Search Results (8,036)

Never has the analysis of bus travel times been so essential to transit planning: travelers complain about a decline in service quality, urban congestion is on the rise, and public transport companies struggle with a structural driver shortage. This research paper aims to address the urgent need to explore new tools to increase commercial speed on transit lines while avoiding costly, potentially inefficient technological investments. A data-driven, cost-neutral, and replicable methodological framework is proposed to provide a first-order estimation of the potential benefits of Transit Signal Priority (TSP) at signalized intersections. The approach is based on Automatic Vehicle Monitoring (AVM) data analysis, which is underpinned by a lean network representation logic built from origin/destination pairs of stops located upstream and downstream of signalized intersections. Bus travel inter-times across network arcs are compared between peak and off-peak periods through a two-level analytical process that progressively refines the estimation of recoverable delay. The methodology is applied to the urban bus network of Pavia (Italy), operated by Autoguidovie S.p.A. (one of the most important Local Public Transport companies in Italy), with a specific focus on the high-frequency PV3 line. Results indicate a potential reduction of up to approximately 6 h and 45 min of operating time per day at the line level (−13.5% of total driving time), and up to 2 min per trip along a 2 km corridor (−6% along the single corridor selected). The procedure integrates both infrastructural and operational perspectives, supporting preliminary decision-making on TSP implementation using only data already collected by transit agencies. Full article

Silicon is increasingly applied in agriculture to improve plant productivity under both abiotic and biotic stress constraints. Nevertheless, its mechanisms of action are often studied separately at the soil, plant, or microbiome levels, limiting a comprehensive understanding of its overall impact on agroecosystem functioning. This review proposes an integrated perspective of the soil–plant–microbiome continuum, linking silicon chemistry in soil solutions with the effects of silicon amendments on soil properties and the processes of uptake, transport, and deposition in the plants. We show that silicon bioavailability depends on maintaining a pool of dissolved silicon dominated by orthosilicic acid, regulated by mineral weathering, adsorption–desorption dynamics, polymerization, pH, iron and aluminum oxides, and organic matter. In soils, silicon inputs can improve structure, modulate acidity and cation exchange balances, influence nutrient availability, and reduce the mobility of certain metals. They may also affect enzymatic activities and microbial community composition. In plants, silicon uptake and transport, mediated by specific transporters, contribute to tissue silicification, the maintenance of leaf architecture, and the regulation of water, ionic, and redox homeostasis. These processes provide a basis for enhanced tolerance to drought, salinity, and metal toxicity, as well as biotic stress caused by pathogens and pests. Finally, we discuss key limitations to the agronomic application of silicon, including the diagnosis of the silicic status of soils, the choice of source and mode of application, and the genotypic variability of acquisition, as well as the need for multi-site tests and more robust mechanistic validations. This synthesis provides a coherent mechanistic framework to better define the conditions under which silicon can serve as a reliable tool for sustainable crop management under climate change. Full article

Rural communities face significant healthcare access barriers that contribute to persistent health disparities. Mobile health clinics (MHCs) have emerged as a promising strategy for expanding healthcare access, yet their effectiveness in rural settings remains understudied. The aim of this review was to examine the literature to determine what is known about access, health outcomes, and the cost-effectiveness of rural MHCs, specifically with regard to their impact on patient access and outcomes, return on investment (ROI)/financial, and program sustainability. We conducted a comprehensive search of peer-reviewed and grey literature sources. Systematic screening yielded 34 documents for full analysis. Thematic analysis was conducted across three domains: patient access, patient outcomes, and ROI/sustainability. All 34 documents provided data on patient access, with common themes including expanded service utilization, multi-service integration, overcoming geographic and transportation barriers, and improved healthcare affordability. Thirty-two documents addressed patient outcomes, reporting improvements in preventive care delivery, chronic disease management, and high patient satisfaction. Twenty-eight documents included ROI/sustainability information, with evidence suggesting cost-effectiveness particularly through emergency department visit avoidance and multi-service integration. Across the literature reviewed, the quality of evidence varied considerably, yet we concluded mobile health clinics demonstrate promise for expanding healthcare access and improving outcomes in rural populations. Key success factors include multi-service integration, diverse funding partnerships, technological integration, and strong community engagement. More rigorous research with longitudinal clinical outcome measures and robust economic analyses is needed. Full article

