Search Results (260)

Stay cables are slender and lightly damped structural members that are highly sensitive to wind action. Although the aerodynamic effects of rain rivulets, ice accretion, snow accretion, and surface roughness on bridge cables have been widely investigated, the influence of architectural illumination attachments installed along stay cables remains insufficiently understood. Such attachments modify the original circular cable cross-section and may alter aerodynamic force coefficients, vortex-shedding characteristics, wake structure, and galloping tendency. This study investigates the aerodynamic characteristics of bridge stay cables modified by illumination attachments with different shapes, dimensions, and installation gaps. Large-eddy simulation was performed using OpenFOAM 5.0 to resolve the unsteady flow around a reference circular cable and ten illumination-modified cable configurations at a representative subcritical Reynolds number of R_e = 9.42 × 10⁴. The numerical model was first verified using benchmark aerodynamic properties of a circular cylinder and then validated against force measurements obtained from closed-circuit wind-tunnel experiments. The results show that illumination attachments significantly affect the drag and lift characteristics of stay cables, particularly at oblique wind attack angles. Among the representative wind attack angles considered, the drag coefficient generally increases when the attachment is exposed laterally or obliquely to the incoming flow, whereas the lift coefficient is strongly affected by attachment shape and angular orientation. Rectangular and double-rectangular attachments produce stronger wake disturbance, greater pressure asymmetry, and more complex vortex structures than circular attachments. The preliminary Den Hartog analysis further indicates that sharp-edged and direct-contact attachments may increase galloping susceptibility, whereas smaller circular attachments and separated-gap configurations show more moderate aerodynamic behavior. These findings indicate that illumination systems should not be treated as purely architectural accessories, but should be considered in the aerodynamic assessment and wind-resistant design of cable-supported bridges. Full article

Ice–snow tourism in Northeast China is developing rapidly. Against this backdrop, revealing the spatial network structure of ice–snow tourism cities and assessing their disturbance resistance capacity is of great significance for achieving high-quality development of regional ice–snow tourism. This study takes 25 cities across the three northeastern provinces as network nodes, using data covering the period from January 2024 to March 2025. Integrating a complex network analysis framework, this paper comprehensively employs an accessibility model, tourism symbiotic linkage intensity model, and core–periphery model to distinguish core and peripheral cities within the network, analyze its structural characteristics and spatial patterns, and evaluate network vulnerability by simulating two scenarios: random attacks and deliberate attacks. The results indicate that: (1) Accessibility presents a concentric zonal pattern that attenuates gradually from the center to the periphery, accompanied by pronounced north–south disparities. Urban symbiosis intensity is strongly influenced by transportation distance, exhibiting a distinct proximity symbiosis pattern. (2) An ice–snow tourism symbiotic network has initially taken shape among northeastern cities. The network displays small-world properties; however, urban development is unbalanced, with marked hierarchical differentiation. Based on geographic location and resource endowments, the network can be divided into four cohesive subgroups. (3) The symbiotic network proves robust under random attacks, whereas connectivity declines sharply under deliberate attacks, embodying typical “robust-yet-vulnerable” structural characteristics. Both expanding the scale of core nodes and optimizing inter-node connection weights can significantly enhance network robustness. The static identification and dynamic dependency evaluation framework constructed in this study can effectively identify key nodes and vulnerable links within ice–snow city networks, and can serve as a reference for the coordinated development and structural optimization of ice-snow tourism in Northeast China. Full article

A long-term, consistent dataset of blowing snow (BLSN), a common phenomenon over Antarctica, is essential for ice sheet mass balance analysis. While space-borne lidar missions such as Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and Ice, Cloud, and Land Elevation Satellite (ICESat-2) have provided valuable continental-scale BLSN observations, their temporal resolutions and spatial reaches present notable limitations. Using CALIPSOs for training, a machine learning approach has been developed to generate hourly Antarctic BLSN data on the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) grid, extending back to the 1980s. ICESat-2 observations are used to assess the post-CALIPSO period; the results demonstrate greater bias with BLSN layer heights and better agreement with BLSN fraction and optical depth. When compared with ground-based observations, MERRA-2 exhibits a comparable and lower BLSN fraction and lower layer heights than those recorded by ceilometers. This data record represents the longest continuous BLSN data record to date. This study provides an overview of key BLSN properties, including occurrence, height, and optical depth. The results highlight strong seasonal patterns: BLSN occurrence peaks during Antarctic winter, while both height and optical depth are higher in summer with no statistically significant trend. Spatially, East Antarctica exhibits higher BLSN occurrence than West Antarctica. Full article

