Search Results (5,708)

Trackless rubber-tired vehicles are the important auxiliary transportation equipment in coal mines. The main difficulty of their unmanned driving is that the underground environment information is complex but the onboard computing resources for perception and measurement are limited. To solve this conflict, this paper establishes a lightweight map dimension reduction framework to assist in path planning. Firstly, motivated by the idea of image convolution, the framework using the simplicity kernel is proposed for the high-resolution grid maps, which can reduce planning time while retaining the useful map information. Secondly, the reverse automata based on the greedy strategy are designed to get suitable machine-selected key points, which can solve the problem that some self-selected key points become impassable because of the dimension reduction. Moreover, a Bezier smoothing method based on slope interpolation is presented to avoid the collision between the smooth path and obstacle grid caused by the small number of path points planned on the reduced-dimension map. Finally, comparison experiments and downhole map experiment are carried out and discussed. The results show that using the proposed method to assist path planning can reduce time by 99.77% and reduce the number of redundant path points by 79.60%, and using the improved smoothing method from the framework can avoid collision risks caused by fewer path points. Full article

H9N2 avian influenza viruses inherently carry cross-species transmission potential, making continuous surveillance critical for pandemic prevention. This study focused on monitoring the 2024 H9N2 epidemic in Jiangsu Province’s external environment, analyzing its molecular evolution and receptor binding properties, assessing cross-species transmission and pandemic risks, and investigating serological antibody levels across different human populations. Environmental samples were collected from live poultry markets, farms, slaughterhouses, and bird habitats across Jiangsu, screened via quantitative PCR (qPCR), with positive samples used for virus isolation and whole-genome sequencing. Receptor binding properties were tested by hemagglutination assay, and H9N2 antibody levels were measured in 370 occupationally exposed individuals and 240 non-exposed individuals using hemagglutination inhibition (HI) assays. Among the 5779 collected samples, 6.89% tested H9N2-positive, and 12 strains belonging to the Eurasian lineage Y280-like clade G57 genotype were successfully isolated. All strains carried the HA-Q226L mutation, with 11 showing preferential binding to human α-2,6 receptors and one strain possessing dual receptor binding capability. Internal genes harbored mammalian adaptation mutations, and M2 proteins contained mutations conferring complete resistance to amantadine-class antiviral drugs. Serological tests revealed antibody positive rates of 4.05% in exposed populations and 2.5% in non-exposed populations, with no statistically significant difference between groups. These findings confirm that Jiangsu’s circulating H9N2 viruses have acquired human receptor preference and mammalian adaptation, posing silent infection and pandemic risks. Enhanced surveillance and the development of candidate vaccine stockpiles are strongly recommended. Full article

Brittle geomaterials such as coal and rock are prone to unstable failure under high stress and dynamic disturbances, where rapid release of stored elastic strain energy can trigger dynamic disasters. Polyurea, a high-strength and high-ductility elastomer, can form a continuous flexible coating on the surface of coal/rock to regulate their deformation–fracture behavior. Here, uniaxial compression tests were performed on coal specimens coated with polyurea layers of different thicknesses (0–1.25 mm). Acoustic emission (AE) and digital image correlation (DIC) were jointly employed to characterize macroscopic deformation, microcrack evolution, fracture-mode transition, and energy partitioning. The results show that polyurea provides passive lateral confinement that suppresses lateral expansion and shifts macroscopic failure from brittle splitting to progressive ductile damage. AE-based AF–RA analysis indicates that thicker coatings increase the normal stress and shear resistance along potential fracture planes, promoting a microfracture transition from shear-dominated to tension-dominated cracking. Energy analysis demonstrates that the coating enhances pre-peak energy dissipation via coordinated deformation with the coal, while thicker coatings (≥1.00 mm) exhibit pronounced post-peak elastic tensile deformation to absorb and buffer fracture-released energy, impeding the instantaneous energy release typical of bare coal. Moreover, the elastic energy index shows that polyurea markedly reduces impact tendency, with an appropriate thickness stabilizing specimens from strong to weak/non-impact propensity. These findings clarify the coupled confinement–fracture–energy regulation mechanisms of polyurea coatings and provide quantitative guidance for coating-thickness design to mitigate dynamic failure hazards in brittle materials. Full article