Cancer remains a major global health challenge, characterized by abnormal cell growth and metastasis. Current limitations of conventional therapies, particularly non-specific toxicity harming healthy cells, highlight the need for more targeted approaches. Nanotechnology offers a revolutionary solution, utilizing nanoparticles (NPs) for precise drug delivery to tumor sites while minimizing off-target effects. These nanometer-scale particles enable superior binding to cancer cell membranes, the tumor microenvironment, or nuclear receptors, facilitating significantly higher local concentrations of therapeutic agents. NPs, synthesized via physical, chemical, or biological methods, are categorized as organic (organic material-based) or inorganic (metallic particle-based). Key delivery mechanisms include the Enhanced Permeability and Retention (EPR) effect and Active Transport and Retention (ATR). This review specifically examines NP applications for the most prevalent cancers in the US (2025): breast, prostate, and lung. Gold and magnetic NPs show significant promise for early breast cancer detection. For lung cancer, polymeric NPs like PCL, PLA, and PLGA are effective carriers for peptides, proteins, and nucleic acids. BIND-014, a docetaxel-loaded NP formulation, represents an emerging strategy for prostate cancer. Clinically established examples include liposomal doxorubicin and albumin-bound paclitaxel. We comprehensively discuss the synthesis methods, delivery mechanisms, and the current landscape of NPs in research and clinical trials for these cancers. This analysis underscores the potential of nanotechnology to provide more effective and targeted therapeutic options for cancer patients in the future. A distinctive feature of this review is its comparative cancer-specific analysis of NP platforms in breast, prostate, and lung cancers. Unlike previous generalized reviews, this work integrates synthesis strategies, delivery mechanisms, translational challenges, and clinically relevant formulations to provide a bench-to-bedside perspective on the future of nanomedicine in oncology. Full article

Objectives: To evaluate how often pain is assessed and treated in pediatric trauma patients transported by Emergency Medical Services (EMS) to a pediatric emergency department (ED), and to compare current practice with national recommendations of the Polish Ministry of Health for prehospital pediatric pain management. Methods: We conducted a retrospective analysis of EMS and ED documentation for all trauma patients under 18 years of age transported to the Pediatric Teaching Hospital of the University Clinical Center of the Medical University of Warsaw between 1 January and 31 December 2021. A total of 981 patients with injury or suspected injury or burns were included without exclusion criteria. For patients with documented pain scores, we analyzed pain intensity (0–10), the scales used [Visual Analog Scale (VAS), Numerical Rating Scale (NRS), Wong–Baker Faces Pain Rating Scale (FACES)], body region injured, Glasgow Coma Scale (GCS) score, suspected alcohol or psychoactive substance use, and type and route of analgesic administration. We further evaluated non-pharmacological interventions, pain reassessment, and achievement of at least 50% pain reduction, as defined in national guidelines. Statistical analysis included Student’s t-test or ANOVA for quantitative variables and maximum likelihood chi-square tests for qualitative variables (α = 0.05). Results: Pain was assessed in 839/981 (85.5%) patients; 651/839 (77.6%) reported pain, most frequently of moderate intensity. Despite this, only 208/981 (21.2%) patients received analgesics prehospitally. Morphine and paracetamol were the most frequently used drugs, predominantly administered intravenously, while non-opioid monotherapy was commonly used in patients with lower baseline pain scores. Less than half of all patients received any non-pharmacological intervention whatsoever. Pain was reassessed in 734/839 (87.5%) patients, with a mean reassessment time of approximately 10 min; however, in many cases reassessment occurred earlier than the expected onset of analgesic action. Overall, only 29.4% of patients with pain and documented reassessment achieved the recommended ≥50% reduction in pain intensity, and at least 70.2% of the cohort had no documented evidence of treatment fully complying with national recommendations. Conclusions: In this real-world prehospital and ED cohort, pediatric trauma pain remains under-treated, and adherence to national guidelines on opioid-based analgesia and pain reassessment is suboptimal. Further efforts are needed to improve documentation, expand the recommended pharmacological options for mild pain, and strengthen education on guideline-concordant pediatric pain management in EMS. Full article

