Search Results (117,300)

A major danger that significantly raises the possibility of deep coal mining accidents is the delayed water influx from the bottom plate, which is brought on by the activation of tiny faults brought on by mining at the working face of the restricted aquifer. This study develops 17 numerical models utilizing FLAC3D simulation software 6.00.69 to clarify the activation and water inburst mechanisms of minor faults influenced by various parameters, incorporating fluid–solid coupling effects in coal seam mining. The developmental patterns of the stress field, displacement field, plastic zone, and seepage field of the floor rock layer were systematically examined in relation to four primary factors: aquifer water pressure, minor fault angle, fracture zone width, and the distance from the coal seam to the aquifer. The results of the study show that the upper and lower plates of the minor fault experience discontinuous deformation as a result of mining operations. The continuity of the rock layers below is broken by the higher plate’s deformation, which is significantly larger than that of the lower plate. The activation and water flow into small faults are influenced by many elements in diverse ways. Increasing the distance between the coal seam and the aquifer will make the water conduction pathway more resilient. This will reduce the amount of water that flows in. On the other hand, higher aquifer water pressure, a larger fracture zone, and a fault that is tilted will all help smaller faults become active and create channels for water to flow into. The gray relational analysis method was used to find out how sensitive something is. The sensitivities of each factor to water influence were ranked from high to low as follows: distance between the aquifer and coal seam (correlation coefficient 0.766), aquifer water pressure (0.756), width of the fracture zone (0.710), and angle of the minor fault (0.673). This study statistically elucidates the inherent mechanism of delayed water instillation in minor faults influenced by many circumstances, offering a theoretical foundation for the accurate prediction and targeted mitigation of mine water hazards. Full article

Citrus, as a globally important economic crop, requires accurate assessment of premium fruit production potential for precise orchard management and enhanced economic benefits. This study develops a method for assessing the production potential of premium citrus using UAV-based multispectral imagery and ground data. Taking citrus orchards in Wuming District, Guangxi, China, as the experimental area, this study investigates techniques for assessing the production potential of premium fruit at the canopy scale of citrus trees in southern hilly regions, aiming to rapidly predict the quality production potential of citrus before fruit ripening. The methodology involved the following: (1) Segmenting the study area using a Digital Surface Model (DSM) and extracting individual tree canopies by integrating NDVI with a marker-controlled watershed algorithm. Canopy fruit boundaries were identified using the NPCI index. (2) Selecting key assessment indicators—NDVI, TCAVI, REOSAVI, canopy area, and fruit area—through correlation analysis with nutritional quality metrics. (3) Establishing threshold levels for these indicators and constructing a production potential assessment model. Experimental results demonstrated an individual tree identification accuracy (precision) of 98.75%, a recall of 98.47%, and an F-score of 98.61%. Canopy area extraction achieved a coefficient of determination (R²) of 0.869 and a root mean square error (RMSE) of 0.489 m². The overall accuracy for production potential assessment reached 85.11%. This study provides a new approach for using UAV multispectral technology to non-destructively assess the production potential of premium citrus in the hilly regions of southern China, offering technical support for precise orchard management. Full article

Investigating the characteristics and influencing mechanisms of urban vitality in mountainous cities can contribute to enhanced urban resilience, optimised resource allocation, and sustainable development. However, most existing studies have focused on static analyses at single spatial scales, making it difficult to fully reveal the evolutionary trends of urban vitality under complex topographic constraints or the spatiotemporal heterogeneity of its influencing factors. This study examines Guiyang, one of China’s fastest-growing cities, focusing on both its economic development and population growth. Based on social media data and geospatial big data from 2019 to 2024, the spatiotemporal permutation scan statistics (STPSS) model was employed to identify spatiotemporal areas of interest (ST-AOIs) and to analyse the spatial distribution and day-night dynamics of urban vitality across different phases. Furthermore, by incorporating transportation and topographic factors characteristic of mountainous cities, the multiscale geographically and temporally weighted regression (MGTWR) model was applied to reveal the driving mechanisms of urban vitality. The main findings are as follows: (1) Urban vitality exhibits a multi-center, clustered structure, gradually expanding from gentle to steeper slopes over time, with activity patterns shifting from an afternoon peak to an all-day distribution. (2) Significant differences in regional vitality resilience were observed: the core vitality areas exhibited stable ST-AOI spatial patterns, flexible temporal rhythms, and strong adaptability; the emerging vitality areas recovered quickly with low losses, while low-vitality areas showed slow recovery and insufficient resilience. (3) The density of commercial service facilities and the level of housing prices were continuously enhancing factors for vitality improvement, whereas the density of subway stations and the degree of functional mix played key roles in supporting resilience during the COVID-19 pandemic. (4) The synergistic effect between transportation systems and commercial facilities is crucial for forming high-vitality zones in mountainous cities. In contrast, reliance on a single factor tends to lead to vitality spillover. This study provides a crucial foundation for promoting sustainable urban development in Guiyang and other mountainous regions. Full article

