Engineering Tools Research Articles

Investigating the Impact of LMS Quality, Technical Support and Perceived Usefulness on Student Satisfaction in Saudi Universities

Educational technology, particularly Learning Management Systems (LMS), has seen significant growth in recent years. As LMS platforms have evolved rapidly, managing them effectively has become crucial for the success of online courses. Many institutions and organizations now focus on developing LMS solutions as part of their e-learning strategies. LMS platforms are used in various educational contexts, including campus-based, distance, classroom, online, traditional, modern, and massive open online courses. They integrate numerous technological tools to support and enhance each stage of the learning process. In this context, user satisfaction is often discussed as a critical measure of LMS success. This study examines the primary factors influencing user satisfaction and the overall impact of LMS usage. It examines how these factors relate to student satisfaction and assesses LMS effectiveness. Data was gathered using a questionnaire based on previous research. The findings reveal that all identified factors positively impact student satisfaction, suggesting that greater user satisfaction leads to improved benefits for students.

Use of active methodologies with ICT in teacher training

The era of knowledge demands educators committed to innovation, which requires providing training to teachers through active methodologies in environments that encourage dialogue, feedback and stimulate their creative and critical thinking. A study is presented that details the experience of upper secondary and higher education teachers, who took part in training that involved the use of active methodologies with the support of technological tools in the classroom. The methodological design was under a qualitative phenomenological approach; A structured interview was applied in which 32 teachers participated. The results show that the combination of active methodologies and the use of ICT in teacher training is effective in providing them with the possibility of transferring their experiences to students. The interviewees agreed that practicing gamification and the flipped classroom method was the most complicated, as it required greater expertise to design and implement it. It is concluded that it is crucial to address the challenges associated with the implementation of these practices, as well as to make the most of the transformative potential of this synergy between innovative pedagogy and digital tools.

Predicting noise and vibration in electric brake systems at mass production stage using q-axis current

This study explores the challenges of noise, vibration, and harshness (NVH) in electric brake systems within the context of the automotive industry's shift towards environmentally friendly technologies. The electrification of vehicles and their braking systems leads to weight reduction and enhanced braking efficiency. However, these advancements also yield new NVH challenges. The intricate motor-driven mechanisms of electric brake systems are prone to cogging torque, magnetic imbalance, and radial electromagnetic forces, which degrade the user experience and necessitate service interventions. Therefore, this study presents an approach to predicting noise and vibration levels using q-axis current measurements obtained during the initial actuation of electric brake systems in the mass production stage. This study aims to establish a predictive model that can identify potential NVH problems early, enhance the quality and reliability of electric brake systems, and improve user satisfaction. This method enhances sustainable vehicle technologies, improving the overall user experience. The study delves into the intricate relationship between electrical currents and NVH phenomena by integrating electromagnetic analysis with mechanical dynamics. Therefore, this paves the way for the development of advanced diagnostic tools in electric vehicle technology.

Case study: structural feasibility analysis for a reinforced concrete building

Currently, the residential building sector in Brazil is experiencing continuous growth, despite the adverse impacts caused by 2 years of the Covid-19 pandemic. Therefore, there is a demand for structural projects that enable the execution of these works from a structure that is viable from the point of view of safety, good conditions of use, comfort and economy. Technical and practical knowledge is a fundamental requirement for a good project, combined with skill in a technological analysis and sizing tool. Having said that, the present work proposed to develop the analysis and structural design, with the help of the Brazilian software TQS®, of a multi-family residential building in reinforced concrete, using both linear and nonlinear static analyses. The central motivation was to address structural challenges and ensure that the design complies with Brazilian standards, thereby improving the quality and safety of residential buildings. The work sought to follow all the design steps of a structure in order to meet the requirements recommended by Brazilian standards. Based on the analyses, it was possible to adopt solutions for the various structural problems that arose during the design and, finally, obtain the final document with all the details of the structure.