Machine condition monitoring increasingly depends on distributed sensing, edge intelligence, and cloud analytics, yet timely and trustworthy health assessment remains constrained by latency, bandwidth, privacy, and reliability requirements. Cloud-only architectures provide scalable computation and historical data integration but often fail to satisfy real-time industrial needs, whereas edge-only deployments are limited by restricted computing resources and fragmented local knowledge. Edge–cloud collaboration has, therefore, emerged as a practical architecture for distributing perception, inference, learning, and coordination across hierarchical industrial systems. This review examines 147 publications on edge–cloud collaboration for machine condition monitoring published between 2019 and February 2026. A four-dimensional taxonomy is developed to organize the literature into model-centric, data-centric, resource and task-centric, and architecture and trust-centric mechanisms, while 13 survey and review papers are considered separately for contextual comparison. On this basis, the review analyzes representative collaboration mechanisms and enabling technologies, with particular attention to federated learning, transfer learning, knowledge distillation, digital twins, and deep reinforcement learning, and surveys their deployment in manufacturing, energy, transportation, and infrastructure monitoring scenarios. The literature remains dominated by model-centric collaboration, while architecture and trust-centric studies increasingly provide the system foundations required for practical deployment. The review further identifies major open challenges, including robust generalization under changing operating conditions, efficient data transmission, real-time resource coordination, interoperability, and trustworthy large-scale deployment, and outlines future directions in foundation-model-based edge–cloud collaboration, continual learning, dual digital twins, trustworthy collaboration, and privacy-preserving industrial ecosystems. Full article

This research, funded by the National Science Foundation (Award #2412054) as part of the NH-LIFT project, provides a critical analysis of a successful public health initiative addressing food insecurity in New Hampshire, which affects nearly 10% of residents and 13.4% of children. The study’s primary objective was to analyze the effectiveness, unique characteristics, and replicability of The Community Kitchen’s Mobile Food Pantry program in collaboration with the Healthy Monadnock Alliance and Cheshire Medical Center. Methods: A survey design was employed over a four-week period (July–August 2025) to collect qualitative data from n = 97 voluntary participants attending mobile pantry events in four rural southwest New Hampshire towns, Gilsum, Richmond, Winchester, and Fitzwilliam, during the period of May-June of 2025. The anonymous, 25-question instrument gathered information on program benefits and needed improvements. Results indicate the model is highly effective in mitigating increased financial stressors and overcoming transportation barriers, which are critical challenges for families and aging adults in this rural region. While demonstrating success in promoting local health and well-being, the research also highlights factors crucial for long-term sustainability. This study contributes to an evidence-based public health model suitable for replication in other food-insecure rural communities. Full article

Hydrogen refueling stations (HRSs) are a critical enabling infrastructure for fuel cell electric vehicles (FCEVs), yet their deployment in dense metropolitan areas often faces a dual challenge: limited travel-time accessibility for users and low public acceptance driven by perceived safety risks. This study develops an integrated, city-scale framework to quantify HRS accessibility and resident acceptance and to identify expansion priorities for Seoul, South Korea. We combine (i) an online perception survey of 1000 adult residents (October 2024) capturing environmental awareness, perceived safety, siting preferences, and willingness-to-travel distance; (ii) spatial demand data on FCEV registrations by administrative dong (n = 2443 vehicles, 2022); and (iii) network-based travel-time analysis using the Seoul road network and the current HRS supply (n = 10, 2024). Accessibility is evaluated under three travel-time thresholds (10, 15, and 20 min), with service-area delineation and demand-weighted underserved-area diagnosis. Candidate expansion sites are generated and screened using operational and regulatory constraints (e.g., site area and proximity to protected facilities), followed by a p-median location-allocation optimization to select five additional sites that minimize demand-weighted travel impedance. Results indicate that, under the 20 min threshold (7.7 km at an average operating speed of 23.1 km/h), 50 of 425 dongs (11.8%) and 244 of 2443 FCEVs (10.0%) are outside the baseline service coverage. After adding five sites (total n = 15), underserved dongs decrease to 5 (1.2%) and underserved FCEVs to 26 (1.1%) for the 20 min threshold, with consistent improvements across shorter thresholds. Survey responses further reveal that only 12.5% of respondents perceive HRSs as safe, while 46.5% report a maximum willingness-to-travel distance of up to 5 km, underscoring the need for both accessibility enhancement and risk-aware communication. The proposed workflow offers a transparent, reproducible approach to support equitable and risk-informed HRS planning by jointly considering network accessibility, demand distribution, and social acceptance, thereby contributing to sustainable urban mobility, low-carbon transport transition, and socially acceptable hydrogen infrastructure deployment. Beyond local accessibility improvement, the study is framed in the broader context of sustainability, as equitable and socially acceptable hydrogen refueling infrastructure can support low-carbon urban transport transitions and more resilient metropolitan energy-mobility systems. Full article