Robust perception in adverse weather conditions remains a significant challenge for autonomous vehicles. Short-wave infrared (SWIR) sensors offer specific physical properties that enable them to penetrate atmospheric disturbances like fog, rain, and snow. However, effectively combining this robustness with the textural and colour information of visible (VIS) cameras is difficult due to signal decorrelation and the limitations of static fusion schemes. To address this, we present VISWIR (Visible and SWIR Weighted Image Reconstruction), a pixel-level fusion method based on a multi-scale pyramid architecture. We introduce an automated strategy for scheduling parameters based on weather conditions using an optimisation framework. Rather than relying on static weights, our method applies offline parameter scheduling to adjust fusion hyperparameters based on the meteorological context. We focus on a multi-objective optimisation approach that maximises perceptual image quality via No-Reference Image Quality Assessment (NR-IQA) metrics. Validated in controlled environment scenarios with varying weather severities, our results confirm the potential of VISWIR as a robust, lightweight algorithmic baseline to enhance the perception capabilities of autonomous vehicles in adverse weather conditions. Full article

Ultra-High Molecular Weight Polyethylene (UHMWPE), the standard base material in ski manufacturing, offers excellent gliding performance but exhibits limited mechanical and scratch resistance on hard and icy snow conditions. In this work, stainless steel is proposed as a mechanically robust alternative, and its inherently higher friction against snow is addressed through surface engineering. The snow friction behavior of 301H stainless steel surfaces decorated with fishbone-like microstructures combined with Laser-Induced Periodic Surface Structures (LIPSSs) was investigated using a custom-built snow tribometer. Several pattern designs, with different pitch distances and depths, were engraved using femtosecond laser pulse irradiation. We conducted morphological, physical, and chemical investigations through microscopy, static contact angle measurements, and X-ray Photoelectron Spectroscopy analyses. Results indicate that the gliding performance is not directly related to the modifications in surface chemistry and wetting behavior of the samples but is affected by the geometry and orientation with respect to the sliding direction of the specific micro- and nano-features. Overall, we achieved friction coefficient values comparable to those found in UHMWPE with a fast and economically sustainable single-step laser-texturing process. This approach allows the industrial up-scaling of the fishbone-texture design to real-size alpine ski prototypes. Full article

Object detection from unmanned aerial vehicle (UAV) imagery is essential for applications such as traffic monitoring, disaster response, and urban surveillance, yet most existing methods are developed and evaluated under clear-sky conditions. In real-world UAV operations, adverse weather including fog, rain, and snow introduces severe image degradation that simultaneously disrupts both the geometric and photometric properties of targets. This paper identifies two fundamental bottlenecks underlying this performance collapse: the lack of geometric invariance in standard convolutional operators and the inability of fixed receptive fields to reconstruct features corrupted by atmospheric interference. To address these bottlenecks, we propose SELPNet (Scene Exploration and Large Format Perception Network), a unified framework that integrates geometric alignment and multi-scale contextual perception into the YOLOv13 head. SELPNet consists of two key modules: (1) The Scene Exploration Convolution (SEC) leverages affine Lie group theory to construct a discrete manifold of rotation and scale transformations, actively probing multiple geometric views and selecting the most coherent response via a Maxout mechanism. (2) The Large Format Perception Module (LPM) introduces a dynamic dilation strategy with depthwise separable convolutions, progressively enlarging the receptive field from fine-grained edge preservation to scene-level contextual perception for semantic completion of degraded regions. We further construct and release AWU-OBB, a large-scale benchmark containing over 18,000 oriented bounding box-annotated UAV images across four representative scene categories. Ablation experiments demonstrate that SEC and LPM yield complementary gains, achieving a combined improvement of +4.26% mAP50 over the YOLOv13-n baseline with only 0.11 M additional parameters and 0.2 extra GFLOPs. The source code will be publicly released upon acceptance of this paper. Full article

