Search Results (1,921)

The aim of this study was to improve the efficiency of in situ uranium leaching by developing a specialized methodology for selecting rational parameters for the chemical treatment of production wells. This approach was designed to enhance the filtration properties of ores and extend the uninterrupted operation period of wells, considering the clay content of the productive horizon, the geological characteristics of the ore-bearing layer, and the composition of precipitation-forming materials. The mineralogical characteristics of ore and precipitate samples formed during the in situ leaching of uranium under various mining and geological conditions at a uranium deposit in the Syrdarya depression were identified using an X-ray diffraction analysis. It was established that ores of the Santonian stage are relatively homogeneous and consist mainly of quartz. During well operation, the precipitates formed are predominantly gypsum, which has little impact on the filtration properties of the ore. Ores of the Maastrichtian stage are less homogeneous and mainly composed of quartz and smectite, with minor amounts of potassium feldspar and kaolinite. The leaching of these ores results in the formation of gypsum with quartz impurities, which gradually reduces the filtration properties of the ore. Ores of the Campanian stage are heterogeneous, consisting mainly of quartz with varying proportions of clay minerals and gypsum. The leaching of these ores generates a variety of precipitates that significantly reduce the filtration properties of the productive horizon. Effective compositions and concentrations of decolmatant (clog removal) solutions were selected under laboratory conditions using a specially developed methodology and a TESCAN MIRA scanning electron microscope. Based on a scanning electron microscope analysis of the samples, the effectiveness of a decolmatizing solution based on hydrochloric and hydrofluoric acids (taking into account the concentration of the acids in the solution) was established for the destruction of precipitate formation during the in situ leaching of uranium. Geological blocks were ranked by their clay content to select rational parameters of decolmatant solutions for the efficient enhancement of ore filtration properties and the prevention of precipitation formation. Pilot-scale testing of the selected decolmatant parameters under various mining and geological conditions allowed the optimal chemical treatment parameters to be determined based on the clay content and the composition of precipitates in the productive horizon. An analysis of pilot well trials using the new approach showed an increase in the uninterrupted operational period of wells by 30%–40% under average mineral acid concentrations and by 25%–45% under maximum concentrations with surfactant additives in complex geological settings. As a result, an effective methodology for ranking geological blocks based on their ore clay content and precipitate composition was developed to determine the rational parameters of decolmatant solutions, enabling a maximized filtration performance and an extended well service life. This makes it possible to reduce the operating costs of extraction, control the geotechnological parameters of uranium well mining, and improve the efficiency of the in situ leaching of uranium under complex mining and geological conditions. Additionally, the approach increases the environmental and operational safety during uranium ore leaching intensification. Full article

The mining sequence of ionic rare earth mineral mining districts is related to the effective utilization of rare earth mineral resources and the protection of ecological environment. This study establishes an optimization model for the mining sequence of ion-adsorption rare earth mining districts that incorporates environmental costs, using the net present value (NPV) of the mining district and the net present value of environmental costs (C_E) as objective functions. The model is applied to optimize the mining sequence of Mining District L. The results demonstrate that (1) Four algorithms, namely NSGA-II, NSGA-III, IBEA, and MOEA/D, were selected for comparison. The analysis based on the distribution of solutions, hypervolume values (HV), and computational time revealed that the IBEA exhibited superior performance. (2) The IBEA was employed to solve the multi-objective optimization problem, yielding a set of 30 optimal solutions. Different NPVs corresponded to different C_E values, with the C_E value increasing correspondingly as the NPV increased. (3) The weighted method was employed to transform the multi-objective optimization problem into a single-objective formulation. Using a genetic algorithm (GA), the optimal solution yielded a decision variable sequence for mining order as [2, 5, 8, 4, 1, 9, 6, 7, 3, 10, 11], with the net present value (NPV) of mining district profits reaching CNY 76,640.65 million and the environmental cost NPV amounting to CNY 19,469.18 million. Compared with the mining sequence optimization scheme that did not consider C_E, although the NPV decreased by CNY 3.3266 million, the C_E was reduced by CNY 10.6993 million. The mining sequence optimization model with environmental costs constructed in this paper provides a scientific decision-making basis for mining enterprises to consider the mining sequence in mining districts, minimize the damage to the ecological environment, and promote the coordinated progress of resource development and sustainable development. Full article

