Articles

High-quality collaborative discourse is essential for deep learning in Problem-Based Learning (PBL). However, novice facilitators frequently struggle with topic drift, which occurs when discussions deviate from core learning objectives. Grounded in Cognitive Load Theory and instructional scaffolding, this paper analyzes the cognitive mechanisms behind topic drift in PBL. It identifies three typical deviation patterns: "Premature Resolution" (bypassing mechanistic deduction), "Detail Fixation" (over-engagement with peripheral details), and "Social Avoidance" (stemming from dysfunctional group dynamics). To address these patterns, this paper presents a literature-based facilitation guide offering specific intervention strategies and discursive prompts. These include mechanistic backtracking, metacognitive monitoring, and cognitive visualization. The study argues that effective pedagogical intervention relies on the strategic timing of facilitation rather than the immediate correction of factual errors. Ultimately, built on a foundation of relational trust, this process requires educators to shift their role from "epistemic authorities" to "cognitive coaches."

Simulation-based learning (SBL) is widely recognized as an effective educational approach that bridges theory and practice in nursing education. Despite its global adoption, limited research has examined the experiences of Filipino nursing students with SBL, particularly in resource-constrained settings. This study explored the perspectives of Bachelor of Science in Nursing students from a university in Metro Manila, Philippines, on the impact of SBL on their skills, emotional responses, and challenges encountered. A descriptive qualitative design was employed using purposive sampling of ten students who had participated in at least one SBL activity. Data were collected through semi-structured interviews and short written reflections and analyzed thematically following Braun and Clarke’s framework to capture nuanced experiences. Three major themes emerged from the analysis. First, students reported initial anxiety, nervousness, and stress during their early SBL experiences, which gradually transformed into confidence, adaptability, and resilience as they gained familiarity and competence. Second, SBL enhanced technical and cognitive skills such as clinical judgment, decision-making, teamwork, and patient-centered care, supporting students’ readiness for real-world practice. Third, students identified resource limitations, insufficient equipment, and time constraints as significant barriers to optimal learning, though these challenges also fostered creativity and perseverance. The findings demonstrate that SBL fosters technical competence, critical thinking, and professional growth but requires institutional support to address resource constraints and faculty development needs. This study underscores the importance of expanding SBL in Philippine nursing curricula to align with international best practices and to contribute to Sustainable Development Goals 3 (good health and well-being), 4 (quality education), and 5 (gender equality).

This research is a collaboration involving a university team, a partnering corporation, and a hemodialysis clinic, which is a cross-disciplinary research initiative in the field of Artificial Intelligence of Things (AIoT) within the medical informatics domain. The research has two objectives: (1) The development of an <i>Internet of Things (IoT)</i><i>-based</i><i> Information System</i> customized for the hemodialysis machines at the clinic, including transmission bridges, clinical personnel dedicated web/app, and a backend server. The system has been deployed at the clinic and is now officially operational; (2) The research also utilized de-identified, anonymous data (collected by the officially operational system) to train, evaluate, and compare <i>Deep Learning-based Intradialytic Blood Pressure</i><i> (BP)</i><i>/Pulse </i><i>Rate (PR) </i><i>Predictive Models</i>, with subsequent suggestions provided. Both objectives were executed under the supervision of the Institutional Review Board (IRB) at Mackay Memorial Hospital in Taiwan. The system completed for objective one has introduced three significant services to the clinic, including automated hemodialysis data collection, digitized data storage, and an information-rich human-machine interface as well as graphical data displays, which replaces traditional paper-based clinical administrative operations, thereby enhancing healthcare efficiency. The graphical data presented through web and app interfaces aids in real-time, intuitive comprehension of the patients’ conditions during hemodialysis. Moreover, the data stored in the backend database is available for physicians to conduct relevant analyses, unearth insights into medical practices, and provide precise medical care for individual patients. The training and evaluation of the predictive models for objective two, along with related comparisons, analyses, and recommendations, suggest that in situations with limited computational resources and data, an Artificial Neural Network (ANN) model with six hidden layers, SELU activation function, and a focus on artery-related features can be employed for hourly intradialytic BP/PR prediction tasks. It is believed that this contributes to the collaborating clinic and relevant research communities.

