Search Results (18,884)

Wildfires are a growing threat in Mediterranean regions where climate variability and land-use practices increase vulnerability to fire risk. Developing effective prediction models is essential for robust wildfire management, particularly in such data-scarce environments. Focusing on data-scarce Mediterranean environments, with reference to environmental conditions observed in Morocco, this review presents prediction models across three methodological categories: spatial risk mapping, temporal forecasting, and fire spread simulation, alongside the satellite data products that support their deployment. Each category is assessed in terms of predictive performance, data requirements, and adaptability to low-resource environments. XGBoost showed strong applicability in data-scarce Mediterranean contexts, while ARIMA was validated for forecasting fire-relevant time series under limited data resources. Freely accessible MODIS-derived products represent a significant asset to the region. Based on this synthesis, a hybrid XGBoost-ARIMA framework incorporating MODIS-derived inputs and SHAP-based interpretability is proposed as a promising candidate architecture to be validated after further investigation. The findings aim to support researchers, land managers, and policymakers in strengthening local wildfire prevention and mitigation efforts by aligning model capabilities with regional data and environmental constraints. Full article

With global warming continuously worsening drought hazards, the Fuhe River Basin urgently requires insight into drought evolution laws to support resilient water resources management. However, traditional univariate indices such as the Standardized Precipitation Index (SPI) and Standardized Soil Moisture Index (SSI) are limited by their inability to capture the coupled meteorological-agricultural drought process and the time-lag effects between precipitation and soil moisture response. Therefore, a multivariate drought index—which integrates both precipitation and soil moisture information—is needed as a core tool for drought early warning and precise regulation. In this study, the calibrated SWAT model was used to simulate monthly soil moisture content in the Fuhe River Basin over the past 60 years. On a 3-month time scale, a Multivariate Standardized Drought Index (MSDI) was established by coupling the Standardized Precipitation Index (SPI) and Standardized Soil Moisture Index (SSI) using the Copula function. The main findings are as follows: (1) The Nash–Sutcliffe efficiency coefficient (NS) of the SWAT (Soil and Water Assessment Tool) model during the validation period reached above 0.70, indicating favorable performance in monthly runoff simulation. (2) The MSDI revealed frequent drought events in two periods, namely 1960–1979 and 2000–2019, demonstrating the periodic fluctuation pattern of droughts in the basin. (3) Wavelet analysis showed that compared with the previous two periods, the frequency of droughts in the basin increased significantly after 2000, with weakened periodic characteristics, intensified extreme drought events, and a further rise in drought risks. This study deepens the understanding of drought dynamics in the Fuhe River Basin and provides a scientific basis for regional sustainable water resource management and the formulation of climate adaptation strategies. Full article

The study introduces a portable sliding sand sieve, transforming traditional stationary systems into an innovative solution for sand separation in the construction industry. This innovative tool offers improved mobility, durability, and operational efficiency, particularly for construction workers, civil technology students, and educators in areas with limited access to advanced equipment. Utilizing a developmental research design, the study involved the conceptualization, fabrication, and evaluation of the prototype. The design incorporated locally available materials, including phenolic boards, mesh screens, steel tubing, and a sliding mechanism supported by bearings and brackets. The Input–Process–Output (IPO) model guided the development, ensuring focus on functionality, affordability, and user safety. To address this gap, the researchers aimed to design, develop, and evaluate a portable sliding sand sieve to enhance sand sieving in construction settings. Expert and student evaluators highly rated the portable sliding sand sieve for its design simplicity, functionality, durability, modularity, and ergonomics. It was praised for its ease of use, time-saving capability, and adaptability to various work environments. The sliding feature enabled continuous sand flow, enhancing productivity and reducing physical strain. Full article

