Search Results (22,746)

Generalized aquifers are widely used in various fields, such as groundwater use, mine water prevention and control, and geothermal energy. This paper presents a transformer-model-based automatic aquifer generalization method using borehole logs in scenarios with scarce experimental parameters. Relying only on basic borehole data, the method used an agent-assisted approach to extract and clean key lithological and coordinate information, which was then fused using a dual embedding mechanism. The model leveraged multi-head self-attention to calculate attention weights between the target stratum and its adjacent strata, capturing the potential contextual correlations in aquifer potential across strata. The resulting deep feature vectors from the transformer’s encoder were fed into a classification head to predict aquifer potential labels. Evaluation results demonstrated a model accuracy of 0.86, significantly outperforming the random classification baseline in precision, recall, the F1-score, and the kappa coefficient. Full article

Food drying is a widely used preservation technique; however, achieving high energy efficiency while maintaining product quality remains a significant challenge. This study aims to analyze comprehensive experimental data obtained during the hot-air drying process of the Paşa pear (regional pear) and the system’s autonomous control structure using an explainable artificial intelligence (XAI)-based method. The intelligent drying system, operating for approximately 17.5 h under two temperatures (50 °C and 65 °C) and two air speeds (0.63 m/s and 1.03 m/s), continuously adjusted the temperature and air speed using a PLC-based control mechanism; it ensured stable control throughout the process by monitoring parameters such as product weight, moisture, inlet–outlet temperatures, and air speed in real time. Experimental results showed that drying performance varied significantly with operating conditions, with product mass decreasing from 450 g to 103 g. The innovative aspect of the study is that it obtained quantitative, interpretable rules without discretization by applying the oscillatory chaotic sunflower optimization algorithm (OCSFO) to multidimensional control and process data for the first time. Thanks to its chaotic search mechanism, OCSFO accurately analyzed complex drying dynamics and created rules that achieved over 90% success for high, medium, and low performance classes. The obtained explainable rules clearly demonstrate that drying temperature and air velocity are the dominant determining parameters for drying efficiency, while energy consumption and cabin temperature distribution play a supporting role in distinguishing between efficiency classes. These rules clearly demonstrate how changes in controlled temperature and air velocity, combined with product weight and heat transfer, affect drying performance. Thus, the study offers a robust framework that identifies critical factors affecting drying performance through a transparent artificial intelligence approach that leverages both the autonomous control system and XAI-based rule mining. Full article

Grouting technology can be employed to repair cracks in an aquifer to maintain its stability; however, existing grouting materials tend to come with problems such as low flexural strength, poor cracking resistance, and the coupled effects of fiber reinforcement and sulfoaluminate cement (SAC) addition on hydrate evolution, and pore-refinement and crack-resistance mechanisms in coal-based solid-waste cementitious grouts remain insufficiently understood. In this paper, fiber-modified coal-based solid-waste grouting (F-CWG) materials were prepared by mixing different contents of sulfoaluminate cement (SAC) and different fibers. The mechanical strength, microstructure, hydration products, and pore evolution characteristics were analyzed by means of mechanical property tests, energy-dispersive X-ray spectroscopy (SEM/EDS), X-ray diffraction (XRD), thermogravimetric analysis (TGA), and nuclear magnetic resonance (NMR). The results show that the mechanical strength decreases at first due to insufficient early-stage hydration products. Specifically, the 28 d compressive and flexural strengths decrease from 15.34 MPa and 4.55 MPa at 0% SAC to 8.18 MPa and 2.99 MPa at 40% SAC but increase again to 13.36 MPa and 3.79 MPa at 60% SAC as the formation of ettringite (AFt) and C–S–H is promoted with higher SAC content. Among the tested fibers, a dosage of 0.6% generally improves mechanical strength and refines pore structure, with PVA and steel fibers showing the most pronounced effects. Our results reveal the mechanism behind the enhancement of cracking resistance in F-CWG materials, providing a scientific basis for grouting and water-preservation mining, and are of great significance in improving the utilization rate of coal-based solid waste. Full article

