1. Introduction

This paper is an extension of work originally presented in the 4th IEEE International Colloquium on Information Science and Technology [1] and was meant to show and explain the important role that traceability plays in different sectors.

Traceability, as defined in ISO (ISO 9001: 2000), is the ability to trace the history, application or location of that which is under consideration. D. Asioli, A. Boecker and M. Canavari say that it is not a new concept but a practice that we need to implement in order to comply with the standards and law rules [2]. Certainly, over the past few years, it has become a necessity in fields where the security or safety of consumers is questioned, especially in medical and food industries.

In software development also, this practice helps in the understanding, capturing, tracking and verification of software artifacts and their relationships and their dependencies during a software life-cycle [3]. As in [4], traceability was initially used to trace requirements from their source to implementation and test, when we talk about software development and now, it plays an increasing role in defect management, change management and project management.

According to the Global Traceability Standard (GS1), Traceability Systems have become an integral part of doing business as they aim to identify and locate unsafe foods and validate the presence or absence of attributes that are important to consumers [5]. Even if they are not yet considered as a catalyst for financial gains, G.G.D. Nishantha, M.K. Wanniarachchige and S.N. Jehan say that they are able to ensure consumer trust, safety, reliability, accuracy and quality [6]. Its importance is reflected through its ability to solve issues and through its power to provide strong proofs or evidences.

These Systems, along with their ability to monitor the composition as well as the position of every lot in a supply chain, are seen as a powerful tool that is capable of defining new management objectives and improve the overall performance [7].

Traceability, if it is used in the right way, can provide a set of critical pieces of information such as the source, the destination, the location, the time, the link, in addition to the actors that were involved in the whole process. As in [8], ubiquitous traceability is achieved automatically, as a result of collecting, analysing, and processing every piece of evidence from which trace data can be inferred and managed.Our personal definition of traceability would be, the ability to keep a detailed history of all activities and changes that a particular object can undergo throughout its entire life cycle, taking into account the different relationships that may appear. This particular object can be a material, a product, a model or even a class in a software development platform.

The remainder of this paper is organized as follows: In Section 2, we will provide a set of definitions extracted from two major sectors, namely the food industry and information technology. Section 3 presents examples of traceability uses in different areas. A list of major techniques that were used to enhance the traceability will be the object of Section 4. In Section 5, we propose a set of definition models related to previously mentioned sectors. We will discuss the proposed traceability definition model in Section 6 and we will give a brief conclusion in Section 7.

2. Definitions

Traceability management is the planning, organization, and coordination of all activities related to traceability, including the creation, maintenance and use of trace links [9], not only in software development, but also in our daily life (e.g. memorizing events, tasks, activities, etc.).

In any area or sector, the definition of “traceability” is based on a number of criteria and limitations according to the used law or standards like the European General Food Law (EGFL) and the GS1 or simply, describes its purpose in a specific context.

Authors in [10] stated that there is no exact, single definition of traceability and that it has a large number of different meanings, which depend on the industry sector, on the supply chain, and on the perspectives of both the suppliers and the users of such information. However, we intend in section 5, to prove that a common definition can be established by means of models.

2.1.  Food Industry

The EGFL defines traceability as the ability to trace and follow a food, feed, food-producing animal or substance through all stages of production and distribution.

According to A. F. Bollen and J.P. Emond, traceability is a well-coordinated and a well-documented movement of product and documented activities associated with the product, from producer, through a chain of intermediaries, to the final consumer [10].

Gooch and B. Sterling say that it is the ability to follow an item, or a group of items (whether animal, plant, food product, or ingredient) from one point in the value chain to another, either backwards or forwards [11]. Thus, food chain traceability goes from raw materials to consumption. This is almost the same definition given by F. Dabbene, P. Gay and C. Tortia, as they assume that products “moving” along the Food Supply Chain (FSC) are both tracked and traced [7].

Tracking is the process by which a product is followed from upstream to downstream in the Supply Chain. Tracing is the reverse process of tracking. The tracing process tends to reconstruct the history of a product through the information recorded in each step of the Supply Chain, identifying the source of a food or group of ingredients and consequently the real origin of a product [12]. These two primary functions of traceability are known as Trace-Back and Trace-Forward, as the movement can be traced one step backwards and one step forward at any point in the supply chain [6].

As in [13], traceability can either be internal or external. Internal traceability is within one company and relates to data about raw materials. While external traceability focuses on the product information from one link in the chain to the next (tracking a product batch and its history through the entire production chain).

In the food industry, traceability requires that each lot or amount or batch of food material is given a unique identifier which accompanies it and is recorded at all the stages of its progress through its food chain [14].

C.C. Martins and R. J. Machado said that a traceability system must record and follow the trail, since products that come from suppliers, are processed and distributed as end products [15]. The traceability presented by these records must contain a set of reliable pieces of information in order to ensure the minimum requirements. In fact, as stated in [11], it has three key essential information components: (1) identification of product attributes, (2) identification of premises and (3) identification of movement.

