Search Results (159)

This study evaluates the potential health risks posed by geogenic arsenic in environments suitable for leisure activities, such as walking, bathing, and playing, for adults and children alike, as well as in neighbouring agricultural areas. The study includes an analysis of environmental characteristics and the main stream originating in the adjacent mining area, with water and sediment samples taken. The study area is representative of other areas in the vicinity of the Mar Menor Lagoon, which is one of the largest and most biodiverse coastal lagoons in the Mediterranean Sea. The general characteristics of the soil and water were determined for this study, as was the concentration of As in the soil and water samples. A granulometric separation was carried out into four different fractions (<2 mm, <250 µm, <100 µm, and <65 µm). The mineralogical composition, total As content, and bioaccessible As content are analysed in each of these fractions. This provides data with which to calculate the danger of arsenic (As) to human health by ingestion and to contribute to As bioaccessibility studies and the role played by the mineralogical composition and particle size of soil ingestion. The conclusions rule out residential use of this environment, although they allow for eventual tourist use and traditional agricultural use of the surrounding soils. Full article

Mining and analyzing information from multiple sources—such as geophysics and geochemistry—is a key aspect of big data-driven mineral prediction. Clustering, which groups large datasets based on distance metrics, is an essential method in multidimensional data analysis. The Two-Step Clustering (TSC) approach offers advantages by handling both categorical and continuous variables and automatically determining the optimal number of clusters. In this study, we applied the TSC method to mineral prediction in the northeastern margin of the Jiaolai Basin by: (i) converting residual gravity and magnetic anomalies into categorical variables using Ward clustering; and (ii) transforming 13 stream sediment elements into independent continuous variables through factor analysis. The results showed that clustering is sensitive to categorical variables and performs better with fewer categories. When variables share similar distribution characteristics, consistency between geophysical discretization and geochemical boundaries also influences clustering results. In this study, the (3 × 4) and (4 × 4) combinations yielded optimal clustering results. Cluster 3 was identified as a favorable zone for gold deposits due to its moderate gravity, low magnetism, and the enrichment in F1 (Ni–Cu–Zn), F2 (W–Mo–Bi), and F3 (As–Sb), indicating a multi-stage, shallow, hydrothermal mineralization process. This study demonstrates the effectiveness of combining Ward clustering for variable transformation with TSC for the integrated analysis of categorical and numerical data, confirming its value in multi-source data research and its potential for further application. Full article

Previous studies documented the contribution of toxic elements (Hg, As, and Sb) from the dismissed Monte Amiata Mining District (Italy), the third largest Hg producer worldwide, to the Mediterranean Sea. Another highly toxic element, thallium (Tl), received less attention. Here we report a reconnaissance study of the spatial variability of Tl content in stream sediments across the Fiora River catchment, which drains part of the Hg and Sb mining districts. Thallium contents are comparatively low (≤0.4 mg/kg) in sediments of creeks directly draining the mining areas, whereas they increase up to 2 mg/kg in the catchment section that drains the Vulsini ultrapotassic volcanic province, where no known mineral deposits occur. Results suggest that Hg and Sb deposits cannot be the only Tl source in the catchment. The most likely alternative/additional candidate are the high-K volcanic rocks. Although no specific data for the Vulsini district exist, a distinct Tl geochemical anomaly linked to the Latium volcanic province is present. The total Tl mass contained in sediments discharged yearly into the Mediterranean Sea from the Fiora River is estimated in the order of 0.1 t. This reconnaissance study indicates a non-negligible potential release of Tl from the mining districts and volcanic catchments in Central Italy and suggests the opportunity of further investigation on Tl distribution and speciation in the area. Full article

