Search Results (867)

The production environments of coal mines are inherently complex, with interrelated disaster risks that challenge safety management. Current prediction systems struggle with fragmented data, limited mechanistic understanding, and inadequate early warnings, falling short of modern coal mine safety needs. This paper advances the thesis that artificial intelligence, including machine learning, deep learning, and Large Language Model, provides essential tools for overcoming these prediction challenges in coal mining. We review AI-based approaches for forecasting coal and gas outbursts, mine fires, water disasters, roof collapses, and dust disasters, analyzing them through technical principles, application scenarios, and empirical outcomes. The analysis clarifies how AI improves risk prediction accuracy, enhances data integration, and enables smarter decision-making for safety. By examining the five major hazards, we highlight ongoing challenges in AI implementation and outline pathways for future development, emphasizing the importance of large models and autonomous agents. Our findings support the creation of advanced AI-driven safety and early warning systems for coal mines. Full article

Industrial processes governed by food safety regulations, such as high-temperature short-time (HTST) pasteurization, rely on continuous sensor monitoring to ensure compliance with standards like Hazard Analysis and Critical Control Points (HACCP). However, extracting actionable process insights from raw sensor data remains a non-trivial task, largely due to the continuous, multivariate, and often high-frequency characteristics of the signals, which can obscure clear activity boundaries and introduce significant variability in temporal patterns. This paper proposes a process mining framework to extract activity-based representations from multivariate sensor data in a pasteurization scenario. By modelling temperature, pH, conductivity, viscosity, turbidity, flow, and pressure signals, the approach segments continuous data into discrete operational phases and generates event logs aligned with domain semantics. Unsupervised learning techniques, including Hidden Markov Models (HMMs), are used to infer latent process stages, while domain knowledge guides their interpretation in accordance with critical control points (CCPs). The extracted models support conformance checking against HACCP-based procedures and enable predictive process-monitoring tasks such as next-activity prediction and remaining time estimation. Experimental results on synthetic (literature-grounded data) demonstrated the method’s ability to enhance safety, compliance, and operational efficiency. This study illustrates how integrating process mining with regulatory principles can bridge the gap between continuous sensor streams and structured process analysis in food manufacturing. Full article

Objective: Medical Visual Question Answering (Med-VQA), a key technology that integrates computer vision and natural language processing to assist in clinical diagnosis, possesses significant potential for enhancing diagnostic efficiency and accuracy. However, its development is constrained by two major bottlenecks: weak few-shot generalization capability stemming from the scarcity of high-quality annotated data and the problem of catastrophic forgetting when continually learning new knowledge. Existing research has largely addressed these two challenges in isolation, lacking a unified framework. Methods: To bridge this gap, this paper proposes a novel Evolvable Clinical-Semantic Alignment (ECSA) framework, designed to synergistically solve these two challenges within a single architecture. ECSA is built upon powerful pre-trained vision (BiomedCLIP) and language (Flan-T5) models, with two innovative modules at its core. First, we design a Clinical-Semantic Disambiguation Module (CSDM), which employs a novel debiased hard negative mining strategy for contrastive learning. This enables the precise discrimination of “hard negatives” that are visually similar but clinically distinct, thereby significantly enhancing the model’s representation ability in few-shot and long-tail scenarios. Second, we introduce a Prompt-based Knowledge Consolidation Module (PKC), which acts as a rehearsal-free non-parametric knowledge store. It consolidates historical knowledge by dynamically accumulating and retrieving task-specific “soft prompts,” thus effectively circumventing catastrophic forgetting without relying on past data. Results: Extensive experimental results on four public benchmark datasets, VQA-RAD, SLAKE, PathVQA, and VQA-Med-2019, demonstrate ECSA’s state-of-the-art or highly competitive performance. Specifically, ECSA achieves excellent overall accuracies of 80.15% on VQA-RAD and 85.10% on SLAKE, while also showing strong generalization with 64.57% on PathVQA and 82.23% on VQA-Med-2019. More critically, in continual learning scenarios, the framework achieves a low forgetting rate of just 13.50%, showcasing its significant advantages in knowledge retention. Conclusions: These findings validate the framework’s substantial potential for building robust and evolvable clinical decision support systems. Full article

