With the advancement of flexible electronics and low-power circuit design, wearable sensing systems have emerged as a interdisciplinary research area within electronic and computer engineering. These kind of systems can support continual identification with non-disruptive, precise sensing of the physical signs of person with pliable, various type sensor arrays. Viewed from the perspective of the systems engineering approach, this paper classifies wearable device architectures into three mutually supportive electrical paths: the analog signal chain, the digital signal chain, and the energy self-sufficiency chain. At the signal chain level, it focuses on analog front-end design for flexible electrochemical, strain sensor, high-input-impedance Transimpedance Amplification design, differential anti-interference design, analog-to-digital conversion design to ensure that the signal-to-noise ratio and low-drift performance of the microampere-level signal are very high. In the digital chain the study is on the signal processing and information transmitting using an embedded MCU unit. Adaptive filtering, dynamic gain adjustment, and event-driven communication have achieved real-time, low-power data management: The energy chain combines biofuel cell (BFC) and power management unit (PMU), it's proposing the hybrid power chain, which would be combining NFC and the energy scheduling algorithm for autonomous power. Looking at it from the system level, it is hard to say that wearable electronics' core competitiveness comes from better analog, digital, but more likely the synergy between them: This provides a solution for field-effect transistors (FETs) and self-powered smart health trackers. It establishes a scalable implementation approach suitable for both low-power signal processing and energy-autonomous circuits.

The rapid development of computer networks has increased the demand for vocational students to master not only theoretical concepts but also practical networking skills. However, many vocational high school students still experience difficulties in applying networking concepts in real or simulated environments. This community service activity aimed to improve students’ understanding and technical skills in computer networking and data communication through simulation-based training using Cisco Packet Tracer. The activity was conducted at SMK Negeri 01 Banyumas and involved students of grade XI majoring in Computer and Network Engineering. The method used consisted of initial observation, preparation of training materials, delivery of theoretical concepts, hands-on simulation practice, and evaluation of participants’ responses. The results showed an improvement in students’ ability to configure basic network devices, understand IP addressing and subnetting, and analyze data communication processes through simulation. In addition, students demonstrated increased awareness of network security and digital ethics. This activity indicates that simulation-based training is an effective approach to strengthen vocational students’ competencies in computer networking and data communication.

This study describes the design, development and evaluation of a directional Yagi-Uda antenna transmitter for an amateur FM radio station at the main campus of Cavite State University. The antenna was made of an aluminum rail with a 9.525 mm diameter that includes two directors, a reflector, and a driving element. The Directional Yagi-Uda Antenna Design is the focus of the research goals for the Amateur Radio Station at Cavite State University's Main Campus, which include increasing signal strength, maximizing antenna gain, and reducing interference from unwanted signals and noise sources. These goals are intended to increase signal transmission range by at least 30%. The antenna has an operational frequency range of 88.7 MHz. A measured gain of 7.88 dBi was obtained after simulation using the YagiMAX ver. 3.11 program to assess the design attributes. Following testing at the Department of Computer and Electronics Engineering building, the prototype antenna successfully transmitted signals from the rooftop. Audible sound reception was accomplished throughout the university, with open sections experiencing particularly good reception. This study showcases the effective use of the directional Yagi-Uda transmission antenna and its enhanced signal reception capabilities for the amateur FM radio station at Cavite State University's Main Campus.

