Search Results (256)

In recent years, interest in artificial intelligence has grown significantly, particularly in the development of advanced computational models for supply chain decision-making. Inventory management is one of the areas in which intelligent algorithms can support demand forecasting, replenishment, stock control, and operational optimization under uncertainty. This study presents an updated systematic literature review of intelligent algorithms applied to inventory management. The review followed PRISMA 2020 guidelines and combined database searches in Scopus, ScienceDirect, Web of Science, IEEE Xplore, SpringerLink, Taylor & Francis, and complementary manual searching. The original search covering January 2020 to December 2024 was updated in July 2026 to include studies published or available online up to June 2026. After applying strict eligibility criteria, 37 primary studies with quantitative evidence were included. The updated corpus confirms the predominance of deep learning, reinforcement learning, and hybrid intelligent models, while also showing the recent emergence of Transformer-based, graph neural network, multi-agent reinforcement learning, and prescriptive analytics approaches. The most frequent application areas were inventory control, inventory optimization, replenishment decision-making, and demand forecasting. Reported improvements were mainly associated with cost efficiency, service level, stockout reduction, and system performance; however, the magnitude of improvement varied across algorithms, data sources, sectors, and simulation or real-world settings. Overall, intelligent algorithms represent a relevant tool for improving inventory management, but their adoption requires careful validation, transparent reporting, and alignment with the operational context. Full article

Background: Early and comprehensive interventions are crucial for preventing substance use in adolescents. In school settings, such interventions can be easily integrated into the curricular and co-curricular activities with the active involvement of school administration, teachers, trained staff, parents, and community members. This review aimed to comprehensively examine preventive, protective, and corrective school-based intervention models for substance use to suggest an evidence-based and effective educational model for preventing substance use in children. Methods: In this scoping review, online databases, including SCOPUS and Web of Science, were searched to identify and collect articles evaluating the effectiveness of school-based prevention models. The PRISMA approach for scoping review (PRISMA-ScR) was employed to assess the scope of relevant literature and synthesise existing school-based intervention models. Two independent reviewers independently screened records (κ = 0.75). Due to heterogeneity, findings were synthesised narratively using a socio-ecological framework. Study quality was appraised using the Mixed Methods Appraisal Tool (MMAT). Results: Forty-three studies, mostly from the United States, were included in the review. All school-based interventions examined were preventive or protective in their approaches. There was no school-based intervention model that was corrective in its approach. Increased knowledge, negative attitudes towards substance use, and reduced substance use in the intervention groups were key outcomes. Effective interventions involved a multi-pronged approach wherein teacher training, student education, and the involvement of parents, senior citizens, police officials, experts, and the community were key components. Additionally, culturally tailored interventions have shown promising results. Conclusions: Among school children, tobacco and alcohol use are most prevalent, and schools can play a crucial role in addressing this issue. Apart from preventive and protective intervention models, the focus needs to shift towards developing integrated school-based corrective models to address the concerns of substance use among school students. Full article

In recent years, interest in machine learning applications has grown rapidly, particularly in the medical domain, where large amounts of data are available for training these models. This review focuses on the potential of machine learning for early diagnosis and prognosis of chronic kidney disease (CKD) by examining the most recent literature. Articles published from 2016 to 2025 were collected from online databases such as PubMed, Web of Science, and Embase. After abstract and full-text screening, 57 articles were included in the results section. Machine learning was applied to clinical and laboratory data, medical imaging, urine samples, retinal images, and at-home measurements to diagnose CKD and predict CKD progression and related complications. Although many studies reported high discriminatory performance, the evidence base was dominated by retrospective, single-center, and methodologically heterogeneous studies, with frequent high-risk-of-bias findings and limited external validation. Furthermore, most published models are not yet sufficiently validated for clinical deployment. Before these tools can be adopted in routine care, prospective, multicenter studies are required that report calibration and clinical utility, adhere to established reporting standards, and demonstrate added value over the current standard of care. Full article

