Search Results (1,626)

Machine learning (ML) algorithms have promoted the development of predictive modeling of mineral prospectivity, enabling data-driven decision-making processes by integrating multi-source geological information, leading to efficient and accurate prediction of mineral exploration targets. However, it is challenging to conduct ML-based mineral prospectivity mapping (MPM) in under-explored areas where scarce data are available. In this study, the Narigongma district of the Qiangtang block in the Himalayan–Tibetan orogen was chosen as a case study. Five typical alterations related to porphyry mineralization in the study area, namely pyritization, sericitization, silicification, chloritization and propylitization, were extracted by remote sensing interpretation to enrich the data source for MPM. The extracted alteration evidences, combined with geological, geophysical and geochemical multi-source information, were employed to train the ML models. Four machine learning models, including artificial neural network (ANN), random forest (RF), support vector machine and logistic regression, were employed to map the Cu-polymetallic prospectivity in the study area. The predictive performances of the models were evaluated through confusion matrix-based indices and success-rate curves. The results show that the classification accuracy of the four models all exceed 85%, among which the ANN model achieves the highest accuracy of 96.43% and a leading Kappa value of 92.86%. In terms of predictive efficiency, the RF model outperforms the other models, which captures 75% of the mineralization sites within only 3.5% of the predicted area. A total of eight exploration targets were delineated upon a comprehensive assessment of all ML models, and these targets were further ranked based on the verification of high-resolution geochemical anomalies and evaluation of the transportation condition. The interpretability analyses emphasize the key roles of spatial proxies of porphyry intrusions and geochemical exploration in model prediction as well as significant influences everted by pyritization and chloritization, which accords well with the established knowledge about porphyry mineral systems in the study area. The findings of this study provide a robust ML-based framework for the exploration targeting in greenfield areas with good outcrops but low exploration extent, where fusion of a remote sensing technique and multi-source geo-information serve as an effective exploration strategy. Full article

The long-term stability of grout-reinforced fractured rock masses in acidic groundwater environments after tunnel fires is critical for the safe operation of underground engineering. In this study, grouting reinforcement tests were performed on fractured diorite specimens using a high-strength fast-anchoring agent (HSFAA), and their mechanical degradation and microstructural evolution mechanisms were investigated under coupled high-temperature (25–1000 °C) and acidic corrosion (pH = 2) conditions. Multi-scale characterization techniques, including uniaxial compression strength (UCS) tests, X-ray computed tomography (CT), scanning electron microscopy (SEM), three-dimensional (3D) topographic scanning, and X-ray diffraction (XRD), were employed systematically. The results indicated that the synergistic thermo-acid interaction accelerated mineral dissolution and induced structural reorganization, resulting in surface whitening of specimens and decomposition of HSFAA hydration products. Increasing the prefabricated fracture angles (0–60°) amplified stress concentration at the grout–rock interface, resulting in a reduction of up to 69.46% in the peak strength of the specimens subjected to acid corrosion at 1000 °C. Acidic corrosion suppressed brittle disintegration observed in the uncorroded specimens at lower temperature (25–600 °C) by promoting energy dissipation through non-uniform notch formation, thereby shifting the failure modes from shear-dominated to tensile-shear hybrid modes. Quantitative CT analysis revealed a 34.64% reduction in crack volume (V_ca) for 1000 °C acid-corroded specimens compared to the control specimens at 25 °C. This reduction was attributed to high-temperature-induced ductility, which transformed macroscale crack propagation into microscale coalescence. These findings provide critical insights for assessing the durability of grouting reinforcement in post-fire tunnel rehabilitation and predicting the long-term stability of underground structures in chemically aggressive environments. Full article

The Shizhuyuan polymetallic deposit in Hunan Province, China, is a world-class ore field rich in tungsten (W), tin (Sn), molybdenum (Mo), and bismuth (Bi), now facing resource depletion due to prolonged exploitation. This study addresses the limitations of traditional geological prediction methods in complex terrain by integrating multi-source datasets—including γ-ray spectrometry, high-precision magnetometry, induced polarization (IP), and soil radon measurements—across 5049 samples. Unsupervised factor analysis was employed to extract five key ore-indicating factors, explaining 82.78% of data variance. Based on these geological features, predictive models including Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) were constructed and compared. SHAP values were employed to quantify the contribution of each geological feature to the prediction outcomes, thereby transforming the machine learning “black-box models” into an interpretable geological decision-making basis. The results demonstrate that machine learning, particularly when integrated with multi-source data, provides a powerful and interpretable approach for deep mineral prospectivity mapping in concealed terrains. Full article

Because of its specific nature, mining activity causes numerous negative impacts on the environment, both during the exploitation phase and after it has ended. An important source of income in the Jiu Valley is represented by the Lupeni Mining Exploitation. