Volume 6, Issue 1

To evaluate and compare methods in computer vision, scientists must use a benchmark dataset and unified sets of measurements. The UCI-EGO dataset is a standard benchmark dataset for evaluating Hand Pose Estimation (HPE) on depth images. To build robotic arms that perform complex operations such as human hands, the poses of the human hand need to be accurately estimated and restored in 3D space. In this paper, we standardized the UCI-EGO dataset to evaluate 3D HPE from point cloud data of the complex scenes. We also propose a method for fine-tuning a set parameter to train the estimation model and evaluating 3D HPE from point cloud data based on 3D Convolutional Neural Networks (CNNs). The CNNs that we use to evaluated currently the most accurate in 3D HPE. The results of the 3D HPE from the point cloud data were evaluated in two branches: using the hand data segment and not using the hand data segment. The results show that the average of 3D joint errors of the 3D HPE is large on the UCI-EGO dataset (87.52mm) and that the error without using the hand data segment is many times higher than the estimated results when using the hand data segment (0.35ms). Besides, we also present the challenges of estimating 3D hand pose and the origin of the challenge when estimating real image dataset.

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Graphene oxide (GO) was prepared by the modified Hummer’s method, but the mass ratio of graphite to sodium nitrate (NaNO3) was varied from 2:1, 1:1, and 1:2. The primary reason for the variation was to determine the optimum conditions that would afford more oxygen functional groups to improve the material for application in adsorption. The final products, termed GO2:1, GO1:1 and GO1:2, were analyzed by several instrumental techniques. The layered structure of the GO sheet was established by transmission electron microscopy, while powder X-ray diffraction showed that the GO2:1 material was more crystalline than either GO1:1 or GO1:2. Raman spectroscopy revealed the presence of a greater defect density in GO2:1. The presence of oxygen functional groups was verified by Fourier transform infrared spectroscopy, and these were quantified by the Boehm titration method. Overall, GO2:1 had a larger oxygen content than either GO1:1 or GO1:2, and a larger specific surface area. A preliminary study on the adsorption properties of the samples revealed that GO2:1 exhibited the highest percentage removal for Cu2+ in aqueous solution. Thus, the preparation of graphene oxide with a smaller amount of NaNO3 yielded a material with a greater oxygen content, which showed suitable properties for the adsorption of contaminants in wastewaters.

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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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Data sensitivity analysis using normalization techniques in decision making has an impact on preference values and rankings in the case of social assistance programs for student online. The distribution of assistance is disproportionate and not on target to potential recipients. This study aims to analyze data sensitivity from simple data normalization techniques and linear techniques in decision making. In particular, a simple data normalization technique is illustrated using Simple Additive Weighting (SAW), and a linear technique using the VIšekriterijumsko KOmpromisno Rangiranje (VIKOR) method. There are five criteria used, obtained from observation through measurements and a questionnaire from 400 students. The confusion matrix testing method is used to measure the value of data sensitivity, which includes precision, accuracy and error rate. The results of the study obtained data analysis sensitivity for each method shows that the distribution of normalized data in the selection of 10% (40 students) of positive target recipients, the sensitivity of the linear technique (Vikor method) is higher than the simple technique (SAW method). However, for the target of 15% (60 students) the simple method is higher. The results show that each data normalization technique for decision-making analysis has a different sensitivity value in terms of social assistance for target groups, although many studies suggest that certain methods may be better than others.

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This research aims to investigate the energy and power management of a battery-supercapacitors Hybrid Energy Storage System (HESS) for Electric vehicles (EVs). A bidirectional DC-DC converter, a battery and a set of supercapacitors were employed to construct the parallel semi-active architecture of HESS. Two strategies of Rule-Base Linear Quadratic Regulators (R-B LQRs) were proposed to manage the power flow in HESS to reduce the overall battery stress during high demand events. The supercapacitors supply the high load demand, while the battery supplies the low load demand. The HESS, EV and the proposed controllers were simulated in a MATLAB/Simulink environment. Three standard drive cycles, namely, Urban Dynamometer Driving Schedule UDDS, New York City Cycle NYCC and Japan1015 drive cycle, were implemented to validate the controller’s responses. The results of the R-B LQR controllers were compared in terms number of possible drive cycles. According to the results achieved, the proposed hybridization achieves stable response of the HESS current over the drive cycles, effectively reducing the battery’s size and extends its life-time.

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Educational organizations often need to distribute academic transcripts and certificates upon student’s request since they are mandatory for admission into new scholarly programs including placement activities. Manual procedures involved with the transmission process of academic document is indeed a tedious task that results in substantial overhead. Thus the necessity for an autonomous electronic system for transfer of records among institutions is on the verge. This paper discuss and portray a hardware approach on the cryptographic elements of blockchain to impart data security and privacy that are found inadequate in its software counterpart.The novelty of this work relies on the design and implementation of a hardware equivalent structure for blockchain which could greatly alleviate the chances of various software attacks and security breach in existence. The solution proposed could cut down the waiting period of students to transmit their credentials and in addition provide a trustworthy verification platform to elude academic deceit. It can be integrated along with existing permission based blockchain framework to form a conjoined hardware-software architecture.

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The objective of this study is to examine the current views of academics on issues and technology development in forensic accounting education. By applying the explanatory sequential mixed method, this study tries to acquire a better understanding of current perception regarding forensic accounting education. This study indicates that demand for forensic accounting services is expected to increase and offered as a separate courses at the graduate and undergraduate levels; respondent’s perceived forensic accounting education as being relevant and beneficial to the business community, the accounting profession, and accounting students. Several technology tools and data analytics in investigation and analysis have been identified as important forensic accounting topics for curriculum developments. The limitation of this study is the small sample. A low response rate could be the result of this study containing bias so that it cannot be generalized, and most respondents are from accounting majors. These results are useful for universities in integrating forensic accounting education into their curriculum or redesigning their forensic accounting courses. The study shows the current view on forensic accounting education in Indonesia. Using an explanatory sequential mixed method is considered as a novelty of the present study.

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The purpose of this article was to determine the incidence between learning strategies and academic goals in the competences of the curricular experience of electronics and digital circuits in engineering students of a private university in Lima, Peru. The objective was to explain how learning strategies and academic goals explain the behavior of engineering students of competencies in electronics and digital circuits. For this study, a sample of 89 students from the III cycle was used, to whom the ACRA test instruments were applied for the learning strategies of Román and Gallego (2001), the CMA academic goals test of Durán and Arias (2015) and a test to assess skills in electronics and digital circuits. According to the results obtained, it was shown that learning strategies and academic goals affect the skills of electronics and digital circuits in engineering students. By obtaining x2 = 83.782, (p = .000 <0.05 and Wald = 16.326 showing that the proposed model is acceptable

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A healthy watershed is important not only for the ecosystem but also for human socioeconomic activities. Therefore, a compatible assessment model is required to recognize watershed health. In Indonesia, the watershed health assessment is directed by the Ministry of Forestry regulation number 60/2014. A critic might be posed to this directive for not including the biotic aspects of the watershed. This research aims to assess the five watersheds in Lampung Province, Indonesia. Afterward, we develop a mathematical model using multiple linear regression analysis to identify influential indicators. In developing the model, we combined indicators in the Ministry of Forestry regulation number 60/2014 with the US-EPA directive to include the biotic indicators. To collect the data, we accessed secondary data officially launched by the authorities and did field observation if the secondary data is not available. Our assessment based on the Indonesian official regulation shows that 3 sub-watersheds are in unhealthy status while the rest can be categorized as healthy watershed. Furthermore, the mathematical model of the sub-watershed health assessment shows that the percentage of critical land and vegetation coverage plays an essential role in determining watershed health status. Besides, investment in the water-related infrastructure also significantly contributes to watershed health.

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In this paper are shown some new results on fixed point related to a ? – ? contractive map in JS – generalized metric spaces X. It proves that there exists a unique fixed point for a nonlinear map f:X?X, using two altering distance functions. Furthermore, it gives some results which related to a couple of functions under some conditions in JS – generalized metric spaces. It provides a theorem where is shown that two maps F,g:X?X under a nonlinear contraction using ultra – altering distance functions ? and ?, which are lower semi-continuous and continuous, respectively, have a coincidence point that is unique in X. In addition, there is proved if the maps F and g are weakly compatible then they have a fixed point which is unique in JS – generalized metric space. As applications, every theorem is illustrated by an example. The obtained theorems and corollaries extend some important results which are given in the references.

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Background: Occupational injuries are an issue of huge significance in the United States. After a work injury, health care providers often utilize functional capacity evaluations to determine readiness of a patient to return to work. However, it can be difficult to determine if a patient is providing maximal “effort” during the evaluation. The aim of this study was to determine if the use of the Polylift could assist in recognizing when a subject reached maximal lift capacity of a manual lift from waist to shoulder. The Polylift is a computerized data collection instrument that measures velocity, acceleration, distance, time, and force during lifting activities. Subjects: 42 healthy college students (20- males, 22- females) ages 20-27. Methods: Participants first performed repeated lifts from waist to shoulder until fatigue and the number of repetitions was noted. Using this information, Brzycki’s 1 Repetition Maximum (1RM) formula was used to predict each subject’s maximal load. Next, the Polylift recorded information during four lifts (25% of 1RM, 50% of 1RM, 75% of 1RM and 100% of 1RM). Results: The Polylift recorded a consistent, significant relationship between time and acceleration. As loads approached subjects 1RM, time required to lift the weight increased, and acceleration decreased in a predictable pattern. Conclusion: The Polylift assisted researchers in determining when a subject reached maximal lift capacity by demonstrating a significant decrease in acceleration and increase in time with progressively increasing loads.

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In this study, we study on the downlink indoor coverage performance of unmanned air vehicle (UAV) base stations. We consider a probabilistic expression for air-to-ground (ATG) path loss, and a deterministic one for additional indoor losses in order to provide a practical model. One of our important assumptions is that the UAV base station operates at the frequencies reserved for 4th Generation (4G) – Long Term Evaluation (LTE) based mission critical services in Turkey–around 2.6 GHz–. Therefore, we are able to neglect intercell interference issue, and we may rely on signal-to-noise ratio (SNR) for our coverage definition. We consider four different SNR requirement throughout our performance evaluation, and investigate the effect of UAV altitude and other related parameters on the radius of service area. We first observe that rural coverage performance is always better than urban conditions –this result is fully compatible with the fact that attenuation levels significantly arise in urban regions–. In addition, we show that increasing quality of service (QoS) requirement and/or using a more directive antenna unit decrease the coverage radius as they are expected. Thereupon, we conclude that it is possible to obtain the optimum altitude level –by employing the framework proposed here– in order to satisfy certain service criteria.

