Search Results (3,504)

Automatically segmenting coal cracks in CT images is crucial for 3D reconstruction and the physical properties of mines. This paper proposes an automatic pixel-level deep learning method called Attention Double U²-Net to enhance the segmentation accuracy of coal cracks in CT images. Due to the lack of public datasets of coal CT images, a pixel-level labeled coal crack dataset is first established through industrial CT scanning experiments and post-processing. Then, the proposed method utilizes a Double Residual U-Block structure (DRSU) based on U²-Net to improve feature extraction and fusion capabilities. Moreover, an attention mechanism module is proposed, which is called Atrous Asymmetric Fusion Non-Local Block (AAFNB). The AAFNB module is based on the idea of Asymmetric Non-Local, which enables the collection of global information to enhance the segmentation results. Compared with previous state-of-the-art models, the proposed Attention Double U²-Net method exhibits better performance over the coal crack CT image dataset in various evaluation metrics such as PA, mPA, MIoU, IoU, Precision, Recall, and Dice scores. The crack segmentation results obtained from this method are more accurate and efficient, which provides experimental data and theoretical support to the field of CBM exploration and damage of coal. Full article

The global transition to renewable energy requires hybrid solutions that address variability while delivering tangible co-benefits and verifiable mitigation outcomes. This study evaluates a novel small hydropower–photovoltaic (SHP–PV) hybrid system in the Kyrgyz Republic that integrates flexible Bitcoin mining loads and waste-heat recovery for greenhouse heating. A techno-economic model was developed for a 10 MW configuration, allocating annual net generation of 57.34 GWh between grid export and on-site mining through a single decision parameter. Mitigation accounting applies a combined margin grid factor of 0.4–0.7 tCO₂/MWh for exported electricity and a diesel factor of 0.26–0.27 tCO₂/MWh_fuel for heat displacement, yielding Article 6–eligible reductions from both electricity and recovered heat. Waste-heat recovery from mining supplies ≈15 MWh_th/year to a 50 m² greenhouse, displacing diesel use and demonstrating visible sustainable development co-benefits. Economic analysis reproduces annual revenues of ≈$1.9 million, with a levelized cost of electricity of $48/MWh and an indicative IRR of ~6%, consistent with positive but modest returns under merchant operation and uplift potential under mixed allocations. This study concludes that componentized accounting—exported electricity credited under grid displacement and diesel displacement credited from recovered heat—ensures Article 6 integrity and positions SHP–PV hybrids as replicable, multi-service renewable models for Central Asia. Unlike prior hybrid studies that treat generation, economics, and mitigation separately, our framework integrates allocation (α), financial outcomes, and Article 6 carbon accounting within a unified structure, while explicitly modeling Bitcoin mining as an endogenous flexible load with thermal recovery—advancing methodological approaches for multi-service renewable systems in climate policy contexts. Full article

In recent years, academic performance prediction has evolved as a research field thanks to its development and exploration in the educational context. Early student performance prediction is crucial for enhancing educational outcomes and implementing timely interventions. Conventional approaches frequently struggle on behalf of the complexity of student profiles as a consequence of single activation functions, which prevent them from effectively learning intricate patterns. In addition, these models could experience obstacles such as the vanishing gradient problem and computational complexity. Therefore, this research study designed an Activation Ensemble Deep Neural Network (AcEnDNN) model to gain control of the previously mentioned challenges. The main contribution is the creation of a credible student performance prediction model that comprises extensive data preprocessing, feature extraction, and an Activation Ensemble DNN. By utilizing various methods of activation functions, such as ReLU, tanh, sigmoid, and swish, the ensembled activation functions are able to learn the complex structure of student data, which leads to more accurate performance prediction. The AcEn-DNN model is trained and evaluated based on the publicly available Student-mat.csv dataset, Student-por.csv dataset, and a real-time dataset. The experimental results revealed that the AcEn-DNN model achieved lower error rates, with an MAE of 1.28, MAPE of 2.36, MSE of 4.55, and RMSE of 2.13 based on a training percentage of 90%, confirming its robustness in modeling nonlinear relationships within student data. The proposed model also gained the minimum error values MAE of 1.28, MAPE of 2.97, MSE of 4.77, and RMSE of 2.18, based on a K-fold value of 10, utilizing the Student-mat.csv dataset. These findings highlight the model’s potential in early identification of at-risk students, enabling educators to develop targeted learning strategies. This research contributes to educational data mining by advancing predictive modeling techniques that evaluate student performance. Full article

