Search Results (5,200)

Service learning (SL) is at a pivotal moment as education systems worldwide confront the challenges and opportunities posed by artificial intelligence (AI) and digital technologies. This scoping review synthesizes regional perspectives on SL and examines the barriers to its implementation in higher education. This study adopts a methodological approach widely used in prior educational research, enriched with selected PRISMA processes, namely identification, screening, and eligibility, to enhance its transparency and rigor. A total of 101 peer-reviewed articles were analyzed, using a mixed methods approach. Results are presented for six regions, Africa, Asia, Latin America, Europe, North America, and Oceania, revealing context-specific constraints, such as technological infrastructure, policy frameworks, linguistic diversity, and socio-economic disparities. Common barriers across regions include limited faculty training, insufficient institutional support, and misalignment with community needs. AI is explored as a potential enabler of SL, not as an empirical outcome, but as part of a reasoned argument emerging from the documented complexity of SL implementation in the literature. Ethical considerations, including algorithmic bias, equitable access, and the preservation of human agency, are addressed, alongside mitigation strategies that are grounded in participatory design and community engagement. This review offers a comparative, context-sensitive understanding of SL implementation challenges, providing actionable insights for educators, policymakers, and researchers, aiming to integrate technology-enhanced solutions responsibly. Full article

Landslides pose persistent threats to mountainous regions in Taiwan, particularly in areas such as Nanfeng Village, Nantou County, where steep terrain and concentrated rainfall contribute to chronic slope instability. This study investigates spatiotemporal patterns of vegetation change as a proxy for identifying potential landslide-prone zones, with a focus on the Tung-An tribal settlement in the eastern part of the village. Using high-resolution satellite imagery from SPOT 6/7 (2013–2023) and Pléiades (2019–2023), we derived annual NDVI layers to monitor vegetation dynamics across the landscape. Long-term vegetation trends were evaluated using the Mann–Kendall test, while spatiotemporal clustering was assessed through Emerging Hot Spot Analysis (EHSA) based on the Getis-Ord Gi* statistic within a space-time cube framework. The results revealed statistically significant NDVI increases in many valley-bottom and mid-slope regions, particularly where natural regeneration or reduced disturbance occurred. However, other valley-bottom zones—especially those affected by recurring debris flows—still exhibited declining or persistently low vegetation. In contrast, persistent low or declining NDVI values were observed along steep slopes and debris-flow-prone channels, such as the Nanshan and Mei Creeks. These zones consistently overlapped with known landslide paths and cold spot clusters, confirming their ecological vulnerability and geomorphic risk. This study demonstrates that integrating NDVI trend analysis with spatiotemporal hot spot classification provides a robust, scalable approach for identifying slope hazard areas in data-scarce mountainous regions. The methodology offers practical insights for ecological monitoring, early warning systems, and disaster risk management in Taiwan and other typhoon-affected environments. By highlighting specific locations where vegetation decline aligns with landslide risk, the findings can guide local authorities in prioritizing slope stabilization, habitat conservation, and land-use planning. Such targeted actions support the Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land), by reducing disaster risk, enhancing community resilience, and promoting the long-term sustainability of mountain ecosystems. Full article

Ultrawideband (UWB) radar has emerged as a compelling solution for noncontact human activity recognition. This study proposes a novel framework that leverages adaptive signal decomposition and video-based deep learning to classify human motions with high accuracy using a single UWB radar. The raw radar signals were processed by empirical wavelet transform (EWT) to isolate the dominant frequency components in a data-driven manner. These components were further analyzed using the Hilbert transform to produce time–frequency spectra that capture motion-specific signatures through subtle phase variations. Instead of treating each spectrum as an isolated image, the resulting sequence was organized into a temporally coherent video, capturing spatial and temporal motion dynamics. The video data were used to train the SlowFast network—a dual-path deep learning model optimized for video-based action recognition. The proposed system achieved an average classification accuracy exceeding 99% across five representative human actions. The experimental results confirmed that the EWT–Hilbert-based preprocessing enhanced feature distinctiveness, while the SlowFast architecture enabled efficient and accurate learning of motion patterns. The proposed framework is intuitive, computationally efficient, and scalable, demonstrating strong potential for deployment in real-world scenarios such as smart healthcare, ambient-assisted living, and privacy-sensitive surveillance environments. Full article

