Search Results (1,291)

Artificial intelligence is transforming higher education, particularly in flipped classroom settings, in which students learn independently prior to class and collaborate during in-person sessions. This study examines the role of ChatGPT as a virtual peer in a veterinary anatomy course centered on cardiovascular and respiratory systems. Over two academic years (2023–2025), 297 first-year veterinary students worked in small groups to explore anatomy through structured prompts in English and Spanish using ChatGPT versions 3.5 and 4. Activities involved analyzing AI output, evaluating anatomical accuracy, and suggesting alternative names for vascular variations. Learning outcomes were assessed using Bloom’s Taxonomy-based questions, and student perceptions were captured via online surveys. Progressive performance improvement was noted across three instructional phases, particularly in higher-level cognitive tasks (Bloom level 4). Responses to English prompts were more accurate than those to Spanish prompts. While students appreciated ChatGPT’s role in reinforcing knowledge and sparking discussion, they also flagged inaccuracies and emphasized the need for critical evaluation. Peer collaboration was found to be more influential than chatbot input. Conclusions: ChatGPT can enrich flipped anatomy instruction when paired with structured guidance. It supports content review, fosters group learning and promotes reflective thinking. However, developing digital literacy and ensuring expert oversight are essential to maximizing the educational value of AI. Full article

Low-power microcontrollers, wireless sensors, and embedded gateways form the backbone of many Internet of Things (IoT) deployments. However, their limited memory, constrained energy budgets, and lack of standardized firmware make them attractive targets for diverse attacks, including bootloader backdoors, hardcoded keys, unpatched CVE exploits, and code-reuse attacks, while traditional single-layer defenses are insufficient as they often assume abundant resources. This paper presents a Systematic Literature Review (SLR) conducted according to the PRISMA 2020 guidelines, covering 196 peer-reviewed studies on cross-layer security for resource-constrained IoT and Industrial IoT environments, and introduces a four-axis taxonomy—system level, algorithmic paradigm, data granularity, and hardware budget—to structure and compare prior work. At the firmware level, we analyze static analysis, symbolic execution, and machine learning-based binary similarity detection that operate without requiring source code or a full runtime; at the network and behavioral levels, we review lightweight and graph-based intrusion detection systems (IDS), including single-packet authorization, unsupervised anomaly detection, RF spectrum monitoring, and sensor–actuator anomaly analysis bridging cyber-physical security; and at the policy level, we survey identity management, micro-segmentation, and zero-trust enforcement mechanisms supported by blockchain-based authentication and programmable policy enforcement points (PEPs). Our review identifies current strengths, limitations, and open challenges—including scalable firmware reverse engineering, efficient cross-ISA symbolic learning, and practical spectrum anomaly detection under constrained computing environments—and by integrating diverse security layers within a unified taxonomy, this SLR highlights both the state-of-the-art and promising research directions for advancing IoT security. Full article

Data-driven forecasting is becoming increasingly central to modern energy management, yet nonspecialists without a background in artificial intelligence (AI) face significant barriers to entry. While Python is the dominant machine learning language, R remains a practical and accessible tool for users with expertise in statistics, engineering, or domain-specific analysis. To inform tool selection, we first provide an evidence-based comparison of R with major alternatives before reviewing 49 peer-reviewed articles published between 2020 and 2025 in Science Citation Index Expanded (SCIE)-level journals that utilized R for energy forecasting tasks, including electricity (regional and site-level), solar, wind, thermal energy, and natural gas. Despite such growth, the field still lacks a systematic, cross-domain synthesis that clarifies which R-based methods prevail, how accessible workflows are implemented, and where methodological gaps remain; this motivated our use of text mining. Text mining techniques were employed to categorize the literature according to forecasting objectives, modeling methods, application domains, and tool usage patterns. The results indicate that tree-based ensemble learning models—e.g., random forests, gradient boosting, and hybrid variants—are employed most frequently, particularly for solar and short-term load forecasting. Notably, few studies incorporated automated model selection or explainable AI; however, there is a growing shift toward interpretable and beginner-friendly workflows. This review offers a practical reference for nonexperts seeking to apply R in energy forecasting contexts, emphasizing accessible modeling strategies and reproducible practices. We also curate example R scripts, workflow templates, and a study-level link catalog to support replication. The findings of this review support the broader democratization of energy analytics by identifying trends and methodologies suitable for users without advanced AI training. Finally, we synthesize domain-specific evidence and outline the text-mining pipeline, present visual keyword profiles and comparative performance tables that surface prevailing strategies and unmet needs, and conclude with practical guidance and targeted directions for future research. Full article