Engineering is suffering a significant change driven by the integration of artificial intelligence (AI) into engineering optimization in design, analysis, and operational efficiency across numerous disciplines. This review synthesizes the current landscape of AI-driven optimization methodologies and their impacts on engineering applications. In the literature, several frameworks for AI-based engineering optimization have been identified: (1) machine learning models are trained as objective and constraint functions for optimization problems; (2) machine learning techniques are used to improve the efficiency of optimization algorithms; (3) neural networks approximate complex simulation models such as finite element analysis (FEA) and computational fluid dynamics (CFD) and this makes it possible to optimize complex engineering systems; and (4) machine learning predicts design parameters/initial solutions that are subsequently optimized. Fundamental AI technologies, such as artificial neural networks and deep learning, are examined in this paper, along with commonly used AI-assisted optimization strategies. Representative applications of AI-driven engineering optimization have been surveyed in this paper across multiple fields, including mechanical and aerospace engineering, civil engineering, electrical and computer engineering, chemical and materials engineering, energy and management. These studies demonstrate how AI enables significant improvements in computational modelling, predictive analytics, and generative design while effectively handling complex multi-objective constraints. Despite these advancements, challenges remain in areas such as data quality, model interpretability, and computational cost, particularly in real-time environments. Through a systematic analysis of recent case studies and emerging trends, this paper provides a critical assessment of the state of the art and identifies promising research directions, including physics-informed neural networks, digital twins, and human–AI collaborative optimization frameworks. The findings highlight AI’s potential to redefine engineering optimization paradigms, while emphasizing the need for robust, scalable, and ethically aligned implementations.

This article presents a gamified digital twin in Minecraft designed to support practical exercises in digital logic in the Computer Engineering I course at the University of Salamanca. Implemented as a Spigot/Paper server plugin based on the Platform for Automatic coNstruction of orGanizations of intElligent Agents (PANGEA) multi-agent architecture, the system orchestrates four virtual organizations and employs a world cloning strategy (via Multiverse and WorldGuard) to ensure individual and isolated workspaces, while also enabling collaborative work. The central contribution is a multi-agent system with an integrated ‘black box’ verification engine that mitigates redstone asynchrony and latency through controlled signal injection and software clock synchronization, enabling real-time deterministic validation of both basic logic gates and more complex sequential circuits. Additionally, the ecosystem includes a specialized suite of logic scenarios and a web-based dashboard for real-time teacher monitoring. In a pilot study (N=30), the system achieved an average task completion rate of 89.1%, and an adapted Unified Theory of Acceptance and Use of Technology (UTAUT) analysis indicated that technical stability is positively associated with student performance.

The development of digital technology has triggered an increase in the frequency of gaming among young people, which has the potential to lead to addiction and affect health, one of which is sleep quality. Teenagers in Vocational High Schools (SMK), especially those who choose technology-based majors, are at higher risk of excessive gaming due to the extensive use of digital devices. This study aims to examine the relationship between the level of game addiction and the sleep quality of students at SMKN 11 Malang. The research methodology uses a correlational descriptive design with a cross-sectional approach. The population of this study includes all tenth-grade students from the Computer and Network Engineering (TKJ) major at SMKN 11 Malang, totaling 134 students, with a total sampling method. The level of game addiction was measured using the Game Addiction Scale (GAS), while sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI). Data analysis was conducted univariately and bivariately, using the Spearman Rank correlation test to examine the relationship between the variables. The study findings indicate that the majority of respondents fall into the low to moderate game dependence category, and nearly half of the respondents experience poor sleep quality. The Spearman Rank test showed a significant relationship between the level of game dependence and adolescent sleep quality (r = 0.216; p-value = 0.012). These findings can serve as a basis for developing nursing interventions to prevent game dependence and improve sleep quality.

ABSTRACT This study presents a bibliometric analysis of computer engineering frameworks applied to IT risk management and intelligent audit systems, based on 393 publications from the Web of Science Core Collection (2024–2025). Findings reveal a rapidly evolving, highly collaborative research domain with an average of 4.66 coauthors per document and 15 international co-authorship. Thematic mapping identifies four core clusters: (1) representation learning and feature engineering, (2) deep learning and detection systems, (3) computational modeling and intelligent surveillance, and (4) real-time tracking and continual learning. The integration of graph-based learning, deep clustering, and transformer architectures enhances audit transparency, anomaly detection, and system resilience. Despite methodological advances, gaps persist in aligning intelligent systems with governance mechanisms and risk controls. The study offers a structured overview of emerging trends, thematic evolution, and future directions, providing valuable insights for researchers, auditors, and system designers aiming to develop secure, adaptive, and intelligent audit infrastructures grounded in robust computer engineering principles.

Abstract The rapid advancement of quantum information science and technology (QIST) has generated significant attention from people in academia and industry as well as the public. Recent advances in QIST have led to both opportunities and challenges for students and researchers who are curious about the true potential of the field amid hype, considering whether their skills are commensurate with what the field needs, and contemplating how collaborating with industries may impact their research including their students’ ability to publish their research. This qualitative study presents perspectives from leading quantum researchers who are educators on three critical aspects shaping QIST’s development: (1) the impact of hype in the field and strategies for managing expectations, (2) approaches to creating conducive environments that attract students and established scientists and engineers from non-physics disciplines, and (3) effective models for fostering university-industry partnerships that can be valuable for students and researchers alike. These aspects, along with several interconnected challenges that the QIST community faces, were explored through in-depth interviews with quantum educa-
tors. Our findings reveal nuanced perspectives on managing the hype cycle, with experts acknowledging both its benefits in attracting talent and funding, and its risks in creating unrealistic expectations. Regarding greater interdisciplinary engagement and attracting more non-physicists in QIST, educators emphasized the need to recognize and leverage existing expertise from fields such as computer science, materials science, and engineering, while developing tailored educational pathways that meet diverse student backgrounds to prepare them for QIST workforce. On university-industry partnerships, respondents highlighted successful models, some of them with
specific focus on student development while noting persistent challenges around intellectual property, confidentiality, and differing organizational goals. These insights provide valuable guidance for educators, policymakers, and industry leaders working to build a sustainable quantum workforce while maintaining realistic expectations about the field’s trajectory as we celebrate the International Year of Quantum Science and Technology.

This study examines the impact of perceived social support on the academic interest in mathematics among vocational high school students, as academic interest is a crucial determinant of engagement and success in learning mathematics. For students in Computer and Network Engineering (TKJ), mathematics is crucial for logical reasoning and technological problem-solving; nonetheless, many exhibit just modest motivation for the subject. Although there is increasing evidence that social support enhances favorable academic achievements, there is insufficient research elucidating the psychological process that connects support to interest in mathematics within vocational education contexts. This study demonstrates that academic self-efficacy fully mediates the association between perceived social support and interest in mathematics. Data were obtained from 260 TKJ vocational students utilizing a quantitative correlational design with a mediation model, employing validated questionnaires and evaluated through regression-based mediation testing. The findings indicated that perceived social support was a significant predictor of academic self-efficacy (β = 0.672, p < .001), and academic self-efficacy was a strong predictor of mathematics interest (β = 0.596, p < .001). The indirect impact was significant (β = 0.359, p < .001) and constituted 93.40% of the overall effect, whereas the direct effect was non-significant (β = 0.026, p = .213), so demonstrating full mediation. The findings suggest that initiatives to increase mathematics interest in vocational schools should focus on bolstering students' academic self-efficacy through continuous social support from family, peers, and educators.

Digital Twin-based laboratories, which are virtual, cloud-connected replicas of physical systems, have become more popular as a result of the growth of remote learning and digital transformation in education. They provide a fresh approach to providing hands-on learning opportunities in engineering education. With an emphasis on computer engineering, specifically in the areas of embedded systems, cloud platforms, and smart networks, this study investigates the effects of remote Digital Twin laboratories on student performance, engagement, and concept mastering. Students learn using physical microcontroller kits (such as Arduino and ARM) in traditional embedded systems education. Students can now engage with cloud-hosted virtual versions of embedded systems called Digital Twins, which allow them to test control logic in real time, view sensor outputs, and replicate microcontroller code. With features like real-time data synchronisation, multi-user collaboration, and scalable simulations, these cloud-connected labs—powered by platforms like AWS IoT, Microsoft Azure, or Google Cloud IoT Core—assist students in comprehending system design and implementation in distributed environments. Additionally, via the use of examples such as smart home system simulations, the integration of smart networks enables students to have practical experience with low-latency data streaming, IoT protocols (MQTT, CoAP, REST), and network optimisation. The study assesses the impact of these virtual labs on learning outcomes in comparison to conventional setups using a mixed-method approach that incorporates engagement measurements, student questionnaires, and performance analytics. According to the findings, laboratories based on digital twins greatly improve accessibility, encourage remote and active learning, and get students ready for workplaces that are cloud-native and driven by the Internet of Things. The results back up the use of digital twin systems as a scalable and successful approach for teaching engineering in the next generation, meeting the needs of smart learning ecosystems and Industry 4.0.

