Search Results (518)

Death feigning (thanatosis) is the antipredator behaviour of grass snake (Natrix natrix) which can contribute to its survival. Since this is difficult to study under natural conditions, in our experiment we used four types of plasticine models of grass snake to mimic its different behaviour types. The advantage of using plasticine models is that they preserve the traces of the predators. The study was conducted at two different locations in Kis-Balaton, one of the largest wetlands in the Carpathian Basin. Only 8.75% (n = 21) of all grass snake models (n = 240) were damaged by predators. The predation activity of birds (76%) was significantly higher than that of mammals (24%), the latter left imprints only on models imitating live (uncoiled, coiled) grass snakes. Among predated models, 62% showed injuries on the midbody, 24% on the tail tip, and 14% on the head. Predation rates were similar in spring and autumn periods, and on the abandoned railway and road. Comparisons of daily survival rates between different model types suggested that the coiled (basking and death feigning) pose may provide an advantage in survival. In some cases, the visible colour pattern of the models may have affected their detectability. The higher survival rates of the grass snake models imitating death feigning indicate the adaptive value of this antipredator behaviour. Full article

Rapid urbanization and extreme rainfall have increased pluvial flood pressure in dense urban areas, yet many drainage problems emerge at microscale interfaces that conventional flood monitoring does not capture well. Focusing on the built-up area of Zhengzhou, this study uses waterlogging complaints from the People’s Daily Online Leadership Message Board to identify resident-perceived flood hotspots. Government replies, field verification, and built-environment indicators are then combined to examine how these sites are described, assigned, and validated. The complaint records reveal recurrent flood pressure at interface settings, including underground-space entrances, residential-compound–road edges, road depressions, and project boundaries. Field checks confirm that several reported hotspots correspond to visible site conditions. Government replies, however, differ in how clearly they recognize local drainage settings and responsibility boundaries. Persistent mismatches are concentrated at sites shared by multiple actors, and project-edge areas where maintenance and construction responsibilities are difficult to separate. Grid-based diagnosis further shows that planning priority is identified more effectively through the overlap of built-environment exposure, observed site pressure, and governance mismatch than through exposure indicators alone. The study treats complaint–reply exchanges as participatory spatial evidence and proposes a diagnostic procedure linking hotspot interfaces, responsibility boundaries, and field verification for pluvial flood planning. Full article

Urban transportation systems face increasing sustainability and resilience challenges due to the growing frequency and intensity of weather extremes. Weather-related congestion may increase travel delays, fuel consumption, and unequal economic costs, thereby undermining urban sustainability. Although previous studies have examined the relationship between weather conditions and traffic congestion, limited attention has been paid to whether street-network design can enhance transportation resilience under extreme weather conditions. This study investigates the relationships among extreme weather, traffic congestion, and street density using daily congestion and meteorological data from 35 major Chinese cities between 2018 and 2024. Fixed-effects regressions estimate the associations between multiple weather extremes and congestion and examine the moderating role of street density. Heavy rainfall, extreme cold, and low visibility are associated with increased congestion, whereas extreme heat is associated with reduced congestion. Street density could buffer congestion under extreme cold and heavy snow cover, suggesting that denser networks may improve resilience to localized road-surface disruptions. Heterogeneity analyses reveal weaker weather-related congestion responses in megacities and clustered cities, and during the COVID-19 period. These findings highlight the potential role of street-network design in supporting sustainable and climate-resilient transportation by reducing vulnerability to weather-related congestion. Full article