Dynamic substructuring for visco-elastic materials using a POD-based model reduction method: application to laminated panels

Dynamic substructuring is now a well established and powerful tool for engineers used to analyze the dynamic response of mechanical systems. The technique is particularly favored for the simulation of mechanical vibrations allowing different aspects to be studied. It consists in splitting the global structure into several substructures from which reduced models are built. In Finite Element software, these reduced models, also called superelements, are generally built using the concept of Component Mode Synthesis. Difficulties arise when structures made of frequency-dependent materials are considered since classical modal reduction algorithms are inapplicable as they are because of the frequency-dependence of the stiffness matrix. Based on the Balanced Truncation approach, a novel methodology for the construction of a superelement for the dynamic analysis of elastic structures made with viscoelastic materials is presented. The methodology takes advantage of the Golla-Hughes-McTavish rheological model (GHM) before reducing the system via the Balanced Proper Orthogonal Decomposition (BPOD). The coupling of the superelement with a host structure is achieved via the use of Lagrange multipliers. The reduced system takes the form of a constant matrix of very small size compared to the original FE model. The method is applied to laminated panels such as an automobile windscreen.

Methanothermobacter thermautotrophicus and Alternative Methanogens: Archaea-Based Production.

Methanogenic archaea convert bacterial fermentation intermediates from the decomposition of organic material into methane. This process has relevance in the global carbon cycle and finds application in anthropogenic processes, such as wastewater treatment and anaerobic digestion. Furthermore, methanogenic archaea that utilize hydrogen and carbon dioxide as substrates are being employed as biocatalysts for the biomethanation step of power-to-gas technology. This technology converts hydrogen from water electrolysis and carbon dioxide into renewable natural gas (i.e., methane). The application of methanogenic archaea in bioproduction beyond methane has been demonstrated in only a few instances and is limited to mesophilic species for which genetic engineering tools are available. In this chapter, we discuss recent developments for those existing genetically tractable systems and the inclusion of novel genetic tools for thermophilic methanogenic species. We then give an overview of recombinant bioproduction with mesophilic methanogenic archaea and thermophilic non-methanogenic microbes. This is the basis for discussing putative products with thermophilic methanogenic archaea, specifically the species Methanothermobacter thermautotrophicus. We give estimates of potential conversion efficiencies for those putative products based on a genome-scale metabolic model for M. thermautotrophicus.

Bulbul Memorial Museum as a Center of Cultural and Historical Memory

The aim of the article is to study and introduce into scientific circulation information about the current activities of the Bulbul Memorial Museum regarding the preservation of the musical heritage of the founder of the professional vocal art of Azerbaijan. The material base of the study consists of the exposition of the Bulbul Memorial Museum and archival sources. Results. The article examines the history of creation and the current activities of the Bulbul Memorial Museum in Shusha after the occupation. The exposition of the museum is characterised, which presents Bulbul’s personal belongings, materials reflecting his creative, scientific, educational and social activities. The article provides a brief overview of the artist’s creative achievements, and also examines Bulbul’s documents and works. In particular, the article begins a discussion about such a peak of Bulbul’s work as the opera “Keroglu”. The activities of the Research Music Cabinet, which plays an indispensable role in the history of the musical culture of Azerbaijan, are highlighted. Information is provided on the unique exhibits known as phonograph records, which are well preserved at the Bulbul Memorial Museum. Scientific novelty. For the first time, the role of information technologies in the presentation of the Bulbul Memorial Museum is studied through the prism of preserving the musical heritage of the founder of professional vocal art of Azerbaijan. Conclusions. The Bulbul Memorial Museum in Shusha contains a powerful display of personal things and documents highlighting Bulbul’s musical legacy as the founder of professional vocal art in Azerbaijan. The use of information technologies for the presentation of the museum’s exposition meets modern requirements of museum activity. The use of a modern technological tool in the Bulbul house-museum — a monitor with a touch screen — is an important feature that arouses interest in the museum.

