Search Results (420)

The study of the relationship between testicular morphology and sperm quality is a pressing issue, for which molecular genetic approaches, including quantitative analysis of gene expression, are being implemented. The aim of this study was to identify correlations between the histomorphological structure of the testes, fresh sperm parameters, and the expression level of key spermatogenesis genes—TGFB2 and DMRT1—in roosters. The experiment was conducted on 10 Russian Snow White roosters aged 28–32 weeks. Sperm quality was assessed by volume, sperm concentration, total and progressive motility, and viability; histological analysis of the rooster testes was performed. The relative expression of the TGFB2 and DMRT1 genes in sperm was analyzed. Multiple correlation analysis of the data was conducted. A positive correlation was found between ejaculate volume and the number of spermatogonia (p = +0.651), a negative correlation between ejaculate volume and the number of second-order spermatocytes (p = −0.704), a negative correlation between the total cross-sectional area of the seminiferous tubules of the testes and sperm viability (p = −0.782), a negative correlation between the number of seminiferous tubules and the average diameter of their cross-section (p = −0.685), and a positive correlation between total and progressive sperm motility (p = +0.794). Analysis of TGFB2 and DMRT1 gene expression in sperm demonstrated a certain relationship between molecular genetic mechanisms and histomorphometric parameters. The expression level of the DMRT1 gene, which plays a key role in sex determination in birds during embryogenesis, had a number of negative correlations with such parameters as testicle weight (r = −0.782), total/progressive sperm motility (r = −0.552; r = −0.612), and viability (r = −0.552). Expression of the TGFB2 gene had no significant relationship with the studied parameters, but correlation analysis revealed a moderate positive relationship (r = +0.321) with DMRT1 gene expression. The data obtained indicate the expediency of integrating morphometric, cellular, and molecular analysis for an objective assessment of rooster reproductive function. Full article

Object detection for autonomous vehicles under adverse weather conditions—such as rain, fog, snow, and low light—remains a significant challenge due to severe visual distortions that degrade image quality and obscure critical features. This paper presents YOLOv11-TWCS, an enhanced object detection model that integrates TransWeather, the Convolutional Block Attention Module (CBAM), and Spatial-Channel Decoupled Downsampling (SCDown) to improve feature extraction and emphasize critical features in weather-degraded scenes while maintaining real-time performance. Our approach addresses the dual challenges of weather-induced feature degradation and computational efficiency by combining adaptive attention mechanisms with optimized network architecture. Evaluations on DAWN, KITTI, and Udacity datasets show improved accuracy over baseline YOLOv11 and competitive performance against other state-of-the-art methods, achieving mAP@0.5 of 59.1%, 81.9%, and 88.5%, respectively. The model reduces parameters and GFLOPs by approximately 19–21% while sustaining high inference speed (105 FPS), making it suitable for real-time autonomous driving in challenging weather conditions. Full article

To enhance the accuracy of discrete element method (DEM) simulation for the snow removal process performed by autonomous robots on membrane structures, this study calibrated the key contact parameters of snow particles used in the simulation. Through literature research, the intrinsic parameters and contact parameter ranges for snow particles and membrane structures were determined. A discrete element model of snow particles was established, and the Hertz–Mindlin with Johnson–Kendall–Robert contact model was selected to simulate the formation process of the repose angle. Using the actual repose angle of snow particles as the target, four significant factors were identified through the P-B experiment, and other factors were set at the intermediate level. Through the steepest slope climbing experiment and response surface design, second-order response equations of the four significant factors were obtained. The optimal parameter combination was calculated as follows: the surface energy of snow particles was 0.23 J/m²; the restitution coefficient, static friction coefficient, and rolling friction coefficient of snow–snow were 0.141, 0.05, and 0.03; and the restitution coefficient, static friction coefficient, and rolling friction coefficient of snow–membrane were 0.2, 0.18, and 0.03. The simulated repose angle was 40.62°, and the relative error with the actual repose angle was 0.32%. These calibration results are reliable and can provide a reliable simulation basis and essential data support for the optimal design of a snow removal robot and the dynamic simulation of the operation process. Full article