Smartphone use among adolescents has surged globally, reshaping communication and learning patterns, especially in developing countries. However, the implications of such digital habits on students in rural or under-resourced areas remain underexplored. This study aims to examine the patterns of smartphone usage and its effects on learning among high school students in Tarutung, a developing region of North Sumatra, Indonesia. Utilizing a quantitative descriptive approach, data were collected from 358 students using structured questionnaires. The results show that 96.05% of students own personal smartphones regardless of socioeconomic background, with an average daily usage of 4 hours and 45 minutes. While 91.81% believe smartphones support their learning, 25.99% report declining academic performance. Alarmingly, 20.62% of students admitted involvement in cyberbullying activities, highlighting a critical digital risk impacting the school environment and student well-being. The study concludes that although smartphones offer educational benefits, their misuse can lead to negative academic, social, and psychological outcomes. This study recommends digital literacy curricula and structured cooperation between parents and educators to prevent risks while optimizing educational opportunities in smartphone use.

The pharmaceutical industry is undergoing a profound transformation driven by advancements in Information Technology (IT), with Enterprise Resource Planning (ERP) systems playing a pivotal role in reshaping operations. These systems offer integrated solutions that streamline key business processes, such as production, inventory management, supply chain optimization, regulatory compliance, and data integration, contributing significantly to operational efficiency and organizational agility. This paper explores the evolution and impact of ERP systems within the pharmaceutical sector, highlighting their contributions to overcoming the industry’s inherent challenges, including complex regulatory requirements, the need for accurate and real-time data, and the demand for supply chain resilience. The integration of cloud-based ERP solutions, the incorporation of emerging technologies like Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT), and enhanced data analytics capabilities have revolutionized pharmaceutical IT. These advancements not only reduce operational costs, improve forecasting accuracy, and enhance collaboration but also ensure compliance with stringent global regulations, such as Good Manufacturing Practices (GMP) and FDA guidelines. Moreover, ERP systems have been instrumental in managing the pharmaceutical supply chain, ensuring product traceability, and improving inventory control and order fulfillment processes. This manuscript examines how ERP systems enable pharmaceutical companies to maintain high standards of product quality, improve decision-making, and ensure the safety and efficacy of drugs through robust tracking and auditing mechanisms. A case study of a pharmaceutical company that implemented an ERP system demonstrates the tangible benefits, including increased operational efficiency, improved compliance rates, and enhanced customer satisfaction. However, despite the clear advantages, challenges such as customization complexities, data integration issues, and resistance to change remain. As the pharmaceutical industry continues to evolve, ERP systems will remain a cornerstone of digital transformation, facilitating smarter decision-making, better resource management, and enhanced collaboration across global operations. This paper also identifies future trends, including the potential of AI and blockchain technologies in further strengthening ERP systems and transforming the pharmaceutical landscape.

Preference learning plays a crucial role in AI applications, particularly in recommender systems and personalized services. Traditional pairwise comparisons, while foundational, present scalability challenges in large-scale systems. This study explores alternative elicitation methods such as ranking, numerical ratings, and natural language feedback, alongside a novel hybrid framework that dynamically integrates these approaches. The proposed methods demonstrate improved efficiency, reduced cognitive load, and enhanced accuracy. Results from simulated user studies reveal that hybrid approaches outperform traditional methods, achieving a 40% reduction in user effort while maintaining high predictive accuracy. These findings open pathways for deploying user-centric, scalable preference learning systems in dynamic environments.

