Search Results (350)

Human activities such as intensive groundwater abstraction and mine dewatering can profoundly disrupt the natural hydrological functioning of karst aquifers. The Transdanubian karst aquifer in Hungary represents one of Central Europe’s most prominent examples, where decades of coal-mine dewatering lowered groundwater levels by more than 40 m and fundamentally altered the natural recharge–discharge regime. Understanding and forecasting recovery in such complex karst systems remain challenging because of heterogeneous conduit–fracture networks, strong climate sensitivity, incomplete monitoring records, and uncertainty in long-term predictions. This study presents an integrated end-to-end machine learning framework for groundwater characterization and climate-constrained probabilistic forecasting. Monthly groundwater-level records (1970–2026) from five monitoring wells were first reconstructed using a hybrid Moving Average–Random Forest gap-filling approach, achieving high reconstruction accuracy (R² = 0.87–0.98). Self-Organizing Maps subsequently identified four hydrogeological states representing the dewatering, transition, recovery, and near-equilibrium phases, while inter-well weight-plane correlations (>0.95) confirmed strong basin-scale hydraulic connectivity. A Bootstrapped Random Forest model forced by bias-corrected COSMO-CLM precipitation projections under the SSP2-4.5 climate scenario generated probabilistic groundwater forecasts through 2030, achieving high predictive performance (NSE > 0.80; RMSE = 0.10–0.35 m). Forecast results indicate that the basin as a whole is approaching hydraulic equilibrium by 2030, with distinct well-specific trajectories including mild steady decline and near-stable water level. The proposed framework provides a robust and transferable methodology for groundwater characterization and long-term forecasting in complex karst and fractured aquifer systems under changing climatic conditions. Full article

Large language models (LLMs) are increasingly relevant to Business Process Management (BPM), particularly when process knowledge is dispersed across documents, conversations, and other unstructured sources. Their probabilistic outputs, however, raise questions about validation, traceability, and accountability. This paper develops a lifecycle-based conceptual framework for allocating and governing LLM use across the six stages of the BPM lifecycle. The framework separates generative interpretation from formal, empirical, and expert validation. It comprises five interdependent layers and six operational principles, implemented through a stage-risk-validation matrix, a principle-intensity map, and four evaluation dimensions. Governance requirements increase as outputs approach live execution or decisions that are difficult to reverse, with controls aligned with the NIST AI Risk Management Framework, the EU AI Act, and the GDPR. A customer complaint-handling scenario demonstrates how the framework can be applied. An illustrative stress test using the BPI Challenge 2017 event log and ten independent LLM generations instantiates the validation layer under information-asymmetric conditions. Although all generated models were structurally valid, the event log revealed incomplete activity coverage and control-flow mismatch. This illustrates the value of an external referent but does not establish comparative performance or a general difference in error detectability between LLM-generated and process-mining artefacts. The framework therefore positions LLMs as tools for turning unstructured information into preliminary process knowledge, while established BPM methods and human expertise remain responsible for validating consequential outputs. Full article

The ubiquity of surveillance networks in public infrastructure presents a significant, yet underutilized, opportunity to assist vulnerable populations, particularly individuals who are blind or have low vision (BLV). However, transforming these passive video feeds into active guidance systems requires accurate camera calibration, a process that is traditionally labor-intensive and unscalable in large facilities. This paper introduces a novel, automated framework that leverages a mobile quadruped robot (Boston Dynamics Spot) as a dynamic calibration agent. We propose a single-pass approach with two planar calibration algorithms, which mines unstructured robotic trajectories to construct distinct geometric features on the ground plane. By synthesizing “virtual rectangles” from the robot’s odometry, our method first recovers camera focal length through vanishing point estimation by constructing virtual rectangles, and then solves for extrinsic 6-DoF pose using two algorithms: our proposed Virtual Rectangle (ViR) algorithm and a standard planar Perspective-n-Point (PnP) algorithm. Experimental validation using real-world data demonstrates the system’s robustness to sensor noise, maintaining focal length errors around $5\%$, rotational errors around $5^{°}$ and relative translation errors of approximately $10\%$, while synthetic simulations indicate focal length errors generally under 10%, rotational errors under ${1.5}^{°}$, and translation errors also around 10% despite heavy image and object disturbances. This work eliminates the need for manual calibration targets for dynamic camera calibration, effectively converting static security infrastructure into a metric sensing network capable of supporting high-fidelity social robotics applications. Full article