The trajectory-tracking capability of autonomous mining trucks is critical for accomplishing transportation tasks efficiently. However, due to the diverse road surfaces and rugged terrains in open-pit mines, the existing vehicle dynamics models struggle to accurately capture the complex tire–ground interactions. As a result, conventional trajectory-tracking control methods that rely on linear vehicle dynamics models suffer from degraded tracking performance. To this end, this paper proposes a novel trajectory-tracking control framework that integrates model predictive control (MPC) with model-free adaptive control (MFAC). A warm-start strategy is employed to improve the computational efficiency of MPC, while MFAC is utilized to provide real-time compensation for the control deviations generated by MPC during the trajectory-tracking process. To validate the effectiveness of the proposed trajectory-tracking control method, co-simulations were conducted on the CarSim and MATLAB/Simulink platforms under various road conditions and driving scenarios. Simulation results demonstrate that the proposed method can effectively enhance the trajectory-tracking performance of autonomous mining trucks. For instance, under the S-condition with Class E road elevation, the proposed method achieves improvements of approximately 90.83%, 15.05%, and 71.93% in the mean error, maximum error, and root mean square error (RMSE), respectively, compared with the conventional LQR-based trajectory-tracking control method. In addition, the computation time of MPC is only 2 ms, which significantly improves the overall performance of the trajectory-tracking controller. Full article

It is widely recognized that benthic sediment plumes generated by deep-sea mining may pose significant potential risks to ecosystems, yet their dispersion behavior remains difficult to predict with accuracy. In this study, we combined laboratory experiments with three-dimensional numerical simulations using the Environmental Fluid Dynamics Code (EFDC) to investigate the dispersion of sediment plumes composed of particles of different sizes. Laboratory experiments were conducted with deep-sea clay samples from the western Pacific under varying conditions for plume dispersion. Experimental data were used to capture horizontal diffusion and vertical entrainment through a Gaussian plume model, and the results served for parameter calibration in large-scale plume simulations. The results show that ambient current velocity and discharge height are the primary factors regulating plume dispersion distance, particularly for fine particles, while discharge rate and sediment concentration mainly control plume duration and the extent of dispersion in the horizontal direction. Although the duration of a single-source release is short, continuous mining activities may sustain broad dispersion and result in thicker sediment deposits, thereby intensifying ecological risks. This study provides the first comprehensive numerical assessment of deep-sea mining plumes across a range of particle sizes with clay from the western Pacific. The findings establish a mechanistic framework for predicting plume behavior under different operational scenarios and contribute to defining threshold values for discharge-induced plumes based on scientific evidence. By integrating experimental, theoretical, and numerical approaches, this work offers quantitative thresholds that can inform environmentally responsible strategies for deep-sea resource exploitation. Full article

This article presents a concept for modernizing the transport system of high-performance coal mines through the transition from traditional monorail to rubber-tyred transport (RTT). The study was conducted based on materials from the V.D. Yalevsky Mine of JSC “SUEK-Kuzbass” with daily longwall output up to 60,000 tons and production capacity up to 10 million tons per year. Analysis of the existing transport system efficiency revealed low equipment utilization rates (52–70%) and significant time losses during shift changeovers (up to 4.3 h/day in development workings). Technical solutions for phased RTT implementation were developed, including six roadway surface scenarios and a fleet composition of 60 specialized equipment units. The research methodology is based on time study observations using the automated “Granch” system, analysis of equipment utilization coefficients, and economic–mathematical modeling using NPV, MIRR, and payback period. The transition to rubber-tyred transport provides a five-fold increase in travel speed (from 4.5 to 25 km/h), reduction in shift changeover time to zero, increase in operating time by 20% in development and 4.5% in extraction, and a reduction in longwall move duration from 173–209 to 88 days. Additional coal production amounts to 6.5 million tons. Economic justification shows NPV of USD 64.2 million with MIRR of 2.4% and a payback period of 4.5 years. Full article