Cadmium is widely recognized as an important environmental toxicant that may give rise to kidney dysfunction, bone disease, and cancer in humans and animals. Kidney dysfunction occurs at very low exposures and is often considered as the most sensitive or critical effect. Cadmium exposures of concern occur in many countries. In low- and middle-income countries with small-scale mining, excessive exposure to cadmium and other metals occurs in occupational and environmental settings. This is of particular importance in view of the growing demand for metals in global climate change mitigation. Since the 1970s, the present authors have contributed evidence concerning the role of metallothionein and other factors in influencing the toxicokinetics and toxicity of cadmium, particularly as it relates to the development of adverse effects on kidneys in humans and animals. The findings gave a background to the development of biomarkers employed in epidemiological studies, demonstrating the important role of metallothionein in protection against cadmium-induced kidney dysfunction in humans. Studies in cadmium-exposed population groups demonstrated how biomarkers of kidney dysfunction changed during 8 years after drastic lowering of environmental cadmium exposure. Other epidemiological studies showed the impact of a good zinc status in lowering the prevalence of cadmium-related kidney dysfunction. Increased susceptibility to Cd-induced kidney dysfunction was shown in a population with high exposure to inorganic arsenic when compared with a group with low such exposure. Several national and international organizations have used part of the reviewed information, but the metallothionein-related biomarkers and the interaction effects have not been fully considered. We hope that these data sets will also be included and improve risk assessments and preventive measures. Full article

Analyzing the spatiotemporal characteristics of microseismic monitoring data is crucial for the monitoring and early prediction of coal–rock dynamic disasters during deep mining. Aiming to address the challenges hampering the early prediction of coal–rock dynamic disasters in deep mining, in this paper, we propose a method for analyzing the spatiotemporal characteristics of microseismic events in deep mining based on the ST-DBSCAN algorithm. First, a spatiotemporal distance metric model integrating temporal and spatial distances was constructed to accurately describe the correlations between microseismic events in spatiotemporal dimensions. Second, along with the spatiotemporal distribution characteristics of microseismic data, we determined the spatiotemporal neighborhood parameters suitable for deep-mining environments. Finally, we conducted clustering analysis of 14 sets of actual microseismic monitoring data from the Xinjulong Coal Mine. The results demonstrate the precise identification of two characteristic clusters, namely middle-layer mining disturbances and deep-seated activities, along with isolated high-magnitude events posing significant risks. Full article

Background: Glioblastoma (GBM) is one of the most challenging malignancies in all of neoplasms. These malignancies are associated with unfavorable clinical outcomes and significantly compromised patient wellbeing. The immunological landscape within the tumor microenvironment (TME) plays a critical role in determining GBM prognosis. By mining data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and correlating them with immune responses in the TME, genes associated with the immune microenvironment with potential prognostic value were obtained. Method: We selected GSE16011 as the training set. Gene expression profiles were substrates scored by both ESTIMATE and xCell, and immune cell subpopulations in GBM were analyzed by CIBERSORT. Gene expression profiles associated with low immune scores were performed by lasso regression, Cox analysis and random forest (RF) to identify a prognostic model for the multiple genes associated with immune infiltration in GBM. Then we constructed a nomogram to optimize the prognostic model using GSE7696 and TCGA-GBM as validation sets and evaluated these data for gene mutation and gene enrichment analysis. Result: The prognostic correlation between the six genes (MEOX2, PHYHIP, RBBP8, ST18, TCF12, and THRB) and GBM was finally found by lasso regression, Cox regression, and RF, and the online database obtained that all six genes were differentially expressed in GBM. Therefore, a prognostic correlation model was constructed based on the six genes. Kaplan–Meier (KM) survival analysis showed that this prognostic model had excellent prognostic ability. Conclusions: Prognostic models based on tumor microenvironment and immune score stratification and the construction of related genes have potential applications for prognostic analysis of GBM patients. Full article