The escalating global threat of antimicrobial resistance (AMR) has intensified efforts to identify safe, effective, and sustainable alternatives to in-feed antibiotics in livestock production. The bovine gastrointestinal microbiome plays a central role in host immunity, nutrient utilization, and disease resilience, positioning microbiome-modulating interventions as promising candidates for antimicrobial stewardship. Despite growing experimental interest, a systematic synthesis of the available evidence in cattle is lacking. This systematic review aimed to evaluate the efficacy of microbiome-modulating interventions, including probiotics, prebiotics, postbiotics, phytogenic feed additives, essential oils, organic acids, and native rumen microbial supplements, as strategies to reduce antimicrobial use in cattle, and to characterize their effects on gut microbial diversity, fermentation characteristics, and host health and performance outcomes. A systematic search of Scopus, Web of Science, and EBSCOhost (including Academic Search Ultimate, MEDLINE with full text, and CAB Abstracts with Full text) was conducted in accordance with PRISMA guidelines. Studies were eligible if they used cattle (dairy cattle, beef cattle, calves, or mixed production systems), employed a microbiome-modulating intervention, and reported at least one microbiological or host outcome. Seventeen peer-reviewed studies published between 2010 and 2025 were included after full-text screening. Risk of bias was assessed using an adapted SYRCLE tool, which identified moderate overall study quality; the majority of included studies were randomized controlled trials or controlled experiments, though reporting of allocation concealment and blinding was inconsistent across studies. Across the 17 included studies, five broad categories of interventions were evaluated: probiotics (n = 5 studies), prebiotics (n = 2), postbiotics and organic acids (n = 4), phytogenic additives and essential oils (n = 4), and native rumen microbial supplements (n = 2). Animals spanned neonatal dairy calves, weaned Holstein calves, dairy heifers, lactating dairy cows, and Bos indicus feedlot beef cattle. Probiotics and organic acids most consistently improved growth performance: benzoic acid supplementation increased average daily gain by 8.4% (p < 0.05) and fructo-oligosaccharide prebiotics elevated body weight at weaning by 6.7% (p < 0.01). Native rumen microbial supplements improved energy-corrected milk yield by up to 3.1% without increasing dry matter intake. Polyphenols and bile acids demonstrated the strongest immunological and disease-preventive effects, reducing calf mortality by approximately 40% and disease severity by approximately 35%, respectively. Microbiome analyses revealed intervention-dependent increases in microbial diversity and shifts toward taxa associated with improved fermentation efficiency, including enrichment of propionate-producing Prevotellaceae, butyrate-associated Ruminococcus, and hindgut Bifidobacterium. Rumen fermentation outcomes included reductions in the acetate:propionate ratio and ammonia-N concentrations and improvements in fiber digestibility of 3.6–4.4 percentage units in dairy cows. Phytogenic additives preserved microbial diversity without inducing broad-spectrum suppression, functioning primarily as microbiome stabilizers rather than direct antimicrobial replacements. This systematic review provides evidence that gut microbiome modulation may enhance growth performance, improve fermentation efficiency, and reduce disease susceptibility in cattle, thereby supporting antimicrobial use reduction across dairy, beef, and mixed production systems. Effect magnitudes varied substantially across intervention categories and production contexts, and study quality was moderate, underscoring the need for larger, pre-registered trials with standardized outcome reporting and direct antibiotic comparator arms. Probiotics, prebiotics, and bile acid metabolites showed the greatest potential as components of integrated antimicrobial stewardship strategies in cattle production. Full article

Rapid identification of maize waterlogging is essential for post-disaster agricultural assessment, but most existing methods rely on multi-temporal imagery that is often unavailable immediately after extreme rainfall events. This study proposes SAB-DeepLabV3+, a semantic segmentation model for mapping waterlogging-affected maize from single-date multispectral imagery within pre-extracted maize planting areas. Built on DeepLabV3+, the model integrates three task-specific modules: a Spectral-Spatial Information Enhancement Module to improve feature discrimination under spectral mixing, an Adaptive Multi-Scale Pooling Module to capture heterogeneous patch sizes, and a Boundary Enhancement Module to refine transition zones. A pixel-level dataset containing 12,198 image patches was constructed from 62 multispectral scenes collected across five major maize-producing cities in Heilongjiang Province, China, during 2022–2024. On the test set, SAB-DeepLabV3+ achieved a waterlogged-class IoU of 68.30%, mIoU of 80.37%, mF1 of 88.62%, and OA of 93.49%, outperforming DeepLabV3+. Leave-one-city-out evaluation further produced an average mIoU of 76.56% and a waterlogged-class IoU of 63.45%. These results indicate that single-date high-resolution multispectral imagery can support rapid and reliable maize waterlogging mapping. Full article

