than several state-of-the-arts that cited in the result section. The proposed models are evaluated on MIT-BIH arrhythmia and PTB Diagnostics datasets. Based on the obtained results, the CNN-LSTM method can improve the accuracy rate, such that 98.1 % and 98.66 % on Myocardial Infarction (MI) and arrhythmia classification, respectively.

This study primarily aims to identify the factors that helped emo kids in 2010 move through the emo-identity discrimination and be able to obtain a certain level of achievement. Facebook is the social network that allows users to track friends’ posts back over 10 years. Content analysis was conducted by using two coders to rate 1,432 Facebook posts published during 2010 until 2019, based on the variables: (1) emo-related content, (2) emotion expression, (3) being a part of fandom, (4) the group of fandom, (5) friend(s) and (6) family appearance. The results from logistic regression analysis reveal that past emo kids’ Facebook posts are increasing in the contents about friends, family, and fan objects over time. However, the number of posts with emo music or emo fan object was reduced. Having social support and belonging to social group, like fandom, might help past emo kids overcome the hard time that they had got prejudiced. Future studies should develop a model or existing theory to explain the complexity of individuals’ overlapping identities blended in social networking profiles.

Storage-Class Memory (SCM) like OptaneTM has advanced as a fast storage medium, and conventional memory management systems designed for the hard disk storage need to be reconsidered. In this article, we revisit the memory management system that adopts SCM as the underlying storage medium and discuss the issues in two layers: file caching and virtual memory paging. Our first observation shows that file caching in the SCM storage is profitable only if the cached data is referenced more than once, which is different from the file caching in hard disks, where a single hit is also beneficial. Our second observation in virtual memory paging shows that the page size in the SCM storage is sensitive to the memory system performance due to the influence of memory address translation and storage access cost. Our simulation studies show that the performance of paging systems can be improved by adjusting the page size appropriately considering application characteristics, storage types, and available memory capacities. However, the page size will not be a significant issue in mobile platforms like Android, where applications are killed before the memory space is exhausted, making situations simpler. We expect that the analysis shown in this article will be useful in configuring file caches and paging systems with the emerging SCM storage.

Brain tumors are a type of tumor with a high mortality rate. Since multifocal-looking tumors in the brain can resemble multicentric gliomas or gliomatosis, accurate detection of the tumor is required during the treatment process. The similarity of neurological and radiological findings also complicates the classification of these tumors. Fast and accurate classification is important for brain tumors. Computer aided diagnostic systems and deep neural network architectures can be used in the diagnosis of multicentric gliomas and multiple lesions. In this study, the Deep Neural Network classification model with Synthetic Minority Over-sampling Technique pre-processing was used on the Visually Accessible Rembrandt Images dataset. The proposed model for the classification of brain tumors consists of 1319 trainable parameters and the proposed method has achieved 95.0% accuracy rate. Precision, Recall, F1-measure values are 95.4%, 95.0% and 94.9% respectively. The proposed decision support system can be used to give an idea to doctors in the detection of glioma type tumors.

Due to high failure rates many students end up spending unnecessary years struggling to qualify and subsequently accumulate unnecessary debt. In this paper, our principal contribution is to provide an expert system that statistically predicts the success of a first year student in an undergraduate Science programme given only academic merit in their subject matter. Over the past decades, much work has been done in the field of predicting student success in first year computer science and in other first year courses. Historically, other authors focused on using linear statistical models to predict student success. These models had limitations as the prediction was designed for inference as compared to machine learning techniques. This paper presents an approach of using the naïve Bayes classifier, support vector machines and decision trees as models that can be used to predict the completion of an undergraduate science degree. This was done by firstly training the classifiers and then testing them. The support vector machine achieved the best accuracy (87%) in predicting the completion of a science degree based only on first year marks, this was followed by the naïve Bayes model (86.36%) and the decision tree (65.62%) came last.

The family can be referred to as a basic nucleus of society, where it must be fully formed as a group and guarantee the safety and development of its members. Increasing social inequality affects society and also the families that comprise it. It is a study with a quantitative approach, with a non-experimental, descriptive and cross-sectional design. The population consisted of 240 heads of families who go to 12 Popular Dining Rooms of a Vulnerable Area of Collique. The data collection technique was the survey-interview and the instrument used was the RFT 5-33 questionnaire of 5 dimensions and 33 items. The total family risk in families who go to Popular Dining Rooms of a Vulnerable Area of Collique in Comas, it is presented as follows, 172 participants representing 72% are threatened families; 41 participants representing 17% are families with low risk and 27 participants representing 11% are families with high risk. Regarding dimensions, threatened families predominated in all, in psycho-affective conditions with 94%, in health services and practices with 91%, in housing and neighborhood conditions with 62%, in socioeconomic situation with 85% and in child management with 85%. The total family risk that predominated is threatened families, followed by families with high risk and families with low risk. Regarding the dimensions of the main variable, threatened families predominate in all of them. The dimension with the highest high-risk value is housing and neighborhood conditions.

The Health-related quality of life corresponds to the perception that people have of their level of well-being, considering aspects of their lives and their impact on their state of health. The studies were initially carried out in a population that presented both acute and chronic pathologies and covered practically all areas of medical specialties; subsequently, the interest focused on studying health-related quality of life in the general population and then in the population of children and adolescents. That is why, in this research work, the evaluation of the adolescent to prevent future problems was raised; to carry out the evaluation, the questionnaire called KIDSCREEN 52 was used, which consists of 52 closed questions divided into 10 dimensions, providing an idea of how the adolescent feels. The following results were obtained: The Health-related quality of life in adolescents of an Educational Institution of Ventanilla, where 430 students representing 59.1% have high quality of life and 298 students representing 40.9% have medium quality of life; according to its dimensions, adolescents from the Educational Institution of Ventanilla, its most affected dimension was economic resources, with 64 students representing 8.8% having a low level. In the relationship with parents’ dimension, it appreciates that 3.4% of students do not have a good family atmosphere since they do not live with their parents, but with uncles and/or grandparents.

Social skills and resilience are very important aspects for mental health; therefore, it is necessary to take into account the positive contribution in the development of adolescents, they are in a vulnerable stage, adapting to physical, mental, emotional, etc. changes. Adolescents, not knowing how to handle the difficulties that may arise, if they do not have the ability to face it and show their positive qualities, can easily fall into depression, student desertion, early pregnancy, addictions to toxic substances (alcohol, tobacco and drugs). That is why in this study to measure social skills, the Elena Gismero Scale of Social Skills questionnaire data collection instrument was used, it has 33 items, 28 refer to the lack of assertion or social skills deficit, 5 of them refers to a positive sense. For resilience, the Connor-Davidson Resilience Scale will be used, it has 25 items. The results obtained with respect to social skills show that in the dimension positive interactions with the opposite sex shows the low level 14.3%, which is equivalent to 45 students, they are presenting difficulties in relating to the opposite sex, regarding resilience in the dimension of spirituality, the low level of 21.7%, indicates that 65 students do not have positive attitudes to fulfill their purposes. The minimum age was 11 years, the maximum age was 17 years, the mean being 13.39 in terms of sex, males predominate with a number of 154 students and 146 females.

Piezoelectric energy harvesters (PEH) can be used in many areas of application, including human walking, railways, pavements and bridges. Piezoelectric energy harvesters are currently based on two types of external forces, namely pressure load and mechanical oscillation or vibration. A vibration energy harvesting (VEH) is a mechanical oscillation in a piezoelectric energy harvester that harvested electric energy. In the market, there is available energy harvesting device in good electric energy harvesting and very sensitivity. However, the price is too high and the fabrication process is too complex. Furthermore, one of the aimed of the research is to install the energy harvesting device at rotary compressor machine which has noise vibration frequency at 1 kHz to 10 kHz. This paper presented a cavity structure-based flexible piezoelectric vibration energy harvester (FPVEH) based on an IDE circuit for low-frequency vibration applications. A cavity structure (IDE circuity) combine with the flexible circuit (polyimide) and flexible membrane (polyvinylidene fluoride, PVDF) will increase the electric energy harvesting and sensitivity of the device. Therefore, the four designs (Design A to D) are used to investigate the effect of the electrode finger width and the gap between the electrode fingers (to investigate the cavity structure applying in the design). All designs have been characterized by FEA simulation using COMSOL Multiphysics 5.0 and experimental work using a sieve shaker vibration machine. A sieve shaker machine is worked as vibration frequency calibrator. However, the sieve machine can operate at 5 kHz and 7 kHz. Since these two vibration frequencies are in targeted vibration frequency. It is used as vibration frequency calibrator in this experimental work. The results from the FEA simulation and experimental work show the Design D has the highest electric energy harvesting compare to other designs. It has electric energy harvesting at 27.3 V for 1 minutes period. Design D has a wide electrode finger width and the wide gap between electrodes compare to other designs. The vibration frequency was also given the impact to energy harvesting whereby the vibration frequency at 5 kHz has the highest electric energy harvesting compare to vibration frequency at 7 kHz.

Technology is advancing faster today than ever before with evidence of its impact in all facets of our lives. With the spread of the novel COVID-19 pandemic across the world, schools were closed as part of lockdown measures to contain the virus thereby disrupting academic curricula. Academic institutions leveraged ICTs to virtually engage students and teachers. Technology adoption will become a new reality for teaching and learning processes. However, the choice of adopting and not adopting this technology is based on an individual’s decision on the benefits or risks in using this technology. The objective of this study is to find topical and relevant studies that have been conducted on technology adoption in education. A systematic literature review was adopted to classify and evaluate articles that fits pre-specified selection criteria using Google Scholar and IEEE databases. 132 papers were found to match the search criteria and filtered to 17 articles using applied exclusion criteria. Current research highlights the trends in technology adoption and provide empirical evidence of applications that have been used to implement technology in educational settings. This research also adds to new literature on COVID-19 in relation to its effect on academic curriculum across the world. Future research will investigate areas that can be expanded and improved on to leverage the benefits of ICTs to limit the adverse impact on the performance of teachers and students in the event of a disruption to academic activities.

This research sought to measure some socio-political indicators using millions of opinionated messages from social network sourced big data. Thus, and using an enhanced mixed method for sentiment analysis and a fusion model algorithm to infer topics from short text, this study attempted to demonstrate the value of computational approaches in measuring some phenomena in the real social world and quantifying public opinion fluctuations in response to certain socio-political issues. The validity of the experimental results was examined by comparing them with data obtained from representative surveys, thus providing a better understanding of the relationships between online and offline opinion dynamics. This contribution is intended to be multidisciplinary, both useful for policymakers and opinion analysts to explore public trends and to inquire into socio-political issues.

In the present paper, a universal approach for determining the maximum flow problem in directed graph for solving different problems is applied. It can be considered as a heuristic and it can be used as a denomination for analyzing an arbitrary system with a mathematical description as a directed graph. The physical nature of the flows passing through the arcs of the system considered can be energy, information, transportation, or material. Then the studied system will be power, communication, transport, or manufacturing, respectively. In the present article five types of maximum flow problems are considered. Each of them is solved analytically by the universal heuristic method. The common between these problems is expressed in the fact that its behavior can be presented with the same mathematical model in the form of a directed graph including one initial (pending) vertex and one final (blocked) vertex, respectively. These vertexes are called either real (if they exist) or fictive (if they are introduced additionally) source and receiver depending on the topology of the associated directed graph’s model. The final solution obtained with this approach for Problem 1 is compared with the similar one found through Ford-Fulkerson’s, Edmonds-Karp’s and Dinic’s algorithms and it has been shown to be better than those determined by them.

Comparing to other types of vehicle accidents, fatality rate of tipped rollover accidents shows significant number. Thus, tripped rollover prevention systems are important in order to keep driver safe. In other hands, different rollover indices are defined to handle the risk. The variable unknown parameters of each index, for instance, current load of the vehicle or center of gravity, are considered as a difficulty. In this work, the recurrent neural networks, which are designed to work on sequential data in order to provide data estimation without additional estimation algorithm, are investigated in purpose to estimate the tripped and untripped rollover index. The vehicle simulation software with industrial standard CarSim is applied to validate the result. The Tanh recurrent neural network is stated in the result to be the most accurate tripped rollover index estimator for the uncertain parameters, for example, sprung mass and the height of the center of gravity. The suitable input features for tripped and untripped rollover index and neural network structure are verified. To prevent and provide warning of rollover, an advance future prediction can also be designed for the future tripped and untripped rollover prediction.

One of the biggest business problems of marketers, is to optimize the return on investments of direct Marketing campaign. This main purpose which can only be ensured by targeting the appropriate customer. The main challenge faced by companies when advertising, is to configure properly a campaign, by choosing the right target, so A high user acceptance rate is ensured to advertisements. However, when dealing with an important size of data, the important specification to consider is the combinatorial aspect of the problem and the limitation of the approach based on mathematical programming methods. In this work, and considering the optimization of targeting offers as an of NP-hard problems, we concluded that the use of a meta heuristic algorithm is more suitable to use a classical (exact) method. We choice to use an improved bat Algorithm hybridized with Genetic Algorithm. The results of computational experiments confirmed that the proposed algorithm gives competitive results.

Wind energy is emerged rapidly as the most important and viable sustainable power source due to the mature technology and wide availability. The integration of wind energy sources and utilization of nonlinear loads having different characteristics results in several challenges in a distribution system. One of which is power quality issue .Hence, in this work, the impact of power quality issues in a distribution system integrated with the wind generation system is studied in the uses of a shunt active power filter (SAPF) and presence of non-linear load conditions. The realization of SAPF is carried out using active power component theory (APCT) for reference current extraction, proportional-integral (PI) controller, and fuzzy logic controller (FLC) for dc-link voltage regulation and a basic hysteresis band current controller method for the extraction of switching pulse for operational the inverter. To enhance power quality in the proposed system, a modified fuzzy logic-controlled (MFLC) is developed to improve the transient performance of shunt APF. The proposed model is developed in MATLAB and results are given so as to show the presentation of the performance of this compensation technique in terms of mitigation of system harmonics, reactive power compensation, and enhancing the power factor.

Learning styles studies have been discussed widely in academic field. It can be considered as a factor that contributes to the achievement of the students in their learning because everyone has a unique learning style when learning. In this paper, we aimed to examine the association of learning styles and attitude towards academic achievement. By identifying the student’s learning styles preference, it will offer benefits to the student and instructor by improving learning achievement. Sample data were collected among 328 pre-university science and engineering students using cross-sectional survey. Attitudes Towards Mathematics Inventory (ATMI) and Index of Learning Style© (ILS) were used as the instruments to collect data from the respondents. Data were analysed using Stata statistical tool to examine the result. Result showed that there was no association of learning styles and attitude towards academic achievement in this study.

A grid is a system that can manage and organize services and resources that spread amongst different control domains, employ interfaces and protocols, and offer a high quality of services. The integration of Multi-Agent Systems (MAS) with a grid environment significantly affects grid performance. MAS is considered a suitable solution for open systems that modify frequently. Grid offers a wide range of resources for its users, and some of these resources might not be used or utilized for some time before any new jobs come to the grid for processing. Usually, these resources are accumulated in massive data centers to cater to the grid users’ growing demand. This accumulated will lead to consuming a considerable amount of electricity for their operation. The ever-increasing usage of grid computing has led to an increase in electrical energy usage by massive servers in their data centers. In this paper, we have proposed an automated system composed of modern agents that can be used the power wastage resulted from inactive servers inside the data centers for a specific amount of time. The proposed technique depends on switching inactive virtual and physical machines to lower power positions (Sleep/Wakeup or switched off) while still preserving customers’ performance requirements. The Automated system has been tested and evaluated using the Jade tools. The results show that the newly proposed method can reduce power wastage for inactive grid resources.

Text classification is considered one of the most fundamental and essential problems that deal with automatically classifying textual resources into pre-defined categories. Numerous algorithms, datasets, and evaluation measurements have been proposed to address the task. Within the era of information redundancy, it is challenging and time-consuming to engineering a sizable amount of data in multi-languages manually. However, it is time-consuming to consider all words in a text, but rather several key tokens. In this work, the authors proposed an effective method to classify Vietnamese texts leveraging the TextRank algorithm and Jaccard similarity coefficient. TextRank ranks words and sentences according to their contribution value and extracts the most representative keywords. First, we collected textual sources from a wide range of Vietnamese news websites. We then applied data preprocessing, extracted keywords by TextRank algorithm, measured similarity score by Jaccard distance and predicted categories. The authors have conducted numerous experiments, and the proposed method has achieved an accuracy of 90.07% on real-world datasets. We have proved that it is entirely applicable in practice.

The article describes a way to increase the efficiency of an abrasive tool by its impregnation in an aqueous solution of chromium diiodide. The device scheme for impregnating the instrument is proposed. Research results of durability of tools impregnated with chromium diiodide and structural condition of material of a surface layer of details after processing are resulted. Within of the researches presented in the article the grinding processing a shafts made from various iron-carbon steels with prepared abrasive tools was conducted. As a result of the machining the scattering fields of the batch sizes of the parts within the grinding wheel resistance were determined. This data for processing with standard grinding wheels and impregnated by chromium diiodide were done. Using data from controlled grinding of workpieces, approximate values of the K? coefficient were determined depending on the diameter of the workpiece surface, the required size accuracy of part and the method application of the surfactants. It is shown that regardless of the material being processed, the use of a grinding wheel impregnated with chromium diiodide leads to a reduction in the specific value of the accumulated deformation energy Espec. Taking into account these results it is possible to predict up to twofold reduction of the cost of machining with the abrasive tool of grinding parts, which will reduce the cost of their manufacture by 20-25%. The developed process of impregnating grinding wheels with chromium diiodide can be used without large economic costs in many machine-building company also during repair works.

Family functionality is when the family meets the needs of its members, especially adolescents. Resilience is the adolescent’s ability to face situations in which there are difficulties, life problems and thus be able to overcome them. The purpose of the study is to determine the Level of Resilience and Family Functionality in Adolescents of two Educational Institutions of a Vulnerable Area in Lima Province. It is a non-experimental study, descriptive of a quantitative approach, it is a cross-sectional correlational investigation, with a population of 204 adolescents, also demographic data and the resilience scale (CD-RISC) and family APGAR. In the results regarding to the level of resilience and family functionality, adolescents present a mild family dysfunction with a medium level of resilience (58.0%), in comparison of the Educational Institutions, in both cases, the majority of students are located in the medium level of resilience, in the case of students from Honorio Manrique Nicho school 48.5% and in the case of students from the Andrés Avelino Cáceres school 54.2%. It is recommended in these institutions to carry out activities that promote the social formation of the adolescent, since this will allow them to communicate with society and the environment.

This paper is carrying out a practical identification of a DC servomotor from input and output data. The system’s step response was obtained from an acquisition card that we have developed using a microcontroller. The data is collected and downloaded to the PC Thought RS232 Protocol to process the identification process using polynomial models (ARX, ARMAX, OE, and BJ). Afterward, a comparison between the resulting models has been made using the validation criterions R² and FPE. The results show that the ARMAX, OE, and BJ models have reproduced a better fit compared to the ARX model.

This paper presents an adaptive step-size and variable leaky least mean square algorithm based on nonlinear adaptive filter with the adaptive lookup table using spline interpolation. An adaptive step-size approach is proposed with the energy of squared previous and present errors to boost up the convergence rate. A modified variable leaky mechanism is proposed with the optimal leaky parameter by using the recursion form. The proposed algorithm merges an adaptive step-size and a modified variable leaky method with least mean square algorithm for linear and nonlinear network part of spline adaptive filtering in term of fast convergence enhancement. Experimental results demonstrate that proposed algorithm can notably achieve a competitive performance on the convergence rate in comparison with the conventional least mean square algorithm for spline adaptive filter. Simulation results suggest that mean square error performance of proposed algorithm can be partially assessed using adaptive step-size with the variable leaky parameters indicating better than the conventional least mean square algorithm by 16.76%.

Nowadays, automatic number plate recognition (ANPR) is very important especially in the era of smart cities and intelligent transport systems. Fully automated number plate detection and recognition system helps in reducing time, error, and cost for tracking of vehicles and for recording traffic violations. The main goal of this paper is to design a low cost fully automated number plate detection and recognition system targeting the Jordanian license plates. Several problems (e.g., cost, wasted efforts, manual intervention, and possible errors) were identified in the currently used Jordanian number plate recognition for recording traffic violations. We hope that the proposed system would mitigate such problems. The proposed system performs two main tasks. First, it automatically detects and recognizes the license plate number of a given Jordanian vehicle using a robust metric; the rectangularity measurement, and identifies the vehicle’s type (e.g., governmental, private, visitor, public, etc.). Second, it recognizes a selected number of trained classes for the make of the vehicle whenever applicable. The experimentation results and the performance evaluations compared to other ANPR approaches show that proposed system achieves the best performance among the tested systems with a plate detection accuracy of 95%, OCR recognition accuracy of 94.68%, make recognition accuracy of 86.84%, and an overall ANPR accuracy of 89% excluding the make results.

This study used 15 VECM analysis models to analyze the relationship among exchange rates of South Korea, China and Japan; and between exchange rates of each country and international financial market variables. The analysis variables are the Won, Yuan, Yen spot exchange rates and international financial market variables. The results of the analysis are as follows: First, there was a long-term cointegration relationship between RMB, KRW, JPY and international financial market variables. Second, the analysis results of the VECM showed that explanatory power of Korea’s offshore Won-Dollar accounts for 50% of the onshore Won-Dollar. The results of the analysis of the Yuan’s VECM showed that the onshore Yuan (CNY) and offshore Yuan (CNH) exchange influence, but each has its own independent characteristics. Overall, the Won is more integrated with the international financial market than the Yuan. The Yen’s relationship with variables in the international financial market was stronger than that of the Won and the Yuan. Third, offshore Won-Dollar, onshore KRW, and JPY had similar Granger causality relationship and impulse responses with international financial market variables. However, CNH and CNY indicated weaker than that of Won and Yen. The onshore Won-Dollar is shown to partially offset the shock from the onshore Yuan and offshore Yuan. The implications of this study are as follows: First, it is important to look at the exchange rate from the perspective of the international financial market. Second, Yuan investment and risk management are necessary considering the characteristics of the onshore and offshore Yuan which are interrelated but also distinctly unique markets. Third, it is necessary to manage the exchange rate position, taking into account the long-term equilibrium relationship among the Won, Yuan, and Yen currencies. Fourth, Korean companies should find a way to actively utilize the Won which shows the characteristics of partial internationalization.

Complete manual annotation of dependency treebank needs resources like annotators and annotation tools and takes long time and has high possibility of inconsistent annotations for free word order languages such as Myanmar. This paper describes a dependency head annotation scheme with Universal part-of-speech and Universal Dependencies for Myanmar dependency treebank. Currently 22,810 sentences and 680,218 tokens were annotated from three corpora for Myanmar dependency treebank. Some language specific issues are also described with examples. Raw syntactic structures were annotated automatically by UDPipe according to the Universal Dependencies based on Universal-part-of-speech tag scheme. Then unsupervised annotated dependency head structures have been manually updated in post processing. To be reliable and speedy post process with reduced errors for manual updating, selected sentences were added to the training data after being updated. After that the model has been retained and the remaining sentences were parsed by UDPipe. Post processing was repeated until all sentences were updated. Some specifications of dependency annotation schemes in sentences encountered in post processing are presented with examples. For parsing performance of annotated data, cross validation tests and parsing experiments were performed. Moreover, annotated treebank data have also been evaluated by CoNLL 2017 evaluation script for parsing performance. Results of parsing experiments and evaluation are also reported by unlabeled and labeled attachment scores and demonstrated that the proposed method is a suitable way for building Myanmar dependency trees. Moreover, syntax structures of treebank are also analyzed and syntax information is also presented. This dependency head annotation for dependency treebank is the first work for Myanmar language as far as we know.

Portuguese organizations that have invoicing software, certified by the Tax and Customs Authority, need to comply with technical requirements that involve business continuity and disaster recovery. The recent tax legislative changes created conditions for the dematerialization of documents, allowing waiving invoice printing, encouraging the adoption of an electronic invoicing and document archiving system. The pandemic situation boosted the need for organizations to integrate this paradigm in their business processes. However, there are some constraints in the implementation of these requirements, due to technical issues, interpretation of tax legislation or the selection of frameworks or good practices for Information and Communication Technologies. The objective of the work is to present a set of concerns underlying the design of a business continuity plan, supported by current tax legislation, by standards and codes of good practice. In view of the constraints of Portuguese business capacity, it is also presented a minimum solution that meets the legal, regulatory, good practices and conceptual requirements of Information and Communication Technologies for initiating the design of a Business Continuity Plan. The method used in this investigation was based on the analysis of international standards ITIL, ISO, CMMI, COBIT through the assertive interconnection with the subject under study with the dispositions stated in the Portuguese legal framework in the field of invoicing. The main result was the conception of a decision support process for designing a guide, concerning the optimization of the business continuity plan design process. In face of the problematic in study, it is considered that the main expected results were achieved, by fostering the design of Business Continuity Plan in Portuguese organizations, in order to reduce the gap between the practices currently in place and the requirements underlying certification, as a way to prepare organizations to deal with disruptive events in invoicing business processes.

