Search Results (7,423)

The transport sector significantly contributes to global greenhouse gas emissions, making electromobility crucial in the race toward the United Nations Sustainable Development Goals. In recent years, the increasing competition among manufacturers, the development of cheaper batteries, the ongoing policy support, and people’s greater environmental awareness have consistently increased electric vehicles (EVs) adoption. Nevertheless, EVs charging needs—highly influenced by EV drivers’ behavior uncertainty—challenge their integration into the power grid on a massive scale, leading to potential issues, such as overloading and grid instability. Smart charging strategies can mitigate these adverse effects by using information and communication technologies to optimize EV charging schedules in terms of power systems’ constraints, electricity prices, and users’ preferences, benefiting stakeholders by minimizing network losses, maximizing aggregators’ profit, and reducing users’ driving range anxiety. To this end, accurately forecasting EV charging demand is paramount. Traditionally used forecasting methods, such as model-driven and statistical ones, often rely on complex mathematical models, simulated data, or simplifying assumptions, failing to accurately represent current real-world EV charging profiles. Machine learning (ML) methods, which leverage real-life historical data to model complex, nonlinear, high-dimensional problems, have demonstrated superiority in this domain, becoming a hot research topic. In a scenario where EV technologies, charging infrastructure, data acquisition, and ML techniques constantly evolve, this paper conducts a systematized literature review (SLR) to understand the current landscape of ML-based EV charging demand forecasting, its emerging trends, and its future perspectives. The proposed SLR provides a well-structured synthesis of a large body of literature, categorizing approaches not only based on their ML-based approach, but also on the EV charging application. In addition, we focus on the most recent technological advances, exploring deep-learning architectures, spatial-temporal challenges, and cross-domain learning strategies. This offers an integrative perspective. On the one hand, it maps the state of the art, identifying a notable shift toward deep-learning approaches and an increasing interest in public EV charging stations. On the other hand, it uncovers underexplored methodological intersections that can be further exploited and research gaps that remain underaddressed, such as real-time data integration, long-term forecasting, and the development of adaptable models to different charging behaviors and locations. In this line, emerging trends combining recurrent and convolutional neural networks, and using relatively new ML techniques, especially transformers, and ML paradigms, such as transfer-, federated-, and meta-learning, have shown promising results for addressing spatial-temporality, time-scalability, and geographical-generalizability issues, paving the path for future research directions. Full article

The Wolf Motor Function Test (WMFT) is widely used to evaluate upper limb motor performance after stroke. However, conventional approaches may overlook domain-specific neuromuscular and kinematic differences during task execution. This study classified WMFT tasks into three functional domains: proximal reaching and transport (PRT), fine motor manipulation (FMM), and gross motor functional control (GMFC). Interlimb differences in muscle activation, joint mobility, and movement amplitude were examined using sensor-based measurements. Twelve individuals with chronic stroke performed 16 WMFT tasks. Surface electromyography (EMG) and inertial measurement units (IMUs) recorded upper limb muscle activity, joint angles, and segmental displacement. Wilcoxon signed-rank tests and Spearman correlations were conducted for each functional domain. Significant asymmetries in EMG, range of motion (ROM), and root mean square (RMS) acceleration were found in PRT and FMM tasks. These results reflect increased proximal muscle activation and reduced distal engagement on the paretic side. GMFC tasks elicited more symmetrical patterns but still showed subtle deficits in distal control. Correlation analyses demonstrated strong interdependencies among neuromuscular and kinematic measures. This finding underscores the integrated nature of compensatory strategies. Categorizing WMFT tasks by functional domain and integrating multimodal sensor analysis revealed nuanced impairment patterns. These patterns were not detectable by conventional observational scoring. These findings support the use of sensor-based, domain-specific assessment to guide individualized rehabilitation strategies. Such approaches may ultimately enhance long-term functional recovery in stroke survivors. Full article

Urban safety is a critical concern for sustainable city development, with crime patterns often linked to localized environmental factors. Understanding the spatial dynamics of safety is critical for informed design and planning of urban environments. This study employs a Geographically Weighted Regression (GWR) approach to investigate how crime in Austin, Texas, correlates with Points of Interest (POIs) such as bars, transit stations, financial businesses, and public spaces, while accounting for localized socio-economic factors. Building on theoretical frameworks like Routine Activity Theory and Crime Pattern Theory, the analysis integrates crime data from the Austin Police Department (APD), POI datasets, and census variables to explore spatially varying relationships often overlooked by traditional global models (e.g., OLS). A novel adaptive geo-grid method refines spatial units by clustering high-density downtown areas into smaller zones and retaining larger grids in suburban regions, ensuring precision without over-fragmentation. Analysis of crime incidents and POI data reveals significant spatial non-stationarity in crime–environment associations. Transportation-related facilities demonstrate strong spatial correlation with crime citywide, particularly forming persistent crime hotspots around transit hubs in areas like Rundberg Lane, South Congress, and East Riverside. Alcohol-related establishments show a strong positive correlation with crime in entertainment districts (coefficient up to 13.5, p < 0.001) but a negligible association in suburban residential areas (coefficient close to 0, p > 0.05). The GWR model significantly outperforms traditional OLS regression, capturing critical local variations obscured by global models. Downtown Austin emerges as a complex hotspot for urban safety where multiple high-risk POI types overlap. This research advances urban design and planning knowledge by providing empirical evidence that environmental factors’ influence on safety is spatially conditional rather than universally consistent, aligning with Crime Pattern Theory and Routine Activity Theory. The findings support place-specific crime prevention strategies, offering policymakers data-driven insights for developing targeted design strategies for urban zones. Full article

Maritime transportation, fishing, scientific research, and other activities rely on various types of ships and platforms, making precise monitoring of ships at sea essential. Synthetic Aperture Radar (SAR) is minimally affected by weather conditions and darkness and is used for ship detection in maritime environments. This study analyzes the differences in backscatter characteristics among various ship types in SAR images and proposes SISS-YOLO, an enhanced model based on YOLOv8. The proposed method addresses the challenge of ship instance segmentation in SAR images involving multiple polarizations, scenarios, and classes. First, the backbone structure was optimized by incorporating additional pooling layers and refining the activation functions. Second, the Coordinate Attention (CA) module was integrated into the C2F template, embedding spatial position information into the channel attention mechanism. Third, a slide loss function was adopted to address the class imbalance across ship categories. The experiments were conducted on the OpenSARShip2.0 dataset, which includes cargo, tanker, passenger and engineering ships. The results show that the SISS-YOLO achieves a mask precision of 88.3%, a mask recall of 86.4% and a mask mAP50 of 93.4% for engineering ships. Compared with YOLOv8m, SISS-YOLO achieved improvements of 15.7% in mask precision and 8.8% in mask recall. The model trained on the OpenSARShip2.0 dataset was directly applied to the FUSAR-Ship1.0 dataset, demonstrating a degree of robustness. When applied to SAR data, the SISS-YOLO model achieves high detection accuracy, demonstrating generalization. Full article

Efficient threat detection in X-ray cargo inspection is critical for the security of the global supply chain. This study evaluates YOLO-based object-detection models from YOLOv5 to the latest, YOLOv11, which is enhanced with modified loss functions and Soft-NMS to improve accuracy. The YOLO model comparison also includes DETR (Detection Transformer) and Faster R-CNN (Region-based Convolution Neural Network). Standard loss functions struggle with overlapping items, low contrast, and small objects in X-ray imagery. To overcome these weaknesses, IoU-based loss functions—CIoU, DIoU, GIoU, and WIoU—are integrated into the YOLO frameworks. Experiments on a dedicated cargo X-ray dataset assess precision, recall, F1-score, mAP@50, mAP@50–95, GFLOPs, and inference speed. The enhanced model, YOLOv11 with WIoU and Soft-NMS, achieves superior localization, reaching 98.44% mAP@50. This work highlights effective enhancements for YOLO models to support intelligent logistics in transportation services and automated threat detection in cargo security systems. Full article