Mining and metallurgical wastes contain reactive Fe-, Al-, Ca-, Mg-, and Mn-bearing phases. These phases can be used for water treatment. Their performance depends on mineral accessibility, processing history, and water chemistry rather than waste identity alone. This review compares tailings, slags, red mud, and leaching residues for inorganic and organic contaminant removal, focusing on mechanisms, modification intensity, and validation. Cations are removed by ion exchange, surface complexation, and precipitation, whereas anion removal relies on Fe/Al sites, Ca-mediated mineral formation, and redox reactions. Organic treatment proceeds through adsorption or catalytic transformation and generally requires activation or additional functional phases. Red mud and blast-furnace slag showed the broadest applicability across both pollutant classes, although no unchanged formulation was validated for both. Under favorable conditions, waste-derived sorbents can achieve pollutant-removal efficiencies approaching 100% in synthetic solutions. However, their performance generally decreases in real wastewater and under continuous or cyclic operating conditions, with reported efficiency losses reaching approximately 25 percentage points. Deployment and machine-learning-assisted selection require application-matched processing, real-water and static/dynamic validation, and standardized reporting of separation, regeneration, cyclic stability, mass loss, secondary leaching, and spent-material fate. Full article

Tailings dam safety is a cornerstone of green and sustainable mine development. The instability of such facilities directly threatens the safety of surrounding ecosystems, human lives, and property, contravening the Sustainable Development Goals regarding clean water and terrestrial ecosystem protection. Rainfall is the primary external factor triggering tailings dam instability, yet the failure mechanisms may differ fundamentally depending on the temporal distribution of rainfall. Against the background of the Poyang Lake Basin, this study integrates observed rainfall data from 2020 to 2025 with a survey of 101 active tailings dams (of which Class IV dams account for 43.6% and upstream-method construction accounts for 88.1%), on the basis of which a representative Class IV upstream-method tailings dam is selected as the prototype. A coupled unsaturated seepage and stability numerical model is constructed, and two sets of rainfall scenarios are designed: one varying the rainfall peak position coefficient t and the other varying the rainfall duration. The results indicate that infiltration induced by short-duration intense rainfall is confined to shallow strata. The earlier the rainfall peak, the more fully the deep infiltration develops and the greater the factor of safety reduction, with the critical variable governing stability degradation being the length of time available for deep redistribution after the rainfall peak. Long-duration sustained rainfall drives an overall rise of the phreatic line, and the seepage response is dominated by the average rainfall intensity. Extending the duration cannot compensate for the attenuation of the infiltration driving force caused by the reduced rainfall intensity, and the core factor determining disaster risk is whether the average rainfall intensity can exceed the drainage threshold of the deep low-permeability layers. Within the Poyang Lake Basin, concentrated heavy continuous rainfall during the Meiyu period lasting 20 to 40 days represents the decisive working condition governing tailings dam stability and should be prioritized in flood-season safety supervision. The rainfall-driven instability mechanisms revealed in this study provide a theoretical basis for establishing differentiated monitoring and early-warning thresholds and for the sustainable operation of tailings dams. Full article