With the increasing frequency of traffic safety issues and the rapid development of autonomous driving technology, traffic sign detection is highly susceptible to adverse weather conditions such as changes in light intensity, fog, rain, snow, and partial occlusion, which places higher demands on the accurate recognition of traffic signs. This paper proposes an improved DAYOLO model based on YOLOv8n, aiming to balance detection accuracy and model complexity. First, the Bottleneck in the C2f module of the YOLOv8n backbone network is replaced with Bottleneck DAttention. Introducing DAttention allows for more effective feature extraction, thereby improving model performance. Second, an ultra-lightweight and efficient upsampler, Dysample, is introduced into the neck network to further improve performance and reduce computational overhead. Finally, a Task-Aligned Dynamic Detection Head (TADDH) is introduced. TADDH enhances task interaction through a dynamic mechanism and utilizes shared convolutional modules to reduce parameters and improve efficiency. Simultaneously, an additional Layer2 detection head is added to the model to strengthen the extraction and fusion of features at different scales, thereby improving the detection accuracy of small traffic signs. Furthermore, replacing SlideLoss with NWDLoss can better handle prediction results with more complex distributions and more accurately measure the distance between predicted and ground truth boxes in the feature space during object detection. Experimental results show that DAYOLO achieves 97.2% mAP on the SDCCVP dataset, which is 5.3 higher than the baseline model YOLOv8n; the frame rate reaches 120, which is 37.8% higher than YOLOv8; and the number of parameters is reduced by 6.2%, outperforming models such as YOLOv3, YOLOv5, YOLOv6, and YOLOv7. In addition, DAYOLO achieves 80.8 mAP on the TT100K dataset, which is 9.2% higher than the baseline model YOLOv8n. The proposed method achieves a balance between model size and detection accuracy, meets the needs of traffic sign detection, and provides new ideas and methods for future research in the field of traffic sign detection. Full article

Permafrost degradation under climate warming has profound implications for ecological processes, hydrology, and human activities. Northeast China, characterized by sporadic and isolated patch permafrost near the southern limit of latitudinal permafrost (SLLP), represents one of the most sensitive and complex permafrost regions. This study aims to improve the reliability of permafrost mapping by incorporating parameter uncertainty into simulations. We developed a Monte Carlo–Temperature at the Top of Permafrost (TTOP) (MC–TTOP) framework that integrates an equilibrium model with Monte Carlo sampling to quantify parameter sensitivity and model uncertainty. Using all-sky daily air temperature data and land use and land cover information, we generated probabilistic estimates of mean annual ground temperature (MAGT), permafrost occurrence probability (PZI), and associated uncertainties. Model validation against borehole observations demonstrated improved accuracy compared with global-scale simulations, with a reduced bias and RMSE. Results reveal that permafrost in Northeast China was relatively stable during 2003–2010 but experienced pronounced degradation after 2011, with the total area decreasing to ~2.79 × 10⁵ km² by 2022. Spatial uncertainty was greatest in transitional zones near the southern boundary, where PZI-based delineations tended to overestimate permafrost extent. Regional comparisons further showed that permafrost in Northeast China is more fragmented and uncertain than that on the Tibetan Plateau, owing to complex snow–vegetation–topography interactions and intensive human disturbances. Overall, the MC-TTOP simulations indicate an accelerated permafrost degradation after 2011, with the highest uncertainty concentrated in southern transitional zones near the SLLP, demonstrating that the MC-TTOP framework provides a robust tool for probabilistic permafrost mapping, offering improved reliability for regional-scale assessments and important insights for future risk evaluation under climate change. Full article

The re-identification of endangered felines is critical for species conservation and biodiversity assessment. This paper proposes the Pose-Guided Network with the Adaptive L2 Regularization (PGNet-AL2) framework to overcome key challenges in wild feline re-identification, such as extensive pose variations, small sample sizes, and inconsistent image quality. This framework employs a dual-branch architecture for multi-level feature extraction and incorporates an adaptive L2 regularization mechanism to optimize parameter learning, effectively mitigating overfitting in small-sample scenarios. Applying the proposed method to the Amur Tiger Re-identification in the Wild (ATRW) dataset, we achieve a mean Average Precision (mAP) of 91.3% in single-camera settings, outperforming the baseline PPbM-b (Pose Part-based Model) by 18.5 percentage points. To further evaluate its generalization, we apply it to a more challenging task, snow leopard re-identification, using a dataset of 388 infrared videos obtained from the Wildlife Conservation Society (WCS). Despite the poor quality of infrared videos, our method achieves a mAP of 94.5%. The consistent high performance on both the ATRW and snow leopard datasets collectively demonstrates the method’s strong generalization capability and practical utility. Full article