Green knowledge management: a bibliometric analysis, research trends and future directions

Purpose Green knowledge management (GKM) has become a critical factor for modern organizations, offering a multifaceted approach to enhancing business operations while promoting environmental sustainability. The purpose of this paper is to review the literature and explore how GKM helps companies reduce their ecological footprint by digitalizing processes and minimizing physical storage needs, thus aligning with growing expectations for corporate environmental responsibility. The necessity for conducting a GKM review is underscored by the increasing recognition of sustainability, environmental performance and as a critical component of organizational corporate success in the contemporary business landscape. Design/methodology/approach This study conducted a bibliometric analysis of 994 papers related to GKM from 2005 until August 2024. Findings Over the 20 years of research on GKM, this bibliometric analysis drew the interlinked relationships between main theme of KM in the context of sustainable practices, processes, social and related theories. This study identifies green innovation, digital transformation, green and lean supply chains, leadership and technology tools. Future research can explore themes such as green and lean processes, barriers and enablers, role of leadership and ambidexterity in green supply chain integration using different theoretical lenses. Research limitations/implications Although the findings of this study are important to GKM research, there are a number of limitations to this investigation. The research database used exclusively was the Web of Science and Scopus, which contained the indexed papers. Future investigations may draw from various databases, such as EBSCO and Google Scholar. Additionally, the use of topic modelling and other artificial intelligence frameworks could enhance research analysis. Subsequently, research papers may expand their scope to encompass conference papers, books and book chapters, in addition to articles. Practical implications This review gives a holistic understanding of GKM practices across various sectors which not only enhances organizational performance but also contributes to broader environmental goals. This study offers an understanding on GKM on holistic approach that combines total quality management (TQM), absorptive capacity and collaborative frameworks to foster innovation and sustainability. Originality/value It is evident from the review that research in GKM is evolving gaining momentum. This review provides a comprehensive understanding of the GKM literature, from its earliest forms of basic concepts aligned with the sustainability and progressing more into the concepts of GKM adoption for green innovation by highlighting multiple theories, factors and gaps in the studies.

Maintenance factory platform for in-space manufacturing: Conceptualizing design architecture

Commerce, long-term habitation, and far-location explorations are the objectives of the next space era, also called Space 2.0. These objectives are the focus of in-space servicing, assembly, and manufacturing (ISAM) research. Current research in this area is focused on demonstrating various manufacturing processes under microgravity using the International Space Station (ISS) as a platform and relies on an Earth-based supply chain. Building operational infrastructure and supply chains enabling in-space manufacturing is critical for the sustained growth of ISAM to meet the goals of Space 2.0. This paper is specifically focused on discussing the need and potential architecture for a maintenance factory in space to enable servicing, maintenance, and repairs in space environments. Space presents a hostile environment and a new set of challenges to establish a robust infrastructure necessary for supporting these objectives. The realization of such an infrastructure, including physical and digital footprints, is demanding new manufacturing science and engineering tools and platforms for maintenance. The authors present requirements of a robust maintenance platform, called a “factory in space” to deliver multi-functional maintenance service station(s) across various operational points in space such as low-earth orbit, lunar surface, and Martian surface. Such factories are projected to be an essential part of the in-space infrastructure for short as well as long-term commerce, habitation, and exploration. A critical analysis of this concept is proposed through an analysis of the state of the art, boundary conditions in space and requirements of maintenance, the role of manufacturing, and the design as well as sustainability considerations for a maintenance factory in space.

A context-aware zero trust-based hybrid approach to IoT-based self-driving vehicles security

With the speedy progression and adoption of IoT devices in modern self-driving vehicles (SDVs), autonomous driving industry is gradually reforming its capabilities to provide better transportation services. However, this domain faces enormous security and privacy challenges and thus has become an attractive target for attackers due to its rapid growth and market worth. Furthermore, the rapid transformation in technological tools in transport industry and speedy evolution of cyber-attacks paved the way for designing efficient IDSs. Motivated by these challenges, we put forward a new secure and efficient IDS approach for the security of SDVs. The propose approach utilizes an emerging strategy to mitigate security vulnerabilities and cyber attacks detection using zero trust (ZT) model. Through this work, we put forward a context-aware zero trust security framework for IoT-based SDVs. The proposed framework utilizes a context-aware design to evaluate the trustworthiness of the devices using multi-source trust and reputation model. Then, to make the framework more effective and reliable, we introduce crawler system into the context of the IoT-devices in SDVs to make the system unbiased. Additionally, an observer module is developed that employs state-of-the-art machine learning algorithm to detect malicious actions. Empirical results on two standard benchmark datasets (i.e., Car Hacking and ToN_IoT) validate the practicality and robustness of propose framework in real-world transport systems with enhanced security and trust management against evolving cyber-threats. Detection results demonstrate that the proposed framework secured the best performance by achieving 99.43% and 99.52% accuracy for Car Hacking and ToN_IoT, respectively. The findings of this study will help the security professionals and researchers to comprehend the importance of ZT architecture in developing effective and robust security solutions for modern IoT-based SDVs.

