Search Results (948)

Snow water equivalent (SWE) can be estimated using Differential SAR Interferometry (DInSAR), which captures changes in snow depth and density between two SAR acquisitions. However, challenges arise due to SAR signal penetration into the snowpack and the intrinsic limitations of DInSAR measurements. This study addresses these issues and explores the use of multi-band SAR data to derive SWE maps in alpine regions characterized by steep terrain, small spatial extent, and a potentially heterogeneous snowpack. We first conducted a performance analysis to assess SWE estimation precision and the maximum unambiguous SWE variation, considering incidence angle, wavelength, and coherence. Based on these results, we selected C-band Sentinel-1 and L-band SAOCOM data acquired over alpine areas and applied tailored DInSAR processing. Atmospheric artifacts were corrected using zenith total delay maps from the GACOS service. Additionally, sensitivity maps were generated for each interferometric pair to identify pixels suitable for reliable SWE estimation. A comparative analysis of the C- and L-band results revealed several critical issues, including significant atmospheric artifacts, phase decorrelation, and phase unwrapping errors, which impact SWE retrieval accuracy. A comparison between our Sentinel-1-based SWE estimations and independent measurements over an instrumented site shows results fairly in line with previous works exploiting C-band data, with an RSME in the order of a few tens of mm. Full article

Meiotic recombination is a key evolutionary process that generates novel allele combinations during prophase I of meiosis, promoting genetic diversity and enabling the selection of desirable traits in livestock breeding. Although its molecular mechanisms are well-characterised in model organisms such as humans and mice, studies in African indigenous cattle, particularly South African breeds, remain scarce. Key regulators of recombination, including PRDM9, SPO11, and DMC1, play essential roles in crossover formation and genome stability, with mutations in these genes often linked to fertility defects. Despite the Bonsmara and Nguni breeds’ exceptional adaptability to arid and resource-limited environments, little is known about how recombination contributes to their unique genetic architecture and adaptive traits. This review synthesises the current knowledge on the molecular basis of meiotic recombination, with a focus on prophase I events and associated structural proteins and enzymes. It also highlights the utility of genome-wide tools, particularly high-density single nucleotide polymorphism (SNP) markers for recombination mapping. By focusing on the underexplored recombination landscape in South African beef cattle, this review identifies key knowledge gaps. It outlines how recombination studies can inform breeding strategies aimed at enhancing genetic improvement, conservation, and the long-term sustainability of local beef production systems. Full article

Landslides are among the most destructive natural hazards in the Western Ghats region of Kerala, driven by complex interactions between geological, hydrological, and anthropogenic factors. This study aims to generate a high-resolution Landslide Susceptibility Level Map (LSLM) using a machine learning (ML)-based Random Forest (RF) model integrated with Geographic Information Systems (GIS). A total of 231 historical landslide locations obtained from the Bhukosh portal were used as reference data. Eight predictive factors—Stream Order, Drainage Density, Slope, Aspect, Geology, Land Use/Land Cover (LULC), Normalized Difference Vegetation Index (NDVI), and Moisture Stress Index (MSI)—were derived from remote sensing and ancillary datasets, preprocessed, and reclassified for model input. The RF model was trained and validated using a 50:50 split of landslide and non-landslide points, with variable importance values derived to weight each predictive factor of the raster layer in ArcGIS. The resulting Landslide Susceptibility Index (LSI) was reclassified into five susceptibility zones: Very Low, Low, Moderate, High, and Very High. Results indicate that approximately 17.82% of the study area falls under high to very high susceptibility, predominantly in the steep, weathered, and high rainfall zones of the Western Ghats. Validation using Area Under the Curve–Receiver Operating Characteristic (AUC-ROC) analysis yielded an accuracy of 0.890, demonstrating excellent model performance. The output LSM provides valuable spatial insights for planners, disaster managers, and policymakers, enabling targeted mitigation strategies and sustainable land-use planning in landslide-prone regions. Full article