Provincial cold-chain investment decisions must reconcile food-loss prevention, digital visibility, logistics capability and the environmental burden of freight-intensive growth. This study develops a sustainability-oriented readiness–intensity framework for 31 provincial-level regions in mainland China. The baseline model uses 14 auditable public-data criteria and combines Criteria Importance Through Intercriteria Correlation (CRITIC) with the standard Combined Compromise Solution (CoCoSo) algorithm. Because the observations combine 2024 statistics, a 2023 digital-finance index and the cumulative 2020–2025 cold-chain-base list, the design is described as an asynchronous cross-sectional snapshot rather than a single-year panel. Municipal sewage and green-space variables are interpreted as regional enabling capacity, not direct cold-chain environmental performance; road freight turnover relative to gross domestic product is treated as a cost-type freight-intensity transition-pressure proxy. A separate diagnostic replaces the earlier inverse-size term with logistics residuals conditional on agri-food output. Shandong, Guangdong, Jiangsu, Henan and Zhejiang form the leading demonstration-readiness group. Equal-weight CoCoSo closely matches the CRITIC result (Spearman ρ = 0.996), while TOPSIS and VIKOR retain the broad ordering but expose local method sensitivity. Dropping either digital criterion, removing the three indirect green proxies, winsorizing the normalization range, varying the CoCoSo compromise parameter and substituting 2022 digital data do not alter the leading pattern. Under an assumed 5% indicator-error perturbation, Shandong and Guangdong remain within ranks 1–2, whereas the ordering of several adjacent provinces is less secure. The framework supports sequenced investment packages rather than a deterministic league table and distinguishes demonstration-ready, scale-led, intensity-led and coverage-building contexts. Full article

High-rise and high-density development is often assumed to reduce the visual quality of urban landscapes, yet building height alone does not fully explain what pedestrians can see at street level. In compact cities such as Seoul, taller buildings may obstruct views, but they may also reduce ground coverage and create more open space around buildings. This study examines how building height and building coverage jointly shape pedestrian-level visibility in high-density urban environments. Using Seoul as a case study, we developed a citywide 3D ISOVIST framework based on building footprint and height data. The city was divided into 500 m × 500 m grid cells, and road-based observation points were used to simulate pedestrian-level visual fields. Urban form was classified into nine height–coverage typologies, and visibility was measured using sky viewing ratio, building obstruction ratio, mean viewing distance, and viewing distance variance. The results show that pedestrian-level visibility varies systematically across height–coverage combinations. Visibility generally decreases as building coverage increases, while the effect of building height is not linear. In some high-rise conditions, reduced ground coverage can partly compensate for vertical obstruction and maintain visual openness at the pedestrian level. These findings suggest that urban landscape regulation should not rely on height control alone. Instead, building height, coverage, spacing, and ground-level openness should be considered together when evaluating the visual effects of dense urban development. This study contributes a scalable geometric visibility framework for urban morphology analysis while acknowledging that visual openness is only one dimension of broader urban landscape quality. Full article

Battery electric vehicle (BEV) fires in road tunnels can intensify thermal, smoke transport, visibility, and CO exposure hazards under confined ventilation. This study evaluated the combined mitigation performance of water mist and semi-transverse mechanical ventilation. A three-dimensional PyroSim/FDS model of a 200 m × 10 m × 5 m tunnel was established with a 7 MW BEV design fire at the midpoint. The prescribed-source model was assessed against a reduced-scale lithium-ion battery tunnel experiment; at the representative monitoring location, the simulated temperature history reproduced the main trend, with deviations of approximately 7% and 10% at the first and second peaks. Thirty-six coupled cases examined ventilation mode, nominal opening velocity, nozzle arrangement and spacing, flow rate input, droplet diameter, and spray cone angle. Supply ventilation improved hot-smoke-layer cooling and visibility, whereas exhaust ventilation more effectively reduced the local CO volume fraction. Under the baseline weighting scheme, the highest-ranked case reduced the peak local ceiling-region and near-fire gas temperatures by 77.8% and 82.2%, increased average visibility during 200–500 s by 42.9%, and achieved a comprehensive relative mitigation index (CRMI) of 56.6%. Two supplementary nominal 10 MW simulations showed that this case retained substantial thermal control, reducing the two peak temperatures by 65.7% and 74.1%, but did not improve local visibility or CO. Thus, the thermal-mitigation trend persisted at the higher nominal input, whereas the full multi-hazard ranking was not transferable across fire sizes. Full article