Empowering healthcare education: A multilingual ontology for medical informatics and digital health (MIMO) integrated to artificial intelligence powered training in smart hospitals.

Objective: A comprehensive understanding of professional and technical terms is essential to achieving practical results in multidisciplinary projects dealing with health informatics and digital health. The medical informatics multilingual ontology (MIMO) initiative has been created through international cooperation. MIMO is continuously updated and comprises over 3700 concepts in 37 languages on the Health Terminology/Ontology Portal (HeTOP). Methods: We conducted case studies to assess the feasibility and impact of integrating MIMO into real-world healthcare projects. In HosmartAI, MIMO is used to index technological tools in a dedicated marketplace and improve partners' communication. Then, in SaNuRN, MIMO supports the development of a "Catalog and Index of Digital Health Teaching Resources" (CIDHR) backing digital health resources retrieval for health and allied health students. Results: In HosmartAI, MIMO facilitates the indexation of technological tools and smooths partners' interactions. In SaNuRN within CIDHR, MIMO ensures that students and practitioners access up-to-date, multilingual, and high-quality resources to enhance their learning endeavors. Conclusion: Integrating MIMO into training in smart hospital projects allows healthcare students and experts worldwide with different mother tongues and knowledge to tackle challenges facing the health informatics and digital health landscape to find innovative solutions improving initial and continuous education.

INFORMAL DIGITAL LEARNING OF ENGLISH TO SUPPORT A FORMAL SPEAKING COURSE: EFL PRESERVICE TEACHERS’ PERCEPTIONS AND IMPLEMENTATION IDEAS

This study aimed at exploring how English as a foreign language (hereafter, EFL) preservice teacher (hereafter, PSTs) who had no teaching experience perceived Informal Digital Learning of English (hereafter, IDLE) and their IDLE implementation ideas for their future students’ out-of-class activities to support formal classroom instruction. The researchers employed a basic qualitative design to understand the participants’ perceptions and implementation ideas. The data were gathered through interviews and document analysis, and subsequently, 14 EFL PSTs with no prior teaching experience were recruited using a purposive sampling strategy. The data were transcribed, coded, and analyzed using inductive thematic analysis. The findings give new insights that the participants had positive perceptions towards IDLE and had various implementation ideas of IDLE for their future students, mostly through disrupting Web 2.0 technology tools, even though they had no teaching experience using technology. Two implications for English educators and teacher education programs are highlighted in this article: changing teachers’ mindsets towards EFL learning and the insertion of IDLE as a topic into pedagogy courses.

BALANCING TRADITION AND TECHNOLOGY: A COMPARATIVE STUDY ON VOCABULARY ACQUISITION IN INTERMEDIATE ENGLISH LEARNERS

This study explores the effectiveness of traditional and technological methods in vocabulary acquisition among intermediate-level English learners. A mixed-methods approach was used, combining quantitative data from pre-tests, weekly exams, and post-tests with qualitative insights gathered from surveys and interviews. Over a six-week period, 30 students participated in lessons utilizing both traditional teacher-led instruction and technological tools such as mobile apps and digital flashcards. The results revealed that while the traditional group showed steady but moderate improvement, the technological group experienced more rapid and significant gains in vocabulary retention. Interviews with participants highlighted the strengths and challenges of both methods, emphasizing the need for a blended learning approach. This study suggests that combining traditional and technological methods can offer a more comprehensive and effective learning experience, catering to diverse learner preferences and enhancing overall vocabulary retention.