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The objective of the metaheuristics, together with obtaining quality results in reasonable time, is to be able to control the exploration and exploitation balance within the iterative processes of these methodologies. Large combinatorial problems present ample search space, so Metaheuristics must efficiently explore this space; and exploits looking in the vicinity of good solutions previously located. The objective of any metaheuristic process is to achieve a ”proper” balance between intensive local exploitation and global exploration. In these processes two extreme situations can occur, on the one hand an imbalance with a bias towards exploration, which produces a distributed search in the search space, but avoiding convergence, so the quality of the solutions will be low, the other case is the bias towards exploitation, which tends to converge prematurely in local optimals, impacting equally on the quality of the solutions. To make a correct balance of exploration and exploitation, it is necessary to be able to control adequately the parameters of the Metaheuristics, in order to infer in the movements taking advantage of the maximum capacity of these. Among the most widely used optimization techniques to solve large problems are metaheuristics, which allow us to obtain quality results in a short period of time. In order to facilitate the use of the tools provided by the metaheuristic optimization techniques, it is necessary to reduce the difficulties in their configuration. For this reason, the automatic control of parameters eliminates the difficult task of obtaining a correct configuration. In this work we implemented an autonomous component to the Intelligent Water Drops algorithm, which allows the control of some parameters dynamically during the execution of the algorithm, achieving a good exploration-exploitation balance of the search process. The correct functioning of the proposal is demonstrated by the Set Covering Problem, which is a classic problem present in the industry, along with this we have made an exhaustive comparison between the standard algorithm and the autonomous version that we propose, using the respective statistical tests. The proposal presents promising results, along with facilitating the implementation of these techniques to industry problems.

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In this work, a study of the process variation effects on the threshold voltage of a floating- gate device is proposed. The study demonstrates the sensitivity of the threshold voltage to five geometrical parameters including gate length, gate width, tunneling gate oxide thickness, bottom oxide-nitride-oxide oxide thickness, and nitride spacer thickness. This paper also proposed a detailed flow to fabricate the floating-gate device for CMOS 180nm process, which is used to design the floating-gate device for the study. This paper used the TCAD tools including Athena, Devedit3D, and Atlas for the simulations.

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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 presents a simple method for correcting the error of commutation windings for sensorless control of a brushless DC motor with a small inductance. Switching error analysis is performed based on the phase currents of the switched-off and switched-on phases. To correct the commutatiuon signals for the inverter, a speed-independent function is used, calculated from the back-EMF, by selecting its coefficients. The back-EMF is calculated for the system obtained by transformed a three-phase system to a two-phase one, which reduces the system dimension. An algorithm for start-up the motor with sensorless control based on the method of align and damping the rotor vibrations in a stable equilibrium position is proposed. The back-EMF is approximated by a function based on a piecewise linear function and an inscribed ellipse, for a more accurate description of the shape of the back-EMF. This approximation is used when simulation the motor. The simulation confirms the effectiveness of the proposed method for correcting the commutation error in the case of sensorless control of a low-power BLDC motor.

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Enrolment figures have been expanding in South African institutions of higher-learning, however, the expansion has not been accompanied by a proportional increase in the percent- age of enrolled learners completing their degrees. In a recent undergraduate-cohort-studies report, the DHET highlight the low percentage of students completing their degrees in the allotted time, having remained between 25.7% and 32.2% for the academic years 2000 to 2017, that is, every year since 2000, more than 67% of the learners enrolled did not complete their degrees in minimum time. In this paper, we set up two prediction tasks aimed at the early-identification of learners that may need academic assistance in order to complete their studies in the allocated time. In the first task we employed six classification models to deduce a learner’s end-of-year outcome from the first year of registration until qualifying in a three-year degree. The classification task was a success, with Random Forests attaining top predictive accuracy at 95.45% classifying the “final outcome” variable. In the second task we attempt to predict the time it is most likely to take a student to complete their degree based on enrolment observations. We complete this task by employing six classifiers again to deduce the distribution over four risk profiles set up to represent the length of time taken to graduate. This phase of the study provided three main contributions to the current body of work: (1) an interactive program that can calculate the posterior probability over a student’s risk profile, (2) a comparison of the classifiers accuracy in deducing a learner’s risk profile, and (3) a ranking of the employed features according to their contribution in correctly classifying the risk profile variable. Random Forests attained the top accuracy in this phase of experiments as well, with an accuracy of 83%.

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Indoor positioning system (IPS) has become a high demand research field to be developed and has made considerable progress in recent years. Wi-Fi fingerprinting is the most promising technique that produces an acceptable result. However, despite the large amount of research that has been done using Wi-Fi fingerprinting, only a few Wi-Fi based IPS in the market can be said to be successful. Doing the research in a controlled environment and ignore the temporal signal changes may be the cause of such scenario. A long-term dataset was built to overcome this issue, yet the distance error of the state of the art was 2.48m. Therefore, we aim to reduce the distance error by combining two positioning algorithms which are Weighted k-Nearest Neighbor (WKNN) and Long-Short Term Memory (LSTM) using ensemble learning. The result shows that our ensemble method can reduce the localization error to 1.89m and improve the performance of the IPS by 23.7% when compared to the state of the art.

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To create a Virtual Reality (VR) system for Wing Chun’s basic hand movement training, capturing, and classifying movement data is an important step. The main goal of this paper is to find the best possible method of classifying hand movement, particularly Wing Chun’s basic hand movements, to be used in the VR training system. This paper uses Oculus Quest VR gear and Unreal Engine 4 to capture features of the movement such as location, rotation, angular acceleration, linear acceleration, angular velocity, and linear velocity. RapidMiner Studio is used to pre-process the captured data, apply algorithms, and optimize the generated model. Algorithms such as Support Vector Machine (SVM), Decision Tree, and k-Nearest Neighbor (kNN) are applied, optimized, and compared. By classifying 10 movements, the result shows that the optimized kNN algorithm obtained the highest averaged performance indicators: Accuracy of 99.94%, precision of 99.70%, recall of 99.70%, and specificity of 99.97%. The overall accuracy of the optimized kNN is 99.71%

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NOX and SO2 gas pollution have a direct impact on health problems and environmental damage. Therefore, to map the emission patterns and predict the resulting impacts, complete data and information on emissions of the two pollutants are needed. In Indonesia, data on NOX and SO2 emissions that are recorded over a long period of time (for example 5 decades) are very difficult to obtain. Meanwhile, REASv3.1 is a global emission inventory that provides complete data on air emissions in Asia during 1950 – 2015. Therefore, this study aimed to analyze NOX and SO2 emission trends, forecast data for 2016 – 2020, and compare the accuracy of calculations from the method used. The processing of both emission data used two methods, namely trend analysis based on exponential and polynomial approaches, and smoothing methods based on Double Moving Average (DMA) and Double Exponential Smoothing (DES). Furthermore, validation of the accuracy from both methods used the value of Mean Absolute Deviation (MAD), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results showed that for the smoothing method, DMA was more accurate than DES. Meanwhile, the indicators are MAD, RMSE, and MAPE values, which are smaller and at a very good category. For forecasting results for 2016 – 2020, it was shown that the total emissions of both NOX and SO2 showed an increase, but with different gains. Furthermore the total NOX emission gain was two times greater than the total of SO2. The road transportation and power plant sectors in NOX emissions showed an increasing trend, with an emission gain ratio of 3:20. Meanwhile, for SO2, the power plant sector experienced a significant increase, while the industrial sector actually showed a downward trend.

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Sizing optimization should be used to design an efficient, sustainable, and feasible hybrid system. In this paper, a hybrid power plant consisting of an off-grid photovoltaic and wind energy system was planned to supply the demand of residential houses in Libya. To minimize installation and operational costs by sizing each part of the hybrid system, the crow search technique was applied. We optimized the number of photovoltaic modules, wind turbine power, and battery capacity and then we compared the performance of the crow algorithm with the particle swarm optimization algorithm for hybrid system design. The results of the crow algorithm suggest better efficiency for sizing lower-cost hybrid power plants consisting of photovoltaic and wind systems.

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The purpose of the study was to describe the experiences of teachers in the distance teaching-learning process in early childhood education during the covid-19 pandemic. The methodology used was qualitative descriptive. Interviews were conducted with thirteen teachers from four state educational institutions at the infant level in Lima city through the virtual platform. As a result of the data analysis, five subcategories emerged: Emotions in remote teaching at the beginning of the school year during the pandemic, communication between teachers and families for student learning, management and adaptation of remote teaching-learning, be a teacher of childhood education during the pandemic, and accompaniment in the teaching-learning process. In conclusion, the distance teaching-learning experience of teachers at the beginning of the COVID-19 pandemic is characterized by the presence of certain negative emotions and attitudes towards their educational work; however, self-training on digital and computer resources and empathy with families have improved the generation of learning situations in children.

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The problem of Pulse Width Modulated (PWM) DC-DC converter simulation is faced in this paper. It is shown how the analysis of this kind of circuits, nonlinear and switching for their nature, can be easily and quickly executed by using symbolic analysis techniques. The paper also presents the program SapWinPE, which performs an automatic symbolic analysis of the considered circuit, and its outputs are in MATLAB compatible format. SapWinpPE features make it more attractive than general-purpose software tools for both academy and industry circuit designers. Other characteristics and potentialities, shown through application examples in the paper, can be advantageously exploited by all the circuit designers and CAD professionals, also at the research and educational level in the academic field.

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This paper presents the generalized integral transform method for solving flexural and elastic stability problems of rectangular thin plates clamped along and simply supported along remaining boundaries (x = 0, x = a) (CSCS plate). The considered plate is homogeneous, isotropic and carrying uniformly distributed transversely applied loading causing bending. Also studied, is a plate subject to (i) biaxial (ii) uniaxial uniform compressive load. The method uses the eigenfunctions of vibrating thin beams of equivalent span and support conditions in constructing the basis functions for the plate deflection and the integral kernel function. The transform is applied to the governing domain equation, converting the problem to integral equations for both cases of bending and elastic buckling. The integral equation reduces to algebraic problems for the bending problem, and algebraic eigenvalue problem for the elastic buckling problem. The deflections are obtained as double infinite series with rapidly convergent properties. Bending moments expressions are double series with infinite terms which are rapidly convergent. Maximum deflections and bending moments values occur at the plate centre in agreement with symmetry. The present results gave double series solutions with good convergent properties in closed form for bending problems. The resulting bending solutions were exact. Solving the resulting eigenvalue equation gave closed analytical equation for the buckling loads. Buckling loads are computed for the cases of biaxial and uniaxial uniform compression of square thin plates using one term approximations. The buckling load obtained for one term approximation of the eigenfunction gave results that are 12.23% greater than the exact solution. The use of more terms in the eigenfunction expansion could give more acceptable results for the eigenvalue problem of buckling of CSCS plates.

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This paper has conducted the study of the impact and effectiveness of Google Classroom in online teaching and learning. Based on the unified theory of acceptance and use of technology (UTAUT2), the first aim was to rank the 8 constructs namely: Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Habit, Behavioral Intention, and the last Use Behavior. Each of the constructs have their respective questions due to the questionnaire formed from the UTAUT2 theory. To evaluate the use of Google Classroom, have been analyzed the feedbacks from every participant based on a 5-likert scale output. Secondly, was completed the rank of the questions based on the most preferred 5-likert scale options. The method proposed for the purposes of this study were fuzzy AHP with triangular fuzzy numbers (TFN) and trapezoidal fuzzy numbers (TpFN). The results suggested that the most preferred construct by fuzzy with TFN numbers was the Behavioral Intention while the least preferred was the Effort Expectancy, whereas for fuzzy TpFN the most preferred construct was the Social Influence and the least preferred was the Effort Expectancy. Based on the questionnaire, the rank resulted to be the same with both methods for the most preferred question and the least important one, that were respectively from Use Behavior construct, and from Performance Expectancy construct, while the ranked questions of other constructs differed slightly with both methods. These results showed that both methods produced the same rank for the 5-likert scale options, where “Agree” option was the more important from the 5-likert scale options and “Strongly disagree” option was less important. From these findings was concluded that these changes in ranking were due to the different defuzzification methods that were used for both types of fuzzy numbers.