Foggy weather degrades road images by reducing visibility, attenuating object contrast, and blurring boundaries, while autonomous ground vehicles require accurate and lightweight environment perception on resource-constrained onboard platforms. This study proposes Fog-YOLO11n, a lightweight traffic object detector based on YOLO11n. A GhostRTA backbone combines RepGhostNet with the proposed C2RTA module: RepGhostNet reduces redundant computation, whereas C2RTA uses channel statistics and multi-scale spatial context to reinforce weak low-contrast responses. FRISA, a dual-branch interactive attention module, separates semantic responses from residual details and uses bidirectional gates to suppress fog- and reflection-related textures while preserving object contours. SD-MPDIoU adaptively modulates corner-distance penalties according to relative box geometry and reweights samples by localization quality, improving regression stability for ambiguous boundaries. Experiments on the RTTS dataset show improvements of 3.80 and 2.52 percentage points in mAP@0.5 and mAP@0.5:0.95, respectively, over YOLO11n, while reducing parameters from 2.59 M to 2.14 M and computation from 6.44 to 5.66 GFLOPs. The resulting accuracy-complexity balance supports real-time foggy-road perception for autonomous unmanned vehicles. Full article

Nighttime driving and adverse weather expose persistent weaknesses in autonomous-driving perception pipelines. Low illumination, fog, rain, snow, glare, wet-road reflections, and motion blur degrade camera, LiDAR, radar, and event-camera inputs in modality-specific ways. This review examines robust perception under low-visibility conditions as a pipeline-level problem requiring joint consideration of image enhancement, sensor fusion, detection, and evaluation. Rather than treating enhancement as an isolated restoration task, it analyzes whether recent methods preserve detector-relevant structures, exploit cross-sensor complementarity, and improve downstream 2D and 3D perception. A taxonomy-driven narrative approach compares representative studies along five axes, from input modality and supervision strategy to evaluation protocol and deployment feasibility, supported by a structured verification search of literature published between January 2020 and July 2026, with the search strategy, eligibility criteria, and corpus composition documented. Recent work indicates a shift from image-quality-oriented restoration toward perception-driven optimization, in which enhancement and fusion modules are evaluated by their effect on object detection and 3D perception. Remaining challenges include generalization to compound degradations, cross-sensor misalignment, scene-dependent sensor reliability, latency on in-vehicle edge platforms, and inconsistent benchmark protocols. Future systems should therefore jointly model degradation severity, sensor reliability, downstream task performance, and real-time constraints rather than optimizing restoration, fusion, and detection modules in isolation. Full article

Conventional solvent-based and hot-melt road marking paints face significant challenges regarding high volatile organic compound (VOC) emissions and limited durability, necessitating the development of eco-friendly, high-performance alternatives. In this study, a waterborne acrylic road marking paint was systematically formulated and optimized using an L₁₆(4⁵) orthogonal experimental design coupled with a comprehensive weighted scoring method integrating subjective and objective (entropy) weights. Four key formulation parameters (pigment-to-binder ratio, titanium dioxide content, ground calcium carbonate content, and coalescing agent dosage) were investigated, with abrasion resistance, hiding power, luminance factor, and stain resistance as evaluation criteria. The optimized formulation was identified through range analysis of comprehensive scores and subsequently subjected to rigorous performance characterization, including retroreflectivity optimization, Taber and accelerated abrasion testing, UV-accelerated weathering, skid resistance, and VOC emissions measurement using a self-designed sealed chamber system. Benchmark comparisons against commercial waterborne and hot-melt paints demonstrated that the developed formulation achieves superior abrasion resistance, exceptional weatherability, and meaningfully lower VOC emissions. Field application on an operational highway section in Liaoning Province, China, confirmed the practical constructability and performance reliability of the optimized paint under real-world construction conditions. This research provides both theoretical guidance and practical validation for the design of sustainable, durable, and highly visible road marking materials, contributing to the advancement of environmentally responsible transportation infrastructure. Full article

