Search Results (3,431)

The exploitation of mineral resources often necessitates groundwater drainage, which may impact surrounding ecosystems, particularly vegetation. In this study, the effects of passive drainage in the Kornica-Popówka chalk mine in eastern Poland were analyzed using Sentinel-2 satellite images and the NDVI vegetation index. Groundwater monitoring wells were used to delineate the extent of the depression cone, representing areas of potentially altered hydrological conditions. NDVI values were analyzed across multiple time points between 2023 and 2024 to assess the condition of vegetation both inside and outside the depression cone. The results indicate no significant difference in NDVI values during the 2023–2024 study period for this specific chalk mine case between areas affected and unaffected by the depression cone, suggesting that vegetation in this region is not experiencing stress due to lowered groundwater levels. This outcome highlights the influence of other environmental factors, such as rainfall and land use, and suggests that the local geological structure allows plants to maintain sufficient access to water despite hydrological alterations. This study confirms the utility of integrating remote sensing with hydrogeological data in environmental monitoring and underlines the need for continued observation to assess long-term trends in vegetation response to mining-related groundwater changes. Full article

Coal gangue, an industrial byproduct of coal mining, was traditionally utilized in concrete production as a coarse aggregate. However, recent advancements have expanded its application by processing it into fine powder for use as a supplementary cementitious material (SCM), partially replacing cement. This approach not only enhances the sustainable reuse of coal gangue but also contributes to reducing cement consumption and associated carbon emissions. Nevertheless, the incorporation of coal gangue may adversely affect the mechanical strength and long-term durability of concrete. This review provides a systematic analysis of recent research on coal gangue-modified concrete. It begins by classifying the functional roles of coal gangue in concrete mixtures, followed by a critical evaluation of its impact on mechanical properties and durability—both as an aggregate an as a mineral admixture. When 30% of the aggregate is replaced with activated coal gangue, the average compressive strength of concrete increases by 15%. When coal gangue replaces less than 20% of the cement, the compressive strength of concrete can reach 95% of the reference strength. Second, the review evaluates the modification effects of various mineral admixtures, elucidating their mechanisms for enhancing mechanical properties and durability in coal gangue-based concrete. Finally, it examines the underlying interaction mechanisms between these admixtures and coal gangue, while identifying key future research directions for optimizing admixture formulations. By providing a comprehensive and critical analysis of current research, this paper serves as a valuable reference for developing high-performance coal gangue concrete with increased substitution rates and tailored admixture systems. Ultimately, this work advances the design of sustainable, low-cement concrete using industrial byproducts, enabling performance-driven applications and supporting next-generation green construction materials. Full article

Multi-source topographic point clouds are of great value in applications such as mine monitoring, geological hazard assessment, and high-precision terrain modeling. However, challenges such as heterogeneous data sources, drastic terrain variations, and significant differences in point density severely hinder accurate registration. To address these issues, this study proposes a robust point cloud registration method named Cauchy-AdaV2, which integrates region-adaptive weighting with Cauchy-based residual suppression. The method jointly leverages slope and roughness to partition terrain into regions and constructs a spatially heterogeneous weighting function. Meanwhile, the Cauchy M-estimator is employed to mitigate the impact of outlier correspondences, enhancing registration accuracy while maintaining adequate correspondence coverage. The results indicate that the proposed method significantly outperforms traditional ICP, GICP, and NDT methods in terms of overall error metrics (MAE, RMSE), error control in complex terrain regions, and cross-sectional structural alignment. Specifically, it achieves a mean absolute error (MAE) of 0.0646 m and a root mean square error (RMSE) of 0.0688 m, which are 70.5% and 72.4% lower than those of ICP, respectively. These outcomes demonstrate that the proposed method possesses stronger spatial consistency and terrain adaptability. Ablation studies confirm the complementary benefits of regional and residual weighting, while efficiency analysis shows the method to be practically applicable in large-scale point cloud scenarios. This work provides an effective solution for high-precision registration of heterogeneous point clouds, especially in challenging environments characterized by complex terrain and strong disturbances. Full article

