Search Results (3,956)

In recent years, the agentive role of youth groups in urban regeneration has garnered increasing academic attention. As vital carriers of urban cultural heritage, the ways in which historic districts can leverage youth dynamics to achieve vitality revitalization have emerged as a key research issue. This study selects the Fayuan Temple Historic and Cultural District in Beijing as an empirical case, constructs a public space evaluation system grounded in the concept of “youth-friendliness,” adopts a mixed-method approach integrating field surveys, questionnaire administration, and multi-source data mining, and employs Importance–Performance Analysis (IPA) modeling for deconstruction. Based on behavioral characteristic differences, youth within the district are categorized into two groups: “resident/local youth” and “transient visiting youth.” The comprehensive experience quality scores were 3.559 (Fair) for resident and local youth and 3.85 (Fair) for temporary visitors, with cultural space scoring highest for both groups. Resident youth demonstrate stronger demands for renewal concerning the completeness of cultural facilities, diversity of commercial formats, and street navigability. Transient visiting youth, by contrast, exhibit greater concern for eight factors: pedestrian safety and comfort, static traffic order, diversity of commercial formats, quality of consumption environment, richness of social venues, pleasantness of spatial scale, landscape interactivity, and street navigability. These findings indicate that youth groups with varying interactive relationships with historic districts possess significantly heterogeneous needs regarding public space utilization, and their expectations for historic district regeneration also manifest differentiated characteristics. This provides a scientific basis for formulating precise, multi-layered strategies for district renewal. Full article

AI digital-human livestreaming has become a key format of e-commerce livestreaming due to its low cost and round-the-clock operation. How the technical features of digital humans work together with consumers’ psychological perceptions to drive consumption is an important academic and practical question. Existing studies mostly adopt linear analytical paradigms. They focus on the anthropomorphic appearance features of digital humans. Few studies interpret the synergistic effects of technical–emotional factors from a configurational perspective. Empirical tests on boundary conditions under different platform ecosystems are also scarce. Drawing on the SOR–PAD theoretical framework, this study takes 18,690 livestream bullet-screen comments collected from 40 AI digital-human livestream rooms across Jingdong, Baidu, and Meituan as the research material. It conducts a mixed empirical analysis, combining text mining, machine learning, and fsQCA. The dependent variable, purchase behavior tendency, is obtained from an SVM model trained by matching bullet-screen comments with desensitized background transaction data. This indicator acts as a proxy measure for subjective purchase intention. The SVM model achieves an accuracy of 0.97, recall of 0.94, and an F1-score of 0.95. The findings are as follows. First, four technical features, including professionalism, simulation fidelity, responsiveness, and personalization, together with the three psychological perceptions of pleasure, arousal, and trust, constitute critical antecedents of high purchase behavior tendencies. Second, no single necessary condition can trigger a high purchase behavior tendency. Five equivalent technical–emotional configuration paths are identified, including response-oriented, pleasure-oriented, and multiple-synergy types. Third, the conversion effects of the configuration paths show clear platform boundaries. Different e-commerce formats match differentiated configuration paths. This study introduces configurational causal logic into the existing SOR–PAD framework. It expands the application boundary of this framework for AI digital-human livestreaming scenarios. It supplements the empirical evidence of consumer behavior under human–computer interactions. It also offers practical references for platforms and merchants to operate AI digital-human livestreams. Full article

This study investigates the impact of critical raw material mining and processing on surface water quality within a representative catchment area, using the Luda Yana River Basin in Southern Bulgaria as a case study. Water quality was evaluated using the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI), based on data collected from five monitoring stations. The analysis focused on key heavy metals, including Cu, Zn, Pb, Cd, Fe, Mn, Ni, and As, reflecting the influence of both active and historical mining activities in the region. Index values were calculated for the period 2008–2024 and revealed considerable temporal variability and pronounced spatial differences in water quality along the river course. This study provides one of the first long-term integrated assessments of heavy-metal-related water quality in a mining-impacted river basin in Bulgaria using the CCME WQI framework and offers new evidence on the cumulative effects of historical and ongoing mining activities on surface waters. The calculated CCME WQI values ranged from very low levels indicating poor conditions to moderate values corresponding to marginal and, occasionally, fair conditions. Overall, the predominant water quality categories were “poor” and “marginal.” The results demonstrate that the waters of the studied river basin remain below the thresholds for “fair” physicochemical status as defined by the European Water Framework Directive (2000/60/EC) and the corresponding Bulgarian legislation, including Regulation No. H-4/2012 on surface water characterization and the 2010 Ordinance on environmental quality standards for priority substances and certain pollutants. The findings highlight the persistent anthropogenic pressure exerted on the river system and emphasize the need for improved water management strategies. The study further underlines the importance of integrating environmental protection measures into the exploitation of critical raw materials in order to balance economic development with the sustainable management of water resources. Full article