The dominant discourse on tourism resilience has traditionally emphasized physical infrastructure, environmental management, and technological solutions as key mechanisms for adapting destinations to climate change and socio-economic disruptions. While these dimensions remain essential, growing evidence suggests that the long-term sustainability of tourism destinations, particularly in remote and vulnerable regions, depends equally on less visible but equally essential community capacities. This paper introduces the concept of human infrastructure as a complementary perspective for understanding resilience in tourism development. Human infrastructure encompasses social relationships, local leadership, collective efficacy, cultural identity, community participation, intergenerational knowledge transfer, and educational capacity, which enable communities to adapt, recover, and innovate under conditions of uncertainty. Drawing upon an integrative conceptual review of the literature on tourism studies, community psychology, rural development, resilience theory, and sustainable development, this conceptual paper proposes a human-centered framework explaining how communities themselves function as adaptive infrastructures. The article argues that resilient tourism destinations emerge not only from investments in roads, visitor centers, and digital technologies, but also from investments in social cohesion, local knowledge systems, community empowerment, and cultural continuity. The paper concludes by discussing policy implications for sustainable tourism planning and proposing future research directions for operationalizing and measuring human infrastructure in tourism contexts. Full article

Infrared–visible image fusion aims to integrate thermal target information from infrared images and structural texture information from visible images into a single informative image. Existing deep fusion methods still face challenges in preserving fine textures, maintaining structural consistency, and balancing complementary information under low-light conditions. To address these issues, this paper proposes MRCDFusion, an unsupervised infrared–visible image fusion network based on residual conditional diffusion and Transformer refinement. Specifically, a shared dense encoder is used to extract modality-specific and cross-modal complementary features from infrared and visible images. A Modality-Level Attention Module (MLAM) is then introduced to aggregate strong responses from infrared and visible features and construct modality-aware condition features for guiding the diffusion process. Instead of generating fused features from scratch, the proposed method adopts a base-plus-residual diffusion strategy, in which base features preserve global structures and residual diffusion enhances local details. A deterministic noise strategy is further introduced to improve inference reproducibility. The diffusion-enhanced features are refined by a window Transformer and depthwise separable convolutions, followed by gated feature fusion and progressive image reconstruction. Experiments are conducted primarily on the low-light LLVIP dataset, while FMB, TNO, and RoadScene are used for zero-shot cross-dataset evaluation without additional fine-tuning. The results show that MRCDFusion achieves particularly strong performance in gradient- and edge-related metrics while remaining competitive in visual information fidelity and cross-modal correlation metrics. Ablation studies verify the effectiveness of the main components, and downstream detection and auxiliary segmentation experiments further demonstrate the potential utility of the fused representations for subsequent visual perception tasks. Full article

Black ice poses a serious winter road-safety hazard because its transparent appearance and localized formation make dangerous pavement conditions difficult to detect before vehicles enter affected areas. Converting this hidden low-temperature risk into visible surface information is therefore important for intuitive driver warning and road management. In this study, groove-filling thermochromic polyurea-based composites were fabricated using aliphatic polyurea resin, reversible thermochromic pigment (R.T.P.), and silica sand, and their colorimetric response, mechanical properties, and groove-filling applicability were investigated. Considering visual response and mechanical performance, the mixture containing 10 wt.% binder and 20 phr R.T.P. was selected as the most balanced candidate. For this mixture, the CIE L*a*b* values changed from 58.36, 3.85, and 13.32 for the control specimen to 47.82, 22.95, and 8.52, respectively, indicating a darker and more reddish appearance. The maximum compressive and flexural strengths were 10.13 MPa and 6.20 MPa, respectively. In addition, the selected mixture retained its low-temperature color response in the groove-filled state, confirming its preliminary groove-filling applicability. These results suggest the potential of thermochromic polyurea-based composites as visual-warning pavement materials for winter road safety. Full article