Global glacier models (GGMs) are effective tools for assessing changes in water resources in mountainous regions and studying glacier degradation. Moreover, with the rapid development and increasing complexity of Earth System Models (ESMs), the incorporation of mountain glaciation parametrizations into ESMs is only a matter of time. GGMs, being computationally efficient and physically well-founded, provide a solid basis for such parametrizations. In this study, we present a new global glacier model, IGRICE. Its dynamic core is based on the Oerlemans minimal model, and surface mass balance (SMB) is explicitly simulated, accounting for orographic precipitation, radiation redistribution on the glacier surface, turbulent heat fluxes, and snow cover evolution on ice. The model is tested on glaciers situated in climatically and topographically contrasting regions—the Caucasus and Svalbard—using observational data for validation. The model is forced with ERA5 reanalysis data and employs morphometric glacial and topographic parameters. The simulated components of the surface energy and mass balance, as well as glacier dynamics over the period of 1984–2021, are presented. The model results demonstrate good agreement with observations, with correlation coefficients for accumulation, ablation, and total SMB ranging from 0.6 to 0.9. The primary driver of glacier retreat in the Caucasus is identified as an increase in net shortwave radiation balance caused by reduced cloudiness and albedo. In contrast, rapid glacier degradation in Svalbard is linked to an increased fraction of liquid precipitation and an extended snow-free period, leading to a sharp decrease in albedo. Full article

The surface albedo, $\alpha$, is one of the key climate parameters since it regulates the shortwave radiation absorbed by the Earth’s surface. An accurate determination of the albedo is crucial in the polar regions due to its variations associated with climate change and its role in the strong feedback mechanisms. In this work, satellite and in situ measurements of broadband surface albedo at the Thule High Arctic Atmospheric Observatory (THAAO) on the northwestern coast of Greenland (76.5°N, 68.8°W) are compared. Measurements of surface albedo were started at THAAO in 2016. They show a large variability mainly in the transition seasons, suggesting that THAAO is a very interesting site for verifying the satellite capabilities in challenging conditions. The comparison of daily ground-based and MODIS-derived albedo covers the period July 2016–October 2024. The analysis has been conducted for all-sky and cloud-free conditions. The mean bias and mean squared difference between the two datasets are −0.02 and 0.09, respectively, for all sky conditions and −0.03 and 0.06 for cloud-free conditions. Very good agreement is found in summer in snow-free conditions, when the mean albedo is 0.17 in both datasets under cloud-free conditions. On the contrary, the capability to determine the surface albedo from space is largely reduced in the transition seasons, when significant differences between ground- and satellite-based albedo estimates are found. Differences for all-sky conditions may be as large as 0.3 in spring and autumn. These maximum differences are significantly reduced for cloud-free conditions, although a negative bias of MODIS data with respect to measurements at THAAO is generally found in spring. The combined analysis of the albedo, cloudiness, air temperature, and precipitation characteristics during two periods in 2023 and 2024 shows that, although satellite observations provide a reasonable picture of the long-term albedo evolution, they are not capable of following fast changes in albedo values induced by precipitation of snow/rain or temperature variations. Moreover, as expected, cloudiness plays a large role in affecting the satellite capabilities. The use of MODIS albedo data with the best value of the quality assurance flag (equal to 0) is recommended for studies aimed at determining the daily evolution of the surface radiation and energy budget. Full article

Sediment connectivity constitutes a valuable metric to assess the most likely areas of sediment transport, providing a preliminary estimate of the areas to be prioritized for sediment control interventions. Assessment spatio-temporal variability in sediment connectivity can help decrease uncertainties in interpreting sediment transport and sediment yield within a catchment. In this regard, we evaluated variations in the index of sediment connectivity (IC) based on a well-established approach in the Gunt River catchment. To achieve a more effective assessment of the temporal variations in IC, we considered changes in surface soil moisture (SSM) along with normalized difference vegetation index (NDVI) in July 2015 and 2024. Also, to better represent and more accurately assess IC within this large catchment (13,700 km²), we applied weighted mean IC values (as a novel metric) based on iso-IC lines. Our results indicate that among the environmental factors affecting IC, including SSM, slope gradient, elevation, and NDVI, SSM is the most influential in such cold, dry mountainous catchments. Also, the findings demonstrated a 38.5% increase in the extent of the medium-high and high categories of IC from 2015 to 2024. Temporal monitoring of IC revealed pronounced variations in the western (close to the outlet) and eastern portions of the catchment, likely associated with the effects of climate warming on sediment connectivity. These results emphasize that SSM is a key parameter for assessing IC in the snow- and ice-melt-dominated dry mountainous catchment. Accordingly, temporal and spatial monitoring of SSM can allow implementation of more effective measures for reducing sediment transfer at the catchment scale. Full article