Empathy is an attribute of importance between the nursing professional and the patient, where it influences the recovery of their health. Several studies carried out have shown worrying results where they reflect a great decrease in empathy during the formative academic years. The objective of the study is to determine empathy in nursing interns from three university institutions in Lima, 2019. The study is a quantitative approach, with a non-experimental, prospective, descriptive and cross-sectional design. The application of the Jefferson Medical Empathic Scale instrument for students in Spanish version S is proposed, where the data was entered into a matrix in the Microsoft Excel 2013 program, then the IBM SPSS Statistics version 24.0 is subsequently analyzed obtaining the percentage of empathy of the internal, also take actions to improve these variables. 216 nursing interns from three universities in Lima participated. 51.4% are from Universidad Norbert Wiener, 89.4% (n = 193) were women, 56.4% (n = 123) are from the ninth cycle, 42.4% (n = 91) they study and work, 58 nursing interns from Universidad Norbert Wiener representing 52.3% have a medium level, 34 nursing interns from the Universidad de Ciencias y Humanidades, representing 30.6% have a medium level and finally 19 nursing interns from the Universidad Privada del Norte representing 17.1% have a medium level. Universidad Norbert Wiener obtained a higher score prevailing the medium level.

The results investigated in this work are toward the optimization of the photonic crystal structures in 1D and 2D scale. One-dimensional distributed Bragg reflectors (DBRs) have demonstrated substantial potential in various optoelectronic applications, due to the observed tunable optical band-gap. Herein, the use of DBRs in light trapping solar cells was simulated and validated, representing its effect as a back reflector structure. In terms of the layer thickness, material selection and number of layer, the optimized DBR structure was modeled and evaluated with respect to previously published numerical and experimental data. The proposed model is capable of designing photonic crystal structures with tunable band-gap varies from 400 nm to 700 nm while controlling the pass-band in both Visible and Near Infra-red regions. On the other hand, 2D grating structure has been simulated where the transmission spectra under various design dimensions have been investigated. Finally, thin film deposition is utilized for experimental validation to our proposed optical model.

In this paper, a modified configuration of Single-Phase Seven-Level Packed U-Cell (PUC) Multilevel Inverter for solar photovoltaic system is presented & investigated for standalone operation. The Seven-Level MPUC Inverter comprises of six semiconductor switches & two DC links which generates seven voltage levels at the inverter output. The maximum amplitude of inverter output voltage is more than that of available maximum DC link value (i.e. sum of two DC link values). The voltage levels generated in the MPUC inverter is more with reduced number of components counts as compare to conventional multilevel inverter topologies like Cascaded H-Bridge (CHB) and Neutral Point Clamped (NPC). The proposed inverter finds application in PV System where green power is derived from two PV panels with different power rating and voltage rating connected to DC links. Several design considerations viz. the RMS value of inverter output voltage, switching frequency & voltage rating of semiconductor switches are taken into consideration to prove the advantages of the MPUC inverter. The simulation results of the MPUC inverter shows the appropriate operation of multilevel inverter.

Recognizing the human behavior and gesture has become important due to the increasing use of wearable devices. This study classifies hand gestures by creating sound in the inaudible frequency range from a smartphone and analyzing the reflected signals. We convert the sound using Short-Time Fourier Transform to magnitude and phase. We trained two types of data on Convolutional Neural Network model. And then we propose a method applying soft voting, an ensemble technique, to improve classification accuracy taking the average of two models’ result. In this paper, the classification accuracy of the Mag model is 96.0% and the classification accuracy of the Phase model is 90.0% for 8 hand gestures. While the ensemble model showed 96.88%, which is better than Mag and Phase models.

This article proposes a cost-aware autoscaler for microservices-based applications deployed with docker containers. This autoscaler decreases the cost of the application deployment as it reduces computing resources. In elastic treatment, microservice resources are scaled when the used metric as the central processing unit (CPU) exceeds the threshold. In case of threshold exceeding, an autoscaler adds many instances of docker containers in order to satisfy the need of the application. In many studies, the autoscaler adds many containers without selecting the appropriate microservices for scaling and without in advance calculation of the adequate number of containers. This may lead to allocating additional resources to inappropriate microservices and a non optimal number of containers. For this reason, we propose our autoscaler ”Docker-C2A” which identifies the adequate microservices to add resources. It also calculates the optimal number of needed containers. ”Docker-C2A” analyses the state of the application, uses the execution history and uses a Particle Swarm Optimization (PSO) algorithm to identify the adequate microservices for scaling resources and to determine the optimal number of containers. As a result, ”Docker-C2A” helps to reduce computing resources and to save extra costs. Experimental measurements were conducted on a microservices-based application as a concrete use-case demonstrating the e d solution.

This paper covers a new topology, a synchronous wind turbine generator, and a solar photovoltaic generator. The Permanent Magnet Synchronous Generator is linked to the grid by back-to-back voltage source converters (BtB VSC), consisting of a two-stage rectifier and a five-stage Neutral Point Clamped inverter, operated by the Space Vector Pulse Width Modulation technique. A solar photovoltaic system has a direct interface with the capacitor of the DC link of the BtB VSCs, with no additional DC/DC conversion stage, while the efficiency of the system is maximized. This work features a Perturb and Observe algorithm Maximum Power Point Tracking for to extract the optimum power from both wind turbine and solar PV generator. The application of Space Vector Pulse Width Modulation control scheme is used for harmonics compensation THD. A proportional integral regulator is used to regulate the DC link voltage. A prototype is tested under various conditions, wind velocity variations as well as under several photovoltaic solar isolations. The present work has been treated using Matlab/Simulink. Simulation results proved efficiency provided by this controlling approach: reduced distortion of harmonic and increased power factor.

This paper is an extension of a research work presented at ICSIoT 2019. An attack continuum against the insider attack in mobile money security in Ghana using a witness based crypto- graphic method proposed by Alornyo et al. resisted the service provider from peddling with users data for economic gains. Our improved scheme achieves a simultaneous benefit of digital signature in public key encryption (PKE). The adoption of signcryption cryptosystem in our scheme achieved a desired security property of EUF-CMA using the random oracle model.

The apple tree (Malus domestica) is an agricultural species of great importance in Morocco. Currently, it occupies about a quarter of the surface of fruit-bearing rosaceae. Apple tree production has evolved rapidly, stimulated by a buoyant market, a varietal range which is tending to diversify and a dynamic profession. The present study aims to determine the physico-chemical properties of soil and apple leaves in Beni Mellal-Khenifra region, Morocco. Data collection was consisted to sample the soils at different depths, three Pearson correlation coefficients were determined: i) Correlation between organic matter and soil mineral elements, ii) Correlation between clay content and soil mineral elements and correlation between CaCO3 content and soil mineral elements. The analytical results of soils were compared to reference values. The results show that there is no correlation between these mineral elements and the rate of CaCO3 and clay fraction. Analysis of the main components showed that the apple leaf contains more sodium, copper and zinc and less magnesium, phosphorus and potassium. The coefficient of variation (CV) of manganese content expressed as a percentage is significantly higher than that of the other elements, while the potassium content is the lowest, in particular for magnesium and sodium the coefficient of variation is zero. In apple orchards in the Middle Atlas, there are great variations in terms of yield levels, fertilizer inputs, soil richness in nutrients and their concentration at leaf level. These variations are due to differences in cultivation practices, in particular fertilization, because the relationship between apple yield and nutrient content at soil and leaf level is significant.

Despite that groundwater is an important and vital water resource, it is not well managed; depletion of aquifers around the world due to overexploitation is of serious concern especially in arid regions where the situation is much more alarming. Tafilalet plain in Morocco which belongs to this type of environment is certainly no exception and is viewing its groundwater disappearing. Artificial aquifer recharge (AAR) is found to be appropriate to such an urgent issue. Thus, the objective of this paper is the exploration of suitable sites to process Artificial aquifer recharge in Tafilalet plain by the joint use of remote sensing (RS), geographic information systems (GIS), and the analytic hierarchy process (AHP) method. For doing so, eight parameters were considered as groundwater influencing parameters such as slope, soil, geology, land Cover/land Use, depth to the water table, aquifer transmissivity, electrical conductivity and drainage density. The laters were integrated and processed in a GIS, their thematic layers were created, and their relative weights were generated by The AHP method based on their significance in recharging the aquifer. Afterward, thematic layers were reclassified and assigned their weights, so the GIS overlay tool was used for inferring artificial groundwater recharge potential regions in the study area. 47 % were identified as suitable while only 12 % were identified as unsuitable. Such studies facilitate groundwater management for stakeholders and water managers because important decisions may be taken in a record time which will preserve water resources and prevent them from being in an alarming situation.

The role of creativity techniques in the design of prototypes is of particular interest given its potential for innovation. At same time, despite the efforts of decades in terms of policies and programs of action, humanity has not yet come close to global sustainability. Sustainability design must involve society and creatively employ all available knowledge sources for creating sustainable software. This paper proposes a prototype design approach rooted in employing creativity techniques, while being guided by the dimensions and principles of the Karlskrona Manifesto. This approach is applied to the development of a multidisciplinary aggregator for the optimization of social services. As a result. guidelines for the use of creativity in requirements engineering will be presented, as well as on how to include sustainability issues, namely the Sustainable Development Goals and the five dimensions of sustainability in the design of prototypes.

This research presents an effectively autonomous method that can save time and increases productivity for an assembly line in industry by using a 6DOF manipulator and computer vision. The objects are flat, aluminum type and randomly stacked in a box. Firstly, 2D color image processing is performed to label the object, then using 3D pose estimating algorithm, the surface normal, angle and position of an object are calculated. To reduce the burden time of 3D pose estimation, a voxel grid filter is implemented to reduce the number of points for the 3D cloud of the objects. As known the 3D image object was obtained by camera in bin often involves both heavy noise and edge distortions, so to prepare for the assembly a manipulator will pick and place it at sub position then a 2D camera is used to estimate the pose of the object correctly. Through implement experiment, the system proved that it is stable and have good precision. Installing time and maintaining is fast and not complicated. It is applicable in production line where mass product is produced. It is also the good foundation for other deep researches.

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Atmospheric pressure plasma was used to treat polypropylene (PP) surfaces. Optical emission spectroscopy (OES) was used to determine the chemical species formed in the plasma as well as the electron temperature and density of the plasma. The flux of species (O and OH) produced during the plasma treatment interact with the polymer surface creating polar groups on the PP surface, which were evaluated by X-ray, Raman, and Attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) analyses. Moreover, the thermal properties of PP were investigated by thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) analysis. The plasma treatment increased the crystallite size and microhardness. This enhanced effect was produced from high concentrations of O and OH that induced functional polar groups containing C–O, C=O, and C=O–OH bonds appended to the PP surface. The polar groups produced by the air plasma can be attributed primarily to the oxygen radicals in the air plasma hitting the PP surface and to heat-induced oxidation rather than the incorporation of oxygen radicals or UV-induced oxidation from the plasma. The increase in hardness is attributed to the introduction of carbonyl and hydroxyl groups, cross-linking, annealing effects, and chemical etching.

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Advances in the world of communications and information technology, as well as the urgent necessity to monitor particular areas and regions, have led to a considerable and influential development in the world of wireless sensor nodes. As they are small, low-cost multi-purpose nodes with limited energy and capabilities. The most important points that deserve research and solution is the problem of energy conservation and increasing the lifetime of the network as a whole. Whereas, many routing protocols have been proposed in the wireless sensor network, some of which are based on homogenous networks while others are not, some are location-based, some are based on hierarchical orientation, some are based on the hybrid approach and many more. Despite the long time that has passed since the discovery and activation of the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol, it and its descendants still have the lion’s share of attention, research, and development. In this research, we will be presenting the most prominent routing protocols while pointing to some pros and cons of each protocol.

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Background: The quality of care of the nursing professional in relation to the hospitalized patient refers to the set of knowledge and attitudes that the nursing professional must provide quality care, preventing risks and improving patient satisfaction, for which its objective is to determine the quality of nursing care in hospitalized patients at the LanFranco la Hoz Hospital. Method: it is a quantitative, non-experimental, descriptive, and cross-sectional study with a study population of 143 patients in total hospitalized at the Carlos LanFranco la Hoz Hospital. Results: The results obtained in the quality of nursing care in hospitalized patients, it can be seeing that 86 patients representing 60.1%, are moderately satisfied, followed by 36 patients that represent 25.2% are satisfied, 12 patients that represent 8.4% are dissatisfied, 7 patients that represent 4.9% are very satisfied and finally 2 patients that represent 1.4% are very dissatisfied. In the dimension, experience of the nurse in the care of hospitalized patients, it can be observed 69 patients that represents 48.3% are moderately satisfied with the experience of the nurse in the care and in the dimension satisfaction with nursing care in hospitalized patients, it can be see 93 patients that represents 65% are satisfied with the quality of care. Conclusions: It is concluded that the high demand for patients sustains a decrease in quality nursing care due to the scarcity of human resources in the hospital and it is recommended that the hospital have a permanent evaluation system of quality of care to meet the expectations of hospitalized patients.

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This research shows the study carried out on the Health Related to Quality of Life (HRQL) focuses on aspects related to the perception of health experienced and declared by the person, in different dimensions such as physical, mental, social, general perception of health and satisfaction achieved measured at different levels; the objective of the study is to determine the quality of life in patients with type 2 diabetes at the Central Hospital of the Peruvian Air Force, 2019. To determine the validity of the instruments, the Kaiser-Meyer-Olkin (KMO) sample adequacy index was used and the Bartlett sphericity test, which result in a significance value of 0.005, being an acceptable validity of both instruments. Among the most relevant results is that the following dimensions: Energy and Mobility, 147 patients representing 73.5% have low level. Likewise, regarding to Social Burden, 156 patients representing 78.0%, have a low level. Lastly, regarding the Sexual Functioning dimension, 178 patients representing 89.0%, have a low level; patients significantly affect quality of life. “Non-adherence to drug treatment” represents 159 patients, of which 115 who represent 72.3% have a low level with respect to their quality of life, of 44 patients who represent 27.7% have a high level regarding their quality of life; “Adherence to pharmacological treatment” 41 patients representing 92.7% have a low level regarding their quality of life and 3 patients who represent 7.3% have a high level regarding their quality of life.

Social skills are the behaviors that the individual adopts on a regular basis at specific moments in everyday life. Resilience is the way in which the individual faces the adversity that life imposes on them, as well as manages to overcome it. In adolescence, problems are perceived because important changes are generated in an accelerated way, many adolescents develop psychological disorders or alterations that could compromise their own lives, if not detected in time, therefore, the objective of this study is to determine the social skills and resilience in adolescents of secondary education of the Kumamoto Educational Institution I 3092, of the Puente Piedra district – Lima, 2019. Applying a quantitative, non-experimental, descriptive, and correlational study, with a population of 626 adolescents from the Kumamoto I 3092 educational institution in the Puente Piedra district, who answered a questionnaire with sociodemographic data and the instruments of the social skills scale and the Connor-Davidson questionnaire for resilience. In the results, the predominance of the medium level in both variables, in social skills with 87.5%, in resilience with 58.3%, concluding the need to carry out interventions in adolescent students, to prevent the inadequate development of their personality.

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Social skills are a set of partially independent and situationally specific verbal or nonverbal responses, in terms of resilience, it is the set of qualities, resources or strengths that favor adolescents to face the adversities of life, therefore the objective of the research work is to determine the social skills and resilience in adolescents of an educational institution in Lima – North. It is a non-experimental study, descriptive of a quantitative approach, it is a cross-sectional correlational research, with a population of 706 students of the secondary level, also with demographic data, and the Social Skills Scale (SSS), and the scale of Resilience (CD-RISC) that will measure the level of resilience of adolescents. The relationship between the variables Social Skills and Resilience. It was determined based on the Pearson’s Chi square test (X2). The significance level of the test obtained a value of 0.000 (p<0.05) (X2 = 28.626; g.l. = 4). So, the dissociation hypothesis is rejected and it can be affirmed with statistical evidence that there is a significant relationship between social skills and resilience. The predominant level of social skills is medium with 648 (91.8%) participants and in terms of resilience, the predominant level is high with 366 (51.8%) of participants. Social skills and resilience are mutually related since both allow the adolescent to face situations of daily life looking the ability to develop their coping capacity.

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The article discusses the possibility of using solar energy for street lighting systems in the city of Gitega, in the Republic of Burundi. Analysis of weather and climate conditions of the city was carried out and an effective street lighting system based on solar mini-power plants using an intelligent control system developed. Calculations of technical and economic efficiencies are made and the principle of operation of the developed system is described. A comparison of the methods of transmitting control signals and the performance of different types of lamps was made as well as an assessment of the battery discharge process. The different attachment types of solar panels to their supports were highlighted, the illumination received on a public space according to the installation height of the luminaire and the used LED power as well as the requirements in terms of illumination and brightness depending on the place to be lit were discussed. The system developed consists of two solar panels, connected in parallel, one battery and six LED lamps with adjustable power. At present, the problem of modernizing the lighting of public places and roads of regional and local significance in Gitega city remains relevant. With the aim of promoting clean energy and reducing energy consumption due to global concern about climate change, the use of modern, cost-effective lighting systems based on the use of LED technology and is one of the most dynamic trends in the world at present and in the near future for the well-being of the population.

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Nowadays, robots are an important learning tool in education and are increasingly used inside and outside the classroom to foster the development of students’ knowledge, skills, and attitudes connecting to a real-world situation. In past years, using robots in STEM (science, technology, engineering, and mathematics) education has been proposed as one of the strategies to integrate the different fields in order to construct more effective projects and innovations. Especially, engineering is the combination of math and science in solving a problem. Nevertheless, students could not understand and appreciate how to apply the knowledge of interdisciplinary integration to operate certain tasks by the engineering method. This research article presents STEM robot-based learning activities (STEM-RoLA) with sixty high-school students who were studying science and engineering. The obtained results show that the proposed STEM-RoLA is beneficial for students, especially when compared with high and low robotics performance students. The results found that high robotics performance students have higher computational thinking than low robot performance students, and they have a positive engagement response in the learning activity.

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Starches have been applied as biomaterials due to their wide availability and biocompatibility. These have also been modified by oxidation, resulting in dialdehyde starch (DAS), to improve their stability in water and mechanical properties. Cassava starch with a low oxidation degree has been introduced into hydrogels based on polyvinyl alcohol (PVA) to improve their properties. However, the behavior of these materials with starch at different oxidation levels has not been previously explored. In the present work, the effect of the oxidation degree of cassava starch on the physical and chemical properties of DAS-PVA hydrogels was evaluated. To modify the degree of oxidation, different concentrations of H2O2 were used, and a high degree of oxidation was achieved by incorporating copper sulfate II as a catalyst. Oxidation was confirmed by quantification of carbonyl groups and Fourier Transformed Infrared Spectroscopy. Hydrogels with low and medium oxidation DAS displayed greater swelling, but also lower stability over time. Similarly, scanning electron microscopy confirmed greater porosity in them. On the other hand, hydrogels with high oxidation DAS had lower water absorption capacity, but greater stability over time. Regarding the controlled release of ibuprofen, as a model drug, hydrogels formulated with low and medium oxidation DAS presented a greater and faster release, compared to the formulations with high oxidation DAS. These results showed that the degree of starch oxidation, for the PVA-DAS hydrogel synthesis has a significant effect on the behavior of the polymeric network.

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The huge number of images on the image sharing websites poses challenges for classification and retrieval of the images. On many image sharing websites, tags can be assigned by the users to an image that describes the contextual and visual description of an image. However, ambiguous or incorrect tags have appeared in frequent tags that affect the performance of an image retrieval system. Thus, assigning appropriate tags to the images plays a very important role in image retrieval and classification. In this paper, the ITR-WTF image tag recommendation method is proposed which explores tags from ranked nearest neighbors of each category. For a given input image, the method first determines the neighbors from training images of each category and ranks the neighbors according to the distance from the input image. In the second step, the weight is assigned to each tag based on the vote from each neighbor. Finally, the weighted tag frequency is determined to recommend appropriate tags to a given image. The experimentation is done on two datasets self-generated and NUS-WIDE. The results obtained using the proposed method ITR-WTF gives good results as compared with the existing methods of tag recommendation.

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This work presents the analysis and formulation for optimizing the dynamic model and parameter estimation of all the six joints of a 6DOF industrial robot manipulator by utilizing swarm intelligence to optimize two excitation trajectories for the first three links at the arm and the last three links at the wrist of the robot manipulator. Numerical techniques were used to reduce the observation matrix to a minimum linear combination of parameters, thereby maximizing the identifiable parameters, and the Linear Least Square method was used for parameter identification. An improved particle swarm optimization algorithm with mutation and archived elite learning was proposed for solving the dynamic optimization problem of the industrial robotic manipulator. The basic parameters of the algorithm have been optimized for robotic manipulator analysis. The proposed algorithm is computationally economical while completely dominating other Evolutionary algorithms in solving robot optimization problems. The algorithm was further used to analyze 36 benchmark functions and produced competitive results.

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In this paper, the effect of plane spacing between electrode planes on Electrical Impedance Tomography (EIT) reconstructed images is investigated. Image properties of models for various plane spacings between electrode planes on EIT imaging were investigated by applying conventional measurement strategies. Sensitivity analysis and spatial resolution analysis were used to study the influence of the different plane spacing between electrode planes on imaging properties. In the sensitivity analyses, the results indicate that there are insignificant differences in sensitivity level for the models with different plane spacings, regardless of measurement strategies applied. From the spatial resolution analyses, the findings are conclusive as there are visible differences in the spatial resolution across the off-electrode plane. A comparative study using reconstructed images was also done. The true distributions with the different number of objects are used as references to assess resulting reconstructed images obtained from models with different plane spacing between electrode planes. Results indicate the model with plane spacing between electrodes planes, which is one quarter to the height of the model, provides the better quality of reconstructed images, in terms of estimations of dimension, and colour contrast of the imaged object.

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Forest mapping by remote sensing is a hot topics in forestry. At present, many researchers focus on the research of forest type classification or tree species identification using different machine learning methods and try to improve the accuracy of classification of satellite image. However, forest type classification using deep belief network (DBN) is still limited in previous literatures. Our research focuses on forest mapping in the western part of Dehua county in southern China. Most important objective was to assess the feasibility of forest mapping from hyperspectral data using deep learning. The HJ-1A hyperspectral data was adopted in this paper. We applied deep belief network and got a thematic map of four forest types, such as coniferous forest, broad-leaved forest, mixed forest and non-forest. Our finding shows that optimal network depth of DBN model is 3 and best node in each layer is 256 in our experiment. Overall accuracy is 85.8% and kappa coefficient is 0.785 with best-fit parameters in DBN model, while for SVM is 73% and 0.6447 respectively. DBN obtain better performance compared with support vector machine. Furthermore, network depth and number of nodes in each hidden layer in DBN model has a significant effect on overall accuracy and Kappa coefficient. In general, DBN is promised to be dominant method of forest mapping by hyperspectral data.

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In this paper we propose an algorithmic approach for Convolutional Neural Network (CNN) for digital watermarking which outperforms the existing frequency domain techniques in all aspects including security along with the criteria in the neural networks such as conditions embedded, and types of watermarking attack. This research addresses digital watermarking in deep neural networks and with comprehensive experiments through computational modeling and algorithm design, we examine the performance of the built system to demonstrate the potential of watermarking neural networks. The inability of intruder towards the retrieval of data without the knowledge of architecture and keys is also discussed and results of the proposed method are compared with the state of the art methods at different noises and attacks.

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The technological evolution and the frequent use of the Internet as a technology within the framework of the Internet of Things and Robotics has affected various fields such as Industry 4.0, smart agriculture, smart cities, home automation and autonomous cars. These advances applied to everyday life have facilitated the implementation of new concepts to minimize pollution, traffic jams and accidents while making life easier. In this article, we propose an intelligent parking concept based on embedded systems. In order to solve the problem of permanent traffic jams and their repercussions on large cities we have implemented this system. It is a complete platform allowing remote reservation from different platforms. It will also collect the various data obtained by parking sensors. These data will be processed and saved if necessary. This concept was first developed to highlight the feasibility and adaptability of this system in the context of smart cities. It has been realized using wireless sensor networks connected to embedded platforms. These platforms are not only connected to motors, sensors and actuators but also to servers to save data and deploy reservations. These reservations are made via web and mobile application.