Microplastics and metals are increasingly recognised as major water contaminants with profound environmental and health consequences. The environmental co-occurrence of microplastics and metals are well documented in waterways, including urban runoff, highway balancing ponds, industrial wastewater, and mine-impacted waters, posing a multifaceted environmental threat. Urgent remedial action is required to remove co-occurring microplastics and metals from water, giving consideration to how their co-occurrence can affect remediative efforts. However, information on the sorption of microplastics and Pb and Zn simultaneously by biochar is lacking. In this current study, changes in the quantity of metal adsorbed by pristine larch biochar and magnetised larch biochar due to the presence of microplastics was assessed using spectroscopic techniques. This study demonstrated that magnetised larch biochar and pristine larch biochar both remove co-occurring microplastics, Pb, and Zn from solution. Neither magnetised larch biochar nor pristine larch biochar show any statistical difference in the sorption of Pb with the inclusion of microplastics into the aqueous matrix. However, the inclusion of microplastics result in the reduced sorption of Zn by 43% for magnetised larch biochar (p < 0.01) and 69% for pristine larch biochar (p < 0.01). Magnetised larch biochar also demonstrated greater sorption than pristine larch biochar for microplastics (p < 0.05), Zn co-occurring with microplastics (p < 0.05), and Zn with no microplastics present (p < 0.01). Despite the effects of competitive sorption between Zn and microplastics, the removal of Pb, Zn, and microplastic from a multi-contaminant system indicate that magnetic larch biochar is a viable option to remove multiple contaminants from aqueous environs where metals and microplastics are seen to co-occur. Full article

Mining and metallurgical activities negatively impact ecosystems due to the release of potentially toxic elements (PTEs). This study assesses PTE pollution and accumulation in native plant species that have spontaneously colonized a historical mining site (Michaly, site A) and a nearby metallurgical smelter site (Varvara, site B) on the Lavreotiki Peninsula, Attika, Greece. Soils were analyzed for As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Sb, and Zn. A total of 89 native plant taxa across 28 families were identified. The aerial parts from dominant species were analyzed for PTE concentrations, and bioconcentration factors (BCFs) were calculated. One-way ANOVA and principal component analysis (PCA) using R were used for statistical evaluation. Soils at both sites showed elevated As, Cd, Cr, Cu, Ni, Pb, Sb, and Zn; Mn was high only at site B, while Co and Fe remained at background levels. Several plant species, especially at Michaly, had elevated concentrations of As, Cd, Co, Cr, Fe, Pb, Sb, and Zn in their aerial parts. BCFs indicated general PTE exclusion from aerial parts, particularly at site B. Native vegetation on these contaminated sites shows resilience and PTE exclusion, highlighting their potential for phytoremediation, especially phytostabilization, and ecological restoration in similarly polluted Mediterranean environments. Full article

This study investigated consumer perception of food insecurity by refining data collected from social media platforms. Text mining and TF-IDF were used to extract core keywords closely related to food insecurity and analyze their meanings. In addition, time series analysis and sentiment analysis were used to examine temporal and emotional changes. The analysis results showed that keywords, such as health, stress, mental, and depression, appeared frequently, indicating that food insecurity is closely related to psychological and mental problems. In addition, consumers showed high emotional sensitivity to essential nutrients, such as vitamin D, magnesium, calcium, and omega. Furthermore, stress indices and mental and physical response indices increased simultaneously during this period, indicating that food insecurity is a factor that causes emotional and physical responses. The results of the sentiment analysis showed that negative emotions (anxiety, fear, and sadness) were higher than positive emotions, suggesting that discussions related to food insecurity have a negative emotional impact. Full article