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The development of wind farms requires an optimal design of wind turbines layout. The main goal of this optimization is to minimize the wake effect through the optimal placement of the wind turbines. The current study aims to standardize the wake losses among all wind turbines in the wind farm and bringing their losses to a similar level. An objective function was developed for this purpose, and used by the genetic algorithm to maximize the farm energy output and prevent the wake concentration on specific wind turbines. The proposed method has been applied to the Gasiri Wind farm through a simulation approach. The applied optimization process has shown very promising results characterized by a 17% possible energy gain after the adoption of the optimized layout. The study has also shown that the new positions of wind turbines characterized by a high rated power, are more on a forward position following the wind direction compared to the original ones. The study has also shown that there is a significant reduction of mechanical fatigue on the wind turbines blades.

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This research designed an image encryption system that focused on securing teledermatology data in the form of skin disease images. The encryption and decryption process of this system is done on the client side using chaos-based encryption with confusion and diffusion techniques. Arnold’s cat map is the chaotic map model used for confusion, while the Henon map is used for diffusion. The initial values of both chaotic maps are obtained from a 30-digit secret key that is generated using Diffie–Hellman key exchange. During Arnold’s cat map generation, different p and q values are used for every iteration. On the other side, the precision of the Henon map’s x and y values is 10–14. From the tests that have been done, histograms of the encrypted images are relatively flat and distributed through all the gray values. Moreover, the encrypted images have average correlation coefficients of 0.003877 (horizontal), -0.00026 (vertical) and -0.00049 (diagonal) and an average entropy of 7.950304. According to the key sensitivity test, a difference of just one number in the secret key causes big differences, as both results have a similarity index of 0.005337 (0.5%). Meanwhile, in the decryption process, that small key difference cannot be used to restore the encrypted image to its original form and generate another chaotic image with average entropies of 7.964909333 (secret key difference) and 7.994861667 (private key difference).

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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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There are pressures to adopt sustainable behaviour more so in generating profits and benefiting the society to accelerate green efforts through a green framework. The overarching goal of the paper is premised through various works of literature, building the ecosystem the elements highlighted by most researchers in the field of environmental entrepreneurship. The various models reviewed consists of generic incubators and entrepreneurship, and societal and environmental factors. Environmental entrepreneurship is often used interchangeably with concepts such as green entrepreneurship and ecopreneurship which under-researched globally, with non-existent efforts on the applicability and modelling of key environmental entrepreneurship within a specific context utilising the system dynamics approach. In order to assess the environmental entrepreneurship ecosystem, the authors adopted a system dynamic approach to determine key variables that enable the development of the system. A literature review was conducted, and of the 135 articles reviewed, n=92 peer-reviewed articles met the criteria that the researchers set. Some of the results emanating from a systematic review are environmental policy, green skills, financial and non-financial support, societal and behavioural factors, environmental agility, ethics and governance, and access to markets. The theoretical results are simulated using system dynamics modelling. Due to limited research on the above-mentioned topic, a possible impacting variable (Exogenous variables) was broadened to add value to, and have an impact on, the study. Upon reviewing the above-mentioned models, the framework emerged signalling elements to be simulated in the system dynamics model, which were then theoretically contextualised for the South African context. The theoretical virtual system dynamic model forming part of the framework will be tested and validated in the next study. The applicability of the theoretical ecosystem to South African context as well as future recommendations are provided in the study.

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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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In the recent past, there has been a renewed shift into biomass and other recycled waste sources for biodiesel production and utilization. This is a critical area of research and study in which this present work intends to review and identify gaps in literature by shifting the focus of review to non-plant based sources for biodiesel production. Traditional biodiesel feedstock sources have always presented a conflict of food security versus energy. This shift will be identified in literature to see if change to non-plant based feedstocks sources has increased food security by discouraging the contribution of commercial farming for the production of biodiesel. This work will identify biodiesel families, generations, traditional and non-traditional feedstocks for biodiesel production. It will also discuss the non-edible biodiesel feedstocks sources in relation to waste to energy recovery. The other factor this work will review is to study how the use of non-plant based feedstocks such as municipal solid waste has improved environmental protection by reducing pollution and landfilling. In other words, this work will review the impact of Using waste municipal solid biomass resources such as waste tyres and waste plastics and changing them into energy sources. This review study aims at increasing environmental awareness, sustainability and reporting the progress made in waste to energy policy shift in many countries globally. This review will look at socio-economic opportunities in recycling besides the academic and research impacts of waste to energy policies adopted in many countries. The review will climax with a conclusion and future trends in waste to energy in relation to municipal solid waste resources.

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In this paper, one of the “problem zones” in mathematics concerned with the surface area of three-dimensional bodies is presented by computer and mathematical modeling with synergetic effects using intelligent system support. The problems of personal self-organization and technological stages of student’s research activity in the process of modern achievements in science adaptation in teaching mathematics by means of computer and mathematical modeling based on intelligent system support are considered. The technology of intelligent system support to choose and investigate the complex generalized constructs in student’s research activity (on the example of multifaceted surfaces of Schwartz cylinder (or “boot”)) using computer and mathematical support are considered. So, the technological stages and personal quality parameters of student’s research activity and content of adaptation the most important generalized constructions of surface area to the actual positions of mathematical knowledge and fractal parameters are considered with synergetic effects and extend Vygotsky’ zones of immediate development as a finding. Intelligent management of student’s cognitive activity on the base of expert systems during the complex knowledge development creates the zones of immediate development and self-development of students as a new methodology of cognitive student’s activity. The original computer design and mathematical modeling of nonlinear dynamics of synergetic effects manifestation are developed step by step and dynamic invariants are revealed in the course of surface area mastering as a realization of student’s educational trajectory. The model of hybrid intelligent system for supporting of student’s research activities is constructed. Details of concretization are described on the example of Schwartz cylinder so it is fractal complex surface with problems zones of student’s understanding on complex concept “surface area”. Moreover, all constructs of mathematical and computer modeling with Schwartz cylinder are new and based on means of the QT Creator environment and GeoGebra. Namely, the means of the QT Creator environment are used for computer design of non-linear dynamics of functional and fractal parameters and technological constructs and students reveal the regularities and irregularities of lateral surface approximations of Schwartz cylinder. The construction of Schwartz cylinder’s fractal surface as an indirect synergetic effect of student’s research activities is new mathematical and computer design result. Conclusion: new methods of intelligent management of student’s research activity and thinking development using neural networks support was carried out on the basis of lateral surface triangulations of the Schwartz’ cylinder with regular and irregular limiting grinding by layered polyhedral complexes with computer and mathematical modeling tools and as one of the educational trajectories of student’s research activity.

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Since its release in 2013, 4.4 billion people around the world have used Kahoot!. Various topics with multiple languages have been made so that there are at least 200 billion games. A very high number for an educational application. Why Kahoot! So interesting? This study aims to analyze the gamification elements found in Kahoot! as a benchmark to create engaging game-based learning in industrial 4.0. The method used is the Octalysis Audit, which examines eight aspects of game psychology: meaning, achievement, empowerment, ownership, social influence, scarcity, unpredictability, and avoidance. The results showed, Kahoot! A value of 441 in Octalysis was obtained. This result shows an excellent balance between positive and negative motivation. Besides, Kahoot also has a balance between Intrinsic and extrinsic motivation.

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The progress of information using websites is developing fast, impacting various sectors, one of which is pharmaceutical companies. Pharmaceutical companies have roles in drug manufacturing, pharmaceutical distribution, and essential services for the community—the most important and challenging activity in the IT department. The researcher is responsible for one of the Bandung pharmaceutical companies. Researchers are trading IT service managers for drug manufacturing, drug distribution, and community service units that have been conducted online. The company has an essential role in distributing drugs to pharmacists and hospitals during the pandemic at COVID-19. The use of the IDT (Innovation Diffusion Theory) and TAM (Technology Acceptance Model) models are used because, according to the agreement, TAM is a concept that researchers consider to be the best and suitable for viewing the user’s use of IT systems. The study will try to convert these methods with TAM variables, such as Perception of Use, Perception of Ease of Use, Attitude Towards, and Behavior Intentions. Also, IDT variables such as Relative Advantage, Compatibility, Complexity, Trialability, Observability. This study’s survey model is the SEM (Structural Equation Model) using a software application. The results showed the pharmaceutical company website could be well received at pharmacists and hospitals in Bandung. The experimental results get a hypothesis, such as finding a significant output. And with the results of this study, getting good results for the people around Bandung.

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In this paper, hexa spacer damper is proposed and its vibration damping effect on the 765 kV power transmission network under the influence of fluctuating wind (10-60 Hz) loading is validated. This asymmetrical loadings led the bundle of Zebra ACSR conductor to be twisted and hence, cause mechanical and electrical instabilities across the span length. These issues may be addressed using the undertaken spacer damper. This paper highlights design, development and field tests of the proposed solution. The product has been validated using CATIA V5 software tools and for the field trial – 27 numbers of these dampers have been placed at various sub-spans across the line at Hydro Québec Test Station, Canada. The damping efficiency has been recorded using system integrated data acquisition set up. The proposed products are an important item of overhead line hardware and are extensively used to ensure that bundled conductors to provide mechanical and electrical performance reliability in service.

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The constant market change and the critical role of the logistics process in the supply chain need to have special attention because it is an essential piece for the global business strategy. This paper presents an assessment of the processes of handling material from Asia suppliers to North America. The data utilized for the analysis is of 12 months of 2019, valuable input to support the proposal to the company to approve the changes to get a financial gain. The proposal aims to create a complete analysis starting on how the containers are loaded currently in Asia (floor loaded) and the complexity that this strategy creates for the whole supply chain. The analysis of the overall process aims how the proposed change (pallet loaded) will reduce demurrage cost annually (USD 147,000), lead time (from 6 days to 3 days), space utilization in the facility (reduce space by 6364 sqrt/ft), reduce operational cost annually (USD 210,000), improve safety (Risk factor savings annually of USD 61,100), and on-time delivery to the customer (Increase 4% ).

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The paper a cubic spline built through a local base spline and the local interpolation bicubic spline models we offer have been selected. The construction details of the models are given, the two-dimensional local interpolation bicubic spline models considered in this study provide high accuracy in digital processing of signals, which helps experts to make the right decision as a result of digital processing of signals. As an example, the initial values of the geophysical signal were digitally processed and error results were obtained. The error results obtained by digital processing of the geophysical signal of the considered models were compared on the basis of numerical and graphical comparisons.