For electric vehicles, accurate real-time estimation of the road friction coefficient is critical for maintaining stability, as the millisecond-level response of electric motors and the integration of regenerative braking demand higher perception fidelity than traditional internal combustion vehicles. This paper proposes a methodological framework for road friction estimation specifically designed for intelligent E-Chassis based on micro-signal features of intelligent tires and deep learning. An intelligent tire system, integrated with tri-axial accelerometers and strain gauges, was installed on the front-left wheel of a test vehicle to capture raw dynamic signals during transitions from cement to snow-covered surfaces across a velocity gradient of 10–50 km/h. The Savitzky–Golay convolutional smoothing algorithm was applied to reconstruct the high-frequency raw signals, enabling the extraction of a five-dimensional feature vector comprising vehicle velocity, peak strain, contact patch width, peak-to-peak acceleration, and signal standard deviation. The study revealed a natural filtering effect originating from the porous elastic properties of snow, resulting in a 60–70% reduction in signal standard deviation compared to cement, accompanied by a cliff-like feature collapse at the moment of snow entry. A BP neural network model with a 5-7-1 architecture achieved an identification accuracy of 96.2% on the test set, facilitating a rapid real-time prediction of the friction coefficient transitioning from 0.75 to 0.23. Unlike traditional methods, the proposed approach does not rely on high slip ratios and can complete identification within the first physical rotation cycle. This provides a robust physical criterion for the torque vectoring and regenerative braking stability of intelligent electric vehicles in extreme environments. Full article

Deploying lightweight convolutional neural networks (CNNs) to provide vision services on resource-constrained Internet of Things (IoT) devices has become the mainstream approach to addressing computing and energy consumption constraints. However, these IoT devices often operate in complex outdoor environments (e.g., fog, rain, and snow), and the quality of the data they collect is easily degraded, causing standard lightweight CNNs to experience a significant performance drop under such corrupted data. To this end, this paper proposes a Generative Nonlinear Transformation Filter (GNTF) method to improve the generalization performance of lightweight CNNs on corrupted data. The core of the GNTF is that only a portion of the filters are used as learnable parameters (named seed filters), while the remaining filters are generated by applying the nonlinear transformation to the seed filters, which is randomly initialized and fixed during training. This design makes the model parameters less dependent on the training data distribution, thereby regularizing the model, mitigating overfitting, and enhancing its robustness to data degradation. The GNTF further analyzes the structural characteristics of lightweight CNNs, showing that significant performance improvements can be achieved simply by replacing the depthwise convolutional modules. Furthermore, this paper examines the properties of various nonlinear transformation functions and finds that model robustness can be improved by applying simple translations. To verify the effectiveness of the GNTF, we conducted extensive experiments on the CIFAR-10/-100, CIFAR-10-C/-100-C, and ICONS-50 datasets, using the MobileNetV2, ShuffleNetV2, EfficientNet, and GhostNet models. The results show that the proposed GNTF can improve the model’s accuracy on corrupted data while reducing the number of trainable parameters in most cases. For example, on the CIFAR-10-C dataset, ShuffleNetV2 with the GNTF improves accuracy by about 3.3% over the original model while slightly reducing the number of trainable parameters. Full article

Mountainous areas are prone to extreme climatic conditions, and the lack of modern infrastructure makes it difficult to achieve sustainable construction. To overcome the challenges of thermal comfort, robustness, and post-occupancy performance in hazard zones like the Neelum Valley in Pakistan, this research proposes a Digital–Vernacular Integration Model (DVIM), which integrates traditional architectural expertise with modern digital technology. The research design was based on mixed-methods research with the integration of qualitative information obtained through interviews and household surveys (n = 120), and quantitative measures of indoor thermal environments and hazards-based spatial analysis. Vernacular buildings made of wood, stone, and mud were digitally reconstructed using geometric modeling with SketchUp and Autodesk Revit with building information (BIM)-based modeling for assigning materials’ properties. Simulations were carried out using DesignBuilder software with EnergyPlus engines for assessing thermal environment, snow resistance, and seismic resistance to local hazards. The incorporation of the double-layered wall resulted in the improvement of heat retention by 12 to 15%. Moreover, the optimized roof and walls of the hybrid model resulted in the reduction of the sensible heating demand by 42% when compared to the conventional log houses and nearly 80% when compared to the conventional concrete block houses of the modern era. The proposed hybrid model resulted in R-values ranging from 33 to 40 m²·K/W, which are significantly higher when compared to the R-values for conventional timber walls (R = 15 m²·K/W) and concrete block walls (R = 1.0 to 1.3 m²·K/W). These results show the effectiveness of the digitally optimized hybrid model in improving the thermal performance in severe climatic conditions. The results clearly show that the integration of traditional architecture with digital simulation can ensure that modern comfort and safety standards are met without affecting the cultural identity of the region. The proposed framework will be implemented in pilot projects to ensure that the hybrid architectural models are incorporated into regional building regulations. Full article

The development of passive microwave sensors traces back to Robert Dicke’s pioneering experiments in the 1940s. Since then, microwave radiometry has evolved into a key tool for Earth observation, strengthened by data from multiple satellite missions operating across different wavelengths. This paper reviews the state of the art in microwave radiometry for monitoring land surfaces. After introducing the theoretical foundations underpinning current missions, we present an overview of major satellite instruments. We then examine early theoretical advances in retrieving soil moisture and snow properties, two applications that contributed to the future development of satellite microwave radiometry missions for the observation of surface variables. Particular attention is given to radiative transfer theory and its solutions, which model the effects of roughness, vegetation, and snow cover. These approaches form the basis of today’s retrieval algorithms and remain central to future missions. Subsequent sections highlight the use of passive microwave data for estimating a variety of surface variables, the role of passive microwave in data assimilation systems and forthcoming missions dedicated to land monitoring. The review concludes with key achievements, ongoing challenges, and open issues—such as soil moisture retrieval under dense vegetation or snow property retrieval in melting conditions. Addressing these limitations is critical to fully exploiting microwave radiometry in the context of climate research and mitigation strategies. Full article

Emerging contaminants (ECs) comprise diverse pollutant classes that are increasingly detected in remote environments due to their persistence and long-range transport potential. In cold regions, atmospheric cold-trapping processes favour their accumulation in high-altitude and high-latitude snow and ice, which act as sensitive archives and secondary sources of contamination. While previous studies have addressed individual environmental compartments (e.g., snowpack, glacier ice, meltwater), focusing on specific contaminant classes, a systematic review integrating the occurrence, behaviour and impacts of major EC groups in polar and alpine snow and ice is still lacking. To fill this gap, this work synthesised current knowledge on the environmental fate of three key EC categories in the cryosphere: metals and metalloids (MMs), industrial chemicals and by-products (ICBs), and pharmaceuticals and personal care products (PPCPs). PRISMA guidelines were accurately followed for research, which was based on a Google Scholar search combining keywords on cryospheric matrices (snow, firn, ice cores), geographical regions (Arctic, Antarctic, Alps, high mountains), and contaminant classes. Of 350 records initially identified, 300 met the eligibility criteria (post-industrial snow, firn, or ice cores studies) after excluding studies focused on aerosol or meltwater-only, method-focused papers, pre-industrial datasets, urban-only investigations, and duplicates. Risk of bias was qualitatively assessed through manual screening, evaluating matrix eligibility, temporal consistency, analytical methods, detection limits, and duplicate data, with particular attention to inconsistencies in ECs classification. Strict operational definitions were therefore applied to ensure methodological coherence. Concentration data were harmonised into a standardised database, and findings were synthesised through a structured narrative supported by tabulated datasets organised by matrix and site. Overall, the evidence indicates widespread occurrence of ECs in the global cryosphere, with spatial variability linked to emission sources, long-range transport pathways, and snow physicochemical properties. Climate-change-driven alterations of snow dynamics, glacier retreat and permafrost thaw are expected to modify partitioning equilibria and enhance the secondary release of legacy and contemporary contaminants. However, significant limitations persist, including geographical gaps, variability in analytical sensitivity, lack of long-term monitoring for certain EC classes, and inconsistencies in contaminant classification frameworks. Despite these constraints, the synthesis highlights consistent emerging patterns and underscores the need to strengthen existing environmental protocols to mitigate potential risks to ecosystems and human health. Full article

With the increasing deployment of autonomous and semi-autonomous road vehicles, Advanced Driver Assistance Systems (ADASs) rely heavily on multi-modal sensing technologies to ensure safe and reliable operation. Among these sensors, Light Detection and Ranging (LiDAR) provides high-resolution three-dimensional environmental perception but is particularly vulnerable to adverse weather conditions such as snowfall. Snowfall can degrade LiDAR performance through signal attenuation, backscattering, false detections, and sensor surface contamination, ultimately reducing visibility and detection reliability. In this study, an experimental investigation was conducted in a climatic chamber to systematically assess LiDAR performance degradation under controlled snowfall conditions. Key parameters influencing sensor behavior, including chamber air temperature, precipitation intensity, and sensor orientation, were isolated and examined. Chamber temperature was varied to generate snow characteristics representative of dry and wet snow, while precipitation intensity was controlled by adjusting snow gun flow rates. Sensor orientation was modified to evaluate its effect on perceived precipitation and snow accumulation. The experimental results confirm the initial hypothesis that snowfall intensity, snow physical properties, and sensor orientation exert a significant influence on LiDAR performance degradation. Increasing precipitation intensity significantly accelerates both 3D target detection loss and 2D visibility reduction, with polynomial regression revealing a non-linear degradation response. Inclined sensor orientations exhibited more rapid performance deterioration compared to a horizontal configuration. These findings provide valuable insights into LiDAR vulnerability in snowy environments and support the development of mitigation strategies to improve ADAS and autonomous vehicle operation in cold climates. Full article