تأثير استعمال ليزر النيوديميوم ياك (Nd-YAG) على البنية الدقيقة وصلابة سطح الفولاذ AISI1012

The unique characteristics of the laser beam such as directionality, monochromaticity, and brightness make it a very important tool in the surface treatment engineering. The concentrated heat in the laser beam can be precisely directed toward the processed region on the surface. The laser beam power, beam size, and scanning velocity are easily controlled during the different processing methods. Laser surface modifications of metallic materials, such as laser surface melting, laser surface alloying, and laser cladding are some of the most important methods to improve the mechanical properties of the processed layer on the surface. A pulsed Nd-YAG laser surface melted specimens of low carbon steel with a maximum power of 90W at different processing parameters. The rapid melt and solidification of the treated layer produced a refinement in the microstructure. The original microstructure of this steel (Ferrite + Pearlite) has been transformed to new phases of martensitic and bainitic structures. The melted layer consists of the melted pool, the heat affected zone, and the substrate. The maximum hardness of the laser melted layer is more than 400 HV. The effect of different lasers such as high power CW CO2 laser on the mechanical properties and corrosion resistance of steel alloys will be carried out in future studies. Keywords: Nd-Yag Laser, Laser surface treatment, Microstructure and hardness of AISI1012 steel

Machine learning techniques for predicting the peak response of reinforced concrete beam subjected to impact loading

To meet the growing need for resilient structures in seismic and high-impact zones, accurate prediction of the response of reinforced concrete (RC) beams under impact loads is essential. Traditional methods, such as experimental testing and high fidelity finite element models, are often time consuming and resource intensive. To address these challenges, this study investigates various ensemble and non-ensemble machine learning techniques—including support vector machine, gaussian process regression (GPR), k-nearest neighbor (KNN), gene expression programming, random forest, decision tree, boosted tree, adaptive boosting tree, gradient boosting algorithm, stochastic gradient descent, and artificial neural network—for predicting the peak response of RC beams under impact loads. A set of 145 experimental data points from 12 different sources is used to train and evaluate these machine learning models. Key parameters in the data include beam width and depth, span, reinforcement ratios, concrete strength, steel yield strength, deflection, and impact characteristics. Except for KNN, all models showed satisfactory generalization capabilities with R2 values over 0.8. Statistical errors such as RMSE, a-10 index, MAE, and a-20 index are within acceptable limits. The GPR model is the most effective with R2 value of 0.95. Moreover, Shapely analysis identified beam depth, impact velocity, and beam breadth as critical factors. Overall, this study demonstrates the efficacy of machine learning in accurately predicting the behavior of RC structures under impact loads, providing valuable tools for civil engineers in design and analysis.

Aggrescan4D: A comprehensive tool for pH-dependent analysis and engineering of protein aggregation propensity.

Aggrescan4D (A4D) is an advanced computational tool designed for predicting protein aggregation, leveraging structural information and the influence of pH. Building upon its predecessor, Aggrescan3D (A3D), A4D has undergone numerous enhancements aimed at assisting the improvement of protein solubility. This manuscript reviews A4D's updated functionalities and explains the fundamental principles behind its pH-dependent calculations. Additionally, it presents an antibody case study to evaluate its performance in comparison with other structure-based predictors. Notably, A4D integrates advanced protein engineering protocols with pH-dependent calculations, enhancing its utility in advising solubility-enhancing mutations. A4D considers the impact of structural flexibility on aggregation propensities, and includes a large set of precalculated predictions. These capabilities should help to open new avenues for both understanding and managing protein aggregation. A4D is accessible through a dedicated web server at https://biocomp.chem.uw.edu.pl/a4d/.

A 45-Year Updating Wind and Wave Hindcast Over the Oman Sea and the Arabian Sea

This study focuses on developing a comprehensive and reliable wind and wave hindcast for the Oman Sea and Arabian Sea, spanning a significant 45-year period (1979-2024). The objective is to capture the intricate wind and wave climate of the region, characterized by distinct monsoon cycles and occasional tropical cyclones. The availability of extensive wave data over four decades would be an irreplaceable tool for researchers and engineers, enabling improved accuracy in extreme value analysis, sediment transport studies, and wave-induced current simulations. Despite limited field observations, all available measured data within the region were collected, analyzed for configuration of the models and utilized for model calibration and validation. The hindcast data was generated using the Weather Research and Forecasting (WRF) model for winds and WAVEWATCH III (WW3) model for waves, and evaluated against observational and measured wind and wave parameters. A comprehensive statistical analysis of the hindcast model's performance reveals adequate agreement with measured data, as evidenced by root mean square errors (RMSEs) of 1.23m/s for wind speeds and 0.37m for significant wave heights in average. These results underscore the model's reliability for research and engineering applications, particularly in the Arabian Sea, with a focus on the northern coastlines of the Oman Sea. The specific model configuration employed in this study holds significant potential for future investigations in the northern Indian Ocean, offering a valuable tool for understanding and predicting climatical conditions in this region.