The Hadamengou gold deposit, hosted in the Precambrian metamorphic basement, is a super-large gold deposit occurring along the northern margin of the North China Craton. Despite extensive investigation, the genesis of the gold mineralization is poorly understood and remains highly debated. This study integrates a comprehensive dataset, including fluid inclusion microthermometry and C-H-O-S-Pb isotopes, to better constrain the genesis and ore-forming mechanism of the deposit. Hydrothermal mineralization can be divided into pyrite–potassium feldspar–quartz (Stage I), quartz–gold–pyrite–molybdenite (Stage II), quartz–gold–polymetallic sulfide (Stage III), and quartz–carbonate stages (Stage IV). Four types of primary fluid inclusions are identified, including pure CO₂-type, composite CO₂-H₂O-type, aqueous-type, and solid-daughter mineral-bearing-type inclusions. Microthermometric and compositional data reveal that the fluids were mesothermal to hypothermal, H₂O-dominated, and CO₂-rich fluids containing significant N₂ and low-to-moderate salinity, indicative of a magmatic–hydrothermal origin. Fluid inclusion assemblages further imply that the ore-forming fluids underwent fluid immiscibility, causing CO₂ effusion and significant changes in physicochemical conditions that destabilized gold bisulfide complexes. The hydrogen–oxygen isotopic compositions, moreover, support a dominant magmatic water source, with increasing meteoric water input during later stages. The carbon–oxygen isotopes are also consistent with a magmatic carbon source. Sulfur and lead isotopes collectively imply that ore-forming materials were derived from a hybrid crust–mantle magmatic reservoir, with minor contribution from the country rocks. By synthesizing temporal–spatial relationships between magmatic activity and ore formation, and the regional tectonic evolution, we suggest that the Hadamengou is an intrusion-related magmatic–hydrothermal lode gold deposit. It is genetically associated with multi-stage magmatism induced by crust–mantle interaction, which developed within the extensional tectonic regimes. Full article

Microservice deployment and request routing can help improve server efficiency and the performance of large-scale mobile edge computing (MEC). However, the joint optimization of microservice deployment and request routing is extremely challenging, as dynamic request routing easily results in asymmetric network structures and imbalanced microservice workloads. This article proposes multi-objective joint optimization for microservice deployment and request routing based on structural symmetry. Firstly, the structural symmetry of microservice deployment and request routing is defined, including spatial symmetry and temporal symmetry. A constrained nonlinear multi-objective optimization model was constructed to jointly optimize microservice deployment and request routing, where the structural symmetric metrics take into account the flow-aware routing distance, workload balancing, and request response delay. Secondly, an improved artificial plant community algorithm is designed to search for the optimal route to achieve structural symmetry, including the environment preparation and dependency installation, service packaging and image orchestration, arrangement configuration and dependency management, deployment execution and status monitoring. Thirdly, a benchmark experiment is designed to compare with baseline algorithms. Experimental results show that the proposed algorithm can effectively optimize structural symmetry and reduce the flow-aware routing distance, workload imbalance, and request response delay, while the computational overhead is small enough to be easily deployed on resource-constrained edge computing devices. Full article