Background: Artificial intelligence (AI) is increasingly proposed to augment early-stage assistance for survivors of sexual and gender-based violence (GBV), including intimate partner and domestic violence, across crisis hotlines, specialist services, digital reporting channels, legal support tools and healthcare pathways. However, the scope, maturity and evaluative strength of the peer-reviewed evidence remain uncertain. We aimed to map the application domains, evaluative maturity, and implementation and safety gaps of this evidence base. Methods: We conducted a scoping review reported according to the PRISMA Extension for Scoping Reviews (PRISMA-ScR), using a Population–Concept–Context framework focused on AI-enabled first-response and early support. Searches in Scopus, Web of Science Core Collection and PubMed were supplemented by targeted searches of IEEE Xplore and ACM Digital Library. Records were screened against predefined criteria, charted using a structured form and synthesised descriptively. Results: Original searches yielded 187 records and 21 included sources of evidence. The supplementary search identified 539 records/candidates; 27 full texts were assessed and 6 additional sources met eligibility criteria, yielding 27 included sources of evidence. Evidence covered survivor-facing conversational support; screening and triage in emergency and specialist services; social-triage and online disclosure models; survivor-informed help-seeking and chatbot design; legal/support routing; and enabling modalities such as speech-based approaches. Most sources reported technical performance, usability, acceptability or systems-audit findings, while no workflow-integrated evaluation was identified and survivor-centred effectiveness outcomes, service uptake and adverse-event monitoring were rarely reported. Conclusions: The evidence remains heterogeneous and early-stage, with limited support for service-integrated effectiveness or safety. Included sources more often assessed models, interfaces or prototypes than downstream pathway outcomes. The findings support cautious, pathway-aware interpretation and identify recurring concerns regarding escalation, accountability, equity, digital trace safety and human handover. The proposed practice considerations and outcome domains are author-informed priorities for future pilot and implementation studies, not validated guidelines. Full article

Background: Game-based learning (GBL) and active engagement strategies have shown promise in health professions education; however, their application in foundational basic science courses, which serve as critical academic gatekeepers in professional pharmacy programs, remains underexplored. Objective: This study evaluated the effect of a multimodal suite of faculty-developed, web-based GBL activities and active engagement strategies on academic performance, learning management system (LMS) engagement, and student perceptions in a required first-year (P1) pharmacy biochemistry course. Methods: A cross-sectional, descriptive study was conducted with 117 P1 pharmacy students. Game-based activities (including Jeopardy, Rapid Fire, and Crossword Puzzle games) were developed using WiscOnline and deployed via Blackboard alongside pre-class activities, a Workbook, and a Padlet discussion board. Engagement was measured via LMS access metrics, academic performance via examination scores, and perceptions via an end-of-semester anonymous Poll Everywhere survey. Results: The cohort achieved a mean examination score of 81.59%, with 83.8% earning a passing grade. A positive association was observed between game access frequency and grade group performance (R² = 0.8241), though individual-level correlations did not reach statistical significance. Student perceptions were overwhelmingly positive, with over 90% of respondents agreeing that GBL activities were enjoyable, facilitated learning, and provided valuable practice opportunities. Conclusions: Low-cost, faculty-developed GBL tools can be successfully integrated into foundational pharmacy science courses, yielding high engagement and positive student perceptions. These findings underscore the importance of extending active learning research to foundational basic science courses and offer a replicable model for health professions programs seeking to enhance student engagement at this critical curricular stage. Full article

Game-based learning (GBL) has gained widespread attention as an innovative pedagogical approach, yet its potential to enhance students’ metacognitive learning remains underexplored. Guided by self-regulated learning (SRL) theory, the review investigates how GBL design features, such as goal-setting, real-time feedback, progress visualization, and reflection tools, scaffold students’ planning, monitoring, and evaluation strategies. A systematic search across Web of Science, Scopus, and ProQuest identified the studies, which included data from physical classrooms, online learning environments, and mixed settings. This scoping review synthesizes evidence from 11 peer-reviewed studies conducted between 2015 and 2025 to evaluate the impact of GBL on metacognitive learning in primary and junior middle school contexts. Findings reveal that GBL effectively supports metacognitive learning through real-time feedback and progress indicators, though planning and evaluation scaffolds are less comprehensively addressed. Furthermore, digital trace data and behavioral logs are emerging as robust tools for assessing metacognitive processes, offering deeper insights than self-reports alone. However, the review identifies critical gaps, including insufficient focus on junior middle school students, limited representation of non-STEM disciplines, and uneven theoretical grounding across studies. The findings underscore the need for theory-driven design and balanced scaffolding to maximize GBL’s potential in fostering metacognitive competence. This study also provides practical insights for educators to foster students’ metacognitive learning by effectively integrating games into educational practices. Full article

