Search Results (6,854)

Numerical modelling of airflow in underground mines is gaining importance in modern ventilation system design and safety assessment. Computational Fluid Dynamics (CFD) simulations enable detailed analyses of air movement, contaminant dispersion, and heat transfer, yet their reliability depends strongly on the accuracy of the geometric representation of excavations. Raw point cloud data obtained from laser scanning of underground workings are typically irregular, noisy, and contain discontinuities that must be processed before being used for CFD meshing. This study presents a methodology for automatic segmentation and regularization of large-scale point cloud data of underground excavation systems. The proposed approach is based on skeleton extraction and trajectory analysis, which enable the separation of excavation networks into individual tunnel segments and crossings. The workflow includes outlier removal, alpha-shape generation, voxelization, medial-axis skeletonization, and topology-based segmentation using neighbor relationships within the voxel grid. A proximity-based correction step is introduced to handle doubled crossings produced by the skeletonization process. The segmented sections are subsequently regularized through radial analysis and surface reconstruction to produce uniform and watertight models suitable for mesh generation in CFD software (Ansys 2024 R1). The methodology was tested on both synthetic datasets and real-world laser scans acquired in underground mine conditions. The results demonstrate that the proposed segmentation approach effectively isolates single-line drifts and crossings, ensuring continuous and smooth geometry while preserving the overall excavation topology. The developed method provides a robust preprocessing framework that bridges the gap between point cloud acquisition and numerical modelling, enabling automated transformation of raw data into CFD-ready geometric models for ventilation and safety analysis of complex underground excavation systems. Full article

Fuel cell commercial vehicles are widely used in commercial transport for their high efficiency and long range. However, in mixed operating scenarios, their energy economy and fuel cell operational stability cannot be fully balanced. Traditional strategies lack adaptability in mixed operating scenarios. Therefore, based on the equivalent factor regulation formula of the Adaptive Equivalent Hydrogen Consumption Minimization Strategy (A-ECMS) and the improved Sparrow Search Algorithm-Long Short-Term Memory (SSA-LSTM) hybrid model, short-term speed prediction and three-stage speed interval division are embedded into the equivalent factor regulation logic. A dynamic equivalent factor regulation strategy integrating SOC deviation is constructed, and an improved Predictive Equivalent Hydrogen Consumption Minimization Strategy (P-ECMS) is finally derived. The SSA-LSTM algorithm is optimized via constrained hyperparameter tuning for short-term speed prediction. A time-decay weighting mechanism enhances recent speed data weight, with weighted results as inputs to boost accuracy. Moving Average Residual Correction (MARC) is used to verify the speed prediction model accuracy and correct residuals. Multi-scenario tests show that the SSA-LSTM model outperforms the Gated Recurrent Unit (GRU) model in prediction accuracy and generalization ability, providing reliable data support for segmented regulation. With battery SOC deviation and the SSA-LSTM-predicted speed trend as core inputs, combined with three-stage speed interval division, A-ECMS’s equivalent factor regulation formula is improved. The model adopts a segmented dynamic regulation logic to integrate dual factors into equivalent factor adjustment, and it reasonably adjusts the energy output ratio of fuel cells and power batteries according to speed intervals and operating condition changes. In scenarios with significant speed fluctuations and frequent operating condition transitions, power shocks are mitigated by the power battery’s peak-shaving and valley-filling function. Simulation results for C-WTVC and NREL2VAIL show that, compared with traditional A-ECMS, the improved P-ECMS has notable energy benefits, with equivalent hydrogen consumption reduced by 3.41% and 5.48%, respectively. The fuel cell’s state is significantly improved, with its high-efficiency share reaching 63%. The output power curve is smoother, start–stop losses are reduced, and the fuel cell’s service life is extended, balancing the energy economy and component durability. Full article