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No other form of technological development had negatively skewed from existing regulations as the siting of petrol filing stations. An audit of the degree to which their operators repeatedly violate the rules regulating their location and operations may encourage the legislature to pass laws that can ensure a balance between the economy and the environment. This study helps to fill this gap, by analyzing the degree to which the siting of local filling stations meets standard norms, taking Cameroon’s district municipalities of Douala as an example. In order to achieve this goal, the current regulations structuring the filling station locations were carefully reviewed. Subsequently, key compliance factors were identified, operationalized, and an audit checklist was developed. Other data collection tools included a structured questionnaire designed to capture the views of neighboring communities on the possible risks to their health, land, and therefore to the environment presented by the station, and a global positioning system receiver for the collection of spatially explicit data. The nearest neighbourhood Index, (Rn) in ArcGIS10.3.1 environment was used to assess the distribution pattern of the stations. Questionnaire data was analysed using SPSS20.0. It is revealed that the distribution pattern of the 152 stations surveyed is mostly random in Douala I (Rn = 0.8573), Douala III (Rn = 0.9879), Douala IV (Rn = 0.6984), and, dispersed in Douala II (Rn = 1.7837) and Douala V (Rn = 1.5764) district municipalities. While most of them, 94 (61.84%) were within the minimum 500 m radius from one another as specified by laws, 99.34% of them didn’t conform with the recommended minimum distance of 7m from the centre of a major road, and the 400m radius from residential areas. The results suggest that the siting of petrol stations in the city neglect the hazards accompanying them. The database created in this study could provide a platform to policy makers for appropriate actions.

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Smart animal husbandries require the adoption of dedicated tools to assess the contribution of each animal to the production process. The IoT platform presented in this article is a real-team monitoring system for voluntary weighing of cattle. To this end, the ISO 18000-6 standard is used for animal identification through an ultra-high-frequency radio link between a reader antenna and suitable ear tags. A customized data processing algorithm has been developed and embedded in the considered system. To demonstrate the effectiveness of the solution, extensive measurements have been carried out in a real-life environment. The proposed IoT platform is useful to farmers as a control tool for selection and breeding work.

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Internet of Things (IoT) is changing the world through a new wave of revolution for communications technologies that are no more limited to the human being. One of the main challenges that result from the exponential spread of IoT technology is the difference in the traffic characteristics between classical human communications and advanced things communications. The IoT traffic characteristics become essential for understanding and studying the parameters affecting the IoT traffic shape and thus all further studies related to traffic aggregation, topologies, and architecture designs. In this paper, a traffic aggregation in both the space domain and time domain is proposed whereas a matrix of traffic parameters is analyzed and simulated through building a practical lab case study to demonstrate the theoretical results. It is proven that the two proposed aggregation techniques could impact the traffic profile shaping existing IoT use cases for optimizing the network efficiency from several perspectives as 20% high throughput gain, 45% low collision probability, network congestion is limited to 800~1600 packets in the space domain and about 300~20 packets in the time domain, and overheads are minimized by about 50~27 Kbytes in the space domain and 9.5~0.59 Kbytes in the time domain.

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This paper presents a new floating-point technology: Bounded Floating Point (BFP) that constrains inexact floating-point values by adding a new field to the standard floating point data structure. This BFP extension to standard floating point identifies the number of significant bits of the representation of an infinitely accurate real value, which standard floating point cannot. The infinitely accurate real value of the calculated result is bounded between a lower bound and an upper bound. Presented herein are multiple demonstrations of the BFP software model, which identifies the number of significant bits remaining after a calculation and displays only the number of significant decimal digits. These show that BFP can be used to pinpoint failure points. This paper analyzes the thin triangle area algorithm presented by Kahan and compares it to an earlier algorithm by Heron. BFP is also used to demonstrate zero detection and to correctly identify an otherwise unstable matrix.

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NFC (Near Field Communication) is a contactless communication technology based on a radio frequency (RF) field using a base frequency of 13.56 MHz. NFC technology is perfectly designed to exchange data between two devices via simple touch gestures. RFID is the process of identifying an object directly by radio frequency. There are two important components of an RFID system is the card or label (tag) and the reader. In each of tags have unique ID. In addition to the ID, the tag also contained a block that is used for security. On the application in the library, all the books that will be read by the reader have an RFID tag in it. With the tag on each book that is used for lending service, it can be made an application on a smartphone that supports NFC (Near Field Communication) which can be used by each user to borrow books independently, that does not need to come to the library staff to borrow books.

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The textile industry is one of the prioritized industries, because it contributes to the country’s foreign exchange, absorbs a large number of workers, and fulfills the need for national clothing. To increase work efficiency and productivity, the textile industry must use ERP. However, ERP implementation still has a relatively high failure rate. ERP readiness assessment is one of the main issues to achieve success in implementing ERP. Previous research is still limited to research about readiness for achieving success in ERP implementation. The research results have indicated that the maturity of the organization is a very significant dimension with a weight of 43.51%. By knowing the maturity level of the organization for ERP implementation can identify factors that become weaknesses for organizations to take corrective steps, so as to reduce the failure rate of ERP implementation in the industry. This research methodology uses a quantitative approach using R software to determine the principal component analysis and uses the Order Preference Technique with the Ideal Solution to weighted the identified factors. This research aims to determine organization readiness by developing the maturity level of ERP implementation in the industry in Indonesia which conducted a case study experiment in the textile industry in Indonesia. The result of this research is the development of an ERP readiness assessment to assess the maturity level of the organizations in ERP implementation.

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Vehicle accidents, particularly in small and large urban areas, are rising tremendously day by day worldwide. As a recent research subject in automaton transportation, the subsequent collision has become a vital issue and emergency. Internet of things (IoT) and the Internet of Vehicles (IoV) have become very popular these days because of their versatility, and robust cybersecurity underpin these new connected services. Aggressive driving among improper driving behaviours is a mainly responsible cause of traffic accidents that endanger human safety and property. Identifying dangerous driving is a significant step in changing this situation by analyzing data recorded through different gathering devices. The focus of aggressive recognition research has recently shifted to the use of vehicle motion data, which has emerged as a new technique for understanding the phenomenon of traffic. As aggressive driving refers to abrupt changes in actions, it is possible to classify them based on the vehicle’s movement data. This paper presents a method to identify driving behaviours categorized into four groups: dangerous, aggressive, safe and normal behaviour to reduce the risk of accidents based on real-time data recorded from vehicles and reference data provided by previous researchers. Comparison and statistical methods have been done to determine the best way to collect driving data based on independent-samples t-test using Statistical Package for the Social Sciences (SPSS) statistics to compare the means between groups on the same continuous, dependent variable. Results have also shown that a small difference of speed between the mobile application and the On-Board Diagnostics (OBD-II) speed with t(4024.1)=1.8, p=.071, which can be considered acceptable. Furthermore, the OBD-II adapter and mobile application speed were significantly different from the independent GPS device with t(3184.9) = 10.8,p= 0 and t(4416.5)= 13.2,p= 0. Consequently, it is expected to improve drivers’ awareness of their driving behaviours.

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Revised Bloom’s Taxonomy (RBT) is proposed in general to look more forward in responding to the demands of the developing educational community, including how students develop and learn and how teachers prepare Learning Objects (LO). The variety of characteristics of students’ abilities in a class has always been a problem that is often faced by a teacher. Unfortunately, cognitive classifications to develop students’ knowledge to a high level have not been used to plan a learning path that is appropriate for their cognitive level. The purpose of this study is to recommend a learning path that matches the cognitive abilities of students from a learning object ontology. The method used in this research is Hybrid Particle Swarm Optimization (HPSO) which integrates Binary Particle Swarm Optimization (BPSO) and Discrete Particle Swarm Optimization (DPSO). The Connection Weight (CW) function is used to test the quality of the connection between the learning objects of an ontology subject controlled by the cognitive class. Based on experimental studies, the HPSO method can recommend a suitable learning path for cognitive classes, namely Low Cognitive (CL), Medium Cognitive (CM), and High Cognitive (CH). The similarity of the sequence of learning paths based on population in CL-class is 87.5%, CM class 75%, and CH class 87.5%.

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We introduce a sign algorithm based on the normalised least mean square with Hammerstein adaptive filtering using adaptive averaging step-size mechanism, which is derived by the minimised absolute a posteriori squared error. To improve the performance by reducing computational complexity, we suggest an adaptive averaging using energy of errors to update step-size variant. The analysis of convergence behaviour and mean square performance are derived. Experimental results reveal that the proposed algorithm can perform better than the least mean square approach based on the Hammerstein model of adaptive filtering.

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In this COVID-19 pandemic, it has been registered that children with autism are not learning properly with this virtual modality in Peruvian education. The main objective of this research work is to design a mobile application with augmented reality so that autistic children can improve their cognitive development in their virtual and face-to-face classes, the chosen method- ology is DesingScrum which is a hybrid of the union of Design Thiking and Scrum, which will have 10 phases (empathise, define, devise, planning meeting, sprint backlog, daily meeting, sprint review, retrospective sprint, prototype, testing), in the case study the Balsamiq tool was used for the design of the mobile application. The results are the responses from the public in Lima – North of the survey carried out on the prototype for its improvement and also the result of the design of the games with augmented reality that was applied with the tools (Tinkercad and App Augmented class). The conclusion drawn from the research work is to be able to help autistic children to improve their cognitive development with the mobile application developed through augmented reality.

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The phasor Measurement Unit is a modern tracking tool mounted on a network to track and manage power systems. PMU is accurate and time-synchronized device that gives voltage phasor measurements in nodes and current phasor measurements connected to those nodes where the PMU is installed. This study introduces the Genetic Algorithm for optimization of allocation of PMUs to enable maximum observation of the power network. The optimal PMU placement (OPP) problem is developed to minimize the quantity of PMU to be placed. The set and optimized model can efficiently position PMU in any network, considering the regular operation and zero injection (ZIN). Thus, the allocation algorithm implemented on IEEE 14-bus systems, the result was compared to that of existing works which achieved the same system of redundancy index. As a further study, the proposed approach is applied to the Nigerian 330kV new 52-bus systems, under operational arrangements for maximum observability of the network system. The technique formulated to handle normal operation and zero injection node succeeded in producing comparable results with other available techniques.

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Assessing academic performance is a common way of evaluating and assessing the abilities of students in tertiary institutions. Usually it is practically performed based on the cumulative grade point average (GPA) at the end of each semester passed. Unwittingly there are many factors that are able to influence student performance results apart from GPA as a performance measure; i.e. gender, hometown, sibling, family status, residence, father education, mother education, family income, motivation, mileage, traveled time, transportation, scholarship, community, social media, and hang-out. Academic performance assessment is proposed through the decision support model (DSM) applying the fuzzy logic (FL) Sugeno technique. The model output generates a decision value (linear or constant equation) for academic performance based on the calculation of the measured fuzzy parameter value (ax) and conventional parameter value (bx). The DSM with the FL Sugeno method is able to provide sharp output in assessing student academic performance. In this case, the model is able to be applied then to assist academics in higher education in determining educational strategies for students with poor academic performance results.