Lactylation, a recently identified post-translational modification (PTM) triggered by excessive lactate accumulation, has emerged as a crucial regulator linking metabolic reprogramming to pathological processes in liver diseases. In hepatic contexts, aberrant lactylation contributes to a range of pathological processes, including inflammation, dysregulation of lipid metabolism, angiogenesis, and fibrosis. Importantly, lactylation has been shown to impact tumor growth, metastasis, and therapy resistance by modulating oncogene expression, metabolic adaptation, stemness, angiogenesis, and altering the tumor microenvironment (TME). This review synthesizes current knowledge on the biochemical mechanisms of lactylation, encompassing both enzymatic and non-enzymatic pathways, and its roles in specific liver diseases. From a therapeutic perspective, targeting lactate availability and transport, as well as the enzymes regulating lactylation, has demonstrated promise in preclinical models. Additionally, combinatorial approaches and natural compounds have shown efficacy in disrupting lactylation-driven pathways, providing insights into future research directions for hepatic diseases. Although the emerging role of lactylation is gaining attention, its spatiotemporal dynamics and potential for clinical translation are not yet well comprehended. This review aims to synthesize the multifaceted roles of lactylation, thereby bridging mechanistic insights with actionable therapeutic strategies for liver diseases. Full article

Accurate maize yield forecasting under climate uncertainty remains a critical challenge for global food security, yet existing studies predominantly rely on single-model frameworks, limiting generalizability and actionable insights. This study selected three regions, specifically Dali, Lijiang, and Zhaotong, and collected data on 12 agronomic traits of 114 varieties, along with eight sets of meteorological data, covering the period from 2019 to 2023. We employed three machine learning models: Random Forest (RF), Support Vector Machine (SVM), and XGBoost. The results revealed a strong correlation between yield and multiple agronomic traits, particularly grain weight per spike (GWPS) and hundred-kernel weight (HKW). Notably, the XGBoost model emerged as the top performer across all three regions. The model achieved the lowest RMSE (0.22–191.13) and a good R² (0.98–0.99), demonstrating exceptional predictive accuracy for yield-related traits. The comparative analysis revealed that XGBoost exhibited superior accuracy and stability compared to RF and SVM. Through feature importance analysis, four critical determinants of yield were identified: GWPS, shelling percentage (SP), growth period (GP), and plant height (PH). Furthermore, partial dependence plots (PDPs) provided deeper insights into the nonlinear interactive effects between GWPS, SP, GP, PH, and yield, offering a more comprehensive understanding of their complex relationships. This study presents an innovative, data-driven methodology designed to accurately forecast corn yield across diverse locations. This approach offers valuable scientific insights that can significantly enhance precision agricultural practices by enabling the precise tailoring of fertilizer usage and irrigation strategies. The results highlight the importance of integrating agronomic and meteorological data in yield forecasting, paving the way for development of agricultural decision-support systems in the context of future climate change scenarios. This study presents an innovative, data-driven methodology designed to accurately forecast corn yield across diverse locations. This approach offers valuable scientific insights that can significantly enhance precision agricultural practices by enabling the precise tailoring of fertilizer usage and irrigation strategies. Full article

Sustainability performance varies significantly across countries, yet global assessments overlook the underlying structural trends. This study bridges this gap using machine learning to uncover meaningful clustering in global sustainability outcomes based on the 2025 Sustainable Development Goals (SDG) Index. We applied K-Means clustering to group 166 countries into five standardized indicators: SDG score, spillover effects, regional score, population size, and recent progress. The five-cluster solution was confirmed by the Elbow and Silhouette procedures, with ANOVA and MANOVA tests subsequently indicating statistically significant cluster differences. For the validation and interpretation of the results, six supervised learning algorithms were employed. Random Forest, SVM, and ANN performed best in classification accuracy (97.7%) with perfect ROC-AUC scores (AUC = 1.0). Feature importance analysis showed that SDG and regional scores were most predictive of cluster membership, while population size was the least. This supervised–unsupervised hybrid approach offers a reproducible blueprint for cross-country benchmarking of sustainability. It also offers actionable insights for tailoring policy to groups of countries, whether high-income OECD nations, emerging markets, or resource-scarce countries. Our findings demonstrate that machine learning is a useful tool for revealing structural disparities in sustainability and informing cluster-specific policy interventions toward the 2030 Agenda. Full article

