Search Results (3,274)

Extensive coal mining activities can exert substantial negative impacts on surface ecosystems. Vegetation indices are widely recognized as effective indicators of land ecological conditions and provide valuable insights into long-term ecological changes in mining areas. In this study, the Zhaogu mining area of the Jiaozuo Coalfield was selected as the study site. Using the Google Earth Engine (GEE) platform, the Kernel Normalized Difference Vegetation Index (kNDVI) was constructed to generate a vegetation dataset covering the period from 2010 to 2024. The temporal dynamics and future trends of vegetation coverage were analyzed using Theil–Sen median trend analysis, the Mann–Kendall test, the Hurst index, and residual analysis. Furthermore, the relative contributions of climatic factors and human activities to vegetation changes were quantitatively assessed. The results indicate that: (1) vegetation coverage in the Zhaogu mining area exhibits an overall improving trend, affecting approximately 77.1% of the study area, while slight degradation is mainly concentrated in the southeastern region, accounting for about 15.2%; (2) vegetation dynamics are predominantly characterized by low and relatively low fluctuations, covering approximately 78.5% of the region, whereas areas with high fluctuations are limited and mainly distributed in zones with intensive mining activities; although the current vegetation trend is generally increasing, future projections suggest a potential decline in approximately 55.8% of the area; and (3) vegetation changes in the Zhaogu mining area are jointly influenced by climatic factors and human activities, with climatic factors promoting vegetation growth in approximately 70.6% of the study area, while human activities exert inhibitory effects in about 24.2%, particularly in regions affected by mining operations and urban expansion. Full article

Blasting operations associated with in-pit ramp construction in open-pit mines generate gaseous emissions originating from both explosive detonation and diesel-powered drilling and loading equipment. The research object of this study is the ramp construction process in an operating open-pit quarry, and the objective is to comparatively evaluate gaseous emissions across alternative blasting scenarios to support emission-aware operational decision-making. Five realistic blasting scenarios are assessed using a combined methodology that integrates laboratory fume index data for ANFO, emulsion explosives, and dynamite with diesel-emission estimates derived from non-road mobile machinery inventory factors. Laboratory detonation tests provide standardized upper-bound emission potentials for CO_x and NO_x, while drilling and loading emissions are quantified using a fuel-based inventory approach. The results show that the dominant contribution to total mass emissions arises from diesel combustion during drilling and loading, consistent with studies on real-world non-road mobile machinery inventory factors. Detonation fumes, although chemically concentrated and relevant for short-term exposure risk, represent a smaller share of the mass-based emission budget. Among the explosive types, bulk emulsions consistently exhibit lower toxic-gas emission indices than ANFO, attributable to their more uniform microstructure and a moderated reaction temperature. Dynamite demonstrates the lowest fume potential but is operationally less scalable for large open-pit patterns due to manual loading. Uncertainty analysis indicates that both laboratory-derived fume indices and diesel emission factors introduce systematic variability: laboratory tests tend to overestimate detonation fumes, while inventory-based diesel estimates may underestimate real-world NO_x and particulate emissions. Notwithstanding these limitations, the scenario-based framework developed here provides a robust basis for comparative evaluation of blasting strategies during ramp construction. The findings support increased use of emulsion explosives and emphasize the importance of moisture management, field-integrated gas monitoring, and improved characterization of diesel-equipment duty cycles. Full article