This study explores the career preferences and influencing factors among computer engineering students at Bulacan State University across various academic years. Utilizing a quantitative survey methodology with a sample of 128 students and graduates, the research identifies key determinants shaping students' career decisions, including job market prospects, personal interests, educational offerings, parental and peer influence, and societal factors. Findings reveal that software development, hardware engineering, data analysis, cybersecurity, and game development are the most preferred career paths, while a significant number of students remain uncertain or consider careers outside the field. The study underscores the necessity for holistic career guidance programs, experiential learning opportunities, mentorship, curriculum review, and continuous student support to enhance career readiness and satisfaction. Recommendations aim to foster informed decision-making, optimize career development strategies, and strengthen university-industry linkages to better align student aspirations with labor market demands.

As information systems (IS), including data-driven solutions, become increasingly prevalent across sectors, the need for ethical data-driven decision-making (DDDM) is gaining recognition. This scoping literature review explores how agency and ethics are addressed in scientific discussions on DDDM. It examines the ethical terminology used, its definitions, and the forms of agency identified in 79 peer-reviewed articles from disciplines such as computer science, education, and engineering. Agency is central to ethical judgment, decision-making, and action. The review reveals a wide range of ethics-related concepts, many of which are loosely defined, incoherent, and problem-oriented. Drawing on a sociomaterial perspective, the study identifies three forms of agency relevant to DDDM: human, technological, and distributed. These forms reflect the complex interplay between individuals, technologies, and institutional contexts. The review concludes that ethics and agency must be considered together to clarify responsibility at different levels of decision-making. Although ethical concerns are increasingly discussed, the debate remains fragmented. In the broader context of IS and the rapid development of generative AI, cross-disciplinary research is essential to address ethical challenges comprehensively. This study contributes both practical insights and theoretical understanding to the evolving discourse on ethical DDDM.

Every learner has a distinct set of preferences that affect how they absorb new information. Some researchers argue that teaching tailored to each student's unique learning style yields better learning outcomes. However, these claims are not sufficiently supported by research data. The inconsistency of findings and the lack of consensus on this issue motivate us to conduct this experimental study. This study aimed to investigate whether there is a correlation between learning style, personality traits, and student achievement. Participants were 54 students from the Faculty of Computer Science and Engineering, St. Cyril and Methodius University in Skopje, RN. Macedonia. The research followed a quantitative research approach. The VARK and TIPI questionnaires are used to measure students’ learning styles and personality traits, respectively. The results of these instruments are analyzed, and the correlational analysis with the students' learning outcomes (measured through the final exam) is conducted. The results show that there is no statistically significant effect of personality type on learning style on student performance, either when the analyses are carried out independently or in combination.

Curriculum and Policies of the Computer Science and Engineering Track in Saudi Secondary Education: International Alignment and Early Challenges

Serverless computing is increasingly integrated with artificial intelligence to address scalability, cost inefficiency, and operational complexity in conventional cloud-based machine learning workflows. This investigation evaluates the performance, scalability, cost efficiency, and statistical robustness of serverless AI architectures for cloud-based machine learning workflows under controlled academic settings. An experimental study was conducted at the Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh, from June to December 2024. Thirty cloud-based machine learning workflows were implemented using serverless architectures. Performance metrics included latency, execution time, throughput, scalability index, resource utilization, and cost efficiency. Statistical analysis employed mean values, standard deviation, and hypothesis testing with significance set at p<0.05. Serverless AI workflows demonstrated a 42.6% reduction in mean execution time compared with baseline container-based systems (3.8±0.9 s vs. 6.6±1.2 s; p=0.001). Average operational cost decreased by 38.4%, with per-inference cost reduced from USD 0.021±0.006 to USD 0.013±0.004 (p=0.003). Scalability efficiency improved by 51.2%, enabling automatic handling of workload surges up to 280% without performance degradation. Mean latency decreased by 34.7% (p=0.008). Resource utilization variance was significantly lower (SD 0.41 vs. 0.88), indicating improved stability. Overall system reliability increased to 96.3%, compared with 88.5% in non-serverless deployments. Serverless AI significantly enhances efficiency, scalability, and cost-effectiveness of cloud-based machine learning workflows, supporting its adoption as a resilient and statistically robust computational paradigm.