Recent advancements in the screening, diagnosis, and management of obstructive sleep apnea (OSA) have significantly improved patient outcomes. For screening, the use of home sleep apnea testing (HSAT) has become more prevalent, offering a convenient and cost-effective alternative to traditional in-lab polysomnography. HSAT devices have shown good specificity and sensitivity, particularly in patients with a high pre-test probability of OSA. In terms of diagnosis, advancements in wearable technology and mobile health applications have enabled continuous monitoring of sleep patterns and respiratory parameters. These tools provide valuable data that can be used to identify OSA more accurately and promptly. Additionally, machine learning algorithms are being integrated into diagnostic processes to enhance the accuracy of OSA detection by analyzing large datasets and identifying patterns indicative of the condition. Management of OSA has also seen significant progress. Continuous positive airway pressure (CPAP) therapy remains the gold standard, but new developments include auto-adjusting CPAP devices that optimize pressure settings based on real-time feedback. Mandibular advancement devices and hypoglossal nerve stimulation are emerging as effective alternatives for patients who are CPAP-intolerant. Furthermore, lifestyle interventions such as weight management, positional therapy, and exercise have been shown to complement medical treatments, leading to better overall outcomes. This review article highlights these advancements that collectively contribute to improved patient adherence, reduced symptoms, and enhanced quality of life for individuals with OSA.

Sentiment analysis, a vital aspect of natural language processing, involves the application of machine learning models to discern the emotional tone conveyed in textual data. The use case for this type of problem is where businesses can make informed decisions based on customer feedback, identify the sentiments of their employees, and make decisions on hiring or retention, or for that matter, classify a text based on its topic like whether it is about a particular subject like physics or chemistry as is useful in search engines. The model leverages a sequential architecture, transforms words into dense vectors using an Embedding layer, and captures intricate sequential patterns with two Long Short-Term Memory (LSTM) layers. This model aims to effectively classify sentiments in text data using a 50-dimensional embedding dimension and 20 % dropout layers. The use of rectified linear unit (ReLU) activations enhances non-linearity, while the SoftMax activation in the output layer aligns with the multi-class nature of sentiment analysis. Both training and test accuracy were well over 80%.

The integration of LiDAR and Artificial Intelligence (AI) has revolutionized feature detection in urban environments. LiDAR systems, which utilize pulsed laser emissions and reflection measurements, produce detailed 3D maps of urban landscapes. When combined with AI, this data enables accurate identification of urban features such as buildings, green spaces, and infrastructure. This synergy is crucial for enhancing urban development, environmental monitoring, and advancing smart city governance. LiDAR, known for its high-resolution 3D data capture capabilities, paired with AI, particularly deep learning algorithms, facilitates advanced analysis and interpretation of urban areas. This combination supports precise mapping, real-time monitoring, and predictive modeling of urban growth and infrastructure. For instance, AI can process LiDAR data to identify patterns and anomalies, aiding in traffic management, environmental oversight, and infrastructure maintenance. These advancements not only improve urban living conditions but also contribute to sustainable development by optimizing resource use and reducing environmental impacts. Furthermore, AI-enhanced LiDAR is pivotal in advancing autonomous navigation and sophisticated spatial analysis, marking a significant step forward in urban management and evaluation. The reviewed paper highlights the geometric properties of LiDAR data, derived from spatial point positioning, and underscores the effectiveness of machine learning algorithms in object extraction from point clouds. The study also covers concepts related to LiDAR imaging, feature selection methods, and the identification of outliers in LiDAR point clouds. Findings demonstrate that AI algorithms, especially deep learning models, excel in analyzing high-resolution 3D LiDAR data for accurate urban feature identification and classification. These models leverage extensive datasets to detect patterns and anomalies, improving the detection of buildings, roads, vegetation, and other elements. Automating feature extraction with AI minimizes the need for manual analysis, thereby enhancing urban planning and management efficiency. Additionally, AI methods continually improve with more data, leading to increasingly precise feature detection. The results indicate that the pulse emitted by continuous wave LiDAR sensors changes when encountering obstacles, causing discrepancies in measured physical parameters.