Maintaining the structural integrity of conveyor belts is essential for safe and reliable mining operations. However, these belts are susceptible to longitudinal tearing and surface degradation from material impact, fatigue, and deformation. Many computer vision-based inspection methods are inefficient and unreliable in harsh mining environments characterized by dust and variable lighting. This study introduces a smartphone-driven defect detection system for the cost-effective, geometric inspection of conveyor belt surfaces. Using Apple’s iPhone 12 Pro Max (Apple Inc., Cupertino, CA, USA), the system captures 3D point cloud data from a moving belt with induced damage via the integrated TrueDepth camera. A key innovation is a 3D-to-2D projection pipeline that converts point cloud data into structured representations compatible with standard 2D Convolutional Neural Networks (CNNs). We then propose a hybrid deep learning and machine learning model, where features extracted by pre-trained CNNs (VGG16, ResNet50, InceptionV3, Xception) are classified by ensemble methods (Random Forest, XGBoost, LightGBM). The proposed system achieves high detection accuracy exceeding 0.97 F1 score in the case of all proposed model implementations with TrueDepth F1 score over 0.05 higher than RGB approach. Applied cost-effective smartphone-based sensing platform proved to support near-real-time maintenance decisions. Laboratory results demonstrate the method’s reliability, with measurement errors for defect dimensions within 3 mm. This approach shows significant potential to improve conveyor belt management, reduce maintenance costs, and enhance operational safety. Full article

This study provides a systematic overview of Natural Language Processing (NLP)-based frameworks for Cyber Threat Intelligence (CTI) and the early prediction of cyberattacks in Industry 4.0. As digital transformation accelerates through the integration of IoT, SCADA, and cyber-physical systems, manufacturing environments face an expanding and complex cyber threat landscape. Following the PRISMA 2020 systematic review protocol, 80 peer-reviewed studies published between 2015 and 2025 were analyzed across IEEE Xplore, Scopus, and Web of Science to identify methods that employ NLP for CTI extraction, reasoning, and predictive modelling. The review finds that transformer-based architectures, knowledge graph reasoning, and social media mining are increasingly used to convert unstructured data into actionable intelligence, thereby enabling earlier detection and forecasting of cyber threats. Large Language Models (LLMs) demonstrate strong potential for anticipating attack sequences, while domain-specific models enhance industrial relevance. Persistent challenges include data scarcity, domain adaptation, explainability, and real-time scalability in operational-technology environments. The review concludes that NLP is reshaping Industry 4.0 cybersecurity from reactive defense toward predictive, adaptive, and intelligence-driven protection, and it highlights the need for interpretable, domain-specific, and resource-efficient frameworks to secure Industry 4.0 ecosystems. Full article

Green innovation is important for environmental sustainability and long-term ecological balance. Using 1129 observations of Chinese listed firms spanning 2014–2024, combined with text mining method to quantify data assets, this paper empirically examines the impact of customer data assets on suppliers’ green innovation. Our model is integrated with fixed effects for both industry and year. We find that there is a significant improvement in suppliers’ green innovation when customers have more data assets, with a one-notch improvement in the customer data assets of a customer firm. This results in an overall 0.06 increase in supplier green innovation output. Specifically, the spillover effect is more pronounced when there is a shorter geographic distance between suppliers and customers, as well as higher customer concentration. After conducting a variety of endogeneity tests, our results are robust. The mechanism analysis shows that customer data assets facilitate supplier digital transformation and improve supplier operational capacity. The heterogeneity analysis also reveals stronger effects when (1) customers are located in eastern regions, (2) customers belong to technology-intensive industries, (3) suppliers are state-owned enterprises (SOEs), and (4) suppliers face lower financial constraints. Further analysis suggests that customers with more data assets also increase suppliers’ R&D investment and improve green innovation quality. Our research contributes to understanding the spillover effect of customer data assets along the supply chain. Full article

Semi-autogenous (SAG) milling represents one of the most energy-intensive and variable stages of copper mineral processing. Traditional deterministic models often fail to capture the nonlinear dependencies and uncertainty inherent in industrial operations such as granulometry, solids percentage in the feeding or hardness. This work develops and validates a stochastic model based on Discrete Bayesian networks (BNs) to represent the causal relationships governing SAG Production and SAG Power under uncertainty or partial knowledge of explanatory variables. Discretization is adopted for methodological reasons as well as for operational relevance, since SAG plant decisions are typically made using threshold-based categories. Using operational data from a Chilean mining operation, the model fitted integrates expert-guided structure learning (Hill-Climbing with BDeu/BIC scores) and Bayesian parameter estimation with Dirichlet priors. Although validation indicators show high predictive performance (R² ≈ 0.85—0.90, RMSE < 0.5 bin, and micro-AUC ≈ 0.98), the primary purpose of the BN is not exact regression but explainable causal inference and probabilistic scenario evaluation. Sensitivity analysis identified water feed and solids percentage as key drivers of throughput (SAG Production), while rotational speed and pressure governed SAG Power behavior. The BN framework effectively balances accuracy and interpretability, offering an explainable probabilistic representation of SAG dynamics. These results demonstrate the potential of stochastic modeling to enhance process control and support uncertainty-aware decision making. Full article