The growing complexity of modern systems has pushed safety science beyond tradition-al analysis methods. In a world where the unknown matters as much as the known, knowledge graphs emerge as a powerful means for representing, connecting, and extending knowledge. However, the intersection between safety science and knowledge graphs remains largely unexplored. Which communities of researchers are leveraging knowledge graphs for safety? Is there any common pattern in how they are being used? This paper addresses these questions by presenting a systematic review of the literature on the use of knowledge graphs in the context of safety. Based on 173 eligible documents, we propose a classification framework structured around three dimensions: the originality of knowledge characterization, the originality of knowledge extraction, and the maturity of safety analysis. The framework identifies three archetypes of knowledge graph users: Assemblers, who rely on existing models and tools; Alchemists, who adapt available knowledge structures or extraction procedures; and Shapers, who develop novel ontologies, extraction methods, or both. The obtained results show how the latter represents the largest group among the reviewed studies, suggesting a tension between analytical maturity and the need for customized solutions. More broadly, the classification framework presented in this review may support researchers from both the safety and the artificial intelligence communities in fostering a shared path for the scientific development of these disciplines. Full article

Accurately tracking the spatial and temporal variations of water vapor is indispensable for weather forecasting and climate adaptation, yet remains challenging due to the sparse coverage and discontinuity of ground-based observations. Satellite remote sensing, particularly from geostationary satellites like Meteosat Third Generation Imager-1 (MTG-I1), offers continuous, high-resolution data. To the best of our knowledge, MTG-I1 is the first geostationary satellite equipped with a near-infrared (NIR) spectral band specifically designed for detecting water vapor. To address the lack of precipitable water vapor (PWV) data derived from the Flexible Combined Imager (FCI) onboard MTG-I1, a novel semi-empirical (SE) algorithm optimized for PWV retrieval is proposed. Validation against ground-based PWV measurements using an initial test set and a temporally independent test set yielded relative errors of no more than 0.10, indicating stable retrieval performance outside the model-development period. The FCI-derived PWV retrievals were also more accurate than the corresponding MODIS PWV data. Compared to the traditional radiative transfer model (RTM)-based retrieval method, the SE method shows greater adaptability to systematic differences between the observed and RTM-simulated FCI reflectance. After correcting for radiometric degradation, the RTM-based algorithm achieves a 41% reduction in absolute error and a 47% reduction in relative error, bringing its accuracy in line with the SE algorithm. Overall, the proposed SE algorithm demonstrates superior robustness and adaptability, and can provide more reliable remote sensing PWV data to support weather forecasting and climate research. Full article

This study proposes a novel simulation-driven intelligent framework for the performance and reliability assessment of renewable energy-integrated pavement systems by unifying coupled multiphysics finite element modeling, structured dataset generation, and graph-based artificial intelligence within a single computational paradigm. The proposed pavement is formulated as a seven-layer multifunctional infrastructure system comprising the asphalt surface, intermediate binder, base layer, thermoelectric energy layer, piezoelectric insert zone, subbase, and subgrade soil, thereby enabling simultaneous consideration of structural load transfer, thermal gradient-driven energy harvesting, moisture-sensitive support behavior, and reliability-oriented performance interpretation. A three-dimensional thermo-hydro-mechanical Abaqus model was developed to simulate the concurrent effects of moving wheel load, solar heat flux, rainfall infiltration, and internal moisture diffusion, and it was subsequently used to construct an AI-ready dataset containing 6000 simulation cases and 68 variables spanning geometric, material, environmental, traffic, uncertainty, structural, thermal, hydraulic, renewable-energy, and probabilistic reliability descriptors. To preserve the physical hierarchy of the layered pavement within the learning process, a Layer-Coupled Reliability Graph Operator Network (LaRGO-Net) was proposed, in which pavement layers are represented as interacting graph nodes linked through adaptive interlayer coupling and optimized through multi-task, physics-aware, and coupling-consistent learning. Experimental evaluation across nine progressive configurations demonstrated a monotonic improvement from baseline dense and graph-convolution models to the full LaRGO-Net formulation. The final model achieved the best overall performance with mean RMSE = 0.040, mean MAE = 0.028, mean $R^2=0.994$, and reliability prediction accuracy characterized by F1 = 99.21 and AUC = 99.53. These results confirm that the proposed framework provides a highly accurate, physically interpretable, and reliability-aware surrogate for next-generation pavement systems capable of simultaneously supporting structural serviceability, renewable-energy functionality, and intelligent decision-making. Full article