Geological Disasters (Geo-disasters) are common in the Ili River Basin, with extreme precipitation being a major triggering factor. As the frequency and intensity of these events increase, the associated risks also rise. This study proposes a hazard assessment framework that integrates extreme precipitation recurrence periods with Geo-disaster susceptibility. Furthermore, based on a comprehensive risk assessment model encompassing hazard, exposure, vulnerability, and disaster mitigation capacity, the study evaluates Geo-disaster risk in the Ili River Basin under extreme precipitation conditions. Hazard levels are assessed by integrating geo-disaster susceptibility with recurrence periods of extreme precipitation, resulting in hazard and risk maps under various conditions. The susceptibility indicator system is refined using K-means clustering, the certainty factor (CF) model, and Pearson correlation to reduce redundancy. Key findings include: (a) Geo-disasters are influenced by a combination of factors. High-susceptibility areas are typically found in moderately sloped terrain (8.5–17.64°) at elevations between 1412 m and 2234 m, especially on east- and southeast-facing slopes. Lithology, soil, hydrology, fault proximity, and the topographic wetness index (TWI) are the primary influences, while high NDVI values reduce susceptibility. (b) The hazard pattern varies with the recurrence period of extreme precipitation. Shorter periods lead to broader high-hazard zones, while longer periods concentrate hazards, particularly in Yining City. (c) Exposure is higher in the east, vulnerability aligns with transportation networks, and disaster mitigation capacity is stronger in the north, particularly in Yining. (d) Low-risk areas are found in valleys and flat terrains, while medium to high-risk zones concentrate in southeastern Zhaosu, Tekes, and Gongliu counties. Some economically active regions require special attention due to their high exposure and vulnerability. Full article

The growing need for sustainable and resource-efficient materials increasingly promotes the use of block-glued laminated timber (glulam) in buildings and civil structures such as bridges. While timber is renewable and sustainable, the formaldehyde-based adhesives commonly used in glulam raise environmental and health concerns. This study addresses this gap by presenting one of the first combined life cycle assessment (LCA) and life cycle cost (LCC) analyses of bio-based versus synthetic adhesives for block-glued glulam. A pedestrian bridge in Zwolle, the Netherlands, serves as a case study. Three synthetic adhesives—melamine-urea formaldehyde (MUF), phenol resorcinol formaldehyde (PRF), and phenol formaldehyde (PF)—and two bio-based alternatives—lignin phenol glyoxal (LPG) and tannin-furfuryl alcohol formaldehyde (TFF)—are analyzed. The LCA covers raw material sourcing, transport, and end-of-life scenarios, with impacts assessed in accordance with EN 15804+A2 using Earthster and the Ecoinvent v3.11 database. The proposed method integrates environmental and economic assessments, with results presented both per kilogram of adhesive and per cubic meter of glulam to ensure comparability. Results show that synthetic adhesives have higher environmental impacts than bio-based adhesives: the carbon footprint of 1 kg of adhesive averages 0.60 kg CO₂-eq for bio-based adhesives and 2.01 kg CO₂-eq for synthetic adhesives. LCC are similar across adhesives, averaging EUR 400 per m³ of glulam. These findings suggest that bio-based adhesives can compete environmentally and economically, but their limited availability and uncertain long-term performance remain barriers. Overall, the study highlights trade-offs between sustainability and structural reliability and provides guidance for sustainable adhesive selection in timber engineering. Full article

Fatigue and distracted driving are among the leading causes of traffic accidents, highlighting the importance of developing efficient and non-intrusive driver monitoring systems. Traditional camera-based methods are often limited by lighting variations, occlusions, and privacy concerns. In contrast, millimeter-wave (mmWave) radar offers a non-contact, privacy-preserving, and environment-robust solution, providing a forward-looking alternative. This study introduces a novel deep learning model, RTSFN (radar-based temporal-spatial fusion network), which simultaneously analyzes the temporal motion changes and spatial posture features of the driver. RTSFN incorporates a cross-gated fusion mechanism that dynamically integrates multi-modal information, enhancing feature complementarity and stabilizing behavior recognition. Experimental results show that RTSFN effectively detects dangerous driving states with an average F1 score of 94% and recognizes specific high-risk behaviors with an average F1 score of 97% and can run in real-time on edge devices such as the NVIDIA Jetson Orin Nano, demonstrating its strong potential for deployment in intelligent transportation and in-vehicle safety systems. Full article

The growing demand for electricity and the urgent need to reduce environmental impact have made sustainable energy utilization a global priority. Street lighting, as a significant consumer of urban electricity, requires innovative solutions to enhance efficiency and reliability. This study presents an off-grid smart street lighting system that combines solar photovoltaic generation with battery storage and Internet of Things (IoT)-based control to ensure continuous and efficient operation. The system integrates Long Range Wide Area Network (LoRaWAN) communication technology for remote monitoring and control without internet connectivity and employs the Perturb and Observe (P&O) maximum power point tracking (MPPT) algorithm to maximize energy extraction from solar sources. Data transmission from the LoRaWAN gateway to the cloud is facilitated through the Message Queuing Telemetry Transport (MQTT) protocol, enabling real-time access and management via a graphical user interface. Experimental results demonstrate that the proposed system achieves a maximum MPPT efficiency of 97.96%, supports reliable communication over distances of up to 10 km, and successfully operates four LED streetlights, each spaced 400 m apart, across an open area of approximately 1.2 km—delivering a practical, energy-efficient, and internet-independent solution for smart urban infrastructure. Full article

