Search Results (4,141)

Ship self-sinking accidents threaten maritime safety, human life, property, and the marine environment, and understanding their causal-factor associations is essential for developing effective preventive measures. This study aims to identify the multi-level factors, recurrent association patterns, and critical structural nodes involved in ship self-sinking accidents. A hybrid framework integrating grounded theory, the Human Factors Analysis and Classification System (HFACS), FP-growth association rule mining, and complex network analysis was applied to 150 accident investigation reports released by the China Maritime Safety Administration between 2014 and 2024. Findings suggest that adverse weather and sea conditions, inadequate ship safety management, and crew incompetence are the most frequent factors. Thirty causal factors were identified and classified into four HFACS levels, and 229 association rules were generated to construct a directed weighted causal-factor association network with 19 nodes and 229 edges. Network results indicate that inadequate ship safety management, crew incompetence, ship unseaworthiness, insufficient maintenance of hull weathertight integrity, and improper or untimely emergency measures occupy critical positions in the association structure. This research offers insight into ship self-sinking accidents and identifies priority intervention points for more targeted maritime supervision, safety management and accident prevention. Full article

Deep-mining operations are increasingly challenged by severe thermal hazards, which have become a critical bottleneck for achieving safe, efficient, and sustainable mineral extraction. While research on deep-mine cooling and heat hazard mitigation has proliferated, the field lacks a systematic, critical review that explicitly examines these advances through the lens of sustainability science. To address this gap, this study conducted a comprehensive bibliometric analysis of 432 publications (1994–2024) retrieved from the Web of Science Core Collection. The methodology employs Bibliometrix, Vosviewer, and CiteSpace to map the intellectual landscape, research hotspots, and evolving frontiers of the field. The results reveal a clear three-stage development trajectory and identify China, the USA, South Africa, and Canada as leading contributors, with national research emphases on ventilation, energy conservation, and refrigeration, respectively. Crucially, keyword clustering and burst detection uncover a notable paradigm shift: the focus has moved from isolated cooling techniques toward integrated, multi-objective strategies—including geothermal energy co-exploitation, phase-change material applications, and system-level energy optimization—signaling a growing alignment with resource efficiency and low-carbon mining principles. However, a critical finding is that the literature remains predominantly techno-centric, overwhelmingly evaluating performance through operational energy savings while largely neglecting life-cycle environmental impacts, holistic sustainability assessment metrics, and the influence of policy drivers. This review thus not only provides a structured overview of the domain, but, more importantly, exposes these critical knowledge gaps. We argue that future research must pivot toward a multi-dimensional sustainability framework that integrates technical, economic, and environmental dimensions, thereby guiding the next generation of research toward truly sustainable deep-mining practices. Full article

Electric Vehicle (EV) charging infrastructure plays a critical role in modern energy systems, affecting energy load distribution, demand-response programs, and grid stability. As EV adoption accelerates globally, the varied charging habits and concurrent interactions among users, stations, and shared infrastructure create operational inefficiencies that existing machine learning and optimization approaches cannot fully diagnose, because these methods rely on aggregated or single-entity representations that discard cross-object process dependencies. To address this gap, we propose EnOptiMine (Energy Optimization Framework for Electric Vehicles through Object-Centric Process Mining), a novel four-phase analytical framework that applies Object-Centric Process Mining (OCPM) to EV charging infrastructure. EnOptiMine operates by transforming raw EV charging data into an Object-Centric Event Log (OCEL 2.0), discovering the complete charging lifecycle as a structured multi-object process through Object-Centric Directly-Follows Graphs (OC-DFGs), performing conformance analysis to detect and quantify process deviations across object-type lifecycles, and proposing process improvement interventions. Applied to the EV charging dataset, EnOptiMine identifies sessions that exhibit post-charge station idle-blocking, departure mismatch, and carry lifecycle ordering violations. In the present work, the real-world simulation confirms that a graduated idle fee policy recovers 22.9% of wasted station-hours, and a departure reconfirmation protocol reduces mismatch sessions by 54.0%. These results demonstrate that OCPM provides process-transparent diagnostic capabilities for EV charging infrastructure that are inaccessible to existing prediction- and optimization-based methods. Full article