<b>Background</b><b>:</b><b> </b>The increasing frequency and intensity of extreme heat exposure is a significant consequence of climate change, with broad public health implications. While many health risks associated with heat exposure are well-documented, less research has focused on its impact on children’s cognitive function. <b>Objectives</b><b>:</b><b> </b>This study examines the relationship between extreme heat exposure and various domains of cognitive function in children. <b>Methods</b><b>:</b><b> </b>Data were drawn from the Adolescent Brain Cognitive Development (ABCD) study. Key variables included race/ethnicity, age, gender, family socioeconomic status (SES), heatwave exposure, and multiple cognitive domains: total composite score, fluid composite score, crystallized intelligence, reading ability, picture vocabulary, pattern recognition, card sorting, and list recall. Structural equation modeling (SEM) was used for data analysis. <b>Results</b><b>:</b><b> </b>A total of 11,878 children were included in the analysis. Findings revealed significant associations between extreme heat exposure and lower cognitive performance across multiple domains. The strongest adjusted effects were observed in pattern recognition (B = −0.064, p < 0.001) and reading ability (B = −0.050, p < 0.001), both within the learning domain, as well as total composite cognitive ability (B = −0.067, p < 0.001), fluid composite (B = −0.053, p < 0.001), and crystallized intelligence (B = −0.061, p < 0.001), all within general cognitive ability. Weaker but still significant associations were found for list recall (B = −0.025, p = 0.006) and card sorting (B = −0.043, p < 0.001) within the memory domain, as well as picture vocabulary (B = −0.025, p = 0.008) within general cognitive ability. These associations remained significant after controlling for demographic factors, race/ethnicity, family SES, and neighborhood SES. <b>Conclusions</b><b>:</b><b> </b>This study underscores the impact of climate change on cognitive function disparities, particularly in learning and general cognitive ability among children exposed to extreme heat. Findings highlight the need for targeted interventions to mitigate the cognitive risks associated with heat exposure in vulnerable populations.

Time series data are increasingly prevalent across domains such as finance, healthcare, manufacturing, and IoT, making accurate forecasting and anomaly detection critical for decision-making and system reliability. Traditional statistical methods (e.g., ARIMA, Holt-Winters) often fail to capture complex temporal dependencies and high-dimensional interactions inherent in modern time series. Recent advances in artificial intelligence particularly deep learning architectures such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), temporal convolutional networks (TCNs), graph neural networks (GNNs) and Transformers have demonstrated marked improvements in modeling both univariate and multivariate series, as well as in detecting anomalies that deviate from learned norms (Darban, Webb, Pan, Aggarwal, &#x26; Salehi, 2022; Chiranjeevi, Ramya, Balaji, Shashank, &#x26; Reddy, 2024) [1,2]. Moreover, ensemble techniques and hybrid signal-processing + deep-learning pipelines show enhanced sensitivity and adaptability in real-world anomaly detection scenarios (Iqbal, Amin, Alsubaei, &#x26; Alzahrani, 2024) [3]. In this work, we provide a unified survey and comparative analysis of AI-driven time series forecasting and anomaly detection methods, highlight key industrial application domains, evaluate performance trade-offs (e.g., accuracy vs. latency, supervised vs. unsupervised learning), and discuss emerging challenges including interpretability, data drift, real-time deployment on edge devices, and integration of causal reasoning. Our findings suggest that while AI approaches significantly outperform classical techniques in many settings, careful consideration of data characteristics, evaluation metrics and deployment environment remains essential for effective adoption.

The most prevalent technique behind security data breaches exists through SQL Injection Attacks. Organizations and individuals suffer from sensitive information exposure and unauthorized entry when attackers take advantage of SQL injection (SQLi) attack vulnerability’s severe risks. Static and heuristic defense methods remain conventional detection tools for previous SQL injection attacks study's foundation is a detection system developed using the Gated Recurrent Unit (GRU) network, which attempts to efficiently identify SQL Injection attacks (SQLIAs). The suggested Gated Recurrent Unit model was trained using an 80:20 train-test split, and the results showed that SQL injection attacks could be accurately identified with a precision rate of 97%, an accuracy rate of 96.65%, a recall rate of 92.5%, and an F1-score of 94%. The experimental results, together with their corresponding confusion matrix analysis and learning curves, demonstrate resilience and outstanding generalization ability. The GRU model outperforms conventional machine learning (ML) models, including K-Nearest Neighbor’s (KNN), and Support Vector Machine (SVM), in terms of identifying sequential patterns in SQL query data. Recurrent neural architecture proves effective in the detection of SQLi attacks through its ability to provide secure protection for contemporary web applications.