This review provides a mechanistic framework to strategically design nanoparticles capable of efficiently crossing the blood–brain barrier (BBB), a critical limitation in neurological treatments. We systematically analyze nanoparticle–BBB transport mechanisms, including receptor-mediated transcytosis, adsorptive-mediated transcytosis, and transient barrier modulation. Essential nanoparticle parameters (size, shape, stiffness, surface charge, and biofunctionalization) are evaluated for their role in enhancing brain targeting. For instance, receptor-targeted nanoparticles can significantly enhance brain uptake, achieving levels of up to 17.2% injected dose per gram (ID/g) in preclinical glioma models. Additionally, validated preclinical models (human-derived in vitro systems, rodents, and non-human primates) and advanced imaging techniques crucial for assessing nanoparticle performance are discussed. Distinct from prior BBB nanocarrier reviews that primarily catalogue mechanisms, this work (i) derives quantitative ‘design windows’ (size 10–100 nm, aspect ratio ~2–5, near-neutral ζ) linked to transcytosis efficiency, (ii) cross-walks human-relevant in vitro/in vivo models (including TEER thresholds and NHP evidence) into a translational decision guide, and (iii) integrates regulatory/toxicology readiness (ISO 10993-4, FDA/EMA, ICH) into practical checklists. We also curate recent (2020–2025) %ID/g brain-uptake data across lipidic, polymeric, protein, inorganic, and hybrid vectors to provide actionable, evidence-based rules for BBB design. Full article

UAV delivery and ground transfer scheduling in emergency scenarios represent critical technological systems for enhancing disaster response capabilities and safeguarding lives and property. This study systematically reviews recent advances across eight core research domains: UAV emergency delivery systems, ground–air integrated transportation coordination, emergency logistics optimization, UAV path planning and scheduling algorithms, collaborative optimization between ground vehicles and UAVs, emergency response decision support systems, low-altitude economy and urban air traffic management, and intelligent transportation system integration. Research findings indicate that UAV delivery technologies in emergency contexts have evolved from single-aircraft applications to intelligent multi-modal collaborative systems, demonstrating significant advantages in medical supply distribution, disaster relief, and search-and-rescue operations. Current technological development exhibits four major trends: hybrid optimization algorithms, multi-UAV cooperation, artificial intelligence enhancement, and real-time adaptation capabilities. However, critical challenges persist, including regulatory framework integration, adverse weather adaptability, cybersecurity protection, human–machine interface design, cost–benefit assessment, and standardization deficiencies. Future research should prioritize distributed decision architectures, robustness optimization, cross-domain collaboration mechanisms, emerging technology integration, and practical application validation. This comprehensive review provides systematic theoretical foundations and practical guidance for emergency management agencies in formulating technology development strategies, enterprises in investment planning, and research institutions in determining research priorities. Full article

The Gonghe–Yushu Expressway (GYE) traverses the degrading permafrost region of the Qinghai–Xizang Plateau, where climate warming has resulted in widespread water ponding, posing significant engineering challenges. However, the spatiotemporal dynamics of this water accumulation and its impacts on permafrost embankment stability remain inadequately understood. This study integrates high-resolution unmanned aerial vehicle (UAV) remote sensing with ground-penetrating radar (GPR) to characterize the spatial patterns of water ponding and to quantify the spatial distribution, seasonal dynamics, and hydrothermal effects of roadside water on permafrost sections of the GYE. UAV-derived point cloud models, optical 3D models, and thermal infrared imagery reveal that approximately one-third of the 228 km study section of GYE exhibits water accumulation, predominantly occurring near the embankment toe in flat terrain or poorly drained areas. Seasonal monitoring showed a nearly 90% reduction in waterlogged areas from summer to winter, closely corresponding to climatic variations. Statistical analysis demonstrated significantly higher embankment distress rates in waterlogged areas (14.3%) compared to non-waterlogged areas (5.7%), indicating a strong correlation between surface water and accelerated permafrost degradation. Thermal analysis confirmed that waterlogged zones act as persistent heat sources, intensifying permafrost thaw and consequent embankment instability. GPR surveys identified notable subsurface disturbances beneath waterlogged sections, including a significant lowering of the permafrost table under the embankment and evidence of soil loosening due to hydrothermal erosion. These findings provide valuable insights into the spatiotemporal evolution of water accumulation along transportation corridors and inform the development of climate-adaptive strategies to mitigate water-induced risks in degrading permafrost regions. Full article