Thermal conductivity is a crucial parameter for heat transfer in asphalt pavements, especially in cold regions where electrically heated snow-melting systems are used. Steel slag, an industrial by-product with high thermal conductivity, holds significant potential to enhance the thermal performance of asphalt mixtures. However, its thermal behavior is influenced by various factors. This study established a thermal conductivity database consisting of 200 samples from published experimental studies, incorporating data collection, graphical digitization, and physically constrained expansion. Mixture composition, volumetric structure, and steel slag properties were used as input variables, with thermal conductivity as the output. Five machine learning models including k-nearest neighbors regression, decision tree, random forest, support vector regression, and gradient boosting were developed. Among them, random forest and gradient boosting showed the highest accuracy and robustness. Feature importance analysis revealed that steel slag content is the primary factor affecting thermal conductivity, while material properties and gradation parameters play secondary roles. This data-driven framework facilitates the efficient prediction and design of thermal conductivity in steel slag asphalt mixtures, supporting the engineering application of functional asphalt pavements. Full article

Accurate estimation of snowpack microwave backscatter is critical for retrieving key physical properties of snow, such as snow depth (SD) and snow water equivalent (SWE), typically modeled using radiative transfer models (RTM). Among the various sources of uncertainty in RTM simulations, snow–ground reflectivity—used as a boundary condition—plays a critical role in influencing the accuracy of simulated backscatter. This study leverages high-resolution X- and Ku-band synthetic aperture radar (SAR) backscatter aircraft measurements using SWESARR and SnowSAR from NASA’s SnowEx campaigns, co-located with in situ snow pit observations in Grand Mesa, Colorado, and uses a Bayesian MCMC parameter optimization model with RTM framework to estimate the key ground parameters such as surface roughness, moisture content, and specular-to-total reflectivity ratio (STRR) governing the estimation of the snow–ground reflectivity and quantify the uncertainties associated with them. At the X-band, increasing ground surface roughness reduced the simulated backscatter by ~1.5 dB across the tested range, increasing the STRR produced an additional ~1.0 dB decrease while the dielectric properties of the ground are highly sensitive to the moisture content of frozen soil, and increasing the moisture content even by 2% increased the backscatter by 2–3 dB. The retrieval sensitivity to the STRR is minimized in the 0.6–0.7 range and it can be fixed at 0.65 without having discernible impact. The Bayesian inversion reveals that the extreme parameter values act as diagnostic indicators of unmodeled complexity rather than retrieval failures, with representativeness error often dominating over instrument noise. The study provides a robust methodology for the estimation of the snow–ground backscatter boundary condition for forward modeling, ultimately aiding SWE and SD retrieval from active microwave observations. While this study relied on Grand Mesa, the framework developed here is general and, along with the model uncertainty, is directly transferable and broadly applicable to other snow-dominated mountain regions where active microwave observations can be used for snowpack monitoring. Full article

The problem of urban surface runoff (USR) treatment is associated with the presence of high concentrations of specific pollutants. One of these pollutants is petroleum product (PP), whose concentration depends on the season and the location of the formation of snow masses, meltwater, and rainwater. For USR treatment, it is possible to use very environmentally friendly and inexpensive technologies. The article discusses natural sorbents based on wood materials, which effectively remove dissolved petroleum products from water. Pine sawdust and shredded branches of maple, birch, and poplar are used as raw materials, which are waste products from the city’s woodworking enterprise and utilities. These materials were pre-microwave (MW) treated to improve their sorption properties. As a result of the experiment, it turned out that modified pine sawdust and crushed maple pinwheels proved to be the most effective sorbents. The maximum sorption capacity values were 0.689 mg/g and 0.952 mg/g for pine and maple sorbents, respectively. This article proposes schemes for filtering devices that can be used in practice in an urban environment. Full article