Spontaneous combustion in fire-affected coal seams can degrade coal quality, alter rock mechanical parameters and reduce mining profitability. Traditional pit limit optimization methods ignore coal fire-induced quality degradation, ignore the coupling effect of economic fluctuation and slope stability, and lack quantitative optimization for fire-affected open-pit mines. Here, we optimize loss-reducing mining boundaries for the southern fire-affected highwall in the first mining district of the Dananhu No. 2 Mine, Hami, Xinjiang. The aim is to move beyond binary decisions that either sterilize or fully extract fire-affected reserves. We instead integrate economic return, slope stability and coal-price uncertainty into a single boundary-optimization framework. First, we established a three-dimensional Cartesian coordinate system for the study area. We then modeled and fitted the coal-seam roof and floor using MATLAB-based multiple integration, reducing edge errors in solid surfaces. Laboratory analyses of borehole coal samples defined how calorific value varied with advance distance. These data were used to derive the coal-quality curve. Net mining profit was then formulated as the objective function, replacing the conventional stripping-ratio criterion. Profit was calculated across advance distances to identify the economically optimal boundary. Mechanical parameters of thermally altered rocks were obtained from laboratory deformation tests. Rhino and FLAC3D 6.0 were then used to evaluate three-dimensional slope stability at critical locations. Coal-price perturbation scenarios were finally introduced to test the sensitivity of net profit and optimal advance distance. Under the baseline coal price, the slope remained stable at an advance distance of 193 m. At this boundary, net profit reached a maximum of RMB 676.608 million. The southern surface boundary contracted by 47 m relative to the initial boundary, reducing unnecessary land disturbance. Sensitivity analysis showed that lower coal prices sharply reduced both the optimal advance distance and maximum net profit. When coal price decreased by 30%, the optimal advance distance contracted to approximately 116.9 m. Maximum net profit fell to approximately RMB 248.239 million. Higher coal prices expanded the optimal boundary outward. Once coal price reached approximately 128.7 yuan/t, or 18.5% above baseline, the optimum reached the upper constraint of 240 m. Net profit then increased substantially with further price growth. These results provide a quantitative basis for dynamic boundary optimization and disturbance-reducing extraction in fire-affected open-pit coal mines. Full article

This study presents a screening-level multitemporal assessment of surface water quality in the Atrato River (Chocó, Colombia), a tropical river system heavily influenced by artisanal and illegal gold mining, elevated sediment loads, and untreated domestic wastewater. Using records from 20 monitoring stations (2020–2025) operated by the regional environmental authority (CODECHOCÓ), six water quality indices (ICA, ICOMO, ICOMI, ICOSUS, ICOMINERÍA, ICOTRO) were analyzed through a framework combining Theil–Sen trend estimation, Kendall’s tau correlation, inter-period median comparison, and an index orientation normalization procedure. Results suggest spatially heterogeneous patterns: organic contamination improved in 11 of 17 evaluable stations, while mining contamination (ICOMINERÍA) showed positive directional slopes in 14 of 17 evaluable stations. Of these, four middle-reach stations reached statistical significance (p < 0.05; Kendall’s τ = 0.618–0.667). At the network level, the mean ICOMINERÍA value increased from 0.191 in 2020 to 0.502 in 2025 (+163%), representing a directional signal that should be interpreted as screening-level evidence requiring confirmation through denser temporal sampling. The proposed framework provides support for potential applicability for detecting environmental change in data-scarce monitoring networks and provide screening-level evidence relevant to the enforcement monitoring of environmental rights granted under Colombia’s landmark Sentencia T-622 (2016). Full article