The development of alternative transportation methods has become paramount in the context of sustainable urban population connectivity. The promise of hyperloop as a high-speed, low-emission travel means motivates both academic and industrial interests. The present work centers on the design of hyperloop levitation systems. A component-level optimization is outlined for the appropriate selection of levitation module geometric parameters, followed by an integration into a capsule and bogie system. Two heteropolar levitation module types are numerically studied in realistic operating conditions: a hybrid electromagnet configuration with permanent magnets and a fully active one. To give means for comparison, both configurations are designed with the aid of a general multi-objective optimization approach. For the hybrid case, a position controller is synthesized with a zero-power policy and a specific frequency response function. The active configuration features comparable behavior. Two main power consumption streams are considered: gap control and magnetic drag. While the former depends on the position control effort, the latter depends on the losses of ferromagnetic elements. The two systems are compared in smooth and irregular track conditions over the studied speed range of 400–700 km/h. This study demonstrates that the hybrid heteropolar case achieves a minimum of 97.6% in specific power consumption reduction at the maximum speed of 700 km/h under smooth track conditions. Under irregular track conditions, a benefit in average specific consumption reduction is noted up to 662 km/h for the hybrid case. The maximum reduction in specific consumption is 57.2% at the minimum speed of 400 km/h. Full article

Urban livability and sustainable development remain major global challenges, yet the interplay between urban planning layouts and actual human activities has not been sufficiently examined. This study investigates this relationship in Beijing by integrating multi-source spatiotemporal data, including point of interest (POI), Land Use Cover Change (LUCC), remote sensing data, and the railway network. Defining urban functional units as “street + railway network”, we analyze the spatial–temporal evolution within the 6th Ring Road over the past four decades and propose targeted strategies for the urban functional layout. The results reveal the following: (1) The evolution of Beijing’s urban functions can be divided into four stages (1980–1990, 1990–2005, 2005–2015, and 2015–2020), with continuous population growth (+142%) driving the over-concentration of functions in central districts. (2) Between 2010 and 2020, the POI densities of medical services (+133.6%) and transport services (+130.48%) increased most rapidly, subsequently stimulating the expansion of other urban functions. (3) High-density functional facilities and construction land (+179.10%) have expanded significantly within the 6th Ring Road, while green space (cropland, forestland and grassland) has decreased by 86.97%, resulting in a severe imbalance among land use types. To address these issues, we recommend the following: redistributing high-intensity functions to sub-centers such as Tongzhou and Xiongan New Area to alleviate population pressure, expanding high-capacity rail transit to reinforce 30–50 km commuting links between the core and periphery, and establishing ecological corridors to connect green wedges, thereby enhancing carbon sequestration and environmental quality. This integrated framework offers transferable insights for other megacities, providing guidance for sustainable functional planning that aligns human activity patterns with urban spatial structures. Full article

Understanding the spatial heterogeneity and driving mechanisms of traditional villages is critical for their tailored preservation and revitalization. Existing studies often overlook intra-regional variations shaped by historical and cultural contexts. In addition, the lack of systematic quantitative approaches limits the formulation of effective conservation strategies. This study addresses these gaps by examining 71 nationally listed traditional villages across five prefectures in southern Sichuan, China. We first mapped spatial patterns using ArcGIS10.5 and Geodetector. Then we applied GWR (adjusted R² = 0.70), K-means clustering, and Kruskal–Wallis tests to examine the spatial heterogeneity. This workflow resulted in three different village clusters related to historical migration: S1-Indigenous (n = 14)—Villages established before the Ming Dynasty, primarily inhabited by indigenous Sichuan residents. S2-Huguang migrants (n = 30)—Villages formed during the late Ming to early Qing “Huguang Migration to Sichuan,” facilitated by proximity to rivers and transport routes. S3-Refugees (n = 27)—Villages settled by war refugees from northern and eastern Sichuan, often located in secure, high-elevation areas. Based on these findings, we propose tailored conservation strategies: preserving historical layout and architectural integrity in S1; maintaining migration-shaped forms and highlighting cultural imprints in S2; and balancing spatial conservation with improved mountain road accessibility in S3. Full article