Search Results (929)

This paper examines Krivelj, a copper mining village in Serbia, as a critical yet overlooked node within global extractive networks. Despite supplying copper essential for renewable energy and sustainable architecture, Krivelj experiences severe ecological disruption, forced relocations, and socio-spatial destabilization, becoming a “sacrifice zone”—an area deliberately subjected to harm for broader economic interests. Employing a hybrid methodology that combines ethnographic fieldwork with Geographic Information Systems (GISs), this study spatializes narratives of extractive violence collected from residents through walking interviews, field sketches, and annotated aerial imagery. By integrating satellite data, legal documents, environmental sensors, and lived testimonies, it uncovers the concept of “slow violence,” where incremental harm occurs through bureaucratic neglect, ambient pollution, and legal ambiguity. Critiquing the abstraction of Planetary Urbanization theory, this research employs countertopography and forensic spatial analysis to propose a counter-cartographic framework that integrates geospatial analysis with local narratives. It demonstrates how global mining finance manifests locally through tangible experiences, such as respiratory illnesses and disrupted community relationships, emphasizing the potential of counter-cartography as a tool for visualizing and contesting systemic injustice. Full article

This paper presents a critical and comprehensive review of the application of mining waste, specifically waste rock and tailings, in asphalt pavements, with the aim of synthesizing performance outcomes and identifying key research gaps. A systematic literature search yielded a final dataset of 41 peer-reviewed articles for detailed analysis. Bibliometric analysis indicates a notable upward trend in annual publications, reflecting growing academic and practical interest in this field. Performance-based evaluations demonstrate that mining wastes, particularly iron and copper tailings, have the potential to enhance the high-temperature performance (i.e., rutting resistance) of asphalt binders and mixtures when utilized as fillers or aggregates. However, their effects on fatigue life, low-temperature cracking, and moisture susceptibility are inconsistent, largely influenced by the physicochemical properties and dosage of the specific waste material. Despite promising results, critical knowledge gaps remain, particularly in relation to long-term durability, comprehensive environmental and economic Life-Cycle Assessments (LCA), and the inherent variability of waste materials. This review underscores the substantial potential of mining wastes as sustainable alternatives to conventional pavement materials, while emphasizing the need for further multidisciplinary research to support their broader implementation. Full article

Spatial association analysis is essential for understanding interdependencies, spatial proximity, and distribution patterns within spatial data. The spatial scale is a key factor that significantly affects the result of spatial association mining. Traditional methods often rely on a fixed distance threshold (bandwidth) to define the scale effect, which can lead to scale sensitivity and discontinuity results. To address these limitations, this study introduces the Fuzzy Geographically Weighted Colocation Quotient (FGWCLQ) method. By integrating fuzzy theory, FGWCLQ replaces binary distance cutoffs with continuous membership functions, providing a more flexible and stable approach to spatial association mining. Using Point of Interest (POI) data from the Beijing urban area, FGWCLQ was applied to explore both intra- and inter-category spatial association patterns among star hotels, transportation facilities, and tourist attractions at different fuzzy neighborhoods. The results indicate that FGWCLQ can reliably discover global prevalent spatial associations among diverse facility types and visualize the spatial heterogeneity at various spatial scales. Compared to the deterministic GWCLQ method, FGWCLQ delivers more stable and robust results across varying spatial scales and generates more continuous association surfaces, which enable clear visualization of hierarchical clustering. Empirical findings provide valuable insights for optimizing the location of star hotels and supporting decision-making in urban planning. The method is available as an open-source Matlab package, providing a practical tool for diverse spatial association investigations. Full article