River sediments act not only as important sinks for heavy metal pollution in watersheds, but also as potential secondary sources under changing environmental conditions. Heavy metals can enter river systems through industrial wastewater discharge, agricultural non-point runoff, urban stormwater and sewage inputs, mining and smelting activities, and atmospheric deposition. During adsorption onto suspended particles, sedimentation, and resuspension, metals such as Cd, Pb, Cr, Cu, Zn, Ni, As, and Hg progressively accumulate in sediments. Because heavy metals are persistent, non-degradable, and bioaccumulative, contaminated sediments can record historical watershed pollution while also releasing metals back into overlying water under hydrodynamic disturbance, pH and redox fluctuations, organic matter mineralization, benthic bioturbation, and dredging activities, thereby threatening aquatic ecosystem stability and human health. Using a global methodological framework with particular emphasis on Chinese river basins, this review systematically summarizes key issues in the study of heavy metal pollution in river sediments, including spatial–temporal distribution and operationally defined fractionation, pollution levels and ecological risk assessment, source apportionment, and remediation and management technologies. Current evidence indicates that heavy metal contamination in river sediments exhibits pronounced spatial heterogeneity and watershed-specific characteristics. Its distribution is jointly controlled by geological background, land use patterns, source input intensity, hydrodynamic conditions, sediment particle size composition, and organic matter content. Methodologically, the field has evolved from single total concentration monitoring and exceedance-based evaluation toward integrated assessment systems that combine total concentrations, operationally defined fractionation, bioavailability, ecological risk, health risk, and source contribution. The joint use of BCR sequential extraction, the geoaccumulation index (Igeo), the pollution load index (PLI), the potential ecological risk index (RI), the risk assessment code (RAC), sediment quality guidelines (SQGs), receptor models, isotope tracing, and machine learning has substantially improved pollution identification, risk zoning, and source apportionment. Overall, research on heavy metal pollution in river sediments has shifted from descriptive judgments of whether contamination exists toward mechanistic and management-oriented questions concerning pollution sources, risk evolution, and remediation strategies. However, important gaps remain in compound pollution transformation mechanisms, regional background values and evaluation benchmarks, uncertainty in model parameters, long-term dynamic monitoring, and engineering-scale verification of remediation technologies. Future studies should strengthen multi-media, multi-scale, and long-term monitoring and further integrate fractionation analysis, toxicological effects, source apportionment models, and remediation technologies to provide a scientific basis for watershed ecological security and precision management of contaminated sediments. Full article

Long-term exploitation of ion-adsorption rare earth deposits has played a vital role in ensuring the supply of strategic mineral resources. However, intensive mining activities have also resulted in severe ecological degradation, including vegetation loss, land degradation, and soil erosion. Although the Remote Sensing Ecological Index has been widely used for ecological environment assessment, it inadequately characterizes land degradation in ion-adsorption rare earth mining areas, while its fixed-weight framework is unable to effectively capture the influence of localized ecological limiting factors. To address these limitations, this study selected a typical ion-adsorption rare earth mining area in southern Jiangxi, China, as the study area. A Desertification Difference Index was incorporated into the conventional RSEI framework to establish a five-dimensional evaluation system consisting of greenness, wetness, dryness, heat, and desertification. Furthermore, a Dynamic Variable-Weight Remote Sensing Ecological Index (DV-RSEI) was developed by integrating variable-weight theory, enabling adaptive adjustment of indicator weights according to local ecological conditions. Using Landsat imagery from 2000, 2005, 2010, 2016, 2020, and 2023, the spatiotemporal evolution and spatial heterogeneity of ecological environmental quality were systematically investigated. The results indicate that: (1) ecological environmental quality exhibited a characteristic evolution process of mining disturbance–ecological degradation–comprehensive restoration–ecological recovery during 2000–2023, with an overall trend dominated by stability and improvement; (2) ecological environmental quality showed significant spatial clustering, with High–High clusters mainly distributed in areas with favorable ecological conditions, whereas Low–Low clusters were concentrated in regions strongly affected by mining activities; and (3) compared with the conventional RSEI, the DV-RSEI better characterized mining-related ecological degradation patterns and the spatial heterogeneity of ecological environmental quality. The proposed approach provides a scientific basis for dynamic ecological monitoring, evaluation of ecological restoration effectiveness, and the construction of green mines in ion-adsorption rare earth mining areas. Full article