Antimicrobial resistant (AMR) infections are a persistent public health issue causing excess death and economic impacts globally. Because AMR in clinical settings is often acquired from nonpathogenic bacteria that surround us, environmental surveillance must be better characterized. It has been well established that metals can co-select for bacterial AMR. Furthermore, recent studies have shown that compromised microbial community diversity may lead to community invasion by antibiotic resistance genes (ARGs). Widespread legacy mining has led to acid mine drainage and metal contamination of waterways and sediments throughout the western United States, potentially compromising microbial community diversity while simultaneously selecting for AMR bacteria. Our study objectives were to survey metal contaminated sediments from the Bonita Peak Mining District (BPMD) in southwestern Colorado, USA, compared to sites downstream in Durango, CO for bacterial and ARG diversity. Sediment bacteria were characterized using 16S rRNA Ilumina and metagenomic sequencing. We found that overall, bacterial diversity was lower in metal-contaminated, acidic sites (p = 0.04). Metagenomic sequencing revealed 31 different ARGs, with those encoding for efflux pumps (mex and spe gene families) substantially more prevalent in the BPMD sites, elucidating a specific AMR marker fingerprint from the high metal concentration sediments. Raising awareness and providing antimicrobial tracking techniques to resource limited communities could help provide information needed for better antibiotic use recommendations and environmental monitoring. Full article

Artificial intelligence technology holds significant importance for building intelligent question-answering systems in the field of coal mine safety and enhancing safety management levels. Currently, there is a lack of specialized large language models and high-quality question-answering datasets in this field. To address this, this study proposes a two-stage fine-tuning method based on Low-Rank Adaptation (LoRA) and Group Sequence Policy Optimization (GSPO) for training a question-answering model tailored to the coal mine safety domain. The research begins by constructing a dedicated question-answering dataset based on domain-specific regulatory documents. Subsequently, using Qwen2.5-7B Instruct as the base model, the study fine-tunes the model through supervised learning with LoRA technology, followed by further optimization of the model’s performance using the GSPO reinforcement learning algorithm. Experiments show that the model trained with this method exhibits significant improvements in coal mine safety-related tasks, achieving superior results on multiple automated evaluation metrics compared to contrast models of similar scale. This study validates the effectiveness of the two-stage fine-tuning method in adapting large language models (LLMs) to specific domains, providing a new technical approach for the intelligentization of coal mine safety. It should be noted that due to the lack of external data, this study relies on a self-constructed dataset and has not yet undergone external independent validation, which constitutes the main limitation of the current work. Full article

This study evaluates the chemical profile and antifungal efficacy of essential oils from Piper glabratum, Piper friedrichsthalii, and Piper cumanense against the cocoa pathogens Moniliophthora roreri and Phytophthora palmivora. Microwave-assisted hydrodistillation followed by GC-MS analysis identified 80 constituents, predominantly monoterpenes and sesquiterpenes, which exhibited significant mycelial inhibition comparable to commercial fungicides. Beyond basic characterization, a comprehensive chemoinformatic analysis was conducted to elucidate the molecular mechanisms driving this bioactivity. The computed physicochemical landscape reveals a dominant lipophilic profile (average LogP 3.4) and low polarity (TPSA 11.5 Ų), characteristics essential for effective fungal membrane penetration. Structural mining identified conserved benzene and cyclohexene scaffolds alongside specific 1,3-benzodioxole moieties, while Maximum Common Substructure (MCS) analysis uncovered high similarity clusters among phenylpropanoids and sesquiterpenes. These findings suggest a synergistic mode of action where conserved structural backbones and interchangeable diastereomers facilitate membrane destabilization and ion leakage. Consequently, the integrative chemoinformatic profiling elucidates the molecular basis of this efficacy, positioning these Piper essential oils not merely as empirical alternatives, but as sources of rationally defined synergistic scaffolds for next-generation sustainable fungicides. Full article