The aim of the study is to evaluate the effectiveness of periacetabular osteotomy as a treatment for hip dysplasia using finite element analysis. The main criterion for the effectiveness of surgical intervention is the reduction of contact pressure on the surface of articular cartilage. Three-dimensional finite element models reflecting the anatomy of the joint before and after osteotomy were built for six patients with hip dysplasia based on computed tomography data. A comparative analysis of the distribution of contact pressure in articular cartilages, as well as the stress-strain state of bone structures, has been performed. The results showed that four out of six patients after osteotomy had a decrease in the maximum contact pressure on the cartilage of the acetabulum, which indicates a positive biomechanical effect of the operation. In one patient the changes were insignificant, and in one an increase in contact pressure was noted. The results obtained confirm that computer engineering technologies make it possible to predict the biomechanical consequences of surgical interventions and evaluate their individual effectiveness: the results vary depending on the patient’s anatomy, which underlines the importance of a personalized approach and the high potential of finite element analysis as a tool for detailed preoperative planning.

Cyber–physical systems (CPS) in healthcare represent a deep integration of computational intelligence, physical medical devices, and human-centric data, enabling continuous, adaptive, and personalized care. These systems combine real-time measurements, artificial intelligence (AI)-based analytics, and networked medical devices to monitor, predict, and optimize patient health outcomes. A key development in the field of CPS is the emergence of patient digital twins (DTs), virtual models of individual patients that simulate biological, behavioral, and social parameters. Using AI, DTs analyze complex medical and social data (genetics, lifestyle, environment, etc.) to support precise diagnosis and treatment planning. The implications of the bibliometric findings suggest that the field emerges from the conceptual phase, justifying the article’s emphasis on both the proposed architectures and their clinical validation. However, most research was conducted in computer science, engineering, and mathematics, rather than medicine and healthcare, suggesting an early stage of technological maturity. Leading countries were India, the United States, and China, but these countries did not have a high number of publications, nor did they record leading researchers or affiliations, suggesting significant research fragmentation. The most frequently observed Sustainable Development Goals indicate an industrial context. Reflecting insights from medical and social research, AI-based DT systems provide a holistic view of the patient, taking into account not only physiological states but also psychological and social well-being. These systems promote personalized therapy by dynamically adapting treatment based on real-time feedback from wearable sensors and electronic medical records. More broadly, CPS and DT systems increase healthcare system efficiency by reducing hospitalizations and supporting remote preventive care. Their implementation poses significant ethical and privacy challenges, particularly regarding data ownership, algorithm transparency, and patient autonomy.