Background/Objectives: Activities of Daily Living (ADLs) are crucial for assessing an individual’s autonomy, encompassing tasks such as eating, dressing, and moving around, among others. Predicting these activities is part of health monitoring, elderly care, and intelligent systems, improving quality of life, and facilitating early dependency detection, all of which are relevant components of personalized health and social care. However, the automatic classification of ADLs from sensor data remains challenging due to high variability in human behavior, sensor noise, and discrepancies in data acquisition protocols. These challenges limit the accuracy and applicability of existing solutions. This study details the modeling and evaluation of real-time ADL classification models based on batch learning (BL) and stream learning (SL) algorithms. Methods: The methodology followed is the Cross-Industry Standard Process for Data Mining (CRISP-DM). The models were trained with a comprehensive dataset integrating 23 ADL-centric datasets using accelerometers and gyroscopes data. The data were preprocessed by applying normalization and sampling rate unification techniques, and finally, relevant sensor locations on the body were selected. Results: After cleaning and debugging, a final dataset was generated, containing 238,990 samples, 56 activities, and 52 columns. The study compared models trained with BL and SL algorithms, evaluating their performance under various classification scenarios using accuracy, area under the curve (AUC), and F1-score metrics. Finally, a mobile application was developed to classify ADLs in real time (feeding data from a dataset). Conclusions: The outcome of this study can be used in various data science projects related to ADL and Human activity recognition (HAR), and due to the integration of diverse data sources, it is potentially useful to address bias and improve generalizability in Machine Learning models. The principal advantage of online learning algorithms is dynamically adapting to data changes, representing a significant advance in personal autonomy and health care monitoring. Full article

The paper presents a study of an intelligent system for personnel positioning, transport, and equipment monitoring in the mining industry using convolutional neural network (CNN) and OpenPose technology. The proposed framework operates through a three-stage pipeline: OpenPose-based skeleton extraction from surveillance video streams, capturing 18 key body joints at 30fps; multimodal feature fusion, combining skeletal key points and proximity sensor data to achieve environmental context awareness and obtain relevant feature values; and hierarchical pose alert, using attention-enhanced bidirectional LSTM (trained on 5000 annotated fall instances) for fall warning. The experiment conducted demonstrated that the combined use of the aforementioned technologies allows the system to determine the location and behavior of personnel, calculate the distance to hazardous areas in real time, and analyze personnel postures to identify possible risks such as falls or immobility. The system’s capacity to track the location of vehicles and equipment enhances operational efficiency, thereby mitigating the risk of accidents. Additionally, the system provides real-time alerts, identifying abnormal behavior, equipment malfunctions, and safety hazards, thus promoting enhanced mine management efficiency, improved safe working conditions, and a reduction in accidents. Full article

Aiming at the problems of poor image quality of traditional cameras and serious noise interference of event cameras under complex lighting conditions in coal mines, an event denoising algorithm fusing spatio-temporal information and a method of denoising event target pose estimation is proposed. The denoising algorithm constructs a spherical spatio-temporal neighborhood to enhance the spatio-temporal denseness and continuity of valid events, and combines event density and curvature to achieve event stream denoising. The attitude estimation framework adopts the noise reduction event and global optimal perspective-n-line (OPNL) methods to obtain the initial target attitude, and then establishes the event line correlation model through the robust estimation, and achieves the attitude tracking by minimizing the event line distance. The experimental results show that compared with the existing methods, the noise reduction algorithm proposed in this paper has a noise reduction rate of more than 99.26% on purely noisy data, and the event structure ratio (ESR) is improved by 47% and 5% on DVSNoise20 dataset and coal mine data, respectively. The maximum absolute trajectory error of the localization method is 2.365 cm, and the mean square error is reduced by 2.263% compared with the unfiltered event localization method. Full article