This work deals with questions of enhancing the scalability and security of linear chain Bitcoin by introducing a D-BTC (Domino Bitcoin) protocol, supported by a simply connected two-dimensional structure. The paper seeks to answer the question: can the linear topology of Bitcoin be replaced by a richer geometric structure that simultaneously (i) enlarges the number of valid positions where parallel mining can occur, and (ii) strengthens the asymptotic decay of the double-spend reversal probability? In the D-BTC protocol, the blocks, called B-dominoes (Bitcoin dominoes) are organized as a finite connected region subset of ${\mathbb{Z}}^2$ without holes, also called a lattice. Simple connectivity plays a central role in D-BTC and to mine a (valid) B-domino, a miner has to compute four PoW (Proof of Work), corresponding to cardinal directions, allowing them to add it to the frontier of the lattice, under the constraint that the new lattice is simply connected. We introduce a new deterministic consensus based on maximization of the lattice surface. By using a simple version of the isoperimetric inequality, we see that the frontier size grows as $\Omega \left(\sqrt{n}\right)$, where n is the lattice size. Following the Nakamoto’s heuristic, and under the honest majority assumption, a double-spending attack is successful with probability decaying exponentially in $k^2$, where k is the minimum Manhattan distance of the concerned B-domino from the lattice frontier. Additionally, we set up implementations and experiments to demonstrate the practical viability of the protocol with authentic gossip-based message propagation and complete Merkle tree verification. Full article

Rainstorm-induced disaster chains are characterized by high suddenness, immense destructive power, and complex chain propagation mechanisms. Traditional static assessment methods rely on fixed parameters and struggle to depict the dynamic evolution of such disasters. Existing dynamic models are mostly based on predefined structures and lack the capability to integrate multi-source data and quantify uncertainty, thereby constraining the accurate prediction of rainstorm disaster chains. To address these issues, this study proposes a rainstorm disaster chain prediction model (SW-DBN) that integrates a large language model (LLM)-optimized sliding window mechanism with a dynamic Bayesian network (DBN). The model first performs dynamic segmentation and feature extraction on multi-source time-series data through the sliding window mechanism and constructs an LLM-driven module for semantic understanding of multi-source information and latent parameter mining. By leveraging the LLM’s in-depth analysis of data pattern variations within the window, the model excavates latent parameters, adaptively adjusts the DBN network topology, and feeds back to optimize the window width and sliding step, thereby maintaining adaptive alignment between the sliding window’s feature extraction and the dynamic evolution of the disaster chain. Ultimately, the cascade propagation process of the rainstorm disaster chain is modeled, reasoned, and validated through the DBN, forming an integrated prediction framework of “perception–reasoning, dynamic regulation, and cascade verification.” A case study in the Xi’an area demonstrates that the proposed model can effectively simulate the temporal evolution of rainstorm disaster chains. The average prediction accuracy for four key types of disaster nodes reaches 84.8%, representing an improvement of 7.5 percentage points over the standard DBN model, with clear advantages in early warning timeliness for critical nodes. The proposed model provides technical support for the probabilistic prediction of rainstorm disaster chains and disaster prevention decision-making, featuring both dynamic adaptability and interpretability. Full article

This study presents an intelligent framework for the analysis of multidimensional geomechanical states in underground mining systems based on numerical simulation and machine learning methods. A three-dimensional geomechanical model of the Zholymbet deposit was developed in the RS3 environment using the generalized Hoek–Brown failure criterion. Numerical simulations were performed for representative mining scenarios characterized by complex excavation interaction and stress redistribution. The modelling results were transformed into a multidimensional geomechanical dataset containing stress, deformation, displacement, and yielding parameters. Principal component analysis (PCA) was applied to investigate the internal structure of the geomechanical state space and identify dominant patterns controlling the rock mass behavior. Clustering analysis revealed several geomechanical regimes corresponding to stable, transitional, and instability-prone conditions. Isolation Forest anomaly detection demonstrated that atypical geomechanical states are not randomly distributed but spatially localized near excavation systems and mining horizons. The obtained results indicate that hazardous geomechanical conditions are governed by complex interactions between stress concentration, deformation intensity, yielding processes, and excavation geometry. The proposed approach provides a basis for intelligent interpretation of large-scale numerical modelling results and may support geomechanical risk assessment in underground mining operations. Full article