With the rapid expansion of user-generated content on social media platforms like Twitter, Facebook, and Reddit, accurately identifying sentiment from textual data has become an essential yet challenging task due to the informal, noisy, and contextually diverse nature of these platforms. To address this, we propose a Hybrid Attention-Driven Recurrent Neural Network (HA-RNN) model that effectively combines Bidirectional Gated Recurrent Units (Bi-GRU) with a sophisticated attention mechanism for sentiment classification. The model utilizes pre-trained GloVe embeddings (300 dimensions) to capture rich semantic features from raw text, enhancing the initial representation of social media data. The Bi-GRU layers are employed to model sequential dependencies Safeyah Tawil Department of Computer Science and Engineering, Faculty of Information Technology, Zarqa University, Zarqa, Jordan. University of Business and Technology, Jeddah, Saudi Arabia stawil@zu.edu.jo I. INTRODUCTION Social media platforms have become primary channels for individuals to express opinions, emotions, and sentiments on a wide range of topics, including politics, products, services, and global events. The explosive growth of platforms such as Twitter, Facebook, and Instagram has led to an overwhelming amount of unstructured textual data that offers valuable insights into public sentiment [1]. Analyzing this vast content can support businesses, governments, and researchers in understanding user perceptions, improving services, and detecting social trends. in both forward and backward directions, ensuring a comprehensive understanding of context within a sentence. The integrated attention layer enables the model to dynamically focus on sentiment-bearing words, thereby improving classification accuracy and interpretability. We evaluated the proposed model on two widely recognized datasets: the Twitter US Airline Sentiment Dataset and the Sentiment140 Dataset. The HA-RNN achieved an accuracy of 90.8% on the Twitter US Airline dataset and 88.5% on Sentiment140, outperforming traditional models such as CNN (84.3% accuracy), LSTM (86.7%), and Bi-GRU without attention (87.1%). Furthermore, the attention mechanism provided insightful visualization, highlighting the critical words influencing sentiment predictions. The model demonstrated a balanced performance with high precision, recall, and F1-scores, validating its robustness across different sentiment classes. Overall, the HA- RNN model presents an effective and interpretable solution for sentiment analysis on noisy and diverse social media texts, supporting applications in social monitoring, brand analysis, and opinion mining.

Abstract Urban administrations increasingly rely on AI and data-driven solutions to address complex societal problems, such as climate change and the distribution of limited resources. Since the 2000s, the term smart city has been used to describe cities that use data and technology to foster efficiency, environmental sustainability, and citizens’ quality of life. Although marketed as a promising way to a digital and data-driven future, numerous examples in recent years show that technology-based solutions may come with unintended and undesired side effects, e.g., excluding certain groups of people from access to resources. This may result in discrimination and increased social inequalities. While computer science and engineering research has been very active in developing smart city technologies, much less is known about the public’s attitudes towards such technologies and whether these attitudes vary across different social groups. To address this gap, we conducted a survey study (N = 2021) on public attitudes towards various smart city applications in May 2023 in a high-quality probability-based online panel in Germany. We presented respondents with eleven smart city technologies across four domains: mobility, social inclusion, public safety, and energy supply. Respondents indicated whether they are familiar with them and how much they would like to see the applications implemented in their neighbourhood. Using latent class analysis, we identified patterns of familiarity and desirability, and examined how these relate to gender, age, education, mobility impairment, migration background, income, and urbanicity. Our analysis reveals distinct attitude profiles towards smart city technologies, with certain socio-demographic characteristics associated with different degrees of familiarity and desirability. The study makes a twofold contribution to the research on citizens’ views on smart city applications: first, it offers a social science perspective that focuses on inequalities in public attitudes. Second, it complements the predominantly qualitative, small-sample literature with a quantitative analysis using a high-quality probability-based sample of the German population.

Transitioning from pre-university studies to a bachelor's degree can be quite challenging, as students often have to choose from a wide range of programs without knowing their academic compatibility. This lack of information can lead to poor performance or even dropout. To tackle this issue, we conducted a study at a Spanish university using machine learning (ML) algorithms on academic data from 2010 to 2022 (about 72,000 records) to develop a degree recommendation tool aligned with pre-university profiles. Our results show an average accuracy of 70% for the top 5 predictions and 90% for the top 10. Moreover, explainability techniques allowed us to identify profiles according to bachelor's degree programs and observe relationships between pre-university subjects and chosen degrees. For example, students who take Geography in their access proofs are less likely to choose Computer Engineering, while Mathematics, English, and Physics negatively affect recommendations for Education degrees. The tool is designed to assist school counselors by providing comprehensive and accurate guidance, considering students' academic profiles, interests, and socioeconomic factors. This is expected to improve academic performance and reduce dropout rates. Future work includes expanding the number of academic records, incorporating additional universities, and introducing new ML algorithms to enhance our results.