Offshore renewable energy is widely recognised as a critical pathway for decarbonising electricity systems, but the integration of floating offshore wind turbines with wave energy converters remains technically challenging. This paper presents a structured literature review of hybrid wave–wind offshore energy platforms, drawing on 114 reviewed sources published between 2000 and 2026. The review classifies hybrid concepts using a three-axis framework based on floating platform type, wave energy converter (WEC) integration approach, and energy-dominance category. It then compares representative configurations, including point absorbers, oscillating water columns, flap-type devices, and heaving torus concepts, with emphasis on hydrodynamic response, energy contribution, structural complexity, mooring implications, validation status, and optimization suitability. The findings show that no single hybrid configuration can be ranked as universally superior because reported performance depends strongly on platform geometry, WEC scale, site wave climate, modelling assumptions, and validation maturity. Point absorber systems offer modularity and lower integration complexity, oscillating water column (OWC)-based systems provide protected power take-off (PTO) integration and moderate hydrodynamic interaction, flap-type systems can provide stronger motion-control potential but impose higher structural and mooring demands, and spar–torus concepts remain geometrically compatible with spar platforms but are generally wind-dominated. The review further shows that optimization method selection should depend on problem class: gradient-based methods are most suitable for local PTO tuning, evolutionary methods for non-convex multi-objective layout problems, surrogate-based methods for high-cost coupled simulations, and data-driven methods for adaptive control. The paper concludes that future progress requires standardized benchmark models, transparent evidence-level reporting, multi-physics co-optimization, techno-economic assessment, and systematic experimental or field validation before definitive concept ranking or commercial-readiness claims can be made. For decision-makers, industry stakeholders, and policymakers, the framework supports early-stage concept screening, identification of technology-specific risk factors, prioritisation of validation and investment pathways, and alignment of hybrid-platform development with site conditions, infrastructure constraints, and policy objectives. Full article

Accurate short-term prediction of network service traffic is essential for communication resource allocation and proactive fault warning in smart grids. However, smart grid service traffic is characterized by nonlinear fluctuations, strong spatio-temporal coupling, and considerable uncertainty, making it difficult for existing methods to capture long-range dependencies, adapt to dynamic topological relationships, and reflect prediction risks. To address these issues, this work develops a deep learning framework that integrates a spatio-temporal Transformer with a diffusion mechanism. The spatio-temporal Transformer extracts temporal evolution patterns and spatial logical correlations from historical traffic matrices, while the diffusion module improves robustness to abrupt traffic variations through latent uncertainty modeling. Furthermore, attention-guided recurrent units are used to generate stable multi-step forecasting sequences. Experiments on a real-world network dataset show that, compared with mainstream benchmark models, the proposed framework reduces Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Root Relative Squared Error (RRSE) by 46.62%, 47.05%, and 44.18%, respectively. These results indicate that the framework improves prediction accuracy and stability while alleviating error accumulation in long-horizon forecasting, thereby providing reliable technical support for smart grid network management. Full article