The rapid growth of organisational textual data has increased the value of Natural Language Processing (NLP) and semantic analytics for strategic decision support. However, many employees still lack the knowledge and skills needed to evaluate AI-generated information critically. This study develops an explainable Management Information Systems (MIS) framework that integrates NLP-based semantic analytics with PLS-SEM to examine the relationship between AI illiteracy and strategic decision quality. A convergent mixed-methods design with sequential analytical integration was used with a valid sample of 200 knowledge workers from public organisations in Saudi Arabia across six industries. The sample included employees from Saudi Arabia, Egypt, Jordan, Sudan, Syria, India, and the Philippines. Quantitative data were analysed using PLS-SEM, while textual data were analysed using Sentence-BERT, BERTopic, semantic network analysis, and Aspect-Based Sentiment Analysis. The results showed that higher AI illiteracy was negatively associated with strategic decision quality and positively associated with automation bias, uncritical trust in AI, and cognitive offloading. Digital proficiency and AI governance awareness weakened the negative association between AI illiteracy and decision quality, while functional-background differences were examined through multigroup analysis. The semantic analysis identified six themes: AI competency, decision trust, AI governance, decision support, organisational learning, and risk awareness. Sentiment analysis showed positive views of productivity and decision support, together with concerns about algorithmic bias, explainability, transparency, and AI governance. The study contributes an integrated human–AI decision vulnerability framework in which semantic evidence complements structural modelling and provides a clearer understanding of AI-related competency, reliance, governance, and decision-support issues. Full article

Bauxite mining causes severe soil degradation in the ecologically fragile karst region of Southwest China, yet targeted vegetation restoration schemes remain poorly developed. This study aimed to screen promising selected restoration species and preliminarily explore their soil improvement characteristics in karst bauxite mine wastelands through a field observational survey. We investigated six vegetation-soil conditions at 0–10 cm topsoil (two-year monocultures of Robinia pseudoacacia, Ligustrum lucidum, Zea mays, Miscanthus sinensis, topsoil from >15-year Z. mays with straw return, and unvegetated bare land), alongside paired 10–20 cm subsoil from the same long-term Z. mays stand as a depth-profile reference, and analyzed soil physicochemical properties, enzyme activities, and bacterial/fungal communities via high-throughput sequencing. Results showed that vegetation cover neutralized strongly acidic mine soil and significantly increased soil organic matter (SOM), total nitrogen (TN), and available nutrients compared with the bare control (p < 0.05). The long-term Z. mays cropland showed the strongest soil nutrient accumulation, with topsoil SOM, available phosphorus, and available potassium, increased by 428.6%, 250.0%, and 65.3%, respectively, relative to the bare control. Notably, the subsoil of long-term Z. mays also maintained high nutrient levels, but its absolute values are not directly comparable with topsoil treatments due to different sampling depths. All vegetation conditions elevated soil enzyme activities and microbial alpha diversity and significantly shifted bacterial and fungal community structure (beta diversity) compared with the control. Several bacterial phyla (including Chloroflexi and Proteobacteria) that have been associated with carbon-cycling functions in prior studies were relatively more abundant in revegetated soils; however, their functional roles in this system remain to be verified. Redundancy analysis identified SOM, TN, and available potassium as key environmental correlates of bacterial community variation. Overall, the long-term crop–straw return pattern shows comprehensive soil improvement effects, providing observational baseline data and reference for ecological restoration of karst bauxite mining areas in Southwest China. Full article