In the same context, P. Olsen and M. Borit have carried out an insightful comparative study of existing definitions [16]. By combining the best parts of these definitions, they concluded by saying that the simplest yet the most complete definition of traceability is the ability to access any or all information relating to that which is under consideration, throughout its entire life cycle, by means of recorded identifications.

2.2.  Information Technology

In the field of software engineering, the IEEE Standard Glossary of Software Engineering Terminology defines traceability as the degree to which a relationship can be established between two or more products of the development process, especially products having a predecessor-successor or master-subordinate relationship to one another [17].

It is the ability to inter-relate any uniquely identifiable software engineering artefact to any other, to maintain the required links over time, and to use the resulting network to answer questions of both the software product and its development process [8]. It is a key element of any rigorous software development process that, provides critical support for many development activities [18].

When we talk about traceability in software development, we often refer to Requirement Traceability, which is an activity that allows creating links between and within software artefacts [19]. The definition of Requirement Traceability (RT), according to O. C. Z. Gotel and A. C. W. Finkelstein, is the ability to describe and follow the life of a requirement, in both a forward and backward direction [20]. Other definitions can be purpose-driven, solution-driven, information-driven or direction-driven.

These authors specify that there are two types of RT: pre-requirements specification traceability (Pre-RST), which is concerned with those aspects of a requirement’s life prior to its inclusion in the Requirement Specification (RS), and post-requirements specification traceability (Post-RST), which is concerned with those aspects of a requirement’s life that result from its inclusion in the RS.

More details about software traceability were listed in [8], including seven research areas and their associated directions which must be addressed in order to achieve ubiquitous traceability.

3. Uses of Traceability

In the food industry, it is considered as a mechanism used to keep the history of a raw or semi-finished unit during manufacturing and until this unit is delivered. It has a great potential to improve food safety as well as to promote consumer protection, by providing quality information [21].

In the field of Information Technology, traceability is used to list all activities of an entity on a system in execution. An example of such is the use of recovery logs or event logs in some cases. As R. Clayton explained, it is the ability to track down the originator of an action (seen as the flip side idea to “anonymity”) and attempts to identify the IP address that caused an action to occur [22]. For instance, Law Enforcement Agencies (LEA) can use traceability to detect “Hi-Tech” crimes through data retention (causing logs to be preserved for a known period) and data preservation (ensuring that logs of special interest are not destroyed).

It is also used to clearly identify the sources behind some statistical analysis. Authors in [23] stated that it is the property which enables the understanding of where the analysis data come from and facilitates transparency. They have proposed a set of traceability pairs (relation criteria and factors) to define all the variables required in an analysis and hence establish the link between the final result and all the sources used. Moreover, traceability can strengthen the link between the requirements put in place, the specifications and the artefacts throughout the phases of a software development, using Requirements Traceability Matrix [19].

As authors in [9] explained in details, it allows creating and using links between software artefacts, which for example allows to connect the origin of a requirement with its specification or development artefacts to each other throughout the software lifecycle. These connections are called trace links, and link a source artefact to a target artefact. These artefacts can be of different types, such as a requirement, a model element, a line of code, or a test case.

In aerospace industry [24], traceability can be used to find the design related causes if a product does not function as expected. It is provided by establishing the relations between the design data and the requirements together with the relations between the components and the identifiers.

In Supply Chain Management (SCM), R.R. Pant, G. Prakash and J. A. Farooquie, traceability is defined in terms of what, how, where, why and when aspects of underlying product along a supply chain [25].

In logistics, traceability may be used to optimize routes and improve planning and management. It may also work with accounting applications to evaluate inventory or with controlling applications to identify process inefficiencies [15].

In electronics, traceability is used to keep track of all information related to changes and transformations which are applied to identified Printed Circuit Board (PCB) or other electronic components. Starting from the original batches and sources, this information is mainly, the Bill of Materials (BOM), the measurements, the list of operations in the process chain and the final destinations to whom or where the boards must be shipped. As stated in [26], it is required for fulfilment of safety standards such as ISO 26262.

Furthermore, traceability is also used in biology. An example of such, is to trace Genetically Modified (GM) animals that may similarly yield improvements in animal breeding, genetics and reproduction [27].

4. Techniques

In order to help users better manage their traceable items, the traceability mechanism has been enhanced by making use of different approaches that vary from using simple information retrieval techniques to the use of ontologies, graphs or even models.

4.1.  Information Retrieval

De Lucia, A. Marcus, R. Oliveto and D. Poshyvanyk explained that Information Retrieval based methods or techniques like probabilistic, vector space and Latent Semantic Indexing models are used to recover traceability links on the basis of the similarity between the text contained in the software artefacts [28]. The higher the textual similarity between two artefacts is, the higher the likelihood that a link exists between them.

As in [29], this approach focuses on automating the generation of traceability links by similarity comparison between two types of artefacts.

4.2.  Ontology

C.C. Martins and R. J. Machado proposed the use of software engineering methods and techniques to aggregate, disambiguate and blend existing knowledge [15]. They have used ontologies as a requirements modeling technique and developed specific traceability taxonomy in order to pursue the continuous improvement and answer the requirements of increased efficiency by tracking manufacturing activities information.