Large-amplitude and long-term vibration deformation under external environmental loads often occurs on tensile membrane structures. Proper sensor placement plays a vital role in effectively achieving the objectives of a structural health monitoring system. In order to optimize the sensor placement to identify the modal vibration parameters for tensile membrane structures, this paper proposes an optimal sensor placement method based on the Fisher information matrix (FIM) and improved random strategy discrete particle swarm optimization algorithm (IRSDPSO). Firstly, the structural modal order is selected by using the two-norm difference and trace change rate of FIM, and the number of sensors is determined based on the QR decomposition and MAC criterion. Secondly, an improved particle swarm optimization algorithm named IRSDPSO, which has the discrete characteristic, is proposed to arrange the placement of sensors. Finally, the convergence, stability and sensitivity are used to evaluate the effectiveness of optimal sensor placement results using a numerical modal test example of the plane bidirectional tensile membrane structure. The results show that the first nineteen modal frequencies can be accurately identified. This indicates that the proposed optimal sensor placement method can determine the number of sensors and arrange the placement of the sensors. The work is reasonable and feasible in the optimal sensor placement for the tensile membrane structure. Full article

Silicone rubber is an important external insulating material for composite bushings, composite insulators, and other power equipment. During long-term service, it is inevitably exposed to coupled environmental and electrical stresses, such as elevated temperature, moisture ingress, strong electric fields, and partial discharge, which may lead to hydrophobicity loss, surface chalking, crack propagation, and particle shedding. To reveal the microscopic degradation mechanism of silicone rubber under complex operating conditions, a molecular model of methyl vinyl silicone rubber was constructed using Materials Studio. A stable silicone rubber molecular structure was obtained through crosslinking, geometry optimization, and ensemble relaxation. Subsequently, a reactive molecular dynamics simulation system under coupled temperature–humidity–electric field conditions was established using LAMMPS and the ReaxFF reactive force field. Different temperature gradients, electric field intensities, and aging–recovery stages were designed to investigate the degradation behavior of silicone rubber. The evolution of the maximum carbon content, maximum silicon content, carbon-containing decomposition products, and typical small-molecule products, including H₂, H₂O, CH₄, C₂H₂, C₂H₄, and C₂H₆, was statistically analyzed. In addition, atomic trajectory tracking was performed to clarify the processes of methyl group detachment, Si-O bond cleavage, water molecule participation, and molecular chain reconstruction. The results show that high temperature mainly promotes methyl group detachment from side chains and fracture of the siloxane main chain, while a strong electric field accelerates the decomposition process and induces the transformation of long siloxane chains into shorter chains. Water molecules can react with broken siloxane chains to form hydroxyl-containing structures, making the structural degradation partially irreversible. The degradation process of silicone rubber under coupled temperature–humidity–electric field stress can be summarized as side-chain detachment, main-chain scission, water-assisted reactions, free-radical recombination, and local molecular aggregation. This study provides a molecular-level theoretical basis for aging mechanism analysis, condition assessment, and lifetime prediction of composite external insulating materials. Full article

Mercury (Hg) is a global pollutant that poses a serious threat to ecosystems and human health. Biochar has shown great potential for mercury removal due to its porous structure and abundant surface functional groups. However, redox-active moieties on biochar can reduce adsorbed Hg²⁺ to volatile Hg⁰, leading to secondary mercury dispersion. To suppress this reduction, this study proposes a strategy of co-pyrolyzing coal gangue with rice husk to prepare composite biochars (RHB/CG), leveraging the abundant metal oxides in coal gangue to tailor the surface chemistry of biochar. The materials were characterized by FTIR, Raman, and XRD; static adsorption, mercury speciation analysis, and kinetic experiments were conducted. The results show that coal gangue incorporation significantly enhances the Hg²⁺ adsorption capacity of biochar, with the equilibrium adsorption capacity calculated by the pseudo-second-order kinetic model, increasing from 20.6 mg/g for pristine RHB to 38.7 mg/g for RHB/CG-1:1. More importantly, RHB/CG composites effectively suppress the reduction of Hg²⁺ to Hg⁰, and the amount of Hg⁰ accumulated in the system is 57.1% lower than that of pristine RHB. Mechanistic studies reveal that coal-gangue-derived basic functional groups (e.g., C–O–C, Si–O–M) inhibit reduction via sequestering Hg²⁺ through coordination and disruption of electron transfer pathways. PHREEQC simulations (pe = 6.0) confirm the decreased tendency of Hg²⁺ reduction to Hg⁰ with increasing pH, in good agreement with the experimental results showing reduced Hg²⁺ reduction. The corresponding results provide a green and sustainable solution for mercury-contaminated water and soil remediation. Full article