Printed circuit boards (PCBs) are critical in the electronics industry. As PCB layouts grow increasingly complex, defect detection processes often encounter challenges such as low image contrast, uneven brightness, minute defect sizes, and irregular shapes, making it difficult to achieve rapid and accurate automated inspection. To address these challenges, this paper proposes a novel object detector, YOLO-UMS, designed to enhance the accuracy and speed of PCB surface defect detection. First, a lightweight plug-and-play Unified Multi-Scale Feature Fusion Pyramid Network (UMSFPN) is proposed to process and fuse multi-scale information across different resolution layers. The UMSFPN uses a Cross-Stage Partial Multi-Scale Module (CSPMS) and an optimized fusion strategy. This approach balances the integration of fine-grained edge information from shallow layers and coarse-grained semantic details from deep layers. Second, the paper introduces a lightweight RG-ELAN module, based on the ELAN network, to enhance feature extraction for small targets in complex scenes. The RG-ELAN module uses low-cost operations to generate redundant feature maps and reduce computational complexity. Finally, the Adaptive Interaction Feature Integration (AIFI) module enriches high-level features by eliminating redundant interactions among shallow-layer features. The channel-priority convolutional attention module (CPCA), deployed in the detection head, strengthens the expressive power of small target features. The experimental results show that the new UMSFPN neck can help improve the AP50 by 3.1% and AP by 2% on the self-collected dataset PCB-M, which is better than the original PAFPN neck. Meanwhile, UMSFPN achieves excellent results across different detectors and datasets, verifying its broad applicability. Without pre-training weights, YOLO-UMS achieves an 84% AP50 on the PCB-M dataset, which is a 6.4% improvement over the baseline YOLO11. Comparing results with existing target detection algorithms shows that the algorithm exhibits good performance in terms of detection accuracy. It provides a feasible solution for efficient and accurate detection of PCB surface defects in the industry. Full article

We present a high-precision endoscopic shape-sensing method using only two calibrated outer cores of a multicore fiber Bragg grating (MC-FBG) array. By leveraging the geometric relationship among two non-collinear outer cores and the central core, the method estimates curvature and bending angle without relying on multiple outer-core channels, thereby reducing complexity and error propagation. On canonical shapes, the proposed method achieves maximum relative reconstruction errors of 1.62% for a 2D circular arc and 2.81% for a 3D helix, with the corresponding RMSE values reported for completeness. In addition, representative endoscope-relevant configurations including the α-loop, reversed α-loop, and N-loop are accurately reconstructed, and temperature tests over 25–81 °C further verify stable reconstruction performance under thermal disturbances. This work provides a resource-efficient and high-fidelity solution for endoscopic shape sensing with strong potential for integration into next-generation image-guided and robot-assisted surgical systems. Full article

Diffusion probabilistic models have demonstrated remarkable superiority in SISR. Yet, their multi-step denoising mechanism incurs prohibitive computational overhead, which severely limits real-world deployment. To address this issue, we propose an Entropy Subtraction-Supported Diffusion Denoising framework for image Reconstruction (ESRDF). The core idea is to shift part of the SR burden from the diffusion model to an image Decoder, with a key focus on recovering the symmetric structural correspondence between LR and HR images that is often degraded during downsampling. Specifically, ESRDF’s main branch employs a CNN that performs one-step feature reconstruction, supervised by a novel entropy-matching loss in addition to the conventional reconstruction loss. This loss adopts a patch-wise entropy matching strategy that enforces regional consistency between the True and the predicted images. Building on L1’s focus on pixel-level details and perceptual loss’s grasp of global semantics, region-wise entropy measurement further completes the global alignment of intra-region information structures. Under this framework, the main branch delivers coarse low-frequency content, drastically reducing the workload of the diffusion branch, which now only needs to sparsely refine high-frequency details. Experimental results on multiple benchmark datasets demonstrate that ESRDF achieves shorter model convergence times and higher generation quality with fewer denoising steps, outperforming previous diffusion-based image reconstruction methods. Full article

Container-based cloud platforms enable flexible and lightweight application deployment, yet container scheduling remains challenged by resource fragmentation, load imbalance, excessive energy consumption, and service-level agreement (SLA) violations. To address these issues, this paper proposes a hybrid multi-objective optimization approach, termed HHO-GWO, which combines Harris Hawks Optimization (HHO) with the Grey Wolf Optimizer (GWO) for container initial placement in cloud environments. A unified fitness function is designed to jointly consider resource utilization, load balancing, resource fragmentation, energy consumption, and SLA violation rate. In addition, a dynamic weight adjustment mechanism and Lévy flight perturbation are incorporated to improve search adaptability and prevent premature convergence. The proposed method is evaluated through extensive simulations under different workload scales and compared with several representative metaheuristic algorithms. The results show that HHO-GWO achieves improved convergence behavior, solution quality, and stability, particularly in large-scale container deployment scenarios. These findings suggest that the proposed approach provides a practical and energy-aware solution for multi-objective container scheduling in cloud data centers. Full article