Roadways driven along the floor of thick coal seams, while retaining the top coal, form “thick coal seam floor roadways.” These large-section roadways feature a composite coal-rock roof and weak coal ribs, leading to low overall strength and poor stability of the surrounding rock. Significant deformation and “necking” often occur, accompanied by roof falls and rib spalling, which are exacerbated under high stress or adverse geology, threatening mine safety and production. In this study, the 2201 haulage gateway in Yingpanhao Coal Mine is investigated to address surrounding rock control in such deep roadways. Using field investigation, theoretical analysis, numerical simulation, and similar simulation tests, the failure mechanisms of ribs and roofs are analyzed. Rib failure is characterized by tensile fracture in the shallow zone, splitting failure in the medium-depth zone, and incomplete conjugate shear in the deep zone. Corresponding mechanical models are established, and a method for calculating total rib failure depth—combining tensile/splitting and shear failure depths—is proposed, along with a bolt length design formula. Based on this, a synergistic roof-and-rib support technology is developed. The failure mechanism and optimal support scheme are validated through simulation tests and successfully applied in the field, demonstrating satisfactory performance. The findings provide a valuable reference for support design in similar mining roadways. Full article

This paper presents a scalable and low-cost Retrieval Augmented Generation (RAG) architecture designed to enhance learning in university-level courses, with a particular focus on supporting students from economically disadvantaged backgrounds. Recent advances in large language models (LLMs) have demonstrated considerable potential in educational contexts; however, their adoption is often limited by computational costs and the need for stable broadband access, issues that disproportionately affect low-income learners. To address this challenge, we propose a lightweight, mobile, and friendly RAG system that integrates the LLaMA language model with the Milvus vector database, enabling efficient on device retrieval and context-grounded generation using only modest hardware resources. The system was implemented in a university-level Data Mining course and evaluated over four semesters using a quasi-experimental design with randomized assignment to experimental and control groups. Students in the experimental group had voluntary access to the RAG assistant, while the control group followed the same instructional schedule without exposure to the tool. The results show statistically significant improvements in academic performance for the experimental group, with p < 0.01 in the first semester and p < 0.001 in the subsequent three semesters. Effect sizes, measured using Hedges g to account for small cohort sizes, increased from 0.56 (moderate) to 1.52 (extremely large), demonstrating a clear and growing pedagogical impact over time. Qualitative feedback further indicates increased learner autonomy, confidence, and engagement. These findings highlight the potential of mobile RAG architectures to deliver equitable, high-quality AI support to students regardless of socioeconomic status. The proposed solution offers a practical engineering pathway for institutions seeking inclusive, scalable, and resource-efficient approaches to AI-enhanced education. Full article

Recycling water offers a powerful way to lower the environmental water impact of mining activities. Pressure-retarded osmosis (PRO) represents a promising pathway for simultaneous water reuse and clean energy generation from salinity gradients. In this study, the performance of a thin-film nanocomposite (TFN) membrane containing functionalized multi-walled carbon nanotubes (fMWCNTs) within a polyacrylonitrile (PAN) support layer, followed by polydopamine (PDA) surface modification, was investigated under a PRO operation using pretreated gold mining wastewater as the feed solution. Unlike most previous studies that rely on synthetic feeds, this work evaluates the membrane performance under a PRO operation using a real mining wastewater stream. The membrane with fMWCNTs and PDA exhibited a maximum power density of 25.22 W/m² at 12 bar, representing performance improvements of 23% and 68% compared with the pristine thin-film composite (TFC) and commercial cellulose triacetate (CTA) membranes, respectively. A high water flux of 75.6 L·m⁻²·h⁻¹ was also obtained, attributed to enhanced membrane hydrophilicity and reduced internal concentration polarization. The optimized membrane, containing 0.3 wt% fMWCNTs in the support layer and a PDA coating on the active layer, produced a synergistic enhancement in the PRO performance, resulting in a lower reverse salt flux and an improved flux–selectivity trade-off. Furthermore, the ultrafiltration (UF) and nanofiltration (NF) pretreatment effectively reduced the hardness and ionic content, enabling a stable PRO operation with real mining wastewater over a longer period of time. Overall, this study demonstrates the feasibility of achieving both reusable water and enhanced osmotic power generation using modified TFN membranes under realistic mining wastewater conditions. Full article

An inorganic–organic hybrid nano-adsorbent was prepared by chemical immobilisation of an organic Schiff base Cu (II) ion receptor, DHB ((E)-N-(1-(2-hydroxy-6-methyl-4-oxo-4H-pyran-3-yl) ethylidene) benzohydrazide), a selective dehydroacetic acid-based chemosensor, onto a mesoporous silica support. In order to prepare the sorbent, the silylating agent was anchored onto the silica. During this procedure, 3-Chloropropyl trimethoxy silane (CPTS) was attached to the surface, increasing hydrophobicity. By immobilising DHB onto the CPTS platform, the silica surface was activated, and as a result the coordination chemistry of the Schiff base generated a hybrid adsorbent with the capability to rapidly sequestrate Cu (II) ions from wastewater, as an answer to combat growing Waste Electrical and Electronic Equipment (WEEE) contamination in water supplies, in the wake of a prolonged consumerism mentality and boom in cryptocurrency mining. The produced hybrid materials were characterised by FTIR, proximate and ultimate analysis, nitrogen physisorption, PXRD, SEM, and TEM. The parameters influencing the removal efficiency of the sorbent, including pH, initial metal ion concentration, contact time, and adsorbent dosage, were optimised to achieve enhanced removal efficiency. Under optimal conditions (pH 7.0, adsorbent dosage 3 mg, contact time of 70 min, and 25 °C), Cu (II) ions were quantitatively sequestered from the sample solution; 93.1% of Cu (II) was removed under these conditions. The adsorption was found to follow pseudo-second-order kinetics, and Langmuir model fitting affirmed the monolayer adsorption. Full article

This study examines the environmental implications of energy-intensive cryptocurrency mining activities within the broader sustainability debate surrounding blockchain technologies. Focusing specifically on Bitcoin’s proof-of-work–based mining process, the analysis investigates the long-run relationship between greenhouse gas emissions, network-specific technical variables, and climate policy uncertainty using advanced cointegration and asymmetric causality techniques. The findings reveal a stable long-run association between mining-related activity and emissions, alongside pronounced asymmetries whereby positive shocks amplify environmental pressures more strongly than negative shocks mitigate them. Importantly, these results pertain to the mining process itself rather than to blockchain technology as a whole. While blockchain infrastructures may support sustainable applications in areas such as green finance, transparency, and energy management, the evidence presented here highlights that energy-intensive mining remains a significant environmental concern. Accordingly, the study underscores the need for active regulatory frameworks—such as carbon pricing and the polluter-pays principle—to reconcile the environmental costs of crypto mining with the broader sustainability potential of blockchain-based innovations Full article

Metaphor recognition plays a key role in natural language understanding and semantic analysis. This paper introduces a metaphor recognition model called EGSNet (Enhanced Gloss Siamese Network). Previous research has shown that the gloss of metaphor words contributes to their comprehension in metaphor recognition. To leverage this information, this paper incorporates gloss annotations of metaphor words into the metaphor recognition model. Mining the deep semantic information of glossed metaphorical words, and combining MIP linguistic rule to use the annotation information of metaphorical words can better perceive the semantic conflicts between the contextual meaning of the target word and its basic meaning, thereby improving the ability to recognize metaphors. Furthermore, by employing data augmentation techniques to reveal the true meaning of target words in contextual environments and combining gloss information, the EGSNet model can effectively capture subtle semantic differences in sentences as well as the linguistic characteristics of metaphors, thereby enhancing metaphor recognition performance. Experiments indicate that the EGSNet model achieves more accurate metaphor recognition results and makes a significant contribution to the field of metaphor recognition. Full article