While robotic technologies have shown great promise in enhancing productivity and safety, their integration into the mining sector, particularly for search and rescue (SAR) missions, remains limited. The success of these systems depends not only on their technical capabilities, but also on the effectiveness of human–robot interaction (HRI) in high-risk, time-sensitive environments. This review synthesizes key human factors, including cognitive load, situational awareness, trust, and attentional control, that critically influence the design and operation of robotic interfaces for mine rescue missions. Drawing on established cognitive theories such as Endsley’s Situational Awareness Model, Wickens’ Multiple Resource Theory, Mental Model and Cognitive Load Theory, we identified core challenges in current SAR interface design for mine rescue missions and mapped them to actionable design principles. We proposed a human-centered framework tailored to underground mine rescue operations, with specific recommendations for layered feedback, multimodal communication, and adaptive interfaces. By contextualizing cognitive science in the domain of mining emergencies, this work offers a structured guide for designing intuitive, resilient, and operator-supportive robotic systems. Full article

Soils in alpine mining areas suffer from severe heavy metal contamination and infertility, yet little is known about the effects of different materials on soil improvement in such regions. In this study, a field experiment was conducted in farmlands contaminated by the Lanping lead–zinc mine in Yunnan, China, to compare the effects of four materials (biochar, organic fertilizer, lime, and sepiolite) on soil properties, heavy metal (lead (Pb), cadmium (Cd), copper (Cu), and zinc (Zn) fractions and their availability, and the growth of Phaseolus coccineus L. Results showed that biochar and organic fertilizer significantly enhanced soil nutrient content and enzyme activities. Lime, biochar, and sepiolite effectively reduced heavy metal bioavailability by promoting their transition to residual fractions. Notably, biochar outperformed other materials by substantially increasing grain yield (by 82%), improving nutritional quality (sugars, protein, and starch contents raised by 20–88%), and reducing heavy metal accumulation in grains (by 36–50%). A comprehensive evaluation based on subordinate function values confirmed biochar as the most effective amendment. Structural equation modeling further revealed that biochar promoted plant growth and grain quality primarily by enhancing soil available nutrients and immobilizing heavy metals. These findings demonstrate the strong potential of biochar for remediating heavy metal-contaminated farmlands in alpine lead–zinc mining regions. Full article

Rockburst disasters have become increasingly prevalent as distinct forms of subsurface geotechnical engineering advanced to the deep earth. Confronted with such a threatening subsurface geopressure disaster that poses a risk to personnel and equipment safety, the microseismic monitoring technology has been employed to track signals generated from rock fracture and collapse in the field. To guide the prevention and control of the hazard, the investigation conducted an effective microseismic data mining method. Through deep feature engineering and interpretable intelligence, a practical and available short-term prediction approach for the rockburst intensity class was developed. On the basis of rockburst case database collected from various underground geotechnical engineering, the neural network-based feature extraction method was conducted in the process of model training. The optimized model was obtained by combining the K-fold cross-validation approach with the structural parameter search methodology. The evaluation among the considered artificial intelligence models on the testing dataset was conducted and compared. Through analyses, the interpretable coupling intelligent model combining convolutional and recurrent neural networks for rockburst prediction were demonstrated with the most robust performance by evaluation metrics. Among them, the proposed adaptive feature extraction method leads the benchmark method by 6% for both accuracy and precision; meanwhile, the proposed metric generalization loss rate (GLR) for accuracy and precision in the validation–testing process reached 1.5% and 0.2%. Furthermore, the Shapley additive explanations (SHAP) approach was employed to verify the model interpretability by deciphering the model prediction from the perspective of the fined impact of input features. Therefore, the investigation demonstrates that the proposed method can predict rockburst intensity with robust generalization and feature extraction capabilities, which possess substantial engineering significance and academic worth. 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