The Chagai porphyry copper belt is a major component of the Tethyan metallogenic domain, which spans approximately 300 km and hosts several giant porphyry copper deposits. The tectonic setting, whether subduction-related or post-collisional, and the deep dynamic processes governing the formation of these giant deposits remain poorly understood. Mafic microgranular enclaves (MMEs), mafic dikes, and multiple porphyries have been documented in the Saindak mining area. This work examines both the ore-rich and non-ore intrusions in the Saindak porphyry Cu-Au deposit, using methods like molybdenite Re-Os dating, U-Pb zircon ages, Hf isotopes, and bulk-rock geochemical data. Geochronological results indicate that ore-fertile and barren porphyries yield ages of 22.15 ± 0.22 Ma and 22.21 ± 0.33 Ma, respectively. Both MMEs and mafic dikes have zircons with nearly identical ²⁰⁶Pb/²³⁸U weighted mean ages (21.21 ± 0.18 Ma and 21.21 ± 0.16 Ma, respectively), corresponding to the age of the host rock. Geochemical and Sr–Nd–Hf isotopic evidence indicates that the Saindak adakites were generated by the subduction of the Arabian oceanic lithosphere under the Eurasian plate, rather than through continental collision. The adakites were mainly formed by the partial melting of a metasomatized mantle wedge, induced by fluids from the dehydrating subducting slab, with minor input from subducted sediments and later crust–mantle interactions during magma ascent. We conclude that shallow subduction of the Arabian plate during the Oligocene–Miocene may have increased the flow of subducted fluids into the sub-arc mantle source of the Chagai arc. This process may have facilitated the widespread deposition of porphyry copper and copper–gold mineralization in the region. Full article

Background/Objectives: This study examines the academic discourse surrounding Critical Incident Stress Debriefing (CISD) and Critical Incident Stress Management (CISM) for first responders using Latent Dirichlet Allocation (LDA) topic modeling. It aims to uncover latent topical structures in the literature and critically evaluate assumptions to identify gaps and limitations. Methods: A corpus of 214 research article abstracts related to CISD/M was gathered from the Web of Science Core Collection. After preprocessing, we used Orange Data Mining software’s LDA tool to analyze the corpus. We tested models ranging from 2 to 10 topics. To guide interpretation and labeling, we evaluated them using log perplexity, topic coherence, and LDAvis visualizations. A four-topic model offered the best balance of detail and interpretability. Results: Four topics emerged: (1) Critical Incident Stress Management in medical and emergency settings, (2) psychological and group-based interventions for PTSD and trauma, (3) peer support and experiences of emergency and military personnel, and (4) mental health interventions for first responders. Key gaps included limited focus on cumulative trauma, insufficient longitudinal research, and variability in procedural adherence affecting outcomes. Conclusions: The findings highlight the need for CISD/M protocols to move beyond event-specific interventions and address cumulative stressors. Recommendations include incorporating holistic, proactive mental health strategies and conducting longitudinal studies to evaluate long-term effectiveness. These insights can help refine CISD/M approaches and enhance their impact on first responders working in high-stress environments. Full article

The generation of fine coal particles during mining and processing presents significant environmental and logistical challenges, particularly in coal-dependent, developing countries like South Africa (SA). This review critically evaluates the technical viability of fine coal briquetting as a sustainable waste-to-energy solution within a SA context, while drawing from global best practices and comparative benchmarks. It examines abundant feedstocks that can be used for valorization strategies, including fine coal and agricultural biomass residues. Furthermore, binder types, manufacturing parameters, and quality optimization strategies that influence briquette performance are assessed. The co-densification of fine coal with biomass offers a means to enhance combustion efficiency, reduce dust emissions, and convert low-value waste into a high-calorific, manageable fuel. Attention is also given to briquette testing standards (i.e., South African Bureau of Standards, ASTM International, and International Organization of Standardization) and end-use applications across domestic, industrial, and off-grid settings. Moreover, the review explores socio-economic implications, including rural job creation, energy poverty alleviation, and the potential role of briquetting in SA’s ‘Just Energy Transition’ (JET). This paper uniquely integrates technical analysis with policy relevance, rural energy needs, and practical challenges specific to South Africa, while offering a structured framework for bio-coal briquetting adoption in developing countries. While technical and economic barriers remain, such as binder costs and feedstock variability, the integration of briquetting into circular economy frameworks represents a promising path toward cleaner, decentralized energy and coal waste valorization. Full article