Bendriss and A. Benabdelhafid used DAML-S which is a generic ontology that can be applied in all areas [30]. They have adapted it by integrating and adding their specific ontology “Product Traceability Service”, which describes all the web services of their traceability system. These services are dedicated to supply chain.

4.3.  Graphs

As detailed in [19], “TraceMe” is an Eclipse module-based plug-in, that can be used to capture and maintain traceability links between different types of artefacts. According to the authors, this plug-in allows the software engineer to define different artefacts categories, capture traceability links between the defined artefacts categories and manage the traceability information through XML files. Traceability dependencies (trace links) are then displayed as graph.

Other researchers tend to use graph-based techniques in order to create trace links of test case scenarios and therefore, enhance the test coverage measurement and analysis [29].

Additionally, there are other plug-ins in the internet which are capable of tracing issues to both requirements and tests and creating the related traceability matrix.

4.4.  Models

According to N. Sannier and B. Baudry, domain-specific modeling, which offers the capability to manipulate business domain concepts and traceability modeling, are Model-Driven Engineering (MDE) techniques that could address various aspects of requirement’s formalization [31]. These authors proposed to combine both MDE and Information Retrieval (IR) techniques to improve requirements organization and traceability while handling textual ambiguous requirements documents.

MDE gives the basic principles for the use of models as primary artefacts throughout the software development phases and presents characteristics which simplify the engineering of software in various domains, such as Enterprise Computing Systems. A model is a symbolic system expressed in a language and each kind of model is represented by an appropriate modeling language and can be applied to certain purposes [3].

Thakur, B. J. Martens and C. R. Hurburgha defined a data model as a coherent representation of objects from a part of reality [32]. They used the modeling technique to create a database model capable of recording all the transformations related to incoming and outgoing grain lots, as well as the transformations that take place internally in the whole supply chain.

By making use of modeling techniques, C. Szabo and Y. Chen proposed “SeMMA” (Semantic Multi-Modeling Architecture), which is a multi-modeling architecture that permits the semantic integration of models defined in various languages, and ensures multi-model consistency when changes across different models occur and relies on three main modules, namely, the Change Analyzer Module, the Consistency Checker Module and the Warning Module [33].

In the same context, S. Bendriss and A. Benabdelhafid proposed a product data model which takes into account the different elements necessary for traceability, namely, the product in its various states, the various operations on the product, the occurred events, the resources used and the spatiotemporal location of the product [30].

In software development, requirement traceability can be described as a feature model to define a product [29]. It consists of a graph with features as nodes and feature relations as edges. If the number of features is very high, then the representation of features and their relations are displayed by tables.

On the other hand, the authors in [18] presented an approach on how to build a multi-domain traceability framework. It consists of defining first a Traceability Information Model (TIM) which represents the core element of any traceability framework (artefacts/relations) and may refer to artefacts (documents, models, databases, project activities context) from different domains, then deriving traceability information from sources, record the information in a Traceability Model (TM) and finally, performing traceability analyses, based on traceability goals.

Another example of such technique is presented in [26], where authors proposed an Eclipse plugin which uses the Eclipse Modelling Framework (EMF) as its base technology and stores the traceability model as an EMF model. This tool helps both users and project managers to create, customize and maintain traceability links, whose types depend on the company, development context and process used.

For more details about MDE techniques, Galvão and A. Goknil have listed many traceability approaches in MDE and evaluated them using five comparison criteria: representation, mapping, scalability, change impact analysis and tool support [3]. They classified these approaches into three categories: requirements-driven approaches, modeling approaches and transformation approaches.

4.5.  Others

Furtado and A. Zisman proposed a new traceability approach called “Trace++”, a traceability technique that extends traditional traceability relationships to support the transition from traditional to agile software development [34]. This technique extends the use of information sets and consists of six elements: the agile related problem, the trace relations, a set of all source artefacts, a set of all target artefacts, a set of additional information and finally, the type of relations.

Other techniques tend to use XML as the main tool to represent models and trace links.

As stated in [29], these techniques are classified as Hypertext-Based techniques. But there are others which can be either Rule based, Event based, Value-based or Scenario-based.

5. Definition Models

As stated before, there is no single or unique definition for traceability, since the term is described according to both its context and its purpose. Based on this and on the elements extracted from the other definitions, we hereby propose a definition model for the main sectors.

In Food Industry, the purpose of traceability is to trace the initial product with the raw materials from the start, till the very end of the production chain. Figure 1 represents the internal traceability in this field, where p is the initial item, P is the final product and i₀, i₁ … i_n are the set of information that describes the movement from one point to another.

By simply adjusting these elements, this representation can also describe the traceability in the Supply Chain or Logistics, tracing lots from the warehouse to every destination of the distribution chain.

Furthermore, it is only when two or more separate representations of this size are connected, that we basically speak about external traceability. Otherwise, it is still internal.

In the field of Information Technology, one example use of traceability is to create links between customer requirements or specifications and the supplier software. As shown in Figure 2, s refers to the initial specification (requirement) or source, while O refers to the final object. The relation between the different stages of development is represented by r₀, r₁ … r_n.