The construction industry remains among the most hazardous, with workers in horizontal transportation infrastructure facing additional risks from dynamic work zones, live traffic exposure, and variable environmental conditions. Immersive technologies such as Virtual Reality (VR) and Augmented Reality (AR) offer new approaches to accident analysis and prevention, yet their applications toward improving occupational safety in transportation construction have not been comprehensively reviewed. This paper presents a systematic review of 54 studies published between 2016 and 2025 collected from two online databases (Transportation Research International Documentation and Web of Science). This review synthesizes how immersive technologies contribute to occupational risk assessment, safety training, and real-time hazard monitoring in the construction of roads, bridges, tunnels, and work zones. Each study is classified across two dimensions: the immersive medium (VR, AR, etc.) and the operational context within the construction lifecycle (onsite tools, offsite monitoring and planning, simulation-based analysis, and workforce education). This dual classification is the first to systematically map immersive technology applications for occupational safety, specifically within horizontal transportation infrastructure. The findings of this review demonstrate the unique use cases of each immersive medium, revealing that VR is primarily used for controlled experimentation and full-immersion remote analysis, whereas AR and handheld devices are preferred for field-deployed applications. Despite these promising capabilities, widespread adoption remains limited by hardware constraints, challenging field conditions, and organizational resistance. This suggests that future work should focus on safety systems tested in real-world settings and rigorously evaluated by domain experts to enable their integration into standard workplace risk management practices. Full article

This scoping review maps AI-based approaches used to infer or measure attention and emotion in technology-enhanced learning (TEL), with a particular focus on tertiary (higher) education and learning analytics-enabled digital environments supporting online and hybrid instruction. Although artificial intelligence (AI) promises personalized digital education, many systems still respond poorly to students’ attentional and emotional fluctuations. We therefore examined the extent to which the literature converges on jointly measuring attention and emotion through AI in educational contexts, especially in virtual and distance-learning settings. Following PRISMA-ScR, we searched Scopus and Web of Science and identified 39 eligible studies. We conducted a methodological quality appraisal using Joanna Briggs Institute tools, a keyword co-occurrence bibliometric analysis, and a narrative synthesis. The evidence shows a rapidly expanding field and a wide range of AI-based techniques, but emotion and attention are typically operationalized and modelled in isolation. Both the bibliometric and narrative results indicate persistent conceptual fragmentation and limitations in the validity of measurement metrics. Overall, the field has not yet established a unified paradigm that integrates attention and emotion within AI-driven educational systems, constraining their adaptive potential. This evidence highlights the need for theory-informed and operational frameworks that enable genuinely holistic, student-centred pedagogical adaptation. Full article

News data have become essential resources across various disciplines. Still, access to full-text news corpora remains challenging due to high costs and the limited availability of free alternatives. This paper presents a novel Python package (gdeltnews) that reconstructs full-text newspaper articles at near-zero cost by leveraging the Global Database of Events, Language, and Tone (GDELT) Web News NGrams 3.0 dataset. Our method merges overlapping n-grams extracted from global online news to rebuild complete articles. We validate the approach on a benchmark set of 2211 articles from major U.S. news outlets, achieving up to 95% text similarity against original articles based on Levenshtein and SequenceMatcher metrics. Our tool facilitates economic forecasting, computational social science, information science, and natural language processing applications by enabling free and large-scale access to full-text news data. Full article