Drug-coated balloons (DCBs) have transformed percutaneous coronary intervention (PCI) by delivering antiproliferative drugs directly to the arterial wall, offering a stent-less approach that mitigates the risks associated with permanent metallic implants. Initially developed for in-stent restenosis (ISR), DCBs have demonstrated robust efficacy in reducing neointimal hyperplasia and target lesion revascularization (TLR) rates across diverse coronary lesions, including small vessel disease (SVD), de novo lesions, and complex anatomies such as bifurcation lesions. Paclitaxel-coated balloons have long been the cornerstone of DCB therapy due to their established clinical outcomes, but sirolimus-coated balloons are emerging as a promising alternative with potentially superior safety profiles and sustained drug release. The pharmacological mechanism of DCBs relies on rapid drug transfer during brief balloon inflation, achieving high local concentrations without residual foreign material. Landmark trials, such as BASKET-SMALL 2, RESTORE SVD, and AGENT IDE, have demonstrated comparable or non-inferior outcomes of DCBs versus drug-eluting stents (DESs) in specific settings, with lower rates of stent thrombosis and shorter dual antiplatelet therapy (DAPT) requirements. Despite these advances, challenges persist, including optimizing drug formulations, ensuring uniform delivery, and addressing calcified lesions. Ongoing research into novel coatings, dual–drug systems, and artificial intelligence (AI)-guided interventions is poised to redefine PCI strategies. This review provides a comprehensive analysis of drug-coated balloon (DCB) angioplasty, not limited to specific clinical scenarios such as in-stent restenosis, small vessel disease, or bifurcation lesions, highlighting their transformative role in coronary artery disease (CAD) management. Instead, it addresses the full spectrum of pharmacological principles, mechanisms of action, clinical indications, comparative efficacy across various coronary artery disease contexts, and future directions of DCBs. Full article

The construction sector, facing a persistent productivity gap compared to other industries, is hindered by fragmented value streams, inconsistent performance metrics, and the limited scalability of process improvements. We introduce a pioneering, four-tiered hierarchical productivity framework to respond to these challenges. This innovative approach integrates operational, tactical, strategic, and normative layers. At its core, the framework applies standardised, repeatable process steps—mapped using Value Stream Mapping (VSM)—to capture key indicators such as input efficiency, output effectiveness, and First-Time Quality (FTQ). These are then aggregated through takt time compliance, schedule reliability, and workload balance to evaluate trade synchronisation and flow stability. Higher-level metrics—flow efficiency, multi-resource utilisation, and ESG-linked performance—are integrated into an Overall Productivity Index (OPI). Building on a modular production model, the proposed framework supports real-time sensing, AI-driven monitoring, and intelligent process control, as demonstrated through an empirical case study of continuous process monitoring for Kelly drilling operations. This validation illustrates how sensor-equipped machinery and machine learning algorithms can automate data capture, map observed activities to standardised process steps, and detect productivity deviations in situ. This paper contributes to a multi-scalar measurement architecture that links micro-level execution with macro-level decision-making. It provides a foundation for real-time monitoring, performance-based coordination, and data-driven innovation. The framework is applicable across modular construction, digital twins, and platform-based delivery models, offering benefits beyond specialised foundation work to all construction trades. Grounded in over a century of productivity research, the approach demonstrates how emerging technologies can deliver measurable and scalable improvements. Framing productivity as an integrative, actionable metric enables sector-wide performance gains. The framework supports construction firms, technology providers, and policymakers in advancing robust, outcome-oriented innovation strategies. Full article