Coal mine ventilation systems face coupled and systemic risks characterized by structural interconnection and disaster chain propagation. In order to accurately quantify and evaluate this overall system risk, this study presents a new method of risk assessment of the coal mine ventilation system based on fuzzy polymorphic Bayesian networks. This method effectively addresses the shortcomings of traditional assessment approaches in the probabilistic quantification of risk. A Bayesian network with 44 nodes was established from five dimensions: ventilation power, ventilation network, ventilation facilities, human and management factors, and work environment. The risk states were divided into multiple states based on the As Low As Reasonably Practicable (ALARP) metric. The probabilities of evaluation-type root nodes were calculated using fuzzy evaluation, and the subjective bias was corrected by introducing a reliability coefficient. The concept of distance compensation is proposed to flexibly calculate the probabilities of quantitative-type root nodes. Through the verification of the ventilation system of H Coal Mine in Shanxi, China, it is concluded that the high risk of the ventilation system is 18%, and the high-risk probability of the ventilation system caused by the external air leakage of the mine is the largest. The evaluation results are consistent with real-world conditions. The results can provide a reference for improving the safety of the ventilation systems. Full article

Maintenance and repair play a crucial role in industry. Smart systems for technical diagnostics can help to save money and to prevent the breakdown of machines and plants. These systems and its classifiers benefit from plausible features because they tend toward robust classification. Although concepts for knowledge discovery are well-known in various scientific fields, they are not established in the field of rotating machines. Knowledge discovery from experimental data is a framework that combines valid methods for knowledge discovery with expert knowledge and automated experiments. For the central data mining step, feature selection algorithms based on heuristic or meta-heuristic search are established. The objective is to identify plausible pattern with a limited number of features and the best combination of these features. The results in this work show which strategies align the best with the requirements of knowledge discovery using experimental data to find plausible features. For this study, well-configured search strategies, namely, sequential forward selection and ant colony optimization, were applied on real data. The data represent several fault severity levels for parallel misalignment and cavitation. The plausible feature vectors and features exhibited good behavior when applied to new targets. It is expected that the obtained knowledge will be transferable to new classification tasks with only minimal optimization of the reference data or the classifier. Full article

Maintaining color constancy in polymer extrusion processes is a key difficulty in manufacturing applications, as fluctuations in processing parameters greatly influence pigment dispersion and the quality of the finished product. Preliminary historical data mining analysis was conducted in 2009. This work concentrates on Opaque PC Grade 5, which constituted 2.43% of the pigment; it contained 10 PPH of resin2 with a Melt Flow Index (MFI) of 6.5 g/10 min and 90 PPH of resin1. It also employs a fixed resin composition with an MFI of 25 g/10 min. This research identified the significant processing parameters (PPs) contributing to the lowest color deviation. Interactions between processing parameters, for the same color formulation, were analyzed using statistical methods under various processing conditions. A principle-driven General Trends (GT) diagnostic procedure was applied, wherein each parameter was individually varied across five levels while holding others constant. Particle size distribution (PSD) and colorimetric data (CIE Lab*) were systematically measured and analyzed. To complete this, correlations for the impact of temperature (Temp) on viscosity, particle characteristics, and color quality were studied by characterizing viscosity, Digital Optical Microscopy (DOM), and particle size distribution at various speeds. The samples were characterized for viscosity at three temperatures (230, 255, 280 °C) and particle size distribution at three speeds: 700, 750, 800 rpm. This study investigates particle processing features, such as screw speed and pigment size distribution. The average pigment diameter and the fraction of small particles were influenced by the speed of 700–775 rpm. At 700 rpm, the mean particle size was 2.4 µm, with 61.3% constituting particle numbers. The mean particle size diminished to 2 µm at 775 rpm; however, the particle count proportion escalated to 66% at 800 rpm. This research ultimately quantifies the relative influence of particle size on the reaction, resulting in a color value of 1.36. The mean particle size and particle counts are positively correlated; thus, reduced pigment size at increased speed influences color response and quality. The weighted contributions of the particles, 51.4% at 700 rpm and 48.6% at 800 rpm, substantiate the hypothesis. Further studies will broaden the GT analysis to encompass multi-parameter interactions through design experiments and will test the diagnostic assessment procedure across various polymer grades and colorants to create robust models of prediction for industrial growth. The global quality of mixing polycarbonate compounding constituents ensured consistent and smooth pigment dispersion, minimizing color streaks and resulting in a significant improvement in color matching for opaque grades. Full article