Ultrasonic inspection of welded steel components remains challenging due to weld-scale material gradients, local anisotropy, attenuation, and aperture limitations. These factors severely distort both the first-arrival wavefield and the late-arriving scattered wavefield. To address this, this study presents a numerical proof of concept for three-dimensional cross-weld virtual-array coda-wave tomography (VACWT). The “virtual array” utilizes a synthetic aperture created by re-indexing sequential source–receiver records from two opposing line scans into midpoint–angle–depth coordinates. This approach enables line-based data acquisition to achieve multi-angle volumetric coverage without requiring a two-dimensional matrix array. A parameterized welded-solid benchmark model was developed, incorporating effective longitudinal and shear wave velocities, attenuation, and out-of-plane tilt fields. Four defect scenarios were evaluated: a cylindrical void, a lack-of-fusion ribbon, a porosity cluster, and an interference case. For each source–receiver path, four observables were extracted from the synthetic records: first-arrival travel time perturbations, coda wave stretching, coda decorrelation, and late-window energy ratios. These observables were then coupled into a volumetric inverse problem to separate smooth slowness variations, distributed scattering strength, and compact defect occupancy. Under the current simulation conditions, VACWT achieved smaller recovered support volumes and higher volumetric overlap compared to the delay-and-sum total focusing method (DAS-TFM), background-corrected TFM, and reverse time migration (RTM). In the interference case, applying a fixed defect-free calibration threshold yielded a centroid error of 0.48 mm, a volumetric intersection over union (IoU) of 0.856, and a false-positive volume fraction of 0.0%. While these findings serve as benchmark results rather than generalized experimental validation, the study demonstrates that late scattered wave observables provide valuable constraints for volumetric support recovery in a controlled welded-solid model. Future experimental verification on welded steel specimens with known defects remains necessary. Full article

Metal oxide semiconductor (MOS) gas sensors are an important part of electronic nose technology because they are sensitive, cheap, and work well with microfabrication for system integration. But sensor drift makes them less useful for long-term, continuous gas monitoring. Changes in how sensors respond over time make pattern recognition models that were trained at first less accurate. This review looks at new ways to deal with sensor drift, with a focus on transfer learning and deep learning methods that have been developing continuously in the last five years. It emphasizes the shift from conventional recalibration and component correction to sophisticated methodologies, including deep domain adaptation, contrastive representation learning, and attention-based models. The review does not just list these methods; it also analyzes their pros and downsides, especially in situations where there is not much labeled data, drift is hard to anticipate, or the computational resources are limited, which is often the case with edge sensors. Full article

To address frequent street-level flooding, inadequate targeted management, and unbalanced cost-effectiveness in the old urban area, this study takes Yong’an Subdistrict in Quanshan District, Xuzhou, as a typical case, regards the street-level as its fundamental analytical unit and constructs a systematic “simulation–assessment–strategy” framework, focusing on evaluating and enhancing flood resilience in old urban districts. First, numerical simulation quantifies water depth under extreme rainfall to identify the flood risk spatial distribution. Second, a flood resilience assessment system is established based on the “exposure–vulnerability–adaptive capacity” framework, using the TOPSIS method to measure and grade street resilience. Finally, differentiated flood management strategies are proposed by integrating assessment results with regional characteristics. This study shows that high-risk flooding zones are clustered, with resilience results significantly correlated with the flood risk distribution. Low-resilience areas highly overlap with high-risk zones, mainly due to deficiencies in engineering, ecological, and social resilience. Accordingly, differentiated strategies—”pipe network upgrades + permeable paving”, “retention facilities + smart drainage”, and “micro-topography modifications”—are applied to old residential areas, core commercial districts, and new development peripheries. This approach balances management costs and effectiveness, providing theoretical and practical support for precise street-level flood management and spatial optimization in old urban districts. Full article