Background: Occupational risk prevention is a critical discipline for ensuring safe working conditions and minimizing accidents and occupational diseases. With the rise of artificial intelligence (AI) and machine learning (ML), these approaches are increasingly utilized for predicting and preventing workplace hazards. This systematic review aims to identify, evaluate, and synthesize existing literature on the use of AI algorithms for detecting and predicting hazardous environments and occupational risks in the workplace, focusing on predictive modeling and prevention strategies. Methods: A systematic literature review was conducted following the PRISMA 2020 protocol, with minor adaptations to include conference proceedings and technical reports due to the topic’s emerging and multidisciplinary nature. Searches were performed in IEEE Digital Library, PubMed, Scopus, and Web of Science, with the last search conducted on 1 August 2024. Only peer-reviewed articles published from 2019 onwards and written in English were included. Systematic literature reviews were explicitly excluded. The screening process involved duplicate removal (reducing 209 initial documents to 183 unique ones), a preliminary screening based on titles, abstracts, and keywords (further reducing to 92 articles), and a detailed full-text review. During the full-text review, study quality was assessed using six quality assessment (QA) questions, where articles receiving a total score below 4.5 or 0 in any QA question were excluded. This rigorous process resulted in the selection of 61 relevant articles for quantitative and qualitative analysis. Results: The analysis revealed a growing interest in the field, with a clear upward trend in publications from 2021 to 2023, and a continuation of growth into 2024. The most significant contributions originated from countries such as China, South Korea, and India. Applications primarily focused on high-risk sectors, notably construction, mining, and manufacturing. The most common approach involved the use of visual data captured by cameras, which constituted over 40% of the reviewed studies, processed using deep learning (DL) models, particularly Convolutional Neural Networks (CNNs) and You Only Look Once (YOLO). Conclusions: The study highlights current limitations, including an over-reliance on visual data (especially challenging in low-visibility environments) and a lack of methodological standardization for AI-based risk detection systems. Future research should emphasize the integration of multimodal data (visual, environmental, physiological) and the development of interpretable AI models (XAI) to enhance accuracy, transparency, and trust in hazard detection systems. Addressing long-term societal implications, such as privacy and potential worker displacement, necessitates transparent data policies and robust regulatory frameworks. Full article

Assessing e-learning students’ satisfaction with lecturers’ interactions in asynchronous forums is essential for enhancing teaching and learning processes. The discussion forum allows students to share comments and ideas with peers or lecturers, stimulating diverse perspectives and improving learning efficacy. However, lecturers’ responses are often similar or redundant to previous students’ comments, limiting feedback depth and potentially reducing students’ perceived value of the interaction. Machine learning classifiers have been widely used to assess satisfaction based on sentiment or semantic similarity. However, integrating sentiment and semantic similarity between students’ comments or opinions and lecturers’ responses in asynchronous online discussion forums has received limited attention and may be improved. Through this research, we propose a novel model called E-learning Satisfaction Assessment using Textual Neural Network (E-SATNet). The E-SATNet model has two main sub-networks. The first sub-network employs a Convolutional Neural Network (CNN) to extract sentiment-related features from students’ reactions to lecturers’ responses. The second sub-network utilizes a Bidirectional Long Short-Term Memory (BiLSTM) to extract semantic features from lecturers’ responses and compute their similarity with the overall discussion content. Evaluation results show that E-SATNet effectively assesses satisfaction, achieving an average F1-score of 88.12. Full article

