Search Results (518)

This paper presents a sensitivity analysis of positioning accuracy in a localization system based on signal time-of-flight measurements, intended for operation in underground roadway workings. The underground environment is characterized by limited installation space, numerous obstacles causing multipath propagation, and the presence of sections with non-uniform geometry, which in practice leads to a “flattening” of the transmitter constellation and a deterioration of the conditioning of the trilateration problem. As a result, even small changes in input parameters (e.g., related to infrastructure geometry, distance-measurement quality, or the adopted model) may cause a significant change in the position-estimation error, thereby reducing the reliability of roadheader localization across the entire working area. In this study, a local sensitivity analysis is employed to identify the parameters that dominate the positioning outcome. Sensitivity coefficients are defined in a normalized form and are determined numerically using a perturbation approach (changing a given input parameter by a prescribed percentage), which avoids analytical differentiation of the complex relationships arising from the trilateration equations. The analysis is performed for a roadway scenario supported by an ŁP10 steel arch yielding support, with transmitters installed under the support arch and the roadheader trajectory represented by a sequence of consecutive position vectors. The obtained results allow the solution’s susceptibility to errors and uncertainties in the parameters to be assessed and indicate which parameters require priority control in practical implementation. On this basis, recommendations are formulated for the design and maintenance of the localization infrastructure, including transmitter placement and reconfiguration rules (relocation or adding an additional transmitter), to maintain stable positioning quality under operational mining conditions. Full article

Building safe and resilient cities is a key objective of China’s urbanisation and a prerequisite for high-quality development. This study assesses urban resilience in 73 mining cities from 2014 to 2023 using a composite index system (30 indicators) structured around robustness, resistance, and recovery. We integrate ARIMA-based forecasting, kernel density estimation, and Dagum Gini decomposition to characterise spatiotemporal dynamics and quantify regional inequality. Urban resilience increases steadily over the study period and can be characterised by three sequential stages, with further gains forecast for 2024–2030. Spatially, high-resilience cities shift from a dispersed pattern to belt-like and clustered agglomerations, consistent with an increasingly stratified centre–periphery structure. Inequality is driven primarily by between-region disparities: the East performs best, followed by the Central region, whereas the West and Northeast lag behind, revealing a pronounced gap between the Northeast and the East, alongside relatively convergent Central–West trajectories. These patterns are associated with interacting differences in location and market development, fiscal capacity and transition pathways, infrastructure endowment and ecological constraints, and institutional and demographic dynamics. The findings underscore the need for place-based regional coordination and targeted investments to strengthen recovery-related capacities. Full article

In a world of over 8.2 billion people, the land footprint of any infrastructure has become a critical factor in sustainable spatial planning. In the case of wind energy deployment, land use primarily involves hardstands, access roads, and interconnection infrastructure. This study focuses on Greece, a country with complex mountainous terrain, where Wind Power Stations are predominantly installed along ridgelines and slopes. Using GIS analysis based on digitization of actual on-site infrastructure, we measured the land coverage of wind energy facilities with a total installed capacity of nearly 2.6 GW. We found an average land-use intensity of 0.33 hectares per megawatt (ha/MW), placing it near the lower end of the range reported in international literature. For the subset of projects with available energy yield data, the value was 1.58 square meters per megawatt-hour (m²/MWh). This approach provides one of the largest, nationally representative, infrastructure-based estimates of actual wind energy land use in complex terrain. Applying these findings to the onshore wind deployment targets of Greece’s National Energy and Climate Plan (NECP) for 2030 and 2050, we estimate that only 0.02–0.03% of the country’s land area will be occupied by wind energy infrastructure. By comparison, lignite mining has already transformed approximately 0.13% of the national territory—almost four times more land than projected for wind energy use in 2050. Further spatial analysis was conducted to identify the land use categories associated with wind energy infrastructure, while for the subset of projects located within Natura 2000 protected areas, the types of affected habitats were also examined. Treating land coverage as a standalone proxy for environmental impact should be avoided; the study demonstrates the need for a context-sensitive interpretation of land use, accounting for ecological context, land-use compatibility, and positive co-benefits, such as improved forest accessibility, fire prevention works and recreation parks. Repowering maximizes land efficiency by extending wind farm lifetimes without expanding their footprint. Full article