Wildfire-induced disturbances to soil and vegetation can significantly impact streamflows for years, depending upon the degree of burn severity. Accurately predicting the effects of wildfire on streamflow at the watershed scale is essential for effective water budget management. This study presents a novel approach to generating a burn severity map on a small scale by integrating unmanned aerial vehicle (UAV)-based thermal imagery with Landsat-derived Differenced Normalized Burn Ratio (dNBR) and upscaling burned severity to the entire burned area. The approach was applied to the Thompson Ridge Fire perimeter, and the upscaled UAV-Landsat-based burn severity map achieved an overall accuracy of ~73% and a kappa coefficient of ~0.62 when compared with the Burned Area Emergency Response’s (BAER) fire product as a reference map, indicating moderate accuracy. We then tested the transferability of burn severity information to a Beaver River watershed by applying Random Forest models. Predictors included topography, spectral bands, vegetation indices, fuel, land cover, fire information, and soil properties. We calibrated and validated the Distributed Hydrology Soil Vegetation Model (DHSVM) against observed streamflow and Snow Water Equivalent (SWE) data within the Beaver River watershed and measured model performance using Nash–Sutcliffe Efficiency (NSE), Kling–Gupta Efficiency (KGE), and Percent Bias (PBIAS) metrics. We adjusted soil (maximum infiltration rate) and vegetation (fractional vegetation cover, snow interception efficiency, and leaf area index) parameters for the post-fire model setup and simulated streamflow for the post-fire years without vegetation regrowth. Streamflow simulations using the upscaled and transferred UAV-Landsat burn severity map and the Burned Area Emergency Response’s (BAER) fire product produced similar post-fire hydrologic responses, with annual average flows increasing under both approaches and the UAV-Landsat-based simulation yielding slightly lower values, by less than 6% compared to the BAER-based simulation. Our results demonstrate that the UAV-satellite integration method offers a cost- and time-effective method for generating a burn severity map, and when combined with the transferability method and hydrologic modeling, it provides a practical framework for predicting post-fire streamflow in both burned and unburned watersheds. Full article

Investigating the optimal planting strategies for brassica vegetables under variable climatic conditions is essential for developing sustainable production systems in northern agricultural regions. However, comprehensive knowledge about how planting timing modulates growth, physiological responses, and yield parameters across different cultivars remains limited. We investigated vegetative development, root morphology, physiological efficiency, and marketable yield in six cauliflower cultivars (‘Amazing’, ‘Cheddar’, ‘Clementine’, ‘Flame Star’, ‘Snow Crown’, and ‘Vitaverde’) subjected to four planting dates (May 1, May 15, June 1, and June 15) across two growing seasons (2023–2024), followed by detailed morphological and physiological profiling. Planting date, cultivar selection, and seasonal variation significantly influenced all measured parameters (p < 0.001), with notable interaction effects observed for fresh root weight, stomatal conductance, water use efficiency, and yield components. Early planted cultivars consistently demonstrated superior performance under variable environmental conditions, maintaining higher growth rates, enhanced root development, and improved physiological efficiency, particularly ‘Flame Star’, ‘Snow Crown’, and ‘Cheddar’, compared to late-planted treatments. Recovery of optimal plant development was most pronounced at May planting dates, with early-established crops showing better maintenance of vegetative growth patterns and enhanced yield potential, including higher curd weights (585.7 g for ‘Flame Star’) and superior marketable grades. Morphological profiling revealed distinct clustering patterns, with early-planted cultivars forming separate groups characterized by elevated root biomass, enhanced physiological parameters, and superior yield characteristics. In contrast, late-planted crops showed reduced performance, indicative of environmental stress responses. We conclude that strategic early planting significantly enhances cauliflower production resilience through comprehensive optimization of growth, physiological, and yield parameters, particularly under May establishment conditions. The differential performance responses between planting dates provide insights for timing-based management strategies, while the quantitative morphological and physiological profiles offer valuable parameters for assessing crop adaptation and commercial viability potential under variable climatic scenarios in northern agricultural systems. Full article