This study utilizes UAV-based LiDAR to analyze doline microtopography within a karst mountainous terrain. The study area, ‘Gulneomjae’ in Mungyeong City, South Korea, features steep slopes, limited accessibility, and abundant vegetation—conditions that traditionally hinder accurate topographic surveying. UAV LiDAR data were acquired using the DJI Matrice 300 RTK equipped with a Zenmuse L2 sensor, enabling high-density point cloud generation (98 points/m²). The point clouds were processed to remove non-ground points and generate a 0.25 m resolution DEM using TIN interpolation. A total of seven dolines were detected and delineated, and their morphometric characteristics—including area, perimeter, major and minor axes, and elevation—were analyzed. These results were compared with a 1:5000-scale DEM derived from the 2013 National Basic Map. Visual and numerical comparisons highlighted significant improvements in spatial resolution and feature delineation using UAV LiDAR. Although the 1:5000-scale DEM enables general doline detection, UAV LiDAR facilitates more precise boundary extraction and morphometric analysis. The study demonstrates the effectiveness of UAV LiDAR for detailed topographic mapping in complex karst terrains and offers a foundation for future automated classification and temporal change analysis. Full article

Monitoring the structural health of bridges in road infrastructure is crucial for ensuring public safety and efficient maintenance. This paper presents a multi-level semi-automatic methodology for bridge monitoring, using Multi-Temporal Differential SAR Interferometry (MT-DInSAR) data. The proposed approach requires a dataset of satellite-derived MT-DInSAR measurements for the Area of Interest. The methodology involves creating a georeferenced database of bridges which allows the filtering of measurement points (generally named Persistent Scatterers—PSs) using spatial queries. Since existing datasets often provide only point geometries for bridge locations, additional data sources such as OpenStreetMaps-derived repositories have been utilized to obtain linear representations of bridges. These linear features are segmented into 20 m sections, which are then converted into polygonal geometries by applying a uniform buffer. Spatial joining between the bridge polygons and PS datasets allows the extraction of key statistics, such as mean displacement velocity, PS density and coherence levels. Based on predefined velocity thresholds, warning flags are triggered, indicating the need for further in-depth analysis. Finally, an upscaling step is performed to provide a practical tool for infrastructure managers, visually categorizing bridges based on the presence of flagged pixels. The proposed approach facilitates large-scale bridge monitoring, supporting the early detection of potential structural issues. Full article

Off-road path planning involves navigating vehicles through areas lacking established road networks, which is critical for emergency response in disaster events, but is limited by the complex geographical environments in natural conditions. How to model the vehicle’s off-road mobility effectively and represent environments is critical for efficient path planning in off-road environments. This paper proposed an improved A* path planning algorithm based on a Delaunay triangular NavMesh model with off-road environment representation. Firstly, a land cover off-road mobility model is constructed to determine the navigable regions by quantifying the mobility of different geographical factors. This model maps passable areas by considering factors such as slope, elevation, and vegetation density and utilizes morphological operations to minimize mapping noise. Secondly, a Delaunay triangular NavMesh model is established to represent off-road environments. This mesh leverages Delaunay triangulation’s empty circle and maximum-minimum angle properties, which accurately represent irregular obstacles without compromising computational efficiency. Finally, an improved A* path planning algorithm is developed to find the optimal off-road mobility path from a start point to an end point, and identify a path triangle chain with which to calculate the shortest path. The improved road-off path planning A* algorithm proposed in this paper, based on the Delaunay triangulation navigation mesh, uses the Euclidean distance between the midpoint of the input edge and the midpoint of the output edge as the cost function $g\left(n\right)$, and the Euclidean distance between the centroids of the current triangle and the goal as the heuristic function $h\left(n\right)$. Considering that the improved road-off path planning A* algorithm could identify a chain of path triangles for calculating the shortest path, the funnel algorithm was then introduced to transform the path planning problem into a dynamic geometric problem, iteratively approximating the optimal path by maintaining an evolving funnel region, obtaining a shortest path closer to the Euclidean shortest path. Research results indicate that the proposed algorithms yield optimal path-planning results in terms of both time and distance. The navigation mesh-based path planning algorithm saves 5~20% of path length than hexagonal and 8-directional grid algorithms used widely in previous research by using only 1~60% of the original data loading. In general, the path planning algorithm is based on a national-level navigation mesh model, validated at the national scale through four cases representing typical natural and social landscapes in China. Although the algorithms are currently constrained by the limited data accessibility reflecting real-time transportation status, these findings highlight the generalizability and efficiency of the proposed off-road path-planning algorithm, which is useful for path-planning solutions for emergency operations, wilderness adventures, and mineral exploration. Full article