Microscopic image automatic recognition is a core technology for mineral composition analysis and plays a crucial role in advancing the intelligent development of smart mining systems. To overcome the limitations of traditional lithium ore analysis and meet the challenges of deployment on edge devices, we propose PS-YOLO-seg, a lightweight segmentation model specifically designed for lithium mineral analysis under visible light microscopy. The network is compressed by adjusting the width factor to reduce global channel redundancy. A PSConv-based downsampling strategy enhances the network’s ability to capture dim mineral textures under microscopic conditions. In addition, the improved C3k2-PS module strengthens feature extraction, while the streamlined Segment-Efficient head minimizes redundant computation, further reducing the overall model complexity. PS-YOLO-seg achieves a slightly improved segmentation performance compared to the baseline YOLOv12n model on a self-constructed lithium ore microscopic dataset, while reducing FLOPs by 20%, parameter count by 33%, and model size by 32%. Additionally, it achieves a faster inference speed, highlighting its potential for practical deployment. This work demonstrates how architectural optimization and targeted enhancements can significantly improve instance segmentation performance while maintaining speed and compactness, offering strong potential for real-time deployment in industrial settings and edge computing scenarios. Full article

To reduce the cost of coal mine filling materials, a novel composite cementitious material was developed by utilizing coal-based solid waste materials, including fly ash, desulfurized gypsum, and carbide slag, along with cement and water as raw materials. Initially, a comprehensive analysis of the physical and chemical properties of each raw material was conducted. Subsequently, proportioning tests were systematically carried out using the single-variable method. During these tests, multiple crucial performance indicators were measured. Specifically, the fluidity and bleeding rate of the slurry were evaluated to assess its workability, while the compressive strength and chemically bound water content of the hardened sample were tested to determine its mechanical properties and hydration degree. Through in-depth analysis of the test results, the optimal formulation of the composite cementitious material was determined. In the basic group, the mass ratio of fly ash to desulfurized gypsum was set at 70:30. In the additional group, the carbide slag addition amount accounted for 20% of the total mass, the cement addition amount was 15%, and the water–cement ratio was fixed at 0.65. Under these optimal proportioning conditions, the composite cementitious material exhibited excellent performance: its fluidity ranged from 180 to 220 mm, the bleeding rate within 6 h was less than 5%, and the 28-day compressive strength reached 17.69 MPa. The newly developed composite cementitious material features good fluidity and high strength of the hardened sample, fully meeting the requirements for mine filling materials. Full article

Generating adversarial examples under black-box settings poses significant challenges due to the inaccessibility of internal model information. This complexity is further exacerbated when attempting to achieve a balance between the attack success rate and perceptual quality. In this paper, we propose QTRL, a query-efficient two-phase reinforcement learning framework for generating high-quality black-box adversarial examples. Unlike existing approaches that treat adversarial generation as a single-step optimization problem, QTRL introduces a progressive two-phase learning strategy. The initial phase focuses on training the agent to develop effective adversarial strategies, while the second phase refines the perturbations to improve visual quality without sacrificing attack performance. To compensate for the unavailability of gradient information inherent in black-box settings, QTRL designs distinct reward functions for the two phases: the first prioritizes attack success, whereas the second incorporates perceptual similarity metrics to guide refinement. Furthermore, a hard sample mining mechanism is introduced to revisit previously failed attacks, significantly enhancing the robustness and generalization capabilities of the learned policy. Experimental results on the MNIST and CIFAR-10 datasets demonstrate that QTRL achieves attack success rates comparable to those of state-of-the-art methods while substantially reducing query overhead, offering a practical and extensible solution for adversarial research in black-box scenarios. Full article

In cold regions, the rock mass of open-pit mine slopes is continuously exposed to freeze–thaw (FT) environments, during which the fracture structures and their infilling materials undergo significant degradation, severely affecting slope stability and the assessment of service life. Conventional laboratory FT tests are typically based on uniform temperature settings, which fail to reflect the actual thermal variations at different burial depths, thereby limiting the accuracy of mechanical parameter acquisition. Taking the Wushan open-pit mine as the engineering background, this study establishes a temperature–depth relationship, defines multiple thermal intervals, and conducts direct shear tests on structural plane filling materials under various FT conditions to characterize the evolution of cohesion and internal friction angle. Results from rock mass testing and numerical simulation demonstrate that shear strength parameters exhibit an exponential decline with increasing FT cycles and decreasing burial depth, with the filling material playing a dominant role in the initial stage of degradation. Furthermore, a two-dimensional fracture network model of the rock mass was constructed, and the representative elementary volume (REV) was determined through the evolution of equivalent plastic strain. Based on this, spatial assignment of slope strength was performed, followed by stability analysis. Based on regression fitting using 0–25 FT cycles, regression model predictions indicate that when the number of FT cycles exceeds 42, the slope safety factor drops below 1.0, entering a critical instability state. This research successfully establishes a spatial field of mechanical parameters and evaluates slope stability, providing a theoretical foundation and parameter support for the long-term service evaluation and stability assessment of cold-region open-pit mine slopes. Full article