Artificial intelligence (AI) is now the computational core of smart building automation, acting across the entire cyber–physical stack. This review surveys peer-reviewed work on the integration of AI with indoor environmental quality (IEQ) and energy performance, distinguishing itself by presenting a holistic synthesis of the complete technological evolution from IoT sensors to generative AI. We uniquely frame this progression within a human-centric architecture that integrates digital twins of both the building (DT-B) and its occupants (DT-H), providing a forward-looking perspective on occupant comfort and energy management. We find that deep reinforcement learning (DRL) agents, often developed within physics-calibrated digital twins, reduce annual HVAC demand by 10–35% while maintaining an operative temperature within ±0.5 °C and CO₂ below 800 ppm. These comfort and IAQ targets are consistent with ASHRAE Standard 55 (thermal environmental conditions) and ASHRAE Standard 62.1 (ventilation for acceptable indoor air quality); keeping the operative temperature within ±0.5 °C of the setpoint and indoor CO₂ near or below ~800 ppm reflects commonly adopted control tolerances and per-person outdoor air supply objectives. Regarding energy impacts, simulation studies commonly report higher double-digit reductions, whereas real building deployments typically achieve single- to low-double-digit savings; we therefore report simulation and field results separately. Supervised learners, including gradient boosting and various neural networks, achieve 87–97% accuracy for short-term load, comfort, and fault forecasting. Furthermore, unsupervised models successfully mine large-scale telemetry for anomalies and occupancy patterns, enabling adaptive ventilation that can cut sick building complaints by 40%. Despite these gains, deployment is hindered by fragmented datasets, interoperability issues between legacy BAS and modern IoT devices, and the computer energy and privacy–security costs of large models. The key research priorities include (1) open, high-fidelity IEQ benchmarks; (2) energy-aware, on-device learning architectures; (3) privacy-preserving federated frameworks; (4) hybrid, physics-informed models to win operator trust. Addressing these challenges is pivotal for scaling AI from isolated pilots to trustworthy, human-centric building ecosystems. Full article

The multi-branch horizontal well for coalbed methane (CBM) is a core technical means to achieve efficient CBM extraction, and its productivity is jointly restricted by geological and engineering factors. To accurately grasp the main controlling factors of the productivity of multi-branch horizontal wells and provide a scientific basis for the optimized design of CBM development, this study takes Well W1 in the Wenjiaba Coal Mine of the Zhina Coalfield in Guizhou, China, as an engineering example and comprehensively uses three-dimensional geological modeling and reservoir numerical simulation methods to systematically explore the key influencing factors of the productivity of multi-branch horizontal wells for CBM. This study shows that coal seam thickness, permeability, gas content, and branch borehole size are positively correlated with the productivity of multi-branch horizontal wells. With the simulation time set to 1500 days, when the coal seam thickness increases from 1.5 m to 4 m, the cumulative gas production increases by 166%; when the permeability increases from 0.2 mD to 0.8 mD, the cumulative gas production increases by 123%; when the coal seam gas content increases from 8 m³/t to 18 m³/t, the cumulative gas production increases by 543%; and when the wellbore size increases from 114.3 mm to 177.8 mm, the cumulative gas production increases by 8%. However, the impact of branch angle and spacing on productivity exhibits complex nonlinear trends: when the branch angle is in the range of 15–30°, the cumulative gas production shows an upward trend during the simulation period, while in the range of 30–75°, the cumulative gas production decreases during the simulation period; the cumulative gas production with branch spacing of 100 m and 150 m is significantly higher than that with spacing of 50 m and 200 m. Quantitative analysis through sensitivity coefficients reveals that the coal seam gas content is the most important geological influencing factor, with a sensitivity coefficient of 2.5952; a branch angle of 30° and a branch spacing of 100 m are the optimal engineering conditions for improving productivity, with sensitivity coefficients of 0.2875 and 0.273, respectively. The research results clarify the action mechanism of geological and engineering factors on the productivity of multi-branch horizontal wells for CBM, providing a theoretical basis for the optimized deployment of well locations, wellbore structure, and drilling trajectory design of multi-branch horizontal wells for CBM in areas with similar geological conditions. Full article

International organizations have highlighted the importance of consistent and reliable environment, social and governance (ESG) disclosure and metrics to inform business strategy and investment decisions. Greater corporate disclosure is a positive signal to investors who prioritize sustainable investment. In this study, economic and climate sentiment are extracted from the integrated and sustainability reports of the top 40 corporates listed on the Johannesburg Stock Exchange, employing domain-specific natural language processing. The intention is to clarify the complex interactions between climate risk, corporate disclosures, financial performance and investor sentiment. The study provides valuable insights to regulators, accounting professionals and investors on the current state of disclosures and future actions required in South Africa. A time series analysis of the sentiment scores indicates a noticeable change in the corporates’ disclosures from climate-related risks in the earlier years to climate-related opportunities in recent years, specifically in the banking and mining sectors. The trends are less pronounced in sectors with good ESG ratings. An exploratory regression study reveals that climate and economic sentiments contain information that explain stock price movements over the longer term. The results have important implications for asset allocation and offer an interesting direction for future research. Monitoring the sentiment may provide early-warning signals of systemic risk, which is important to regulators given the impact on financial stability. Full article