Artificial intelligence (AI), as an emerging interdisciplinary field dedicated to simulating and extending human intelligence, is increasingly integrating into the domain of infectious disease medicine with unprecedented depth and breadth. This narrative review is based on a systematic literature search in databases such as PubMed and Web of Science for relevant studies published between 2018 and 2025, with the aim of synthesizing the current landscape. It demonstrates transformative potential, particularly in the realm of diagnostic assistance. Confronting global challenges such as pandemic control, emerging infectious diseases, and antimicrobial resistance, AI technologies offer innovative solutions to these pressing issues. Leveraging its robust capabilities in data mining, pattern recognition, and predictive analytics, AI enhances diagnostic efficiency and accuracy, enables real-time monitoring, and facilitates the early detection and intervention of outbreaks. This narrative review systematically examines the application scenarios of AI within infectious disease diagnostics, based on an analysis of recent literature. It highlights significant technological advances and demonstrated practical outcomes related to high-throughput sequencing (HTS) for pathogen surveillance, AI-driven analysis of digital and radiological images, and AI-enhanced point-of-care testing (POCT). Simultaneously, the review critically analyzes the key challenges and limitations hindering the clinical translation of current AI-based diagnostic technologies. These obstacles include data scarcity and quality constraints, limitations in model generalizability, economic and administrative burdens, as well as regulatory and integration barriers. By synthesizing existing research findings and cataloging essential data resources, this review aims to establish a valuable reference framework to guide future in-depth research, from model development and data sourcing to clinical validation and standardization of AI-assisted infectious disease diagnostics. Full article

This study investigates premature fatigue failure in rocker arm gears of large-mining-height shearers operating at alternating ±45° working angles, where insufficient lubrication generates non-uniform thermal -stress fields. In this study, an integrated multiphysics framework combining transient thermal–fluid–structure coupling simulations with fatigue life prediction is proposed. Transient thermo-mechanical coupling analysis simulated dry friction conditions, capturing temperature and stress fields under varying speeds. Fluid–thermal–solid coupling analysis modeled wet lubrication scenarios, incorporating multiphase flow to track oil distribution, and calculated convective heat transfer coefficients at different immersion depths (25%, 50%, 75%). These coupled simulations provided the critical time-varying temperature and thermal stress distributions acting on the gears (Z6 and Z7). Subsequently, these simulated thermo-mechanical loads were directly imported into ANSYS 2024R1 nCode DesignLife to perform fatigue life prediction. Simulations demonstrate that dry friction induces extreme operating conditions, with Z6 gear temperatures reaching over 800 °C and thermal stresses peaking at 803.86 MPa under 900 rpm, both escalating linearly with rotational speed. Lubrication depth critically regulates heat dissipation, where 50% oil immersion optimizes convective heat transfer at 8880 W/m²·K for Z6 and 11,300 W/m²·K for Z7, while 25% immersion exacerbates thermal gradients. Fatigue life exhibits an inverse relationship with speed but improves significantly with cooling. Z6 sustains a lower lifespan, exemplified by 25+ days at 900 rpm without cooling versus 50+ days for Z7, attributable to higher stress concentrations. Based on the multiphysics analysis results, two physics-informed engineering optimizations are proposed to reduce thermal stress and extend gear fatigue life: a staged cooling system using spiral copper tubes and an intelligent lubrication strategy with gear-pump-driven dynamic oil supply and thermal feedback control. These strategies collectively enhance gear longevity, validated via multiphysics-driven topology optimization. Full article