Methane emissions from abandoned coal mining operations represent a persistent environmental and safety challenge in post-mining regions undergoing urban redevelopment. As urban infrastructure expands over former underground workings, the uncontrolled migration of mine gas can compromise public safety, exacerbate greenhouse gas emissions, and undermine sustainable development goals. This study investigates the origin of methane emissions detected in an urban area of the municipality of Lupeni, Romania, following the commissioning of a new natural gas distribution pipeline installed within a historically mined perimeter. The emissions had not been previously reported and were unexpectedly discovered during technical inspections conducted after the gas network installation, highlighting the absence of historical data on gas presence in this area. This is the first documented case of an accidental discovery of methane emissions in an urban perimeter overlying historical coal mine workings in Romania, granting this study a pioneering status, both scientifically and in terms of urban risk management. The findings emphasize that administrative mine closure does not equate to risk closure, as latent methane emissions may reactivate during urban transformations (e.g., excavations, utility upgrades, drainage changes). To ensure a scientifically sound and sustainable risk assessment, an integrated diagnostic framework was applied, combining chronological field monitoring with chromatographic gas composition analysis. This methodology enabled precise attribution of the methane source to abandoned underground mine workings, excluding the public gas network as a contributor. Based on this diagnosis, a controlled drainage and methane recovery system was implemented, resulting in the elimination of detectable concentrations at all monitoring points by February 2025. The captured methane was redirected for local energy use, transforming an environmental liability into a usable resource. This intervention supports circular economy principles and aligns with EU climate and energy transition goals. The proposed methodological framework provides a replicable model for identifying and managing residual mine gas in post-industrial urban environments. Although emission fluxes were not quantified, concentration-based screening enabled risk diagnosis, prioritization, and targeted intervention. These findings are relevant to EU Regulation (2024/1785) on methane emission reduction, emphasizing the need to include post-mining methane (AMM) in urban planning and environmental monitoring strategies. Limitations of the study include the absence of baseline data and the inability to calculate total methane flux. However, the results support immediate and practical risk mitigation and highlight the need for future work focused on long-term monitoring and emission quantification. This case provides critical insights for other post-mining cities in Central and Eastern Europe facing similar challenges at the intersection of legacy coal infrastructure and modern urban development. This study is designed as a concentration-based diagnostic and risk-oriented case study and does not aim to quantify methane emission fluxes. Full article

This article presents a systematic synthesis of variationally grounded approaches for the design and optimization of mining structural infrastructure. This study is motivated by the critical need to ensure stiffness, reliability, and operational availability under severe loading, mass constraints, and aggressive environmental conditions. Methodologically, the study situates structural modeling and synthesis within the continuity of the principle of stationary action. It demonstrates that, in the quasi-static regime, structural equilibrium is obtained as the stationarity of the total potential energy; consequently, the finite element method (FEM) arises naturally as a Ritz–Galerkin approximation of this underlying variational statement. On this basis, topology optimization is interpreted as a physics-constrained optimization problem wherein the design is posed as an outer optimality level acting over an energetically defined state. It is worth noting that SIMP-based formulations require explicit regularization to define the effective problem being solved. Emphasis is placed on the traceability between physical assumptions, discretization choices, regularization, and the resulting structural interpretations. The core contribution of this paper is a systematic literature review that consolidates evidence across variational mechanics, FEM-based optimization, and industrial applications, identifying recurrent methodological patterns and gaps that currently limit transfer to mining practice. Furthermore, a fully specified illustrative case is included to demonstrate reporting discipline and methodological consistency, rather than as a validation of a new optimization method. The conclusions highlight that a variational reading provides a coherent theoretical backbone for structural analysis, synthesis, simulation, and physics-based digital twins, while also clarifying the extensions required for industrial deployment, such as stability constraints, manufacturability, and multiphysics coupling within Mining 4.0 workflows. Full article