Early detection of faults in artificial satellites is crucial for the success of missions, but it is hindered by the scarcity of data on faults and the limitations of traditional monitoring methods. As an alternative, technological trends such as data mining have emerged. This study presents a literature review to provide an in-depth examination of the landscape of data mining applications for early fault detection in satellites. Following the PRISMA protocol, the available scientific corpus from seven scientific databases was reviewed, and 52 primary studies were selected from an initial set of 2726 records published between 2011 and 2024. The results indicate that this is a rapidly expanding field, with an annual growth rate of 35.72%, characterized by a significant geopolitical concentration of research and funding led by China. From a methodological point of view, unsupervised approaches (~40%) predominate, a response to the lack of labeled in-flight data. However, supervised and hybrid models demonstrate superior performance, achieving F1 scores above 97% when selected or simulated data are available. A misalignment was identified in the domain, although research clearly favors the EPS due to the availability of data. Operational statistics, however, confirm that the ADCS system is the primary cause of mission failure. It is essential to note that the limited availability of public datasets and models, with less than 15% of studies providing access, is the main obstacle to reproducibility and progress. The primary conclusion of this work is that the field is expanding, and all stakeholders must contribute to its continued growth. Key actions include establishing public benchmarks that enable transparent evaluation, exploring physics-based models that account for uncertainty to address data scarcity, and concerted efforts to bridge the transfer gap from academic satellite operations to the real world. Full article

Addressing the challenges of online measurement of oil-gas-water three-phase flow under high gas–liquid ratio (GVF > 90%) conditions (fire-driven mining, gas injection mining, natural gas mining), which rely heavily on radioactive sources, this study proposes an integrated, radiation-source-free three-phase measurement scheme utilizing a “Venturi tube-microwave resonator”. Additionally, a physics-informed neural network (PINN) is introduced to predict the volumetric flow rate of oil-gas-water three-phase flow. Methodologically, the main features are the Venturi differential pressure signal ($\mathrm{\Delta }P$) and microwave resonance amplitude ($V$). A PINN model is constructed by embedding an improved L-M model, a cross-sectional water content model, and physical constraint equations into the loss function, thereby maintaining physical consistency and generalization ability under small sample sizes and across different operating conditions. Through experiments on oil-gas-water three-phase flow, the PINN model is compared with an artificial neural network (ANN) and a support vector machine (SVM). The results showed that under high gas–liquid ratio conditions (GVF > 90%), the relative errors (REL) of PINN in predicting the volumetric flow rates of oil, gas, and water were 0.1865, 0.0397, and 0.0619, respectively, which were better than ANN and SVM, and the output met physical constraints. The results indicate that under current laboratory conditions and working conditions, the PINN model has good performance in predicting the flow rate of oil-gas-water three-phase flow. However, in order to apply it to the field in the future, experiments with a wider range of working conditions and long-term stability testing should be conducted. This study provides a new technological solution for developing three-phase measurement and machine learning models that are radiation-free, real-time, and engineering-feasible. Full article