The expansion of copper mining operations has led to the accumulation of a large amount of phyllite waste rock. Re-purposing this material into manufactured sand presents a promising solution for its large-scale consumption. In this study, phyllite waste rock from the Dexing Copper Mine was used as raw materials to prepare manufactured sand. A precise mineralogical analysis was conducted using Tescan Integrated Mineral Analyzer (TIMA) to determine the mineral composition, intergeneration and distribution relationships, particle size and shape, and elemental distribution. The performance of the resulting manufactured sand was comprehensively evaluated. Key findings showed a needle and flake particle content of 5.2%, a methylene blue (MB) value of 1.3, and a stone powder content of 9%. The physical properties, including solidity, crushing index, density, and porosity, as well as mica content, complied with the national standard GB14684-2022 (Sand for Construction). Additionally, phyllite-sand concrete exhibited a third-month expansion rate below the standard limit of 0.1%, indicating no potential risk for alkali-silica reaction. The radioactive index of the material met the standard requirements, posing no radiation hazard. However, the excessive sulfur compounds in phyllite present a risk of corrosion of the concrete structures, necessitating mitigation measures. Full article

The deep-sea mining pump is a core component in deep-sea mineral resource extraction, whose performance directly determines the transportation efficiency of coarse-grained ore and overall system reliability. However, deep-sea mining pumps suffer from severe abrasion of internal components due to continuous impact by coarse ore particles, leading to short service life and high maintenance costs. These issues adversely impact the economics and continuity of mining operations. Consequently, studying the solid-liquid flow to understand wear mechanisms and develop optimized, wear-resistant designs is crucial for enhancing pump performance. This paper establishes a fully coupled solid-liquid two-phase flow platform by integrating Fluent and EDEM, based on an artificial diffusion-based coarse-particle CFD-DEM (Computational Fluid Dynamics-Discrete Element Method) approach, to systematically investigate the critical technical issue of internal pump wear. The study finds that wear in traditional spiral centrifugal pump blades is primarily concentrated on the leading edge and the middle section. On the leading edge, wear comprises 56.4% cutting wear and 44.7% impact wear; in contrast, cutting wear accounts for 96.8% of the total wear in the middle section. To address the premature failure of traditional impeller blades caused by localized wear concentration, this paper proposes an optimized design for a novel spiral centrifugal impeller with segmented blades. By modifying the impeller structure, the proposed design relocates the primary wear zones to the leading edges of the two blade segments, thereby facilitating the application of anti-wear treatments. Full article