When this definition is applied to the model driven engineering (Figure 3), relations are replaced by a set of transformations t₀, t₁ … t_n between predecessors and successors, representing the same system S. Elements s and O will be replaced respectively by m for the initial model and M the final model. Not to mention that each transformation can be represented likewise.

To sum up, we can say that the three proposals have a set of elements in common:

• Items: units that need to be traced and followed.
• Stages: positions where the units are processed.
• Relations: links between predecessors and successors.
• Activities: set of processes that were applied to the units

These elements will lead us to set a common model for traceability, which will be the basis of our future studies.

6. Discussion

Traceability in our point of view, as stated before, is the ability to keep a detailed history of all activities and changes that a particular object can undergo throughout its entire life cycle, taking into account the different relationships that may appear. It can be internal or external and can be used in two different ways either forward or backward.

As presented in the previous section, every traceable item is moving from an initial state to a final state, through numerous stages. In each stage, the output is the result of an activity that takes into account inputs from the previous stage and keeps the link to the origin. At the end, and since inputs and outputs are interrelated, tracing forward and backward is possible.

Thus, we can combine these facts to establish a common definition model that can be used to define “Traceability” everywhere (Figure 4).

Here, i refers to the initial traceable item, where I is the final traceable item. The activities that the traceable item undergoes are represented by a₀, a₁ … a_n. O is the origin or the representation of all original characteristics of the traceable item. These characteristics do not change and are only updated if a new property was discovered when moving from one stage to another.

Certainly, once this generic model is deployed on a particular platform, it will be a subjected to a large amount of data, of different types. Hence, is it mandatory to consider the following challenges:

• How to address the problem of time vs Big Data during information access?
• How can we manage to order the accessed traceability information by degrees of priority or importance?

We intend on enhancing our model by adding a set of rules and other traceability related properties.

For instance, a “weight” or a “priority” measure can be introduced and assigned to each traceability information, after the classification process, or we can improve the representation of trace links by including additional factors. Thus, only the most important set of information is shown when tracing an item, either backward or forward.

7. Conclusion

Traceability can ensure quality, safety, reliability and accuracy. Furthermore, it can help companies improve productivity, reduce costs and gain consumer’s trust.

According to GS1 [5], traceability may assess other business systems and tools such as quality management, risk management, information management, logistical flows, commercial advantage and evaluation of management demands.

In this paper, we have listed recent definitions related to traceability from two major sectors. We have presented definition models for these sectors and proposed our generic traceability model, by combining a set of common elements, which stands as the basis of our research. In the same context, we have listed also, the uses and purposes of traceability as well as the major techniques applied in this research field.

In future work, we intend to refine our model with new elements and then, deploy it and use it in E-learning environments. Furthermore, a study will be initiated to decide whether or not this model can be applied to other fields or needs further enhancements. The final purpose will be to implement a general model, able to satisfy all traceability needs and requirements.

Conflict of Interest

The authors declare no conflict of interest.