Over the past few decades, cardiovascular disease and related complications have surpassed all others as the important causes of death on a universal scale. At the moment, they are the important cause of mortality universal, including in India. It is important to know how to find cardiovascular problems early so that patients get better care and prices go down. This project utilizes the UCI Heart Disease Dataset to develop ML and DL models capable of detecting cardiac diseases. Heart illness was categorized using Convolutional Neural Network (CNN) models, which are able to detect intricate patterns in supplied data. A confusion matrix rating, an F1-score, a ROC curve, accuracy, precision, and recall were some of the measures used to grade the model. It did much better than the Neural Network, Deep Neural Network (DNN), and Gradient Boosted Trees (GBT) models, with 91.71% accuracy, 88.88% precision, 82.75% memory, and 85.70% F1-score. Comparative study showed that CNN was the most accurate model. Other models had different balances between accuracy and recall. The experiment results show that the optional CNN model is a decent way to identify cardiovascular disease. This means that it could be used in healthcare systems to find diseases earlier and treat patients better.

The theme of the study that catches our attention is the initial conceptions of learners in Science and school performance; this theme is based on the competency-based approach in force in Cameroon, which is implemented in several African countries. Insofar as learning is not the accumulation of new knowledge but a cognitive reorganization of old knowledge experienced, it is therefore a question of knowing what is the influence of initial conceptions on the academic performance of learners in science. The objective of this research was to show that taking into account the initial conceptions of learners, Biology “SVT” has a lasting influence on learning and thus on the academic performance of learners. To achieve this objective, the study uses the mixed and quasi-experimental method, where two groups of learners were used: a control group and an experimental group. The experimental group was subjected to the teaching-learning system designed for this purpose, and in which the initial conceptions of the learners were taken into account according to do with or go against. In the light of the different hypotheses adopted and the different results of this study, it can be observed that the didactic consideration of the learners' initial conceptions improves their academic performance through the data of the experimental group. In relation to the field of education, this study shows that in order to enable learners to learn and build knowledge in the long term, their initial conceptions must be taken into account in concrete didactics; Otherwise, learning will be sporadic, learners' conceptions will be significant, which will lead to a learning defect perceptible by school failure.

Making students efficiently and flexibly understand and master the knowledge of biological/biomedical sciences by prompting active learning and critical thinking and making biomedical science courses form an integrated system are critical for more successful and effective student education, future and career. However, the current biological/biomedical education system worldwide tends to produce graduates best suited for postdoctoral training, and academic careers, in highly specialized areas. Many institutions worldwide currently favor densely packed curricula with fast-paced instruction focused on detailed subject matter, with little room left for skill training in both active learning and critical thinking, creative problem solving, professional skills, putting what was learned into a larger context that creates meaning, and teaching students with different learning styles, despite their significance. In this article, we will discuss the current defects in biological/biomedical science education and the need to reform biomedical science teaching and education. We will also describe some suggestions and approaches to improve biological/biomedical science learning and education, including the establishment of a more effective learning/teaching repository.

Whereas Physical Education and Sports (PES) have been reported to be crucial to learners, such claims have often been made without empirical support. Given this, this paper reports on a systematic review of the relevance of PES to learners. The review involved 20 empirical studies. Most studies were conducted in the United States, using quantitative research design and focusing on preschool to high school. The studies also demonstrated that PES improves academic performance, motivation, attention, and behaviour. It is recommended that PES be taken seriously at all levels of academics and integrated into the curriculum. While the studies reviewed did not involve higher educational institutions, the benefits can be extended to higher education institutions such as colleges of education and universities.