Rapid urbanization and increasing traffic density have increased the crash risk of vulnerable road users (VRUs), particularly pedestrians and cyclists. Meanwhile, the conventional statistical models have difficulties in capturing the nonlinear and complex nature of crash data, limiting their safety analysis effectiveness. This study utilized advanced Gradient Boosting machine learning and integrated it with SHapley Additive exPlanations (SHAP) using five years of crash data from Michigan, USA, employing a two-tiered modeling design consisting of a 4-class joint structure and binary subset frameworks. Rigorously evaluated using 10-fold stratified cross-validation to predict crash severity for VRUs, the CatBoost model had better predictive performance (AUC = 0.917) than LightGBM, Random Forest and the traditional Logistic Regression models. The analysis further indicated that prior crash actions, particularly risky crossing behaviors, are the most significant determinants of injury severity for both user groups. However, the pedestrian crash severity is strongly associated with lighting conditions and speed limits, while cyclist crash severity is more heavily influenced by intersection involvement and roadway geometry. Moreover, SHAP interaction analysis showed that the speed effect on severity significantly increases when it interacts with hazardous actions or poor visibility. The findings provide a critical insight into the implementation of effective measures and infrastructure improvements to increase the safety of VRUs. Full article

Robust object detection for autonomous driving requires perception models that remain reliable when visible imagery is degraded by darkness, glare, rain, fog, motion blur, or long-range small targets. Visible and thermal infrared cameras provide complementary evidence, yet many RGB–thermal detectors fuse modalities, mainly as aligned tensors, and may underuse relational structure in channel responses, spatial layouts, semantic scales, and modality-specific uncertainty. This paper presents TopoGraph-Fusion, a hierarchical graph-guided dual-modal object detector that formulates fusion as topology-aware reasoning rather than direct feature concatenation. The proposed framework builds a dual-stream backbone for RGB and thermal images, constructs channel-wise topology through a channel-topology graph aggregation module, derives relation-aware spatial and channel global attention from affinity graphs, and replaces fixed feature-pyramid communication with a Graph-Guided Feature-Pyramid Network. A topology-regularized detection objective further encourages stable cross-modal correspondence while suppressing noisy all-to-all connections. Experiments on M3FD, FLIR, RGBTDronePerson, and VEDAI512 cover road scenes, adverse illumination, drone–person perception, and aerial vehicle detection. Within this validation scope, the results and visual analyses indicate that topology-guided fusion improves small-object recall, cross-modal consistency, and robustness under modality imbalance. Full article

Material extrusion additive manufacturing, commonly implemented as fused deposition modeling (FDM) or fused filament fabrication (FFF), has evolved into a manufacturing route for reinforced thermoplastic composites, including particle-filled, short-fiber-reinforced, and continuous-fiber-reinforced systems. The process is governed by a layer-by-layer thermal cycle. Deposited roads cool rapidly, are repeatedly reheated by subsequent material deposition, and finally cool non-uniformly as part of the growing structure. This thermal history generates residual-stress that may cause warpage, build–platform detachment, delamination, dimensional error, and reduced mechanical performance. This review synthesizes residual-stress formation, measurement, modeling, parameter effects, and mitigation in material-extruded reinforced thermoplastic composites, with emphasis on short and continuous-fiber systems. Stress formation is discussed in terms of constrained thermal contraction, crystallization shrinkage, anisotropic stiffness, fiber-constrained deformation, porosity, and fiber–matrix thermal expansion mismatch. Experimental methods, including hole drilling, layer removal, curvature methods, embedded fiber Bragg gratings, digital image correlation, photoelasticity, and warpage metrology, are critically compared for anisotropic and porous printed composites. Analytical and numerical models are reviewed from layerwise shrinkage formulations to crystallization-coupled thermo-viscoelastic finite element simulations. Finally, mitigation strategies are evaluated. A central conclusion is that reinforcement can suppress visible distortion while increasing stress retained in a stiffer structure. Therefore, warpage alone is not a sufficient residual stress metric. Full article