Background: Vaccine hesitancy (VH) remains a major threat to global health that can reverse the progress in tackling vaccine-preventable diseases. Vaccine uptake among adolescent Girls and young women (AGYW) is often low. We assessed VH using a validated scale among AGYW in Uganda, Zambia, and South Africa. Methods: From June 2023 to February 2024, we recruited AGYW from fishing communities in Uganda, urban and peri-urban locations in Lusaka and Ndola, Zambia, and mining communities in Rustenburg, South Africa. Eligible participants were aged 15–24 years, sexually active, and HIV-negative but at risk for HIV acquisition. We collected demographic, HIV-related behavioral data, and vaccine hesitancy data using a structured questionnaire. Vaccine confidence was assessed using the 10-question Vaccine Hesitancy Scale that describes two factors, i.e., “vaccine confidence” and “risk tolerance”. Exploratory and Confirmatory Factor Analyses were performed to assess scale validity and internal consistency. Logistic regression was used to determine associations between demographics and vaccine confidence. Results: A total of 1213 AGYW participated in the study, with a mean age of 19.4 (SD ± 2.6) years. More than half (54%) were aged between 15 and 19 years. The majority of AGYW (94%) strongly believed that vaccines were important for their health and the community and that vaccination is a good way to protect them from diseases. About two-thirds of the AGYW (66%) indicated that they were concerned about the adverse effects of vaccines, while 30% responded that they did not need vaccines for diseases that were not common. We observed that 951 (78%) of the AGYW reported high vaccine confidence, while 494 (41%) reported low concerns over risks. Vaccine confidence varied across countries, with Zambia and Uganda showing lower vaccine confidence (adjusted odds ratios of 0.28 and 0.45, respectively, p < 0.005) in comparison to South Africa. Conclusions: A high level of vaccine confidence was observed among AGYW. Vaccine confidence among AGYW was driven more by the trust in vaccine safety and the need to protect communities against diseases. These findings suggest the potential for acceptance of vaccines, including future HIV vaccines, among AGYW. Despite high levels of vaccine confidence, concerns over vaccine risks remain substantial and must be addressed. Full article

Diamond traceability has been a major challenge for the gemological industry in recent decades. In this context, this paper presents new studies using UV-VIS-NIR spectroscopy to identify the traceability and geographical origin of diamonds. The aim of the work is to identify characteristic centers of fancy-color diamonds collected from Cullinan Mine, Democratic Republic of Congo (DRC), and the geographical regions with unknown origin. Depending on the origin of the diamonds, the UV-VIS-NIR spectra can be differentiated as follows: (i) the diamonds collected from Cullinan Mine show absorption bands assigned to N₁⁰, NV⁰, NV⁻, N₃V⁰, N₄V₂, and N₄V centers, which are accompanied by a vibronic structure localized between 415 and 394 nm (2.987–3.147 eV) and (ii) the diamonds from DRC show absorption bands attributed to N₁⁰, NV⁻, N₃V⁰, N₁⁺, and NVH centers. Using Raman spectroscopy, nitrogen concentration values of diamonds collected from the Cullinan mines and DRC between 41 and 185 ppm and 204–336 ppm, respectively, were reported. We prove that the simultaneous applicability of UV-VIS-NIR spectroscopy and Raman scattering as comparative tools for assessing diamond provenance can be a valuable strategy for an initial attribution of diamonds with unknown geographical origin, knowing the optical features of diamonds collected from Cullinan Mine and DRC. Full article

Deep-sea polymetallic nodule provinces harbor rich benthic microbial communities that underpin biogeochemical cycles and sustain abyssal ecosystem functions. Recent studies have begun to map their abundance, diversity and community structure, emphasizing the role of environmental gradients and spatial heterogeneity. Yet the spatiotemporal dynamics and assembly mechanisms of these microbes remain largely unresolved. Mining-induced sediment plumes further complicate the picture: they modify microbial biomass, activity and composition, but the trajectories of community succession and the functional consequences of disturbance are still unclear. Thresholds used to gauge plume impacts also differ markedly among studies, hampering consistent risk assessments. In summary, a stark contrast exists between the limited in situ observational data, the widely varying impact thresholds reported across studies, and the pressing need for unified standards in environmental impact assessments for deep-sea mining. It recommends future work that integrates multi-omics, time-series in situ monitoring, cross-regional comparisons and standardized evaluation frameworks to refine microbial indicators and ecological thresholds for deep-sea mining impact assessments. Full article