This study introduces a refined multi-scale micromechanical framework for analyzing C/C-SiC ceramic matrix composites (CMCs) using a dedicated Parametric High-Fidelity Method of Cells (PHFGMCs). A three-level geometric model is constructed from scanning electron microscope (SEM) micrographs and computed tomography (CT) scans. Specialized dual micro-meso nested PHFGMCs are employed to accurately generate the effective properties and spatial distributions of local stress fields in the highly heterogeneous microstructure of an 8-harness C/C-SiC representative volume element (RVE). The proposed refined framework recognizes the different micro- and meso-scales, ranging from the carbon fiber and amorphous carbon matrix to intra-yarn segmentation and weave regions. All are nested in a complete 8-harness architecture. The refined PHFGMC analyses showed good agreement between predicted mechanical properties and experimental data for C/C-SiC. The model’s ability to resolve local spatial deformation in the complex microstructure of C/C-SiC CMCs is demonstrated. These findings highlight the need for a refined multi-scale analysis that captures microstructural complexity and constituent interactions influencing both macroscopic and local responses in C/C-SiC CMCs. The proposed PHFGMC-based framework provides a robust theoretical and computational foundation for the future integration of nonlinear and progressive damage models within C/C-SiC CMC material systems. Full article

Construction progress monitoring remains predominantly manual, labor-intensive, and reliant on subjective human interpretation. Human dependence often leads to redundant or unreliable information, resulting in scheduling delays and increased costs. Advances in drones, point cloud generation, and multisensor data acquisition have expanded access to high-resolution as-built data. However, transforming data into reliable automated indicators of progress poses a challenge. A limitation is the lack of robust material-level segmentation, particularly for structural materials such as concrete and steel. Concrete and steel are crucial for verifying progress, ensuring quality, and facilitating construction management. Most studies in point cloud segmentation focus on object- or scene-level classification and primarily use geometric features, which limit their ability to distinguish materials with similar geometries but differing physical properties. A consolidated and systematic understanding of the performance of multispectral and multimodal segmentation methods for material-specific classification in construction environments remains unavailable. The systematic review addresses the existing gap by synthesizing and analyzing literature published from 2020 to 2025. The review focuses on segmentation methodologies, multispectral and multimodal data sources, performance metrics, dataset limitations, and documented challenges. Additionally, the review identifies research directions to facilitate automated progress monitoring of construction and to enhance digital twin frameworks. The review indicates strong quantitative performance, with multispectral and multimodal segmentation approaches achieving accuracies of 93–97% when integrating spectral information into point cloud or image-based pipelines. Large-scale environments benefit from combined LiDAR and high-resolution imagery approaches, which achieve classification quality metrics of 85–90%, thereby demonstrating robustness under complex acquisition conditions. Automated inspection workflows reduce inspection time from 24 h to less than 2 h and yield cost reductions of more than 50% compared to conventional methods. Additionally, deep-learning-based defect detection achieves inference times of 5–6 s per structural element, with reported accuracies of around 97%. The findings confirm productivity gains for construction monitoring. Full article

Remote sensing acquires Earth surface information without physical contact through sensors operating at diverse spatial, spectral, and temporal resolutions. In high-resolution remote sensing imagery, objects often exhibit large scale variation, complex spatial distributions, and strong inter-class similarity, posing persistent challenges for accurate semantic segmentation. Existing methods still struggle to simultaneously preserve fine boundary details and model long-range spatial dependencies, and lack explicit mechanisms to decouple low-frequency semantic context from high-frequency structural information. To address these limitations, we propose the Multi-Perspective Information Fusion Network (MPIFNet) for remote sensing semantic segmentation, motivated by the need to integrate global context, local structures, and multi-frequency information into a unified framework. MPIFNet employs a Global and Local Mamba Block Self-Attention (GLMBSA) module to capture long-range dependencies while preserving local details, and a Double-Branch Haar Wavelet Transform (DBHWT) module to separate and enhance low- and high-frequency features. By fusing spatial, hierarchical, and frequency representations, MPIFNet learns more discriminative and robust features. Evaluations on the Vaihingen, Potsdam, and LoveDA datasets through ablation and comparative studies highlight the strong generalization of our model, yielding mIoU results of 86.03%, 88.36%, and 55.76%. Full article