This study addresses the challenges of high-dimensional data, such as the curse of dimensionality and feature redundancy, which can be viewed as an inherent asymmetry in the data space. To restore a balanced symmetry and build a more complete feature representation, we propose an enhanced feature engineering model (EFEM) that employs a novel dual-strategy approach. First, we present a symmetrical feature selection algorithm that combines an improved Dolphin Swarm Algorithm (DSA) with the Maximum Relevance–Minimum Redundancy (mRMR) criterion. This method not only selects an optimal, high-relevance feature subset, but also identifies the remaining features as a complementary, redundant subset. Second, an ensemble learning-based feature reconstruction algorithm is introduced to mine potential information from these redundant features. This process transforms fragmented, redundant information into a new, synthetic feature, thereby establishing a form of information symmetry with the selected optimal subset. Finally, the EFEM constructs a high-performance feature space by symmetrically integrating the optimal feature subset with the synthetic feature. The model’s superior performance is extensively validated on nine standard UCI regression datasets, with comparative analysis showing that it significantly outperforms similar algorithms and achieves an average goodness-of-fit of 0.9263. The statistical significance of this improvement is confirmed by the Wilcoxon signed-rank test. Comprehensive analyses of parameter sensitivity, robustness, convergence, and runtime, as well as ablation experiments, further validate the efficiency and stability of the proposed algorithm. The successful application of the EFEM in a real-world product demand forecasting task fully demonstrates its practical value in complex scenarios. Full article

Open-world object detection (OWOD) aims to build detectors that can recognize known categories while simultaneously identifying unknown objects and incrementally learning novel classes. Despite recent advances, existing OWOD approaches still struggle with two critical challenges: the severe bias toward head classes in long-tailed data distributions and the misclassification of unknown objects as background. To address these issues, we introduce TAPM (Tail-Calibrated Transformer Autoencoding with Prototype-Guided Mining), a novel framework that explicitly enhances tail-class representation and robustly reveals unknown objects. TAPM integrates three core innovations: (1) a transformer-based autoencoder that reconstructs region features to calibrate embeddings for rare categories, mitigating the dominance of frequent classes; (2) a prototype-guided mining strategy that leverages class prototypes to localize both overlooked tail instances and candidate unknowns; and (3) an uncertainty-aware soft-labeling mechanism that assigns probabilistic supervision to pseudo-unknowns, reducing noise in incremental learning. Extensive experiments on the MS-COCO and LVIS benchmarks demonstrate that TAPM significantly improves unknown-object recall while maintaining strong known-class accuracy, achieving state-of-the-art performance across both the superclass-separated (S-OWODB) and superclass-mixed (M-OWODB) benchmarks. In particular, TAPM achieves a +20.4-point gain in U-Recall over the strong PROB baseline, underscoring its effectiveness in detecting novel objects without sacrificing mean Average Precision (mAP). Furthermore, TAPM achieves better generalization on cross-dataset evaluations, highlighting its robustness in diverse open-world scenarios. Full article

This study evaluates the techno-economic feasibility of integrating wind power with hydrogen-based storage to decarbonize the Raglan Mine in northern Canada. Using HOMER simulations with real 2021 operational data, six progressive scenarios were modeled, ranging from partial substitution of diesel generators to complete site-wide electrification, including heating, transport, and mining equipment. Results show that complete decarbonization (Scenario 6) is technically achievable and could avoid up to 143,000 tCO₂eq annually (~2.15 Mt over 15 years), but remains economically prohibitive under current technology costs. In contrast, Scenario 2 Case 2, which combines solid oxide fuel cells with thermal charge controllers, emerges as the most viable near-term pathway, avoiding ~61,000 tCO₂eq annually (~0.91 Mt over 15 years) while achieving improved return on investment. A qualitative multi-criteria framework highlights this configuration as the best trade-off between technical feasibility, environmental performance, and economic viability. At the same time, complete decarbonization remains a longer-term target contingent on cost reductions and policy support. Overall, the findings provide clear evidence that hydrogen storage, when coupled with wind power, can deliver substantial and measurable decarbonization benefits for Arctic mining operations. Full article