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Monoacylglycerol (MAG) and diacylglycerol (DAG) are structured lipids that have been widely used in various pharmaceutical, cosmetic, and food industries. MAG and DAG are generally produced by chemical glycerolysis. Chemical catalysts have been shown to be more efficient, economical, and effective. This study summarizes and discusses the factors that affect the synthesis of MAG and DAG by chemical glycerolysis, such as temperature, reaction time, and type and concentration of catalysts that affect the resulting MAG and DAG concentrations. Homogeneous catalysts such as KOH and NaOH are very effective for generating MAG and DAG conversions up to 91%, but they have a disadvantage, mainly because they cannot be used repeatedly. However, heterogeneous catalysts have great potential to be developed into catalysts with high activity, environmentally friendly, and can be used repeatedly.

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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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The Coriolis force in the ocean at mid to high latitudes can cause significant deviation of flow over bottom topography, including formation of Taylor columns. Structures in a tidal zone will experience zero inertial current between every tidal change. Around periods of directional change, the Coriolis force may be tapped into for energy. Factors like timescales and other environmental factors like local currents could influence the flow characteristics in an undesirable way and are outside of the scope of this study. The focus of this study is to assess how the design of a structure influences the asymmetric flow patterns produced around it by an incident quasigeostrophic flow. Analytical solutions existing for inviscid quasigeostrophic flow over isolated elongated elliptical topography are used for flows with small Rossby numbers. These solutions are used to predict and explore the characteristics of the flows expected during a change in the tidal cycle. Results show that a linear array placed perpendicular to a quasi-geostrophic flow will experience flow acceleration on the left-hand side when looking downstream. On the other hand, a linear array placed parallel to the quasi-geostrophic flow will experience a sharp velocity gradient over the array. This suggests that an array placed perpendicular to the quasi-geostrophic flow will provide for a more robust design when compared to a linear array placed parallel to the flow.

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The angle of attack is a critical flight parameter for commercial aviation aircraft, because automatic envelope protection systems rely on it to keep the aircraft within its safe flight envelope. Faulty measurements of the angle of attack could have catastrophic effects, leading to aircraft loss of control in flight and fatalities, as demonstrated by the recent accidents involving the Boeing 737-MAX. This paper presents a novel approach to the measurement of the angle of attack, which uses one virtual sensor and two physical sensors to implement a physical-analytical redundant system that is robust to a single fault of the physical sensors. The virtual sensor is based on an innovative and reliable estimator of the angle of attack. It was originally developed to provide General Aviation pilot with an accurate indication of trend toward stall, and has been suitably customized to fit its application to commercial aviation. One of the peculiarities of the redundant measurement system is that its implementation on-board several existing commercial aviation aircraft only needs the integration of a software code and does not require any installation of additional physical sensors. The proposed approach demonstrated very interesting performance, assessed in simulation through several Monte Carlo analyses. Its exploitation could contribute to reduce the angle of attack related accidents, improving the safety of the air transport system.

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Capturing similarity in gene sequences of a target organism to detect significant regions of comparison will most likely occur because genes share a related descendant. Local sequence alignment for the targeted organisms can help preserve associations among sequences of related organisms. Such homologous genes possess identical sequences with common ancestral genes. The genes may be similar to common traits, and varying purposes, but they descend from a common ancestor. Basic local alignment search tool (BLAST) from the National Center for Biotechnology Information. (NCBI) has been used by different researchers to resolve the various forms of alignment problems. However, much literature to bare the efficacy of standard protein-protein BLAST (BLASTp) on the MATLAB platform has not been seen. In this study, a position-specific iteration BLASTp of 20 anopheles insecticide target protein sequence was performed on NCBI Ensembl against genomes of Anopheles (target organism), then against humans, fruit-fly, zebrafish, and chicken genomes (non-target organisms) to eliminate the targets with homology to non-target organisms. Furthermore, the same iteration was repeated for the genomes of Anopheles and non-target organisms using a posterior probability algorithm built into MATLAB as a tool for protein to protein search BLAST. Outputs from NCBI and MATLAB were put forward to determine the optimality of an optimized search algorithm on MATLAB. The MATLAB-Blastp method based on the application of posterior probability has helped to avoid errors occurring in the early stages of alignment. Moreover, the same results were obtained for the sought features on NCBI Blastp with a refined understanding of how feature values are generated from MATLAB posterior probability built-in algorithm for position-specific BLAST.

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E-learning, among the most prominent modes of learning, offers learners the opportunity to attend online courses. To improve the quality of online learning, social learning through social networks promotes interaction and collaboration among learners. As part of the learning process management in these environments, the implementation of recommendation systems facilitates the provision of content adapted to the needs and requirements of learners and generates recommendations likely to arouse their interest. Many researchers have been involved in several recommendation techniques such as the development of Machine Learning algorithms and the incorporation of social interactions between learners. However, the behavior within a learning environment can diverge from one learner to another. This must therefore be taken into consideration when generating recommendations, i.e., it is initially important to form groups of homogeneous learners prior to proposing recommendations. In this respect, the recommendations generated will be more appropriate to the learners’ profiles and level of interaction. On this basis, we raise an important issue which is the importance of grouping learners into homogeneous groups in a recommendation system. In the recommendation system we advocate, we group learners based on the degree of interaction within the learning environment before generating the recommendation list based on a hybrid approach for each cluster. The overall system is, therefore, based on the identification of communities based on the k-means algorithm and the generation of recommendations list for each community separately. Finally, we compare the results of the system integrating the classification of learners as a preliminary step to the system excluding the k-means algorithm. The results reveal that the integration of the clustering algorithm leads to improvements in terms of performance and accuracy.

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This paper aims to clarify the lower limit of the gaze time required during reading by investigating at a display time of 98 msec or less using the visual information processing analyzer developed in the previous research. The image display range and display time at the point of gaze was controlled by moving the window in conjunction with the eye movement. As a result, it was found that normal reading, as if there were no window or visual ?eld restriction, could not be performed with a display time of 42 msec or less, but that normal reading could be performed when sentences were displayed in a window of 7 characters at 56 msec. In this way, we believe we can obtain useful knowledge that is necessary for studying more e?ective methods of displaying text on electronic device.

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Many countries all over the world are promoting the recycled aggregate concrete for its advantage to solve the problem of shortage of natural resources in aggregates as well as promoting the recycling of waste and the reuse of materials.
Recycled aggregate concrete replaces natural concrete with various rates of restitution of natural aggregates into recycled aggregates. The choice of rate is essential in the quality of the finished concrete and depends on the mechanical and durability characteristics. Tests in the civil engineering laboratory of the Mohammadia School of Engineers – Rabat Morocco have shown that above a rate of 30%, the mechanical characteristics drop. The addition of adjuvants / side products helps to increase this rate and a concrete with a 50% restitution can be used instead of natural concrete.
Our study, the subject of this document, aims to study the durability of this concrete for various percentages of restitution. The experimental tests focused on determining the porosity of hardened concrete. The results were between 13and 14% for all of the restitution percentages.
This study concluded that for ordinary constructions, this concrete meets the criteria of the Perfertial Approach for durability. For projects with special criteria, other more in-depth studies will have to be carried out.

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University operational activities are a routine part of university operations and supervisory control and monitoring function. The low controlling and monitoring of operational activities can cause irrelevance in the teaching and learning process. A graduate may have a graduation document but has never attended the teaching and learning process. An official institution can issue this graduation document, but it is fake because no teaching-learning activity occurred. It happens because the data is easily being manipulated and changed in the current system. From the problem, this is what drives this research to be carried out. With the characteristics of distributed, secure, and traceable information, Blockchain will solve this problem. Based on a previous study, blockchain technology facilitates university operational activities so that it will solve the current problems. This research uses qualitative research methods. The research process starts with literature studies and forum group discussions conducted on nine universities in Indonesia (public and private universities). This study used the User-Centered Design Technique. This research focuses on the user, so the results possibly are applied. The research results prove that Blockchain technology can record student attendance as part of the graduation process’s teaching and learning process. Blockchain’s immutable, unchangeable, and distributed characteristics will ensure the student attendance record’s validity in the teaching and learning process.

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A comparative analysis of formulas for blast wave overpressure is presented in the paper, and models were compared with available experimental data. The Kinney and Shin models show the best agreement with experimental data (Kingery-Bulmash) for free airburst, while for surface burst, Swisdak, Vanuci, and Jeon models predict test data most accurately. One of the novelties in the paper is introduction of new exponential and power functions for blast overpressure estimation, giving good agreement with experimental data. Also, several numerical simulations of free airburst explosions were performed to introduce methodology, and compare the data obtained with experimental data. A detailed description of the procedure for these simulations was provided – a contribution to numerical modeling of blast wave phenomena.

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Blockchain technology has proven to be the best solution for digital data storage today, which is decentralized and interconnected via cryptography. Many consensus algorithms can be options for implementation. One of them is the PoA consensus algorithm, which is proven to provide high performance and fault tolerance. Blockchain has been implemented in many sectors, including the healthcare sector that has different characteristics of larger and more diverse record sizes. Implementing blockchain costs a lot of money. We used a blockchain network simulator as the best alternative in our research. The main problems with blockchain implementation are having a dynamic characteristic network and providing a blockchain system that is adaptive to network characteristics. Therefore, we propose a method to optimize the simulated PoA blockchain networks using Deep Deterministic Policy Gradients by adjusting the block size and block interval. The simulation results show an increase in effective transaction throughput of up to 9 TPS for AIH and 5 TPS for the APAC data models, and without affecting other important aspects of the blockchain.

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This study builds the implementation of the traceability process by conducting simulation tests using business process simulations with the implementation of blockchain technology to track drugs. This research focus involved stakeholders, including the pharmaceutical industry, pharmaceutical wholesalers (distributors/wholesalers), health services (drug stores, hospitals), consumers. Simulation methods are used to describe the distribution and traceability of drugs. Finally, the research contribution in incorporating blockchain technology to supply chain management could potentially help in drug traceability. This study provides an overview of blockchain technology capabilities to find out which stakeholders and assets are transacted on the blockchain system. A decentralized Autonomous Organization is an approach to organizing data on the blockchain that defines all stakeholders identities associated with different addresses. This process can organize each address’s transactions on a special blockchain platform in this study using multichain. Furthermore, transactions that have occurred cannot be updated or deleted. This simulation also illustrates some of the blockchain characteristics that must exist, among others, transparent, distributed, immutable, and peer to peer transactions. This contribution gives supply chain management, in particular on drug distribution, stronger control over distribution.

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Harmful algal blooms (HAB) or “Red tides” are organisms which produce toxins which are harmful to humans, fish, and other marine mammals. The said toxins come from dinoflagellates of genus Pyrodinium Bahamense var Compressum that cause Paralytic Shellfish Poisoning (PSP) and Dinophysis Caudata and Prorocentrum lima which results to Diarrhetic Shellfish Poisoning (DSP). In the coast of Western Samar, Philippines, there are reported occurrences of red tide toxins based on the laboratory result conducted by the Bureau of Fisheries and Aquatic Resources (BFAR) and the Local Government Unit (LGUs). HAB or Red tide has an impact on economy in terms of the loss of livelihood, sales, and exports, and also in terms of human life. This study developed a system that automatically detects the presence of red tide toxins and water quality parameters. Red Tide toxins and water quality parameters are determinant factors in detecting and forecasting the occurrence of HAB. In particular, the system utilizes a Surface Plasmon Resonance (SPR) and Water Quality parameter sensors used in detecting red-tide toxins and water quality. Data or information collected and generated by the system can be used for a faster, effective and efficient way of detecting and predicting the possible occurrence of HAB. Results positively indicate that the system performance can indeed be used as a Method in the detection and prediction of Harmful Algal Bloom. Specifically, for a time period of 80 minutes, 16 different sets of water parameters are captured and transmitted to the centralize system – making the system to be 100% functional as it is being set to operate in 5 minutes interval. Likewise, SPR bio toxin detection has a deviation rate of (+/-) 20%. Operational performance of the system is found to be 100%.