To establish a quantitative mapping relationship between macro- and micro-parameters in the PFC^2D parallel bonding model, and in view of the inherent complexity of the mutual validation process between laboratory experiments and parameter calibration, this paper takes uniaxial compression tests as the mechanical reference. By combining orthogonal experimental design, Pearson correlation analysis and multivariate analysis of variance, this study systematically investigates the effects of 10 micro-parameters on 6 macro-mechanical indicators (modulus of elasticity E, Poisson’s ratio ν, uniaxial compressive strength ${\sigma }_c$, friction-to-cohesion ratio FCR, crack initiation strength ${\sigma }_{ci}$ and crack damage stress ${\sigma }_{cd}$). To reduce the coupling dimension between cohesion and internal friction angle in the calibration of PFC macro–micro parameters, this paper defines the Friction-to-Cohesion Ratio (FCR) as the ratio of the equivalent macroscopic angle of internal friction to the equivalent macroscopic cohesion, and systematically conducts sensitivity analyses of uniaxial compression simulations. The results indicate that the elastic modulus E is primarily governed by $E^{*}$, ${\overline{E}}^{*}$, ${\overline{k}}^{*}$ and $R_f$; the Poisson’s ratio ν is mainly influenced by $E^{*}$, $k^{*}$, ${\overline{E}}^{*}$, ${\overline{k}}^{*}$ and $R_f$; the uniaxial compressive strength ${\sigma }_c$, the crack initiation strength ${\sigma }_{ci}$ and the crack damage stress ${\sigma }_{cd}$ are primarily regulated by ${\overline{\sigma }}_c$ and $R_f$; whilst the Friction-to-Cohesion Ratio (FCR) is mainly affected by ${\overline{\sigma }}_c$, $\overline{\phi }$, $R_f$, $\overline{c}$ and $\beta$; Elasticity parameters and strength parameters are governed by different micro-mechanisms, reflecting the fundamental decoupling of stiffness and strength in the PFC model. This study established a progressive ‘screening–validation–quantification’ sensitivity analysis framework, revealing the directional regulation patterns of various micro-parameters on macroscopic responses, thereby providing a theoretical basis for the targeted optimisation and efficient calibration of micro-parameters in PFC discrete element simulations. Full article

Existing cross-chain bridges over-rely on static collateralization and post-event penalties, leaving them vulnerable to concealed on–off attacks and rational group collusion. To address these limitations, this paper proposes a Dynamic Trust Evaluation and Risk Control (DTERC) mechanism for heterogeneous cross-chain relay nodes. First, DTERC develops a multidimensional trust quantification model that combines temporal decay, robust multi-observer latency aggregation, verification accuracy, online stability, and an asymmetric one-strike penalty triggered only by cryptographic evidence. Second, DTERC constructs a threshold-aware N-player evolutionary game model to characterize the k-of-N signature structure of cross-chain relay consensus and introduces a dynamic staking function to reduce the economic incentive for collusion under bounded attack-value and parameter conditions. Third, DTERC designs a threshold-preserving FastPath mechanism to reduce redundant verification for low-risk transactions while retaining committee-level confirmation and challenge-based fallback. The empirical evaluation combines multi-agent simulation, smart-contract prototype testing, whitelist-compromise stress tests, malicious-oracle robustness analysis, network-jitter experiments, repeated trials, and parameter-sensitivity analysis. The results show that, under the tested settings, DTERC reduces the malicious transaction success rate to 0.15% under a 50% initial collusion scenario, lowers core contract Gas overhead by 35.7%, and reduces average end-to-end latency by approximately 10% in benign FastPath conditions. These findings indicate that DTERC improves the security–efficiency trade-off of heterogeneous cross-chain relay networks while making its assumptions and limitations explicit. 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