Background: Developmental coordination Disorder (DCD) is a prevalent and persistent neurodevelopmental condition characterized by motor learning difficulties that significantly affect daily functioning and participation. Despite growing interest in artificial intelligence (AI) applications within healthcare, the extent and nature of AI use in the evaluation and intervention of DCD remain unclear. Objective: This scoping review aimed to systematically map the existing literature on the use of AI and AI-assisted approaches in the evaluation, screening, monitoring, and intervention of DCD, and to identify current trends, methodological characteristics, and gaps in the evidence base. Methods: A scoping review was conducted in accordance with the PRISMA extension for Scoping Reviews (PRISMA-ScR) guidelines and was registered on the Open Science Framework. Systematic searches were performed in Scopus, PubMed, Web of Science, and IEEE Xplore, supplemented by snowballing. Peer-reviewed studies applying AI methods to DCD-relevant populations were included. Data was extracted and charted to summarize study designs, populations, AI methods, data modalities, clinical purposes, outcomes, and reported limitations. Results: Seven studies published between 2021 and 2025 met the inclusion criteria following a literature search covering the period from January 2010 to 2025. One study listed as 2026 was included based on its early access online publication in 2025. Most studies focused on AI applications for assessment, screening, and classification, using supervised machine learning or deep learning models applied to movement-based data, wearable sensors, video recordings, neurophysiological signals, or electronic health records. Only one randomized controlled trial evaluated an AI-assisted intervention. The evidence base was dominated by early-phase development and validation studies, with limited external validation, heterogeneous diagnostic definitions, and scarce intervention-focused research. Conclusions: Current AI research in DCD is primarily centered on evaluation and early identification, with comparatively limited evidence supporting AI-assisted intervention or rehabilitation. While existing findings suggest that AI has the potential to enhance objectivity and sensitivity in DCD assessment, significant gaps remain in clinical translation, intervention development, and implementation. Future research should prioritize theory-informed, clinician-centered AI applications, including adaptive intervention systems and decision-support tools, to better support occupational therapy and physiotherapy practice in DCD care. Full article

The rapid digitalization of financial services in the European Union (EU) has not only enhanced convenience and inclusion but also increased exposure to sophisticated online financial fraud. Digital financial literacy (DFL) is widely promoted as a key tool for empowering consumers and reducing fraud victimization. However, the empirical and conceptual landscape linking DFL to fraud reduction within the specific sociolegal context of the EU remains fragmented. This study uses bibliometric analysis to map the research area, define major themes within the field, and determine the role of DFL in reducing online financial fraud in the EU. Peer-reviewed journal articles were targeted to ensure academic rigor, with a publication window of 2010–2025 reflecting key fintech and regulatory developments. After adhering to PRISMA principles, 87 peer-reviewed publications were chosen out of a total of 568 records identified through OpenAlex and Web of Science, coauthorship, keyword co-occurrence, citation, temporal, and density representations were analyzed using VOSviewer. Findings indicate an increasingly diffuse research field with new clusters concentrating on macroeconomic policy, business technology, social psychology, and interdisciplinary foundations. Results demonstrate that successful implementation of DFL interventions combines behavioral insights, technological protection, and non-discriminatory policy considerations. The study concludes by identifying major gaps in research and providing a path forward for future evidence-based policy efforts toward enhancing consumer protection in the EU digital financial market. Full article

Background/Objectives: Caregivers of infants with congenital muscular torticollis (CMT) frequently seek information online, although the accuracy, clarity, and safety of web-based content remain variable. As large language models (LLMs) are increasingly used as health information tools, their reliability for caregiver education requires systematic evaluation. This study aimed to assess the reproducibility and quality of ChatGPT-5.1 responses to caregiver-centered questions regarding CMT. Methods: A set of 17 questions was developed through a Delphi process involving clinicians and caregivers to ensure relevance and comprehensiveness. ChatGPT generated responses in two independent sessions. Reproducibility was assessed using TF–IDF cosine similarity and embedding-based semantic similarity. Ten clinical experts evaluated each response for accuracy, readability, safety, and overall quality using a 4-point Likert scale. Results: ChatGPT demonstrated moderate lexical consistency (mean TF–IDF similarity 0.75) and high semantic stability (mean embedding similarity 0.92). Expert ratings indicated moderate to good performance across domains, with mean scores of 3.0 for accuracy, 3.6 for readability, 3.1 for safety, and 3.1 for overall quality. However, several responses exhibited deficiencies, particularly due to omission of key cautions, oversimplification, or insufficient clinical detail. Conclusions: While ChatGPT provides fluent and generally accurate information about CMT, the observed variability across topics underscores the importance of human oversight and content refinement prior to integration into caregiver-facing educational materials. Full article