Autonomous drones may be called on to perform search-and-rescue operations in environments without access to signals from the global navigation satellite system (GNSS), such as underground mines, subterranean caverns, or confined tunnels. While technology to perform such missions has been demonstrated at events such as DARPA’s Subterranean (Sub-T) Challenge, the hardware deployed for these missions relies on heavy and expensive sensors, such as LiDAR, carried by costly mobile platforms, such as legged robots and heavy-lift multicopters, creating barriers for deployment and training with this technology for all but the wealthiest search-and-rescue organizations. To address this issue, we have developed a custom four-rotor aerial drone platform specifically built around low-cost low-weight sensors in order to minimize costs and maximize flight time for search-and-rescue operations in GNSS-denied environments. We document the various issues we encountered during the building and testing of the vehicle and how they were solved, for instance a novel redesign of the airframe to handle the aggressive yaw maneuvers commanded by the FUEL exploration framework running onboard the drone. The resulting system is successfully validated through a hardware autonomous flight experiment performed in an underground environment without access to GNSS signals. The contribution of the article is to share our experiences with other groups interested in low-cost search-and-rescue drones to help them advance their own programs. Full article

Mining waste presents significant environmental and public health risks due to the potential release of toxic substances when improperly managed. In this study, four tailings samples were taken to evaluate the environmental risks in the Ponce Enríquez mining area in Ecuador. Chemical characterization and X-ray Fluorescence Spectrometry (XRF) were used to analyze the content of potentially toxic elements (PTEs) of interest (As, Cd, Cr, Cu, Ni, Pb, and Zn), and X-ray Diffraction (XRD) for mineralogical characterization. The contamination index (IC) was calculated to assess the potential hazard associated with the content of PTEs in the mining wastes. To assess environmental risks, leaching tests were carried out to evaluate the potential release of PTEs, and Acid-Base Accounting (ABA) tests were conducted to determine the likelihood of acid mine drainage formation. The results revealed that the PETs concentration exceeded the maximum permissible limits in all samples, according to Ecuadorian regulations: As, Pb, and Cd were identified as critical contaminants. Mineralogically, quartz was the dominant phase, followed by carbonates (calcite, dolomite and magnesite), phyllosilicates (chlorite and illite), and minor amounts of pyrite and talc. The IC indicated high to very high contamination risk levels, with As being the predominant contributor. Although leaching tests met the established limits for non-hazardous mining waste, the ABA test showed that all samples had a high potential for long-term acid generation. These results underscore the need for implementing management strategies to mitigate the environmental impacts and the development of plans to protect local ecosystems and communities from the adverse effects of mining activities. Full article

To address the limitations of existing knowledge graph-based recommendation algorithms, including insufficient utilization of semantic information and inadequate modeling of user behavior motivations, we propose SKGRec, a novel recommendation model that integrates knowledge graph and semantic features. The model constructs a semantic interaction graph (USIG) of user behaviors and employs a self-attention mechanism and a ranked optimization loss function to mine user interactions in fine-grained semantic associations. A relationship-aware aggregation module is designed to dynamically integrate higher-order relational features in the knowledge graph through the attention scoring function. In addition, a multi-hop relational path inference mechanism is introduced to capture long-distance dependencies to improve the depth of user interest modeling. Experiments on the Amazon-Book and Last-FM datasets show that SKGRec significantly outperforms several state-of-the-art recommendation algorithms on the Recall@20 and NDCG@20 metrics. Comparison experiments validate the effectiveness of semantic analysis of user behavior and multi-hop path inference, while cold-start experiments further confirm the robustness of the model in sparse-data scenarios. This study provides a new optimization approach for knowledge graph and semantic-driven recommendation systems, enabling more accurate capture of user preferences and alleviating the problem of noise interference. Full article

Using graphs as a data structure is a simple way to represent relationships between objects. Consequently, it has raised the need for algorithms to process, analyze, and extract meaningful information from graphs. Therefore, frequent subgraph mining (FSM) algorithms have been reported in the literature to discover interesting, unexpected, and useful patterns in graph databases. Frequent subgraph mining involves discovering subgraphs that appear no less than a user-specified threshold; this can be performed exactly or approximately. Although several algorithms for mining frequent approximate subgraphs exist, mining this type of subgraph in graph collections has scarcely been addressed. Thus, we propose AGCM-SLV, an algorithm for mining frequent approximate subgraphs within a graph collection that allows structural and label variations. Unlike other FSM approaches, our proposed algorithm tracks subgraph occurrences and their structural dissimilarities, allowing user-defined partial similarities between node and edge labels, and captures frequent approximate subgraphs (patterns) that would otherwise be overlooked. Experiments on real-world datasets demonstrate that our algorithm identifies more patterns than the most similar state-of-the-art algorithm with a shorter runtime. We also present experiments in which we add white noise to the graph collection at different levels, revealing that over 99% of the patterns extracted without noise are preserved under noisy conditions, making the proposed algorithm noise-tolerant. Full article