Background: Platinum-based chemotherapy is the frontline treatment for high-grade serous ovarian cancer (HGSOC); however, the development of therapy resistance greatly limits clinical response. Increasing evidence suggests that platinum agent-driven metabolic programming, particularly within redox-associated pathways, may contribute to chemoresistance. Methods: A syngeneic pair of patient-derived HGSOC cell lines representing cisplatin-sensitive (SE) and cisplatin-resistant (CR) states were evaluated using a multi-omics approach. Differential metabolite abundance and gene expression were assessed, followed by gene set and pathway enrichment analyses to identify coordinated metabolic shifts. In silico analysis of an additional sensitive and resistant HGSOC cell line validated the glutathione pathway upregulation seen in the patient-derived model. The functional contribution of the glutathione pathway on cisplatin resistance was evaluated following glutathione inhibition. Results: Chronic cisplatin exposure induced extensive metabolic rewiring in CR cells, characterized by enrichment of glutathione metabolism at both the metabolite and gene levels. Increased reduced glutathione was observed alongside upregulation of key enzymes involved in its de novo biosynthesis, recycling, and utilization, consistent with enhanced detoxification capacity relating to cisplatin-induced oxidative stress. Additionally, taurine was highly enriched, further highlighting a metabolic shift towards enhanced antioxidant mechanisms. CR cells also demonstrated an increase in NADPH-generating pathways, including amino acid metabolism and fatty acid β oxidation, to support redox balance and biosynthetic demands of increased glutathione metabolism. Transcriptional remodeling of the γ-glutamyl cycle further indicated a shift toward increased glutathione turnover, suggesting that the coordinated changes seen may define a metabolic state enhanced in oxidative stress tolerance and therapeutic resistance. These transcriptional changes were also seen in another model of platinum sensitivity/resistance, indicating a conserved response associated with platinum-induced resistance. Finally, concurrent cisplatin treatment and glutathione inhibition significantly increased sensitivity within the CR cells. Conclusions: These findings suggest that cisplatin-resistant cells, previously exposed to a platinum-based agent, may undergo distinct metabolic rewiring towards antioxidant pathways to survive chronic chemotherapeutic stress. Targeting components of these systems may represent a viable strategy to overcome platinum resistance and improve therapeutic outcomes. Full article

The AS/EN9100 standard represents the primary quality management framework governing aerospace supply chains. However, its implementation remains challenging for small and medium-sized enterprises (SMEs) due to limited resources, fragmented processes, and insufficient integration of digital support mechanisms. Existing studies primarily focus on standard interpretation, certification outcomes, or isolated implementation practices, while lacking a structured process–chronological implementation architecture suitable for SME environments. This study develops and empirically validates a digitally supported AS/EN9100 implementation framework using a Design Science Research (DSR) approach combined with Action Research principles. The proposed framework transforms the traditional clause-based interpretation of the standard into a coordinated implementation architecture integrating process management principles, risk-based thinking, and a digital support layer. The framework was validated in a real organizational environment through implementation. The integrated digital support environment also improved the coordination of responsibilities, monitoring of implementation milestones, and management of documentation workflows. From a systems perspective, the study conceptualizes quality management implementation as a socio-technical transformation process rather than a compliance-driven activity. The contribution of the study lies in the development of a transferable organizational and process innovation artifact that integrates process structuring, digital coordination, and adaptive management principles into a unified implementation framework for regulated SME environments. Full article

Urban street lighting remains a significant source of energy consumption in cities, largely due to static operation and limited responsiveness to real-time conditions. This inefficiency increases operational costs and environmental impact, especially in rapidly urbanizing regions. To address this issue, this study investigates IoT-enabled smart street lighting as an adaptive and data-driven solution within smart city frameworks. The work focuses on the growing body of research in this domain and examines its evolution, technical structure, and emerging environmental role. The study aims to provide a structured synthesis that connects research trends with system-level design, while highlighting the transition from energy-focused systems to multifunctional urban platforms. A bibliometric-driven and thematic review approach is adopted. A dataset of 151 publications was analyzed using Bibliometrix and Biblioshiny tools to extract trends, collaboration patterns, and research themes. This analysis is complemented by a qualitative evaluation of system architectures, sensing technologies, communication models, and control strategies. The findings indicate a sustained annual growth rate of 14.87% and a highly collaborative research landscape, with an average of 3.97 authors per study. The results also reveal that energy efficiency remains the dominant focus, while environmental integration is emerging but still underrepresented. The study further identifies key gaps related to scalability, sensor reliability, and the lack of standardized evaluation metrics. The outcomes provide a comprehensive roadmap for future research and support the development of scalable, intelligent, and sustainable lighting systems. The proposed insights are applicable to urban environments globally, particularly in regions seeking cost-effective and energy-efficient infrastructure solutions. Full article