The wave-making characteristics and resistance performance of a naval vessel are fundamental to its hydrodynamic design, directly impacting its speed, stealth, and energy efficiency. To reveal the performance trade-offs inherent in different design philosophies, a systematic comparative study on the hydrodynamic performance of two representative mainstream naval destroyers from China and the United States was conducted using Computational Fluid Dynamics (CFD). Full-scale three-dimensional models of both vessels were established based on publicly available data. Their flow fields in calm water were numerically simulated at both economical (18 knots) and maximum (30 knots) speeds using an unsteady Reynolds-Averaged Navier–Stokes (RANS) solver, the Volume of Fluid (VOF) method for free-surface capturing, and the SST k-ω turbulence model. The performance differences were meticulously compared through qualitative observation of wave patterns, quantitative measurements (such as the transverse width of the wave-making region), and analysis of resistance data. Numerical results indicated that the wave-making generated by the vessel of the United States was more pronounced during steady navigation. To validate the reliability of the CFD results, supplementary towing tank tests were performed using a small-scale model (1.1 m in length) of the vessel from China. The test speed (1.5 m/s) was scaled to correspond to the full-scale ship speed through dimensional analysis. The experimental data showed good agreement with the simulation results, jointly confirming the aforementioned performance trade-off. This study clearly demonstrates that, at the economic speed, the design of the mainstream vessel from China tends to prioritize superior wave stealth performance at the expense of higher resistance, whereas the mainstream vessel from the U.S. exhibits the characteristics of lower resistance coupled with more significant wave-making features. These findings provide an important theoretical basis and data support for the future multi-objective optimization design of surface vessels concerning stealth, speed, and comprehensive energy efficiency. Full article

ISO 22002-100:2025 introduces stringent and more technically explicit prerequisite programme (PRP) requirements for allergen management, food fraud mitigation, and the control of chemical and packaging-related contaminants across the food, feed, and packaging supply chain. This review examines how advanced chromatographic methods provide the analytical basis required to meet these requirements and to support alignment with GFSI-recognized certification schemes. Recent applications of liquid and gas chromatography coupled with mass spectrometry for allergen quantification, authenticity assessment, and the determination of packaging migrants, auxiliary chemical residues, lubricants, and indoor pest-control pesticides are presented to demonstrate their relevance as verification tools. Across these PRP-related controls, chromatographic methods enable trace-level detection, structural specificity, and reproducible measurement performance, thereby shifting PRP compliance from a documentation-based activity to a process verified through measurable analytical evidence. The review highlights significant progress in method development and simultaneous multi-target analytical approaches while also identifying remaining challenges related to matrix-appropriate validation, harmonization, and analytical coverage for chemical contamination, which is now formally defined as a measurable PRP requirement under ISO 22002-100:2025. Overall, the findings demonstrate that chromatographic analysis has become essential to demonstrating PRP effectiveness under ISO 22002-100:2025, supporting the broader shift toward evidence-based, scientifically robust food safety assurance. Full article

This research addresses the limitations of traditional assembly line equipment, which is costly and impractical for narrow terrains, as well as the challenges of portable devices in large-scale detection. We propose a non-destructive testing method for assessing the internal quality of Sanhua Plums using a free-fall approach that integrates near-infrared spectroscopy and images. Through analysis of models created from spectral data collected under optimal conditions (motor speed: 6.6 r/min, integration time: 14 ms, spot diameter: 20 mm), we processed near-infrared data from 120 plums. The spectral data underwent preprocessing with polynomial smoothing (SG) and Standard Normal Variate (SNV) calibration, followed by feature extraction using Competitive Adaptive Reweighted Sampling (CARS), resulting in a prediction model for soluble solid content with R² of 0.8374 and RMSE of 0.5014. Simultaneously, a prediction model based solely on visual image data achieved an R² of 0.3341 and RMSE of 1.0115. We developed a multi-source information fusion model that incorporated Z-score normalization, linear weighted fusion, and Partial Least Squares Regression (PLSR), resulting in an R² of 0.8871 and RMSE of 0.4141 for the test set. This model outperformed individual spectroscopy and visual models, supporting the development of an automated non-destructive system for evaluating Sanhua Plum’s internal quality. Full article