Adaptive forest and fire management in parks and protected areas is becoming increasingly complex as climate change alters the frequency and intensity of disturbances (wildfires, pest and disease outbreaks, etc.), while park visitation and the number of people living adjacent to publicly managed lands continues to increase. Evidence-based, climate-adaptive forest and fire management practices are critical for the responsible stewardship of public resources and require the continued availability of long-term ecological monitoring data. The US National Park Service has been collecting long-term fire monitoring plot data since 1998, and has continued to add monitoring plots, but these data are housed in databases with limited access and minimal analytic capabilities. To improve the availability and decision support capabilities of this monitoring dataset, we created the Trends in Forest Fuels Dashboard (TFFD), which provides an implementation framework from data collection to web visualization. This easy-to-use and updatable tool incorporates data from multiple years, plot types, and locations. We demonstrate our approach at Rocky Mountain National Park using the ArcGIS Online (AGOL) software platform, which hosts TFFD and allows for efficient data visualizations and analyses customized for the end user. Adopting interactive, web-hosted tools such as TFFD allows the National Park Service to more readily leverage insights from long-term forest monitoring data to support decision making and resource allocation in the context of environmental change. Our approach translates to other data-to-decision workflows where customized visualizations are often the final steps in a pipeline designed to increase the utility and value of collected data and allow easier integration into reporting and decision making. This work provides a template for similar efforts by offering a roadmap for addressing data availability, cleaning, storage, and interactivity that may be adapted or scaled to meet a variety of organizational and management use cases. Full article

Assessment practices are central to higher education, particularly critical in theory-based programs, where they facilitate the development of conceptual understanding and higher-order cognitive skills. They also support Saudi Arabia’s Vision 2030 agenda, which aims to drive educational innovation. This narrative review examines assessment practices in theory-based programs at a Saudi public university, identifies discrepancies with learning objectives, and proposes potential solutions. A narrative review synthesised peer-reviewed literature (2015–2025) from Scopus, Web of Science, ERIC, and Google Scholar, focusing on traditional and alternative assessments, barriers, progress, and comparisons with international standards. The review found that traditional summative methods (quizzes, final exams) still dominate and emphasise memorisation, limiting the development of higher-order skills. Emerging techniques, such as projects, portfolios, oral presentations, and peer assessment, are gaining traction but face institutional constraints and resistance from faculty. Digital adoption is growing: 63% of students are satisfied with learning management system tools, and 75% find online materials easy to understand; yet, advanced analytics and AI-based assessments are rare. A comparative analysis reveals that international standards favour formative feedback, adaptive technologies, and holistic competencies. The misalignment between current practices and Vision 2030 highlights the need to broaden assessment portfolios, integrate technology, and provide faculty training. Saudi theory-based programs must transition from memory-oriented evaluations to student-centred, evidence-based assessments that foster critical thinking and real-world application. Adopt diverse assessments (projects, portfolios, peer reviews), invest in digital analytics and adaptive learning, align assessments with learning outcomes and Vision 2030 competencies, and implement ongoing faculty development. The study offers practical pathways for reform and highlights strategic opportunities for achieving Saudi Arabia’s national learning outcomes. Full article

Background: Plant-based diets are associated with reduced risk of chronic diseases and improved health outcomes. However, sustaining dietary changes remains challenging. Digital interventions—including virtual culinary medicine programs, web-based nutrition coaching, SMS and email reminders, mobile application–based self-management, and hybrid community programs—offer promising strategies to support behavior change, enhance cooking skills, and improve dietary adherence. These approaches are relevant for both healthy individuals and those living with chronic conditions. Methods: We conducted a narrative review of studies published between 2000 and 2025 in PubMed/MEDLINE, Scopus, and Web of Science, supplemented with manual searches. Included studies comprised randomized controlled trials, quasi-experimental designs, feasibility studies, and qualitative research. Interventions were categorized by modality (SMS, email, web platforms, mobile apps, virtual culinary programs, and hybrid formats) and population (healthy adults, patients with chronic diseases). Outcomes examined included dietary quality, self-efficacy, psychosocial well-being, and program engagement. Results: Most studies reported improvements in dietary quality, cooking skills, nutrition knowledge, and psychosocial outcomes. Virtual cooking programs enhanced dietary adherence and engagement, particularly among individuals at cardiovascular risk. Digital nutrition education supported behavior change in chronic disease populations, including patients with multiple sclerosis. SMS and email reminders improved self-monitoring and participation rates, while mobile applications facilitated real-time feedback and goal tracking. Hybrid programs combining online and in-person components increased motivation, social support, and long-term adherence. Reported barriers included limited technological access or skills, lack of personalization, and privacy concerns. Conclusions: Virtual culinary medicine programs and other digital microinterventions—including SMS, email, web, mobile, and hybrid formats—are effective tools to promote plant-based diets. Future interventions should focus on personalized, accessible, and hybrid strategies, with attention to underserved populations, to maximize engagement and sustain long-term dietary change. Full article