Rainbowfish of the genus Melanotaenia are highly endemic freshwater fishes found only in Australia and New Guinea. Although widespread, most species have narrow geographic ranges, making them particularly vulnerable to environmental change. Currently, 43 described (and many undescribed) Melanotaenia species occur in the Bird’s Head and Bird’s Neck region of Western New Guinea, 29 of which are currently classified as critically endangered, endangered, or vulnerable by the IUCN Red List, including two that may be extinct in the wild. We generated a high-spatial-resolution baseline land cover classification of rainbowfish habitats using low-cloud Planet Labs quarterly basemap mosaics and compared it with a moderate-resolution Landsat 8 OLI-derived classification to assess how spatial resolution influences land cover classification. Using the full 40-year Landsat archive, we quantified decadal land cover change around species type localities and identified localized disturbance events that may affect rainbowfish habitats. For species described from large rivers and lakes, changes in water-body extent over time were quantified. Deforestation varied widely, ranging from little or no detectable change in remote, difficult-to-access locations (e.g., M. misoolensis, M. sneideri), to landscapes heavily modified by logging, urbanization, mining, and agriculture (e.g., M. boesemani, M. arfakensis). Around the type localities, from the high-resolution imagery, we detected ~2939 ha of cleared land, whereas from the Landsat classification we identified only 31 ha of clearing, indicating that most of the fine-scale deforestation was not resolved at the Landsat scale. Time-sequence analyses indicate that over one-third of type localities experienced one or more localized disturbance events over the last 40 years. Land cover change in this region is highly dynamic and differs from commonly studied frontier deforestation patterns elsewhere. It also underscores a critical conservation challenge where rainbowfish species are being discovered in landscapes that are simultaneously undergoing rapid, spatially heterogeneous change. The same infrastructure that enables biological exploration also accelerates habitat modification. These changes threaten the persistence of highly endemic rainbowfish and underscore the value of multi-scale spatial and temporal remote sensing approaches for assessing habitat change in remote, biodiverse regions. The framework presented here is also broadly applicable to other narrowly distributed endemic taxa. Full article

The accelerated closure of hard coal mines across Europe contrasts with Poland’s continued structural reliance on coal extraction and coal-based power generation, increasing the urgency of credible post-mining development models. This article investigates the potential transformation of the end-of-life Bobrek coal mine in Bytom (Poland), drawing on methodological and business-model insights from the European Union (EU) Research Fund for Coal and Steel (RFCS) POTENTIALS and GreenJOBS projects. A combined methodological framework is applied, including structural analysis to identify key transformation variables, morphological analysis to explore alternative redevelopment pathways, and multicriteria assessment to configure coherent scenarios integrating renewable energy systems and circular-economy activities. The results show that an industrial eco-park backed by a virtual power plant (VPP), comprising photovoltaic installations, a mine-water-based geothermal heating system, and small-scale wind turbines, is technically feasible and environmentally sustainable. In parallel, three circular-economy business lines, the recycling of end-of-life photovoltaic panels, waste electrical and electronic equipment (WEEE), and refrigeration units, were assessed as possible economic cores of the envisaged eco-park. Overall, the proposed model enables effective reuse of mining infrastructure, supports low-emission industrial activity, and aligns with EU climate policy objectives. The Bobrek site may serve as a reference for post-mining redevelopment in other coal regions. Full article

As coastal ultra-deep mine shafts advance to greater depths, shaft lining concrete may experience sustained humid–hot conditions. Elevated temperature is induced by geothermal heat and early-age hydration heat, while high humidity is maintained in water-rich underground environments, which can compromise long-term performance. Such late-age deterioration may increase maintenance demand and pose safety concerns for ultra-deep shaft construction and long-term service. This study experimentally evaluates a high-strength shaft lining concrete designed with a composite cementitious system and cured at 40, 60, and 80 °C (95% RH) for 30–180 days, considering the engineering scenario of the 2500 m shaft at the Sanshan Island Gold Mine. The selected temperature range was determined based on in situ temperature monitoring in the target shaft. P-wave velocity measurements and uniaxial compression tests were conducted, while acoustic emission (AE) monitoring and energy evolution analysis were used to interpret damage progression. P-wave velocity decreased with curing temperature, with the most pronounced reduction at 80 °C. Compressive strength increased at early ages and then declined at later ages; by 180 d, the strength loss relative to the peak level is more pronounced at higher temperatures. AE results show four typical damage stages, with activity increasingly concentrated near peak stress as temperature and age increase. AF–RA analysis indicates tensile cracking dominates, with a slight increase in shear-related events at higher curing temperatures and longer ages. Energy analysis further confirms that most input energy is stored as elastic strain energy prior to peak stress, and higher curing temperatures increase the proportion of input energy stored elastically, implying a higher tendency toward brittle failure. These results suggest optimizing curing regimes and toughness-enhancement strategies for durable shaft infrastructure. Full article