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The adoption and diffusion of mobile applications (mobile apps) has become the base of modern activities in Africa owing to the services and values that are obtained through mobile apps innovations. More emphasis has been on the development of mobile apps whereas the adoption and diffusion process as well as their predictors are ignored or given less attention. This study explores and develops a framework for the adoption and diffusion of mobile apps in Africa. A survey was conducted on the basis of the diffusion of innovation framework in five-selected African nations. A total of 1285 of the 2300 distributed questionnaires were returned, giving a response rate of 55.87%. The results indicated that many factors/predictors drive the acceptance and use of mobile apps and these factors were structured to develop a common framework for mobile apps adoption and diffusion in Africa.

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Quantum computers are heterogeneous device. It consists of a main CPU and a quantum accelerator. True quantum accelerator (or coprocessor) is analog and probabilistic device. Qubits are the basic building blocks of quantum computers. But qubits can be digital. A digital qubit is similar to RISC processor pipeline and is an unique chain of digital gates.
In this work, it is proposed to execute quantum routines in quantum computer not on the quantum chip but on the chip of a digital FPGA. This paper presents the architecture of such FPGA – an architecture of digital quantum coprocessor. The paper presents two types of digital quantum coprocessors – heterogeneous and homogeneous. The advantage of a homogeneous coprocessor is shown.
The IP Core generator was developed to create VHDL descriptions of digital quantum elements and digital quantum coprocessors in general.
In this paper heterogeneous quantum computer which consists of a main CPU and a FPGA-based quantum accelerator (coprocessor) has been proposed. And these FPGA-based digital quantum coprocessors can have a homogenous or heterogeneous structure. Quantum coprocessors have up to 1024 qubits in one FPGA. A homogeneous quantum coprocessor performs better than a heterogeneous one. Also, its implementation is easier.
The measured ratio of correct results for a 1024-qubit homogenous coprocessors is more then 50%.

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Diabetes Mellitus Type 2 is a chronic disorder that affects the way the body metabolizes sugar (glucose) in the blood and depends on a combination of risk factors, such as genes and lifestyle. Although certain risk factors such as family history, age, or ethnicity cannot be changed, those related to diet, physical activity, and weight can be changed. These lifestyle changes can affect the probability of developing type 2 diabetes and its complications. In this research work, the prevention of the patient to develop type 2 diabetes with the option of leading a healthy lifestyle is proposed and reinforcing the population of each establishment with health education. It is a quantitative approach, a non-experimental, descriptive, and correlational study, with a population of 300 outpatients from a district of North Lima, who answered a questionnaire with sociodemographic data and the instruments of the level of physical activity IPAQ and risk of FINDRISK type 2 diabetes mellitus. In the results with respect to the level of physical activity, low 125 (41.7%) predominated and at risk of type 2 diabetes mellitus, 113 (27.7%) predominated. In conclusion, the population should be educated to have an adequate lifestyle by improving physical activity to prevent the risk of contracting diabetes.

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The growing popularity of social media sites has generated a massive amount of data that attracted researchers, decision-makers, and companies to investigate people’s opinions and thoughts in various fields. Sentiment analysis is considered an emerging topic recently. Decision-makers, companies, and service providers as well-considered sentiment analysis as a valuable tool for improvement. This research paper aims to obtain a dataset of tweets and apply different machine learning algorithms to analyze and classify texts. This research paper explored text classification accuracy while using different classifiers for classifying balanced and unbalanced datasets. It was found that the performance of different classifiers varied depending on the size of the dataset. The results also revealed that the Naive Byes and ID3 gave a better accuracy level than other classifiers, and the performance was better with the balanced datasets. The different classifiers (K-NN, Decision Tree, Random Forest, and Random Tree) gave a better performance with the unbalanced datasets.

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Today, Drowning is the 3rd major cause for unintentional injury death accounting for 7% of deaths of all injury deaths. Drowning is a state of suffocation when water or other fluids accumulate the lungs, resulting in respiratory impairment, ultimately leading to death. The predominant problem during rescue operations of such accidental drowning is to locate or track the person underwater, facilitated with the help of our bare eye sight, which makes the process too cumbersome. Also, in case of moving water bodies such as rivers and sea, the process of tracking the person becomes too difficult. Thus, there is pressing need for development of an engineered solution to solve this problem. This research involves development of such a robotic technology which will facilitate the work of locating the position of the drowning person underwater. This method comprises of Image processing along with GPS tagging embedded on a Remote Operated Vehicle maneuvering underwater at the site of accident to detect the exact location of the drowning person along with the GPS coordinates in order to ease the process of rescue. This robotic system was tested on a real time environment in order to demonstrate the efficacy of the system under practical circumstances. Thus, this system can be used at places where emergency rescue operations are needed.

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Android Operating system (OS) has been used much more than all other mobile phone’s OS turning android OS to a major point of attack. Android Application installation serves as a major avenue through which attacks can be perpetrated. Permissions must be first granted by the users seeking to install these third-party applications. Some permissions can be subtle escaping the attentions of the users. Some of these permissions can have adverse effects like spying on the users, unauthorized retrieval and transference of the data and so on. This calls for the need of a heuristic method for the identification and detection of malware. In this discourse, testing of classification algorithms including Random forest, Naïve Bayes, Random Tree, BayesNet, Decision Table, Multi-layer perceptron (MLP), Bagging, Sequential Minimal Optimization (SMO)/Support-Vector Machine (SVM), KStar and IBK (also known as K Nearest Neighbours classifier (KNN)) was carried out to decide which algorithm performs best in android malware detection. Two dataset was used in this study and were gotten from figshare. They were trained and tested in the Waikato Environment for Knowledge Analysis (WEKA). The performance metrics used are Root Mean Square Error (RMSE), Accuracy, Receiver Operating Curve (ROC), False positive rate, F-measure, Precision and recall. It was discovered that the best performance with an accuracy of 99.4% was the multi-layer perceptron on the first dataset. Random Forest has the best performance with accuracy, 98.9% on the second dataset. The implication of this is that MLP or random forest can be used to detect android application malwares.

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The objective of this article is to highlight the existing relationship between the tourist’s destination brand image of the city of Rabat (Morocco) and its choice by foreign tourists to be visited once again. We suggest a reliable measurement scale able to measure the dimensions and associations of the brand image most memorized by tourists, so as to further attract the researchers and stakeholders curiosity in the tourism sector which face, every day, new challenges of attractiveness and sustainability of tourist destinations in the new Moroccan regionalization context. The survey is carried out on a sample of 454 foreign tourists in the city of Rabat because of the importance that this city requires in terms of overnight stays, that is to say nearly one million in 2018 according to the statistics of the Moroccan Tourism Observatory. The results obtained from the survey show that the functional and abstract associations jointly constitute the brand image of the city of Rabat in the foreign tourist’s memory and that the abstract associations are more significant in the destination’s choice.

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The purpose of this study is to show how to design the character used as the symbol or Mascot of the educational institution and how to apply the design results into advertising and Brand identity design such as printing art, poster, digital media, toy, and souvenir. In this study, the ideas of mascot design have been leaded to present a contemporary design under the conceptual framework from local culture, organizational culture, theories of society, arts and design through the analysis and synthesis of the organization, which is CAMT from Chiang Mai University, as the model of this study. CAMT has 7 bachelor’s degree majors, namely Animation and Visual effects (ANI), Software Engineering (SE), Digital Film (DF). Modern Management and Information Technology (MMIT), Digital Game (DG), Digital Industry Integration (DII) and Knowledge Innovation Management (KIM). These Mascot design group were applied into Art product such as poster, Line sticker and end up with Toy product as a final stage, it was using digital 3D printing for action figure product in prototype level. This study including Toy creating concept designing and how the outcome product affected to Mascot identity, institution brand and the target groups of study which are widely from generation X to Z, mostly age range 18-23 years old. As a result, it founded that this Mascot package advertising becomes the distinctive character creating the new image of CAMT and the enhanced of Toy design product that raise CAMT remembered as a contemporary and digital organization in Chiang Mai University. Accordingly, it produced the impact results to receivers in many dimensions involving trend, creative behaviors such as Fan Art design, Doujinshi, or Caricature, Cosplay, Arts, and craft also Subculture from the public user.

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The Internet of Things (IoT) is a technology that enables communication between everyday life using different sensor actuators that work together to identify, capture, and distribute critical data from the planet. Massive machines and devices are therefore linked and communicate with them. The use of resources in this area presents new challenges for this technology. The goal was to find a green IoT that focuses on energy efficiency and IoT efficiency. Green IoT is an energy-efficient way to reduce or eliminate the greenhouse effect of current applications. Radio Frequency Identification (RFID) is one of the Green IoT and Master IoT components that identifies a person or entity in a high-frequency electromagnetic spectrum when combining electromagnetic or electrostatic couplings. If the predictions are also correct, energy use issues arise as active battery-powered RFID detection needs to be addressed by incorporating new solutions for Green IoT technology. Past studies and assessments have attempted to evaluate RFID technology and its functions. Unfortunately, however, they concentrated on a single RFID view of technique and technology. This paper examines holistically and systematically the impact of RFID applications on green IoT, focusing on three categories: the challenges, environmental consequences, and the benefits of green IoT RFID applications. The impacts, performance and safety of RFID IoT applications have been carefully described. The benefits and examples of RFID applications, including their key advantages and disadvantages, are also discussed. Overall, this paper highlights the potential efforts of RFID to address existing Green IoT issues.

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The Internet of Things (IoT) is a technology that enables communication between everyday life using different sensor actuators that work together to identify, capture, and distribute critical data from the planet. Massive machines and devices are therefore linked and communicate with them. The use of resources in this area presents new challenges for this technology. The goal was to find a green IoT that focuses on energy efficiency and IoT efficiency. Green IoT is an energy-efficient way to reduce or eliminate the greenhouse effect of current applications. Radio Frequency Identification (RFID) is one of the Green IoT and Master IoT components that identifies a person or entity in a high-frequency electromagnetic spectrum when combining electromagnetic or electrostatic couplings. If the predictions are also correct, energy use issues arise as active battery-powered RFID detection needs to be addressed by incorporating new solutions for Green IoT technology. Past studies and assessments have attempted to evaluate RFID technology and its functions. Unfortunately, however, they concentrated on a single RFID view of technique and technology. This paper examines holistically and systematically the impact of RFID applications on green IoT, focusing on three categories: the challenges, environmental consequences, and the benefits of green IoT RFID applications. The impacts, performance and safety of RFID IoT applications have been carefully described. The benefits and examples of RFID applications, including their key advantages and disadvantages, are also discussed. Overall, this paper highlights the potential efforts of RFID to address existing Green IoT issues.

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The need of add-on features (secondary contacts) for current paths of air insulated high voltage disconnectors switching capabilities (e.g. bus transfer switching) is introduced. The relevant product components for the switching capability and their functionality is described, which is giving boundary conditions for adding features necessary to achieve the switching capability. Possible features are discussed with necessary properties and performance. Those are separated in passive and active solutions, which are focused. Given solutions successfully applied, are explained and capabilities are shown based on experimental design and testing – where calculations and/or computational analysis are not shown (consequently no resulted data of such approaches), as those have not been used for the given solutions (the disconnector is still a low-cost product within industrial business circumstances, where invests for comprehensive design activities are unfortunately very limited). The testing of the solutions is covered with information for limited switching capability values and measures and/or further, partially theoretical, alternative solutions, for increased values. Also, the testing itself and possible laboratories with certain test execution opportunities and/or challenges is elaborated. The conclusion provides a market view, product users perspectives, in regards of the applicability for passive solutions and the need for active solutions.

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There is a crisis in basic education during this pandemic which affected everyone worldwide, we see that teaching and learning have gone online which has effected student perfor- mance. Student’s academic performance needs to be predicted to help an instructor identify struggling students more easily and giving teachers a proactive chance to come up with supplementary resources to learners to improve their chances of increasing their grades. Data is collected on KAGGLE and the study is focusing on student’s engagement, how often they check their announcements, number of raised hands, number of accessed forum and number of accessed resources to predict student academic performance. Various ma- chine learning models such as Support vector machine, Decision tree, Perceptron classifier, Logistic regression and Random forest classifier is used. From the results, it was proven that Support vector machine algorithm is most appropriate for predicting student academic performance. Support vector machine gives 70.8% prediction which is relatively higher than other algorithms.

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Healthy lifestyles were proposed to improve the health status of the university population, they are a set of behaviors that are reflected according to the type of situation and behavior of each person who performs it during the period of their training, the inappropriate lifestyles provides many problems, which are non-communicable diseases that manifest in the health status of each individual, which most of the time students choose to consume less nutritious foods and they are not healthy and affect their health; inadequate lifestyles do not provide good academic performance and also generate a bad psycho-emotional state. The objective of the study is to determine the Lifestyle in nursing students at a university in North Lima, 2019. As results, regarding the Lifestyle in nursing students at a university in North Lima; 51.5% have healthy lifestyle and 48.5% have an unhealthy lifestyle. Regarding the dimensions, interpersonal relationships predominate 73.8% have an unhealthy lifestyle followed by stress management, 65.8% have an unhealthy lifestyle, responsibility for health, 63.3% have a lifestyle unhealthy, Physical activity dimension, 60.8% have an unhealthy lifestyle, spiritual growth, 48.8% have an unhealthy lifestyle, healthy nutrition, 29, 5% have an unhealthy lifestyle; it is important to know those results to make decisions.

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The use of wireless sensor networks (WSN) can be a valuable contribution in disaster situations or life-threatening exploration. Using wireless mobile robots, it is possible to explore vast areas without human intervention. However, the wireless network coverage that can keep mobile robots connected to the base station / gateway is a major limitation. With this in mind it was created a prototype of an extensible WSN using mobile robot nodes that cooperate amongst themselves. The strategy adopted in this project proposes using three types of nodes: master node, static node, and robot node. Three different algorithms were also developed and proposed: Received Signal Strength Indication (RSSI) Request; Automovement; Robot Cooperation and Response to Static Node. The performance evaluation of the prototype was carried out using a real-world testbed with each developed algorithm. The results achieved were very promising to continue the evolution of the prototype.

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Empathy in the care of the patient by the nursing students is important because it allows having the capacity of response towards the patient, this study aimed to determine the level of empathy in nursing students who attend in clinical practices of the Universidad de Ciencias y Humanidades. This is a cross-sectional study, with a population of 289 nursing students who answered a questionnaire and the Jefferson medical empathy scale. 210 participants (72.7%) had a medium level of empathy, 74 participants (25.6%) with a high level of empathy and 5 participants (1.7%) with a low level of empathy. Regarding the empathy dimensions, the high level in the perspective taking dimension predominated with 81.7%, followed by the average level of the capacity dimension to put oneself in the patient’s place with 59.9% and the compassionate care dimension predominated in the low level with a percentage of 45%. An improvement program is required in terms of professional training so that nursing students can carry out compassionate care when performing their clinical practices.

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For many years, the most popular technique for Internet video streaming is hypertext transfer protocol-based adaptive streaming, known as HAS (HTTP Adaptive Streaming). However, a seamless viewing experience can not be just simply guaranteed by HAS only. In the management network, the adaptation of HAS copes with a huge challenge since client- driven schemes lead to unfair share of available bandwidth when multiple players request adaptive bitrates (i.e bandwidth) through a bottleneck network link. Each client’s requesting to maximize its needed bandwidth leads to the competition of network resources. This causes great QoE (Quality of Experience) reduction in terms of main metrics for each player: fairness, efficiency, and stability. In this paper, we propose an integration scheme of bitrate adaptation and Software Defined Networking-based resource allocation that can improve the QoE of competing clients. Our experiments show that the proposed scheme increases at least 20% up to 124% in terms of QoE scores compared with some existing methods as well as gains smoother viewing experience than the solutions of the traditional Internet.

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Road safety has become an object of research and many research institutes have invested in this field because a lot of people die and many others are injured every year due to road accidents. The deployment of wireless communication technologies for vehicular networks can considerably improve road safety. It can enable new services such as traffic management, collision detection, and additional communication ease between moving vehicles. This paper presents a complete implementation of Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communications. Raspberry Pi boards, ultrasonic sensor, infrared obstacle detector, and line follower sensors are used in order to implement the complete prototype. The results show the usefulness of this road safety prototype named PrOMor (Prevention of Obstacles in Morocco). Based on these results, it can be concluded that the presented scenarios can be applied to the field of road safety related to the Moroccan case. This should reduce the number of accidents and save more human lives.

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The widespread team project method is more effective when used in conjunction with heuristic methods. The large number of heuristic methods and the variety of their descriptions create a problem to prepare students for the use of these methods. A method based on two areas of knowledge – heuristics and psychology – is proposed. The personality types of students STEM specialties according to Myers-Briggs are considered. An analysis of interaction of personality types from the point of view of application of heuristic methods is performed. The survey for percentage composition personality types of student STEM specialties was carried out and predominantly types of student STEM specialties was determine. Heuristic methods are consideration as sum of heuristic techniques and procedure. It is shown that many methods involve the same heuristic techniques and differ only in procedures. A generalized method has been developed that allows replacing most of the methods based on collective discussion. This method included five heuristic techniques: collective discussion, pause between the presentation of ideas and their criticism, random associations, analogy, expert evaluation, using a matrix. This method is mainly aimed at teaching students of STEM specialties. A project team is formed to use the method. The composition of this team includes a discussion group, a criticism group and a expert evaluation group. These groups are formed in accordance with the personal types of participants. The method includes an algorithm for team members to interact when using heuristic techniques and procedures.

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The Blockchain technologies enable decentralized networking consisting of large number of nodes. To determine the shared states and failures of all nodes in a fully distributed peer-to-peer system, the appropriate consensus algorithm needs to be selected for each Internet of Things system. In this paper, a novel hierarchical voting-based byzantine fault tolerance (HBFT) consensus algorithm is proposed. The proposed HBFT algorithm utilizes a typical PBFT algorithm hierarchically to guarantee low latency. The message complexity of HBFT shows that our proposed algorithm has better scalability. We also mathematically calculate the optimal number of groups based on the total number of nodes to determine the ratio of allowable faulty nodes per group. In addition, we analyze the reliability of byzantine fault tolerance to compare the reliability of group case with the reliability of non-group case. Finally, we introduce the methods of real-time Blockchain considering the service level agreement (SLA). The real-time processing performance of transactions is analyzed for the service level agreement (SLA).

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“This paper is an extension of work originally presented in the 4th International Conference on Systems of Collaboration, Big Data, Internet of Things & Security -SysCoBIoTS’19”.
The use of complex and dynamic systems modelling to social systems is quite recent and its pertinence in the case of an educational system is continually increasing. For the concrete management of educational systems, a global approach is required. This approach must take into consideration the effects of many parameters that can act and interact together thus making, as a result, the system more or less efficient. Our aim is to develop a model that can capture the dominant dynamics of these systems while being at the same time simple enough to be useful for analyzing, simulating, and quantifying the impact of different parameters on the global performances of educational systems. By viewing education systems as skills production systems and by applying Business Processing modelling methods, a modelling of education systems is proposed in the present work which allows studying the effects of a set of parameters on the behavior of the system and its performance. The focus will be done on the study of the impact of learners’ input competence on the performance of a training unit and on the performance of a training program. The obtained simulation results allow us to analyze the evolution of a training program’s behavior as well as estimates its performance under the effect of the variation of simulation factors. These results enable to measure the performance variation according to the learners’ input competence, their ability to acquire skills, and to the class size. This modelling enables us to test solutions for performance improvement.

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Internet Protocol version 6 is being adopted on slow pace and it is taking a long time. This paper intends to discuss the transition process between IPv4 and IPv6 and the major obstacles that prevent deploying IPv6 worldwide. It presents the IPv4 exhaustion reports results and where are the IPv4 address pool. Then it presents the methods that have been used to prolong the life expectancy of IPv4. After that it describes and discusses the mechanisms that have been used to deploy IPv6. Additionally, it describes the recently proposed mechanisms to overcome the problems encountered by the ISPs in migrating to IPv6. Furthermore, it shows the mechanisms that have been proposed to motivate the ISPs to start deploying IPv6 on their access networks. Finally, it presents a comparison between these mechanisms from the authors’ point of view.

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IoT platforms play an important role on modern measurement systems because they allow the ingestion and processing of huge amounts of data (big data). Given the increasing use of these platforms, it is important to characterize their performance and robustness in real application scenarios. The paper analyzes the ThingSpeak platform by measuring the latencies associated to data packets sent to cloud and replied back, and by checking the consistency of the returned data. Several experiments were done considering different ways to access the platform: REST API, MQTT API, and MQTT broker alone. For each experiment, the methodology is explained, results are presented, and conclusions are extracted. The REST and MQTT APIs have similar performances, with roundtrip times between 1 s and 3 s. The MQTT broker alone is more agile, with roundtrip times below 250 ms. In all cases, the up and down links are far from being symmetric, with the uplink delay showing higher variance than the downlink delay. The obtained results can serve as a reference for other IoT platforms and provide guidelines for application development.

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Solar energy becomes widely used in the global power grid. Therefore, enhancing the accuracy of solar energy predictions is essential for the efficient planning, managing and operating of power systems. To minimize the negatives impacts of photovoltaics on electricity and energy systems, an approach to highly accurate and advanced forecasting is urgently needed. In this paper, we studied the use of Deep Learning techniques for the solar energy prediction, in particular Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). The proposed prediction methods are based on real meteorological data series of Errachidia province, from 2016 to 2018. A set of error metrics were adopted to evaluate the efficiency of these models for real-time photovoltaic forecasting, to achieve more reliable grid management and safe operation, in addition to improve the cost-effectiveness of the photovoltaic system. The results reveal that RNN and LSTM outperform slightly GRU thanks to their capacity to maintain long-term dependencies in time series data.

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Sales behavior and the technical development of products influence their fabrication. As market influences become increasingly volatile and unpredictable, factories will have to adapt their manufacturing to market trends even more in the future. Adaptation is referred to as convertibility and can be achieved, among other things, by mobile and intercompatible machines. Enabling machines to be mobile, its power supply must be wireless and it should be possible to locate it on the shop floor at any time. With the help of the infrastructure platform Intelligent Floor, machines in future factories can be made more mobile than they are today. In combination with the novel autonomous guided vehicle BoxAGV, the platform offers a cost-efficient and highly flexible solution for in-house transport tasks. The transport of goods can be performed based on lot size one and thereby opens a wide field in logistics automation. This paper is an extension of work originally presented in MELECON 2020 and describes the concept and the functionality of the open platform that is implemented so far. It shows how to apply the platform to a real industrial manufacturing environment and highlights the resulting manufacturing benefits. Finally, the next development steps on the platform and machine side are presented.

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Data aggregation, gathering and gossiping are all vital communication tasks in wireless sensor networks (WSNs). When all networking devices are always active, scheduling algorithms for these communication tasks have been extensively investigated under both the protocol and physical interference models. However, wireless devices usually switch between the sleep state and the active state for the purpose of energy saving. A networking device with duty-cycled scenarios having sleep/awake cycles may need to transmit the message to all neighbors more than once. Taking the duty-cycled scenarios into consideration, communication scheduling algorithms for these tasks have been extensively investigated under the protocol interference model. As far as we know, scheduling algorithms for these communication tasks have not yet been investigated in WSNs with duty-cycled scenarios under the physical interference model. In this paper, we propose minimum latency scheduling algorithms for these communication tasks in duty-cycled WSNs under the physical interference model. Our innovative scheduling algorithms for both data gathering and gossiping achieve approximation ratios at most a constant time of |T|, where |T| is the length of a scheduling period. The approximation ratio of our proposed data aggregation scheduling algorithm is less than or equal to a constant times T with bounded maximum degree of the network.

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The article describes the concept and architecture of a decentralized control system for a solid-state voltage regulator (SSVR). The SSVR is a universal device for controlling the mode and operation parameters of medium voltage electrical networks. SSVR manage the amount of current in line using the vector voltage control method. The SSVR control system consists of two levels: the SSVR semiconductor converter control level (the technological control system), SSVR cluster management level (intelligent control system). The objective of the study is to develop an algorithm for managing the SSVR cluster located in the nodes of the distribution electric network. The Raft consensus algorithm for managing the computing cluster is applied to ensure reliable decentralized network management. The algorithm is iterative. Ethernet and PLC architectures are proposed for constructing a data transmission network between SSVR nodes. A simulation model of the SSVR cluster and its control system is developed to study the operation of the control system (node shutdown, loss of communication). The criteria for the normal operation of the intelligent control system are formulated and an algorithm for its operation in emergency situations is presented. The studies of the SSVR control system confirmed the operability of the developed control system in normal and emergency operation of the cluster on a simulation. The dependence of the control system response time on the number of cluster nodes is investigated. The maximum number of nodes in the SSVR cluster depending on the tasks being solved by the SSVR will be limited by the speed of the control system. If the number of cluster nodes increases, it is necessary to enlarge the minimum time interval between requests for control commands.