In the present study, major oxide, trace, and rare-earth element (REE) contents in the stream sediments of the Ikuno and surrounding areas of the central part of Hyogo Prefecture in the Kinki district in southwestern Japan were analyzed. Several abandoned mines that contain Au, Ag, Cu, Pb, Zn, Fe, W, and As exist in these areas, including the Ikuno and Akenobe mines, which are famous historical mines. A total of 156 stream sediments over approximately 1300 km² in these areas were analyzed using X-ray fluorescence (XRF) and inductively coupled plasma mass spectrometry (ICP-MS). The spatial distribution patterns of elemental concentrations in the stream sediments in the Ikuno area were determined by three primary factors: the surface geology, the localized deposition of ore minerals, and the influence of the sedimentation of heavy minerals in the basin on local distribution. The mean value of the spatial distributions of the ore deposits was greater than the median, primarily due to the presence of concentrated regions near the mining sites. A Kolmogorov–Smirnov test indicated abnormal distribution patterns of Pb, Zn, Cu, Cr, and Ni due to the presence of exceptionally high concentrations of these elements at the mine sites. The stream sediments showed higher levels of light REEs, mainly La, Ce, and Nd, in comparison with the heavy REEs. This pattern, deviating from the global abundance, suggests the dominating influence of mining sites on local REE distributions. These findings are essential for assessing the environmental impacts of historical mining and developing strategies for responsible resource management in the region. By understanding the geochemical signatures of mining-affected areas, these data could contribute to future environmental monitoring and mitigation efforts, enhancing our understanding of environmental sustainability and responsible resource utilization. Full article

K-means clustering is a fundamental tool in data mining, yet its scalability and efficacy decline when faced with massive datasets. In this work, we introduce BiModalClust, a novel clustering algorithm that leverages a bimodal optimization paradigm to overcome these challenges. Our approach simultaneously optimizes two interdependent modalities: the input data stream and the neighborhood structure of the solution landscape, which emerges from iterative restrictions of the Minimum Sum-of-Squares Clustering (MSSC) objective function to sampled subsets of the data. By integrating the Variable Neighborhood Search (VNS) metaheuristic, we systematically explore and refine these landscapes through dynamic reinitialization of degenerate centroids and adaptive exploration of expanding neighborhoods. This dual-stream optimization not only transforms traditional local search into a more global and robust process but also ensures computational scalability and precision. Extensive experimentation on diverse real-world datasets demonstrates that BiModalClust achieves superior clustering performance among K-means-based methods in big data environments. Full article

Acid mine drainage (AMD) generated during the exploitation and utilization of mineral resources poses a severe environmental problem globally within the mining industry. The Xiaomixi Stream in Ziyang County, Shaanxi Province, is a primary tributary of the Han River, which is surrounded by historically concentrated mining areas for stone coal and vanadium ores. Rainwater erosion of abandoned mine tunnels and waste rock piles has led to the leaching of acidic substances and heavy metals, which then enter the Haoping River and its tributaries through surface runoff. This results in acidic water, posing a significant threat to the water quality of the South-to-North Water Diversion Middle Route within the Han River basin. According to this study’s investigation, Xiaomixi’s acidic water exhibits yellow and white precipitates upstream and downstream of the river, respectively. These precipitates stem from the oxidation of iron-bearing minerals and aluminum-bearing minerals. The precipitation process is controlled by factors such as the pH and temperature, exhibiting seasonal variations. Taking the Xiaomixi Stream in Ziyang County, Shaanxi Province, as the study area, this paper conducts field investigations, systematic sampling of water bodies and river sediments, testing for iron and aluminum pollutants in water, and micro-area observations using field emission scanning electron microscopy (FESEM) on sediments, along with analyzing the iron and aluminum content. The deposition is analyzed using handheld X-ray fluorescence (XRF) analyzers, X-ray diffraction (XRD), and visible–near-infrared spectroscopy data, and a geochemical model is established using PHREEQC software. This paper summarizes the migration and transformation mechanisms of iron and aluminum pollutants in acidic water and proposes appropriate prevention and control measures. Full article

Traditional approaches to data mining are generally designed for small, centralized, and static datasets. However, when a dataset grows at an enormous rate, the algorithms become infeasible in terms of huge consumption of computational and I/O resources. Frequent itemset mining (FIM) is one of the key algorithms in data mining and finds applications in a variety of domains; however, traditional algorithms do face problems in efficiently processing large and dynamic datasets. This research introduces a distributed incremental approximation frequent itemset mining (DIAFM) algorithm that tackles the mentioned challenges using shard-based approximation within the MapReduce framework. DIAFM minimizes the computational overhead of a program by reducing dataset scans, bypassing exact support checks, and incorporating shard-level error thresholds for an appropriate trade-off between efficiency and accuracy. Extensive experiments have demonstrated that DIAFM reduces runtime by 40–60% compared to traditional methods with losses in accuracy within 1–5%, even for datasets over 500,000 transactions. Its incremental nature ensures that new data increments are handled efficiently without needing to reprocess the entire dataset, making it particularly suitable for real-time, large-scale applications such as transaction analysis and IoT data streams. These results demonstrate the scalability, robustness, and practical applicability of DIAFM and establish it as a competitive and efficient solution for mining frequent itemsets in distributed, dynamic environments. Full article