In characterizing unconventional reservoirs, conventional Digital Rock Physics (DRP) has long been constrained by three fundamental bottlenecks: the trade-off between imaging resolution and field of view, challenges in reconstructing multiscale pore topology, and the prohibitive computational cost of direct numerical simulation (DNS) at the pore scale. The deep integration of artificial intelligence and rock physics has given rise to a new paradigm—Intelligent Digital Rock Physics (IDRP). This paper provides a systematic review of the evolutionary trajectory of IDRP, with a focus on how machine learning is reshaping the end-to-end workflow from imaging and segmentation to reconstruction and simulation. First, we survey image super-resolution and 3D pore structure generation techniques based on convolutional neural networks (CNNs), generative adversarial networks (GANs), and diffusion models, elucidating their mechanisms for surpassing optical diffraction limits and incorporating macroscopic petrophysical constraints. Second, we outline algorithmic strategies for fusing multi-source heterogeneous data (e.g., Micro-CT and SEM) and representing dual-porosity or multi-continuum systems. Third, we critically examine the application of machine learning surrogates in single- and multiphase flow prediction, highlighting how physics-informed machine learning (PIML) and reinforcement learning (RL)—by embedding governing equations such as Navier–Stokes or Muskat–Leverett into loss functions—achieve both computational acceleration and physical consistency. We further identify key limitations of current IDRP approaches, including insufficient validation of generated topological realism, narrow generalization across lithologies, inadequate representation of dynamic wettability, and limited model interpretability. Finally, we propose a forward-looking roadmap centered on multimodal foundation models for rocks, coupled with neural operators and uncertainty quantification frameworks, emphasizing the critical pathways for translating IDRP into engineering digital twins for unconventional hydrocarbon development, coalbed methane production enhancement, Enhanced Geothermal Systems, and geological CO₂ storage. This review offers a comprehensive reference for researchers at the intersection of geophysics, rock mechanics, and artificial intelligence. Full article

The Ordos Basin contains abundant deep coalbed methane (CBM) resources, whose efficient development largely depends on the effective implementation of large-scale volumetric fracturing technologies. To comprehensively evaluate the fracability of deep coal reservoirs in this basin, this study focuses on the No. 8 coal seam of the Benxi Formation. Based on rock mechanical experiments and well-logging data, multivariate linear regression models were established to predict Young’s modulus (E) and Poisson’s ratio (μ). The Huang model was applied to determine the three principal in situ stresses of the coal seam. Furthermore, a comprehensive fracability evaluation model was constructed by integrating three key indicators, namely brittleness index (BI), horizontal stress difference (Δσ_h), and tensile strength (S_t). The entropy evaluation method was used to determine the weights of these indicators, and the fracability index (F) of deep coal reservoirs was calculated. The results show that the weights of the factors controlling fracability decrease in the following order: tensile strength (0.434), brittleness index (0.332), and horizontal stress difference (0.234). The No. 8 coal seam in the northern and southern parts of the basin, including the Daning–Jixian, Shenfu, Jiaxian, northern Yulin, and southern Yanchuan areas, exhibits relatively favorable fracability, whereas northern Liulin and southern Yulin show comparatively poor fracability. In addition, the fracability index shows a clear positive correlation with the peak gas production of vertical CBM wells. Based on this relationship, the deep coal reservoirs were classified into three categories: Class I reservoirs (F > 0.55), characterized by high fracability and high production potential; Class II reservoirs (0.50 ≤ F ≤ 0.55), characterized by moderate fracability and moderate production potential; and Class III reservoirs (F < 0.50), characterized by low fracability and low production potential. These findings provide a scientific basis for identifying fracturing sweet spots and for the classification evaluation of deep CBM resources in the Ordos Basin. Full article

In multi-variety, small-batch electric oven manufacturing, sequence-dependent setup times (SDST) and worker skill heterogeneity jointly affect makespan, labor cost, and energy consumption. This study addresses a multi-objective hybrid flow shop scheduling problem with SDST and multi-skilled worker assignment (HFSP-SDST), in which symmetry and asymmetry coexist: the three objectives require balanced trade-offs, whereas sequence-dependent setups and skill–speed compatibility impose asymmetric constraints. A mixed-integer linear programming model is formulated to minimize the three objectives, embedding a skill–speed downward compatibility mechanism that couples worker assignment with processing time, power demand, and labor cost. To solve it, a hybrid algorithm integrating NSGA-II, variable neighborhood search (VNS), and multi-objective simulated annealing (MOSA) is designed on a four-matrix encoding with problem-specific crossover, neighborhood, and feasibility-repair operators. On 24 test instances of varied scale and structure, NSGA-II-VNS-MOSA attains the highest mean hypervolume (2.05) and the best average rank (2.07) against classical and recent Q-learning-guided algorithms, with its advantage growing as setup asymmetry intensifies; an ablation study shows that VNS and MOSA jointly increase hypervolume by 89.5% and reduce the inverted generational distance (IGD) by 45.2% relative to baseline NSGA-II. A real electric oven case confirms that the resulting Pareto set offers decision-makers actionable trade-offs among the three objectives. Full article