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Rescuers escorted search dogs into the disaster area, using their unique sense of smell to find the injured. First, researchers summarize the design requirements in the search process from interviews with rescuers, and construct a conceptual prototype to confirm the interaction mode between the user and the dog. User central design invited people melt into the situation to identify product features. The ideas were selected based on the viability which increases efficiency. The main design proposal includes a strap and a smart collar. Smart sensing (heartbeat, speed, temperature, and GPS) can improve communication and increases the efficiency of rescue. The search area is large in many cases; therefore, we selected the WiFi or Ultra-wideband module as the wireless transmission medium when the rescue team enters this domain. The pre-deploy nodes connect and position with the smart collars. The instructor sends voice commands remotely to prompt the dog to return when the temperature is high. The smart collar design includes an elastic O-ring waterproof shell. Rescuers click the recall button, and the remote device sends a signal of dog returning. This proposed work looks more at user’s needs through multi-disciplinary aspects of view, which enhance usability. The case consists of customer interviews, observation, concepts, evaluation (science/ device/ electronic packaging/ and App software); the design process also demonstrated a possible teamwork perspective in the industry. This scenario encourages cross-field extension for design education.

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In the Global sphere, the social, environmental, and economic pillars are the main contributors and accelerators to the sustainable development goals. As a result, the latter creates a platform for interdisciplinary researchers, society and decision-makers to collaborate in formulating ways to minimize factors contributing to environmental concerns. Energy is currently referred to as one of the scarce resources. The scarcity of electricity is mainly experienced in the rural areas of most countries in the world. The mandate of the green economy is to introduce innovative ways to redress the inequalities and lack of access, especially when it comes to Energy. Based on the sector’s efforts, questions arise as to what comprises the ecosystem that can be accelerated to enhance entry to the sector. Hence, the researchers focus on Renewable Energy with specific reference to the entrepreneurial motives to meet sustainable goals. The applicable sustainable goals are goal 7 (affordable and clean Energy) and Goal 8 (decent work and economic growth). Furthermore, Energy contributes to modern access and poverty reduction to accelerate the transitioning to a Green economy. The current paper hopes to answer the following questions: Firstly, how Renewable Energy enterprise can contribute to sustainable development goals theoretically. Secondly, how can the theoretical energy enterprise ecosystem be contextualized in the South African context? A theoretical review was conducted through a literature review of which n=47 sources met the criteria that the researchers set for ecosystem variables. The overarching goal of the paper is premised on various works of literature building the ecosystem of the elements highlighted by most researchers in the field of renewable energy enterprises or business ventures. From the various models, the framework emerged singling out the critical success factors of the ecosystem of the Renewable Energy enterprise. The theoretical ecosystem consists of accelerators, social factors, sustainable development goals, as well as selected business models. The latter ecosystem was then contextualized in the South African context for a complete framework. Some of the critical drivers derived from the latter broad ecosystem are: Renewable Energy Feed-in Tarrif (REFIT), Utility Renewable Energy business model, Customer renewable energy business model, Energy Justice (distributive justice), Off-grid (Mini-grid), Saurian Lilting lamp, Renewable powered irrigation system.

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Power converters are electronic devices widely applied in industry, and in recent years, for renewable energy electronic systems, they can regulate voltage levels and actuate as interfaces, however, to do so, is needed a controller. Proportional-Integral-Derivative (PID) are applied to power converters comparing output voltage versus a reference voltage to reduce and anticipate error. Using PID controllers may be complicated since must be previously tuned prior to their use. Many methods for PID controllers tunning have been proposed, from classical to metaheuristic approaches. Between the metaheuristic approaches, bio-inspired algorithms are a feasible solution; Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) are often used; however, they need many initial parameters to be specified, this can lead to local solutions, and not necessarily the global optimum. In recent years, new generation metaheuristic algorithms with fewer initial parameters had been proposed. The Grey Wolf Optimizer (GWO) algorithm is based on wolves’ herds chasing habits. In this work, a comparison between PID controllers tunning using GWO, PSO, and GA algorithms for a Boost Converter is made. The converter is modeled by state-space equations, and then the optimization of the related PID controller is made using MATLAB/Simulink software. The algorithm’s performance is evaluated using the Root Mean Squared Error (RMSE). Results show that the proposed GWO algorithm is a feasible solution for the PID controller tunning problem for power converters since its overall performance is better than the obtained by the PSO and GA.

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Cardiovascular diseases are the group of conditions produced in the heart or blood vessels. This is one of the main causes of death in Peru and the world, produced mostly by non-communicable diseases and harmful habits, which makes it an extremely predictable disease. These factors include body mass index, smoking, diabetes, age, blood pressure, total cholesterol, and high-density lipoproteins. Therefore, this study aims to identify patients who go to the medical office of a private health center in North Lima who do not have a prior history of a cardiovascular accident, using the cardiovascular risk calculator provided by the Organization World Health. The present research work had a quantitative, non-experimental, descriptive, and cross-sectional approach, in a population of 99 adult and elderly patients. Regarding the results, it was found that 46.5% presented a low cardiovascular risk, 37.4% a moderate risk, 11.1% a high risk and 5.1% an extremely high risk. The information found contrasts with the number of deaths caused by this disease and may be an indicator of greater prevention by populations with higher economic income. Finally, it is concluded that diabetes, smoking and the age group are predisposing factors to an increased cardiovascular risk.

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The article proposes an approach to evaluate human-computer interaction in the collective decision-making model. It is believed that all team members interact with each other through a distributed information system. The approach involves considering, when modelling, the personality characteristics of perception, each member of the team as a set of cognitive styles. Within the scope of the proposed technique, it is believed that information flows are interconnected with the processes of collective decision-making, which makes it possible to model the process of collective decision-making, monitor and analyse the effectiveness of the collective’s activities. Experimental studies accomplished with statistical data processing were carried out and discussed.

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Soil moisture is one of the most important variables to monitor in agriculture. Its analysis gives insights about strategies to utilize better a particular area regarding its use, i.e., pasture for cows (or similar), production forests, or even to answer what crops should be planted. The vertical structure of the soil moisture plays an important role in several physical processes such as vegetation growth, infiltration process, soil – atmosphere interactions, among others. Despite a set of tools are currently being evaluated and used to monitor soil moisture, including satellite images and in-situ sensor, several drawbacks are still persisting. In situ data is expensive for high spatial monitoring and vertical measurements and satellite data have low spatial resolution and only retrieval information of soil moisture for the top few centimeters of the soil. The present work shows an experiment design for collecting soil moisture data in a specific Andean basin with in-situ sensors in different kinds of soils as a promising tool for reproducing soil moisture profiles in areas with scarce information, employing only surface soil moisture and simple soil characteristics. Collected data is used to train machine learning supervised parametric (Multiple Linear Regression – MLR) and non-parametric models (Artificial Neural Networks – ANNs and Support Vector Regression – SVR) for soil moisture estimation in different depths. Conclusions show that parametric methods do not meet goodness of fit assumptions; so, non-parametric methods must be considered, and SVR outperforms parametric methods regarding regression accuracy allowing to reproduce the soil moisture content profiles. The proposed SVR model represents a high potential tool to replicate the soil moisture profiles using only surface information from remote sensing or in-situ data.

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With the increase of smart factories and smart cities following the recent 4th industrial revolution, internal user authentication and authorization have become an important issue. The user authentication model using the server-client structure has a problem of forgery of the access history caused by the log manipulation of the administrator and unclearness of the responsibility. In addition, users must independently manage the authentication method for each service authentication. In this paper, to solve the above problem, the researchers propose an integrated ID model based on a hybrid blockchain. The proposed model is implemented as two layers of Ethereum and Hyperledger Fabric: the former layer is responsible for integrated authentication, and the latter layer is responsible for access control. The physical pass or application for user authentication and authorization are integrated to one ID through the proposed model. In addition, the decentralized blockchain ensures the integrity and transparency of the stored access history, and it also provides non-repudiation of authority and access history.

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Modulation type classification is a part of waveform estimation required to employ spectrum sharing scenarios like dynamic spectrum access that allow more efficient spectrum utilization. In this work multiple classification features, feature extraction, and classification algorithms for modulation type classification have been studied and compared in terms of classification speed and accuracy to suggest the optimal algorithm for deployment on our target application hardware. The training and validation of the machine learning classifiers have been performed using artificial data. The possibility to use instantaneous values of the time domain signal has shown acceptable performance for the binary classification between BPSK and 2FSK: Both ensemble boosted trees with 30 decision trees learners trained using AdaBoost sampling and fine decision trees have shown optimal performance in terms of both an average classification accuracy (86.3 % and 86.0 %) and classification speed (120 0000 objects per second) for additive white gaussian noise (AWGN) channel with signal-to-noise ratio (SNR) ranging between 1 and 30 dB. However, for the classification between five modulation classes demonstrated average classification accuracy has reached only 78.1 % in validation. Statistical features: Mean, Standard Deviation, Kurtosis, Skewness, Median Absolute Deviation, Root-Mean-Square level, Zero Crossing Rate, Interquartile Range and 75th Percentile derived from the wavelet transform of the received signal observed during 100 and 500 microseconds were studied using fractional factorial design to determine the features with the highest effect on the response variables: classification accuracy and speed for the additive white gaussian noise and Rician line of sight multipath channel. The highest classification speed of 170 000 objects/second and 100 % classification accuracy has been demonstrated by fine decision trees using as an input Kurtosis derived from the wavelet coefficients derived from signal observed during 100 microseconds for AWGN channel. For the line of sight fading Rician channel with AWGN demonstrated classification speed is slower 130 000 objects/s.

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The paper treats a problem for detection and recognition objects in computer vision sector, where researchers recommended OpenCV software and development tool, it’s several and better-remembered library resource for isolating, detecting, and recognition of particular objects, what we would find an appropriate system for detecting and recognition traffic roads signs. The robustness with optimization is a necessary element in the computer vision algorithms, we can find a very large number of technics in object detection, for example, shapes transformation, color selection, a region of interest ROI, and edge detection, combined all these technics to reach high precision in animated video or still image processing. The system we are trying to develop, is in high demand in the automotive sector such as intelligent vehicles or autonomous driving assist systems ADAS, based on intelligent recognition, applying Artificial Intelligence, by using Deep Learning, exactly Convolutional Neural Network (CNN) architecture, our system improves the high accuracy of detection and recognition of traffic road signs with lower loss.

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Collaborative filtering of recommended systems (CFRSs) suffers from overrun false rating injections that diverge the system functions for creating accurate recommendations. In this paper, we propose a three-stage unsupervised approach. Starts by defining the mechanism(s) that makes recommendation vulnerable to attack. Second, find the maximum-paths or the associated related items valued by the user. We then rule out the two attacks; we will need to pull two different measures. (a) We will pull user ratings across all reviews and measure their centre variance. (b) We will then pull each individual user rating and measure them according to the original rating. Detected attack profiles are considered untrusted and, over time, if the same user is detected as untrusted, the profile is classified as completely untrusted and eliminated from being involved in the generation of recommendations. Thus, protect CFRS from creating tweaked recommendations. The experimental results of applying the algorithm to the Extensive MovieLens dataset explicitly and accurately filter users considering that a user could seem normal and slightly diverge towards attack behaviours. However, the algorithm used assumes that the framework has already begun and manages user accounts to manage the cold start scenario. The proposed method would abstractly protect users, irrespective of their identity, which is a positive side of the proposed approach, but if the same user reenters the system as a fresh one, the system will reapply algorithm processing for that user as a normal one.

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Addiction such as tobacco smoking affects the human brain and thus causes significant changes in the brainwaves. The changes in brain wave due to smoking can be identified by focusing on changes in electroencephalogram pattern, extracting different time-frequency domain features. In this aspect, a laboratory-based study has been presented in this paper, for assessing the brain signal changes due to the tobacco addiction. Four classifier models, namely, Logistic Regression (LR), K- Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest Classifier (RFC) were trained and tested for assessing the performance of the time domain, frequency domain and fusion of time-frequency domain features, with a five-fold cross-validation. Four different performance measures (sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve) were used to measure the overall performance, and the results suggested that the classifiers based on time-frequency domain features perform the best while using combinedly. Using the utilized fusion of the time-frequency domain features, the classification models can identify the smoker group with an accuracy ranged from (86.5-91.3%), where the RFC shows the best accuracy of 91.3%, which is higher than the three other classifiers models.

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Different colleges and universities have different approaches to dealing with low-performance learners. However, in most cases, analgesics do not deal with root problems. This research suggests a model of three layers of variables sequentially adaptable to a deep-root issue. The suggested model can identify early pupils who could be at risk because of inaccurate or lack of match sequences and suggest rehabilitation. The approach proposed was implemented at three levels. First, we examined the personality type for 180 learners from different majors: Security and Forensics, Networking, and Application Development, using the MBTI test. Second, we build a knowledge matrix for courses by dividing each learning outcome into its knowledge segments. Then, we build the skills matrix for courses by decomposing each learning outcome into its skills segments. We then use machine learning (SVM, DT and association rules) algorithms to mine student performance on a smaller scale of knowledge and skills, taking into account their personality types instead of measuring an entire course’s holistic performance. Finally, we developed a system of recommendations to detect performance deviations in knowledge and skills and provide adaptive learning materials that fit the examined students’ personality. The proposed approach demonstrates its validity and effectiveness. However, it needs regular updates on learners’ performance, which could be automated and linked to evaluation tools. The framework also has a minor impact on learners’ privacy since it exposes individual personalities to their advisors.

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The purpose of the research is an investigation of different accounting software products, their functions, and specific features to make easier choice among variety of similar products and analysis of their pros and cons that can influence on companies’ performance. Authors classified accounting software according to its capabilities to serve the different managerial purposes. Because accounting software contains hundreds, some of them even thousands of features, the grouping method gave a possibility to assort similar models that might suit the company’s specific requirements – size, cost, customizing, formats, appointments, models, and providers. Observation and comparing of data showed that the cost of accounting programs is critical to making the right choice. As the global accounting software market has a tendency to abrupt change to e-accounting, so that makes it impossible to predict the future behavior of accounting software users. To determine the objectives of this research statistical procedures are conducted. Received results can help potential users of accounting software products to choose the appropriate one based on listed advantages and disadvantages among the best sellers – customization tools, foreign currencies handling, financial and managerial reporting system and analytical capabilities. Lack of prior research studies on the topic and lack of available data have caused significant limitation of the analysis scope. The obtained results gave possibility to identified the main elements in formation the list of features necessary for making right choice of accounting software products. Facts showed managers, who don’t consider specific needs and features of accounting software, encounter with problem of discrepancy to company’s requirements.
The research is based on theoretical and empirical data. To collect the necessary data for research there was used a quantitative approach. Analytical method helped to analyze and evaluate the ponderable factors which must be considered in selecting process the most appropriate accounting software for companies. The research is dedicated to problems connected with an uncertainty that appears in the accounting software market. This research adds new knowledge to the accounting field as there was disproving theoretical and practical knowledge about accounting software.

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Despite the significant progress made in data mining technologies in recent years, breast cancer risk prediction and diagnosis at an early stage using DNA microarray technology still a real challenging task. This challenge comes especially from the high-dimensionality in gene expression data, i.e., an enormous number of genes versus a few tens of subjects (samples). To overcome this problem of data imbalance, a gene selection phase becomes a crucial step for gene expression data analysis. This study proposes a new Decision Tree model-based attributes (genes) selection strategy, which incorporates two stages: fisher-score-based filter technique and the gene selection ability of the C5.0 algorithm. Our proposed strategy is assessed using an ensemble of machine learning algorithms to classify each subject (patients). Comparing our approach with recent previous works, the experiment results demonstrate that our new gene selection strategy achieved the highest prediction performance of breast cancer by involving only five genes as predictors among 24481 genes.

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Presented paper discusses possibilities related to the recovery of the damaged lithium batteries after the short-circuit. The recovery procedure was applied on the selected traction LiFePO4 40Ah cell which was initially short-circuited. After the short-circuit, the damaged cell has visible damage of the electro-mechanical properties. For the recovery of damaged traction cell as much as possible, the experimental recovery procedure has been proposed. For the realization of this recovery procedure, the automated workplace for the cell discharging and charging with the proposed algorithm was created. For verification of the proposed recovery algorithm, the traction cell was tested with a delivered ampere-hour test at the various discharging currents. Results of the delivered ampere-hour test of the recovered cell were compared to results of delivered ampere-hour tests of the new cell. From the final evaluation is seen that the proposed recovery algorithm can recover up to 90% of capacity within a wide range of discharge and charge current.

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The current concept of open innovation corresponds to the R&D products transfer model – “role changes”. One of the fundamental provisions of the model is that R&D products are considered for commercialization not only at the final stage of technological readiness, but at any of them. In today’s changing market environment, special attention is paid to the transfer and commercialization of R&D products at the early stages of readiness, but this process is characterized by significant problems from the point of view of technological forecasting. To solve the problems, the article substantiates the method of technological forecasting of market behaviour of R&D products at the early stages of technological readiness, which is based on taking into account the strengths and weaknesses, development factors and limiting factors of R&D product. The method allows you to predict indicators of product behaviour relative to the market where its commercialization is planned.
As a component of the above method and in order to increase the level of reliability of calculations and validity of results, a method for determining the correction factor of indicators of market behaviour of R&D product has been developed. The method was developed on the basis of fuzzy set theory algorithms using the fuzzy logic toolbox (MATLAB), which made it possible to integrate a set of different types of forecast data on the market behaviour of an R&D product, taking into account the relationships and interdependencies between them, into one correction factor. This coefficient contains the characteristics of signs of the impact of R&D product on the market (in particular, market effects, types of market changes) and the impact of market effects on R&D product (effects generated by R&D products, organizational and technological changes in R&D products). To justify the correction factor, a knowledge base of responses from subject area experts has been formed. In order to further select a commercialization strategy for R&D product, a system with normative indicators has been developed that interpret the following types of strategies: zero-level commercialization of R&D product; first-level commercialization of R&D product; commercialization of the second level of R&D product.
The author’s method of technological forecasting of market behaviour of R&D products and choosing a commercialization strategy for a product is universal, can be applied to R&D product of any type of economic activity, transfer method, etc. Testing of the method on the example of a number of R&D products presented by the developers of the Lviv Polytechnic National University (Lviv, Ukraine) showed the validity of the author’s method and its relevance in modern conditions of market singularity.

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This paper proposes an active disturbance rejection control (ADRC) design for a haptic display platform structure. The motivation for the following scheme originates from the shortcomings faced by classical proportional integral derivative (PID) controllers in control theory. The ADRC is an unconventional model-independent approach, acknowledged as an effective controller in the existence of total plant uncertainties, and these uncertainties are inclusive of the total disturbances and unknown dynamics of the plant. The design and simulation for ADRC are established in MATLAB/ Simulink. The concerned electro-mechanical platform consists of dual ball screw driving system and DC motors. This overall physical system constitutes the haptic interface. Modelling of the two- dimensional physical platform is also explained in this article. Designing of ADRC controller and the human-machine interface (HMI) is followed by their integration, in order to obtain simulation results, thus proving the practicality and validity of the overall system. The results of the proposed controller are compared with the Proportional Integral (PI) controller, which suggests that the ADRC controller performs better as compared to the conventional PI controller.

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This paper presents the method of simulating the personal knowledge management (PKM) processes, based on Get-Understand-Share-Connect (GUSC) Model, using real event logs data from an online learning platform. The method used in here is process discovery and conformance, which are the process mining techniques. Having the model proven at granular level of multi-agent system, this research is found significant in proving that PKM indeed exists in students’ online learning behavior and needs to be monitored to ensure that they are managing knowledge in a complete cycle, to support their credibility as future graduates and knowledge workers in organizations. The ideal process starts from Get, then Understand, and followed by Share and Connect, but this study proves that the sequence may vary although the original theory is construed. This depends on the way the online activities being mapped to the Get, Understand, Share and Connect processes during the data processing stage. The results from this paper include the simulation of the GUSC model as discovered from real event logs data.

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This article copes with the formal verification of properties of the missions building module of PILOT’s software. PILOT is a language dedicated to remote control of robots. An incremental syntax-oriented editor was built in order to increase the dependability of PILOT’s missions and we showed that, under a maximum size of plan, this editor allows building only all plans that are syntactically correct. The limitation in size was due to state space explosion problem inherent to the Model-checking approach used for the proof. In order to extend the proof to all plans without any limitation in size, we investigated the theorem-proving approach, and especially PVS (Prototype Verification System). This paper therefore focuses more on modeling of PILOT plans and related building operations and the use of PVS to verify properties of the built models, in view of proving the aforementioned properties of PILOT software’s missions building module.

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In this paper, we have modeled and designed a metal mountable tag antenna that is applied to cover two major UHF RFID bands, i.e., European (EU) (865-867 MHz) and U.S. bands (902-928 MHz). It is applied for many applications, especially in the transport and logistics fields. The tag antenna configuration utilizes microstrip configuration with open bent stub feed network to attain conjugate matching w.r.t. Monza R6 chip impedance. The proposed microstrip patch-based tag antenna structure is simple without using any shorting pin/holes, thus making it easy and inexpensive to manufacture. Additionally, the proposed tag’s impedance has been easily tuned in order to achieve conjugate matching in regard to the employed chip impedance. The presented tag antenna has been fabricated and experimentally characterized to measure its read-range performance in the desired bands.
Further, the differential probe set up is used to measure the designed tag’s impedance. Also, the designed tag read-range is measured using a reader setup and is observed to exhibit read-range up to 11 m and 9 m in European and U.S. UHF RFID bands, respectively. The tag exhibits an impedance of 9.7 – j 130 ohms at 866 MHz and 8.7 – j 124 ohms at 915 MHz. The proposed tag antenna design’s performance is verified, analyzed, and optimized by CST Studio Suite software. The performances of the designed tag are evaluated and analyzed in terms of conjugate matching, reflection coefficient, and read range measurement. From the results, it is noticed that the designed tag exhibit dual-band behavior with good impedance matching, Reflection coefficient, and high read range.

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Nowadays almost every PLC manufacturer offer a so-called safety PLC. It is a specific category of PLC, which in recent years have become a commonly used means of performing safety functions, especially in industrial applications. In this area of specific applications, a maximum of SIL 3 is normally required. However, the guaranteed safety features of the PLC lead to the consideration or discussion, whether they could be used in applications with higher safety requirements. This paper deals with the possibility of using the safety PLC to implement safety functions with SIL 4. The paper presents the long-term experience of the authors in the development of control systems for railway applications with the required level of SIL4.

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Nowadays the sustainable awareness trend is increasing. The consumers’ attitude has changed, causing companies to pay more attention to management in a sustainable way. Effective sustainable supply chain management (SSCM) can increase social, economic, and environmental benefits. Important factors from literatures were gather and organized to be a framework for SSCM. The proposed framework incorporates the whole supply chain for both internal and external activities, which can be applied to a manufacturer. The case study factory, which is an automotive elastomer producer has planned to adopt SSCM, so it needs to know the main factors for its operations. Logarithmic fuzzy preference programming method (LFPP) was used to rank SSCM criteria. The results of ranking important criteria showed that external factors (government and competition) were the most significant criteria that the factory has determined. Government and competitors are significant drivers that initiate the company to implement SSCM. Regulations and standards were good guidelines to SSCM for the factory. Next, the Triple Bottom Line (TBL) criteria (social, economic, environment) were considered in the overall operations. Not only concerning about cost and profit, but also environmental effect and social responsibility are cooperated. Finally, internal factors (supplier, consumption, and company) were considered with low level of importance. The proposals of actions of the company were also shown as a guideline for a manufacturer.

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Nowadays, the Internet has become an indispensable tool for the realization and continuity of activity at a different time, place, and technological context (e.g., mobile, pc, tablet), so that interaction techniques through the use of multi-device support have become of great interest. From this perspective, continuity in interactions is an essential concept in the face of changes in context environments where an interaction develops. There are works related to the continuity and support of multi-device environments through software platforms that are useful to improve continuity support; however, reducing steps to resume an activity on a different device is an aspect that needs to be studied in greater detail. In support of the above, this paper presents an exploratory study that shows that continuity is a useful feature for users; however, there are still aspects that need to be studied. Therefore, in this paper, we propose a platform to implement continuity in a workflow that reduces the steps necessary to continue and resume activity in a different device context and a case study which serves as a method to evaluate the platform proposal and the models on which it is based.

