Search Results (3,878)

Mercury in jarosites is crucial for environmental management and metallurgy. These minerals can incorporate highly toxic heavy metals from mining waste into their structure. This study analyzes the decomposition of mercury jarosite in a Ca(OH)₂ medium, focusing on its topological, kinetic, and modeling characteristics. Topological analysis, XRD and SEM−EDS were performed. ICP−OES was used to analyze the mercury and sulfur ions diffusing from the mercury jarosite into the Ca(OH)₂ solution. The kinetic model that best fit the data was that of spherical particles of constant size with an unreacted core under chemical control. The XRD results did not show new crystallographic phases. SEM−EDS showed a partially decomposed particle indicating a halo and core. The experimental conditions included temperatures from 298.15 to 333.15 K, concentrations of 0.0071–0.23210 mol L⁻¹ Ca(OH)₂, particle diameters of 25–53 µm, and pH of 11.12–12.85. During the induction period, reaction orders of 1.04 and 0.44 were obtained, along with an activation energy of 77.580 kJ mol⁻¹. For the progressive conversion period, the reaction orders were 0.59 and 0.15, with an activation energy of 52.124 kJ mol⁻¹. The overall kinetic modeling showed favorable results, supporting the evolutionary process of the mercury jarosite decomposition reaction in an alkaline medium under different conditions. This allows prediction of when mercury could be released back into the environment in alkaline soils or lime barriers. Full article

The beneficial use of mining waste aligns with circular economy thinking: saving primary resources can extend their lifetime and maintain availability, reduce the volume of legacy mining waste and its environmental impacts, and develop a resource beneficiation industry that is less energy and water intensive; mining lower grades at larger scale inevitably requires more beneficial reuse. Existing classifications applicable to different types of mine waste were reviewed. These include factors such as the mode of origin during the mining operation, grain size, chemical composition and stability. The result shows that these factors also largely control their civil engineering applications, suitability for end use sectors and potential hazards. Long-term liabilities related to chemical stability were identified as the most difficult challenge. When developing a reuse project, either by the end users or by the mine operator, it is likely that resource screening covering a comprehensive range of factors will be required, as none of the existing schemes individually cover all of the aspects needed to fully assess suitability for beneficial use. In conclusion, there is a need for a systematic and structured approach to classification of mining waste to facilitate reuse as raw materials, such as that presented in our review. Full article

Ecological restoration is increasingly applied as an effective strategy for mitigating environmental risks associated with tailings impoundments. However, plant establishment and ecological recovery in tailings substrates are often limited by unfavorable physicochemical properties and potential toxicity. This study investigated the changes in soil microbial community structure and diversity under the synergistic remediation of compound organic fertilizer and plants. Field plots subjected to combined organic fertilizer–plant remediation in a tailings impoundment in northern China were selected. The high-throughput sequencing of bacterial 16S rRNA genes and fungal ITS regions was performed alongside analyses of soil physicochemical properties. Compared to the untreated tailings soil, remediated soils showed pH values closer to neutrality, lower electrical conductivity, and significantly higher organic matter content, indicating an overall reduction in environmental stress and potential toxicity. The relative abundance of copiotrophic bacteria, such as Proteobacteria, increased, whereas that of stress-tolerant taxa adapted to extreme environments, such as Firmicutes , decreased. Although slight variations in dominant groups were observed among plots with different plant species, key microbial groups contributing to soil environmental improvement were largely consistent. These findings demonstrate that this combined remediation effectively improves the physicochemical properties and microbial community structure of tailings soil, providing a risk-oriented and ecologically sustainable strategy for the ecological restoration of similar sites. Full article

To investigate the mechanisms of coal seam reservoir modification and the efficient development of surface coalbed methane (CBM), the coal with different structural formations in the 13-1 coal seam of Huainan Mining Area was selected as the research object. Fracturing numerical simulation technology was employed to analyze the effect of hydraulic fracturing on tectonic coal reservoirs and explore the mechanism of fracturing-induced gas production. The results show that fragmented coal contains well-developed face and butt cleats, and distinct fracture models were constructed for the three tectonic coal types. Granulated and mylonitic structural coals exhibit larger total pore volumes and higher proportions of pores larger than 10 nm than fragmented coal. Both tectonic coal types exhibit a high proportion of methane flow space, with rapid methane desorption and diffusion under high pressure and stable behavior under low pressure. Pore volume compressibility calculations indicate that tectonic coal exhibits poor compressibility. Numerical simulations indicate that direct horizontal well fracturing produces short, wide fractures, whereas roof-strata horizontal well fracturing generates longer, more effective fractures, primarily due to large-scale depressurization and induced fracturing associated with horizontal well drilling and staged fracturing. Full article

Long-distance coal gangue slurry transportation pipelines are critical components of underground coal mine green backfilling systems, yet blockage failures severely threaten their safe and efficient operation. Existing distributed acoustic sensing (DAS)-based monitoring methods for such pipelines suffer from three key limitations: insufficient fixed-point quantitative accuracy, lack of verified blockage-specific characteristic indicators, and limited quantitative severity assessment capability. To address these gaps, this paper proposes a novel feature-level fusion monitoring method integrating DAS, fiber Bragg grating (FBG), and piezoelectric accelerometers for accurate blockage identification and quantitative evaluation in coal gangue slurry pipelines. A slurry pipeline circulation test platform with gradient blockage simulation (0% to 76.42%) and a synchronous multi-sensor monitoring system were developed. Through multi-domain signal analysis, three blockage-correlated characteristic frequencies were identified and cross-validated by synchronous multi-sensor data: 1.5 Hz (system background vibration), 26 Hz (blockage-induced fluid–structure resonance, verified by the Euler–Bernoulli beam theory with a theoretical value of 25.7 Hz), and 174 Hz (transient flow impact). The DAS phase change rate exhibited a unimodal nonlinear response to blockage degree, with the peak occurring at 40.94% blockage. On this basis, a sine-fitting quantitative inversion model was developed, achieving a high goodness of fit (R² = 0.985), and leave-one-out cross-validation confirmed its excellent robustness with a mean relative prediction error of 3.77%. Finally, a collaborative monitoring framework was built to fully leverage the complementary advantages of each sensor, realizing full-process blockage monitoring covering global blockage localization, precise quantitative severity calibration, and high-frequency transient risk early warning. The proposed method provides a robust experimental and technical foundation for real-time early warning, precise localization, and quantitative diagnosis of long-distance slurry pipeline blockages and holds important engineering application value for the safe and efficient operation of underground coal mine green backfilling systems. Full article

Remote sensing object detection is severely hindered by background clutter and uneven object spatial distribution, limiting the performance of traditional algorithms and the original RT-DETR. To address these issues, this paper proposes an improved RT-DETR-based algorithm, CB-DETR. First, a symmetry-guided Density-Adaptive Attention (DAA) module is designed to tackle insufficient intra-scale feature interaction and poor adaptability to uneven density regions in RT-DETR. Centered on a density estimation network, it predicts target density, generates normalized weights via temperature scaling and softmax, and dynamically adjusts receptive fields through a multi-branch structure to symmetrically adapt to high- and low-density regions, outperforming RT-DETR’s fixed receptive field design. Second, a cross-attention-fused Posterior Dynamic Query Decoder (PDQD) is constructed to overcome fixed query interaction and weak small/occluded object detection in the original decoder. A dynamic query update mechanism optimizes vectors via multi-round iterations, breaking fixed-layer limitations and mining detailed features in complex scenarios, thus improving small/occluded target detection accuracy. Comparative experiments on RSOD, DIOR, and DOTA datasets show that CB-DETR outperforms the original RT-DETR comprehensively: ${\mathrm{mAP}}_{50}$/${\mathrm{mAP}}_{50:95}$ improve by 2.8%/2.1% and Precision (P)/Recall (R) by 4%/2.4% on RSOD; ${\mathrm{mAP}}_{50}$ improves by 1.3% on DIOR and 3% on DOTA. All core metrics surpass the original model and mainstream improved algorithms, verifying the effectiveness and innovation of the proposed improvements. Full article

Coal mine gas disasters have always been a major threat to coal mine safety production. With the increasing depth and intensity of mining, the importance of studying gas geological laws is becoming increasingly prominent. In the actual mining process in coal mines, there is often a phenomenon of sudden increase in gas accumulation and gas emission in local areas. The study and prediction of the main influencing factors of gas enrichment are important research foundations for guiding the precise implementation of gas control engineering and avoiding coal and gas outburst accidents. Research shows that gas accumulates in local areas (such as abnormal structural and coal thickness areas), and gas pressure also increases locally; in areas where coal seam thickness changes dramatically, there is a sharp increase in gas content in mines. Prominent accidents all occurred in the coal seam area with a thickness exceeding 5 m. There is a significant spatial coupling between gas enrichment zoning and outburst accidents. The strip-shaped high-enrichment area based on gas content gradient division has a northeast southwest distribution consistent with the direction of structural extension. This study reveals the cross scale occurrence law of coalbed methane under multiple disturbances during the mining process, elucidates the non-equilibrium occurrence characteristics of methane, delineates local gas enrichment areas, uses theoretical models to predict gas emission and distribution laws, and provides parameter support for constructing gas geological attribute models. Full article

The dynamics of the processes shaping the quality of rainwater discharged by sewer systems is very complex. The use of hydrodynamic models to simulate surface runoff and the dynamics of changes in pollutants, including heavy metal (HM) concentrations, requires the collection of a lot of data that is difficult to obtain, and model calibration is complex and time-consuming. This paper presents a machine learning model and investigates the possibility of applying data mining methods to simulate changes in the concentrations of selected heavy metals (Ni, Cu, Cr, Zn and Pb) based on rainwater quality studies conducted in three urban catchments located in Kielce, southern Poland, with the aim of developing a model with broader applicability. Simulations of HM content in rainwater were performed using regression and classification trees (RF), neural networks (MLP) and support vector machines (SVMs). The MLP (MAPE ≤ 21.6) and SVM (MAPE ≤ 23.5) methods were shown to have the highest accuracy in simulating HM content. These models produced satisfactory simulation results based on rainfall amount and meteorological conditions, and they had relatively simple model structures and short simulation time. The study demonstrated that the proposed approach provides a transferable tool for estimating HM content in rainwater based on air quality, expressed in terms of visibility, and the type of catchment development. Full article

Illegal artisanal and small-scale mining (galamsey) and climate stress jointly degrade ecosystems and livelihoods in Ghana. This paper demonstrates how community-driven governance can realign incentives toward environmental stewardship and inclusive livelihoods. Using an explanatory sequential mixed-methods design—quantitative difference-in-differences followed by qualitative case analysis and Participatory Action Research—we evaluate a structured program combining vocational training, financial literacy, environmental stewardship, and governance alignment. We operationalize Environmental, Social, and Governance (ESG) outcomes via transparent composite indices and triangulate survey, administrative, and focus group evidence. The study identifies conditions under which alternative livelihoods reduce participation in illegal mining, strengthen women’s economic agency, and improve adoption of climate-smart practices. Implications include practical guidance for program design (community delivery, matched incentives, oversight), policy (local climate finance and accountability mechanisms), and research (scalable indicators and rigorous impact evaluation in resource-dependent communities). Full article

Autonomous mobile robots are transforming industries from e-commerce logistics to field exploration, but their effectiveness depends on onboard energy storage. This study addresses the challenge of selecting optimal battery technologies for autonomous mobile robots, balancing performance, energy efficiency, thermal stability, and cost across diverse applications. We focus on lithium-ion, lithium-polymer, and nickel-metal hydride batteries, the most common power solutions, each with distinct advantages and disadvantages in energy density, form factor, thermal stability, and cost. A dynamic modeling and simulation framework in MapleSim evaluated these chemistries under defined and representative operating conditions, tracking state of charge and temperature during charging and discharging. A Particle Swarm Optimization algorithm evaluated 37 battery configurations by thermal stability, energy efficiency, and cost across five use cases. Key results indicate that for logistics and warehousing, lithium nickel manganese cobalt oxide with graphite is optimal; for healthcare, lithium nickel manganese cobalt oxide with lithium titanate oxide excels; for manufacturing, lithium nickel cobalt aluminum oxide with graphite leads; for agricultural robots, lithium manganese oxide with graphite is best; and for exploration and mining, lithium iron phosphate with graphite is most reliable. These results provide a structured basis for battery selection, showing how simulation-driven, multi-criteria decision-making enhances energy management and operational reliability. Full article