This literature review examines the relationship between the number and type of deficiencies identified during Port State Control (PSC) inspections and a ship’s overall risk. The main objective is to synthesise the current academic evidence, detailing the analytical methodologies employed and highlighting key research contributions. The selection of literature has focused on peer-reviewed articles and relevant doctoral theses addressing detention risk prediction, accident risk and ship risk profiling. The findings indicate a consistent correlation between PSC deficiencies and ship risk, although the nature and strength of this correlation may vary depending on the type of risk considered and the specific deficiencies. A methodological evolution is observed in the field, from descriptive statistical analyses and regressions towards more complex predictive models, such as Machine Learning (ML) and Bayesian Networks (BNs). This transition reflects a search for greater accuracy in risk assessment, going beyond simple numerical correlation to improve the selection of ships for inspection. Multivariate statistical techniques, on the other hand, focus on the identification of risk patterns and the evaluation of the PSC system. The conclusions underline the importance of deficiencies as indicators of risk, the need for differentiated inspection approaches and the persistent challenges related to data quality and model interpretability. Full article

This study investigates the influence of Trait Emotional Intelligence (TEI) on affective dimensions of English language learning among 515 Pakistani EFL learners, addressing a key gap in Global South research. Using bootstrapped multiple regression and culturally adapted instruments (Cronbach’s α = 0.724–0.857), findings reveal that in Pakistan’s exam-driven, teacher-centered classrooms, well-being significantly enhances attitudes (β = 0.172, p < 0.001), motivation (β = 0.219, p = 0.002), and engagement (β = 0.179, p < 0.001). Emotionality, however, increases anxiety (β = 0.192, p < 0.001) and lowers engagement (β = −0.092, p = 0.025), contradicting global models due to punitive error correction. Sociability shows no significant effect (attitudes: β = 0.038, p = 0.366; engagement: β = 0.019, p = 0.613), reflecting limited peer interaction in hierarchical classrooms. Notably, an emergent auxiliary facet—contextual adaptability—strongly predicts motivation (β = 0.269, p < 0.001) and anxiety (β = 0.109, p = 0.020), highlighting the role of competencies like Urdu–English code-switching. These results call for a Contextually Stratified TEI Framework, emphasizing that while well-being is universal, other TEI dimensions are context-dependent. Implications urge educators to foster well-being, reframe emotionality as a risk-detection skill, and promote adaptability to local linguistic realities. Full article

Currently, citizens of the Western world are struggling with (1) the rapid growth and increasing densification of cities and (2) the excessive time spent of citizens indoors. This article aims to contribute to changing this paradigm through proposals for organizing outdoor spaces in schools that are easy to implement and that allow new generations to experience the multiple benefits of using the outdoors. To encourage such use, we propose using school outdoor spaces for teaching/learning activities, complementing spaces traditionally committed to play, sports, and peer socialization. The presented proposals were developed using a collaborative methodology involving the school community, supervised by final-year students and professors from the University of Lisbon’s MSc. in Architecture program. The results demonstrate a wide range of ideas capable of stimulating learning among children and young people, as well as encouraging teachers to increase outdoor teaching activities. The implementation of these proposals will certainly have a positive impact on the planning and construction/qualifying of cities, providing their citizens with greater and better use/enjoyment of outdoor space, as well as all the benefits associated with it. Full article

(1) Background: Quantitative EEG (QEEG) offers potential for identifying objective neurophysiological biomarkers in psychiatric disorders and guiding neurofeedback interventions. This study examined whether three nonlinear QEEG metrics—Lempel–Ziv Complexity, Tsallis Entropy, and Renyi Entropy—can distinguish children with autism spectrum disorder (ASD) from typically developing (TD) peers, and assessed their relevance for neurofeedback targeting. (2) Methods: EEG recordings from 19 scalp channels were analyzed in children with ASD and TD. The three nonlinear metrics were computed for each channel. Group differences were evaluated statistically, while machine learning classifiers assessed discriminative performance. Dimensionality reduction with t-distributed Stochastic Neighbor Embedding (t-SNE) was applied to visualize clustering. (3) Results: All metrics showed significant group differences across multiple channels. Machine learning classifiers achieved >90% accuracy, demonstrating robust discriminative power. t-SNE revealed distinct ASD and TD clustering, with nonlinear separability in specific channels. Visual processing–related channels were prominent contributors to both classifier predictions and t-SNE cluster boundaries. (4) Conclusions: Nonlinear QEEG metrics, particularly from visual processing regions, differentiate ASD from TD with high accuracy and may serve as objective biomarkers for neurofeedback. Combining complexity and entropy measures with machine learning and visualization techniques offers a relevant framework for ASD diagnosis and personalized intervention planning. Full article

Water hyacinth (Eichhornia crassipes), native to South America, is a highly invasive aquatic plant threatening freshwater ecosystems worldwide. Its rapid proliferation negatively impacts water quality, biodiversity, and navigation. Remote sensing offers an effective means to monitor such aquatic environments by providing extensive spatial and temporal coverage with improved resolution. This systematic review examines remote sensing applications for monitoring water hyacinth and water quality in studies published from 2014 to 2024. Seventy-eight peer-reviewed articles were selected from the Web of Science, Scopus, and Google Scholar following strict criteria. The research spans 25 countries across five continents, focusing mainly on lakes (61.5%), rivers (21%), and wetlands (10.3%). Approximately 49% of studies addressed water quality, 42% focused on water hyacinth, and 9% covered both. The Sentinel-2 Multispectral Instrument (MSI) was the most used sensor (35%), followed by the Landsat 8 Operational Land Imager (OLI) (26%). Multi-sensor fusion, especially Sentinel-2 MSI with Unmanned Aerial Vehicles (UAVs), was frequently applied to enhance monitoring capabilities. Detection accuracies ranged from 74% to 98% using statistical, machine learning, and deep learning techniques. Key challenges include limited ground-truth data and inadequate atmospheric correction. The integration of high-resolution sensors with advanced analytics shows strong promise for effective inland water monitoring. Full article

The pandemic spared most children and adolescents in Ghana from severe clinical disease, but it exposed long-standing gaps in services and protection methods. Methods: We conducted a desk-based narrative review of peer-reviewed studies, national and international reports, and grey literature from January 2020 to May 2025. The evidence was organised across eight domains of child and adolescent well-being. Across mental health, gambling and other risky behaviours, access to health services, economic hardship and child labour, nutrition, education, early childhood development, and WASH, the pandemic disrupted essential services and social safety nets. Examples include declines in routine care and immunisation, wider digital exclusion during remote learning, a rise in child labour linked to income loss, and persistent hygiene constraints. Preparedness in Ghana should focus on mental health, digital inclusion, early childhood services, and strong social protection. Ghana’s specific empirical data are uneven, so we triangulate peer-reviewed evidence with official reports, appraised the grey literature, and calibrated claims to the strength of sources. Full article

Traffic congestion remains a major urban challenge, impacting economic productivity, environmental sustainability, and commuter well-being. This systematic review investigates how artificial intelligence (AI) techniques contribute to detecting traffic congestion. Following the SPAR-4-SLR protocol, we analyzed 44 peer-reviewed studies covering three data categories—spatiotemporal, probe, and hybrid/multimodal—and four AI model types—shallow machine learning (SML), deep learning (DL), probabilistic reasoning (PR), and hybrid approaches. Each model category was evaluated against metrics such as accuracy, the F1-score, computational efficiency, and deployment feasibility. Our findings reveal that SML techniques, particularly decision trees combined with optical flow, are optimal for real-time, low-resource applications. CNN-based DL models excel in handling unstructured and variable environments, while hybrid models offer improved robustness through multimodal data fusion. Although PR methods are less common, they add value when integrated with other paradigms to address uncertainty. This review concludes that no single AI approach is universally the best; rather, model selection should be aligned with the data type, application context, and operational constraints. This study offers actionable guidance for researchers and practitioners aiming to build scalable, context-aware AI systems for intelligent traffic management. Full article