A shallow, seasonal snowpack is rarely homogeneous in depth, layer characteristics, or surface structure throughout an entire winter. Aerodynamic roughness length ($z_0$) is typically considered a static parameter within hydrologic and atmospheric models. Here, we present observations showing $z_0$ as a dynamic variable that is a function of snow depth ($d_s$). This has a significant impact on sublimation modeling, especially for shallow snowpacks. Terrestrial LiDAR data were collected at nine different study sites in northwest Colorado from the 2019 to 2020 winter season to measure the spatial and temporal variability of the snowpack surface. These data were used to estimate the geometric $z_0$ from 91 site visits. Values of $z_0$ decrease during initial snow accumulation, as the snow conforms to the underlying terrain. Once the snowpack is sufficiently deep, which depends on the height of the ground surface roughness features, the surface becomes more uniform. As melt begins, $z_0$ increases, when the snow surface becomes more irregular. The correlation value of $z_0$ was altered by human disturbance at several of the sites. The $z_0$ versus $d_s$ correlation was almost constant, regardless of the initial roughness conditions that only affected the initial $z_0$. Full article

With increasing urbanization and population growth, the volume of municipal solid waste (MSW) continues to rise. Efficient and environmentally responsible waste processing has become a core issue in sustainable development. Incineration plays a key role in reducing landfill usage and recovering energy from waste, contributing to circular economy initiatives. However, fluctuations in furnace temperature significantly affect combustion efficiency and emissions, undermining the environmental benefits of incineration. To address these challenges under dynamic operational conditions, this paper proposes a hybrid model combining an Improved Snow Ablation Optimizer (ISAO), Dual-Branch Convolutional Neural Network (DBCNN), and Bidirectional Long Short-Term Memory (BiLSTM). The model extracts dynamic features from control and condition variables and incorporates time series characteristics for accurate temperature prediction, thereby enhancing the overall efficiency of the incineration process. ISAO integrates Lévy flight, differential mutation, and elitism strategies to optimize parameters, contributing to better energy recovery and reduced emissions. Experimental results on real MSWI data demonstrate that the proposed method achieves high prediction accuracy and adaptability under varying operating conditions, showcasing its robustness and application potential in promoting sustainable waste management practices. By improving combustion efficiency and minimizing environmental impact, this model aligns with global sustainability goals, supporting a more efficient, eco-friendly waste-to-energy process. Full article

With the advancement of autonomous driving technology, the performance of LiDAR in adverse weather conditions has garnered increasing attention. Traditional denoising algorithms, including intensity-based methods like LIOR (a representative intensity-based filter that relies solely on signal intensity), have limited effectiveness in handling snow noise, especially in removing dynamic noise points and distinguishing them from environmental features. This paper proposes a Dynamic Vertical and Low-Intensity Outlier Removal (DVIOR) algorithm, specifically designed to optimize LiDAR point cloud data under snowy conditions. The DVIOR algorithm, as an extension of intensity-based filtering augmented with vertical height information, dynamically adjusts filter parameters by combining the height and intensity information of the point cloud, effectively filtering out snow noise while preserving environmental features. In our experiments, the DVIOR algorithm was evaluated on several publicly available adverse weather datasets, including the Winter Adverse Driving Scenarios (WADS), the Canadian Adverse Driving Conditions (CADC), and the Radar Dataset for Autonomous Driving in Adverse weather conditions (RADIATE) datasets. Compared with both the mainstream dynamic distance–intensity hybrid algorithm in recent years, Dynamic Distance–Intensity Outlier Removal (DDIOR), and the representative intensity-based filter LIOR, DVIOR achieved notable improvements: it gained a 10.2-point higher F1-score than DDIOR and an 11.8-point higher F1-score than LIOR (79.00) on the WADS dataset. Additionally, DVIOR performed excellently on the CADC and RADIATE datasets, achieving F1-scores of 87.35 and 86.68, respectively—representing an improvement of 19.82 and 36.9 points over DDIOR and 4.67 and 17.95 points over LIOR (82.68 and 68.73). These results demonstrate that the DVIOR algorithm outperforms existing methods, including both distance–intensity hybrid approaches and intensity-based filters like LIOR, in snow noise removal, particularly in complex snowy environments. Full article

Using a camera system developed earlier for monitoring the behavior of lemmings under the snow, we are now able to record a large number of short image sequences from this rodent which plays a central role in the Arctic food web. Identifying lemming species in these images manually is wearisome and time-consuming. To perform this task, we present a deep neural network which has several million parameters to configure. Training a network of such an immense size with conventional methods requires a huge amount of data but a sufficiently large labeled dataset of lemming images is currently lacking. Another challenge is that images are obtained in darkness in the near infrared spectrum, causing the loss of some image texture information. We investigate whether these challenges can be tackled by a transfer learning approach in which a network is pretrained on a dataset of visible spectrum images that does not include lemmings. We believe this work provides a basis for moving toward developing intelligent software programs that can facilitate the analysis of videos by biologists. Full article