This study aims to investigate the sedimentary facies characteristics of the coal seam roof in the Qinglong and Longfeng coal mines and their control over water abundance. By collecting core samples and well logging data from both mining areas, multiple methods were employed, including core observation, thin-section analysis, sedimentary microfacies distribution mapping, nitrogen adsorption tests, and nuclear magnetic resonance analysis, to systematically analyze the depositional environments, types of sedimentary microfacies, and their distribution patterns. Results indicate that the roof of Qinglong Coal Mine is predominantly composed of sandy microfacies with well-developed faults, which not only increase fracture porosity but also provide water-conducting pathways between surface water and aquifers, significantly enhancing water abundance. In contrast, Longfeng Coal Mine is characterized mainly by muddy microfacies, with small-scale faults exhibiting weak water-conducting capacity and relatively low water abundance. Hydrochemical analysis indicates that consistent water quality between Qinglong’s working face, karst water, and goaf water confirms fault-induced aquifer–surface water connectivity, whereas Longfeng’s water quality suggests weak aquifer–coal seam hydraulic connectivity. The difference in water hazard threats between the two mining areas primarily stems from variations in sedimentary microfacies and fault structures. Full article

Alpine permafrost plays a vital role in regional hydrology and ecology. Alpine permafrost is highly sensitive to climate change and human disturbance. The Muri area, which is located in the headwaters of the Datong River, northeast of the Tibetan Plateau, has undergone decadal mining, and the permafrost stability there has attracted substantial concerns. In order to decipher how and to what extent the permafrost in the Muri area has responded to the decadal mining in the context of climate change, daily MODIS land surface temperatures (LSTs) acquired during 2000–2024 were downscaled to 30 m × 30 m. The active layer thickness (ALT)–ground thaw index (DDT) coefficient was derived from in situ ALT measurements. An annual ALT of 30 m × 30 m spatial resolution was subsequently estimated from the downscaled LST for the Muri area using the Stefan equation. Validation of the LST and ALT showed that the root of mean squared error (RMSE) and the mean absolute error (MAE) of the downscaled LST were 3.64 °C and −0.1 °C, respectively. The RMSE and MAE of the ALT estimated in this study were 0.5 m and −0.25 m, respectively. Spatiotemporal analysis of the downscaled LST and ALT found that (1) during 2000–2024, the downscaled LST and estimated ALT delineated the spatial extent and time of human disturbance to permafrost in the Muri area; (2) human disturbance (i.e., mining and replantation) caused ALT increase without significant spatial expansion; and (3) the semi-arid climate, rough terrain, thin root zone and gappy vertical structure beneath were the major controlling factors of ALT variations. ALT, estimated in this study with a high resolution and accuracy, filled the data gaps of this kind for the Muri area. The ALT variations depicted in this study provide references for understanding alpine permafrost evolution in other areas that have been subject to human disturbance and climate change. Full article

Backfilling mining methods control the surrounding pressure and ground subsidence by backfilling goaf and managing the ground pressure, providing a safety guarantee for mining in complex environments and serving as a key means of achieving the deep mining of metal minerals. However, in the design of backfill strength, material mix ratios are determined under indoor standard constant temperature and humidity conditions, which differ significantly from the in situ curing environment. Strength measurements obtained from field samples are notably higher than those from indoor test specimens. To address this issue, this study designed a curing device simulating the in situ thermal-hydraulic multi-field environment of the mining site and tested the strength and porosity of the backfill under different curing temperatures, curing pressures, and pore water pressures. The results indicate that curing pressure and pore water pressure significantly altered the pore structure of the specimens. Specifically, when the curing pressure increased to 750 kPa, the maximum pore diameter decreased from 3110.52 nm to approximately 2055 nm, accompanied by a continuous reduction in porosity. Pore water pressure exhibited a positive linear correlation with specimen porosity, which increased continuously as the pore water pressure rose. With increasing curing temperature, the strength of the backfilled specimens first increased and then decreased, reaching a maximum at 45 °C. As the curing pressure increased, the strength of the backfilled specimens rose, but the rate of increase gradually slowed. With increasing pore water pressure, the strength of the backfilled specimens showed a gradual decreasing trend. Full article