The increasing depth and complexity of underground metal mining has raised ventilation energy demands and safety risks, driving the need for intelligent and more adaptive ventilation systems. Ventilation on Demand (VOD) systems dynamically adjust airflow using real-time operational and environmental data to improve energy efficiency while maintaining safety. Although VOD has been applied for over a decade, deeper and more extreme mining environments associated with critical minerals extraction introduce new challenges and opportunities. VOD systems rely on the tight integration of hardware, sensing, optimization-based control, and flexible infrastructure as mining operations evolve. The application of Artificial Intelligence (AI) introduces significant opportunities to further enhance and adapt VOD systems to these emerging challenges. This work presents a comprehensive review of the state of the art in AI integration within VOD technologies, covering sensing and prediction models, control strategies, and optimization frameworks aimed at improving energy efficiency, safety, and overall system performance. Findings show an increasing use of hybrid deep learning architectures, such as CNN-LSTM and Bi-LSTM, for forecasting, as well as AI-enabled optimization methods for sensor and actuator placement. Key research gaps include a reliance on narrow AI models, limited long-term predictive capabilities for maintenance and strategic planning, and a predominance of simulation-based validation over real-world field deployment. Future research directions include the integration of generative and generalized AI approaches, along with human–cyber–physical system (Human-CPS) designs, to enhance robustness and reliability under the uncertain and dynamic conditions characteristic of deep underground mining environments. Full article

Tailings dams represent one of the most environmentally sensitive infrastructures in the mining industry. To address the need for continuous and accurate monitoring, this paper presents an adaptive forecasting framework that combines Internet of Things (IoT) technologies with machine learning (ML) models to detect early signs of structural and ecological risks. The proposed system architecture is modular and scalable and enables the automated training, selection, and deployment of predictive models for multivariate sensor data. Each sensor data flow is independently analyzed by using a configurable set of algorithms (including linear, convolutional, recurrent, and residual models). The framework is deployed via containers with a CI/CD pipeline and includes real-time visualization through Grafana dashboards. A use case involving tiltmeters and piezometers in an operational tailing dam shows the system’s high predictive accuracy, with mean relative errors below $4\%$ across all variables (in fact, many of them have a mean relative error below $1\%$). These results highlight the potential of the proposed solution to improve structural and environmental safety in mining operations. Full article

Contamination of drinking water by potentially toxic elements (PTEs) remains a critical public-health concern in Uganda. This systematic review compiled and harmonized quantitative concentrations (mg/L) for key PTEs, lead (Pb), cadmium (Cd), arsenic (As), chromium (Cr), mercury (Hg), copper (Cu), zinc (Zn), nickel (Ni), cobalt (Co), manganese (Mn), and iron (Fe), across various potable and informal water sources used for drinking, including municipal tap water, boreholes, protected and unprotected springs, wells, rainwater, packaged drinking water, rivers, lakes, and wetlands. A comprehensive search of different databases and key institutional repositories yielded 715 records; after screening and eligibility assessment, 161 studies met the inclusion criteria, and were retained for final synthesis. Reported PTE concentrations frequently exceeded WHO and UNBS drinking water guidelines, with Pb up to 8.2 mg/L, Cd up to 1.4 mg/L, As up to 25.2 mg/L, Cr up to 148 mg/L, Fe up to 67.3 mg/L, and Mn up to 3.75 mg/L, particularly in high-risk zones such as Rwakaiha Wetland, Kasese mining affected catchments, and Kampala’s urban springs and drainage corridors. These hotspots are largely influenced by mining activities, industrial discharges, agricultural runoff, and corrosion of aging water distribution infrastructure, while natural geological conditions contribute to elevated background Fe and Mn in several regions. The review highlights associated health implications, including neurological damage, renal impairment, and cancer risks from chronic exposure, and identifies gaps in regulatory enforcement and routine monitoring. It concludes with practical recommendations, including stricter effluent control, expansion of low-cost adsorption and filtration options at household and community level, and targeted upgrades to water-treatment and distribution systems to promote safe-water access and support Uganda’s progress toward Sustainable Development Goal 6. Full article