Atopic dermatitis (AD) is a chronic, relapsing inflammatory skin disorder characterized by immune dysregulation and epidermal barrier dysfunction. Advances in understanding the interplay of genetic predisposition, cytokine signaling, and environmental triggers have led to the emergence of targeted therapies. Although biologic agents such as dupilumab, tralokinumab, and lebrikizumab have revolutionized AD management, their high costs, injectable administration, and limited global accessibility highlight the need for alternative options. Small molecule therapies are gaining momentum as they target intracellular pathways central to AD pathogenesis and offer oral or topical administration routes. This review provides a comprehensive analysis of key agents including Janus kinase (JAK) inhibitors (upadacitinib, abrocitinib, baricitinib, ruxolitinib, delgocitinib), phosphodiesterase 4 (PDE4) inhibitors (crisaborole, difamilast, roflumilast, apremilast), as well as STAT6 degraders (KT621, NX3911), aryl hydrocarbon receptor modulators, histamine H4 receptor antagonists (adriforant, izuforant), and sphingosine-1-phosphate receptor modulators (etrasimod, BMS-986166). We summarize their mechanisms of action, pharmacological profiles, and pivotal clinical trial data, emphasizing their potential to address unmet therapeutic needs. Finally, we discuss safety concerns, long-term tolerability, and future directions for integrating small molecule therapies into precision treatment strategies for moderate-to-severe AD. Full article

Pale lager dominates global beer markets. However, rising living standards and changing consumer expectations have reshaped sensory preferences, highlighting the importance of understanding consumers’ true sensory priorities. In this study, a twenty-eight-item questionnaire, refined through multiple rounds of optimization, was distributed across China and yielded 1837 valid responses. Spearman correlation analysis and partial least-squares regressions showed that educational background and spending willingness exerted the strongest independent effects on sensory priorities. A hybrid analytic hierarchy process–entropy weight method–Delphi procedure was then applied to quantify sensory attribute importance. Results indicated that drinking sensation (30.92%) emerged as the leading driver of pale lager choice, followed by taste (26.60%), aroma (24.77%), and appearance (17.71%), confirming a flavor-led and experience-oriented preference structure. Weighting patterns differed across drinking-frequency cohorts: consumers moved from reliance on overall mouthfeel, through heightened sensitivity to negative attributes, to an eventual focus on subtle hedonic details. Based on these findings, a new sensory evaluation scale was developed and validated against consumer preference rankings, showing significantly stronger alignment with consumer preferences (ρ = 0.800; τ = 0.667) than the traditional scale. The findings supply actionable metrics and decision tools for breweries, supporting applications in product development, quality monitoring, and targeted marketing. Full article

Large language models (LLMs) rapidly transform healthcare by automating tasks, streamlining administration, and enhancing clinical decision support. This rapid review assesses current and emerging applications of LLMs in diagnostic-related group (DRG) assignment and clinical decision support systems (CDSS), with emphasis on radiology and nuclear medicine. Evidence shows that LLMs, particularly those tailored for medical domains, improve efficiency and accuracy in DRG coding and radiology report generation, providing clinicians with actionable, context-sensitive insights by integrating diverse data sources. Advances like retrieval-augmented generation and multimodal architecture further increase reliability and minimize incorrect or misleading results that AI models generate, a term that is known as hallucination. Despite these benefits, challenges remain regarding safety, explainability, bias, and regulatory compliance, necessitating ongoing validation and oversight. The review prioritizes recent, peer-reviewed literature on radiology and nuclear medicine to provide a practical synthesis for clinicians, administrators, and researchers. While LLMs show strong promise for enhancing DRG assignment and radiological decision-making, their integration into clinical workflows requires careful management. Ongoing technological advances and emerging evidence may quickly change the landscape, so findings should be interpreted in context. This review offers a timely overview of the evolving role of LLMs while recognizing the need for continuous re-evaluation. Full article

In response to increasing global sustainability demands, this study examines how green dynamic capabilities influence business performance in Bali Island’s tour and travel SMEs, with green service innovation as a mediating mechanism. Drawing on the resource-based view (RBV) and dynamic capability theory, the research adopts a quantitative approach using survey data from 387 SMEs and employs structural equation modeling (SEM) to analyze the relationships among green dynamic capabilities, green service innovation, and business performance. Findings reveal that green dynamic capabilities significantly enhance both green service innovation and business performance. Notably, green service innovation partially mediates this relationship, underscoring its pivotal role in transforming internal sustainability-oriented capabilities into tangible performance outcomes. The key contribution of this study lies in extending RBV by integrating green service innovation as a strategic conduit that links eco-centric capabilities to competitive advantage in a tourism SME context—a perspective that remains underexplored in emerging economies. Practically, the study provides actionable insights for SME owners and policymakers to prioritize innovation in service design and delivery as a pathway to sustainable tourism performance. Full article