With the advancement of Light Detection and Ranging (LiDAR) technology and computer science, LiDAR–Inertial Simultaneous Localization and Mapping (SLAM) has become essential in autonomous driving, robotic navigation, and 3D reconstruction. However, dynamic objects such as pedestrians and vehicles, with complex terrain conditions, pose serious challenges to existing SLAM systems. These factors introduce artifacts into the acquired point clouds and result in significant vertical drift in SLAM trajectories. To address these challenges, this study focuses on controlling vertical drift errors in LiDAR–Inertial SLAM systems operating in dynamic environments. The research focuses on three key aspects: ground point segmentation, dynamic artifact removal, and vertical drift optimization. In order to improve the robustness of ground point segmentation operations, this study proposes a method based on a concentric sector model. This method divides point clouds into concentric regions and fits flat surfaces within each region to accurately extract ground points. To mitigate the impact of dynamic objects on map quality, this study proposes a removal algorithm that combines multi-frame residual analysis with curvature-based filtering. Specifically, the algorithm tracks residual changes in non-ground points across consecutive frames to detect inconsistencies caused by motion, while curvature features are used to further distinguish moving objects from static structures. This combined approach enables effective identification and removal of dynamic artifacts, resulting in a reduction in vertical drift. Full article

Repeated sowing of winter cereals represents one of the adaptive dryland approaches to make more sustainable the rainfed agriculture activities in southern Kazakhstan. This study conducted a multi-year reconstruction of crop transitions using Sentinel-2 imagery for 2018–2025, based on the combined analysis of Normalized Difference Vegetation Index (NDVI) temporal profiles and the Plowed Land Index (PLI), enabling the creation of a field-level harmonized classification set. The transition “spring crop → winter crop” was used as a formal indicator of repeated winter sowing, from which annual repeat layers and an integrated metric, the R-index, were derived. The results revealed a pronounced spatial concentration of repeated sowing in foothill landscapes, where terrain heterogeneity and locally elevated moisture availability promote the recurrent return of winter cereals. Comparison of NDVI composites for the peak spring biomass period (1–20 May) showed a systematic decline in NDVI with increasing R-index, indicating the cumulative effect of repeated soil exploitation and the sensitivity of winter crops to climatic constraints. Precipitation analysis for 2017–2024 confirmed the strong influence of autumn moisture conditions on repetition phases, particularly in years with extreme rainfall anomalies. These findings demonstrate the importance of integrating multi-year satellite observations with climatic indicators for monitoring the resilience of agricultural systems. The identified patterns highlight the necessity of implementing nature-based solutions, including contour–strip land management and the development of protective shelterbelts, to enhance soil moisture retention and improve the stability of regional agricultural landscapes. Full article

Aiming at issues such as reactive power distribution fluctuations and insufficient local support caused by large-scale integration of renewable energy in new power systems, as well as the poor adaptability of traditional methods and bottlenecks of deep reinforcement learning in complex power grids, a two-stage intelligent optimization method for grid reactive power based on dynamic voltage partitioning is proposed. Firstly, a comprehensive indicator system covering modularity, regulation capability, and membership degree is constructed. Adaptive MOPSO is employed to optimize K-means clustering centers, achieving dynamic grid partitioning and decoupling large-scale optimization problems. Secondly, a Markov Decision Process model is established for each partition, incorporating a penalty mechanism for safety constraint violations into the reward function. The DDPG algorithm is improved through multi-experience pool probabilistic replay and sampling mechanisms to enhance agent training. Finally, an optimal reactive power regulation scheme is obtained through two-stage collaborative optimization. Simulation case studies demonstrate that this method effectively reduces solution complexity, accelerates convergence, accurately addresses reactive power dynamic distribution and local support deficiencies, and ensures voltage security and optimal grid losses. Full article