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13. Richard Romero Izurieta, Segundo Moisés Toapanta Toapanta, Luis Jhony Caucha Morales, María Mercedes Baño Hifóng, Eriannys Zharayth Gómez Díaz, Oscar Marcelo Zambrano Vizuete, Luis Enrique Mafla Gallegos, José Antonio Orizaga Trejo, "Prototype to Identify the Capacity in Cybersecurity Management for a Public Organization", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 1, pp. 108–115, 2023. doi: 10.25046/aj080113
14. Sathyabama Kaliyapillai, Saruladha Krishnamurthy, Thiagarajan Murugasamy, "An Ensemble of Voting- based Deep Learning Models with Regularization Functions for Sleep Stage Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 1, pp. 84–94, 2023. doi: 10.25046/aj080110
15. Mikhail Lavrentiev, Andrey Marchuk, Konstantin Oblaukhov, Mikhail Shadrin, "Natural Tsunami Wave Amplitude Reduction by Straits – Seto Inland Sea", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 161–166, 2022. doi: 10.25046/aj070616
16. Fabrizio Striani, Chiara Colucci, Angelo Corallo, Roberto Paiano, Claudio Pascarelli, "Process Mining in Healthcare: A Systematic Literature Review and A Case Study", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 151–160, 2022. doi: 10.25046/aj070615
17. Segundo Moisés Toapanta Toapanta, Rodrigo Humberto Del Pozo Durango, Luis Enrique Mafla Gallegos, Eriannys Zharayth Gómez Díaz, Yngrid Josefina Melo Quintana, Joan Noheli Miranda Jimenez, Ma. Roció Maciel Arellano, José Antonio Orizaga Trejo, "Prototype to Mitigate the Risks, Vulnerabilities and Threats of Information to Ensure Data Integrity", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 139–150, 2022. doi: 10.25046/aj070614
18. Angela Pearce, "The Perceptions of Students and Teachers When using ICTs for Educational Practices Matter: A Systematic Review", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 1–12, 2022. doi: 10.25046/aj070601
19. Ferdinand Friedrich, Christoph Ament, "Model Order Reduction and Distribution for Efficient State Estimation in Sensor and Actuator Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 5, pp. 146–156, 2022. doi: 10.25046/aj070516
20. Jabrane Slimani, Abdeslam Kadrani, Imad EL Harraki, El hadj Ezzahid, "Long-term Bottom-up Modeling of Renewable Energy Development in Morocco", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 5, pp. 129–145, 2022. doi: 10.25046/aj070515
21. Javier Calle, Itziar Sagastiberri, Mikel Aramburu, Santiago Cerezo, Jorge García, "Automatic Counting Passenger System using Online Visual Appearance Multi-Object Tracking", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 5, pp. 113–128, 2022. doi: 10.25046/aj070514
22. Bouassale Nasr-Eddine, Sallaou Mohamed, Aittaleb Abdelmajid, Benaissa Elfahim, "DEM models Calibration and Application to Simulate the Phosphate Ore Clogging", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 5, pp. 79–90, 2022. doi: 10.25046/aj070511
23. Kelebaone Tsamaase, Japhet Sakala, Kagiso Motshidisi, Edward Rakgati, Ishmael Zibani, Edwin Matlotse, "Performance Adjustment Factor for Fixed Solar PV Module", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 4, pp. 98–104, 2022. doi: 10.25046/aj070413
24. Zhumakhan Nazir, Temirlan Zarymkanov, Jurn-Guy Park, "A Machine Learning Model Selection Considering Tradeoffs between Accuracy and Interpretability", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 4, pp. 72–78, 2022. doi: 10.25046/aj070410
25. Lu Xiong, Spendylove Duncan-Williams, "Generalized Linear Model for Predicting the Credit Card Default Payment Risk", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 3, pp. 51–61, 2022. doi: 10.25046/aj070306
26. Haruka Motohashi, Hayato Ohwada, "Interpretable Rules Using Inductive Logic Programming Explaining Machine Learning Models: Case Study of Subclinical Mastitis Detection for Dairy Cows", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 2, pp. 143–148, 2022. doi: 10.25046/aj070214
27. Nils Finke, Ralf Möller, "On the Construction of Symmetries and Retaining Lifted Representations in Dynamic Probabilistic Relational Models", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 2, pp. 73–93, 2022. doi: 10.25046/aj070207
28. Rachida Hassani, Younès El Bouzekri El Idrissi, "IT Project Management Models in an Era of Digital Transformation: A Study by Practice", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 2, pp. 53–62, 2022. doi: 10.25046/aj070205
29. Alexander Núñez, Fernando Solares, Alejandro Crisanto, "Estimation of Non-homogeneous Thermal Conductivity using Fourier Heat Equation Considering Uncertainty and Error Propagation", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 1, pp. 90–99, 2022. doi: 10.25046/aj070109
30. Nganyang Paul Bayendang, Mohamed Tariq Khan, Vipin Balyan, "Thermoelectric Generators (TEGs) and Thermoelectric Coolers (TECs) Modeling and Optimal Operation Points Investigation", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 1, pp. 60–78, 2022. doi: 10.25046/aj070107
31. Xiqin Lu, Nobuo Funabiki, Htoo Htoo Sandi Kyaw, Ei Ei Htet, Shune Lae Aung, Nem Khan Dim, "Value Trace Problems for Code Reading Study in C Programming", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 1, pp. 14–26, 2022. doi: 10.25046/aj070103
32. Lucie Böhmová, Antonín Pavlíček, "Innovations in Recruitment—Social Media", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 6, pp. 88–97, 2021. doi: 10.25046/aj060613