The dynamic development of the food delivery sector and the resulting increase in last-mile distribution operations generate the need to simultaneously improve the efficiency of delivery processes and reduce the environmental impacts of urban logistics. In this context, shortening delivery time contributes not only to higher service quality and competitiveness but also to lower energy consumption and carbon dioxide emissions, which are key elements of sustainable urban mobility and logistics. Therefore, the aim of this study is to develop a delivery time optimization algorithm for the food delivery sector using selected machine learning methods, supporting the implementation of sustainable development principles in the operations of transport enterprises. This study presents an integrated approach to modelling delivery time in food distribution as a tool for building the competitive advantage of logistics enterprises under the conditions of implementing sustainable development principles. The study combines a literature review on sustainable last-mile logistics and data-driven optimization with an empirical analysis using traditional methods such as multiple regression and selected machine learning methods: decision trees, the Gradient Boosting Machine (GBM) method, and the XGBoost algorithm. The operational data include parameters related to delivery execution, such as supplier characteristics, vehicle type, order execution date, weather conditions and traffic situation. The developed mathematical models enable high-accuracy prediction of delivery time and the identification of the most important factors affecting both timeliness and potential energy consumption in the delivery process. The comparative assessment of the applied methods makes it possible to indicate the algorithms that provide the best forecast quality and practical usefulness in logistics decision-making. The proposed delivery time optimization algorithm supports data-driven decision-making that leads to shorter delivery times and lower energy intensity and thus to a reduction in the carbon footprint of last-mile operations, simultaneously strengthening the competitiveness and environmental responsibility of logistics enterprises. The results contribute to the development of sustainable urban logistics by linking predictive modelling with the economic, environmental and operational dimensions of efficiency in last-mile transport processes. Overall, this study offers an original, high-quality contribution to sustainable last-mile food delivery by integrating large-scale operational data with advanced machine learning models to deliver practically relevant, highly accurate delivery time predictions for logistics enterprises. Full article

Kuwait experiences persistently high levels of air pollution driven by industrial emissions, transportation, oil-related activities, and frequent desert dust storms. This study aims to synthesize and critically evaluate the available evidence on the relationship between air pollution, Air Quality Index (AQI), and health outcomes in Kuwait using a guided narrative review approach. A guided literature search identified 26 peer-reviewed studies published between 2014 and 2026 about Kuwait air pollution, which were assessed for methodological characteristics, pollutant types, health outcome categories, and vulnerable populations. The most frequently examined pollutants were particulate matter (PM_2.5: 69%; PM₁₀: 38%), followed by NO₂ (23%), multi-pollutant and AQI-based (19%), O₃ (12%), SO₂ (12%), VOCs and PAHs (8%). Health-related investigations most commonly addressed mortality and respiratory morbidity, while cardiovascular, metabolic, biomarker-based, and cancer-related outcomes were less frequently represented. Among studies reporting direct health outcomes, elevated PM_2.5 exposure was generally associated with increased risks of respiratory hospitalizations, cardiovascular events, and all-cause mortality. Susceptible populations identified across the literature include children, older adults, individuals with pre-existing chronic conditions, and outdoor workers, who may experience higher exposure levels and greater health vulnerability. However, a substantial proportion of the included studies focused primarily on exposure characterization or pollutant modeling without direct assessment of health outcomes. These studies nonetheless indicate consistently elevated pollutant levels and seasonal variability, which may plausibly contribute to population health risks. Overall, while the available Kuwait-specific evidence suggests potential adverse health effects linked to air pollution, the strength of direct epidemiological evidence remains limited. Important gaps persist, including the scarcity of long-term cohort studies, limited multi-pollutant analyses, and insufficient integration of AQI categories with health outcomes. These limitations highlight the need for more robust and longitudinal research to better quantify health risks and inform public health policy in Kuwait. Full article

The estuarine and coastal regions of India and Taiwan are under increasing threat from pollutants such as microplastics (MPs) and heavy metals including cadmium (Cd). These contaminants are known to have adversely affect biodiversity and water quality. In this study, the combined toxic effects of polyethylene microplastics (PE-MPs) and Cd were evaluated using Poecilia sphenops, a euryhaline fish species, selected for its adaptability to varying salinity conditions. P. sphenops were exposed to Cd (20, 40, and 60 μg/L), MPs (8, 16, 24 mg/L), and co-exposure combinations ranging from Cd 5 μg/L + MPs 4 mg/L to Cd 20 μg/L + MPs 16 mg/L Results showed significant (p< 0.05) negative effects on growth parameters including body weight gain, specific growth rate (SGR), and survival rate. Hematological analysis revealed significant (p< 0.05) decreases in hemoglobin (Hb), red blood cells (RBCs), and white blood cells (WBCs), indicating impaired oxygen transport and compromised immune function. Elevated blood glucose levels indicated physiological stress, while reduced total protein levels suggested a compromised nutritional status. Antioxidant enzyme activities, including catalase (CAT), superoxide dismutase (SOD), and glutathione peroxidase (GPx), were significantly (p < 0.05) decreased in the toxicant-treated groups compared with the control. Digestive enzyme activities (proteases, amylases, and lipases) were also reduced, suggesting impaired digestion and nutrient assimilation. The study also included a comparative assessment of water quality between the exposed and control tanks. Water quality parameters such as turbidity, salinity, hardness, alkalinity, chloride, fluoride, and total suspended solids (TSSs) were elevated in the toxicant-treated media, accompanied by a notable decline in dissolved oxygen (DO) levels. These findings highlight the urgent need for integrated pollution control and water quality monitoring, particularly in coastal regions vulnerable to desalination discharges and plastic contamination. Sustainable management strategies must address these complex interactions between multiple pollutants to protect aquatic ecosystems. Full article

Extreme hydrological events fundamentally alter sediment transport dynamics across grain, reach, and watershed scales, rendering classical equilibrium-based transport formulations inadequate. This review synthesizes recent advances in multiscale sediment transport modeling under highly unsteady and high-magnitude forcing conditions. At the grain scale, particle-resolved simulations demonstrate that sediment entrainment is governed by turbulence intermittency and transient force exceedance rather than mean bed shear stress thresholds, particularly when the hydrograph rise timescale (T_h) becomes comparable to particle response times (T_p). At the reach scale, non-equilibrium transport emerges when the unsteadiness ratio T_h/T_a∼O(1), where T_a is the sediment adaptation timescale representing the time required for sediment flux to adjust toward transport capacity. Under these conditions, pronounced hysteresis between discharge and sediment flux is observed, requiring relaxation-based transport formulations instead of instantaneous equilibrium laws. At the watershed scale, the sediment delivery ratio (SDR), defined as the ratio of sediment yield at the basin outlet to total hillslope erosion, becomes highly time-dependent. Extreme precipitation events can activate hillslope-channel connectivity, increasing SDR by orders of magnitude relative to baseline conditions. A unified dimensionless scaling framework is presented based on mobility intensity (θ/θ_c, where θ is the Shields parameter and θ_c is its critical value for incipient motion), unsteadiness ratio (T_h/T_a), and morphodynamic coupling (T_f/T_m, where T_f is the hydraulic advection timescale and T_m is the morphodynamic adjustment timescale). This framework enables classification of sediment transport regimes ranging from quasi-equilibrium to cascade-dominated states. The synthesis demonstrates that predictive uncertainty increases nonlinearly across scales due to timescale compression, threshold activation, and feedback between flow hydraulics and evolving morphology. Recent developments in hybrid physics-AI approaches show promise in improving predictive capability by enabling dynamic transport closures, surrogate modeling of computationally expensive microscale processes, and data assimilation for real-time forecasting. However, these approaches remain limited by extrapolation uncertainty and the need to enforce physical constraints. Overall, this review concludes that regime-aware multiscale coupling, combined with uncertainty quantification and adaptive modeling strategies, is essential for robust sediment hazard prediction and climate-resilient infrastructure design under intensifying hydrological extremes. Full article