Land use/land cover (LULC) change is a key driving factor influencing the dynamics of terrestrial ecosystem carbon storage. In high-groundwater-level coal resource-based cities (HGCRBCs), the interplay of urban expansion, mining disturbances, and land reclamation makes the carbon storage evolution process more complex. This study takes Jining, Zaozhuang, and Heze cities in Shandong Province as the research area and constructs a coupled analytical framework of “mining–reclamation–carbon storage” by integrating the Patch-generating Land Use Simulation (PLUS), Probability Integral Method (PIM), InVEST, and Grey Multi-Objective Programming (GMOP) models. It systematically evaluates the spatiotemporal characteristics of carbon storage changes from 2000 to 2020 and simulates the carbon storage responses under different development scenarios in 2030. The results show that: (1) From 2000 to 2020, the total carbon storage in the region decreased by 31.53 Tg, with cropland conversion to construction land and water bodies being the primary carbon loss pathways, contributing up to 89.86% of the total carbon loss. (2) Among the 16 major LULC transition paths identified, single-process drivers dominated carbon storage changes. Specifically, urban expansion and mining activities individually accounted for nearly 70% and 8.65% of the carbon loss, respectively. Although the reclamation path contributed to a recovery of 1.72 Tg of carbon storage, it could not fully offset the loss caused by mining. (3) Future scenario simulations indicate that the ecological conservation scenario yields the highest carbon storage, while the economic development scenario results in the lowest. Mining activities generally lead to approximately 3.5 Tg of carbon loss, while post-mining reclamation can restore about 72% of the loss. Full article

Data preprocessing and feature engineering play key roles in data mining initiatives, as they have a significant impact on the accuracy, reproducibility, and interpretability of analytical results. This review presents an analysis of state-of-the-art techniques and tools that can be used in data input preparation and data manipulation to be processed by mining tasks in diverse application scenarios. Additionally, basic preprocessing techniques are discussed, including data cleaning, normalisation, and encoding, as well as more sophisticated approaches regarding feature construction, selection, and dimensionality reduction. This work considers manual and automated methods, highlighting their integration in reproducible, large-scale pipelines by leveraging modern libraries. We also discuss assessment methods of preprocessing effects on precision, stability, and bias–variance trade-offs for models, as well as pipeline integrity monitoring, when operating environments vary. We focus on emerging issues regarding scalability, fairness, and interpretability, as well as future directions involving adaptive preprocessing and automation guided by ethically sound design philosophies. This work aims to benefit both professionals and researchers by shedding light on best practices, while acknowledging existing research questions and innovation opportunities. Full article

In low-risk and open environments, such as farms and mining sites, efficient cargo transportation is essential. Despite the suitability of autonomous driving for these environments, its high deployment and maintenance costs limit large-scale adoption. To address this issue, a modular unmanned ground vehicle (UGV) system is proposed, which is adapted from existing platforms and supports both autonomous and manual control modes. The autonomous mode uses environmental perception and trajectory planning algorithms for efficient transport in structured scenarios, while the manual mode allows human oversight and flexible task management. To mitigate the control latency and execution delays caused by platform modifications, an enhanced transformer-based general dynamics model is introduced. Specifically, the model is trained on a custom-built dataset and optimized within a bicycle kinematic framework to improve control accuracy and system stability. In road tests allowing a positional error of up to 0.5 m, the transformer-based trajectory estimation method achieved 94.8% accuracy, significantly outperforming non-transformer baselines (54.6%). Notably, the test vehicle successfully passed all functional validations in autonomous driving trials, demonstrating the system’s reliability and robustness. The above results demonstrate the system’s stability and cost-effectiveness, providing a potential solution for scalable deployment of autonomous transport in low-risk environments. Full article

Former coal mining in the Ruhr area of North Rhine-Westphalia, Germany, leads to significant challenges in flood management due to drainless sinks in urban areas caused by ground depression. Consequently, pumping stations have been constructed to enable the drainage of incoming river discharge, preventing overland flooding. However, in the event of the failure of pumping stations, these areas are exposed to a higher flood risk. To address this issue, a methodology has been developed to assess the probability of pumping failures by identifying the most significant failure mechanisms and integrating them into a Bayesian network. To evaluate the impact on the flood inundation probability, a new approach is applied that defines pump failure scenarios depending on available pump discharge capacity and integrates them into a flood inundation probability map. The result is a method to estimate the flood inundation probability stemming from pumping failure, which allows the integration of internal failure mechanisms (e.g., technical or electronic failure) as well as external failure mechanisms (e.g., sedimentation or heavy rainfall). Therefore, authorities can assess the most probable pumping failures and their impact on flood risk management strategies. Full article