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Cardiovascular risk today is one of the non-communicable diseases that occurs in the population of the third age where risk factors further compromise its health, therefore the objective of the study is to determine the evolution of the Cardiovascular Risk indicators in hypertensive elderly from a facility in North Lima. This is a quantitative, non-experimental, descriptive, and cross-sectional study, with a population of 47 hypertensive elderly people over 60 years of age, whose cardiovascular risk was determined with the PAHO cardiovascular risk calculator. In the results, we can see that in the month of January, 17 (36.2%) presented a moderate risk, 19 (40.4%) presented high risk, 8 (17%) presented very high risk and 3 (6.4 %) presented critical risk, in June, 17 (36.2%) presented low risk, 24 (51.1%) presented moderate risk, 4 (8.5%) presented high risk, 2 (2.1 %) presented very high risk and 1 (2.1%) presented critical risk and in December, 25 (53.2%) presented low risk, 17 (36.2%) presented moderate risk, 4 (8.5%) presented high risk and 1 (2.1%) presented critical risk. In conclusion, prevention of cardiovascular problems in the elderly should be expanded, to contribute to their health status and quality of life due to the increase in population.

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The article presents some results of the research of the LoRa module. These modules can be the basis of possible IoT technologies are implementing, providing enough good range of receiving and transmitting messages. The SX1276 transceiver has been testing to determine the signal loss in the propagation channel. These experiments took in a highly-populated Kyiv district and one of the passageways of a Podbryantsevsky salt mine near the Solar town. The measured parameters are the maximum radio communication range in the mine, the signal-to-noise ratio, the number of bit errors and losses of the signal transmission. The data of the study we plan to use for the engineering of the radio-messaging networks based on LoRa radio modules.

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The research paper aims to focus on the issues and challenges in developing a sustainable model of an ideal heritage village project by using descriptive and empirical investigation methods. To capture the perception and understanding of the concept of sustainability of a Heritage Village, a mixed-methods approach was conducted by the researcher where document where reviewed, observations were done, structured interviews and a questionnaire survey was conducted involving resident’s in the heritage villages of Pragpur and Garli in Himachal Pradesh, India. Through this research, the objective was to catalogues the resident’s outlook and understand their belongingness towards their rural settlement. The analysis conducted, was also to understand their attachment to the heritage fabric which would act as a catalyst for their sustainable development. Due to the diversification in terms of architecture, social and cultural aspects, it was important to analyze the resident’s perception towards the built heritage as it may vary to be more or less important to different people, community groups, or generations.

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This article proposes a compact and simple design of electronically adjustable voltage-mode biquadratic filter using fundamental active cell implemented on a single integrated circuit (IC) package as LT1228. The proposed circuit having triple inputs and single output (TISO) employs namely one resistor and two capacitors as the passive components. All the five possible biquadratic filtering responses, namely low-pass (LP), band-pass (BP), high-pass (HP), band-stop (BS) and all-pass (AP), are realized by the appropriate selection of the relevant input signals. The pole angular frequency and the quality factor of the proposed TISO filter are electronically tunable through the bias current of the IC chip LT1228. Non-ideal effects and sensitivity performance are carried out. The theoretical results are satisfactorily validated by both PSPICE simulation results and experimental measurements using commercially available LT1228.

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The use of heuristic methods in teaching is not possible without the cooperation of all or part of the students’ academic group. That is, the teacher who made the decision to apply the heuristic method de facto solves the issues of organizing project method in teaching. There are a large number of indications on the relationship between the effectiveness of the use of heuristic methods, taking into account the personality differences of students. As well as the importance of taking into account the personality differences in the project method. However, there is a lack of information about experimental studies in which three components: the project method, the heuristic method and the Myers Briggs personality types methodology, would be considered simultaneously. This prompted us to conduct this study. As part of the project method, a tournament among students of prospective mathematics teachers was held during 2014-2020. Teams of three types to participate in the competition were formed. There was a team whose members were not previously trained. The team whose members studied the heuristic method – “Creativity enhancement method”. And also a team whose members, along with the study of the heuristic method, were selected in a special way. Students included in this group had personality types most suitable for performing heuristic techniques, which are components of the heuristic method. The task of the tournament was to compile a set of educational problems in geometry that can be used in the school curriculum. The problems developed by the team were evaluated by the panel. Members of other teams acted as opponents and reviewers. Using the heuristic method allowed teams to prepare more problems and systematize them. The best results in the use of the heuristic method showed the team, the composition of which was selected in a special way. The survey conducted according to the results of the tournament showed an increase in students’ interest both in the studied discipline and in the project method, as well as a willingness to use the project method in their future work.

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Wireless sensor networks (WSNs) are having large demands in enormous applications for the decade. The main issue in WSNs is estimating the exact location of unknown nodes. All applications are dependent on the location information of unknown nodes in WSNs. Location information of mobile anchor node is used to estimate the location of unknown nodes. A new approach is implemented in this paper for the localization of unknown nodes using Artificial Neural Networks. Specifically, a neural feed network is used for the indoor position process. Also several neural network configuration sets have been tested, which includes Bayesian regularisation (BR), Levenberg-Marquardt (LM), resilient back propagation (RP), Scaled Conjugate Gradient (SCG) and Degree Descent (SCG),etc. At the end results are simulated using MATLAB and Mean Square Error is calculated and compared with other existing approaches. The proposed approach is energy efficient and uses only a two-way message to obtain inputs for the localization. Even the cost is minimized as in the proposed system only one mobile anchor node is used.

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The objective of this paper is to allocate transmission congestion cost to responsible generators using a novel method. Deregulation of the electrical power system leads to the compulsion of open access to the transmission system for all entities of the power system. There is a trend to utilize cheaper generators by all loads. This leads to a violation of the operational and physical constraints of transmission corridors connected to those generators. It is not possible to utilize cheaper generators all the time due to the operational and physical constraints of the transmission lines. Hence there is an increase in the cost of energy produced. This increase in energy cost is taken into account as total congestion cost. Allocation of total congestion costs among various entities is always a complex task. Here, generators liable for the increase in total congestion cost identified using Bialek’s algorithm. Bialek’s upstream algorithm was applied to allocate congestion costs to generators. Results are obtained on IEEE-14 bus and IEEE-30 bus standard test systems.

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In this paper, we investigate and analyze parameters degradation in a typical photovoltaic (PV) cell, which lead to power loss under dark as well as light condition using measured current-voltage (I-V) data. A nonlinear least squares method to extract the parameters such as the reverse saturation currents, the ideality factors, the series and shunt resistances of the cell from the dark current-voltage (I-V) curves is used. In order to analysis the sensitivity of the dark current-voltage (I-V) measurement to each of the six extracted parameters as a function of the voltage as well as the temperature and the density current, we simulate the operation of a silicon solar cell (KXB0022-12X1F). The analysis of the dark current-voltage (I-V) curves permit us to detect variation as small as 15% in the series resistance. We also extends the use of dark as well as light current-voltage (I-V) measurements to modules configurations of cells and uses a nonlinear least squares method to evaluate the cell efficiency parameters in the modules. Results obtained show a degradation of the values of the maximum power (Pmax) as compared to initial values by about 12, 3%, 12, 06% and 10, 21 % respectively in Total-Cross-Tied (TCT), Bridge-Link (BL) and Honey-Comb (HC) configurations.

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As a result of digitalization, services become highly dependent on information systems thus increasing the criticality of security management. However, with system complexity and the involvement of more human resources, it becomes more arduous to monitor and track tasks and responsibilities. This creates a lack of visibility hindering decision making. To support operational monitoring, we propose a method composed of i) a core of security concepts from International Standard Organization (ISO) standards ii) a graphical modeling language iii) a guiding process and iv) a tool that provides verification through formal Object Constraint Language (OCL) queries. Applying this method to the case of the Capital One data breach showcases incident prevention through task supervision. The resulting work product is a formal comprehensive map of assets, actors, tasks and responsibilities. The SysML formalism allows different actors to extract information from the map using OCL queries. This allows for regular task and responsibility verification thus closing any window of attack possible.

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Motivation significantly influences the outcome in the rehabilitation of patients. Several developments have been made to assess and increase patient motivation by addressing factors linked to motivation such as the personality of the patient, professional administering rehabilitation, and the rehabilitation environment. The main objective of the study is to evaluate the reliability of a gamified environment for the rehabilitation of stroke patients by testing its functionalities within standard physical therapy time and intervals. To achieve this, calibration was characterized. Also, user feedback was taken in the form of questionnaires based on the System usability scale (SUS) and Intrinsic Motivation Inventory (IMI). Based on the SUS scale, results show that the game manipulability is good, the game concept and design is satisfactory, and the game comprehensibility is also good based on the qualitative conclusion per SUS score. For the IMI ratings, it was found out that the highest rating was the perceived choice which indicates their voluntary participation in the game. Some improvements can still be added to the game itself to increase the motivation of patients. The balance board manipulability and the recalibration time interval can be further improved for comfort and ease of use by the patients.

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In this work we propose an algorithm for Peruvian counterfeit banknotes detection. Our algorithm operates in banknotes with 50, 100 and 200 soles denominations that were manufactured from 2009 onwards. This algorithm offers an automatic diagnosis based on digital image processing and support vector machines (SVM). Current Peruvian counterfeit detection systems are specially designed to analyze relevant characteristics in dollars and euros. Then, some counterfeiters can fool these systems. We made our detection system robust because we focus on the image acquisition and the segmentation of intaglio marks engraved over the banknotes. After segmentation, we applied embossing and Sobel filters followed by an aperture morphological operation to obtain special characteristics that were then classified by an SVM. We have validated our methodology using real and fake banknotes from a dataset of 240 samples provided by Central Reserve Bank of Peru (BCRP). Our final identification accuracy was 96.5%.

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Non-communicable chronic diseases are more frequent in developing countries, having a significant impact on morbidity, mortality, health care costs and productivity. A study indicates that physical exercise, glucose control, complications, hypertension, duration of diabetes, diet and depression are associated with the quality of life in patients with type 2 diabetes. The purpose of the study is to determine the quality of life in people with Type 2 Diabetes who reside in a vulnerable area of Los Olivos. The focus of this study is quantitative, its design is descriptive because it allows the study population to be described at a given moment. The population was 173 people with type 2 diabetes, from the Juan Pablo II Confraternity Maternal and Child Center in Los Olivos. The diabetes 39 questionnaire (D-39) was applied, made up of 39 items grouped into five dimensions, which measures the quality of life in diabetes. The results showed a half quality of life (51.4%), followed by high (26%) and low (22.5%). These results support the importance of promoting and educating about healthy habits in the diabetic patient, so that they can maintain an adequate quality of life. Type 2 diabetes is a silent and progressive disease in its initial stage, public health systems must increase efforts to make timely diagnoses, where the disease can be controlled avoiding the presence of complications and sequelae that can be fatal for the patient.

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The psychological impact is alarming in families since they are exposed to high risks of contagion because their mental health is altered, therefore, the objective of the research study is to determine the Psychological Impact on families due to positive cases of COVID – 19 in the Puente Piedra district, 2020. This is a quantitative, non-experimental, descriptive, and cross-sectional study, with a population of 22 families with household members infected with COVID – 19 from the Puente Piedra district, who answered a questionnaire with sociodemographic data and the Depression, Anxiety and Stress Scale (DASS-21). In the results where can be observed, with respect to the sex data of the head of the family to whom the questionnaire was carried out, where we can observe with respect to the psychological impact on families by positive cases of COVID-19 at home in the district of Puente Piedra, where the male head of the family 13 (76.5%) of the total have a medium psychological impact and 4 (23.5%) have a low psychological impact, in the female head of the family 4 (80 %) of the total have a medium psychological impact and 1 (20%) have a low psychological impact. In conclusion, greater attention should be paid to vulnerable groups such as the young, the elderly, and women since they are more prone to contracting the disease.

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In this paper, a novel approach to design a Process Design Kit Digital for CMOS 180nm process is presented. This work proposes a detailed flow to design a PDK Digital using Ocean language, which is a vital element in the semi-custom design and applied in education purposes in universities in Vietnam. The PDK digital includes Standard Cell Library containing 47 standard cells and Wire-Load Model. The library is designed based on the CMOS 180nm process with a supply voltage of 1.8V.

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Higher efficiency and lower losses are widely considered as the best metrics to optimize, in a high-power converter performance context. To provide a solution to the ever-increase of high switching frequencies challenges, we must explore soft-switching technologies to resolve interface issues and reduce the switching losses. This manuscript describes a comparative analysis between the fixed-bandwidth (FBW) and the variable-bandwidth (VBW) of the hysteresis modulation (HM) based on the conventional sliding mode (CSM) strategy. The two adopted techniques are applied to a bidirectional multichannel DC-DC asynchronous Buck converter. The cells are parallel-connected and operating in continuous conduction mode (CCM). The objective is to have a system that is more stable, more efficient and able to cope with variations in input voltage, load and desired output voltage. That requires, first, to attenuate the non-linearity phenomenon of the conventional sliding mode by placing a hysteresis modulation. Then, after applying this technique, we confronted the dilemma of the variable switching frequency. Our hypothesis was to incorporate a variable bandwidth of the hysteresis modulation. The results obtained under parametric variation clearly show the areas where significant differences have been found between the two approaches. Likewise, they both share several key features. Simulation studies in the MATLAB® / Simulink™ environment are performed to analyze system performance and assess its robustness and stability.

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Solar ultraviolet radiation (UVR) is considered a relevant health risk for the workers of the maritime and port sectors, but scant data are available on actual exposure measured using personal dosimeters. Moreover, in outdoor workers sun protection habits are usually poor, while some promising data suggest that sun-safety campaigns can be effective in increasing self-protection at work. Accordingly, our aim was to conduct an assessment of solar UVR exposure in dockworkers and fishermen using personal dosimeters, and to evaluate the use of sun protection measures at work after a sun-safety training. We performed two different UVR measurements campaigns in spring-summer 2018, investigating 7 fishermen and 14 dockworkers. Electronic dosimeters have been placed on the workers for at least a half work-day. Only at the port it was also possible to register the environmental UVR exposure with a specrto-radiometer, while for fishermen we estimated the corresponding environmental exposure using an algorithm. Our results demonstrate a high erythemal UVR dose received by the workers, with an individual exposure up to 542 J/m2 for fishermen in spring and up to 1975 J/m2 for dockworkers in summer. This data indicates an excessive occupational risk, needing more effective prevention. Accordingly, we offered a sun-safety training to the workers. Before the training, protective behaviour of the workers was rather poor: about the 50% never used the hat, the 40% never wore sunglasses and none of the workers referred to apply sunscreens at work. After the training, fishermen reported a relevant improvement in the use of individual UV protections, as hat (+9.6%), sunglasses (+28.5%) and clothes (+5%), even if the use of sunscreens at work was not increased.

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European counties continue to receive an increasing number of migrants and refugees from an also increasing number of both European and non-European countries. This results in a huge societal challenge which is societal inclusion of people speaking different languages and of diverse backgrounds. Key for their inclusion is job finding which comes with hurdles like the language, the difficulty in assessing and certifying their skills and many more. In this paper, we present the architecture of a novel platform that aspires to provide migrants with a) assistance in discovering and assessing their hard and soft skills by employing Artificial Intelligence technologies, b) recommendations of appropriate job sectors and positions based on their profile, c) recommendations about training that would allow them to find jobs in the country/region they are located and d) practical information regarding the integration process. Furthermore, the proposed platform aims at assisting host authorities, non-governmental organizations and companies in detecting the needs of the target populations (migrants and refugees) through data analytics and supports them in reaching them. Apart from the technical architecture, we provide the results from the initial testing of the platform in real-life pilots in two countries.

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The objective of this research is to study the behaviour of the anaesthesia monitor Conox during natural sleep to open the gate for this devices to assess subjects during this stage. The values of qCON and qNOX indices and EEG frequency bands are analysed during night sleep of 10 volunteers when they lose consciousness, in order to determine if they can be used for monitoring sleep. The possibility of using these indexes to differentiate between NREM/REM cycles of night sleep is studied. A reduction in the hypnotic index was observed while the nociception index stayed significantly higher. Statistical differences where found for qCON between sleep cycles, allowing this index to detect REM intervals and possibly opening the gate to use depth of anaesthesia devices to monitor sleep.

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Software evolution is one of the software process activities that occupies a major percentage of software development cost. Since requirements change continually and new technologies emerge, software should be adapted to satisfy these new changes to continue to survive. Despite software evolution being performed after software validation and deployment, software developers should predict at earlier stages how software would evolve in the future to avoid surprises. Although many works focus on how to enhance the program structure to facilitate maintenance tasks, only few works treat software evolution in earlier phases of software development process. In this direction, we propose an UML profile that permits to tackle software maintenance issues at the early phases of software development process. The proposed approach helps software developers to predict in design phase the kind of maintenance tasks that could occur in the future.

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The optimum performance of power plants has major technical and economic benefits. A case study in one of the Malaysian power plants reveals an escalating harmonic failure trend in their Continuous Ship Unloader (CSU) machines. This has led to a harmonic filter failure causing performance loss leading to costly interventions and safety concerns. Analysis of the harmonic parameter using Power Quality Assessment indicates that the power quality is stable as per IEEE standards; however, repetitive harmonic failures are still occurring in practice. This motivates the authors to explore whether other unforeseen events could cause harmonic failure. Usually, post-failure plant engineers try to backtrack and diagnose the cause of power disturbance, which in turn causes delay and disruption to power generation. This is a costly and time-consuming practice. A novel event-based predictive modelling technique, namely, Event Modeller Data Analytic (EMDA), designed to inclusive the harmonic data in line with other technical data such as environment and machine operation in the cheap computational effort is proposed. The real-time Event Tracker and Event Clustering extended by the proposed EMDA widens the sensitivity analysis spectrum by adding new information from harmonic machines’ performance. The added information includes machine availability, utilization, technical data, machine state, and ambient data. The combined signals provide a wider spectrum for revealing the status of the machine in real-time. To address this, a software-In-the-Loop application was developed using the National Instrument LabVIEW. The application was tested using two different data; simulation data and industrial data. The simulation study results reveal that the proposed EMDA technique is robust and could withstand the rapid changing of real-time data when events are detected and linked to the harmonic inducing faults. A hardware-in- the-Loop test was implemented at the plant to test and validate the sensitivity analysis results. The results reveal that in a single second, a total of 2,304 input-output relationships were captured. Through the sensitivity analysis, the fault causing parameters were reduced to 10 input-output relationships (dimensionality reduction). Two new failure causing event/parameter were detected, humidity and feeder current. As two predictable and controllable parameters, humidity and feeder velocity can be regulated to reduce the probability of harmonic fluctuation.

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Real-time embedded systems are widely adopted in applications such as automotive, avionics, and medical care. As some of these systems have to provide a guaranteed worst-case execution time to satisfy the time constraints, understanding the timing behaviour of such systems is of the utmost importance regarding the reliability and the safety of these systems. In the past years, various timing analysis techniques have been developed. Probabilistic timing analysis has recently emerged as a viable alternative to state-of-the-art deterministic timing analysis techniques. Since a certain degree of deadline miss is still tolerable for some systems, instead of deriving an estimated worst-case execution time that is presented as a deterministic value, probabilistic timing analysis considers execution times as random variables and associates each possible execution time with a probability of occurrence. However, in order to apply probabilistic timing analysis, the measured execution times must be independent and identically distributed. In the particular case of hybrid timing analysis, since the input and the initial processor state of one software component are influenced by the preceding components, it is difficult to meet such prerequisite. In this article, we propose a hybrid probabilistic timing analysis method that is able to (i) reduce the dependence in the measured execution times to facilitate the application of extreme value theory and (ii) reduce the dependence between software components to make it possible to use convolution to calculate the probabilistic WCET of the overall system.

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In a content-based image retrieval system(CBIR) feature classification,identification, and ex- traction play an important role. The retrieval of images using a single feature is a challenging task in CBIR systems. The high retrieval rates are reported based on combining multiple features, multiple algorithms and preprocessing steps, feature classification, and segmentation because the image retrieval are mainly based on the content in an image. This paper presents a texture feature extraction for the image retrieval system from semivariogram and robust semivariogram technique. A semivariogram is a statistical approach that provides the textural information based on the lag distance ’h’.The proposed method is tested on various standard image databases such as Corel-1k, Corel-10k, and Coil-100 database. The semivariogram and robust semivariogram methods are tested for the Corel 1k database using four distance metrics i.e. Euclidean, Manhatten, Canberra, and Chord distance to check which distance measure is appropriate for the CBIR system. The proposed method is also tested on three types of databases to investigate the performance of the CBIR system. The Matlab simulation results show that the e clidean distance.

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Computer Vision is a domain which deals with the challenge of enabling technology with vision capabilities. This goal is accomplished with the use of Convolutional Neural Networks. They are the backbone of implementing vision applications on embedded systems. They are complex but highly efficient in extracting features, thus, enabling embedded systems to perform computer vision applications. After AlexNet won the ImageNet Large Scale Visual Recognition Challenge in 2012, there was a drastic increase in research on Convolutional Neural Networks. The convolutional neural networks were made deeper and wider, in order to make them more efficient. They were able to extract features efficiently, but the computational complexity and the computational cost of those networks also increased. It became very challenging to deploy such networks on embedded hardware. Since embedded systems have limited resources like power, speed and computational capabilities, researchers got more inclined towards the goal of making convolutional neural networks more compact, with efficiency of extracting features similar to that of the novel architectures. This research has a similar goal of proposing a convolutional neural network with enhanced efficiency and further using it for a vision application like Image Classification on NXP Bluebox 2.0, an autonomous driving platform by NXP Semiconductors. This paper gives an insight on the Design Space Exploration technique used to propose A-MnasNet (Augmented MnasNet) architecture, with enhanced capabilities, from MnasNet architecture. Furthermore, it explains the implementation of A-MnasNet on Bluebox 2.0 for Image Classification.

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This manuscript intending to publicize the improvements incorporated in the BrcLightning application, including the Risk Analysis module with the help of a pop-up, which provides the result and assists in the identification of mitigating measures by the professional, which must be defined to reduce the calculated risks. Other points addressed in this extension are the improvements added to the database to meet the corporate demands of companies, referring to the Risk Analysis module. It also incorporates flexibilities to perform the sizing separately, in the design, evaluation and scaling modules of the LPS – Lightning Protection System that using rolling sphere method and Angle Method, incorporating, in some of the modules, the issue of opinions or alerts. These modules use the mathematical approach methodology. In addition to these improvements, this review included the reporting module of facilities in the filter system, which allows the use of the database more selectively for the emission of these documents. This filter has a structure for issuing corporate demands of reports. The results can be obtained quickly and easily, on-screen or printing several reports. The reliability and safety of the results can be assessed through the check with the examples of the standards that define the criteria and methodology, that must be followed to carry out for cases of Risk Analysis or through graphic drawings on AutoCad platform or similar for the sizing modules. Other improvements in this extension are the addition of topics for new modules for which we already have the equations modeled in Excel, although we have not yet coded in the programming language.

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A very large number of workers is occupationally exposed to Optical Radiation (OR) worldwide, while indeed nowadays an exposure to Electromagnetic fields (EMF) can occur in almost all workplaces. OR origin can be natural, including the most relevant source, i.e. the sun, or artificial, that can be further classified in incoherent and coherent, i.e. the LASERs. Solar radiation (SR) exposure, and in particular its most harmful component, the ultraviolet radiation (UVR), is a significant occupational risk in “outdoor workers”, including e.g. farmers and construction workers. UVR is mainly absorbed in the eye and the skin, there inducing various short-term and chronic adverse health effects, as burns, cataract and skin cancers. At least in Europe, for SR exposed workers no specific obligations currently exist regarding the Health Surveillance (HS), that is instead required for occupational exposures to artificial OR according to the legislation of the European Union (EU, Directive 2006/25/EC). Considering now EMF, the EU Directive 2013/35/EU provides an obligation for the HS of exposed workers, aimed at the prevention of the possible direct short-term effects, as involuntary contractions or temperature increase of tissues, and indirect effects, as shocks and interference. Conversely, long-term effects are not considered in the Directive as data on causal relationship, including reliable mechanisms, are considered inadequate. Direct short-term and indirect effects can appear solely in case of high exposures, usually occuring only accidentally, but a specific group of workers, defined “at particular risk”, exists, and it includes e.g. persons with implanted active medical devices, as cardioverter defibrillators or pacemakers. In these workers, adverse effects can be induced at lower EMF levels. The identification and an adequate protection of the workers at particular risk is one of the main goals of the HS of occupational EMF exposure.
The main HS criteria applicable for workers with exposure to OR and EMF are discussed in this article.