Underground coal mine safety monitoring relies on multi-source sensor networks, but abnormal-state detection remains challenging because methane, airflow, dust, and equipment-operation signals are non-stationary, heterogeneous, and constrained by ventilation and mining disturbances. This study proposes GasNet, an interpretable engineering-prior graph–temporal–spectral framework for precursor-related anomaly detection. Variables are organized into methane-related core sensors and environmental–operational modulation sensors. A directed sensor graph is constructed using ventilation causality, sensor deployment, and shearer-coupling relationships. GasNet then integrates a graph convolutional network for spatial–topological modeling, TimesNet for temporal–spectral pattern extraction, and cross-attention for adaptive feature fusion. An unsupervised reconstruction strategy identifies intervals deviating from learned normal production patterns. Field validation was conducted on the 31002 fully mechanized working face of the Xinyuan Coal Mine, where eight precursor-related abnormal intervals were annotated from monitoring data and field records. GasNet achieved a Precision of 0.881, a Recall of 1.000, an F1-score of 0.937, a false-alarm rate of 0.0017, and zero missed detections, with the highest F1-score among seven time-series baselines. Interpretability analysis further provided feature-fusion and sensor-time evidence for warning review. These results support the feasibility of GasNet for interpretable anomaly detection in the investigated working face. Full article

Background: Renal cell carcinomas (RCCs) represent neoplasms with variable biological behaviour, some of which remain difficult to classify within the current diagnostic framework. Clear cell papillary renal cell tumor (CCPRCT) is now recognised as an indolent entity, whereas RCC with fibromyomatous stroma (RCCFMS) remains a provisional subtype with partially overlapping morphological features. Methods: We analysed a multifocal RCC with clear cell morphology and prominent fibromyomatous stroma via whole-genome and RNA sequencing. The obtained molecular profile was compared with The Cancer Genome Atlas (TCGA) pan-cancer dataset, which includes 885 RCC cases, and histological re-evaluation of 10 identified similar cases was performed. Transcriptional data were mined for potential markers, which were validated in an independent cohort. Results: The 10 TCGA cases with similar transcriptomic features were characterised by diploid genomes, absence of recurrent chromosomal alterations, and lack of VHL mutations. Reduced VHL mRNA expression was observed, with increased methylation at selected CpG sites consistent with possible epigenetic down-regulation. Diagnostic variability was identified during histological re-evaluation of the 10 similar cases by three urological pathologists. Differential expression analysis highlighted cytokeratin 17 (CK17) and collagen 17A1 (COL17A1) as candidate markers. Immunohistochemical evaluation in a small (n = 6) independent CCPRCT cohort demonstrated expression of both markers, whereas tissue microarrays from 257 clear cell and 68 papillary RCC cases were found to be negative. Conclusions: These findings suggest that a subset of renal tumors with overlapping morphological features of CCPRCT and RCCFMS may share common molecular characteristics. CK17 and COL17A1 emerged as candidate markers for recognising these tumors, although their diagnostic sensitivity and specificity require validation across a broader spectrum of renal neoplasms. These observations are exploratory and hypothesis-generating, and further studies in large, well-characterised cohorts are required to clarify the biological and diagnostic significance of this subgroup. Full article

Classification is a fundamental technique in data mining that predicts categorical labels by analyzing input features. However, training Artificial Neural Networks (ANNs) using traditional methods often encounters challenges, such as getting stuck in local minima and slow convergence. To address these issues, this study proposes a novel hybrid model, IPA-ANN, which integrates the Immune Plasma Algorithm (IPA) to optimize the ANN’s connection weights and biases. The IPA, inspired by the immune plasma treatment process, utilizes a unique donor-receiver mechanism to balance exploration and exploitation in the search space. The proposed model was evaluated on nine benchmark datasets from the UCI repository and compared with 18 state-of-the-art metaheuristic algorithms, including Grey Wolf Optimization (GWO), Differential Evolution (DE), and Particle Swarm Optimization (PSO). Experimental results were analyzed using accuracy, F1-score, confusion matrices, and convergence graphs. The findings indicate that IPA-ANN achieves competitive and stable classification performance across different datasets while demonstrating favorable convergence characteristics in several cases. Furthermore, the study investigates the influence of donor–receiver parameters on the optimization process, highlighting the adaptability of the proposed framework. The reliability of these findings was further examined through repeated stratified 5-fold cross-validation and paired Wilcoxon signed-rank tests with Holm–Bonferroni correction on representative datasets, confirming that a subset of the observed performance differences are statistically significant, and through a computational cost analysis showing that IPA-ANN incurs no additional overhead relative to the majority of the compared algorithms. This study contributes to the literature by presenting the first documented application of IPA in ANN training and by providing a modular infrastructure for future metaheuristic-based ANN optimization studies. Full article