High-Performance Computing (HPC) is indispensable for large-scale scientific simulations, but achieving optimal performance on modern supercomputers is increasingly challenging. As HPC systems scale toward exascale, they face escalating complexity in hardware, software, and workloads. Traditional optimization methods (manual tuning and heuristic algorithms) struggle to cope with dynamic workloads and intricate system behaviors. Artificial Intelligence (AI) techniques offer a promising approach to address these challenges. This article provides an overview of AI-powered optimization in HPC, focusing on how machine learning and related AI methods enhance performance, efficiency, and scalability of scientific simulations. We survey key AI techniques applied to HPC optimization including machine learning for performance modeling, deep reinforcement learning for resource management, and AI-driven surrogate models for accelerating simulations and illustrate their impact through case studies in domains such as job scheduling and fluid dynamics. We discuss the practical applications of these techniques, highlighting reported performance gains (e.g., substantial reductions in simulation run time and improved resource utilization). We also examine the challenges in integrating AI with HPC (such as training overhead, data movement, and reliability concerns) and outline future directions for research. The convergence of AI and HPC is poised to produce “smart” simulation workflows that intelligently adapt and optimize in real time, pushing the frontiers of scientific computing.

The purpose of this study was to examine the challenges affecting the teaching and learning of Social Studies in the Colleges of Education in the Ashanti Region. Qualitatively, the study adopted a case study research design. The population of the study comprised fifteen (15) Social Studies teachers in the Ashanti region of Ghana. A purposive sampling technique was used to select the colleges and Social Studies teachers. The main instrument for data collection was an unstructured open-ended interview guide. Four criteria proposed by Lincoln and Guba in 1985 used to measure the trustworthiness of data collected are credibility, transferability, dependability and confirmability. Data obtained from the study were subjected to thematic analysis using themes developed from the data collected based on the research question. The findings of this study indicate that adequate textbooks, availability of teaching and learning materials such as projectors, the ability of tutors to engage in research, and the ability to vary teaching techniques are some factors that affect the teaching and learning of Social Studies in Colleges of Education. Students can remember a greater percentage of what was taught when techniques used by teachers are in their interest. It is recommended that T-TEL and Universities to which Colleges of Education are affiliated should continue to provide seminars and sessions to tutors in areas of material, resource development and research. This will continue to ensure that tutors are competent in their field of specialization. It is also recommended that the government should conduct a nationwide programme evaluation exercise on Social Studies to identify the loopholes needed to ensure the effectiveness of the subject.

Consumer behavior is evolving, demanding a wide range of products with fast shipping and reliable service. The automotive aftermarket industry, worth billions, requires efficient distribution systems to stay competitive. Manufacturers strive to balance growth with product and service excellence. Distributors and retailers face the challenge of maintaining competitive pricing while keeping inventory levels low. An adequate supply chain and accurate product data are crucial for product availability and reducing stock issues. This ultimately increases profits and customer satisfaction.

Challenges are part and parcel of human activities. Quantitatively, a relational survey model research was adopted for the study. The population for this study comprised all Junior High School (JHS) Religious and Moral Education (RME) students in Bokro M/A JHS and the Eziom Methodist JHS in the Nzema-East Municipality of the Western Region. Purposive and random sampling techniques were used to select the schools and respondents for the study. The main instruments for data collection were a test and a questionnaire. The data from both the control and experimental groups on the challenges learners face in using the flipped classroom model in RME were analysed using means and standard deviations. The study indicates the challenges learners face by employing the flipped classroom model to learn include lack of data to access the internet, frequent light outs, lack of technological devices to access contents, frequent power outages, and difficulty in comprehending some of the materials given to them. Given that learners face frequent power outages during periods that they watch videos assigned to them by their teachers, it is recommended that the government and the Electricity Company of Ghana ensure that there is a constant flow of power to allow learners trying to use the flipped classroom to learn are without any hindrance. It is also recommended that the Ghana education service should organise continuing professional development for RME teachers on effective uses of technology in teaching and learning subject concepts. The government should also supply the basic schools with all the needed technological devices to promote effective teaching, learning and assessment.