While space weather indices (e.g., F10.7, Dst index) are commonly employed to characterize ionospheric activity levels, the Global Mean Electron Content (GMEC) provides a more direct and comprehensive indicator of the global ionospheric state. This metric demonstrates greater potential than space weather indices for calibrating empirical ionospheric models such as IRI-2020. The COSMIC-2 constellation enables continuous, all-weather global ionospheric monitoring via radio occultation, unimpeded by land–sea distribution constraints, with over 8000 daily occultation events suitable for GMEC modeling. This study developed two lightweight GMEC models using COSMIC-2 data: (1) a POD GMEC model based on slant TEC (STEC) extracted from Level 1b podTc2 products and (2) a PROF GMEC model derived from vertical TEC (VTEC) calculated from electron density profiles (EDPs) in Level 2 ionPrf products. Both backpropagation neural network (BPNN)-based models generate hourly GMEC outputs as global spatial averages. Critically, GMEC serves as an essential intermediate step that addresses the challenges of utilizing spatially irregular occultation data by compressing COSMIC-2’s ionospheric information into an integrated metric. Building on this compressed representation, we implemented a convolutional neural network (CNN) that incorporates GMEC as an auxiliary feature to calibrate IRI-2020’s global ionospheric maps. This approach enables computationally efficient correction of systemic IRI TEC errors. Experimental results demonstrate (i) 48.5% higher accuracy in POD/PROF GMEC relative to IRI-2020 GMEC estimates, and (ii) the calibrated global IRI TEC model (designated GCIRI TEC) reduces errors by 50.15% during geomagnetically quiet periods and 28.5% during geomagnetic storms compared to the original IRI model. Full article

Camera-centric perception has matured into a cornerstone of modern autonomy, from self-driving cars and factory cobots to underwater and planetary exploration. This review synthesizes more than a decade of progress in vision-based robotic navigation through an engineering lens, charting the full pipeline from sensing to deployment. We first examine the expanding sensor palette—monocular and multi-camera rigs, stereo and RGB-D devices, LiDAR–camera hybrids, event cameras, and infrared systems—highlighting the complementary operating envelopes and the rise of learning-based depth inference. The advances in visual localization and mapping are then analyzed, contrasting sparse and dense SLAM approaches, as well as monocular, stereo, and visual–inertial formulations. Additional topics include loop closure, semantic mapping, and LiDAR–visual–inertial fusion, which enables drift-free operation in dynamic environments. Building on these foundations, we review the navigation and control strategies, spanning classical planning, reinforcement and imitation learning, hybrid topological–metric memories, and emerging visual language guidance. Application case studies—autonomous driving, industrial manipulation, autonomous underwater vehicles, planetary rovers, aerial drones, and humanoids—demonstrate how tailored sensor suites and algorithms meet domain-specific constraints. Finally, the future research trajectories are distilled: generative AI for synthetic training data and scene completion; high-density 3D perception with solid-state LiDAR and neural implicit representations; event-based vision for ultra-fast control; and human-centric autonomy in next-generation robots. By providing a unified taxonomy, a comparative analysis, and engineering guidelines, this review aims to inform researchers and practitioners designing robust, scalable, vision-driven robotic systems. Full article

In this paper, a discrete element method (DEM) coupled with a finite element method (FEM) was used to elucidate the impact of packing structures and size ratios on the cold die compaction behavior of pure copper powders. HCP structure, SC structure, and three random packing structures with different particle size ratios (1:2, 1:3, and 1:4) were generated by the DEM, and then simulated by the FEM to analyze the average relative density, von Mises stress, and force chain structures of the compact. The results show that for HCP and SC structures with a regular stacking structure, the average relative densities of the compact were higher than those of random packing structures, which were 0.9823, 0.9693, 0.9456, 0.9502, and 0.9507, respectively. Compared with their initial packing density, it could be improved by up to 21.13%. For the bigger particle in HCP and SC structures, the stress concentration was located between the adjacent layers, while in the small particles, it was located between contacted particles. During the initial compaction phase, smaller particles tend to occupy the voids between larger particles. As the pressure increases, larger particles deform plastically in a notable way to create a stabilizing force chain. This action reduces the axial stress gradient and improves radial symmetry. The transition from a contact-dominated to a body-stress-dominated state is further demonstrated by stress distribution maps and contact force vector analysis, highlighting the interaction between particle rearrangement and plasticity. Full article