Intelligent sensors and Internet of Medical Things (IoMT) platforms are rapidly changing smart healthcare by enabling continuous capture of physiological, behavioral, and clinical events outside conventional hospital settings. Yet the value of connected sensing depends on more than signal acquisition alone. A practical remote-monitoring ecosystem must also convert sensor alerts, clinician-facing summaries, and historical electronic clinical records (ECRs) into ranked evidence that supports care decisions. This study reframes a large-AI clinical retrieval model as the intelligence layer of an edge–cloud IoMT architecture. The proposed framework combines Transformer-Based Sequence (TBS) encoding, BioBERT-driven representation learning, explicit retrieval, and domain-guided re-ranking to connect sensor-originated narratives, patient records, and clinician queries. The empirical evaluation is conducted on Medical Information Mart for Intensive Care III (MIMIC-III) and i2b2, two de-identified clinical text benchmarks that approximate the documentation layer of real-world remote patient monitoring. Compared with strong baselines, including DeepBio, UniT2T, Web4IR, A2A-API, CoLTiD, VLRG, ColBERT, DeepSDH, BiRex, and DL4BTM, the proposed model achieves the best overall performance, reaching F1/Pre/NDCG scores of 0.8399/0.8338/0.5235 on MIMIC-III and 0.8090/0.8100/0.5129 on i2b2. Ablation experiments confirm the importance of exploratory data adaptation, critical feature modeling, critical token learning, cross-disciplinary supervision, and data-driven regularization. Parameter sensitivity analysis shows stable behavior for beta values greater than or equal to 1, with the strongest results at beta = 5. The study concludes that large-AI retrieval can strengthen the clinical interpretation layer required for IoMT-enabled remote monitoring, while future work should validate the approach on live multimodal sensor streams and privacy-preserving deployments. Full article

Lymphoid neoplasms such as multiple myeloma (MM), indolent non-Hodgkin lymphoma, and chronic lymphocytic leukemia are increasingly managed as chronic, relapsing conditions characterized by prolonged surveillance, repeated treatment transitions, and cumulative self-management demands. These trajectories expose patients and caregivers to persistent illness uncertainty, fluctuating fear of progression, symptom and comorbidity burden, communication challenges, and treatment-related workload. This theory-informed framework development paper uses an overview of selected psycho-oncological, hematological, nursing, theoretical, and patient-reported outcome literature to propose the LUMINA framework: Longitudinal illness trajectory, Uncertainty fields, Multidimensional symptom and comorbidity load, Information and interaction context, Navigation work and self-management load, and Adaptive outcomes and alignment. LUMINA is intended as a hypothesis-generating conceptual structure to organize clinically relevant domains, clarify potential relationships among uncertainty, symptom burden, communication, navigation work, and adaptive outcomes, and guide future assessment, validation, and intervention research in chronic lymphoid neoplasms. The framework builds on prior theories of illness uncertainty, treatment burden, workload–capacity balance, fear of recurrence/progression, and lymphoma-specific qualitative work on uncertainty management and psychosocial adaptation. Potential research applications include structured assessment, shared decision-making research, and domain-matched supportive-care concepts; however, these applications remain theoretical and require empirical testing. Future studies should evaluate feasibility, acceptability, construct validity, domain overlap, predictive validity beyond quality of life, and the clinical utility of LUMINA-informed research profiles. Until such validation is available, LUMINA should be interpreted as a conceptual model rather than a validated clinical tool or care pathway. Full article