The intestinal microbiome plays a fundamental role in canine health and well-being, regulating functions, including digestion, immunity, metabolism, and behavior. Dysbiosis refers to the disruption of the balanced composition of resident commensal communities, and gut bacteria can influence behavior via neurological, metabolic, endocrine, and immune-mediated pathways. Growing evidence supports the existence of a bidirectional communication between the gut and the central nervous system, known as the gut–brain axis, through which intestinal microorganisms may influence behavior via neurological, metabolic, endocrine, and immune-mediated pathways. Despite the expanding interest in this field, the contribution of intestinal dysbiosis to the development and severity of behavioral and neurological disorders in companion dogs remains poorly understood. This review aims to critically analyze the literature from 2011 to 18 September 2025 concerning the association between dysbiosis, the gut–brain axis, and both gastrointestinal and non-gastrointestinal illnesses in dogs. To our knowledge, this review represents the first application of Text Mining (TM) in this domain: TM facilitates the identification and analysis of valuable information from extensive datasets, converting unstructured content into structured data, thereby enabling quantitative analysis. We used the following search terms on three bibliographic databases (PubMed, Scopus, and Web of Science): “dysbiosis” AND “canine” OR “dog” AND “gut–brain axis” AND “behavior”. Of the 1176 records retrieved, 35 studies were checked following the PRISMA guidelines, and they met the predefined inclusion criteria in the final analysis. Full article

Uranium mining generates complex multi-element contamination that affects interconnected ecosystem components, posing long-term ecological and sanitary risks; this review places these impacts in a broad environmental context and aims to synthesize current knowledge on the distribution, migration, and accumulation of uranium and associated heavy metals in water, soil, and plants. A structured analysis of international peer-reviewed literature was conducted, focusing on documented pathways of metal release from tailings and waste dumps, geochemical controls on mobility, and biological uptake by vegetation. The reviewed studies consistently show that tailings and disturbed ore-bearing strata act as persistent sources of uranium and heavy metals (e.g., Cd, Pb, Cr, Ni, Zn, Mn, As), which migrate through infiltration, acid mine drainage, and atmospheric dispersion, leading to elevated concentrations in surface and groundwater and long-term accumulation in soils. Soils function as the principal sink controlling metal bioavailability, while vegetation reflects the bioavailable fraction and exhibits pronounced species-specific accumulation patterns. These processes establish an active “soil–water–plant” transfer chain that facilitates entry of contaminants into food webs. The synthesis indicates that combined uranium and heavy metal contamination represents a sustained ecological and public health concern in uranium-mining regions and underscores the need for integrated monitoring of soils, waters, and vegetation, along with quantitative risk assessment and scientifically grounded remediation strategies. Full article

In China’s stock-based renewal agenda, many old residential communities display pronounced intergenerational overlap, in which grandparental childcare becomes a dominant pattern of outdoor-space use. Against the backdrop of age-structure shifts, population ageing, and persistently low fertility, community-level outdoor-space supply, distributive equity, and environmental adaptability have become key concerns in renewal practice. Yet, practitioners still lack a rankable, low-cost, and implementable evaluation-to-decision workflow. Using Xingshe Community in Dalian, China as an empirical case, this study establishes and tests an integrated “NLP–AHP–GBDT” assessment framework. Guided by policy discourse and planning theory, over 50 semi-structured interviews were processed via NLP-based semantic analysis and keyword mining to derive a two-tier indicator set (criterion and indicator layers). Seven specialists then applied the analytic hierarchy process to elicit indicator weights, and a resident survey was administered to generate weighted performance scores for diagnosing deficiencies. In the feedback-validation stage, we adopted both a qualitative Framework Method and a quantitative GBDT approach, first using the Framework Method to conduct feedback validation based on community residents’ open-ended evaluations. Subsequently, gradient boosting decision trees were used for supervised verification with renewal-scenario data, providing empirical backing for the weighting scheme and the resulting priority order for interventions. The findings suggest that outdoor spaces are broadly serviceable but fall short in intergenerational friendliness, reflecting a structural misalignment between intergenerational activity patterns and spatial provision. Based on the validated priorities, the study proposes modular, incremental micro-renewal measures focusing on safety and emergency accessibility, environmental comfort and caregiving–recreation coupling, and place identity with community organizational mobilization. Full article