Accurate estimation of terrestrial water storage change (TWSC) remains challenging in regions where hydrological variability interacts with complex geological conditions and intensive human activities. Taking the Loess Plateau (LP) in the middle Yellow River region as a case study, this work integrates GLDAS simulations, GRACE observations, GNSS vertical-displacement records, a joint GNSS–GRACE inversion, and meteorological data for 2013–2024 to investigate regional TWS variability and model-dependent discrepancies. The results show that GLDAS, GRACE, GNSS, and the joint solution exhibit distinct temporal trends and spatial patterns. GRACE indicates a stronger long-term depletion signal, whereas GNSS-derived equivalent water height (EWH), which relies on the assumption of elastic surface loading, shows a weaker trend but stronger seasonal variability. This discrepancy suggests that GNSS-based inversion over the LP may be affected by non-elastic or non-loading deformation processes, such as wetting-induced loess collapse, aquifer compaction, mining-related subsidence, and other near-surface effects. In contrast, GRACE may include non-TWS mass redistribution associated with soil erosion and mineral exploitation. The joint solution is more consistent with the GLDAS-derived hydrological model benchmark than either single geodetic estimate, but this agreement should not be interpreted as direct proof of higher accuracy or complete removal of non-hydrological effects. Overall, this study highlights the need to diagnose model-dependent discrepancies, effective spatial resolution, and non-loading deformation when applying GRACE- and GNSS-based approaches to TWSC estimation in geologically and anthropogenically complex regions. Full article

Amidst the intensification of strategic mineral exploration, the discovery of concealed and deep-seated deposits within covered areas and the peripheries of existing mines has become a critical challenge and a central focus of contemporary research. “Dongchuan-type” (DCT) copper deposits represent a key mineralization style within the SW Yangtze Block. However, significant exploration breakthroughs have stagnated over the past three decades. Consequently, advanced exploration prediction research is urgently required to guide deep-resource evaluation. Using the Shizishan copper deposit in the Yimen area as a representative case, this study applies the theory of “metallogenic geological body prediction” to construct a tripartite model encompassing “metallogenic geological bodies, metallogenic structures and structural planes, and metallogenic characteristic markers”. The Shizishan deposit is classified as a sedimentary-reformed type, characterized by multi-episodic mineralization involving initial sedimentary processes, non-magmatic hydrothermal activity, and subsequent structural-magmatic hydrothermal overprinting. Key metallogenic geological bodies include ore-bearing stratigraphic associations, basin-margin faults, NE-trending fold–fault systems, and deep-seated concealed intrusions. Metallogenic structural planes comprise primary ore-controlling surfaces, secondary fold-related structures, and potential deep-seated magmatic intrusive interfaces. Alteration patterns display a distinct vertical zonation: shallower levels show medium-to-low temperature hydrothermal processes (silicification, carbonatization, sericitization, and chloritization), whereas deeper portions reveal high-temperature magmatic–hydrothermal indicators, specifically skarnization and intense silicification. This study proposes that the integration of “stratigraphy + structures + concealed intrusions” should serve as the primary criterion for target screening in deep exploration. By integrating geophysical and geochemical datasets into this model, three promising exploration targets were delineated within the Yimen area. These findings provide a refined theoretical framework for large-scale prospecting of DCT. Full article

Natural products remain a major source of structurally diverse and biologically active small molecules, yet traditional activity-guided discovery is labor-intensive and prone to rediscovery, while untargeted genome mining often lacks efficient prioritization criteria for biosynthetic gene clusters (BGCs). Self-resistance-gene guided discovery has emerged as a powerful strategy to address this limitation. In producing organisms, toxic metabolites are typically accompanied by genetically encoded self-protection mechanisms, such as resistant target homologs, duplicated housekeeping genes, detoxification enzymes, repair systems, or transporters. When co-localized with BGCs, these determinants serve as functional markers for predicting bioactivity and, in some cases, molecular targets prior to compound isolation. Over the past decade, this concept has evolved into a target-directed genome mining framework supported by tools and databases including ARTS, FunARTS, antiSMASH, and MIBiG. This review summarizes the biological basis, workflow, representative advances, and limitations of this strategy. Self-resistance genes can thus be viewed as functional beacons for accelerating bioactive natural product discovery. Full article