33. Toshiki Watanabe, Hiroyuki Kameda, "Designing a Model of Consciousness Based on the Findings of Jungian Psychology", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 356–361, 2021. doi: 10.25046/aj060540
34. Ibnu Daqiqil Id, Masanobu Abe, Sunao Hara, "Acoustic Scene Classifier Based on Gaussian Mixture Model in the Concept Drift Situation", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 167–176, 2021. doi: 10.25046/aj060519
35. Rim Mrani Alaoui, Abderrahim El-Amrani, Ismail Boumhidi, "Model Reduction H? Finite Frequency of Takagi-Sugeno Fuzzy Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 53–58, 2021. doi: 10.25046/aj060507
36. Olena Nosovets, Vitalii Babenko, Ilya Davydovych, Olena Petrunina, Olga Averianova, Le Dai Zyonh, "Personalized Clinical Treatment Selection Using Genetic Algorithm and Analytic Hierarchy Process", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 406–413, 2021. doi: 10.25046/aj060446
37. Zhiyuan Chen, Howe Seng Goh, Kai Ling Sin, Kelly Lim, Nicole Ka Hei Chung, Xin Yu Liew, "Automated Agriculture Commodity Price Prediction System with Machine Learning Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 376–384, 2021. doi: 10.25046/aj060442
38. Ahmed R. Sadik, Christian Goerick, "Multi-Robot System Architecture Design in SysML and BPMN", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 176–183, 2021. doi: 10.25046/aj060421
39. Montaño-Arango Oscar, Ortega-Reyes Antonio Oswaldo, Corona-Armenta José Ramón, Rivera-Gómez Héctor, Martínez-Muñoz Enrique, Robles-Acosta Carlos, "Multidisciplinary Systemic Methodology, for the Development of Middle-sized Cities. Case: Metropolitan Zone of Pachuca, Mexico", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 80–90, 2021. doi: 10.25046/aj060410
40. Kwun-Ping Lai, Jackie Chun-Sing Ho, Wai Lam, "Exploiting Domain-Aware Aspect Similarity for Multi-Source Cross-Domain Sentiment Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 01–12, 2021. doi: 10.25046/aj060401
41. Mark Gourary, Sergey Rusakov, "Technique to Simulate an Oscillator Ensemble Represented by the Kuramoto Model", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 3, pp. 311–318, 2021. doi: 10.25046/aj060335
42. Svetlana Segarceanu, George Suciu, Inge Gavăt, "Environmental Acoustics Modelling Techniques for Forest Monitoring", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 3, pp. 15–26, 2021. doi: 10.25046/aj060303
43. Marlene Ofelia Sanchez-Escobar, Julieta Noguez, Jose Martin Molina-Espinosa, Rafael Lozano-Espinosa, "Supporting the Management of Predictive Analytics Projects in a Decision-Making Center using Process Mining", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 1084–1090, 2021. doi: 10.25046/aj0602123
44. Mamudu Hamidu, Jerry John Kponyo, "Closed Loop Capacitive Accelerometer Model using Simple Regression Test for Linearity", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 1038–1045, 2021. doi: 10.25046/aj0602118
45. Natalia Yevtushenko, Nataliia Kuzminska, Tetiana Kovalova, "Dependence of the Knowledge Structure of the Company Employees on a Set of the Competencies", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 699–708, 2021. doi: 10.25046/aj060281
46. Antoni Wibowo, Inten Yasmina, Antoni Wibowo, "Food Price Prediction Using Time Series Linear Ridge Regression with The Best Damping Factor", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 694–698, 2021. doi: 10.25046/aj060280
47. Marion Olubunmi Adebiyi, Oludayo Olufolorunsho Olugbara, "Homology Modeling of CYP6Z3 Protein of Anopheles Mosquito", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 580–585, 2021. doi: 10.25046/aj060266
48. Pritesh Shah, Ravi Sekhar, Iswanto Iswanto, Margi Shah, "Complex Order PI\(^{a+jb}\)D\(^{c+jd}\) Controller Design for a Fractional Order DC Motor System", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 541–551, 2021. doi: 10.25046/aj060261
49. Abdulla M. Alsharhan, "Survey of Agent-Based Simulations for Modelling COVID-19 Pandemic", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 439–447, 2021. doi: 10.25046/aj060250
50. Amany Khalil, Osama Tolba, Sherif Ezzeldin, "Design Optimization of Open Office Building Form for Thermal Energy Performance using Genetic Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 254–261, 2021. doi: 10.25046/aj060228
51. Amine Mounaam, Ridouane Oulhiq, Ahmed Souissi, Mohamed Salouhi, Khalid Benjelloun, Ahmed Bichri, "A Model-Driven Digital Twin Framework Development for Sulfur Dioxide Conversion Units Simulation", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 122–131, 2021. doi: 10.25046/aj060215
52. Yousra Karim, Abdelghani Cherkaoui, "Fuzzy Analytical Hierarchy Process and Fuzzy Comprehensive Evaluation Method Applied to Assess and Improve Human and Organizational Factors Maturity in Mining Industry", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 75–84, 2021. doi: 10.25046/aj060210
53. Futra Zamsyah Md Fadzil, Alireza Mousavi, Morad Danishvar, "Event Modeller Data Analytic for Harmonic Failures", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1343–1359, 2021. doi: 10.25046/aj0601154
54. Naeem Ahmed Haq Nawaz, Musab Bassam Al-Zghoul, Hamid Raza Malik, Omar Radhi Aqeel Al-Zabi, Bilal Radi Ageel Al-Zabi, "Wireless Sensor Networks Simulation Model to Compute Verification Time in Terms of Groups for Massive Crowd", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1229–1240, 2021. doi: 10.25046/aj0601140
55. Thinh Dang Cong, Toi Le Thanh, Phuc Ton That Bao, Trang Hoang, "A Novel Approach to Design a Process Design Kit Digital for CMOS 180nm Technology", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1191–1198, 2021. doi: 10.25046/aj0601135