The first golden mussel, Limnoperna fortunei (Dunker, 1857), specimens in North America were discovered on 17 October 2024 at the Port of Stockton on the lower San Joaquin River in California (United States). The golden mussel is native to southern China and is one of the highest-risk aquatic invasive species worldwide. Golden mussels colonize hard surfaces and cause significant biofouling, affecting vital infrastructure such as hydroelectric plants and water delivery systems. It spreads rapidly through hydrological connectivity and human-mediated transport, with water conveyance systems functioning as invasion highways. The Sacramento–San Joaquin River Delta is vital to endangered species and provides water to 30 million people and 790,000 ha of farmland in central and southern California, but faces severe ecological and economic threats from this invasion. The detection of golden mussels was received with concern due to their impact on ecosystems and infrastructure. One year after detection, the invasion front moved 545 km south of the initial detection site (in a straight line) into Silverwood Lake in San Bernardino County near Los Angeles. By April 2026, the invasion front had already advanced 707 km south to the Sweetwater Reservoir in San Diego County (detection date: 15 January 2026). The invasion path coincides with California’s major water delivery systems. Ballast water was the most likely introduction vector, further underscoring the inefficiency of well-intentioned ballast water management policies and the need to implement better ones. This article addresses five objectives: (1) document the introduction and current distribution; (2) highlight key invasive traits to guide management; (3) assess putative impacts in California; (4) review tested management strategies; and (5) propose an innovation-driven framework for golden mussel management. Full article

High-temperature stress severely reduces the photosynthetic efficiency of radish (Raphanus sativus L.), a cool-season crop. This study evaluated five nitrogen (N) levels {0 N, 0.5 N, 1 N (234 kg urea ha⁻¹, based on RDA), 2 N, and 4 N} through an open-field experiment under high-temperature stress conditions. Analysis of OJIP transients revealed that high temperatures severely inhibited photosynthetic capacity in the 0 N, 0.5 N, and 4 N treatment groups. These groups exhibited a simultaneous increase in K and J-steps, signifying electron transport bottlenecks and structural damage to the oxygen-evolving complex (OEC). Consequently, energy absorption and trapping decreased, while heat dissipation increased. In contrast, the 2 N treatment maintained superior F_m(maximum fluorescence) and energy flux, demonstrating enhanced photosynthetic resilience. However, despite improved photosynthetic stability, the 2 N group did not show a significant increase in yield compared to the 0.5 N or 1 N treatment groups. These results suggest that photosynthetic protection under heat stress does not necessarily guarantee higher yields, highlighting the need to identify optimal fertilization points for sustainable production. Overall, the findings of this study provide fundamental data for strategic nitrogen management in open-field radish cultivation to mitigate the impacts of increasing climatic instability. Full article

This review examines microplastics (MPs) in aquatic environments, their interactions with organic pollutants (OPs), effects on organisms, and implications for human and ecological health. MPs are ubiquitous, persistent contaminants. Their small size and large surface area enhance adsorption of diverse OPs; however, the extent to which MPs influence pollutant transport, fate, and bioavailability remains highly context-dependent and is still under scientific debate. Sorption processes are influenced by polymer type, pollutant properties, environmental factors, and aging processes that increase surface reactivity, further contributing to the variability of MP–OP interactions. Detection of MPs in human tissues raises concerns about long-term health effects, including inflammatory, immune, gastrointestinal, respiratory, and endocrine responses. Despite advances in analytical techniques, challenges remain in identifying and quantifying small particles in complex matrices. This review emphasizes the need for integrated, multi-technique, and environmentally realistic studies to understand MP–OP interactions and support risk assessment. Future research should focus on standardizing methodologies, improving nano-sized particle detection, and elucidating long-term effects, including trophic transfer and potential tissue accumulation. Full article