To investigate the rockburst failure characteristics and underlying mechanisms of deep straight-wall arch roadways containing structural planes, deep-mined limestone was selected as the rock material. True triaxial rockburst experiments were conducted on cubic limestone specimens containing a straight-wall arch roadway. A high-speed camera and an acoustic emission system were employed to monitor, in real-time, the initiation and evolution of the rockburst process. In addition, numerical simulations of straight-wall arch roadways containing structural planes with different spacings were carried out using the PFC software, and the failure patterns and rockburst evolution characteristics of surrounding rock with different structural-plane spacings were systematically analyzed. The results indicate that the presence of structural planes significantly alters the stress and energy transmission paths within the rock mass, leading to local stress concentration, enhanced rockburst impact intensity, and more complex microscopic morphologies of the ejected rock fragments. Compared with specimens without structural planes, specimens containing structural planes exhibited higher cumulative acoustic emission ring-down counts and cumulative absolute energy, accompanied by pronounced transient high-amplitude acoustic emission activity. Moreover, the proportion of shear failure in specimens containing structural planes was higher than that in intact specimens without structural planes. With increasing structural-plane spacing, the failure mode of the surrounding rock gradually changed, while the mutual constraint between the rock mass and the structural planes weakened. As the structural-plane spacing increased, the failure pattern of the surrounding rock changed, and the constraining effect of the rock mass on the structural planes gradually weakened. Consequently, crack propagation paths became increasingly oriented toward the free surface, resulting in a progressive decrease in the propagation angle of wing cracks. Based on the experimental data, a theoretical relationship was established between structural-plane spacing and the stress characteristic parameter of the straight-wall arch roadway, σ_i/σ_max. These findings can provide a useful reference for disaster prevention and mitigation, as well as rockburst prediction, in underground openings containing structural planes under impact disturbance. Full article

The coupled characterization of overburden movement, surrounding-rock failure evolution, and mine-pressure response remains difficult during high-intensity longwall mining because these processes are commonly measured and interpreted using separate data streams. In this study, a multi-source sensing and interpretation framework was established for the S8310 longwall face of Yangmei No. 1 Mine by integrating physical similarity simulation, underground monitoring, UDEC numerical modeling, region-of-interest (ROI) image-feature extraction, and sliding-window long short-term memory (LSTM) analysis. Fiber Bragg grating (FBG) sensors and conventional monitoring were used to obtain key-stratum deformation and support-pressure responses. A joint-state-based damage index (DI) was constructed from fixed-ROI UDEC outputs to quantify progressive structural activation beneath the key stratum. The results indicate that the overburden evolved from global bending and local crack initiation to fracture expansion, interface degradation, and interlayer slip. In the physical model, the initial weighting interval was approximately 37.5 cm, and the periodic weighting intervals were mainly 13.3–15.7 cm; these correspond to prototype-scale distances of approximately 37.5 m and 13.3–15.7 m, respectively. Peak abutment pressure occurred approximately 9–17 cm ahead of the face at the model scale, with a peak coefficient of 1.40–1.68. When the prototype advance distance increased from 37.5 m to 80.3 m, the ROI exhibited enhanced joint activation and a transition from local slip to continuous tensile opening and slip. For the pressure-sequence analysis, the 24-step-window LSTM produced lower errors than the 36- and 48-step-window LSTM models under the same preprocessing and training settings. Additional persistence, moving-average, and random forest baselines were added to clarify the prediction context: the persistence baseline performed strongly because of the high short-term autocorrelation of the pressure series, whereas the 24-step-window LSTM outperformed the moving-average and random forest baselines. Accordingly, the LSTM module is interpreted as an auxiliary temporal-pattern identification tool rather than as an exclusive optimal predictor. The comparison between DI evolution and pressure-prediction behavior suggests that rapid DI growth is mechanically consistent with stronger pressure-sequence non-stationarity and increased prediction deviation near active mine-pressure events. The proposed framework provides a sensor-oriented and physically interpretable approach for linking overburden failure evolution with mine-pressure response in longwall mining. Full article