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A sensor fusion concept integrating the optical and microfabricated eddy-current sensor for the non-destructive testing of the grinding burn is reported. For evaluation, reference grinding burn with varying degrees are fabricated on 42CrMo4 tool steel cylinder. The complementary sensing nature of the proposed sensors for the non-destructive testing of the grinding burn is successfully achieved, wherein both the superficial and an in-depth quantitative profile information of the grinding zone is recorded. The electrical output (voltage) of the optical sensor, which is sensitive to the optical surface quality, dropped only by 20 % for moderate degree of grinding burn and by ca. 50 % for stronger degree of grinding burn (i.e. by exclusively considering the superficial surface morphology of the grinding burn). Moreover, a direct correlation among the average surface roughness of the grinding burn, the degree of grinding burn and the optical sensor’s output voltage was observed. The superficial and in-depth information of the grinding burn was recorded using a microfabricated eddy-current sensor (planar microcoil with circular spiral geometry with 20 turns) by measuring the impedance change as function of the driving frequency. The depth of penetration of induced eddy-current in the used 42CrMo4 workpiece (with a sensor to workpiece distance of 700 µm) varied from 223 µm to 7 µm on increasing the frequency of the driving current from 1kHz to 10 MHz, respectively. A very interesting nature of the grinding burn was observed with two distinct zones within the grinding zone, namely, the superficial zone (starting from the workpiece surface to 15 µm in grinding zone) and a submerged zone (>15 µm within the grinding zone). The impedance of the microcoils changed by ca. 8 % and 4 % for the superficial and submerged zone for regions with stronger degree of grinding burn at a frequency of 10 MHz and 2.5MHz, respectively. Furthermore, a correlation between the microhardness of the grinding burn and the impedance change is also observed.

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For its importance nowadays in a wide range of applications such as the anechoic chamber, we introduce a microwave ultra-broadband polarization-independent metamaterial absorber (MA) in the Ultra High Frequency (UHF)/ Super High Frequency (SHF) frequency bands. Through this work, we improved the Relative Absorptive Bandwidth (RAB) of the conventional pyramidal absorber (CPA) by modifying its altitude to a curved shape. As a result, the RAB increased from 25.9 % to 71.82 % with an absorptive level greater than 90% paving the way to an optimized structure for a broader band of absorption. As a second target, we looked for widening the broadband absorption of the CPA in the low-frequency region. To achieve this aim, we introduced two new prototypes. The first with a total thickness of 12.7 cm, consisting of 35 curved resonant layers where numerical simulations show an enhanced design with an absorption band from 0.3 GHz to 30 GHz referring ta a RAB of 182%. The second prototype consists of a cell containing different pyramidal absorbers grouped in-plane in a unit cell; such structures operate in complementary bands. This prototype is dedicated to combining these bands of absorption. After that, an enhancement is presented of this latest to reach a well-combined band with a RAB of 128.69%. We used for simulation, testing, and collecting results the High-Frequency Structure Simulator (HFSS) tool.

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In underwater photographs are look like low-quality images, the main reason is behind that due to attenuation of the propagated light, absorption and scattering effect. The absorption significantly reduces the light energy, while the dispersion causes changes in the light propagation path. They result in foggy appearance and degradation of contrast, causing misty distant objects. So, for getting the most effective result from that type of image, there must be an enhancement technique that has to be applied. We propose an efficient technique to enhance the images captured underwater by applying a fusion-based technique using super-resolution. For enhancing images, we have followed two steps. The first one illumination adjustment and another one is color correction. Then fusion technique is applied to the resultant image from illumination adjustment and color correction as two inputs and combined them with their maximum coefficient value and received output from there. After that, on the fused output image, we used the Super-Resolution method. In the Super-resolution procedure, low resolution and high-resolution images are used then a bi-cubic interpolation algorithm and finally, VDSR (very-deep super-resolution) neural network has been used to get the most effective result from an obscure underwater image. For getting the most effective result from an obscure image, a new high-quality and efficient image enhancement method has been proposed in this paper.

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Robotic arms or industrial robots are a machinery that is widely used in the medical and military industries because it is a flexible, highly accurate and reliable. It is very necessary to work in complex tasks requiring more accuracy than humans can work. This paper presents an estimate of the standard uncertainty of 6 DOF robotic arm, KUKA KR5 ARC robot, and describes the experimental setup of a laser tracker to measure the position of the reflector mirror installed on a robot end-effector. This research describes the method of testing and experimenting to calculate the errors of each joint by using the inverse kinematic model, calculating the actual angle of their joint in comparing it with a nominal joint angle. The Jacobian matrix was applied to calculate the robotic position error. The calculation of uncertainties of each joint was conducted by using the Jacobian matrix to calculate the uncertainty in the robot and the four points testing were designed for estimating the error value and uncertainty value. The results showed that the error and uncertainty of each test point were within the range of the average error and the average uncertainty of the robot specification. The position errors and the position uncertainties of all test points within the robotic moving space were calculated and estimated by the proposed method and model. Therefore, the position error tolerance of each required moving target point must be smaller than the position errors and the position uncertainties that are estimated from this proposed model. These estimated robot linear position end effector uncertainties were used to compare and adjust the robotic path based on the required robotic position target and tolerance control.

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This study tests the feasibility of manufacturing concrete blocks made of recycled materials. The paper is an extension of work originally presented in ASET conference in Dubai. The paper, depicts and analyzes how the characteristics of the blocks (strength/durability) are affected by the presence of recycled concrete ingredients (recycled aggregate (RA)) and recycled water (RW). The recycled materials (RA and RW) were mixed in 16 different configurations; from each one 10 samples were prepared for testing. In each concrete configuration the RA and RW gradually replaced the fresh materials at 25%, 50%, 75%, and 100%. The RA moderately impacted the bearing capacity but significantly impacted the durability. The results show that using recycled aggregate decreases the bearing capacity by 22% (at the 100% replacement), and the recycled water slightly affected the bearing capacity (at the 100% replacement). To boost the durability, the ground granulated blast furnace slag (GGBS) was used, in the concrete mix, instead of the ordinary Portland cement (OPC). The GGBS was used at 3 magnitudes: 25%, 50%, and 75% of OPC. As a result the carbon foot-print footprint (1000 kg/m3) was significantly lowered. Besides, the strength and durability of the blocks are reasonably enhanced. Generally, producing blocks from recycled materials is economical and feasible. The use of GGBS helps to lower the carbon footprint and enhance the strength and durability.

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The leading place among diseases of the musculoskeletal system is occupied by various feet deformations. Clinical movement analysis and posturological examination are required to objectively assess the distribution for load caused by the weight of human body on the feet and its locomotion effect. In normal conditions, the foot is exposed to elastic deformations. When analyzing the foot loads, it`s necessary to consider shock loads as one of dynamic load types. The foot is the first to perceive the shock impulse by support reaction, and the further nature for interaction with the environment directly depends on its functional capabilities. However, the foot supporting properties haven`t been fully researched. The purpose for this research is to increase the accuracy of estimating the human foot biomechanical parameters, by assessing the dynamic impact, namely short-term shock loads by step cycle relevant phases. This goal is solved by developing a method of static-dynamic load analysis, which allows to estimate dynamic and shock loads on foot and is reduced to determining the capacity coefficients, dynamic and shock loads. In the course of studies, conducted in this research, it was found that the maximum contact per unit time has front section (repulsion phase), then – the rear section (landing phase) and the smallest – the foot middle section (rolling phase), the greater speed and length step – so the greater shock loads coefficient, and their peak falls on the front and rear sections. The practical significance of the obtained results is to improve the existing methods of researching biomechanical parameters by comprehensively assessing by standing and gait features, foot step cycle and support properties.

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The behavior of rivers’ hydrology and flow under changing climate has been an objective of interest for long time. In this study the impacts of climate change on streamflow of the Arkansas River will be investigated. The paper is an extension of work originally presented in ASET conference in Dubai. The Arkansas River is a crucial element in the economy of the Colorado state in the USA. It is a vital transportation channel and main source of water for irrigated agriculture. In order to understand the direction and magnitude of climate change, the changes in the monthly flow regimes of the Arkansas River were projected using two future climate scenarios. The projections extend over 100 years (2000 – 2100). The projections were carried out in the period from April to September because this is the period of the river’s significant runoff. For better presentation the monthly flows were aggregated and presented on decadal time scale. Project stream flow is simulated using a neural network that was developed to autonomously model the relationship between different flow levels and the resultant changes in temperature and precipitation. In general, the projections depict a rise in the magnitude of the flow in the river. In general the increases concurred with the patterns of temperature and precipitation projected for the region. Noticeably, the high temperatures cause the precipitation to melt earlier shifting the peak flow to April instead of June. Statistical analysis show that in the future the current levels of flow would be surpassed more frequently. The probability of exceedance fluctuates between from month to month – reaching its peak in April-July; before retreating to a very low level in August and becoming almost negligible in September. Overall, the results reveal profound implications for regional water resource planning and management.

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The literature shows a growing interest in taking into account human and organizational factors (HOFs) to achieve safe and successful human performance by reducing the risk of errors. In this sense, the concept of maturity models aims to help companies in the integration of these factors by assessing the current level of maturity and define future areas for improvement. The HOFs maturity model shown in this article is based on the five main factors that can impact human performance and safety positively. The measurement methodology consists in applying the Fuzzy Analytic Hierarchy Process (FAHP) method to calculate the weighting of the elements of the model since they do not have the same importance. Next, the Fuzzy Comprehensive Evaluation Method (FCEM) is used to determine the maturity level in terms of HOFs among the five proposed by performing an assessment of the sub-factors using a questionnaire. The purpose of using fuzzy logic is to deal with vagueness and uncertainty of the human reasoning . The proposed model and methodologies are implemented to bring out the current situation of a Moroccan mining organization and identify the elements that require more effort to reach the next level of maturity.

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This paper describes an experimental setup that employs ultrasound to scan an area. This method utilizes ultrasonic waves to scan the surface of a submerged object in a water-coupled medium. A pulse-echo mode is used, and quantitative data are collected at various positions using a two- dimensional automated table. A microcontroller controls the motion of the scanner, whereas a script developed in MATLAB controls the ultrasonic pulser receiver process. The MATLAB script ultimately controls and correlated between the scanner movement and ultrasonic pulser receiver process. The intensities of the reflected waves are captured and used to generate the A-scan image for the external surface. The surface profile of the scanned object can be clearly obtained using the time arrival of the reflected waves. The experimental results based on a one-pound coin indicate that the precision of the proposed process. This simple and efficient method can be used in different engineering applications with minimum errors.

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This study attempts to develop theoretical criteria for verifying the morality of the actions of artificial intelligent agents, using the Turing test as an archetype and inspiration. This study develops ethical criteria established based on Kohlberg’s moral development theory that might help determine the types of moral acts committed by artificial intelligent agents. Subsequently, it leverages these criteria in a test experiment with Korean children aged around ten years. The study concludes that the 10-year-old test participants’ stage of moral development falls between the first and second types of moral acts in moral Turing tests. We evaluate the moral behavior type experiment by applying it to Korean elementary school students aged about ten years old. Moreover, this study argues that if a similar degree of reaction is obtained by applying this experiment to future healthcare robots, this healthcare robot can be recognized as passing the moral Turing test.

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Sustainable urban development and utilization of Internet of Things (IoT) technology is driving cities globally to evolve into Smart Cities (SC). The power of IoT services and applications will enable public agencies to provide personalized services to the citizens and inevitably improves their much-needed quality of life. However, although the use of IoT technology proves to be advantageous to citizens, it is not without challenges, particularly concerning with the management of information security. As agencies prepare towards SCs with the utilization of IoT, their Information Systems (IS) security management is even more critical. Current IS security management approaches must be reviewed and potentially revise appropriately in tandem with the increasing commercial use of the IoT technology. Therefore, this paper aims to discuss challenges in the IS management specifically in protecting and assuring information accuracy and completeness. Document analysis on relevant literature has been carried out to identify and analyse the challenges. The result discusses that the IS security management for IoT-enabled SC is challenged in five aspects: governance, integrity, interoperability, personalization, and self-organizing. Considerations of these challenges will support SC development concerning the IS security management in IoT-enabled SC.

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This paper is an extension of previous research that has been done on factors affecting digital payment adoption in the UAE. This study focuses on analyzing which relevant demographic factors affect new e-payment technologies, mainly in the smart city Dubai, with more complexities and dynamics of variables that affect users’ behavior toward adopting new technologies. The current research included a wider range of demographic factors compared to previous studies. Quantitative methods were conducted using a survey of 270 individuals living and working in Dubai. This study revealed that e-payment adoption is very high, which could be aligned with the national digital transformation strategy of the UAE. The results of the chi-square test for independence indicate that using e-payment technologies is positively associated with the level of education and the level of income. This is confirmed by the fact that the UAE’s demographic shape is identified by its high-income groups, positively influencing the residents’ e-payment adoption. Surprisingly, the significant results for independence were not found between using e-payments and the gender, marital status, age group, and the current professional position in Dubai. This research’s contribution adds to both academia and industry in the digital transformation and technology adoption field. Based on the results, it is recommended for decision-makers to leverage education, digital literacy, and income to accelerate moving toward a cashless economy. However, not having statistically significant differences between the rest demographic variables and adoption will encourage businesses and e-payment service providers to deliver new innovative e-payment models and technologies in a smart city context.

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In the phosphate industry, sulfuric acid is a key compound in phosphoric acid and fertilizer production. Industrially, the sulfuric acid H2SO4 is made generally in a sequence of three main steps: burning liquid sulfur with air, catalytic oxidation of sulfur dioxide SO2 to sulfur trioxide SO3, and formation of H2SO4 by the reaction of H2O with the SO3. The catalytic conversion of the SO2 into the SO3 is considered as the crucial reaction that affects the gas emissions and the performance of the process. In this paper, an industrial SO2 conversion unit of four catalytic beds reactors with vanadium pentoxide as a catalyst, and three heat exchangers were modeled. The model was based on heat transfer, energy and mass balance equations, and the kinetic reaction of the SO2 catalytic conversion was proposed and calibrated using the experimental plant data. The simulation of the four catalytic beds was carried out in steady-state and dynamic mode using Unisim Design R451 simulator. The proposed model was tested and validated using the studied plant measurements, and the accuracy of the model has exceeded 97%. A graphical interface of the SO2 conversion unit was integrated to make it suitable for industrial use and operator training. Finally, a digital twin (DT) of the studied conversion unit was developed based on an architecture integrating the plant, the virtual system, and the communication part in a Distributed Control System (DCS) context. The developed DT in this work makes it possible to simulate in real-time the SO2 conversion unit, predict the process performance, and optimize the unit efficiency.

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The main purpose of this paper is to present a reusability approach that helps the designer to assess the best practice to restore a heritage building. Based on the literature review, the reusability process and attributes was used as a method to restore the heritage building. Considering these approaches helps the designer to achieve useful results in terms of the built environment and building performance; moreover, it helps the designer to identify the suitable new usage of the building. Also, the designer validated the building performance through using the TAS to assess the thermal comfort of the building after using passive techniques and design restorations. The obvious finding was the successful achievement through considering this approach and decreasing the interior temperature two degrees. This study can be assessed as one of the optimistic practices that considered the sustainability dimensions during the restoration process, as well as improving the thermal comfort of the building for the end user. The research paper provides a useful understanding of designers, restorers and researchers.

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Human have the god gifted ability to focus on the essential part of a visual scenery irrespective of its background. This important area is called the salient region of an image. Computationally achieving this natural human quality is an attractive goal of today’s scientific world. Saliency detection is the technique of finding the salient region of a digital image. The color contrast between the foreground and background present in an image is usually used to extract this region. Seam Map is computed from the cumulative sum of energy values of an image. The proposed method uses seam importance map along with the weighted average of various color channels of Lab color space namely boundary aware color map to extract the saliency map. These two maps are combined and further optimized to get the final saliency output using the optimization technique proposed in a previous study. Some intermediate combinations which are closer to the proposed optimized version but differ in the optimization technique are also presented in this paper. Several standard benchmark datasets including the famous MSRA 10k dataset are used to evaluate performance of the suggested procedure. Precision-recall curve and F-beta values found from the experiments on those datasets and comparison with other state of the art techniques prove the superiority of the proposed method.

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The design of microelectronic systems is often complex, therefore metaheuristics can be of a great interest, because in most cases these systems have conflicting objectives and constraints. In this paper, we demonstrate the application of multi-criteria design strategies to a CMOS current conveyor. This provides designers with the ability to develop solutions that can meet several objectives respecting the design constraints. Therefore, three evolutionary algorithms well-known for their best performance in the resolution of more difficult multi-objective problems are proposed. They are first applied to the well-known benchmark functions and then for the optimal design of the current conveyor transistors in the framework of the 0.18µm CMOS technology. The aim is to maximize the bandwidth and minimize the parasitic input resistance respecting the technological constraints of the circuit. The obtained results are integrated in Cadence tool to show their validities. Final performances obtained by the three methods are in agreement and are better compared to the state-of-art-results.

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Software maintenance contributes the majority of software system life cycle costs. However, existing approaches with automated code analysis are limited by accuracy and scope. Using human-assessed methods or implementing quality standards are more comprehensive alternatives, but they are much more costly for smaller organizations, especially in open- source software projects. Instead, bugs are generally used to assess software quality, such as using bug fixing time as an estimate of maintenance effort. Although associated bug reports contain useful information that describe software faults, the content of these bug reports are rarely used. In this paper, we incorporate quality standards with natural language processing techniques to provide insight into software maintainability using the content of bug reports and feature requests. These issues are classified with an automated approach into various maintainability concerns whose generalizability has been validated against over 6000 issue summaries extracted from nine open source projects in previous works. Using this approach, we perform a large empirical study of 229,329 issue summaries from 61 different projects. We evaluate the differences in expressed maintainability concerns between domains, ecosystems, and types of issues. We have found differences in relative proportions across ecosystem, domain and issue severity. Further, we evaluate the evolution of maintainability across several versions in a case study of Apache Tomcat, identifying some trends within different versions and over time. In summary, our contributions include a refinement of definitions from the original empirical study on maintainability related issues, an automated approach and associated rules for identifying maintainability related quality concerns, identification of trends in the characteristics of maintainability related issue summaries through a large-scale empirical study across two major open source ecosystems, and a case study on changes in maintainability over versions in Apache Tomcat.

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To this extent, the delineation of techno-economic evaluations for transformers becomes more intricate through a lens of Distributed Solar Photovoltaic (DSPV) market in South Africa. Essentially, the transformer price and loss evaluation techniques should be tailored for calculating the Total Ownership Cost (TOC) of transformers facilitating decentralized energy systems. In South Africa, the traditional coal power generation and renewables operate concurrently under liberalized energy markets but have distinct operational requirements and therefore have distinct methods for evaluating their generating states, service loss costs and TOC. As a result, their techno-economic evaluations should be different. In this work, new formulae have been developed to contemplate on a comprehensive technique for calculating the transformer prices and losses necessitated to estimate the cost of service losses and TOC for DSPV transformers. These formulae are based on experimental studies undertaken on a fleet of DSPV transformers ranging from 1.25 to 250MVA. In order to substantiate these new formulae, 4 case studies have been presented. The calculated losses and associated cost results against the pragmatic values from the case studies yield an error of estimation of less than 1% and 2% respectively in all cases. Further, these results are used to calculate the cost of losses and TOC using a methodology that has been proposed in previous work exclusively for power producers who are proprietors of DSPV generation systems.

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Compared to other types of autonomous vehicles, the single-seat is the simplest when designing, since its compact design makes it an option that can simplify different mechanical aspects and enhance those of greater importance such as the steering and the braking system. Likewise, the electronic and electrical design may be a great improvement on the vehicle. It enhances the safety on road by interacting with the mechanical parts of the vehicle and increasing the driver’s perspective or reaction in a larger range of scenarios. For an electric vehicle is also important to clarify that, as an internal combustion engine vehicle, it needs to be regulated and have all the necessary equipment to circulate on the streets. Other interesting information is that an electric vehicle can be recharged with electricity and it can come from renewable energy, diminishing its already lower carbon footprint. Therefore, to achieve autonomy over the detection and evasion of objects, the application of intelligent algorithms is dispensable. To achieve the obtained result, a Q-Learning algorithm was applied on the complete 3D model of the vehicle in a simulation environment, which allows finding the best parameters of forward and turning speed. In this way, by reaching a design that meets the requirements and applying the results obtained in the aforementioned algorithm, it allows their interaction in a real environment to be satisfactory.

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Coronavirus pandemic and transition to distance learning have significantly accelerated the introduction of Education 3.0 – 4.0 technologies into traditional educational process. This paper discusses questions of training of IT- specialists in context of Education 4.0. Based on our experience, approaches to the organization of the educational process of IT- students are considered. It is discussed, what elements of mobile learning, self-directed learning and informal learning are used by students. Informal learning in traditional educational process of IT- students is considered in such aspects as the source of knowledge, personalization, teaching/learning methods. The paper discusses a stage-by-stage approach to formation of interdisciplinary educational content for IT-students. In conclusion, the strategies of teaching / learning of Education 4.0, useful for forming of competencies for Industry 4.0, are discussed.

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South Africa is aiming to achieve a generation capacity of about 11.4GW through wind energy systems, which will contribute nearly 15.1% of the country’s energy mix by 2030. Wind energy is one of the principal renewable energy determinations by the South African government, owing to affluent heavy winds in vast and remote coastal areas. In the design of newfangled Wind Turbine Generator Step-Up (WTGSU) transformers, all feasible measures are now being made to drive the optimal use of active components with the purpose to raise frugality and to lighten the weight of these transformers. This undertaking is allied with numerous challenges and one of them, which is particularly theoretical, is delineated by the Eddy currents. Many times the transformer manufacturer and also the buyer will be inclined to come to terms with some shortcomings triggered by Eddy currents. Still and all, it is critical to understand where Eddy currents emanate and the amount of losses and wherefore the temperature rise that may be produced in various active part components of WTGSU transformers. This is the most ideal choice to inhibit potential failure of WTGSU transformers arising from excessive heating especially under distorted harmonic load conditions. In the current work, an extension of the author’s previous work, new analytical formulae for the Eddy loss computation in WTGSU transformer winding conductors have been explicitly derived, with appropriate contemplation of the fundamental and harmonic load current. These formulae allow the distribution of the skin effect and computation of the winding Eddy losses as a result of individual harmonics in the winding conductors. These results can be utilized to enhance the design of WTGSU transformers and consequently minimize the generation of hotspots in metallic structures.

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This study is a review of the literature on open access (OA), seeking to identify trends in research on the subject. This review was conducted in the SCOPUS database and focused on the following as the main topics: 1. Financial aspects, 2. Repositories, 3. Education, 4. Academic community’s perception of OA resources, 5. Tools, 6. Policies, 7. Institutions, 8. Stakeholders, and 9. Impact. Out of these topics, the financial aspect, especially in OA’s publication costs, was identified as driving great interest among researchers in the field. On the other hand, the study of the impact of OA is a subject little examined. Although research on OA in the higher education sector analyzes different perspectives and describes advances, challenges, and concerns, it is fair to conclude that OA encourages the creation and dissemination of knowledge and academic communication.

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The article suggests four circuit topologies for the practical simulation of grounded and floating lossy inductors. All the suggested circuits use commercially available integrated circuit LT1228 chips as active elements, and only two passive elements, namely one resistor and one capacitor. The first two of the proposed circuits employ only a single LT1228 active element and can realize grounded lossy inductors without the need for element-matching conditions. The last two of the proposed circuits can realize synthetic floating lossy inductors with only two LT1228s. The values of simulated equivalent elements can be tuned electronically by simply adjusting the external DC bias current of the LT1228. Non-ideal transfer error effects of the LT1228 on the synthetic inductor performance are inspected. Sensitivity performance concerning transfer errors and active and passive elements is also demonstrated. PSPICE simulation results and experimental measurements of the commercially available integrated circuit, LT1228, are incorporated to corroborate all our theoretical analyses.

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Fresh water is already scarce in the world, especially in the Middle East (ME). Desalination industry is the main supplier of the potable water to the municipalities in the ME region. It is well known the high cost of a liter of water produced by the desalination process. Unfortunately, car-washing service consumes substantial amount of this desalinated water. This paper describes a filtration system designed and tested for treatment and reuse of the wastewater generated in car-washing stations. The filtration system assembled from two filters: (1) sand and gravel mix, and (2) activated carbon. The paper is an extension of work originally presented in ASET conference in Dubai. The quality of the effluent (treated wastewater) was investigated and determined in Dubai central laboratories. Wastewater samples were grabbed from different car service stations. Representative samples were prepared and the concentrations of the following parameters were measured in each sample of the effluent: (1) Biological oxygen demand (BOD), (2) Chemical oxygen demand (COD), (3) Total dissolved solids (TDS), (4) Total suspended solids (TSS), and (5) Oil and grease (OG). The results show that the filter system removes the BOD and COD at an efficiency as high as 97.5%, the TSS at 90%, and the TDS and OG at 85.5%. In general, the quality of the effluent was found to fall within the standards set by Dubai regulatory authorities. Further research is recommended to enhance the filtration system performance and make it commercially applicable.