Surface deformation induced by underground coal mining is characterized by strong nonlinearity and spatial heterogeneity, which complicates early warning and ecological assessment. While InSAR-driven data assimilation models and remote sensing-based ecological indices are widely used for long-term monitoring, three fundamental limitations remain unresolved: (1) severe spatial imbalance in deformation samples biases data-driven models toward mean-reverting predictions, (2) recursive multi-step forecasting accumulates errors, leading to instability in long-horizon extrapolation, and (3) in ecological monitoring, vegetation resilience further induces a multi-year observation lag, resulting in a “pseudo-stable” bias in optical indicators. To address these issues, this study proposes an unified framework integrating multi-step deformation prediction and ecological time-lag analysis. Taking the Datong Coalfield as the study area, we utilized 231 Sentinel-1A images from March 2017 to December 2024 for SBAS-InSAR deformation inversion. A spatial stratified sampling strategy is used to extract 5894 representative points. A 24-step backward and 15-step forward windows were reconstructed to systematically compare six predictive models. Simultaneously, the Remote Sensing Ecological Index (RSEI) derived from Landsat data is used for cross-lagged analysis. The results demonstrate that: (1) The maximum deformation rate reached −276.75 mm/year, with cumulative subsidence exceeding −2000 mm. (2) At 3-step short-term forecasting, all models proved robust, with LSTM performing best (RMSE = 5.78 mm). At 15-step extreme extrapolation, however, traditional recursive models diverged significantly (Kalman, RMSE = 45.70 mm), whereas N-BEATS maintained stability and effectively mitigated temporal error cascades with an RMSE of 17.98 mm. (3) The core collapse zone exhibited concurrent ecological degradation (Lag 0), while the marginal basin presented a hidden degradation period of one to two years. It provides reliable scientific support for precise tracking and proactive safety management in complex mining areas. Full article

To address dimensional reduction for continuous single-label and multi-label data, this paper proposes an improved variable precision rough set method based on maximal consistent blocks. We formulate dimensional reduction as an attribute reduction problem in continuous decision information systems, construct a distance-based tolerance relation, and design a maximal consistent block generation algorithm based on pivoted Bron–Kerbosch maximal clique mining for direct continuous data modeling. We establish a generalized variable precision rough set model, define $\beta$-approximation sets and distribution reduction objectives for single- and multi-label scenarios, analyze the stage-wise complexity of the procedure, separating polynomial stages from output-sensitive enumeration stages, and develop a discernibility matrix-based reduction algorithm. Experiments on fourteen public benchmark datasets against seven baselines under Equal-d (fixed feature number) and Nested-d (training-partition tuning) protocols show that the proposed method attains the lowest average rank under Equal-d, where the Friedman test indicates overall differences among methods and Holm-adjusted Wilcoxon comparisons confirm significant advantages over MCLS and the neighborhood rough-set dependency baseline; under Nested-d, the comparison with MCLS remains significant after Holm adjustment. Parameter sensitivity analysis, distance metric comparison, ablation study, and a resource audit further confirm the robustness and feasibility of the method. Full article