Digital transformation has become an essential component of sustainable rural development, yet substantial inequalities persist in how communities access, adopt, and benefit from digital technologies. Understanding these disparities requires not only information about technological resources but also knowledge of the relational structures through which information, support, and opportunities circulate. This article presents a reproducible synthetic socio-digital network dataset designed to support the analysis of digital gaps in community-based tourism (CBT) environments. Rather than containing original respondent-level observations, the repository was computationally reconstructed from aggregate statistics derived from field studies conducted in three rural communities in the province of Guayas, Ecuador: Bucay (5 de Septiembre), Manglares Churute, and Ruta de los Chirijos. All node-level records, survey variables, and support relationships included in the repository were synthetically generated to preserve aggregate community characteristics while protecting participant confidentiality and preventing individual re-identification. The repository contains synthetic actor metadata, reconstructed socio-digital variables, directed support networks, graph representations in interoperable formats, and precomputed Social Network Analysis (SNA) indicators. The dataset includes 90 synthetic actors, more than one thousand generated support interactions distributed across multiple socio-digital dimensions, machine-readable metadata, and reusable scripts for preprocessing, validation, graph construction, and metric computation. The represented dimensions include financial assistance, training support, information exchange, technological support, social media promotion, institutional collaboration, trust, and emotional closeness. To facilitate reuse, all resources are distributed in standardized formats compatible with NetworkX, Gephi, Neo4j, and graph-learning frameworks. The repository follows FAIR principles and includes documentation intended to support transparency, reproducibility, and methodological benchmarking. Potential applications include social network analysis, graph mining, graph neural networks, digital inequality research, computational social science, community resilience studies, and educational activities. By providing an openly documented synthetic dataset and reproducible computational workflow, the repository contributes to the study of socio-digital systems, privacy-preserving data sharing, and community-level digital transformation processes. Full article

The Huainan Mining Area features extensively developed, fragmented-soft and low-permeability coal seams, characterized by low porosity and permeability, complex geological structures, and significant difficulty in coalbed methane (CBM) drainage. Horizontal wells with staged fracturing in the coal seam roof have become a key method for regional gas control. To further enhance the volume fracturing stimulation effect and single-well gas production, this study targets the horizontal well group in the roof of the No. 8 coal seam in the Huainan Mining Area as the research object. A saturated volume fracturing technology for horizontal wells in the coal seam roof, centered on the concept of a high pump rate (18–20 m³/min) and a high proppant volume (>250 m³/stage), is proposed. This study investigates the fracture propagation mechanisms and fracturing parameter optimization of this technology, and conducts engineering application to verify its stimulation effect. Increasing the fracturing pump rate improves the proppant-carrying capacity of the fracturing fluid, successfully enabling high-rate and high-volume proppant placement. Optimization of the perforation parameters—12 holes per m per cluster and a cluster spacing of 15–25 m—utilizes high perforation friction and moderate stress interference to promote balanced initiation and propagation of multiple fractures within a stage. The optimized ‘saturated’ injection mode, with a single-stage fluid volume exceeding 2400 m³, a single-stage proppant volume exceeding 250 m³, and a maximum sand ratio exceeding 20%, combined with a multi-size proppant mixture, enables full propping of both main and branch fractures. Microseismic monitoring shows that the hydraulic fracture extension length increased by approximately 50% compared to conventional wells, significantly enlarging the stimulated reservoir volume (SRV). Saturated fracturing achieved stable gas production of 2000 to 3000 m³/d, with average production ramp-up rates of 21.47–26.40 m³/d (five times higher than the 5.34 m³/d of the conventional well), and the stable plateau period was notably extended from 36 days to over 150 days. The saturated volume fracturing technology proposed in this study provides an important reference for efficient CBM extraction and surface gas control in mining areas with similar geological conditions. Full article

LiDAR-based place recognition is critical for long-term autonomous navigation in Global Navigation Satellite System (GNSS)-denied environments, yet existing methods struggle to balance accuracy and efficiency under substantial yaw rotations. This paper proposes a robust framework based on a multi-channel polar bird’s-eye-view (BEV) representation. Under yaw-dominated revisits, the polar BEV image transforms yaw rotation into cyclic column shifts, providing a useful structural prior for rotation-equivariant feature extraction. Raw point clouds are projected onto polar BEV grids encoding density, height, and intensity. A rotation-equivariant feature extractor comprising a Radial Compression Module and a rotation-equivariant Transformer module captures long-range azimuthal dependencies via Conditional Positional Encoding and Circular Relative-Position Bias. The equivariant features are aggregated by NetVLAD into a compact global descriptor, trained end-to-end with a hard-example mining triplet loss. Extensive experiments on the public KITTI and NCLT datasets, as well as our self-constructed LiDAR Place Recognition Revisit (LPRR) dataset, demonstrate competitive performance on KITTI and superior performance on NCLT and LPRR among the compared methods. The proposed framework achieves a favorable trade-off between performance and computational cost, and shows promising cross-dataset generalization on the evaluated NCLT and LPRR datasets without fine-tuning. Full article