The construction of transition rules is the core and difficulty faced by the cellular automata (CA) model. Dynamic mining of transition rules can more accurately simulate urban land use change. By introducing a graph attention network (GAT) to mine CA model transition rules, the temporal and spatial dynamics of the model are increased based on the construction of a real-time dynamic graph structure. At the same time, by adding an agent-based model (ABM) to the CA model, the simulation evolution of different human decision-making behaviors can be achieved. Based on this, an urban expansion scenario prediction (UESP) model has been proposed: (1) the UESP model employs a multi-head attention mechanism to dynamically capture high-order spatial dependencies, supporting the efficient processing of large-scale datasets with over 50,000 points of interest (POIs); (2) it incorporates the behaviors of agents such as residents, governments, and transportation systems to more realistically reflect human micro-level decision-making; and (3) by integrating macro-structural learning with micro-behavioral modeling, it effectively addresses the existing limitations in representing high-order spatial relationships and human decision-making processes in urban expansion simulations. Based on the policy context of the Outline of the Beijing–Tianjin–Hebei (BTH) Coordinated Development Plan, four development scenarios were designed to simulate construction land change by 2030. The results show that (1) the UESP model achieved an overall accuracy of 0.925, a Kappa coefficient of 0.878, and a FoM index of 0.048, outperforming traditional models, with the FoM being 3.5% higher; (2) through multi-scenario simulation prediction, it is found that under the scenario of ecological conservation and farmland protection, forest and grassland increase by 3142 km², and cultivated land increases by 896 km², with construction land showing a concentrated growth trend; and (3) the expansion of construction land will mainly occur at the expense of farmland, concentrated around Beijing, Tianjin, Tangshan, Shijiazhuang, and southern core cities in Hebei, forming a “core-driven, axis-extended, and cluster-expanded” spatial pattern. Full article

Sjögren’s Disease (SjD), Rheumatoid Arthritis (RA), and Systemic Lupus Erythematosus (SLE) are autoimmune diseases with overlapping genetic features, yet the etiologies of these diseases are poorly understood. Using these rheumatic diseases as an example of proof of concept, our aim was to develop a tool that simplifies analysis of gene–disease associations applicable to any disease and to perform comparisons. This tool is meant to provide insights into associated gene symbols and gene expression data to identify candidate biomarkers in common among these diseases. The Diseasesv2.0 and GTExv8 databases were utilized for data collection, providing searchable disease names, affiliated gene symbols, confidence scores (ranging from 0 to 5, with 5 being the most confident), and gene expression across the panel of 54 tissue types present in GTExv8. Data infrastructure was established on a Postgres database using Plotlyv5.17.0 and Streamlitv1.27.2 Python packages. The resulting database was used to investigate the genetic associations among SjD, RA, and SLE, including confidence scores from 2.50 to 5.00. STRINGv12 analysis determined significant pathways (FDR < 0.05). Analysis using our tool revealed the following refined gene associations for each disease: SjD based on ‘Sjogren’ search term (n = 12 genes), RA (n = 231 genes), and SLE (n = 137 genes). We found seven genes in common, namely, CD4, CD8A, IL6, IL17A, TNFS13B, TNF, and TRIM21. With the exception of IL17A, these genes were expressed in tissue types known or suggested to be affected by SjD. STRINGv12 determined significant KEGG pathways involving interleukin signaling, cytokine signaling, and the immune system. We developed a tool that simplifies the data mining process, allowing users to search for diseases of interest and view common gene associations and gene expression. Some of the genes identified through our tool may be further explored to better understand SjD pathogenesis and systemic impact. Full article