Specific computational methods, such as machine learning algorithms, can assist mining professionals in quickly and consistently identifying and addressing classification issues related to mineralized horizons, as well as uncovering key variables that impact predictive outcomes, many of which were previously difficult to observe. The integration of numerical and categorical variables, which are part of a dataset for defining ore grades, is part of the daily routine of professionals who obtain the data and manipulate the various phases of analysis in a mining project. Several supervised and unsupervised machine learning methods and applications integrate a wide variety of algorithms that aim at the efficient recognition of patterns and similarities and the ability to make accurate and assertive decisions. The objective of this study is the classification of gold ore or gangue through supervised machine learning methods using numerical variables represented by grade and categorical variables obtained through drillholes descriptions. Four groups of variables were selected with different variable configurations. The application of classification algorithms to different groups of variables aimed to observe the variables of importance and the impact of each one on the classification, in addition to testing the best algorithm in terms of accuracy and precision. The datasets were subjected to training, validation, and testing using the decision tree, random forest, Adaboost, XGBoost, and logistic regression methods. The evaluation was randomly divided into training (60%) and testing (40%) with 10-fold cross-validation. The results revealed that the XGBoost algorithm obtained the best performance, with an accuracy of 0.96 for scenario C1. In the SHAP analysis, the variable As was prominent in the predictions, mainly in scenarios C1 and C3. The arsenic class (Class_As), present mainly in scenario C4, had a significant positive weight in the classification. In the Receiver Operating Characteristic (ROC) and Area Under the Curve (AUC) curves, the results showed that XGBoost/scenario C1 obtained the highest AUC of 0.985, indicating that the algorithm had the best performance in ore/gangue classification of the sample set. The logistic regression algorithm together with AdaBoost had the worst performance, also varying between scenarios. Full article

Fully mechanized mining involves high energy consumption, particularly during cutting and transportation. Scraper conveyors, crucial for coal transport, face energy efficiency challenges due to the lack of accurate dynamic coal flow models, which restricts precise energy estimation and optimization. This study constructs dynamic coal flow and scraper conveyor energy efficiency models to analyze the impact of multiple variables on energy consumption and lump coal rate. A dynamic coal flow model is developed through theoretical derivation and EDEM simulations, validated for parameter settings, boundary conditions, and numerical methods. The multi-objective optimization model for energy consumption is solved using the NSGA-II-ARSBX algorithm, yielding a 33.7% reduction in energy consumption, while the lump coal area is reduced by 27.7%, indicating a trade-off between energy efficiency and coal fragmentation. The analysis shows that increasing traction speed while decreasing scraper chain and drum speeds effectively lowers energy consumption. Conversely, simultaneously increasing both chain and drum speeds helps to maintain lump coal size. The final optimization scheme demonstrates this balance—achieving improved energy efficiency at the cost of increased coal fragmentation. Additional results reveal that decreasing traction speed while increasing chain and drum speeds results in higher energy consumption, while increasing traction speed and reducing chain/drum speeds minimizes energy use but may negatively affect lump coal integrity. Full article

E-commerce live streaming in China has become a major retail channel, yet hosts often employ subtle phonetic or semantic “morphs” to evade moderation and make unsubstantiated claims, posing risks to consumers. To address this, we study the Live Auditory Morph Resolution (LiveAMR) task, which restores morphed speech transcriptions to their true forms. Building on prior text-based morph resolution, we propose an LLM-enhanced training framework that mines three types of explanation knowledge—predefined morph-type labels, LLM-generated reference corrections, and natural-language rationales constrained for clarity and comprehensiveness—from a frozen large language model. These annotations are concatenated with the original morphed sentence and used to fine-tune a lightweight T5 model under a standard cross-entropy objective. In experiments on two test sets (in-domain and out-of-domain), our method achieves substantial gains over baselines, improving $F_{0.5}$ by up to 7 pp in-domain (to 0.943) and 5 pp out-of-domain (to 0.799) compared to a strong T5 baseline. These results demonstrate that structured LLM-derived signals can be mined without fine-tuning the LLM itself and injected into small models to yield efficient, accurate morph resolution. Full article