Exogenous fires in underground coal mines are characterized by low illumination, smoke occlusion, heavy dust loading and pseudo fire sources, which jointly degrade image quality and cause missed and false alarms in visual detection. To achieve accurate and real-time early warning under such conditions, this paper proposes a mine exogenous fire detection algorithm based on an improved YOLOv9m, termed PPL-YOLO-F-C. First, a lightweight PP-LCNet backbone is embedded into YOLOv9m to reduce the number of parameters and GFLOPs while maintaining multi-scale feature representation suitable for deployment on resource-constrained edge devices. Second, a Fully Connected Attention (FCAttention) module is introduced to perform fine-grained frequency–channel calibration, enhancing discriminative flame and smoke features and suppressing low-frequency background clutter and non-flame textures. Third, the original upsampling operators in the neck are replaced by the CARAFE content-aware dynamic upsampler to recover blurred flame contours and tenuous smoke edges and to strengthen small-object perception. In addition, an MPDIoU-based bounding-box regression loss is adopted to improve geometric sensitivity and localization accuracy for small fire spots. Experiments on a self-constructed mine fire image dataset comprising 3000 samples show that the proposed PPL-YOLO-F-C model achieves a precision of 97.36%, a recall of 84.91%, mAP@50 of 96.49% and mAP@50:95 of 76.6%, outperforming Faster R-CNN, YOLOv5m, YOLOv7 and YOLOv8m while using fewer parameters and lower computational cost. The results demonstrate that the proposed algorithm provides a robust and efficient solution for real-time exogenous fire detection and edge deployment in complex underground mine environments. Full article

As power systems expand and grow smarter, the safe and steady operation of substation equipment has become a prerequisite for grid reliability. In cluttered substation scenes, however, existing deep learning detectors still struggle with small targets, multi-scale feature fusion, and precise localization. To overcome these limitations, we introduce PBZGNet, a defect-detection network that couples a gradual parallel-branch backbone, a zoom-fusion neck, and a global channel-recalibration module. First, BiCoreNet is embedded in the feature extractor: dual-core parallel paths, reversible residual links, and channel recalibration cooperate to mine fault-sensitive cues. Second, cross-scale ZFusion and Concat-CBFuse are dynamically merged so that no scale loses information; a hierarchical composite feature pyramid is then formed, strengthening the representation of both complex objects and tiny flaws. Third, an attention-guided decoupled detection head (ADHead) refines responses to obscured and minute defect patterns. Finally, within the Generalized Focal Loss framework, a quality rating scheme suppresses background interference while distribution regression sharpens the localization of small targets. Across all scales, PBZGNet clearly outperforms YOLOv11. Its lightweight variant, PBZGNet-n, attains 83.9% mAP@50 with only 2.91 M parameters and 7.7 GFLOPs—9.3% above YOLOv11-n. The full PBZGNet surpasses the current best substation model, YOLO-SD, by 7.3% mAP@50, setting a new state of the art (SOTA). Full article

Following the persistent evolution of terrestrial 5G wireless systems, a new field of underwater communication has emerged for various related applications like environmental monitoring, underwater mining, and marine research. However, establishing reliable high-speed underwater networks remains notoriously difficult due to the severe RF attenuation in conductive seawater, which strictly limits range coverage. In this article, we focus on a comprehensive review of different antenna types for future underwater communication and sensing systems, evaluating their performance and suitability for Autonomous Underwater Vehicles (AUVs). We critically examine and compare distinct antenna technologies, including Magnetic Induction (MI) coils, electrically short dipoles, wideband traveling wave antennas, printed planar antennas, and novel magnetoelectric (ME) resonators. Specifically, these antennas are compared in terms of physical footprint, operating frequency, bandwidth, and realized gain, revealing the trade-offs between miniaturization and radiation efficiency. Our analysis aims to identify the benefits and weaknesses of the different antenna types while emphasizing the necessity of innovative antenna designs to overcome the fundamental propagation limits of the underwater channel. Full article

This study investigates the environmental and economic dynamics of sustainable aviation through the perspectives of senior executives in Türkiye’s civil aviation sector. As global aviation continues to face increasing pressure to decarbonize, understanding how industry leaders perceive and respond to carbon emission challenges is critical. The research employs a qualitative methodology based on semi-structured interviews with ten executives across airlines, airports, and aviation authorities. Using Python-based data mining techniques and thematic analysis, three core themes emerged: (1) sustainable aviation experience and economic dimensions; (2) carbon emissions reduction and efficient aviation systems; (3) sustainable energy and alternative fuel technologies. Findings reveal that while environmental sustainability is a growing concern, operational costs, technological constraints, and regulatory uncertainties significantly influence implementation. Stakeholders emphasized the importance of coordinated action among governments, industry, and international organizations, especially in scaling Sustainable Aviation Fuels (SAFs) and enhancing infrastructure for electric and hydrogen-powered aircraft. The study concludes that achieving net-zero aviation by 2050 requires an integrated approach that balances technological innovation, policy incentives, and stakeholder engagement. This multidimensional insight contributes to the ongoing discourse on low-carbon transition strategies in aviation, offering policy-relevant implications for developing countries. Full article