Currently, there is a lack of methods for detecting the mechanism of gas explosion propagation within flameproof enclosures and the dynamic behavior of flameproof enclosures under explosion impact. Therefore, this paper studies a method for detecting the vibration characteristics of coal mine explosion-proof equipment under internal gas explosions using laser Doppler. First, a model of gas explosion propagation and explosion transmission response in flameproof enclosures is established to reveal the mechanism of gas explosion transmission inside coal mine flameproof enclosures. Second, a laser Doppler measurement method for coal mine flameproof enclosures is proposed, along with a step-by-step progressive vibration characteristic analysis method. This begins with a single-frequency dimension analysis using the Fourier transform (FFT), extends to time–frequency joint analysis using the short-time Fourier transform (STFT) to incorporate a time scale, and then advances to a three-dimensional linkage of scale, time, and frequency using the wavelet transform (DWT) to solve the limitation of the fixed window length of the STFT, thereby achieving a dynamic characterization of the detonation response characteristics. Finally, a non-symmetric Gaussian impact load inversion model is constructed to validate the overall scheme. The experimental results show that the FFT analysis identified a 2000 Hz main frequency, along with the global frequency components of the flameproof enclosure vibration signal, the STFT analysis revealed the dynamic evolution of the 2000 Hz main frequency and global frequency over time, and the wavelet transform achieved higher accuracy positioning of the frequency amplitude in the time domain, with better time resolution. Finally, the experimental platform showed an error of less than 5% compared with the actual measured impact load, and the error between the inverted impact load and the actual load was less than 15%. The experimental platform is feasible, and the inversion model has good accuracy. The laser Doppler measurement method has significant advantages over traditional coal mine flameproof equipment measurement and analysis methods and can provide further failure analysis and prevention, design optimization, and safety performance evaluation of flameproof enclosures in the future. Full article

In water-stressed regions of South Africa, the expansion of extractive industries is compounding the effects of climate change and poor governance, threatening local water security and socio-ecological resilience for hydrosocial justice. This chapter examines the proposed Jindal iron-ore mine in Melmoth, KwaZulu-Natal and its anticipated impact on water availability, quality, and governance. Drawing on in-depth interviews with farmers, residents, and environmental stakeholders, the findings reveal a region already suffering from recurrent droughts, El Niño-related climate variability, and over-allocated water resources. Findings reveal concern that the mine would further strain surface and groundwater systems, especially given the industrial demands already placed on the Goedertrouw dam. Other concerns about potential water contamination from tailings, dust, and runoff echo experiences from neighbouring mining areas, where degraded water quality has affected both domestic use and cultural practices. The study also uncovers governance gaps, including weak regulatory oversight, non-compliance with environmental safeguards, and flawed consultation processes that overlook downstream impacts. By situating Melmoth within wider debates on extractivism, climate stress, and environmental justice, the paper calls for an urgent reconsideration of extractive approvals in ecologically vulnerable regions that threaten water security, livelihoods, cultural practices, and sense of place. Ignoring interconnected dimensions risks reinforcing existing vulnerabilities, undermining resilience, and entrenching long-term injustices. Full article

Innovative technologies have been helping to improve comfort and safety at work in high-risk sectors for years. The study analysed the impact, along with an assessment of potential implementations (opportunities and limitations) of innovative technological solutions for improving occupational safety in two selected sectors of the economy: mining and construction. The technologies evaluated included unmanned aerial vehicles and inspection robots, the Internet of Things and sensors, artificial intelligence, virtual and augmented reality, innovative individual and collective protective equipment, and exoskeletons. Due to the extensive nature of the obtained materials, the research description has been divided into two articles (Part I and Part II). This article presents the first three technologies. After the scientific literature from the Scopus database was analysed, some research gaps that need to be filled were identified. In addition to the obvious benefits of increased occupational safety for workers, innovative technological solutions also offer employers several economic advantages that affect the industry’s sustainability. Innovative technologies are playing an increasingly important role in improving safety in mining and construction. However, further integration and overcoming implementation barriers, such as the need for changes in education, are needed to realise their full potential. Full article