Urban blue–green space (UBGS) plays a critical role in mitigating the urban heat island (UHI) effect and reducing land surface temperatures (LSTs). However, existing research has not sufficiently explored the optimization of UBGS spatial configurations or their interactions with urban morphology. This study takes New York City as a case and systematically investigates small-scale urban cooling strategies by integrating multiple factors, including adjustments to the blue–green ratio, spatial layouts, vegetation composition, building density, building height, and layout typologies. We utilize multi-source geographic data, including LiDAR derived land cover, OpenStreetMap data, and building footprint data, together with LST data retrieved from Landsat imagery, to develop a prediction model based on generative adversarial networks (GANs). This model can rapidly generate visual LST predictions under various configuration scenarios. This study employs a combination of qualitative and quantitative metrics to evaluate the performance of different model stages, selecting the most accurate model as the final experimental framework. Furthermore, the experimental design strictly controls the study area and pixel allocation, combining manual and automated methods to ensure the comparability of different ratio configurations. The main findings indicate that a blue–green ratio of 3:7 maximizes cooling efficiency; a shrub-to-tree coverage ratio of 2:8 performs best, with tree-dominated configurations outperforming shrub-dominated ones; concentrated linear layouts achieve up to a 10.01% cooling effect; and taller buildings exhibit significantly stronger UBGS cooling performance, with super-tall areas achieving cooling effects approximately 31 percentage points higher than low-rise areas. Courtyard layouts enhance airflow and synergistic cooling effects, whereas compact designs limit the cooling potential of UBGS. This study proposes an innovative application of GANs to address a key research gap in the quantitative optimization of UBGS configurations and provides a methodological reference for sustainable microclimate planning at the neighborhood scale. Full article

This article presents a methodology for developing a three-dimensional terrain model based on numerical data in the form of a point cloud, with an emphasis on reducing mesh surface errors and using a surface smoothing factor. Initial surface generation was based on a point cloud with a square mesh, and an adopted algorithm for mesh conversion to the input form for the computer aided design (CAD) environment was presented. The use of a bilinear interpolation algorithm was proposed to reduce defects in the three-dimensional surface created in the reverse engineering process. The terrain mapping accuracy analyses were performed for three samples of different geometry using two available options in the Siemens NX program. All obtained surfaces were subjected to shape deviation analysis. For each of the analyzed surfaces, changing the smoothing factor from 0% to 15% did not cause significant changes in accuracy depending on the method adopted. For flat regions, in the Uniform Density (UD) method, the size of the area outside the tolerance was 6.16%, and in the Variable Density (VD) method, it was within the range of 5.01–6%. For steep regions, in the UD method, it was 6.25%, and in the VD method, it was within the range of 5.39–6.47%, while for concave–convex regions, in the UD method, it was 6.5% and in the VD method, it was within the range of 4.96–6.36%. For a smoothing factor value of 20%, a sudden increase in the inaccuracy of the shape of the obtained surface was observed. For flat regions, in the Uniform Density (UD) method, the size of the area outside the tolerance was 69.84%, and in the Variable Density (VD) method, it was 71.62%. For steep regions, in the UD method, it was 76.07%, and in the VD method, it was 80.94%, while for concave–convex regions, in the UD method, it was 56.08%, and in the VD method, it was 62.38%. The developed methodology provided high accuracy in the reproduction of numerical data that can be used for further analyses and manufacturing processes, such as 3D printing. Based on the obtained data, three fused deposition model (FDM) prints were made, presenting each of the analyzed types of terrain geometry. Only FDM printing was used, and other technologies were not verified. Full article

Mapping dominant forest habitats is essential for guiding reforestation practices, especially in areas affected by fires. This study focuses on identifying dominant forest habitats in selected forested areas in Cyprus using supervised, pixel-based classification algorithms to support the planning of post-fire reforestation actions. For this study, three classifiers were provided by the Google Earth Engine (GEE) platform. Specifically, the Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Trees (CART) were implemented utilizing Sentinel-1 and Sentinel-2 data as well as topographic features and the tree density. Eight dominant forest habitats were mapped, including the Mediterranean pine forests with endemic Mesogean pines, Sarcopoterium spinosum phrygana, Thermo-Mediterranean and pre-desert scrub, Olea and Ceratonia forests, scrub and low forest vegetation with Quercus alnifolia, endemic forests with Juniperus, Cedrus brevifolia forests and Mediterranean pine forests with endemic Mesogean pines. The results revealed that RF and SVM outperformed CART. While SVM achieved the highest overall accuracy (OA) of 84.67%, it exhibited sensitivity to hyperparameter adjustments. In contrast, RF demonstrated greater stability and generalization across habitat types, attaining a reliable OA of 82.24%, making it the preferred classifier for this study. Full article