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The research objectives were to identify, analyze needs, as well as designing a mobile application for student online learning at PT. Ruang Raya Indonesia, particularly in the process of working on tasks assigned by the teacher. The methods used in this research are data collection method using Slovin theory, systems analysis by focusing on the current business processes, designing object-oriented systems (OOAD) method. The result is the design of mobile applications in the form of an application for students to be able to do the tasks assigned by the teacher, change private tutoring schedule, and view academic progress reports of the student during private tutoring. The Conclusion of this research is PT. Ruang Raya Indonesia requires a system that can provide students the information about student’s private tutoring activity.

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The dramatic growth of social media during the last years has been associated with the emergence of a new bullying types. Platforms such as Facebook, Twitter, YouTube, and others are now privileged ways to disseminate all kinds of information. Indeed, communicating through social media without revealing the real identity has emerged an ideal atmosphere for cyberbullying, where people can pour out their hatred. Therefore, become very urgent to find automated methods to detect cyberbullying through text mining techniques. So, many researchers have recently investigated various approaches, and the number of scientific studies about this topic is growing very rapidly. Nonetheless, the methods are used to classify the phenomenon and evaluation methods are still under discussion. Subsequently, comparing the results between the studies and identifying their performance is still difficult. Therefore, the current systematic review has been conducted with the aim of survey the researches and studies that have been conducted so far by the research community in the topic of cyberbullying classification based on text language. In order to direct future studies on the topic to a more consistent and compatible perspective on recent works, we undertook a deep review of evaluation methods, features, dataset size, language, and dataset source of the latest research in this field. We made a choice to focus more on techniques that adopted neural networks and machine learning algorithms. After conducting systematic searches and applying the inclusion criteria, 16 different studies were included. It was found that the best accuracy was achieved when a deep learning approach is used particularly CNN approach. It was found also that, SVM is the most common classifier in both Arabic and Latin languages and outperformed the other classifiers. Also, the most widely used feature is N-Gram especially bigram and trigram. Furthermore, results show that Twitter is the main source for the collected datasets, and there are no unified datasets. There is also a shortage of studies in Arabic texts for cyberbullying identification in contrast with English texts.

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This paper aims to simulate the current trajectory of the pandemic growth in the UAE; when it is likely to end and at what cost? It also examines the current and additional possible measures to contain the second wave of the pandemic. The method used is a simple Susceptible-Infected-Recovered (SIR) model called covid19_scenarios. The key finding suggests current intervention is 35 – 45% and effective, and based on keeping them, the pandemic curve in the UAE is expected to be flattened around the fourth quarter of 2022 with the maximum saved lives and lowest burden on the healthcare system. In contrast, it can end earlier at the end of the second quarter of 2021 but at a much higher fatality rate and a health system ready with 3,600 intensive care units. It also revealed that country closure has a minor impact, and severe and fatal cases will continue to appear even after vaccinating the whole community.

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This paper proposes a modeling framework (analytical modeling) for the case of fish cannery supply chain (FCSC) to optimize the environmental impact of the set of its processes; indeed, for our knowledge, there were few studies attempting to address this case study as a model of green supply chain. Implementation of the proposed model is done using first MCDM methods (AHP, TOPSIS) in order to select and classify processes and the corresponding environmental impact, as well as dealing with environmental analysis. Furthermore; a flowchart is proposed as an addition to improve the other processes in terms of reducing environmental impact, and the numerical resolution is carried out using the LINDO software. The proposed framework will guide researcher both as well as practitioners in establishing an optimal model for the green fish cannery supply chain (FCSC).

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This article presents an analytical model of a single-phase ferromagnetic core power in- ductor (reactor) based on a magnetic equivalent circuit (MEC). The MEC model consists of magnetomotive forces (MMFs) and reluctances for all flux paths: magnetic core and leakage flux paths. The MEC elements are found established on the characteristics of the ferromagnetic material and the reactor’s dimensions. Then the inductances of the reactor are determined using the MEC for a group of Silicon Steel (Si-Fe) sheets, which are juxtaposed with the inductances obtained using the finite element analysis (FEA) method. This comparison corroborates the MEC presented here. Furthermore, for the unsaturated (linear) region of the B-H characteristics of the same sets of Si-Fe materials, the inductive reactance closed-form solution of the reactor is obtained as a function of the design parameters. As an application example of the presented analytical model, reactor design optimizations (single-objective and multi-objective) are formulated and solved based on the derived closed-form reactance expression resulting in a reactor not only optimized in reactance but also in terms of material use and size.

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The main issue that is being presented in 2020 is the impact that all organizations are having due to the COVID-19 pandemic, and it is not for less given the global collapse that is occurring in all aspects. Many organizations have been affected by this catastrophe and in the face of an unforeseen scenario, the disruptions in the different supply chains have revealed the lack of some essential products for human consumption.
For organizations that are looking for alternatives of what to do and that are constantly analyzing how to reinvent their processes to mitigate the impacts of the pandemic and thus stay current during the contingency, they have before them the challenge of strengthening their supply chains, however It is difficult to think that the contagion of this virus that has brought the great powers of the planet to their knees, collapsing their productive, economic and especially health systems.
This research aims to propose a model that allows the development of an action plan in the event of the COVID-19 contingency in the sector of companies classified as essential, such as the retail industry. It is not enough to have well-defined and structured processes, these must also be dynamic and interconnected to privilege the distribution of essential products and for this it is important to be clear about the pillars of supply chain management and the key elements that they proposed.
Organizations must learn to protect their supply chains and to achieve it research offers a perspective on how to assess the level of risk of the processes of the retail industry and thus have identified the opportunities that will have to improve to build a resilient supply chain and strengthened.

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The implementation of green advertising is relatively low for credibility but has an impact on green customer behavior. Based on the phenomenon, the purpose of this study is to examine factors affecting green advertising development, which is based on experienced customers towards products and advertisements and environmental issues. This research focuses on customers to create an implementation model for green advertising among companies. The study was conducted through a survey of 215 customers in West Java (Indonesia) who experienced green advertising and bought environmental-friendly products. Data were collected through a quantitative questionnaire and processed with SmartPLS to test and evaluate Confirmatory Factor Analysis (CFA). In emphasizing the study results, a fit test of the research model and research hypotheses were also being carried out by valuing the KMO. Research findings show several dimensions involved in developing green advertising, such as experience, theme, message, claim, emotion, interaction, and impact. The dimensions of green advertising were plotted in the CFA model so that the priority scale from the implementation of green advertising measurement can be detected. Customers assume green advertising as advertising that takes environmental issues of “global warming,” and this issue can adopt by companies in implementing the green marketing strategy.

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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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Classic surgical skill evaluation is performed by an expert surgeon examining an apprentice in a hospital operating room. This method suffers from being subjective and expensive. As surgery becomes more complex and specialized, there is an increase need for an automated surgical skill evaluation system that is more objective and determines more exactly the skills (or lack thereof) the apprentice has. The main purpose of our proposed approach is to use an existing skill database with known proficiency levels to evaluate the skills of a given apprentice. The skill of the apprentice will be assessed to be similar to the closest skill example found in the database (case-based reasoning). A key element of the system is the skill distance measure employed, as each skill example is a multidimensional time series (sequence) with widely varying values. In this paper, we discuss a new surgery skill distance measure denoted as Procrustes dynamic time warping (PDTW). PDTW integrates the search for exact alignment between two skill sequences using DTW and Procrustes distance as a measure for the similarity. The Procrustes approach is a shape distance analysis that involves rotation, scaling, and translation. We evaluated our proposed distance on three surgical motion data, a widely used JIGSAWS robot surgery dataset, a wearable sensor dataset, and a Vicon motion system dataset. The results showed that the proposed framework produced a better performance for surgeon skill assessment when PDTW was used compared to other time series distances on all three datasets. Also, some experimental results for the JIGSAWS dataset outperformed existing deep learning-based methods.

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Disruptive innovation has created fast changes in the business environment and competition among companies, especially on information technology companies. Knowledge creation and entrepreneurial orientation are two variables that can improve firm performance. There is still limited study on how knowledge creation and entrepreneurial orientation both affects firm performance. This study aims to discuss how to effectively apply knowledge creation and entrepreneurial orientation to develop firm performance. A questionnaire has been conducted to 55 medium-large IT companies in Jakarta, Indonesia, and analyzed using structural equation modeling (SEM). The result showed that knowledge creation did not directly affect firm performance but indirectly affected entrepreneurial orientation. Knowledge creation also had a positive and significant effect on entrepreneurial orientation, and so does entrepreneurial orientation towards firm performance. Therefore, IT companies should consider both variables to improve their performance. Future studies may consider using qualitative or mixed-method approaches, conducting research for small IT companies and in other countries.

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This paper aims to understand the impact of eLearning capabilities on organizational performance. It also addresses the obstacles of organizational learning using eLearning methods and highlighting some emerging trends and technologies that will impact the eLearning experience in organizations. It examines a brief history of knowledge management and how it is related to learning, organizational learning, and performance. It also explores different eLearning technologies and trends. A systematic literature review was used to examine previous papers between 2016–2020. Results show eLearning can impact organizational performance in many ways, and human factors can be one of the most challenging obstacles in deploying eLearning solutions in organizations, and many emerging eLearning trends were explored including open educational resources, gamification, flipped classrooms, and many others.

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In the modern marketing system in a mass consumer society, the central place is given to the formation and promotion of brands. Professional conceptual approaches to brand management are able to preserve and increase the value and stability of the brand and allow the brand to survive in the most difficult competitive and crisis market conditions. In view of the fact that there is a fairly large number of theoretical developments, the purpose of the study was to develop practical recommendations regarding the strategic management of brand positioning in the market. The authors have developed an Algorithm for the strategic management of brand positioning in the market, which consists of 5 stages and 11 tasks. The tools proposed by the authors, which were discussed in detail and clearly demonstrated in the article, are of practical value. The product positioning process is not an easy process, and therefore even experienced professionals are not immune from mistakes. This research will help better to understand the brand positioning strategy in the market.

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In this paper, we are concerned to investigate the efficiency of the second-order Redlich-Kister Finite Difference (RKFD) discretization scheme together with the Four Point Explicit Group Kaudd Successive Over Relaxation (4EGKSOR) iterative method for solving two-point boundary value problems (TPBVPs). In order to apply this block iteration to solve any linear system, firstly we discretize all derivative terms via the second-order RKFD discretization scheme over the proposed problem in order to get the second-order RKFD approximation equation. Due to the main characteristics of the coefficient matrix for the generated linear system which are large-scale and sparse, the best choice for solving this linear system is using one of the iterative methods. Therefore, the formulation of the Kaudd Successive Over Relaxation method together with the Explicit Group iteration method mainly on the Four-Point Explicit Group Kaudd Successive Over Relaxation (4EGKSOR) iterative method has been presented to solve this linear system iteratively. In order to show the efficiency of the 4EGKSOR, another two iterative methods have also been considered which are the Gauss-Seidel (GS) and the Kaudd Successive Over Relaxation (KSOR) to solve three examples of the proposed problems in which all numerical results obtained were recorded based on the number of iterations, execution time and maximum norm. Based on the performance analysis, clearly, the 4EGKSOR iterative method shows substantiated improvement in terms of the number of iterations and execution time.