Accurate characterization of underground geological conditions is essential for gas control, geological hazard assessment, and safe coal mining operations. However, conventional geological interpretation methods often suffer from limited spatial accuracy due to borehole deviation, sparse geological control, and insufficient integration of multi-source borehole data. To address these limitations, this study proposes an integrated geological characterization framework combining resistivity-based image logging, borehole trajectory correction, and BIM-based three-dimensional geological modeling using 135 gas extraction boreholes from the Coal Seam 15-21050 working face of Pingdingshan No. 8 Coal Mine, China. Multi-parameter logging data, including natural gamma, apparent resistivity, natural potential, and borehole image observations, were used to identify coal seam lithology, stratigraphic interfaces, and structural characteristics. Borehole trajectory analysis revealed systematic deviation patterns controlled by borehole inclination, lithological heterogeneity, and drilling conditions, highlighting the necessity of trajectory correction for accurate spatial positioning. Trajectory-corrected borehole coordinates were subsequently integrated into a BIM-based three-dimensional geological reconstruction workflow using spatial interpolation methods. The resulting model successfully reproduced coal seam geometry, interburden distribution, and localized concealed structural anomalies. Coal Seam 15 exhibited thicknesses ranging from 2.69 to 3.47 m, while Coal Seam 16–17 ranged from 1.51 to 2.38 m. The proposed workflow improved the reliability of geological interpretation and the accuracy of spatial characterization, providing an effective technical basis for gas drainage optimization, geological hazard assessment, and intelligent underground coal mining. Full article

The foundational element of any ventilation system assessment is the precise definition of the primary airflow intake. In underground mine networks, even a marginal error in primary inputs triggers a series of inaccuracies, resulting in significant volumetric errors across the entire network. This study explores the sensitivity of the iterative Hardy Cross algorithm for ventilation network analysis towards the main intake, and demonstrates that the intake overestimation error reaches 77%, creating a false sense of security. Furthermore, when utilizing the Hardy Cross approach, evaluating a model based solely on its mathematical tendency to balance is misleading; analysis of relative error evolution demonstrates that a converged network can achieve mathematical balance while remaining fundamentally uncoupled from the mine’s physical reality due to flawed input data. While technical fields currently diverge into two primary paths for airflow definition, overly simplistic approximations or specialist-dependent numerical CFD models, this study proposes a middle ground alternative. The proposed methodology relies on direct anemometric field measurements synthesized through cartographic mapping integration techniques. The suggested technique offers a detailed graphical representation of air velocity across the excavation, derived from the isovel mapping. This visualization illustrates that airflow behaviour through rock excavations is fundamentally non-uniform and dictated by wall roughness and structural irregularities. Full article

Freeze–thaw cycles cause mechanical deterioration and instability of slope rock masses in open-pit coal mines located in the cold regions of Northwest China. In this study, the research object is fine-grained sandstone from the Yan’an Formation in the Tarangole mining area of the Ordos Basin. Here, indoor freeze–thaw cycling, uniaxial compression, and triaxial compression tests were conducted to systematically analyze the deformation behavior, strength evolution, and failure modes of the sandstone under varying numbers of freeze–thaw cycles, freezing temperatures, and confining pressures, thereby revealing its freeze–thaw damage mechanism. The results show that the number of freeze–thaw cycles is the dominant factor affecting the elastic modulus. Freezing temperatures (especially between −5 °C and −15 °C) and the number of freeze–thaw cycles (particularly the first 10 cycles) significantly reduce peak strength. In addition, confining pressure can significantly enhance the resistance to deformation (under 15 freeze–thaw cycles, the elastic modulus increases by 181.8% as confining pressure rises from 0 to 2 MPa). Within the low confining pressure range (0–1.5 MPa), peak strain decreases monotonically with increasing confining pressure and is independent of the number of freeze–thaw cycles. Finally, the increase in the number of freeze–thaw cycles and the decrease in temperature jointly promote crack development, and the failure mode shifts from pure shear to a shear-tension composite mode. The underlying cause lies in the evolution of interparticle cementation within the soil skeleton and in the associated pore–crack structure. In addition, based on fracture damage mechanics and the modified Weibull distribution, a damage evolution equation and a constitutive model for sandstone considering freeze–thaw cycles and temperature effects were established and validated. Therefore, the research findings can provide a theoretical basis for slope support, freeze–thaw disaster prevention and mitigation, and stability assessment in the Tarangole mining area and other cold regions. Full article

Rock pillars in deep underground mines are subjected to complex stress environments. The combined effects of in situ stress and cyclic disturbances from mining activities lead to a redistribution of the surrounding rock mass stress field, which readily triggers instability and failure, posing severe threats to mining engineering safety. To investigate the damage mechanism of cyclic loading on rock and its weakening effect on the bearing capacity of mine pillars, this study takes limestone as the research object. A series of uniaxial compression tests were conducted on limestone specimens subjected to triaxial cyclic pre-damage, complemented by numerical simulations to further characterize the energy and deformation evolution of the damaged limestone under cyclic loading conditions. The findings are as follows: (i) Triaxial cyclic tests on limestone show that both the input energy and dissipated energy follow similar trends, decreasing rapidly in the initial stage before stabilizing. The elastic strain energy remains largely constant, with most of the input energy being stored as elastic strain energy. Under constant stress levels and cycle numbers, increases in confining pressure and frequency reduce the rock’s input energy, elastic strain energy, and dissipated energy. (ii) The peak stress of damaged limestone exhibits a positive correlation with the pre-damage confining pressure and cyclic frequency, while it decreases with an increasing number of cycles. Higher confining pressure and frequency raise the input energy, elastic potential energy, and dissipated energy at the peak stress point. (iii) Deformation and failure in damaged limestone originate from the development and propagation of localized deformation zones. Increased lateral displacement within these zones promotes the formation of macroscopic fractures. Due to significant structural heterogeneity inside the localized areas, the evolution of deformation energy is influenced by regional characteristics. (iv) Simulation results indicate that the uniaxial compressive failure of limestone involves the accumulation and propagation of micro-scale tensile cracks, which ultimately coalesce into macro-scale shear fracture surfaces. During uniaxial loading of pre-damaged limestone, newly generated cracks predominantly initiate around pre-existing cracks, with only a limited number distributed randomly. Their peak intensity shows a positive correlation with the pre-damage confining pressure. Full article

Large-diameter rescue shafts serve as critical infrastructure for emergency response in mining disaster scenarios, and their structural deformation directly affects the safe passage of rescue capsules. In this paper, we investigate three-dimensional (3D) reconstruction techniques for large-diameter rescue shaft environments and develop a Neural Radiance Fields (NeRF)-based reconstruction and deformation assessment scheme. The proposed workflow integrates no reference signal-to-noise-ratio (NR-SNR), image-quality filtering, SfM-based camera-pose estimation, Nerfacto reconstruction, point-cloud export, and circular-section fitting. The NR-SNR retention-ratio experiment shows that retaining approximately 35% high-quality images provides a practical efficiency–quality trade-off for the present dataset, reducing the computational burden of SfM pose estimation while preserving sufficient geometric information for subsequent reconstruction. The reconstructed radiance field is further exported as a dense point cloud and evaluated using relative radius error, circle-fitting residuals, and image-level rendering metrics. Experiments on a simulated large-diameter rescue shaft platform show that the proposed NeRF-based scheme provides favorable geometric measurement applicability and visual reconstruction quality under weak-texture and low-illumination conditions. Compared with conventional MVS and the tested 3DGS baseline, the proposed scheme produces a point-cloud output that is more suitable for subsequent circular-section fitting and deformation-related assessment. In addition, comparison with a representative SDF-based baseline indicates that direct implicit surface recovery remains challenging for the tested hollow cylindrical shaft-wall scene. The results demonstrate the potential of the proposed NeRF-based workflow for rescue-shaft inner-wall reconstruction and engineering-oriented deformation evaluation. Full article

Geological disasters are frequently triggered in steep mountainous mining areas due to the coupling effects of underground excavation and slope stability, yet the applicability of traditional rock movement theories in such terrains remains unclear. This study investigates an extremely steep coal mine in southwestern China, integrating engineering geological surveys, unmanned aerial vehicle (UAV) measurements, InSAR monitoring, and rock movement theoretical calculations to analyze the impact of mining on mountain deformation and slope stability. The results show that the study area exhibits steep slopes (55–85°) and gently inclined, reverse-layered rock masses controlled by structural fracture zones, creating a geological environment prone to mining-induced landslides. The 1151 working face lies at a depth of 286–470 m, with a protective coal pillar of approximately 160 m left between the excavation and the cliff zone. InSAR monitoring indicates cumulative LOS deformation rates of −0.98 to 0.55 cm/a, with subsidence concentrated above the working face, while existing landslides in the cliff zone show no significant deformation. Comparison between theoretical calculations and InSAR inversion reveals that InSAR boundary angles (downslope 61–68°, upslope 67–73°) exceed theoretical predictions (downslope 48–52°, upslope 55°), indicating that complex topography and rock mass structure constrain mining-induced deformation propagation. The findings demonstrate that appropriately designed protective coal pillars and avoidance of unstable slopes can effectively mitigate the impact of mining-induced disturbances on existing hazards. This study provides valuable reference for landslide risk assessment and disaster prevention in extremely steep mining regions. Full article