56. Laurent Nana, François Monin, Sophie Gire, "Formal Proof of Properties of a Syntax-Oriented Editor of Robotic Missions Plans", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1049–1057, 2021. doi: 10.25046/aj0601116
57. Shahrinaz Ismail, Faes Tumin, "Simulating Get-Understand-Share-Connect Model using Process Mining", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1040–1048, 2021. doi: 10.25046/aj0601115
58. Syeda Nadiah Fatima Nahri, Shengzhi Du, Barend Jacobus van Wyk, "Active Disturbance Rejection Control Design for a Haptic Machine Interface Platform", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 898–911, 2021. doi: 10.25046/aj060199
59. Sana Elhidaoui, Khalid Benhida, Said Elfezazi, Yassine Azougagh, Abdellatif Benabdelhafid, "Model of Fish Cannery Supply Chain Integrating Environmental Constraints (AHP and TOPSIS)", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 798–809, 2021. doi: 10.25046/aj060189
60. Abdulla M. Alsharhan, "Simulating COVID-19 Trajectory in the UAE and the Impact of Possible Intervention Scenarios", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 791–797, 2021. doi: 10.25046/aj060188
61. Deddy Kurniawan, Ditdit Nugeraha Utama, "Decision Support Model using FIM Sugeno for Assessing the Academic Performance", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 605–611, 2021. doi: 10.25046/aj060165
62. Arman Mirmanov, Aidar Alimbayev, Sanat Baiguanysh, Nabi Nabiev, Askar Sharipov, Azamat Kokcholokov, Diego Caratelli, "Development of an IoT Platform for Stress-Free Monitoring of Cattle Productivity in Precision Animal Husbandry", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 501–508, 2021. doi: 10.25046/aj060155
63. Mochammad Haldi Widianto, "Analysis of Pharmaceutical Company Websites using Innovation Diffusion Theory and Technology Acceptance Model", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 464–471, 2021. doi: 10.25046/aj060150
64. Eugeny Smirnov, Svetlana Dvoryatkina, Sergey Shcherbatykh, "Technological Stages of Schwartz Cylinder’s Computer and Mathematics Design using Intelligent System Support", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 447–456, 2021. doi: 10.25046/aj060148
65. Lixin Wang, Jianhua Yang, Sean Gill, Xiaohua Xu, "Data Aggregation, Gathering and Gossiping in Duty-Cycled Multihop Wireless Sensor Networks subject to Physical Interference", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 369–377, 2021. doi: 10.25046/aj060142
66. Najat Messaoudi, Jaafar Khalid Naciri, Bahloul Bensassi, "Mathematical Modelling of Output Responses and Performance Variations of an Education System due to Changes in Input Parameters", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 327–335, 2021. doi: 10.25046/aj060137
67. Eva Rolia, Dwita Sutjiningsih, Yasman, Titin Siswantining, "Modeling Watershed Health Assessment for Five Watersheds in Lampung Province, Indonesia", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 99–111, 2021. doi: 10.25046/aj060111
68. Murtada Khalafallah Elbashir, Saleh N. Almuayqil, "Time-to-Event Analysis for Recovery from Coronavirus Disease (COVID-19): A Case Study on Wuhan and Elsewhere in China from Jan 1 to Feb 11, 2020", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1609–1617, 2020. doi: 10.25046/aj0506192
69. Hakimjon Zaynidinov, Sayfiddin Bakhromov, Bunyod Azimov, Sarvar Makhmudjanov, "Comparative Analysis Spline Methods in Digital Processing of Signals", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1499–1510, 2020. doi: 10.25046/aj0506180
70. Abubakar Umar, Zhanqun Shi, Lin Zheng, Alhadi Khlil, Zulfiqar Ibrahim Bibi Farouk, "Parameter Estimation for Industrial Robot Manipulators Using an Improved Particle Swarm Optimization Algorithm with Gaussian Mutation and Archived Elite Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1436–1457, 2020. doi: 10.25046/aj0506174
71. Nicolò Speciale, Rossella Brunetti, Massimo Rudan, "Solution of the Semiconductor-Device Equations by the Numerov Process", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1414–1421, 2020. doi: 10.25046/aj0506171
72. Marcin Kuropatwi´nski, Leonard Sikorski, "Empirical Probability Distributions with Unknown Number of Components", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1293–1305, 2020. doi: 10.25046/aj0506154
73. Othmane Rahmaoui, Kamal Souali, Mohammed Ouzzif, "Towards a Documents Processing Tool using Traceability Information Retrieval and Content Recognition Through Machine Learning in a Big Data Context", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1267–1277, 2020. doi: 10.25046/aj0506151
74. Aaron Don M. Africa, Emmanuel T. Trinidad, Lawrence Materum, "Projection of Wireless Multipath Clusters Using Multi-Dimensional Visualization Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1064–1070, 2020. doi: 10.25046/aj0506129
75. Aarthi Ramachandran, Amudha Joseph, Shunmuga Velayutham, "Feature Gate Computational Top-Down Model for Target Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1001–1006, 2020. doi: 10.25046/aj0506120
76. Gehad Ali Alsayed, Zahraa Ismail, Sameh O. Abdellatif, "Investigating the Optical Behavior of Single/Multi-Dimensional Photonic Crystal Structures for Photovoltaic Applications", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 951–958, 2020. doi: 10.25046/aj0506113