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The article investigated the banking determinants of loans’ growth using the quarterly data of Albanian banking sector during 2003-2018. We estimated the linear model of control variables to examine the bank’s sound effects on loan’s capacity. Empirical results demonstrated the initial positive outcome of the growth of non – performing loans on the loans’ growth, but negative effect incorporating with the structural changes. This divergence emphasized the optimum loans’ supply through the existence of U relationship amongst them and confirming the banking shrinking behavior related to the lending policy. Also, we confirmed the negative impact of capital regulation and long run correlation to capital regulation growth and growth ratio of loans.

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Modern power systems are topologically and structurally complicated due to their complex interconnections. Consequently, the complexity of the dynamic stability assessment be-comes more tedious, most especially, when considering a power electronics-based power system operating under faulty conditions. This paper, therefore suggests an alternative approach of Network Structural-Based Technique (NSBT) for the analysis and enhancement of transient stability of a power system considering Flexible Alternating Current Transmission Systems (FACTS) devices integration. The mathematical formulations based on the NSBT as well as the dynamic swing equations, required for carrying out the stability analysis, are presented. The structural characteristics of the network are captured by considering the interconnections of the network elements and the impedances between them. The eigenvalue analysis is then explored to identify suitable and possibly weak load node locations where the influence of FACTS device placement within the network, could be most beneficial. The transient stability analysis before and after critical outage conditions is investigated. The transient stability of the network operating under critical outage condition is then enhanced considering the integration of a multi- UPFC controller, which is suitably located as identified by NSBT. The effectiveness of the suggested approach is tested using the modified standard IEEE 5-bus, 30-bus networks as well as the practical Nigerian 28-bus grid incorporating a multi-FACTs controller. The results obtained show that the FACTS device contributes significantly to improving the transient stability of a multi-FACTS-based power network. The information provided by this study is highly beneficial to the system operators, utilities investors and power engineers, most especially, for predicting system collapse during critical outage conditions.

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This manuscript exhibits a perfect design for a flexible number of modified DC nanogrids within an open energy distribution network (OEDN) that interconnected via a DC bus. Each modified nano-grid implies a single-input multi-output switched boost inverter (SIMO-SBI). The DC-output for each inverter is robustly controlled to keep the DC-bus interconnected voltage constant. The proposed control uses model reference technique to defeat the non-linearity of the system. In addition, a control algorithm is proposed to manage the optimum power flow and increase the system reliability. The MATLAB/Simulink program is used in modelling and simulation of the suggested arrangement to achieve the robustness of the proposed OEDN with multiple 5-Kw interconnected nanogrids fed from photovoltaic (PV) arrays. Several simulation results are introduced for both open and closed loop control to verify the robustness and validity of the system against the main parameters’ changes. In accession, the smoothing power flow between nanogrids indicates that the proposed algorithm for the controller is sophisticated and able to supervise the power among all nanogrids of an OEDN.

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Online fashion market is constantly growing, and an algorithm capable of identifying garments can help companies in the clothing sales sector to understand the profile of potential buyers and focus on sales targeting specific niches, as well as developing campaigns based on the taste of customers and improve user experience. Artificial Intelligence approaches able to understand and label humans’ clothes are necessary, and can be used to improve sales, or better understanding users. Convolutional Neural Network models have been shown efficiency in image c1assification. This paper presents four different Convolutional Neural Networks models that used Fashion-MNIST dataset. Fashion-MNIST is a dataset made to help researchers finding models to classify this kind of product such as clothes, and the paper that describes it presents a comparison between the main classification methods to find the one that better label this kind of data. The main goal of this project is to provide future research with better comparisons between classification methods. This paper presents a Convolutional Neural Network approach for this problem and compare the classification results with the original ones. This method could enhance accuracy from 89.7% (the best result in the original paper, using SVM) to 99.1% (with a new cnn model called cnn-dropout-3).

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The Ocean plays an important role in hosting investigations in underwater astronomy and enabling the realization of new research prospects. This paper discusses synergistic prospects of the blue economy from the perspective of underwater astronomy and scientific inquiry, technological and economic development. The presented research investigates how the synergy enhances computing applications. The paper presents the overloaded application paradigm that explores the ability of underwater telescope networks to accommodate additional applications. The investigated metrics for computing applications are the accessible computational resources, and power usage effectiveness (PUE) that is investigated in a scenario where onshore computing stations used in underwater astronomy observations are integrated with existing terrestrial data centers. This is necessary as onshore computing stations benefit from free cooling being located near natural maritime resources. The performance evaluation investigates how the proposed synergy and the emerging crowd–sourcing can enhance the observation resolution for underwater astronomy observations. Investigation shows that the synergy enhances accessible computational resources, PUE and observation resolution by an average of 48.8%, 1.6% and 41.3%, respectively.

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Social media attracts a lot of users around the world. Many reasons drive people to use social media sites such as expressing opinions and ideas, displaying their diaries and sharing them with others, social communication with family and friends and building new social relationships, learning and sharing knowledge. Written text is one of the most common forms used for communication while using social media sites. People use written texts in different languages, and due to the increased usage of social networking sites around the world, the amount of texts and data resulting from this use is large. These generated data considered as a valuable source of information that attracted business owners, companies, government institutions, and of course, it attracts researchers and data scientists as well. Researchers and data scientists increasingly presented great efforts in investigating and analyzing Arabic Language texts. Most of these efforts targeted the Modern Standard form of Arabic Language. While exploring the social media sites, most of the Arab users tend to use their dialects while utilizing Social Media sites, which results in generating a massive amount of Arabic Dialects texts. The number of researches and analysis of Dialects’ form of the Arabic language are limited, however, it is increasing recently. This literature review aims to explore approaches and methods used for Sentiment Analysis of Arabic Dialects text.

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The objective of this article is to determine if the self-perception of professional competences has been affected, in systems engineering students, during the health emergency declared in Peru by Covid-19; The results will allow the public university of Peru to make corrective decisions and formulate proposals to improve the functioning of the variable under study. Initially, the comparative analysis was carried out, where it is observed that in the academic semester 2020-I (during the health emergency), there is a greater number of students who present a better self-perception of the 10 indicators of the professional skills dimension, compared to the semester 2019-II (before the health emergency). Likewise, the indicators “To solve problems and cases of the specialty” and “To master practical professional skills” present a higher rate of improvement in self-perception, of 8.14% and 10.37%, respectively. Finally, when carrying out the statistically validation of the association of the two mentioned indicators, using the contingency table, it is observed, by the Chi-square statistic, a significance (bilateral) lower than ? = 0.05, with this, it is verified , the significant association of the indicators; This is supported by the percentage obtained in the contingency table, where it is shown that 90.6% of the systems engineering students in the 2019-II semester and 95.5% of the students in the 2020-I semester , who are satisfied with the indicator “To master practical professional skills”, have experienced a positive impact with the indicator “To solve problems and cases of the specialty”.

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In vital energy applications especially the agricultural environments, the service of adaptive power utilization plays an essential role in facilitating the usage of Internet of Things systems. Such environments are distinguished by the large range of lands where most of the region lacks the commercial power lines. Reaching some high or deep sensing points is also difficult in such environments. The adaptive power system will pave the way to make a smart service for users to create a platform for real time interaction. It can enhance the reliability, stability and sustainability of power supply, and provide more humanized and various intelligent services for the users. The usage of adaptive power can be improved effectively using IoT technology with its strong data processing and reliable communication. In this paper, an algorithm is proposed to offer a sustainable power service for smart agriculture system to guarantee continuous system operation. It mainly rely on controlling the load demands and managing the renewable energy. A model is built on Matlab that governs the proposed algorithm and the results of the simulation are discussed.

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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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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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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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Fiber-Wireless (FiWi) network is an integration of fiber optic and wireless connections in the same network. It is one of the best solutions to overcome rapid increment of Internet users and bandwidth-hungry services. To facilitate fundamental knowledge and further understanding on FiWi for students and researchers at the university level, this article proposes the development of a fast integration and scalable FiWi router testbed using Raspberry Pis as the embedded system-based hardware for lab-scale experiments. The performance of the router testbed in terms of end-to-end delay and throughput for upstream and downstream are evaluated. The delay values comply with IEEE 802.15.4 routing scheme. The performance of the router testbed is compared with the industrial grade off-the-shelf router in terms of throughput for each network. A testbed stress test is conducted by sending two data traffics simultaneously, and the performance test is repeated for Wireless-Fiber-Wireless and Fiber-Wireless-Fiber network architecture. The results show the proposed router testbed is scalable, flexible, and capable of fast integration.

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The emergence of new digital payment technologies has introduced both opportunities and challenges across all industries. This research aims to examine the significant factors that influence the adoption of new e-payment technologies, specifically in smart cities, as in Dubai. A comprehensive theoretical framework based on several previous studies included the following factors; Perceived Usefulness, Perceived Trust, Perceived Personal Innovativeness, Perceived Ease of Use, Perceived Risk, and Generation Cohort. The results of this research confirm that all proposed factors significantly affect the adoption of e-payment in Dubai as a case of a smart city; however, the perceived usefulness is not as significant as the other factors. Moreover, comparative analysis across the three generations showed almost similar patterns of adopting e-payment systems.

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Music is one of the most popular entertainments, and the music industry continues to increase over time. There are many types of genres in music, and everyone has their own choice of the type of music they want to listen to. The recommendation system is an important function in the application, especially when there are a large number of choices for a particular item. With a good recommendation system, users will be able to get help from the suggestions given and can improve the user experience of the application. By using collaborative filtering (CF) methods to recommend products related to personal preference history, this feature can be better provided. However, the CF method still lacks in integrating complex user data. Hybrid technology may be a solution to perfect the CF method. The combination of neural network and CF also called NCF is better than using CF alone. The focus of this research is a CF method combined with neural networks or neural collaborative filtering. In this study, we use 20,000 users, 6,000 songs, and 470,000 records of ratings then predict the score using CF and NCF approach. We aim to compare the recommendation systems using CF and NCF. The study shows that NCF is better in gathering certain playlists according to one’s preferences, but it takes more time to build compared to user-based collaborative filtering.

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The Bolivian higher education system comprises 59 universities; these institutions have the challenge of increasing their scientific production since it is one of the factors to provide quality education and meet one of the 17 Sustainable Development Goals (SDGs). But currently, the GDP assigned to the research and development (R&D) sector of Bolivia is 0.16%. In this regard, this research aims to develop a scientific production assessment of the publications generated by Bolivian universities and indexed in SCOPUS in the last ten years. On the other hand, the universities included in the 2020 SIR World and SIR Iber rankings will be identified, and their scientific production will be reviewed. After conducting the analysis, it was observed that the country still has a low scientific production compared to the rest of the countries in the region, with a total of 3,451 publications indexed in SCOPUS and 303.97 publications per million inhabitants in the last ten years. The 2020 SIR World ranking shows that only one Bolivian university is in the ranking and holds the 774th position. For their part, 26 Bolivian universities entered the 2020 SIR Iber ranking. Among all these institutions, they produced 1,010 publications indexed in SCOPUS for the five years analyzed.