Ground subsidence in mined-out areas has irreversible impacts on residents’ lives and infrastructure, making its monitoring and prediction crucial for ensuring safety, protecting the ecological environment, and promoting sustainable development. This study employed the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to process Sentinel-1A satellite images of Liaoyuan’s Northern New District from August 2022 to March 2025, deriving ground deformation data. The SBAS-InSAR results were validated using unmanned aerial vehicle (UAV) measurements. Monitoring revealed deformation rates ranging from −26.80 mm/year (subsidence) to 13.12 mm/year (uplift) in the area, with a maximum cumulative subsidence of 59.59 mm observed near the Xi’an Sixth District. Based on spatiotemporal patterns, most mining-induced subsidence in the study area is in its late stage, primarily caused by progressive compaction of fractured rock masses and voids within the collapse and fracture zones. Using subsidence data from August 2022 to March 2024, three prediction models—LSTM, GRU, and TCN-GRU—were trained and subsequently applied to forecast subsidence from March 2024 to August 2025. Comparisons between the predictions and SBAS-InSAR measurements showed that all models achieved high accuracy. Among them, the TCN-GRU model yielded predictions closest to the actual values, with a correlation coefficient exceeding 0.95, validating its potential for application in time-series settlement monitoring. Full article

With rapid urbanization, urban renewal has become increasingly important. Traditional research has relied on expert assessments and objective indicators, lacking scalable frameworks that effectively translate street-level conditions into actionable renewal strategies. This study proposes a Vision–Language Model (VLM)-based framework to address these gaps, using the Hongshan Central District of Urumqi, China, as a case study. Specifically, we collected 4215 street-view images (SVIs) and employed VLMs to assess six perceptual dimensions (i.e., safety, liveliness, beauty, wealthiness, depressiveness, and boringness), together with textual descriptions. The best-performing model, selected by a 500-respondent perception survey validation, was used to conduct spatial pattern and text mining analyses to inform targeted urban renewal strategies. Results show that (1) VLMs have a high consistency with humans in evaluating the spatial perception of six dimensions; (2) spatial clustering analysis successfully delineated four distinct renewal priority tiers, confirming the method’s capability in translating perceptual data into actionable spatial strategies; and (3) textual mining of the VLM’s rationales revealed that areas with lower perceptual scores are predominantly characterized by deficiencies in foundational infrastructure and street-level order, thereby providing explanatory evidence directly linked to the generated renewal priorities. This study provides a generative artificial intelligence (GAI)-driven and interpretable evaluation framework for urban renewal decision-making, facilitating precision-oriented and intelligent urban regeneration. Full article

High-entropy alloys (HEAs) present a fundamental design paradox: their exceptional properties arise from complex, high-dimensional composition–process–microstructure–property (CPMP) relationships, yet the knowledge needed to navigate this space is fragmented across a vast and unstructured literature. Large language models (LLMs) offer a transformative interface to this complexity. By extracting structured facts from text, they can convert dispersed and heterogeneous evidence (i.e., findings scattered across many studies and reported with inconsistent test protocols or characterization standards) into queryable knowledge graphs. Through code generation and tool composition, they can automate simulation pipelines, surrogate model construction, and inverse design workflows. This review analyzes how LLMs can augment key stages of HEA research—from intelligent literature mining and multimodal data integration (using LLMs to automatically extract and structure data from texts and to combine information across text, images, and other data sources) to model-driven design and closed-loop experimentation—illustrated by emerging case studies. We propose concrete evaluation protocols that measure direct scientific utility, including knowledge-graph completeness, workflow setup efficiency, and experimental validation hit rates. We also confront practical limitations: data sparsity and noise, model hallucination, domain bias (where models may exhibit superior predictive performance for specific, well-represented alloy systems over others due to imbalances in training data), and the imperative for reproducible infrastructure. We argue that domain-specialized LLMs, embedded within grounded, verifiable research systems, can not only accelerate HEA discovery but also standardize the representation, sharing, and reuse of community knowledge. Full article

Increasing the permissible travel speed of suspended monorails in underground mines improves the efficiency and profitability of hard coal mining. However, increasing the maximum speed requires addressing a number of issues affecting the safety of the crew and the mine infrastructure. The concept of a new emergency braking device presented in this article is intended to protect against excessive temperature increases on friction surfaces during braking. The article presents the results of preliminary numerical simulations, the purpose of which was to calculate the temperature of a wet multi-plate brake, its propagation, and verify the condition for not exceeding the maximum permissible temperature of external surfaces in contact with a potentially explosive atmosphere. Full article