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Automatic Speech Recognition (ASR) technology has the potential to improve the learning experience of students in the classroom. This article addresses some of the key theoretical areas identified in the pursuit of implementing a speech recognition system, capable of lesson summary generation in the educational setting. The article discusses: some of the applica- tions of ASR technology in education; prominent feature extraction and speech enhancement techniques typically applied to digital speech; and established neural network-based machine learning models capable of keyword spotting or continuous speech recognition. Following the theoretical investigation, a model is proposed for the implementation of an automatic speech recognition system in a noisy educational environment to facilitate automated, speech-driven lesson summary generation. A prototype system was developed and improved based on this model, ultimately proving itself capable of generating a lesson summary intended to bolster students’ secondary contact with lesson content. This topic-oriented lesson summary provides students with a lesson transcript, but also helps them to monitor educator-defined keyword terms, their prevalence and order as communicated in the lesson, and their associations with educator- defined sections of course content. The prototype was developed using the Python programming language with a modular approach so that its implemented Continuous Speech Recognition system and noise management technique could be chosen at run-time. The prototype contrasts the performance of CMUSphinx and Google Speech Recognition for ASR, both accessed via a cloud-based programming library, and compared the change in accuracy when applying noise injection, noise cancellation or noise reduction to the educator’s speech. Proof of concept was established using the Google Speech Recognition System, which prevailed over CMUSphinx and enabled the prototype to achieve 100,00% accuracy in keyword identification and association on noise-free speech, contrasted with a 96,93% accuracy in keyword identification and association on noise-polluted speech using a noise-cancellation technique.

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Industry 4.0 implementation stipulates effective actuator control. In the present work, a complex order PI^{a+ jb} D^{c+ jd} (COPID) controller was designed for a fractional order model of a direct cur- rent (DC) motor system. For comparisons, the DC motor system model was also controlled using the conventional proportional integral (PI), proportional integral derivative (PID), proportional resonant (PR) and fractional order PID controllers (FOPID). Time domain results indicated that the PR controller performed exceedingly well for output signal responses, but fared poorly in case of control signal specifications. The PI controller responses suffered from high time domain characteristics for both control and output signals. The PID controller performed moderately in terms of time domain and peak overshoot metrics. The FOPID controller attained the best time domain characteristics, but was unable to effectively limit the control and output signal peak overshoots. It was only the COPID controller, that successfully minimised / eliminated peak overshoots in control and output signals (0.1 % and 0.0 % respectively). Moreover, the COPID controller was also successful in limiting the rise, peak and settling times. In addition, Bode diagram, root locus plot were obtained and system gain parameters were varied to confirm the robustness of the proposed COPID controller. Thus, COPID controller promises to be an effective solution towards accurate and robust actuator control in modern manufacturing.

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In this article supervised classification methods for the analysis of local Panamanian rice crops using Near-Infrared (NIR) spectral signatures are assessed. Neural network ( Multilayer Perceptron-MLP) and Tree based (Decision Trees-DT and Random Forest-RF) algorithms are used as regression and supervised classification of the spectral signatures by rice varieties, against other crops and by plant phenology (days after planting). Also, satellite derived spectral signature is validated with a field collected spectral model. Results suggest that MLP networks, either for regression or classification, were more efficient (RMSE of 8.78 and 0.068, respectively) than either tree based methods to regress/classify the rice spectral signature (RMSE of 19.37, 19.09 and 0.979, respectively). The validation made using satellite derived spectral signatures resulted in MLP models with RMSE of 0.216 and 7.318, respectively, leaving room for further improvement of the models. This work aimed to present a practical example of the employment of recent supervised classification algorithms for the determination of regression and classification models from reflectance spectral signatures in local rice varieties.

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The Anopheles gambiae’s CYP6Z3 protein belongs to the Cytochrome P450 family and functions in oxidation-reduction processes, many studies including our previous work on elucidating insecticide resistance genes of the Anopheles also implicated her in pyrethroid insecticide resistance. Model prediction, functional analysis, and enrichment of the target gene with triplex binding sites may become a useful diagnostic biomarker for the disease subtype, but wrong classification of the model by various existing alignment algorithms is a daunting issue that complicates and misleads in decision making during pathway and functional analysis. The aim of this study is to predict five in-silico model of CYP6Z3 Anopheles protein by homology modeling, evaluate and classify them to elucidate the performance of the sequence alignment algorithm deployed, then characterize the top model that is correctly classified. Template selection from three alignment algorithms with sequence of the target-protein, (Anopheles-CYP6Z3) obtained from UNIPROT served as input, Clustal omega and Clustalw2 algorithms was used to generate alignment files for homologous template search to the target-protein. Best template was sought, and the 3D model built in an-automated-mode. PROCHECK was used to evaluate the best-of-the-five-obtained models. Estimating the quality of all models, the prime model emerged from ClustalW2 alignment algorithm, but was wrongly classified as a homo-tetramer-state. These provided a misleading-information which was revealed during model evaluation and interpretation, that resulted to an inappropriate pathway and functional-analysis, false positive model was then isolated, and the current best model emerged from clustalo alignment algorithm having 87.7% amino residues in the most favorable regions, 0.7% in the disallowed regions at monomer oligo state. Functional analysis of the best Anopheles CYP6Z3 secondary structure showed characteristics that explain the different degrees of genetic regulation translating to resistance mechanism in the malaria vector.

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In grid connected wind turbine (WT) systems, the maximum power point tracking (MPPT) approach has a crucial role in optimizing the wind energy efficiency. To search for the maximum power value of the wind turbine, this contribution proposes a new Maximum Power Point Tracking System (MPPT) for wind turbine related to a permanent magnet synchronous generator (PMSG)). The new proposed MPPT combines two techniques: Artificial Neural Network (ANN) and Fuzzy Logic (FL). The ANN is employed to estimate the maximum voltage of the WT, for various values of wind speed, while the control of DC–DC boost converter operation is executed by applying Fuzzy Logic technique. The comparison of our proposed algorithm to P&O technique has shown that it ensures more efficiency, and we used for that a simulation under Matlab/Simulink.

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The Public Administration is forced to transform itself by taking advantage of the contribution of ICT to in the process of reducing bureaucracy and increase transparency, promoting the dematerialization of processes, increasing the quality of online services, allowing greater ubiquity of access, reducing response times, in the search for improvement of the quality of life of its citizens. Decision-making should consider objectives not only technical but also financial, environmental, and social objectives, ideally aligning with Green IT. The objective of the paper is to present a framework, supported by international standards and frameworks, that allows measuring and guiding the alignment of ICT with Green IT for the optimization of practices instituted in organizations, namely in Local Government. This framework includes a qualitative component with several phases and quantitative component with a metric that allows the evaluation of alternative strategies for a given goal. The phases considered are Problem Identification; Problem assessment; Study and planning; Project; Telecommunications and Printing; Information Security; Innovation; Improvement of Citizen Service; Evaluation / opinion. The complete metric includes four valences: IT, financial, environmental, and social. The IT valence, its indicators and corresponding weights are illustrated in a practical example. The proposed framework is an innovative contribution to the area, clearly integrating the support of the perspective of Green IT and thus actively contributing to the implementation of sustainability policies and alignment with Green IT best practices in Local Government, as well as presenting with greater level of detail the components of the structure that emphasize Green IT concerns. The main expected results of the application of the framework are contributing to the implementation, in local government, of sustainable policies and good practices aligned with Green IT, whist targeting cost reduction and optimization, ubiquity of access, increased productivity and ensuring safety standards.

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With the rising number of web services created to build complex business processes, selecting the appropriate web service from a large number of web services respond to the same client request with the same functionality are developed independently but with different quality of service (QoS) attributes. From this point, there are many approaches to web service selection. Nevertheless, this is still deficient due to a considerable number of discovered web services. The prefiltering is a solution to reduce the number of web services candidates. In this paper, the K-means clustering is applied to determine similar services based on QoS information. The results of this prefiltering are considered at the selection task using the Branch and Bound Skyline (BBS) algorithm. The experimental evaluation performed on real Dataset proves that our approach presents efficient results for web service selection.

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Internet of Things provides the support for devices, people and things to collaborate in collecting, analyzing and sharing sensitive information from one device onto the other through the internet. The internet of things is thriving largely due to access, connectivity, artificial intelligence and machine learning approaches that it supports. The stability and enhanced speed of the internet is also attributable to the huge adoption rate that IoT continues to enjoy from Governments, industry and academia in recent times. The increased incidences of cyber-attacks on connected systems in recent times, has inspired the heightened efforts from Governments, industry practitioners and the research world towards improving existing approaches and the engineering of new innovative schemes of securing devices, the software or the platforms for the deployment of IoT. Security solution for Internet of things includes the use of secure ciphers and key exchange algorithms that ensures the provisioning of a security layer for the: devices or hardware, communication channels, cloud, and the life cycle management constituting the Internet of things. The use of key exchange algorithms in resilient cryptographic solution that have less computational requirements without compromising the security efficiency in the encryption of messages for IoT continues to be the preferred approach in securing messages in a node-node exchange of data. This paper aims at providing a cryptographic solution that uses a key exchange cryptographic primitive and a strong cipher in encrypting messages for exchange between nodes in an IoT. Towards achieving this goal, the Diffie-Hellman key exchange (DHE) protocol was used to provide a secure key exchange between the communicating nodes, whiles the Twofish block cipher was used in the encryption and decryption of messages, assuring the security, privacy and integrity of messages in a node-node IoT data exchange. The cryptographic solution has a high throughput.

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This article aims at comparing two controls to follow the maximum power point, making use of DC-DC converters for PV uses. All transformers operate continuously. To fulfil maximum power, we will exploit two MPPT controls: a traditional perturb – observe ‘P&O’ and a smart one – the fuzzy logic ‘FL’. The goal of this article is two-fold: to scrutinize the efficiency of DC-DC transformers (Boost, Buck, Cuk and SEPIC), and to assess the outcomes of the simulation. For the construction of models and simulations, the Matlab / Simulink environment is employed.

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Information and Communications Technologies are developing faster today than ever before, giving an important contribution to the global economy. Organizations in developed and developing economies explore new technologies to gain advantage and add value. That evolution also brings an increasing complexity to the organizations’ management. The alignment of organizational practices with international standards and best practices worldwide accepted in this domain is a relevant topic. To identify gaps in Information and Communications Technologies management area, a brief analysis of international standards will be considered in the state-of-the-art. Considering that Information Technology Infrastructure Library and Control Objectives for Information and related Technology are the most used in the literature review, this paper will propose an Information and Communications Technologies management framework based on those two standards. The approach pretends to solve some gaps found in process alignment, continuing improvement of Information and Communications Technologies services in the context of the organization, driving stakeholder satisfaction and cost optimization. Concluding, the final goal of this paper is to present the framework features analysed, to allow an integrative and multidisciplinary vision, leading to cost optimization, increasing communication, and stakeholder satisfaction.

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Brain-computer interface (BCI) has extensively been used for rehabilitation purposes. Being in the research phase, the brainwave based wheelchair controlled systems suffer from several limitations, e.g., lack of focus on mental activity, complexity in neural behavior in different conditions, and lower accuracy. Being sensitive to the color stimuli, the EEG signal changes promises a better detection. Utilizing the Electroencephalogram (EEG changes due to different color stimuli, a methodology of wheelchair controlled by brainwaves has been presented in this study. Red, Green, Blue (primary colors) and Yellow (secondary color) were chosen as the color stimuli and utilized in a 2 × 2 color window for four-direction command, namely left and right, forward and stop. Alpha, beta, delta and theta EEG rhythms were analyzed, time and frequency domain features were extracted to find the most influential rhythm and accurate classification model. Four classifiers, namely, K- Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest Classifier (RFC) and Artificial Neural Networks (ANN) were trained and tested for assessing the performance of each of the EEG rhythm, with a five-fold cross-validation. Four different performance measures, i.e. sensitivity, specificity, accuracy and area under the receiver operating characteristic curve were utilized to examine the wholescale performance. The results suggested that Beta EEG rhythm performs the best apart from all the rhythms for the color stimuli based wheelchair control. While comparing the performance of the classifiers, ANN-based classifier shows the best accuracy of 82.5%, which is higher than the performance of the three other classifiers.

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In the Internet of Things era it is expected that low-end processor domination of the embedded market will be further reaffirmed. Then, a question will arise, on whether it is possible to enhance performance of such processors without the cost of high-end architectures. In this work we propose a better-than-worst-case (BTWC) methodology which enables the processor pipeline to operate at higher clock frequencies compared to the worst-case design approach. We employ a novel timing analysis technique, which calculates the timing requirements of individual processor instructions statically, while also considering the dynamic instruction flow in the processor pipeline. Therefore, using an appropriate circuit that we designed within this work, we are able to selectively increase clock frequency, according to the timing needs of the instructions currently occupying the processor pipeline. In this way, the error-free instruction execution is preserved without requiring any error-correction hardware. We evaluate the proposed methodology on two different RiscV Rocket core implementations. Results with the SPEC 2017 CPU benchmark suite demonstrate a 12% to 76% increase on the BTWC design performance compared to the baseline architectures, depending on the appearance rate of instructions with strict timing requirements. We also observe a 4% to 37% increase on power consumption due to the operation of the pipeline at higher clock frequencies. Nevertheless, the performance increase is up to nine times larger than the power consumption increase for each case.

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Within the results of scientific research, vibrations and their reduction have been described for the brushless direct current motor with permanent magnets (BLDC motor). In this paper, calculations (the finite element method using commercial Finite Element Software Ansys) and measurements were performed to identifying the sources of vibrations in BLDC motor. The article presents numerical and experimental research on the resonant frequencies of the stator and the rotor; transient vibrations of the stator due to Maxwell forces in the motor have been analysed. It was shown that the natural frequencies were the main source of vibrations. The vibration sources indentification made possible formulation of better principles of choice of constructional motor parameters with the aim of attaining minimalization of vibrations.

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Haze removal is an extremely challenging task, and object detection in the hazy environment has recently gained much attention due to the popularity of autonomous driving and traffic surveillance. In this work, the authors propose a multiple linear regression haze removal model based on a widely adopted dehazing algorithm named Dark Channel Prior. Training this model with a synthetic hazy dataset, the proposed model can reduce the unanticipated deviations generated from the rough estimations of transmission map and atmospheric light in Dark Channel Prior. To increase object detection accuracy in the hazy environment, the authors further present an algorithm to build a synthetic hazy COCO training dataset by generating the artificial haze to the MS COCO training dataset. The experimental results demonstrate that the proposed model obtains higher image quality and shares more similarity with ground truth images than most conventional pixel-based dehazing algorithms and neural network based haze-removal models. The authors also evaluate the mean average precision of Mask R-CNN when training the network with synthetic hazy COCO training dataset and preprocessing test hazy dataset by removing the haze with the proposed dehazing model. It turns out that both approaches can increase the object detection accuracy significantly and outperform most existing object detection models over hazy images.

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Depth prediction plays a key role in understanding a 3D scene. Several techniques have been developed throughout the years, among which Convolutional Neural Network has recently achieved state-of-the-art performance on estimating depth from a single image. However, traditional CNNs suffer from the lower resolution and information loss caused by the pooling layers. And oversized parameters generated from fully connected layers often lead to a exploded memory usage problem. In this paper, we present an advanced Dilated Fully Convolutional Neural Network to address the deficiencies. Taking advantages of the exponential expansion of the receptive field in dilated convolutions, our model can minimize the loss of resolution. It also reduces the amount of parameters significantly by replacing the fully connected layers with the fully convolutional layers. We show experimentally on NYU Depth V2 datasets that the depth prediction obtained from our model is considerably closer to ground truth than that from traditional CNNs techniques.

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In this paper, second order generalized integrator (SOGI) is discretized to design the control algorithm of unified power quality conditioner (UPQC). A UPQC is combination two voltage source converter (VSC) and VSC are connected with back to back DC link. The first one VSC is in series to maintain the desire voltage at point of common coupling (PCC) and second is connected with shunt VSC to share the reactive power demand of load. Moreover, it protects the AC mains from pollution of the load. A phase lock loop (PLL) based SOGI model is implemented to design an algorithm for UPQC under light polluted load. Whereas under highly polluted load the Laplace Transformation based PLL-SOGI model is fail to eliminate harmonics of current and voltage at PCC. Thus, a discretization of PLL-SOGI is needed to meet disadvantage. In this paper, reference current is generated under polluted load using discretization of PLL-SOGI model and compared it with actual current to pulse the gate of shunt VSC. Moreover, a feedback unit (m) is proposed to pulse the gate of series VSC under voltage sag/swell condition. A hardware setup is performed in the lab to verify the proposed algorithm under highly polluted load.

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Formal methods are one of the efficient tools to verify and validate designs for different kinds of systems. Smart systems are attracting researchers’ attention due to the rapid spread of new technologies all over the world. Modeling a smart system requires connecting heterogeneous subsystems together to build it. Our contribution to this work is in focusing on using formal methods to prove that a design model meets its specifications. We have chosen to design a smart school building system due to the lack of research in this particular area, and to prove that formal methods are appropriate for different systems applications. In this paper, we have used UML diagrams and the formal specification language TLA+ to design a smart school building system. We validate our design using the TLC model checker. The smart school system has many subsystems connected together including a secure access system, lighting control system, climate control system, and smoke detection system. Safety is a very important attribute in this system. Our goal is to have a smart system that satisfies its functional requirements as well as any non-functional requirements like safety. The system provides safety for employees and students in the smart school.

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Harassment and violence against women have become one of the social security problems in Bangladesh. In this paper, we aim to develop safety devices for women named BOHNNI and BADHON which resemble legitimate jewelry. We used a microcontroller for the hardware device to make it most decisive and less immoderate. BOHNNI, a locating device, is the imitator of a locket including a voice recognizer, Bluetooth, Arduino, GPS, and GSM module. BADHON which imitates a bracelet is a rescue device for the victim whenever she thinks of herself being in a very deliberate situation. Both devices are activated by the user’s voice commands and also by a manual switch. The devices are aesthetically designed which will make the users enthusiastic to wear them. The device will generate messages to the predefined relative’s numbers with the victim’s location and relevant surrounding information. The device can also be used as a self-defending weapon as it can produce a shock up to 10 mA with an interval of two seconds which can temporarily paralyze or freeze a person. After calculating, we have obtained the lowest response time of BOHNNI and BADHON which is 1.95s, and the highest accuracy level of 91.67% in different situations that ensure the superlative performance level of our devices. We found our device as an all-in-one device that combines all the features in it regarding safety.

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Fossil fuel can no longer supply the constantly spiking demands of energy around the world, hence the increasing research on renewable energies as an alternative. The Stirling Engine is an external combustion engine, giving us a wide range of heat sources: solar, nuclear. The Stirling engine makes best of use of solar sources in an environmentally friendly way. It has no emissions and live longer as compared to Photovoltaic cells. The Stirling engine can operate at Low Temperature difference, which makes it prominent. In order to study the efficiency of a conversion from thermal energy to work, we need to take into account the energy efficiency, which is a key parameter in Low Temperature Difference Stirling Engine, even if its efficiency is lower than those of high temperature Stirling engine. In this article, we are studying the efficiency of the Stirling engine as a first step using a parabolic mirror to focus the sun’s radiation onto the engine. In this article, we are studying the efficiency of the Stirling engine as a first step, by making isothermal and adiabatic analysis of the engine to detail the operation throughout its process, and be able to act on the various input parameters that impact the value of the final yield, and in a second step, using a parabolic mirror to focus the sun’s radiation onto the engine.

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The main contribution of this paper is the framework of Application Programming Interface (API) to be integrated on a smartphone app. The integration with algorithm that generates fingerprints from the method ST-PSD with several parameter configurations (Windows size, threshold, and sub-score linear combination coefficient). An approach capable of recognizing an audio piece of music with an accuracy equal to 90% was further tested based on this result. In addition the implementation is done by algorithm using Java’s programming language, executed through an application developed in the Android operating system. Also, capturing the audio from the smartphone, which is subsequently compared with fingerprints, those present in a database.

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It has become difficult to handle traditional networks because of extensive network developments and an increase in the number of network users, and also because of new technologies like cloud computing and big data. Traditional networks are experiencing an increase in VM load and in the time taken for processing tasks. Hence, it has become essential to modify the traditional network architecture. A notion called Load balancing techniques that increases the conformance of network management was presented recently to deal with this problem. The critical need for load balancing emerges due to network resources limitations and requirements fulfillment that facilitates traffic distribution through various resources to enhance the efficiency and reliability of network resources. This task has been carried out by several researchers before, who have presented various algorithms with their benefits and shortcomings. The focus of this research is on the notion of cloud computing load balancing and on the advantages and disadvantages of a chosen load balancing algorithm. Furthermore, it examines the metrics and issues of these algorithms.

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With a view to detecting whether economic activity deterioration for elderly people at age of sixty-five or over could be observed, anonymous data (AD) were used as analysis data, which were obtained from the National Survey of Family Income and Expenditure (NSFIE) conducted by the Ministry of Internal Affairs and Communications (MIC). We have developed a method to detect dissaving risk among elderly people. In our previous analysis, AD were divided into test data and training data. Three kinds of methods were performed on the basis of income and savings. Then two-step methods were processed to determine dissaving risk. Nevertheless, in utilizing AD as it is, the security of anonymity could be questionable. Therefore, in order to enhance the anonymity of the data, random data (RD) were generated based on AD in this paper. Then RD were compared with the case of analyzing mere AD as it is, for the purpose of performance evaluation. Further analysis results suggest that using both RD and AD would be as effective as using only AD in evaluating the performance of the proposed method.

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The use of Unmanned Aerial Vehicle (UAV) can contribute to find solutions and add value to several agricultural problems, favoring thus productivity, better quality control processes and flexible farm management. In addition, the strategies that allow the acquisition and analysis of data from agricultural environments can help optimize current practices such as crop counting. The present research proposes a methodology based on the exploitation of deep learning approach, especially Convolutional Neural Networks (CNN) on UAV data for fruit tree detection and counting. We build models for the automatic extraction of fruit trees. This approach is divided into main phases: dataset pre-treatment, implementing a fruit trees detection model by exploiting several CNN architectures, validating and comparing the performances of different models. The exploitation of RGB UAV images as input information will allow the learning models to find a statistical structure, which will result in rules capable of automating the detection task. They can be applied to new images for automatically identify and count fruit trees. The application of the methodology on collected data has made it possible to reach estimates of detection and counting until 96 %.

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Before the COVID-19 crisis, the Southern African Developing Countries (SADC) had a varied energy mix including renewable energy, fossil fuels, and military energy production. The use of fossil fuels in the energy mix is known to be the source of the growing levels of greenhouse gases in the atmosphere. However, there was a reduction in GHG emissions following the pandemic, which reduced travel and trade, and worldwide disruption in economic activities. The priority of priority B in the 2015-2020 Regional Indicative Strategic Development Plan, which is Energy, continues. As a result, the availability of affordable and renewable energy is still a priority for south of the equator countries and their growth agenda. This paper is aimed at exploring the sustainability of SADC countries’ electricity sectors by using three sustainability pillars: Social, Environmental and Economic (SEE). SEE offers the main concepts of renewable energy, in a way that is socially, environmentally appropriate and economically viable. Study shows a gap in access rate in SADC countries with only Mauritius and Seychelles reaching 100% access to modern energy services (electricity) for both rural and urban areas. Currently all the member countries have set their RE goals for the year 2030. However, the subsidies by SADC member countries indicate that they are practiced as a way to make electricity affordable, and also to make electricity available to lower income households. In the period 2014-2017, big national budget deficits happened in various Southern African countries because of subsidies. Thus, this paper is of crucial importance to the foundational advancement of sustainable electricity sector growth in the country. The findings of this paper play a crucial role in helping and guiding politicians to better understand the existing and challenges future in the energy market and alternatives to address these problems. Additional research is given on how to arrive at sustainable decisions for the electricity sector in the region.

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A new approach for designing RF energy harvester with a single stage converter circuit is presented in this paper. The proposed converter configuration is integrated with an antenna that is based on the coplanar waveguide (CPW) transmission line with improved gain resonated at 2.45 frequency ISM band. The CPW patch antenna as a harvester antenna is designed in a rectangular shape that uses FR-4 substrate with a loss tangent and relative permittivity of 0.025 and 4.3 respectively. The output from the harvester antenna is connected to the converter circuit using only two Schottky diodes. The rectifier design achieves between 0.1% to 37% of RF-DC power conversion efficiency over the ambient RF input signal range from -20 dBm to 0 dBm and the antenna exhibits a directivity of 3.896 dBi as well as a return loss of -48.85 dB. For an input power of 0 dBm, the proposed circuit can rectify an AC signal up to 6.09 V. Moreover, the proposed CPW antenna that is integrated with a converter circuit agrees for the harvesting of ambient electromagnetic energy to power low power electronic devices.