The spontaneous combustion of sulfide ores (SOSC) is an extremely dangerous mining disaster that directly threatens safety production in mines and causes far-reaching negative impacts on the surrounding ecosystem. In this study, oxidation weight gain experiments, self-heating temperature and ignition temperature tests, and thermogravimetric analysis (TGA) were conducted to detect the spontaneous combustion characteristics of sulfide ores with different sulfur contents (40.29%, 34.56%, 24.81%, and 14.2%). The results show that the sulfur content significantly affects the spontaneous combustion characteristics of sulfide ores. As the sulfur content decreased, the oxidized weight gain rate decreased overall, and the self-heating temperature (135, 152.5, 162.5, and 176.9 °C) and ignition temperature (425.3, 438.6, 455.4, and >500 °C) increased. The three combustion stages of the SOSC were divided based on the TG and DTG curves: low-temperature oxidation stage, combustion decomposition stage, and slow burnout stage. Furthermore, KAS and FWO methods were used to obtain the apparent activation energy in the combustion decomposition stage. The apparent activation energy decreased significantly with the increase in the sulfur content. The results of all experiments and analyses showed that sulfide ores with high sulfur content have a stronger tendency to undergo spontaneous combustion. The research results have important theoretical and practical implications for the prevention of SOSC. Full article

In response to the complex challenges posed by gob-side entry retaining in medium-thick coal seams—specifically, severe stress concentrations and unstable surrounding rock under composite roof structures—this study presents a comprehensive field–numerical investigation centered on the 5-200 working face of the Dianping Coal Mine, China. A three-dimensional coupled stress–displacement model was developed using FLAC3D to systematically evaluate the mechanical behavior of surrounding rock under varying roof cutting configurations. The parametric study considered roof cutting heights of 6 m, 8 m, and 10 m and cutting angles of 0°, 15°, and 25°, respectively. The results indicate that a roof cutting height of 8 m combined with a 15° inclination provides optimal stress redistribution: the high-stress zone within the coal rib is displaced 2–3 m deeper into the coal body, and roof subsidence is reduced from 2500 mm (no cutting) to approximately 200–300 mm. Field measurements corroborate these findings, showing that on the return airway side with roof cutting, initial and periodic weighting intervals increased by 4.0 m and 5.5 m, respectively, while support resistance was reduced by over 12%. These changes suggest a delayed main roof collapse and decreased dynamic loading on supports, facilitating safer roadway retention. Furthermore, surface monitoring reveals that roof cutting significantly suppresses mining-induced ground deformation. Compared to conventional longwall mining at the adjacent 5-210 face, the roof cutting approach at 5-200 resulted in notably narrower (0.05–0.2 m) and shallower (0.1–0.4 m) surface cracks, reflecting effective attenuation of stress transmission through the overburden. Taken together, the proposed roof cutting and pressure relief strategy enables both stress decoupling and energy dissipation in the overlying strata, while enhancing roadway stability, reducing support demand, and mitigating surface environmental impact. This work provides quantitative validation and engineering guidance for intelligent and low-impact coal mining practices in high-stress, geologically complex settings. Full article

With the accelerated development of economic globalization, it is of great significance to strengthen the ability to measure, evaluate, and warn of systemic financial risks for preventing and defusing financial risks. Thus, this research established the Time-Varying Parameter Factor-Augmented Vector Autoregression model (TVP-FAVAR), combined with the Markov Regime Switching Autoregressive Model, to dynamically measure China’s systemic financial risk. The network public opinion index is constructed and introduced into the financial risk early warning system to capture the dynamic impact of market sentiment on financial risks. After testing the nonlinear causal relationship between financial indicators based on the transfer entropy method, the Transformer deep learning model is applied to build a financial risk early warning system, and the performance is compared to traditional methods. The experimental results showed that (1) the trend of the systemic financial risk index based on the dynamic measurement of the TVP-FAVAR model fitted the actual situation well and that (2) the Transformer model public opinion index could fully and effectively mine the nonlinear relationship between data. Compared to traditional machine learning methods, the Transformer model has significant advantages in stronger prediction accuracy and generalization ability. This study provided a new technical path for financial risk early warning and has important reference value for improving the financial regulatory system. Full article