Mining-induced subsidence represents one of the most significant long-term geomechanical hazards associated with underground coal exploitation, often continuing for decades after mining activities have ceased. Understanding the persistence and spatial distribution of post-mining ground deformation is essential for evaluating residual geological hazards and improving long-term monitoring strategies in former mining regions. This study investigates the long-term evolution of mining-induced subsidence in the Maleia sector of the Jiu Valley Coal Basin (Romania), an area historically affected by intensive underground coal extraction. A geodetic monitoring network consisting of seventeen permanent benchmarks, initially established in 2006, was reoccupied and remeasured using Global Navigation Satellite System (GNSS) technology in 2026. The comparative analysis was performed against historical measurements acquired during the 2007 monitoring campaign, providing a nineteen-year temporal framework for deformation assessment. Analysis of vertical displacements revealed persistent subsidence at all monitored benchmarks, confirming the continued post-mining adjustment of the geological structure. Measured cumulative vertical displacements ranged from −0.082 m to −3.853 m, with the highest deformation recorded at benchmark R14. The calculated average annual subsidence rates reached values of up to −0.203 m/year and are reported as normalized indicators of cumulative deformation over the nineteen-year observation interval. The results demonstrate that mining-induced geomechanical instability may persist for decades after underground mining has ceased, emphasizing the necessity of long-term monitoring strategies in former coal mining regions affected by residual geological hazards. This study provides one of the few long-term GNSS field datasets documenting delayed mining-induced subsidence over a nineteen-year observation period in an underground coal basin, contributing rare field evidence of persistent post-mining geomechanical evolution in Eastern Europe. Full article

This study employs the Kaya identity and Shapley value decomposition to analyse the main drivers of CO₂ emissions per capita in Australia and its states/territories from 2008–2009 to 2023–2024. This study extends the traditional three-factor Kaya model to a five-factor framework that explicitly incorporates renewable energy dynamics. Within this environment, we introduce an index for evaluating energy transition in each state over time, captured through the combined effects of renewable energy share and the fossil fuel-to-renewable ratio. The results indicate that energy intensity improvements are the dominant driver of emission reductions, contributing well over 100% of the total decline in most jurisdictions. However, this pattern does not hold universally: in the Northern Territory, energy intensity increased and contributed positively to emissions. In contrast, economic growth continues to exert upward pressure on emissions, partially offsetting these reductions and highlighting the ongoing challenge of decoupling. The Energy Transition Index (ETI) rose more than fivefold nationally (0.0018 to 0.0096), with Tasmania the highest among all states (0.185–0.291) and Western Australia and the Northern Territory the lowest. The Energy Transition Effect (ETE), defined as the natural logarithm of the ETI, follows the same regional pattern, while the Net Energy Transition Contribution (NETC) derived from the Shapley decomposition shows that the transition’s effect on emissions has been episodic rather than linear, turning negative in years when growth in fossil-fuel use outpaced renewable displacement. Marked regional variation is observed, with resource-intensive states such as Western Australia, Queensland, and the Northern Territory exhibiting distinct emissions patterns driven by mining and LNG activities, while more service-oriented states show more stable reductions. Full article

To investigate the quantitative relationship and dynamic response evolution between fault activation and coal pore morphology, this study employs coal samples from faults with varying throws to simulate the fault throw activation process. Taking the 120910 working face of the Longfeng Coal Mine in Guizhou as the study area, six groups of coal samples with gradient throws were collected. High-pressure mercury intrusion porosimetry (MIP), low-temperature nitrogen adsorption (LTNA), and fractal theory were integrated to systematically reveal the evolutionary characteristics of coal pores driven by fault activation. The results indicate a three-stage dynamic evolution characterized by distinct critical thresholds. (1) In the initial fracture development stage corresponding to fault throws not exceeding 1.2 m, fault activation promotes the propagation and coalescence of primary fractures, manifested as slight increases in specific surface area and pore volume. (2) As the throw increases to 1.2–1.6 m, corresponding to the strong compaction stage, intense compression triggers extensive collapse of pore throats ranging from 2 to 50 nm, resulting in a sharp 57.8% decline in specific surface area and the poorest pore connectivity; (3) When the throw exceeds 1.6 m and the process enters the secondary fracturing stage, the coal undergoes brittle failure, generating abundant ultrafine pores. This drives an exponential rebound in specific surface area and increases the micropore volume to 0.0069 m³/g, indicative of significant pore refinement. Collectively, this evolutionary process exhibits a three-stage dynamic pattern of “fracture development-strong compaction-secondary fracturing” with well-defined critical points. This study elucidates the pore structure evolution characteristics of coal during fault activation, providing guidance for safe gas drainage and coal and gas outburst prediction. Full article