Lunar laser ranging (LLR) has currently achieved millimeter-level ranging accuracy, establishing itself as a powerful tool for testing general relativity, particularly the equivalence principle. However, atmospheric delay introduces spurious signals in LLR-based equivalence principle tests, significantly degrading parameter constraint precision. Through analysis of observational data from the Grasse station—which has contributed the most normal point data in recent years—we demonstrate that atmospheric delay may significantly affect the test of equivalence principle. Moreover, this paper provides a comprehensive analysis of how temporal and elevation-angle non-uniformity in atmospheric delay distribution affect equivalence principle tests. Simulation results demonstrate that fixing the elevation angle significantly enhances the precision of equivalence principle tests. Therefore, to achieve more stringent constraints, it is recommended to analyze segments from the long-term ranging archive that have minimal variation in elevation angle. Full article

Forests are a critical component of global carbon sequestration, biodiversity, and ecosystem services, making accurate mapping essential for long-term monitoring. In North Korea, limited field access, rugged topography, and inconsistent national statistics necessitate reliable remote sensing–based observation. However, frequent cloud contamination challenges the use of optical time-series imagery for forest monitoring. This study introduces a framework that integrates a ConvLSTM-based autoencoder into a U-Net segmentation model to improve tree cover classification from Sentinel-2 time-series data. The autoencoder was pretrained to reconstruct cloud-contaminated or missing observations using multi-octave Perlin-noise perturbations, providing standardized inputs that enhanced segmentation robustness under noisy conditions. Results show that tree cover accuracy exceeded 96% when all five time steps were available and remained stable (94–95%) even with one missing step. Accuracy declined below 90% with three missing steps but remained above 80%, enabling draft classifications under limited data. Confidence analysis further indicated that model certainty is a practical quality-control metric. Annual mapping for 2019–2024 showed a general increase in tree cover, aligning with reported afforestation efforts in North Korea. Taken together, the framework advances long-term monitoring, carbon accounting, and risk assessment in North Korea, while also enabling robust, region-adapted monitoring in cloud-prone, data-limited settings. Full article

Dental caries remains a prevalent diagnostic challenge, particularly in periapical radiographs, where geometric distortions and anatomical overlap complicate interpretation. Although deep learning has advanced dental image analysis, most segmentation models depend on fully annotated datasets and rarely exploit anatomical context. This study proposes a hybrid Multi-Task Learning (MTL) framework that jointly performs anatomical segmentation and caries detection in scenarios with partial and asymmetric annotations. The method integrates a U-Net++ dual-head architecture with a shared EfficientNet-B4 encoder, supplemented by pseudo-label generation and selective loss masking to handle incomplete ground truth. We hypothesize that learning healthy dental structures provides a contextual scaffold that enhances the identification of pathology. Experiments on an independent test set validate this hypothesis: the proposed MTL model achieved an DSC of 0.6706 and an IoU of 0.5044, outperforming a specialized single-task baseline. Most notably, sensitivity improved by 7.47%, reducing false-negative pixels by 19.9%. These findings demonstrate that context-aware supervision substantially improves detection robustness on complex periapical radiographs, even when full-pixel-level annotations are unavailable. The proposed framework offers a scalable pathway for developing clinically oriented diagnostic tools in real-world settings where annotation completeness is limited. Full article

Terrace construction is a critical engineering practice for soil and water conservation as well as sustainable agricultural development on the Loess Plateau (LP), China, where high-precision dynamic monitoring is essential for informed regional ecological governance. To address the challenges of inadequate extraction accuracy and poor model generalization in time-series terrace mapping amid complex terrain and spectral confounding, this study proposes a novel Swin Transformer-based Terrace Dual-Branch Deformable Boundary Network (STDBNet) that seamlessly integrates multi-source remote sensing (RS) data with deep learning (DL). The STDBNet model integrates the Swin Transformer architecture with a dual-branch attention mechanism and introduces a boundary-assisted supervision strategy, thereby significantly enhancing terrace boundary recognition, multi-source feature fusion, and model generalization capability. Leveraging Sentinel-2 multi-temporal optical imagery and terrain-derived features, we constructed the first 10-m-resolution spatiotemporal dataset of terrace distribution across the LP, encompassing nine annual periods from 2017 to 2025. Performance evaluations demonstrate that STDBNet achieved an overall accuracy (OA) of 95.26% and a mean intersection over union (MIoU) of 86.84%, outperforming mainstream semantic segmentation models including U-Net and DeepLabV3+ by a significant margin. Further analysis reveals the spatiotemporal evolution dynamics of terraces over the nine-year period and their distribution patterns across gradients of key terrain factors. This study not only provides robust data support for research on terraced ecosystem processes and assessments of soil and water conservation efficacy on the LP but also lays a scientific foundation for informing the formulation of regional ecological restoration and land management policies. Full article