Against the backdrop of accelerating global carbon neutrality and the full implementation of China’s “Dual Carbon” strategy, the mining industry, as an energy-intensive sector that guarantees resource supply, plays a critical supporting role in the green transformation of the industry and achieving national carbon emission reduction targets. Based on panel data from 29 provinces in China from 2000 to 2021, this study combines the Tapio decoupling index and the LMDI decomposition method to systematically characterize the evolution of carbon emissions in China’s mining industry, to accurately identify the decoupling state between carbon emissions and economic growth, and to reveal the core driving mechanism, presenting quantifiable and interpretable empirical and technical results. The results show that carbon emissions and raw ore output in China’s mining industry generally followed an evolutionary trend of “first rising, then peaking, and continuously declining”. Carbon emissions peaked in 2013 and decreased steadily afterward, reflecting remarkable achievements in green development. The decoupling relationship has shifted from weak decoupling to stable strong decoupling in 2019 and has been maintained in this state ever since, indicating that the mining industry has entered a high-quality development stage featuring coordinated economic growth and carbon emission reductions. The decomposition results confirm that the output expansion effect is the main driver of the increase in carbon emissions, while the reduction in energy intensity, optimization of the energy structure, and improvement in output efficiency constitute the key forces driving the reduction in carbon emissions, with technological progress, industrial upgrading, and clean energy substitution as the core pathways. In summary, this study empirically verifies the feasibility and effectiveness of low-carbon transformation in China’s mining industry. The realization of a stable strong decoupling state shows that this paradigm can be replicated in the green development of other energy-intensive industries. In the future, precise policy incentives, energy structure upgrades, energy efficiency technological innovation, and standardized construction of green mines can further consolidate the decoupling effects and further encourage the comprehensive transition towards a low-carbon mining industry. The findings of this study can provide a solid theoretical basis and empirical support for the formulation of carbon emission reduction policies and the design of green development pathways in China’s mining industry, with important theoretical and practical value for ensuring national resource security and facilitating the realization of the “Dual Carbon” goals. Full article

What drives persistent urban–rural tourism satisfaction gaps: whether from promotional over-promising or structural service deficits? This distinction fundamentally determines whether territorial development resources should target marketing sophistication or productive capacity, yet remains empirically unresolved. Text-mining for 33,174 attractions across 349 Chinese cities reveals that both rural and urban destinations systematically under-promise, with description sentiment falling consistently below actual ratings, contradicting the “digital facade” hypothesis. Urban attractions nonetheless generate more positive surprises through superior service delivery (gap = 0.62 vs. 0.55). Sentiment measurement robustness is validated through triangulation of two independent dictionary-based methods ($r=0.58$, $p<0.001$) and cross-paradigm verification using a pre-trained BERT transformer ($\tau =1.000$ ranking stability). SHAP decomposition quantifies the policy implication: controllable service quality indicators, including description quality (23.2%), information richness (30.7%), and price positioning (16.5%), collectively explain over 70% of the variance in satisfaction, while fixed geographic factors (rural classification 14.9% and city-tier 14.7%) account for 29.6%, yielding a controllable-to-geographic ratio of 2.4:1. Propensity score matching with six covariates confirms a 0.074–0.100-point rural penalty persists after controlling for confounders, while non-linear analysis demonstrates that rural attractions face no marginal productivity disadvantage, and the challenge is baseline capacity, not investment efficiency. For policymakers pursuing Sustainable Development Goals 8, 10, and 12 through tourism-led regional strategies, these results mandate redirecting resources from demand-side expectation management toward supply-side infrastructure and workforce development, the true binding constraint on rural competitiveness. Full article