Development of an optical measurement system for strain determination in concrete structures

Health monitoring activities in concrete structures are crucial tools in Civil Engineering for both maintenance processes and durability assessment. These activities carry significant economic implications within the field of civil projects. At the regional level, locally produced products and services for such monitoring are lacking. This deficiency is likely due to the high costs associated with importing and deploying such systems, thus limiting the implementation of structural health monitoring practices. However, strain measurement is a key parameter in structural monitoring techniques, and optical sensors have emerged as attractive alternatives due to their inherent advantages over conventional technologies. Recognizing the potential benefits and economic impact of such systems in civil project management, and noting the void in the regional market, this article presents the results of a technological development undertaken as part of a start-up technological project. Specifically, an optical system was designed, implemented, and tested for strain measurement in concrete structures using locally manufactured fiber optic sensors based on Bragg gratings.

Vein Visualisation Technology for Peripheral Intravenous Access in Paediatric Patients: A Clinical Decision-Making Tool.

The aim of this study is to develop a clinical decision-making tool to guide utilisation of vein visualisation technologies and enhance chances of successful peripheral intravenous catheter (PIVC) insertion, using data collected from a vascular access team in a large paediatric medical centre in the United States. Quantitative two-phase, cluster analysis design. The study consisted of the following two phases: (1) a quantitative retrospective chart review to evaluate clinician utilisation and preference for vein visualisation technologies and (2) a quantitative prospective design, including a post-discharge retrospective chart review, to confirm utilisation of vein visualisation technologies and factors influencing clinician decision-making. A total of 16 groups were created based on the cluster analysis and expert clinician input. The results of first-attempt success analyses identified optimal device recommendations for each of the 16 patient groups. For patients older than 2 years old, the NIR device was more likely to result in first-attempt success regardless of BMI or access site and the NIR device was most optimal for almost all categories of paediatric patients regardless of BMI or access site. The transilluminator was found to be the most optimal device to use with younger patients (< 2 years old) who are underweight. Vein visualisation technology is recommended by professional nursing organisations to improve PIV access. Yet, adoption of this useful technology to aid selection of an optimal vein for insertion and visualisation during insertion is limited. This is the first study to develop a clinical decision-making tool for vein visualisation technology in PIVC insertion. Vein visualisation technology allows for a rapid, thorough assessment of patients' vasculature to determine an optimal site for PIVC placement beyond what is visible to the naked eye or achievable using traditional methods. The tool was designed to guide healthcare professionals towards successful first attempt PIVC insertions, thereby improving patient outcomes and efficiency of care. None.

Интеллектуальная обработка текстовой информации: обзор автоматизированных методов суммаризации

Interest in innovative technological strategies and modern digital tools has increased significantly due to the need to manage large amounts of unstructured data. This paper reviews current paradigms and services for automated summarization, developed based on interdisciplinary research in linguistics, computer technologies, and artificial intelligence. It focuses on syntactic and lexical techniques employed by neural network models for text compression. The paper presents performance examples of such AI-powered services as QuillBot, Summate.it, WordTune, SciSummary, Scholarcy, and OpenAI ChatGPT. The contemporary automated models proved effective in using extractive and abstractive methods to generate summaries of varying quality and length. The extractive approach relies on identifying the most significant sentences from the original text, while abstractive algorithms create new sentence structures that preserve the main idea of the original content. Automated summarizers effectively utilize text compression techniques that are inherent to human approach to text processing, e.g., they exclude redundant information, simplify complex structures, and generalize data. These technologies provide high accuracy and coherence in the generated summaries, though each summarization model has its limitations. Optimal results depend on the specifics of the task at hand: extractive models provide brevity and precision while abstractive ones allow for deeper semantic processing. Automated summarization is becoming an important tool in various fields that require effective analysis and processing of large text data.