To elucidate the mechanisms responsible for the low strength, pronounced variability, and long-term deformation of carnallitite, and to provide a basis for stope parameter design in deep potash mines, two carnallitite seams from a potash mine were investigated. Group C carnallitite and Group D halite-dominated rock salt were subjected to short-term compression tests and multiscale comparative analyses, while Groups A and B carnallitite specimens were tested under multistage creep loading. Particle Flow Code (PFC) simulations were conducted to evaluate the influence of particle size distribution. The results indicate the following: (1) The representative Group C specimens exhibited an average uniaxial compressive strength of 7.83 MPa, which was substantially lower than that of Group D. The acoustic emission (AE), scanning electron microscopy (SEM), and computed tomography (CT) analyses revealed greater heterogeneity in damage evolution and failure behavior, mainly associated with polymineralic composition, weak particle–matrix interfaces, local pores, and insufficient particle connectivity. (2) Particle-scale heterogeneity influenced the load-bearing capacity of carnallitite. In the PFC sensitivity analysis, narrowing the prescribed particle-size-distribution range from 0.4–8.0 mm to 4.0–4.0 mm at a mean particle size of 4.0 mm was associated with an increase in simulated strength from 7.82 to 10.40 MPa. Because quantitative contact-network descriptors were not extracted, the corresponding contact-network interpretation is treated as mechanistic rather than direct quantitative evidence. (3) The long-term uniaxial strengths of Groups A and B were estimated as 3.3 MPa and 4.8 MPa, respectively, using the adopted specific-failure-energy method. The modified Burgers model provided a good fit to the creep data within the tested stress levels, yielding coefficients of determination of 0.957 and 0.964 and root-mean-square error (RMSE) values of 0.0803 and 0.0552 percentage points. Based on the long-term strength constraints and the site-specific design assumptions adopted in this study, the calculated inter-room pillar widths were 6 m for Group A and 4 m for Group B. These findings provide insights into the multiscale damage mechanisms and long-term stability assessment of carnallitite stopes in deep potash mines. Full article

Digital transformation is reshaping the mining industry by improving resource efficiency and environmental performance. However, empirical evidence explaining how, why, and under which organizational and institutional conditions digitalisation enhances sustainable operational performance (SOP) in Sub-Saharan African mining remains limited, despite the region’s strategic role in global mineral supply. This study examines the effect of digitalisation on sustainable operational performance using longitudinal panel data from 48 mining companies operating in Sub-Saharan Africa between 2013 and 2022. Digitalisation is conceptualized as a multidimensional organizational capability and measured through a Digitalisation Index. The index was systematically derived from corporate annual environmental, social and governance reports using transparent coding procedures and Principal Component Analysis, enhancing measurement transparency and reproducibility. SOP is measured using a composite index encompassing operational efficiency, equipment utilization and maintenance effectiveness, resource utilization and environmental sustainability, and occupational health and safety. Fixed effects panel regression serves as the primary estimator, while the two-step System Generalized Method of Moments addresses endogeneity and dynamic persistence, with robustness analyses confirming result stability. The findings show that digitalisation significantly enhances sustainable operational performance by transforming digital resources into organizational capabilities that strengthen operational resilience, optimize resource allocation, and improve sustainability outcomes. By integrating the Resource-Based View, Dynamic Capabilities Theory, the TOE framework, and the Natural Resource-Based View into a unified explanatory framework, this study advances theory while providing practical guidance for digital capability development and Industry 4.0 investment and informing policies that strengthen digital infrastructure, institutional readiness, and regulatory support for sustainable mining in Sub-Saharan Africa. Full article

Background: Small-scale illegal gold mining, or “Galamsey”, is an important livelihood in Ghana but exposes workers to serious occupational health risks. This study aimed to explore the lived experiences, perceptions of health hazards, and coping strategies of workers from illegal small-scale gold mines in the Atwima Mponua District to inform context-specific interventions and policies. Methods: This qualitative study employed a phenomenological design to explore the occupational health experiences of workers from illegal small-scale gold mines in the Atwima Mponua District. A total of 16 purposively selected workers, ten men and six women, aged 27 to 44 years, with mining experience ranging from 3 to 12 years, working across roles including excavators, washers/panners, chemical processors, ore grinders, and support workers, were interviewed in depth, using a semi-structured interview guide. Thematic analysis was used to analyse the data according to Braun and Clarke’s model. Findings: There were four dominant themes: (1) normalization of risk and fatalistic attitudes about mining risks; (2) Respiratory Manifestations and Dust-Related Ailments; (3) chemical exposures and dermatological conditions; and (4) traumatic injuries from pit collapses, mining equipment accidents, and heavy physical labour. Participants demonstrated low levels of knowledge about long-term health outcomes and reported substantial obstacles to accessing healthcare, including fear of legal action, financial constraints, and geographic isolation. Conclusions: Workers from illegal small-scale gold mines in the Atwima Mponua District bear a serious, hidden occupational health burden normalized by poverty and criminalization. Urgent, comprehensive interventions are needed that address legal protection, economic alternatives, accessible healthcare, and harm reduction. Full article