The SPEcies At Risk index for metals (${\mathrm{SPEAR}}_{metal}$ index) was refined using updated physiological sensitivity data and validated to assess the ecological impact of metal contamination on benthic macroinvertebrate communities in the upper Nakdong River, near a Zn smelter in Korea. Biosurvey and chemical monitoring data were collected at 18 sites surrounding the smelter and nearby mines. Acute ecotoxicity tests on 20 indigenous species from the Korean peninsula were conducted and used to update taxon-specific metal sensitivity data. The refined ${\mathrm{SPEAR}}_{metal}$ index, based on this updated sensitivity, was significantly lower than previous versions, with most values below the severe impact threshold (0.5) in the main stream. The correlation between hazard quotients in water and the SPEAR index improved, with the correlation coefficient increasing from 0.63 to 0.70. Despite consistently high benthic macroinvertebrate indices (BMIs) across the study area, generic ecological indices, such as total richness, EPT (Ephemeroptera, Plecoptera, and Trichoptera taxa richness), and Shannon’s diversity index, showed correlations with metal contamination levels. Principal component analysis identified the ${\mathrm{SPEAR}}_{metal}$ index as the primary indicator associated with metal contamination in both water and sediment. These findings highlight the improved performance of the refined ${\mathrm{SPEAR}}_{metal}$ index as a more sensitive and specific tool for assessing the ecological status of metal-impacted aquatic ecosystems compared to traditional indices. Full article

In recent years, recommender systems—which provide personalized recommendations by analyzing users’ historical behavior to infer their preferences—have become essential tools across various domains, including e-commerce, streaming media, and social platforms. Recommender systems play a crucial role in enhancing user experience by mining vast amounts of data to identify what is most relevant to users. Among these, deep learning-based recommender systems have demonstrated exceptional recommendation performance. However, these “black-box” systems lack reasonable explanations for their recommendation results, which reduces their impact and credibility. To address this situation, an effective strategy is to provide a personalized textual explanation along with the recommendation. This approach has received increasing attention from researchers because it can enhance users’ trust in recommender systems through intuitive explanations. In this context, our paper introduces a novel explainable recommendation model named GCLTE. This model integrates Graph Contrastive Learning with transformers within an Encoder–Decoder framework to perform rating prediction and reason generation simultaneously. In addition, we cleverly combine the neural network layer with the transformer using a straightforward information enhancement operation. Finally, our extensive experiments on three real-world datasets demonstrate the effectiveness of GCLTE in both recommendation and explanation. The experimental results show that our model outperforms the top existing models. Full article