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The suitability of incorporating waste transfer stations (WTS) in likely future Municipal Solid Waste Management (MSWM) systems for Harare city and neighbouring urban centres was assessed under this study. WTS will bring about location of landfills and other MSWM facilities further away from population centres, increasing recycling, reducing waste collection costs and burden on the overall MSWM budget, an increase in waste collection effectiveness and efficiency, reduction in waste collection derived greenhouse gases (GHG) emissions and other associated impacts. Life cycle Assessment (LCA) on contribution of waste collection to human health impact potential of 34 DALYs as well as acidification, global warming and eutrophication impact potentials of 0.012, 0.065 and 0.0002 species.year respectively under all the six MSWM options were observed. Highest impacts in the species extinction impact categories was realised in the global warming impact category resultant of GHG emissions from fuel combustion during waste collection. The unavailability of land and the above factors support the incorporation of WTS in future MSWM options for Harare City and surrounding urban centres under a separation at source waste collection system to derive maximum benefits. Citizens drop off centres (CDOPs) and buy-back centres (BBCs) could also compliment the WTS leading to increased recycling. Though there is a relative sound supportive legislative, regulatory and policy framework that supports the need for waste recycling consequently supporting WTS, CDOPS and BBCs due to their recycling promotion capabilities, there is need for specific legislation, regulation and policies that support the development and operation of such facilities that will bring interest amongst would be operators, effectiveness and efficiency resultantly reducing associated human health and environmental impact. Further studies that determine the breakeven distance and LCA studies that specifically assess the associated environmental loads of incorporating WTS within the likely future MSWM systems are recommended.

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Municipal Solid Waste (MSW) is composed of items that are discarded or disposed of daily including paper, plastics, glass, metals, used gadgets, paint and old furniture. The plastic waste stream has proven to be problematic to manage sustainably on a global scale. Various researchers are trying to come up with innovative ways of alleviating the detrimental effects of plastic on the environment. Examples include the production of liquid fuel and synthetic gas through pyrolysis and gasification of plastic waste, use of microbial strains that can break down polyethylene, manufacture of plastic-infused tar, use of plastic waste in cement and concrete as well as its use in the manufacture of bricks. Conducting public awareness and outreach programmes has also been found to be beneficial in reducing plastic littering. This paper reviews South Africa’s strengths, weaknesses, and opportunities in plastic waste management as well as lessons from other jurisdictions that can be adopted in South Africa making it a role model for Africa with regards to plastic waste management. There exists an untapped opportunity for improvement of post-consumer plastic recycling rates to levels comparable to other recyclables in the country through compulsory separation of waste at source. Hence an enabling environment should be created to encourage this practice. Since this will require a fully functional waste management infrastructure, collection services should expand to cover rural areas and informal settlements while industries can assist municipalities to upgrade infrastructure through the extended producer responsibility (EPR) scheme. In addition, there is potential for more jobs to be created in the waste sector through recycling as compared to landfilling, thus urgent attention is needed to divert 100% waste from the landfill. Finally, the integration of informal waste pickers into the waste management chain should be prioritised.

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Municipal Solid Waste Management (MSWM) decentralisation brings about reductions in the amount of municipal solid waste (MSW) earmarked for landfilling worse still under situations where MSW is being sent to dumpsites. It also reduces the distances MSW collection vehicles travel during MSW collection, maintenance and transport costs due to the establishment of local level decentralised MSWM and treatment facilities. Subsequently fuel use, greenhouse gas and other emissions together with MSWM associated environmental and human health risks are reduced. The Zimbabwe National Integrated Solid Waste Management Plan (ZNISWMP) provides for the need for decentralisation in MSWM. This study therefore assessed the framework along which MSWM decentralisation can be achieved in Harare. The study noted the presence of various opportunities for MSWM decentralisation in Harare namely household backyard composting, community level and industrial scale anaerobic digestion or composting of organic MSW fraction, anaerobic co-digestion of organic MSW fraction and dewatered sewage sludge, SW source separation for material recovery, establishment of waste transfer stations, citizens drop off centers, buy back centers and thermal treatment facilities associated with energy recovery. Though the NISWMP plan provides for concrete actions for MSWM decentralisation, it was observed that almost all of the proposed actions have not been implemented hence the need for urgent review and subsequent operationalization and implementation of the review findings. MSWM has also been hindered by the lack of legislative and institutional reforms with ULAs remain reluctant to devolve and delegate some of the MSWM responsibilities and functions to other players prompting the need for such reforms to be implemented as provided in goal 10 of plan. The Presidential national cleanup day proclamation needs to be complemented with other initiatives that will increase residents’ interest in participation, allow for different types of participation such as provision of resources and equipment and above all the development of sustainable MSW disposal facilities unlike dumpsites.

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This article extends novelty of modeling capacitive accelerometer with PID controller to provide PI controller for better tuning and statistical test to determine the linear validation characteristics of closed capacitive accelerometer. Capacitive accelerometer is a sensor which uses the dynamic law of physics model Position-Velocity-Accelerator (PVA) by the movement of an electrode coupled to mass proof sandwich between parallel plates to detect vehicle/object displacement. The modelling of closed loop system helps to mitigate steady state error accumulation of measurements in open loop model. The accelerometer gives linear time dependence on output displacement after an input step-like function of acceleration. The closed model can predict the desired output signal. The linearity of the model is tested statistically using simple regression of 120 dataset. This shows a p-value of 2e-16 indicating that at any time, the acceleration predicts the displacement/position of vehicle/object.

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This paper presents a design of parallel gripper finger for robotic dual-arm working with an automatic push-down bottle crown cork cap opener on ABB’s Yumi collaborative bartender robot. A safe gripper finger is made from ABS plastic by 3D printing for human-based interaction design for grasping and holding a glass bottle. Rack design and proposed automatic push-down bottle cap opener using pneumatics are presented to support a gripper finger. Experimental tests as bartender environment with 4-different types of carbonated soft drink with crown cork cap show that can be achieved effectively with average of 91.5% percentage of successful cap opener.

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A Decision-Making Centers (DMCs) Environment facilitates stakeholders’ decision-making processes using predictive models and diverse what-if scenarios. An essential element of this environment is the management of Decision Support Components (e.g., models or systems) that need to be created with mature methodologies and good delivery time. However, there has been a gap in the understanding of project management best practices in DMC environments and in the application of methodologies to ease project execution. In the following paper, we address that gap by analyzing six predictive analytics projects executed in a Mexican DMC using Process Mining techniques. We perform process discovery using a detailed activity event log, which has not been possible in previous studies. Additionally, we perform a compliance evaluation versus the de facto methodology to identify the current process alignment gaps, and finally, we analyze the social networks present in the process execution. The research reveals that (1) process mining models are helpful to address management issues of PA/DM projects (2) PA/DM projects require alignment to mature methodologies to improve process performance and avoid execution problems (3) PA/DM project execution should be revised at the activity level to identify issues and to propose specific strategies. This study’s findings can help project managers to perform process analyses and to make informed decisions in PA/DM projects. The following paper is an extension of the article “Applying Process Mining to Support Management of Predictive Analytics/Data Mining Projects in a Decision-Making Center¨ presented in the 2019 International Conference on Systems and Informatics (ICSAI 2019).

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Environmental sounds detection plays an increasing role in computer science and robotics as it simulates the human faculty of hearing. It is applied in environment research, monitoring and protection, by allowing investigation of natural reserves, and showing potential risks of damage that can be deduced from the environmental acoustic. The research presented in this paper is related to the development of an intelligent forest environment monitoring solution, which applies signal analysis algorithm to detect endangering sounds. Environmental sounds are processed using some modelling algorithms based on which the acoustic forest events can be classified into one of the categories: chainsaw, vehicle, genuine forest background noise. The article will explore and compare several methodologies for environmental sound classification, among which the dominant Deep Neural Networks, the Long Short-Term Memory, and the classical Gaussian Mixtures Modelling and Dynamic Time Warping.

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A methodology of medical signal-based biometrics has been proposed in this paper for implementing a human identification system controlled by electroencephalogram in respect of different color stimuli. The advantage of biosignal based biometrics is that they provide more efficient operation in simple experimental condition to ensure accurate identification. Red, Green, Blue (primary colors) and Yellow (secondary color) were chosen as the color stimuli for making more comfortable EEG regenerating environment. Four supervised classification models, namely, Logistic Regression (LR), K- Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest Classifier (RFC) were trained and tested for assessing the performance of the EEG based biometric identification, with five-fold cross-validation. Four different measures (sensitivity, specificity, accuracy and area under the receiver operating characteristic curve) were used to evaluate the overall performance. The results suggested that Blue color stimuli perform the best among all the color stimulus with an accuracy ranging from (77.2-88.9%). The classifiers identify each of the subjects with any color having an accuracy ranged from (70.9-88.9%), and the RFC shows the best accuracy which is 88.9% in the case of blue color stimuli.

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The construction industry has undergone several changes in recent years linked to new laws and international conventions aimed at protecting the environment and combating pollution. Construction industry alone produces tons of waste annually due to debris produced either during construction or during deconstruction. To combat this, companies are forced to control their debris either by reusing it on site or by sending it to specialized landfills. Thus, new materials appear constantly based on the recycled materials. Recycled aggregate concrete was thus born. It is a concrete based on the use of recycled aggregates retrieved from the demolished structures to replace natural aggregates. Characteristics of this type of concrete depends of the chosen replacement percentage of natural aggregates specially the mechanical properties. This article is part of my research studies done in the civil laboratory of the Mohammadia School of engineers. The study is based on the identification of the recycled aggregates, the determination of physicals and mechanical characteristics of the aggregates, the determination of the effect of the use of recycled aggregates on the concrete characteristics and finally the improve of the quality of the concrete to prove so that it can replace ordinary concrete. The test results described in this article show that the increase of the replacement by recycled aggregates decreases the mechanical properties especially when it is up to 25-30%. The results also prove that the ad of additives especially plasticizer with 1% replacement of cement improves the compressive strength of concrete and allows us to use up to 50% of replacement.

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Fuel Cells (FCs) and Combined Cooling, Heating and Power (CCHP) systems are becoming very popular due to their environmental friendliness and immense applications. This extended review paper commenced by introducing the rampant South Africa electricity crisis as the basis for the study, followed by some structural analyses of up to forty four miscellaneous power electronics converters case studies applicable to fuel cells including at least sixteen FCs energy management systems (EMSs) applicable case studies. The review rationale is to determine innovative best practices that can be applied to devise an efficient power converter and EMS for an energy efficient FC CCHP system. From these analyses, it is realized that each power converter and EMS scheme has its merits and demerits depending on the targeted applications and most importantly the research project objectives ? that is, whether the goals are to reduce costs, enhance efficiency, reduce size, boost performance, simplicity, durability, reliability, safety etc. Therefore, the conclusion drawn is, there is no “one size fits all” approach, as all the various reviewed case studies reported relatively good results based on their chosen schemes for their targeted applications. Notwithstanding, this review highlights are the interleaved boost converter and variants as well as maximum power point tracking (MPPT) technique, which are the most widely used schemes as they are reasonably effective and simple to implement. The contributions brought forward are i) a single reference study that presents a quick topological insight and synopsis of assorted FCs power converters as well as EMS and ii) our proffered FC CCHP system undergoing research to offer an innovative energy efficient solution for basic household energy needs.

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This paper details an extension to the SLIP model named spring-loaded inverted pendulum model with swing legs (SLIP-SL). SLIP-SL extends the SLIP model by introducing swing leg dynamics while keeping its passive nature. This way, reference trajectories for the center of mass and swing foot trajectories can be simultaneously obtained which was not possible with the SLIP. This makes implementation easier and can increase tracking performance. We show how a variety of feasible two-phased walking trajectories can be obtained for this template model using direct collocation optimization methods. It is also shown through simulation studies that reference SLIP-SL trajectories can be used to control a fully actuated bipedal robot with the proposed feedback linearization controller to reach a stable cyclic gait.

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The rapid transformation of the energy sector in South Africa towards renewable energy (RE) production calls for the management of assets to keep pace with the ongoing developments in a reliable manner. Aging assets, increasing energy needs and reliable supply of energy without load shedding are some of the challenges utilities are facing in South Africa. In resolving these challenge, imaginative solutions are required to maintain the installed assets and determining the viability of refurbishment, replacement or upgrading. In the current work, an extension of the author’s previous work, a novel approach for estimating the service lifetime of transformers within Distributed Solar Photovoltaic (DSPV) Systems in South Africa is introduced. This experiential form has been derived by extensive experimental trials. The proposed approach is initially employed to evaluate the Degree of polymerization (DP) of cellulose insulation based on measured furan (2FAL) contents of 9 case scenarios. The calculated DP is then used to evaluate the service lifetime of these units. In efforts to authenticate the proposed approach, a comparative study is conducted against 5 other models. Finally, the proposed approach is compared with the results of the measured DP. It is observed that the proposed approach produce accurate estimates with an approximation not exceeding 1% and 2.2% from the measured DP and service lifetime respectively.

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Distributed Solar Photovoltaic (DSP) Plants are one of the fastest growing renewable energy systems in South Africa. The primary components forming an integral part of the point of common coupling (PCC) are, the inverter used to convert dc voltage to ac voltage and step-up transformers which step-up low voltage input to the desired output level. However, DSP plant step-up transformers are considered to be one of the most sensitive equipment on the plant. These transformers are challenged with various electrical problems including abnormal levels of harmonics. The presence of harmonics in these transformers results in higher service losses thereby raising the hotspot (HS) temperature, in which, consequently introduce the stray gassing phenomena of the insulating oils. This calls for understanding of the nature of the problem and possible remediation to ensure enhanced power quality. Present work, an extension of previous work, investigate a reported case of peculiar stray gassing of transformer insulation oils during service. Initially, the harmonic spectrum of the DSP plant is presented and the related service losses at fundamental and under harmonic conditions are computed. Furthermore, the thermal performance of the transformer under these conditions is investigated. Lastly, the Dissolved Gas Analysis (DGA) results of the oil samples are presented. Novelty, findings of this work indicate that the generation of hydrogen arising from stray gassing may stem from severely hydro-treated mineral oil, but is also strenuously affected by transformer thermal aging of polymers, choice of core steel grade, zinc tank walls and vanishes. The production surplus of methane and ethane are also witnessed in the first years of service and reaches substantial concentration levels. Potentially, these occurrences also arises from the thermal aging of polymers. The authors make some recommendations to utility owners to make a distinction of stray gassing from transformer fault by means of routine inspection aside from DGA value basis withal to the increase in gas diffusion rate. Further, the authors make some significant contribution by further recommending procedures that can be employed as remedies during the design phase and manufacturing processes. Lastly, the authors highlight the need to establish standards that will provide support for transformers intended to operate in DSP applications.

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The TAM is a model that is widely used to understand IT adoption and usage process accordingly and the reason for its popularity is that the model clarifies variances like behavioral intention (BI) relevant to IT appropriation and use over a broad range of settings. The model’s main factors for system use is perceived usefulness (PU) and perceived ease of use (PEOU). Likewise, the T-O-E framework is a popular framework for three stimuli that influence organizational adoption namely technology, organization, and environment. Much literature has dealt with the use of TAM and T-O-E frameworks together with their derivatives without looking at the shortfall of these models. This paper reviewed one hundred and seventeen papers that used or reviewed TAM or TOE models. The contribution of this paper is the address of the usefulness, limitations, and criticism of the two models and also how the TAM and the T-O-E frameworks can be integrated into a hybrid model using a generic framework. In conclusion these models can be used separately or as a hybrid depending on the situation at hand. In future it important to harmonize the so many factors of the models that have been suggested and used in literature.

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In this paper a synchronization methodology of two fractional-order chaotic oscillators under the framework of identical synchronization and master-slave configuration is introduced. The proposed methodology is based on a fractional-order feedback control design under the frame of control theory, the feedback controllers provide synchronization convergence. A comparative study between a proportional control, a nonlinear fractional-order proportional-integral control and an active control is presented. The above is showed via an analysis of the dynamic of the called synchronization error. Numerical experiments using the mathematical model of the fractional-order unified chaotic system and its equivalent electronic circuit corroborate the satisfactory results of the proposed schemes.

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During the past years, a lot of research work have been done on the topic of smart grids and more specifically on the charging of electric vehicles (EVs), which will become an essential aspect in the coming years. The various works carried out on these themes have allowed the development of efficient tools to organize energy exchanges within these networks and to make this energy available to electric vehicles on certain time intervals. However, the problems related to the compatibility between the different elements of these networks seem to be largely underestimated. The collaborative work between IETR and Dropbird highlights the technological challenges that significantly hinder the deployment of relevant charging algorithms and experiments with dynamic programming-based algorithms to circumvent these obstacles.

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The effectiveness and suitability for the reliability and test of the embedded software of the automotive EHPS (Electrical Hydraulic Power Steering) pump are extensively explored in this paper. The Crow-AMSAA analysis has been applied to evaluate the embedded software reliability growth based on the failure data collected in the prototype phase and in the field. The slope ? of the Crow-AMSAA plot is smaller than 1 which indicates that the reliability of the embedded software is increasing and failure rate is decreasing. The field performance and reliability of the embedded software, which is the key indicator to evaluate the effectiveness and suitability of the reliability management and testing methods used in design and development, are also summarized in this paper. Using the real field and zero mileage data to evaluate the effectiveness and suitability of DFR (Design for Reliability) is also beneficial for the company to make the continuous improvement for the future embedded system/software design and development.

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Collaborative E-learning is highly dependent on the well functioning of a complex socio-technical system that comprises information technology and various social processes. Large-scale infrastructures like the National Research and Education Networks (NRENs) provide access to research and educational resources and provide collaboration between educational and research organizations, thus providing a set of essential services for e-learning. Currently, the lack of data integration between e-learning systems is still a problem in NREN domains, and a hurdle to collaborative e-learning. We address systematic cross-organizational collaboration and data integration between large-scale e-learning systems by designing an architecture for NREN e-learning systems to support open access education and learning. In particular, we design and provide a reference implementation for an e-learning broker that can provide the needed data integration and processes, and takes into consideration the strategies and policies for open access in education and training. We develop the architecture and reference implementation applying the eXtreme Model-Driven Development (XMDD) paradigm for software design and development, using the DIME low-code development environment for modelling data, processes, and user interface. We consider here two specific application settings: The national network of e-learning collaboration in AfgREN, centered on the Kabul University in Afghanistan, and the newly started collaboration between the Athlone Institute of Technology (AIT) and Limerick Institute of Technology (LIT) in Ireland, that are forming a new consortium under the newly introduced Technological University structure.

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Erosion affects large parts of Moroccan land, particularly in the mountains leading to soil quality deterioration and less vegetation cover. Located in the South-west of Morocco, the Site of Biological and Ecological Interest (SBEI) of Ain Asmama, where erosion threatens a major part of the region was investigated. The site has a terraced transitional bioclimate, between arid to sub-humid, of local conservation importance. The varied, dense, continuous, and well-preserved vegetation of the area is crucial to protect the soils against erosion. Qualitative observations show that soils are increasingly degraded, water erosion is developing, and sediments accumulate in dams and ponds. In this study we have used the Revised Universal Soil Loss Equation (RUSLE) to assess the erosion risk in this area. It helped to develop a synthetic map of erosion susceptibility. Our results show that the integration of the different parameters in the water erosion process estimated the loss of soil amounting 339,03 tons/ha/year over the whole site. This is equivalent to a value of soil lowering of 2,82 cm which is considered extremely high.

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The field of view (FOV) and spatial resolution (SR) are the major image quality parameters which are being optimized in neutron radiography (NR) technique. This requires effective components selection during the design of NR detector systems. The selection is a discouraging task owing to often having conflicting experimental requirements and related constraints. In this work, models were developed to study the relationship between detector system components. These allow the simulation of detector system components whilst taking cognizance of specific experimental requirements and constraints in order to aid the design. Various commercial detector system components combinations were simulated to evaluate their performance. Results were benchmarked with the result from secondary data. 100% agreement between these data demonstrated the accuracy of the models, allowing purposeful selection. The ?90% negative correlation between SR and FOV was identified as a tradeoff between these variables. Currently selected best combination offers a monotonic SR range of 25.5 – 170.92 µm pixel size, over FOV range of 52.3 × 52.3 mm2 – 350 × 350 mm2. The results also show that components can be selected for design of desired detector system within constraints to manage the field of view effectively; thereby optimizing the SR.

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In this study, 6 unique microstrip patch antennas with quarter-circular and semi-circular slots in patches are proposed. It is aimed to investigate the effect of these slots in the designed topologies. The antennas operate for wireless applications including Personal Communication Service (PCS), 3rd Generation (3G), The Standard for Wireless Fidelity (WiFi)/Wireless Local Area Network (WLAN)/Bluetooth, Long Term Evolution (LTE) and Global System for Mobile Communications at 1.8 GHz (GSM-1800). Simulation results for antenna performance parameters such as fractional bandwidth, gain, radiation pattern, radiation efficiency and total efficiency are presented.

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The Hamming neural network is an effective tool for solving problems of recognition and classification of objects, the components of which are encoded using a binary bipolar alphabet, and as a measure of the objects’ proximity the difference between the number of identical bipolar components which compared include objects and the Hamming distance between them are used. However, the Hamming neural network cannot be used to solve these problems if the input network object (image or vector) is at the same minimum distance from two or more reference objects, which are stored in the weights of the connections of the Hamming network neurons, and if the components of the compared vectors are encoded using a binary alphabet. It also cannot be used to assess the affinity (proximity) binary vectors using the functions of Jaccard, Sokal and Michener, Kulchitsky, etc. These source network Hamming disadvantages are overcome by improving the architecture and its operation algorithms. One of the disadvantages of discrete neural networks is that binary neural networks perceive the income data only when it’s coded in binary or bipolar way. Thereby there is a specific apartness between computer systems based on the neural networks with different information coding. Therefore, developed neural network that is equally effective for any function of two kinds of coding information. This allows to eliminate the indicated disadvantage of the Hamming neural network and expand the scope of discrete neural networks application for solving problems of recognition and classification using proximity functions for discrete objects with binary coding of their components.

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Lab sessions in Engineering Education are designed to reinforce theoretical concepts. However, there is usually not enough time to reinforce all of them. Remote and virtual labs give students more time to reinforce those concepts. In particular, with remote labs, this can be done interacting with real lab instruments and specific configurations. This work proposes a flexible configuration for Remote Lab Sessions, based on some of 2019 most popular programming languages (Python and JavaScript). This configuration needs minimal network privileges, it is easy to scale and reconfigure. Its structure is based on a unique Reception-Server (which hosts students database, and Time Shift Manager, it is accessible from the internet, and connects students with Instruments-Servers) and some Instrument-Servers (which manage hardware connection and host experiences). students always connect to the Reception-Server, and book a time slot for an experience. During this time slot, User is internally forwarded to Instrument-Server associated with the selected experience, so User is still connected to the Reception-Serer. In this way, Reception-Server acts as a firewall, protecting Instrument-Servers, which never are open to the internet. A triple evaluation system is implemented, user session logging with auto-evaluation (objectives accomplished), a knowledge test and an interaction survey. An example experience is implemented, controlling a DC source using Standard Commands for Programmable Instruments. This is an example regarding how systems enable students to interact with hardware, giving the opportunity of understand real behaviour.

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Multi-Robot System (MRS) is a complex system that contains many different software and hardware components. This main problem addressed in this article is the MRS design complexity. The proposed solution provides a modular modeling and simulation technique that is based on formal system engineering method, therefore the MRS design complexity is decomposed and reduced. Modeling the MRS has been achieved via two formal Architecture Description Languages (ADLs), which are Systems Modeling Language (SysML) and Business Process Model and Notation (BPMN), to design the system blueprints. By using those abstract design ADLs, the implementation of the project becomes technology agnostic. This allows to transfer the design concept from on programming language to another. During the simulation phase, a multi-agent environment is used to simulate the MRS blueprints. The simulation has been implemented in Java Agent Development (JADE) middleware. Therefore, its results can be used to analysis and verify the proposed MRS model in form of performance evaluation matrix.

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Educators should be considered the learning style of students so that the best practice approach can be applied in learning activities. As students understand their learning style, they will be able to integrate it into their learning process. Kolb Learning Style was the learning style that was widely used based on the theory of learning experiences. Therefore, this study aimed to describe engineering and non-engineering students’ learning style. The survey research design with a quantitative approach was applied in this study. A total of 300 respondents were selected randomly from all faculties in Universiti Tun Hussein Onn Malaysia. The survey questionnaire consisted of two main sections representing Learning Goals, Learning Style, and Learning Activities. The result explains that both engineering and non-engineering students are more dominant to adopt the Accommodator learning style, followed by the Converger learning style, and then Assimilator learning style and Diverger learning style. It is concluded that the engineering and non-engineering students are more incline to be a kinesthetic learner. These learning preferences and learning styles will contribute to their engagement in the concept of learning and for educators to plan teaching strategies.

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