Amid the digital transformation of logistics and supply chains, Extended Reality (XR) technologies have emerged as promising tools for enhancing education and training. However, existing studies are fragmented, often limited to case-specific applications with minimal theoretical or longitudinal depth. This study conducts a systematic literature review of 1172 publications from 2009 to December 2024, using PRISMA protocols and VOSviewer-based text mining to identify trends and research gaps. A total of 59 peer-reviewed articles were selected for in-depth analysis based on relevance, methodological transparency, and educational scope. Five key themes emerged: immersive instructional innovation, XR-enabled safety training in high-risk logistics environments, simulation-based development of practical competencies, intelligent learning environments with personalized features, and competency alignment with Industry 4.0. These themes span higher education, vocational training, and community-based learning. A temporal analysis reveals a three-phase evolution: exploratory (2009–2013), applied implementation (2016–2020), and integrative innovation (2021–2024). Despite increasing interest, the field remains dominated by descriptive methods and lacks systematic evaluation frameworks. XR shows strong potential to bridge the theory–practice gap and support scalable, interdisciplinary education models. Future research should prioritize evidence-based frameworks and cross-contextual validation to support the effective adoption of XR in LSCM education. Full article

Remote sensing technology has significant technical advantages over traditional geological methods in geological mapping and mineral resource exploration, especially in high-altitude and steep topography areas. Geochemical sampling and geological mapping methods in these areas are difficult to use directly in mountainous regions such as West Kunlun. Therefore, in the face of Li-Be-Nb-Ta mineralization of the Dahongliutan rare-metal pegmatite deposit in West Kunlun, remote sensing has become an effective means to identify areas of interest for exploration in the early stage of the exploration campaigns. Several methods have been developed to detect pegmatites. Still, in this study, this methodology is based on spectral analysis to select bands of the ASTER and Landsat-8 OLI satellites, and methods, such as principal component analysis (PCA) and mixture tuned matched filtering (MTMF), to delineate the prospective areas of pegmatite. The results proved that PCA could map the hydrothermal alteration and structure information for pegmatites. To define new locations of interest for exploration, we introduced the spectra of spodumene-bearing pegmatites and tourmaline-bearing pegmatites as endmembers for the MTMF approach. The results indicate that the location of pegmatite areas on the ASTER and Landsat-8 OLI images overlaps with the ore deposits, and the location of potential ore-bearing pegmatites is delineated using remote sensing and geological sampling. Although this does not guarantee that all prospective areas have the mining value of ore-bearing pegmatites, it can provide basic data and technical references for early exploration of Li. Full article

The rapid development and deployment of artificial intelligence (AI) technologies have sparked intense public interest and debate. While these innovations promise to revolutionise various aspects of human life, it is crucial to understand the complex emotional responses they elicit from potential adopters and users. Such findings can offer crucial guidance for stakeholders involved in the development, implementation, and governance of AI technologies like OpenAI’s ChatGPT, a large language model (LLM) that garnered significant attention upon its release, enabling more informed decision-making regarding potential challenges and opportunities. While previous studies have employed data-driven approaches towards investigating public reactions to emerging technologies, they often relied on sentiment polarity analysis, which categorises responses as positive or negative. However, this binary approach fails to capture the nuanced emotional landscape surrounding technological adoption. This paper overcomes this limitation by presenting a comprehensive analysis for investigating the emotional landscape surrounding technology adoption by using the launch of ChatGPT as a case study. In particular, a large corpus of social media texts containing references to ChatGPT was compiled. Text mining techniques were applied to extract emotions, capturing a more nuanced and multifaceted representation of public reactions. This approach allows the identification of specific emotions such as excitement, fear, surprise, and frustration, providing deeper insights into user acceptance, integration, and potential adoption of the technology. By analysing this emotional landscape, we aim to provide a more comprehensive understanding of the factors influencing ChatGPT’s reception and potential long-term impact. Furthermore, we employ topic modelling to identify and extract the common themes discussed across the dataset. This additional layer of analysis allows us to understand the specific aspects of ChatGPT driving different emotional responses. By linking emotions to particular topics, we gain a more contextual understanding of public reaction, which can inform decision-making processes in the development, deployment, and regulation of AI technologies. Full article