77. Ismail Ktata, Naoufel Kharroubi, "A Model-Driven Approach for Reconfigurable Systems Development", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 801–810, 2020. doi: 10.25046/aj050695
78. Luisella Balbis, "Optimal Irrigation Strategy using Economic Model Predictive Control", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 781–787, 2020. doi: 10.25046/aj050693
79. Yohei Yamauchi, Mitsuyuki Saito, "Adaptive Identification Method of Vehicle Model for Autonomous Driving Robust to Environmental Disturbances", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 710–717, 2020. doi: 10.25046/aj050685
80. Khalid Chennoufi, Mohammed Ferfra, "Fast and Efficient Maximum Power Point Tracking Controller for Photovoltaic Modules", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 606–612, 2020. doi: 10.25046/aj050674
81. Jojo Blanza, Lawrence Materum, "Interface for Visualization of Wireless Propagation Multipath Clustering Outcomes", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 544–549, 2020. doi: 10.25046/aj050665
82. Jojo Blanza, Lawrence Materum, "Variation Between DDC and SCAMSMA for Clustering of Wireless MultipathWaves in Indoor and Semi-Urban Channel Scenarios", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 538–543, 2020. doi: 10.25046/aj050664
83. Alexander Raikov, "Accelerating Decision-Making in Transport Emergency with Artificial Intelligence", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 520–530, 2020. doi: 10.25046/aj050662
84. Mokhlis Salah-eddine, Said Sadki, Bahloul Bensassi, "Microcontroller Based Data Acquisition and System Identification of a DC Servo Motor Using ARX, ARMAX, OE, and BJ Models", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 507–513, 2020. doi: 10.25046/aj050660
85. Robert Antonio Romero-Flores, "An Economic Theory Perspective for the Fight Against Poverty in the Peruvian Andes", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 497–506, 2020. doi: 10.25046/aj050659
86. Gede Putra Kusuma, Jonathan, Andreas Pangestu Lim, "Emotion Recognition on FER-2013 Face Images Using Fine-Tuned VGG-16", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 315–322, 2020. doi: 10.25046/aj050638
87. Ravi Sekhar, Tejinder Paul Singh, Pritesh Shah, "Complex Order PI\(^{\alpha + j\beta} \)D\(^{\gamma+j\theta}\) Design for Surface Roughness Control in Machining CNT Al-Mg Hybrid Composites", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 299–306, 2020. doi: 10.25046/aj050636
88. Eugen Harinda, Hadi Larijani, Ryan M. Gibson, "Trace-Driven Simulation of LoRaWAN Air Channel Propagation in an Urban Scenario", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 211–220, 2020. doi: 10.25046/aj050625
89. Sergiy Kostrikov, Rostyslav Pudlo, Dmytro Bubnov, Vladimir Vasiliev, Yury Fedyay, "Automated Extraction of Heavyweight and Lightweight Models of Urban Features from LiDAR Point Clouds by Specialized Web-Software", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 72–95, 2020. doi: 10.25046/aj050609
90. Fadhillah Moulita Andiani, Faizal Abid, Hendri, Abba Suganda Girsang, "Business Intelligence for Generating Comprehensive Report in Electronic Completion and Handover", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 45–51, 2020. doi: 10.25046/aj050606
91. Redha Touati, Max Mignotte, Mohamed Dahmane, "A Circular Invariant Convolution Model-Based Mapping for Multimodal Change Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 1288–1298, 2020. doi: 10.25046/aj0505155
92. Nhu-Tung Nguyen, Dung Hoang Tien, Do Duc Trung, "Development of the Surface Roughness Model in the Grinding Processes", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 1184–1188, 2020. doi: 10.25046/aj0505143
93. Emad Kareem Mutar, "Matrix-based Minimal Cut Method and Applications to System Reliability", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 991–996, 2020. doi: 10.25046/aj0505121
94. Gcobisile Matafeni, Ritesh Ajoodha, "Using Big Data Analytics to Predict Learner Attrition based on First Year Marks at a South African University", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 920–926, 2020. doi: 10.25046/aj0505112
95. Gene Patrick Rible, Nicolette Ann Arriola, Manuel Ramos Jr., "Modeling and Implementation of Quadcopter Autonomous Flight Based on Alternative Methods to Determine Propeller Parameters", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 727–741, 2020. doi: 10.25046/aj050589
96. Abdelghani Lakhdar, Aziz Moumen, Laidi Zahiri, Mustapha Jammoukh, Khalifa Mansouri, "Experimental and Numerical Study of the Mechanical Behavior of Bio-Loaded PVC Subjected to Aging", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 607–612, 2020. doi: 10.25046/aj050574
97. Kerin Augustin, Natasia, Ditdit Nugeraha Utama, "Butterfly Life Cycle Algorithm for Measuring Company’s Growth Performance Based on BSC and SWOT Perspectives", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 554–558, 2020. doi: 10.25046/aj050568
98. Józef Pawelec, "The Newtonian Model of the Smolensk Catastrophe", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 550–553, 2020. doi: 10.25046/aj050567
99. Abdi Sukmono, Arief Laila Nugraha, Arsyad Nur Ariwahid, Nida Shabrina, "Growth Models and Age Estimation of Rice using Multitemporal Vegetation Index on Landsat 8 Imagery", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 506–511, 2020. doi: 10.25046/aj050563
100. Nghia Duong-Trung, Nga Quynh Thi Tang, Xuan Son Ha, "Interpretation of Machine Learning Models for Medical Diagnosis", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 469–477, 2020. doi: 10.25046/aj050558