In recent years, the RRT* algorithm has been widely applied in industrial fields because of its asymptotic optimality. However, the traditional RRT* algorithm exhibits limitations in terms of convergence speed and quality of generated paths, and its path exploration capability in complex environments remains inadequate. To address these issues, this study proposes a self-adaptive bidirectional APF-RRT* (SA-Bi-APF-RRT*) algorithm. Specifically, a hierarchical node expansion mechanism is established, enabling dynamic adjustment of the new node expansion strategy. Furthermore, a bidirectional artificial potential field (APF) guidance strategy is introduced to enhance obstacle avoidance performance. An obstacle range density evaluation module, which autonomously adjusts APF parameters according to the density distribution of surrounding obstacles, is then incorporated. Additionally, the algorithm integrates a segmented greedy approach with Bézier curve fitting techniques to achieve simultaneous optimization of path length and smoothness, while ensuring path safety. Finally, the proposed algorithm is compared against RRT*, GB-RRT*, Bi-RRT*, APF-RRT*, and Bi-APF-RRT*, demonstrating superior adaptability and efficiency in environments characterized by low iteration counts and high obstacle density. Results indicate that the SA-Bi-APF-RRT* algorithm constitutes a promising optimization solution for USVs path planning tasks. Full article

The purpose of this study was to determine the effects of coordination training and strength training on the lower extremity inter-segmental coordination during instep kicking for novices. Thirty-two male college students with no soccer-specific training experience participated and were randomly assigned to either a coordination training group, a strength training group, or a kicking training group. Both the coordination and strength training groups also performed the same kicking training as the kicking training group. Each participant executed exercise training three times a week for eight weeks. The instep kicking test was performed before and after the three training sessions. Two-way ANOVAs were conducted to determine the training effects on the kicking performance and the inter-segmental coordination. The maximum ball speed significantly increased for all three training groups (p < 0.001, effect size = 0.638). In contrast, improvements in kicking accuracy were specific to the coordination training group (p = 0.001, effect size = 0.326), with no significant changes observed in the strength (p = 0.052, effect size = 0.138) or kicking groups (p = 0.953, effect size < 0.001). The time spent percentage of the knee-ankle shank-phase coordination pattern in the leg-cocking phase was significantly increased (p = 0.003, effect size = 0.268), but the time spent percentage of the hip-knee thigh-phase in the back swing phase significantly decreased after the three trainings (p = 0.031, effect size = 0.150). A significant reduction in the relative activity of the tibialis anterior and gastrocnemius muscles occurred exclusively after coordination training (p = 0.024, effect size = 0.188). This study confirms that coordination training provides a unique contribution to skill acquisition in novices, specifically enhancing kicking accuracy and neuromuscular control, whereas improvements in maximal ball speed were generic to all training types. Full article

This article addresses selected issues concerning the formulation and implementation of public policies on aging both at the global and regional levels. Selected key initiatives undertaken by the World Health Organization (WHO), the United Nations (UN), and the European Union (EU) are discussed. The article further examines three distinct models of aging policy as exemplified by Norway, Lithuania, and Poland. These countries share commonalities, including very high rates of demographic aging and membership in the Organization for Economic Co-operation and Development (OECD). However, each nation adopts a different model of social policy, consequently reflecting diverse models of senior citizen support. The research aims to elucidate the determinants and impacts of social policies targeting a particularly vulnerable segment of the population—older adults requiring long-term services and support. Central to this investigation is the comparative analysis of general characteristics distinguishing the different national models. This was achieved through a synthesis and evaluation of selected quantitative and qualitative indicators. Methodologically, the study utilizes expert interviews, comprehensive literature reviews, secondary analyses of international and national statistical data, including data from Eurostat and OECD databases, European Commission policy documents, and an extensive review of grey literature encompassing pertinent Internet sources. Findings reveal marked differences in the design and execution of aging policies across the three countries, highlighting variations in how the welfare function of the state is operationalized in addressing the needs of the elderly. Full article