In recent years, there have been significant advances in the application of technology in professional kitchens. This evolution of “smart kitchens” has transformed the food processing sector, ensuring higher standards of food safety through continual microbial monitoring, quality control, and hygiene improvements. This review critically discusses the recent developments in technology in commercial kitchens, focusing on their impact on microbial safety, operational efficiency, and sustainability. The literature was sourced from peer-reviewed journals, industry publications, and regulatory documents published between 2000 and 2025, selected for their relevance to the assurance of food safety using emerging technologies especially for use in commercial kitchens. Some of the most significant of these technologies currently being employed in smart kitchens include the following: smart sensors and IoT devices, artificial intelligence and machine learning systems, blockchain-based traceability technology, robotics and automation, and wearable monitoring devices. The review evaluated these technologies against criteria such as adherence to existing food safety regulations, ease of integration, cost factors, staff training requirements, and consumer perception. It is shown that these innovations will significantly enhance hygiene control, reduce the levels of waste, and increase business revenue. However, they are constrained by high installation costs, integration complexity, lack of standardized assessment measures, and the need for harmonizing automation with human oversight. Thus, for the widespread and effective uptake of these technologies, there is a need for better collaboration between policymakers, food experts, and technology innovators in creating scalable, affordable, and regulation-compliant solutions. Overall, this review provides a consolidated evidence base and practical insights for stakeholders seeking to implement advanced microbial safety technologies in professional kitchens, highlighting both current capabilities and future research opportunities. Full article

Smart-beehive technologies represent a paradigm shift in beekeeping, transitioning from traditional, reactive methods toward proactive, data-driven management. This systematic literature review investigates the current landscape of intelligent systems applied to beehives, focusing on the integration of IoT-based monitoring, sensor modalities, machine learning techniques, and their applications in precision apiculture. The review adheres to PRISMA guidelines and analyzes 135 peer-reviewed publications identified through searches of Web of Science, IEEE Xplore, and Scopus between 1990 and 2025. It addresses key research questions related to the role of intelligent systems in early problem detection, hive condition monitoring, and predictive intervention. Common sensor types include environmental, acoustic, visual, and structural modalities, each supporting diverse functional goals such as health assessment, behavior analysis, and forecasting. A notable trend toward deep learning, computer vision, and multimodal sensor fusion is evident, particularly in applications involving disease detection and colony behavior modeling. Furthermore, the review highlights a growing corpus of publicly available datasets critical for the training and evaluation of machine learning models. Despite the promising developments, challenges remain in system integration, dataset standardization, and large-scale deployment. This review offers a comprehensive foundation for the advancement of smart apiculture technologies, aiming to improve colony health, productivity, and resilience in increasingly complex environmental conditions. Full article

Background/Objectives: Artificial Intelligence (AI) is improving dentistry through increased accuracy in diagnostics, planning, and workflow automation. AI tools, including machine learning (ML) and deep learning (DL), are being adopted in oral medicine to improve patient care, efficiency, and lessen clinicians’ workloads. AI in dentistry, despite its use, faces an issue of acceptance, with its obstacles including ethical, legal, and technological ones. In this article, a review of current AI use in oral medicine, new technology development, and integration barriers is discussed. Methods: A narrative review of peer-reviewed articles in databases such as PubMed, Scopus, Web of Science, and Google Scholar was conducted. Peer-reviewed articles over the last decade, such as AI application in diagnostic imaging, predictive analysis, real-time documentation, and workflows automation, were examined. Besides, improvements in AI models and critical impediments such as ethical concerns and integration barriers were addressed in the review. Results: AI has exhibited strong performance in radiographic diagnostics, with high accuracy in reading cone-beam computed tomography (CBCT) scan, intraoral photographs, and radiographs. AI-facilitated predictive analysis has enhanced personalized care planning and disease avoidance, and AI-facilitated automation of workflows has maximized administrative workflows and patient record management. U-Net-based segmentation models exhibit sensitivities and specificities of approximately 93.0% and 88.0%, respectively, in identifying periapical lesions on 2D CBCT slices. TensorFlow-based workflow modules, integrated into vendor platforms such as Planmeca Romexis, can reduce the processing time of patient records by a minimum of 30 percent in standard practice. The privacy-preserving federated learning architecture has attained cross-site model consistency exceeding 90% accuracy, enabling collaborative training among diverse dentistry clinics. Explainable AI (XAI) and federated learning have enhanced AI transparency and security with technological advancement, but barriers include concerns regarding data privacy, AI bias, gaps in AI regulating, and training clinicians. Conclusions: AI is revolutionizing dentistry with enhanced diagnostic accuracy, predictive planning, and efficient administration automation. With technology developing AI software even smarter, ethics and legislation have to follow in order to allow responsible AI integration. To make AI in dental care work at its best, future research will have to prioritize AI interpretability, developing uniform protocols, and collaboration between specialties in order to allow AI’s full potential in dentistry. Full article