High-silica adakites exhibit specific compositions, as follows: SiO₂ ≥ 56 wt.%, Al₂O₃ ≥ 15 wt.%, Y ≤ 18 ppm, Yb ≤ 1.9 ppm, K₂O/Na₂O ≥ 1, MgO < 3 wt.%, high Sr/Y (≥10), and La/Yb (>10). Devonian I-type adakitic granitoids in the northern Appalachians of New Brunswick (NB, Canada) share geochemical signatures of adakites elsewhere, i.e., SiO₂ ≥ 66.46 wt.%, Al₂O₃ > 15.47 wt.%, Y ≤ 22 ppm, Yb ≤ 2 ppm, K₂O/Na₂O > 1, MgO < 3 wt.%, Sr/Y ≥ 33 to 50, and La/Yb > 10. Remarkably, adakitic intrusions in NB, including the Blue Mountain Granodiorite Suite, Nicholas Denys, Sugar Loaf, Squaw Cap, North Dungarvan River, Magaguadavic Granite, Hampstead Granite, Tower Hill, Watson Brook Granodiorite, Rivière-Verte Porphyry, Eagle Lake Granite, Evandale Granodiorite, North Pole Stream Suite, and the McKenzie Gulch porphyry dykes all have associated Cu mineralization, similar to the Middle Devonian Cu porphyry intrusions in Mines Gaspé, Québec. Trace element data support the connection between adakite formation and slab break-off, a mechanism influencing fertility and generation of porphyry Cu systems. These adakitic rocks in NB are oxidized, and are relatively enriched in large ion lithophile elements, like Cs, Rb, Ba, and Pb, and depleted in some high field strength elements, like Y, Nb, Ta, P, and Ti; they also have Sr/Y ≥ 33 to 50, Nb/Y > 0.4, Ta/Yb > 0.3, La/Yb > 10, Ta/Yb > 0.3, Sm/Yb > 2.5, Gd/Yb > 2.0, Nb + Y < 60 ppm, and Ta + Yb < 6 ppm. These geochemical indicators point to failure of a subducting oceanic slab (slab rollback to slab break-off) in the terminal stages of subduction, as the generator of post-collisional granitoid magmatism. The break-off and separation of a dense subducted oceanic plate segment leads to upwelling asthenosphere, heat advection, and selective partial melting of the descending oceanic slab (adakite) and (or) suprasubduction zone lithospheric mantle. The resulting silica-rich adakitic magmas ascend through thickened mantle lithosphere, with minimal affect from the asthenosphere. The critical roles of transpression and transtension are highlighted in facilitating the ascent and emplacement of these fertile adakitic magmas in postsubduction zone settings. Full article

Turbidity, as a key indicator of water quality linked to underwater light attenuation, is crucial for evaluating water quality. Control in high-turbidity water environments plays a critical role in navigable rivers. For this purpose, our study proposed a framework for analyzing the spatio-temporal variation of turbidity and its driving factors in a navigable and turbid river using in situ measurement data, satellite data, socioeconomic data, a power index function model, and correlation analysis. The results show that the proposed model is feasible for quantitative turbidity monitoring of the Xitiaoxi River. Its upstream turbidity is lower than downstream, with seasonal averages for spring, summer, autumn, and winter of 93.9, 111.3, 113.5, and 120.9 NTU, respectively. Furthermore, the turbidity in the middle and lower reaches of the Xitiaoxi River continuously increased before 2005 and began to decline after 2005 due to the policy of mining moratorium. This trend is especially noticeable at monitoring points along the main stream of the Xitiaoxi River, such as downstream of the Xitiaoxi River (S1), Gangkou station (S2), middle reaches of the Xitiaoxi River (S4), Hengtangcun station (S6), upper stream of the Xitiaoxi River (S7), and Huxi River (S8). Mining and shipping have significantly contributed to the turbidity of the target river. This framework offers a practical approach for assessing the environmental impacts of both natural and anthropogenic factors, thereby providing valuable insights for river management practices. Full article

The development in the fields of clean energy, particularly wind and photovoltaic power, generates a large amount of data streams, and how to mine valuable information from these data to improve the efficiency of power generation has become a hot spot of current research. Traditional classification algorithms cannot cope with dynamically changing data streams, so data stream classification techniques are particularly important. The current data stream classification techniques mainly include decision trees, neural networks, Bayesian networks, and other methods, which have been applied to wind power and photovoltaic power data processing in existing research. However, the data drift problem is gradually highlighted due to the dynamic change in data, which significantly impacts the performance of classification algorithms. This paper reviews the latest research on data stream classification technology in wind power and photovoltaic applications. It provides a detailed introduction to the data drift problem in machine learning, which significantly affects algorithm performance. The discussion covers covariate drift, prior probability drift, and concept drift, analyzing their potential impact on the practical deployment of data stream classification methods in wind and photovoltaic power sectors. Finally, by analyzing examples for addressing data drift in energy-system data stream classification, the article highlights the future prospects of data drift research in this field and suggests areas for improvement. Combined with the systematic knowledge of data stream classification techniques and data drift handling presented, it offers valuable insights for future research. Full article