Street trees are vital components of urban ecosystems, contributing to air purification, microclimate regulation, and visual landscape enhancement. Thus, accurate segmentation of individual trees from point clouds is an essential task for effective urban green space management. However, existing methods often struggle with noise, crown overlap, and the complexity of street environments. To address these challenges, this paper introduces a multi-constraint and shortest path optimization method for individual urban street tree segmentation from point clouds. In this paper, object primitives are first generated using multi-constraints based on graph segmentation. Subsequently, trunk points are identified and associated with their corresponding crowns through structural cues. To further improve the robustness of the proposed method under dense and cluttered conditions, the shortest-path optimization and stem-axis distance analysis techniques are proposed to further refine the individual tree extraction results. To evaluate the performance of the proposed method, the WHU-STree benchmark dataset is utilized for testing. Experimental results demonstrate that the proposed method achieves an average F1-score of 0.768 and coverage of 0.803, outperforming superpoint graph structure single-tree classification (SSSC) and nyström spectral clustering (NSC) methods by 17.4% and 43.0%, respectively. The comparison of visual individual tree segmentation results also indicates that the proposed framework offers a reliable solution for street tree detection in complex urban scenes and holds practical value for advancing smart city ecological management. Full article

Landslide disasters are complex spatiotemporal phenomena. Existing deep learning (DL) models for remote sensing (RS) image analysis primarily exploit shallow visual features, inadequately incorporating critical geological, geographical, and environmental knowledge. This limitation impairs detection accuracy and generalization, especially in complex terrains and diverse vegetation conditions. We propose Knowledge Graph Embedding in Swin Feature Pyramid Networks (KGE–SwinFpn), a novel RS landslide segmentation framework that integrates explicit domain knowledge with deep features. First, a comprehensive landslide knowledge graph is constructed, organizing multi-source factors (e.g., lithology, topography, hydrology, rainfall, land cover, etc.) into entities and relations that characterize controlling, inducing, and indicative patterns. A dedicated KGE Block learns embeddings for these entities and discretized factor levels from the landslide knowledge graph, enabling their fusion with multi-scale RS features in SwinFpn. This approach preserves the efficiency of automatic feature learning while embedding prior knowledge guidance, enhancing data–knowledge–model coupling. Experiments demonstrate significant outperformance over classic segmentation networks: on the Yuan-yang dataset, KGE–SwinFpn achieved 96.85% pixel accuracy (PA), 88.46% mean pixel accuracy (MPA), and 82.01% mean intersection over union (MIoU); on the Bijie dataset, it attained 96.28% PA, 90.72% MPA, and 84.47% MIoU. Ablation studies confirm the complementary roles of different knowledge features and the KGE Block’s contribution to robustness in complex terrains. Notably, the KGE Block is architecture-agnostic, suggesting broad applicability for knowledge-guided RS landslide detection and promising enhanced technical support for disaster monitoring and risk assessment. Full article

Accurate scene understanding is crucial for the safe and stable operation of underground utility tunnel inspections. Addressing the characteristics of low-light environments, this paper proposes an object recognition method based on low-light enhanced image semantic segmentation. Secondly, by analyzing image data from real underground utility tunnel environments, the visual language model undergoes scene image fine-tuning to generate scene description text. Thirdly, integrating these functionalities into the system enables real-time processing of captured images and generation of scene understanding results. In practical applications, the average accuracy of the improved recognition model increased by nearly 1% compared to the original model, while the accuracy and recall of the fine-tuned visual-language model surpassed the untuned model by over 70%. Full article