In this study, density functional theory (DFT) was used to analyze the processes that govern the interactions among triethylaluminum (TEAL), the Ziegler–Natta (ZN) catalyst, and the inhibitory compounds dimethylamine (DMA) and diethylamine (DEA) during olefin polymerization. The structural and charge characteristics of these inhibitors were examined through steric maps and DFT calculations. Combined DFT calculations (D3-B3LYP/6-311++G(d,p)) and IR spectroscopic analysis show that the most efficient way to deactivate the ZN catalyst is via the initial formation of the TEAL·DMA complex. This step has a kinetic barrier of only 27 kcal mol⁻¹ and a negative ΔG, in stark contrast to the >120 kcal mol⁻¹ required to form TEAL·DEA. Once generated, TEAL·DMA adsorbs onto the TiCl₄/MgCl₂ cluster with adsorption energies of −22.9 kcal mol⁻¹ in the gas phase and −25.4 kcal mol⁻¹ in n-hexane (SMD model), values 5–10 kcal mol⁻¹ more favorable than those for TEAL·DEA. This explains why, although dimethylamine is present at only 140 ppm, its impact on productivity (−19.6%) is practically identical to that produced by 170 ppm of diethylamine (−20%). The persistence of the ν(Al–N) band at ~615 cm⁻¹, along with a >30% decrease in the Al–C/Ti–C bands between 500 and 900 cm⁻¹, the downward shift of the N–H stretch from ~3300 to 3200 cm⁻¹, and the +15 cm⁻¹ increase in ν(C–N) confirm Al←N coordination and blockage of alkyl transfer, establishing the TEAL·DMA → ZN pathway as the dominant catalytic poisoning mechanism. Full article

Hysteresis is a nonlinear phenomenon found in many physical systems, including soft viscoelastic actuators, where it poses significant challenges to their application and performance. Consequently, developing accurate hysteresis models is essential for the effective design and optimization of soft actuators. Moreover, a reliable model can be used to design compensators that mitigate the negative effects of hysteresis, improving closed-loop control accuracy and expanding the applicability of soft actuators in robotics. Physics-based approaches for modeling hysteresis in soft actuators offer valuable insights into the underlying material behavior. Nevertheless, they are often highly complex, making them impractical for real-world applications. Instead, phenomenological models provide a more feasible solution by representing hysteresis through input–output mappings based on experimental data. To effectively fit these phenomenological models, it is essential to rely on sensing data collected from real actuators. In this context, the primary objective of this work is a comprehensive comparative evaluation of the efficiency and performance of representative phenomenological hysteresis models (e.g., Bouc–Wen and Prandtl-Ishlinskii) using experimental data obtained from a pneumatic bending actuator made of a viscoelastic material. This evaluation suggests that the Generalized Prandtl–Ishlinskii model achieves the highest modeling accuracy, while the Preisach model with a probabilistic density function formulation excels in terms of parameter compactness. Full article

Generative Bayesian Computation (GBC) provides a simulation-based approach to Bayesian inference. A Quantile Neural Network (QNN) is trained to map samples from a base distribution to the posterior distribution. Our method applies equally to parametric and likelihood-free models. By generating a large training dataset of parameter–output pairs inference is recast as a supervised learning problem of non-parametric regression. Generative quantile methods have a number of advantages over traditional approaches such as approximate Bayesian computation (ABC) or GANs. Primarily, quantile architectures are density-free and exploit feature selection using dimensionality reducing summary statistics. To illustrate our methodology, we analyze the classic normal–normal learning model and apply it to two real data problems, modeling traffic speed and building a surrogate model for a satellite drag dataset. We compare our methodology to state-of-the-art approaches. Finally, we conclude with directions for future research. Full article