The Encyclopaedia Cloacae is a novel and centralised digital platform designed to support and advance wastewater-based epidemiology (WBE) by cataloguing pathogens detectable in wastewater and their relevance to public health surveillance. The platform is hosted on the EU Wastewater Observatory for Public Health (EU4S) website, where it is populated with peer-reviewed research through a structured workflow under harmonised criteria which address the presence of pathogens in human excreta, detectability in wastewater, and integration into public health systems. This tri-criteria approach ensures that the database is both scientifically robust and operationally actionable. Complemented by the Visualising the Invisible dashboard, the platform offers geospatial insights into global WBE research activity. By consolidating peer-reviewed evidence on pathogen detectability in wastewater and human excreta, the Encyclopaedia Cloacae enables early detection of infectious diseases, whether already known or newly emerging. The continuously updated repository and geospatial dashboards help to identify surveillance gaps and research hotspots, to support timely public health responses, enhance pandemic preparedness, and strengthen global health security. In addition, it supports One Health strategies, connecting the health of humans, animals, and the shared environment. This article outlines the platform’s architecture, data curation methodology, and future directions, including automation and expansion to encompass broader health determinants such as antimicrobial resistance and chemical hazards. Full article

Over the past two decades, patient safety and clinical risk management have become strategic priorities for healthcare systems worldwide. In this context, the London Protocol has emerged as one of the most influential methodologies for investigating adverse events through a systemic, non-punitive lens. The 2024 edition, curated by Vincent, Adams, Bellandi, and colleagues, represents a significant evolution of the original 2004 framework. It integrates recent advancements in safety science, human factors, and digital health, while placing a stronger emphasis on resilience, proactive learning, and stakeholder engagement. This article critically examines the structure, key principles, and innovations of the London Protocol 2024, highlighting its departure from incident-centered analysis toward a broader understanding of both failures and successes. The protocol encourages fewer but more in-depth investigations, producing actionable and sustainable recommendations rather than generic reports. It also underscores the importance of involving patients and families as active partners in safety processes, recognizing their unique perspectives on communication, care pathways, and system failures. Beyond its strengths—holistic analysis, multidisciplinary collaboration, and cultural openness—the systemic approach presents challenges, including methodological complexity, resource requirements, and cultural resistance in blame-oriented environments. This paper discusses these limitations and explores how leadership, staff engagement, and digital technologies (including artificial intelligence) can help overcome them. Ultimately, the London Protocol 2024 emerges not only as a methodological tool but as a catalyst for cultural transformation, fostering healthcare systems that are safer, more resilient, and committed to continuous learning. Full article

The Multi-load Automated Guided Vehicle (M-AGV) has emerged as a key enabling technology for intelligent and sustainable workshop logistics owing to its potential to enhance transportation efficiency and reduce system costs. To address the limitations in energy optimization caused by simplified AGV speed and payload modeling in existing scheduling models, this study develops a multi-factor coupled energy consumption model—integrating vehicle speed, travel distance, and dynamic payload—to minimize the total energy consumption of M-AGV systems. To effectively solve the model, a hybrid optimization algorithm that combines the State–Action–Reward–State–Action (SARSA) learning algorithm with the Triangulation Topology Aggregation Optimizer (TTAO), complemented by a similarity-based individual generation strategy, is designed to jointly enhance the algorithm’s exploration and exploitation capabilities. Comparative experiments were conducted across task scenarios involving three different handling task scales and three levels of M-AGV fleet heterogeneity, demonstrating that the proposed SARSA-TTAO algorithm outperforms Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the Hybrid Genetic Algorithm with Large Neighborhood Search (GA-LNS) in terms of solution accuracy and convergence performance. The study also reveals the differences between homogeneous and heterogeneous M-AGV fleets in task allocation and resource utilization under energy-optimal conditions. Full article