Aiming at the existing problems in practical teaching in higher education, we construct an intelligent teaching recommendation model for a higher education practical discussion course based on naive Bayes machine learning and an improved k-NN data mining algorithm. Firstly, we establish the naive Bayes machine learning algorithm to achieve accurate classification of the students in the class and then implement student grouping based on this accurate classification. Then, relying on the student grouping, we use the matching features between the students’ interest vector and the practical topic vector to construct an intelligent teaching recommendation model based on an improved k-NN data mining algorithm, in which the optimal complete binary encoding tree for the discussion topic is modeled. Based on the encoding tree model, an improved k-NN algorithm recommendation model is established to match the student group interests and recommend discussion topics. The experimental results prove that our proposed recommendation algorithm (PRA) can accurately recommend discussion topics for different student groups, match the interests of each group to the greatest extent, and improve the students’ enthusiasm for participating in practical discussions. As for the control groups of the user-based collaborative filtering recommendation algorithm (UCFA) and the item-based collaborative filtering recommendation algorithm (ICFA), under the experimental conditions of the single dataset and multiple datasets, the PRA has higher accuracy, recall rate, precision, and F1 value than the UCFA and ICFA and has better recommendation performance and robustness. Full article

Advances in the field of asteroid dynamics continue to yield new knowledge regarding the behavior and characteristics of asteroids, allowing unprecedented levels of accuracy for predicting trajectories and contributing to impact avoidance strategies. Meanwhile, more detailed information regarding the physical composition of asteroids has reignited interest in asteroid mining as a potential new resource sector. This article considers some of the technical, ethical, legal and social issues facing global planetary defense efforts and off-world mining proposals. It considers issues such as claim jumping, weaponization of the space environment and ownership issues for resources extracted from space. Full article

Rare earth elements (REEs) are critical mineral resources that play a pivotal role in modern technology and industry. Currently, the global supply of light rare earth elements (LREEs) remains adequate. However, the supply of heavy rare earth elements (HREEs) is associated with substantial risks due to their limited availability. Ion-adsorption type rare earth deposits (IAREDs), which represent the predominant source of HREEs, have become a focal point for exploration activities, with a notable increase in global interest in recent years. This study systematically collected stream sediments and stream water samples from the Jiaping IARED in Guangxi, as well as from adjacent granitic and carbonate background areas, to investigate the exploration significance of geochemical surveys for IAREDs. Additionally, mineralized soil layers, non-mineralized soil layers, and bedrock samples from the weathering crust of the Jiaping deposit were analyzed. The results indicate that stream sediments originating from the Jiaping IARED and granite-hosted background regions display substantially elevated REE concentrations relative to those from carbonate-hosted background areas. Moreover, δEu values in stream sediments can serve as an effective indicator for differentiating weathering products derived from granitic and carbonate lithologies. Within the mining area, three coarse-grained fractions of stream sediments (i.e., +20 mesh, 20–60 mesh, and 60–150 mesh) exhibit REE concentrations comparable to those observed in both granite-hosted and carbonate-hosted background regions. However, the HREEs content in the finer -150-mesh stream sediments from Jiaping IARED is markedly higher than that in the two background regions. The (La/Sm)_N versus (La/Yb)_N ratios of -150-mesh stream sediments in the Jiaping IARED may reflect the mixing processes involving HREE-enriched ore layer, non-mineralized layer, and LREE-enriched ore layer. This observation implies that fine-grained (-150-mesh) stream sediments can partially inherit the REE characteristics of mineralized layers within IAREDs. Scanning electron microscopy (SEM) observations indicate that the enrichment of REEs in fine-grained stream sediments primarily originates from REE-rich accessory minerals derived from parent rocks and mineralized weathering crusts. A comparative analysis reveals that the concentrations of REEs in stream water collected during the rainy season are significantly higher than those collected during the dry season. Moreover, the levels of REEs, especially HREE, in stream water from the Jiaping IARED substantially exceed those in background areas. Collectively, these findings suggest that the geochemical signatures of REEs in rainy season stream water possess diagnostic potential for identifying IAREDs. In conclusion, the integrated application of geochemical surveys of stream water and -150-mesh stream sediments can effectively delineate exploration targets for IAREDs. Full article