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Everyone needs fast response or output against its request or need. Therefore, technologies are used to make the processing fast and accurate according to our needs. But in some situations, still we need to do more. Especially, when we need to process a massive or huge crowd of people in limited time frame such as at airport, religious gatherings, at stations etc. As the verification time increases crowd also increases and causing problems such as missing of the next flight, causality in religious gathering. Also, percentage of usefulness of verification system decreases and vice versa. Currently, different approaches are being employed to reduce verification time such as decentralized, distributed, queuing etc. But each of the approach has its merits and demerits. In order to minimize this problem, one of the solutions is to perform group (cluster) based verification. The reason behind is that as religious or tourist visits are done in groups, therefore, we can easily perform the group verification to make the verification fast in context of time. Further, we can set the limit where we need the group verification and where we can go through the normal verification (one by one). In this paper, we presented a Wireless Sensor Network (WSN) simulation model to calculate verification time in the groups form for massive crowd. We discussed the different scenarios for a group such as all group (cluster) members (CMs) are same and within the range or out of the range of cluster head (CH), CMs and non-CMs are within the range or out of range of the CH. We considered the pilgrimage as use case to compare the verification time taken by existing system and proposed system in context of time. We also compared the verification time with respect to verified and unverified CMs (CMs) in a group verification model. By optimizing the number of CM members in a group will decrease the number of unverified CMs (the drop rate), hence the performance of the group verification will be increased by minimizing the group verification time.

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The study aims to verify the capacity of Facebook Translation Service in translating English Facebook posts into Arabic in terms of two criteria: adequacy and fluency in line with the Translation Automation User Society (TAUS) scales. To ensure consistency and objectivity as recommended by TAUS, six evaluators, native speakers of Arabic and near-native speakers of English, rated the same data on each scale. The evaluators were acquainted with fluency and adequacy scales along with MT limitations and potentials. Once the corpus was uploaded and sent to the evaluators using TAUS tools, they had to assign scores online on 1-4 rating scales. Then, each report was displayed online on the TAUS reports tool. Evaluators’ responses were combined in thematic categories and were calculated to obtain frequencies and percentages. The study found that FTS provided fluent output with highest percentage of the scale good equal to 3 on a scale from 1 to 4, where the output is assessed as flowing smoothly with minor linguistic errors. Moreover, FTS succeeded in generating an adequate output with the highest percentage of responses as ‘most’ equal to 3 on a scale from 1 to 4, where almost the full meaning of the source is deemed to be transferred in the target language. This study is useful since it highlights the role of Facebook Translation service in translating, educating the public and fighting COVID-19. Consequently, such research would encourage the use and research on the potentiality of MT and FTS in dealing with abrupt crises, such as COVID-19.

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It has been observed that the smartwatches have emerged quickly on the digital era with the ability to significantly influence daily life and improve users’ wellbeing, decisions, and behaviour. Nonetheless they are in their stages of adoption, smartwatches are marked the most widespread type of wearable technologies. Considering this, present work has been carried out to intensify the scholarly understanding of determinants that affecting consumers’ behaviour of purchasing intention, to reach this objective, an integrated model based on Technology Acceptance Model (TAM) was designed and examined. An online questionnaire was utilized for the collected data (n=106). The empirical analysis based on partial least square method, using SmartPLS software. The findings exposed that visibility, social influence, and perceived ease of use are the proximate factors that drive adoption intention. Further, the analysis reveals that Consumers’ purchasing intention is significantly influenced by intention to use and cost. The extent of these factors is influenced by consumers’ perception of smartwatches as a technology and as a fashion accessory. Present study has also attempted to scholarly discuss the theoretical and managerial implications.

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This paper proposes a new method of implementation of the part of SIFT (Scale-Invariant Feature Transform) algorithm used to extract the feature of an image of a size 256?256 of pixels, which is mainly based on the using the LEON3 soft core processor .With this method it is possible to detect points of interest and so perform matching. This process allows several real time applications as robotic navigation, stereovision, object recognition etc. Obtained results show a very robustness in rotation, scale invariant as well as luminosity change. SIFT algorithm saw big success in various applications of computer vision. However, its high computation complexity has been a challenge for the most part of embedded implementations. This paper presents a partial implementation of the SIFT algorithm, which is to implement just the extraction of the characteristics that is based on the LEON3 processor. This implementation method overcomes the existing problems, in particular, the high dependence of existing implementations on the hardware architecture used. Indeed, the high flexibility of the processor allows the possibility to develop the application independent of the target board.

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Autonomous vehicles (AVs) technology is expected to provide many benefits for the society such as providing safe transportation to the community and reducing the number of accidents on the roads. With the emergence of AVs, the conventional safety infrastructure in which humans drive their vehicles will need to be upgraded in order to take full advantage of the new technology. AVs are responsible for the different aspects of driving, namely: perception, decision making and taking action. Capturing data and diagnosing issues becomes imperative in this new transportation system because an error might be an indication of a systemic problem which may lead to future accidents or failures. Therefore, any AV accident must be dealt with seriously. Unfortunately, current procedures and the type of data collected during the investigation of an accident is not sufficient; For example, AV minor accidents are not investigated in depth as it is with AV major accidents and therefore, if the cause of the accident is a systemic issue, then it might cause more accidents in the future. The main goal of this paper is to explore the requirements for accident reports for incidents involving AVs and the procedures to escalate issues to avoid systemic risks. All the information available about AV accidents, regardless of the severity of the accident, will be analyzed and studied. This paper will present three recommendations; An updated law enforcement accident report, an escalation procedure that depends on the diagnosis of the fault and a database of AV accidents to enable ongoing learning to find systemic issues.

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Diploma certificate and transcript forgery is a serious issue in education. The government is still finding the best way to prevent and minimize this issue in the future. The diploma certificate and transcript are very easy to duplicate, not only from third parties. Even the university can easily produce a fake diploma certification and transcript for their benefit. Blockchain technology is a new technology and now can be used to prevent the fake graduation documents. This research shows the simulation of blockchain technology used (Multichain) for data recording of academic transcript as one of the graduation documents. FGD has been carried out by inviting several reputable universities to validate the prototype. As a result, the FGD participants validated the prototype and agreed if it possible to be implemented in university, especially for recording the academic transcript data. Recording the academic transcript on blockchain technology can contribute to prevent forgery.

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For systems using rotating machinery, diagnosing the faults of the rotating machinery is critical for system maintenance. Recently, a machine learning algorithm has been employed as one of the methods for diagnosing the faults of rotating machinery. This algorithm has an advantage of automatically classifying faults without an expert knowledge. However, despite a good training performance of the deep learning model, there remains a challenge of performance degradation arising from noise when the model is applied in a real environment. In this study, to solve this problem, we identified the faults of a rotating machinery after applying the continuous wavelet transform (CWT) and then we extracted the images for detecting the faults of rotating machinery and apply them to the convolution neural network (CNN). Subsequently, we compared it with a commonly used artificial neural network technique according to load and noise. When we added the white noise from 1dB to 20dB to vibration signal, the proposed method converged to 100% accuracy from 8dB at no load, at 10dB at presence of load. we verified that the proposed method improved the performance in diagnosing the faults of rotating machinery.

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Recently, numerous scholars have suggested fuzzy time series (FTS) models to forecast many different fields. One of the vital issues for high accurate forecasting in FTS model is method of partitioning in Universe of discourse (UoD). In this research, we propose a novel FTS model, which is established by using hedge algebra (HA) and particle swarm optimization (PSO) for forecasting the different problems. In this model, HA is considered an algebraic structure for partitioning the UoD into unequal – size intervals based on optimal parameters which is determined by PSO. After making the intervals with unequal – length, the data values of times series on each interval are symbolized by fuzzy sets and then, these fuzzy sets are utilized to make fuzzy relation groups. Lastly, we keep using the PSO to adjust the size of each interval with view to reaching the better accurate prediction rate. The effectiveness of the proposed method is demonstrated on two datasets which are often applied in many studies in literature as enrolments data of the University of Alabama and Car road accident data in Belgium. The obtained results show that the proposed model produces higher accuracy forecasting when compared with the some of the recent FTS prediction models for all orders of model.

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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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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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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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This is a report describing the impact of calcination on the morphological and optical properties of nanoparticles Iron doped-nickel oxide. By synthetic precipitation, the technique makes use of it. Three samples have been calcined in different temperatures and were characterized by X-ray diffraction (XRD), Fourier transforms infrared spectroscopy (FTIR), UV spectroscopy, and scanning electron microscopy (SEM), energy dispersive X-Ray (EDX). The study showed that the increase in the temperature enhances the structural and optical properties of the samples, and makes the samples take a more crystalline structure. It is also shown that there has been an expansion of the volume of the samples, making the samples having a small bandgap. UV-Visible absorption spectra of Iron doped-nickel oxide nitrate shows a peak of absorption between 350 to 400 nm. The bandgap value is calculated to be 1.86 eV at a calcination temperature of 350oC. The required structural and optical properties of Iron doped-nickel oxide nanoparticles make it a promising material for optoelectronic applications.

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In this paper, a new polynomial shear deformation theory for the static flexural analysis of anisotropic rectangular thick plate was developed. The plate which carries a uniformly distributed load is clamped on the three edges, and free of support on the other edge (CCFC), is analyzed to determine the in-plane displacement, vertical displacement, bending moment, and shear force, bending and transverse shear stress. The General variation approach was used to obtain the general governing equation and its associated boundary conditions, thereafter the coefficient of deflection and shear deformation along the direction of x and y coordinate was determined by minimizing the energy equation obtained using the new established theory. The study revealed that: (i) as the displacement and stress decrease, the plate’s span-thickness ratio increases (ii) as the length to breadth ratio of the plate increases, the value of displacement and stresses increase. To validate the theory, the numerical results are obtained and compared with an available solution in the literature. The result showed good agreement with those in the literature.

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A Japanese method in teaching at classroom show good result by implementing Kamishibai. On the other hand, technology is inseparable from daily life. Computer-based learning media innovations are fast and diverse, ranging from 2D animation to 3D environments. The hologram is one example of 3D object visualization to deliver the learning material. Based on Kamishibai’s opportunity and hologram multimedia utilization to enhance the environmental education teaching activity, research about the adaptation of 3D pyramid hologram for teaching environmental education in elementary school is proposed. Firstly, the kamishibai model for teaching environmental education from Japan is explored and modified to fit Indonesia’s condition. The kamishibai and hologram multimedia utilization in teaching environmental education has been experimented with in class. The results of learning kamishibai and multimedia holograms show that students will improve their abilities in environmental problems. The students lead the literacy and caring attitudes were following their level of knowledge and skills as well as their attitudes about caring about disposing of garbage in its place, learning to clean sewerage at school, learning to farm in the yard of schools, and understand how plants exist in school gardens.

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