Lithium is an essential material for lightweight batteries. Traditional mining of soluble salts expanded to include the extraction of hard rocks, which requires their solubilization through roasting. Among hard lithium rocks, spodumene has recently received attention from the scientific community. Its metallurgical processing can be classified according to the type of reagents, as well as the operating temperature and pressure. The use of calcium carbonate as a natural alkali avoids aggressive chemicals such as sulfuric acid or caustic soda. In this article, 0.5 g of jewelry-grade spodumene was loaded into a ceramic crucible with 2.5 g of reducing agent in a tandem of roasting at 1050 °C-1 bar-30 min and leaching with neutral water at 90 °C-1 bar-20 min at a water/clinker mass ratio of 25. Measurements by XRD, ICP-OES, and SEM-EDX suggest a pathway of spodumene cracking because of poor contact with the reductant. Potassium present in the crucible acts as a flux and encapsulates spodumene crystals, causing lithium to end up bound to silica. While lithium metasilicate is barely soluble in water, leaching potassium aluminate hoards in the liquid. The empirical observations were supported with thermodynamic spontaneity studies, which required compiling the mineral properties based on open reference tabulations. Full article

Urban railway systems are critical for the daily lives of citizens in cities. Considering that urban railways are a core infrastructure, it is important for urban and railway practitioners to operate and maintain urban railway systems effectively and to maximize user satisfaction. However, despite the importance of this topic, research on the factors that contribute to high levels of railway user satisfaction in the context of Southeast Asian developing countries remains limited. To address this gap, this study conducted an exploratory case study using the Jakarta Mass Rapid Transit (MRT). This study collected 406 valid responses regarding Jakarta MRT user satisfaction through a face-to-face questionnaire survey and analyzed them using regression analysis, fuzzy-set qualitative comparative analysis (fsQCA), and text-mining techniques, which have seldom been applied in previous research on factors influencing railway user satisfaction. The results indicate that high levels of satisfaction with railway fares and social considerations—particularly the combination of both—may be the simplest configuration associated with higher overall user satisfaction, while various other combinations of satisfaction dimensions could also lead to elevated satisfaction. The results also suggest that all dimensions may serve as necessary and/or sufficient conditions for high satisfaction, implying the importance of considering all dimensions. These findings are specific to this case study and may differ depending on the socio-cultural contexts. To advance the understanding of satisfaction factors, further comparative research on the Jakarta MRT and rail systems in other countries is warranted. Full article

While BioTRIZ is widely employed in biomimetic design to facilitate creative ideation and standardize workflows, accurately formulating domain conflicts and assessing design schemes during critical stages—such as initial concept development and scheme evaluation—remains a significant challenge. To address these issues, this study proposes an advanced BioTRIZ method. Firstly, the theory of technological evolution is integrated into the domain conflict identification stage, resulting in the development of a prompt framework based on patent analysis to guide large language models (LLMs) in verifying the laws of technological evolution (LTE). Building on these insights, domain conflicts encountered throughout the design process are formulated, and inventive principles with heuristic value, alongside standardized biological knowledge, are derived to generate conceptual solutions. Subsequently, a main parameter of value (MPV) model is constructed through mining user review data, and the evaluation of conceptual designs is systematically performed via the integration of orthogonal design and the fuzzy analytic hierarchy process to identify the optimal combination of component solutions. The optimization case study of a floor scrubber, along with the corresponding experimental results, demonstrates the efficacy and advancement of the proposed method. This study aims to reduce the operational difficulty associated with implementing BioTRIZ in product development processes, while simultaneously enhancing its accuracy. Full article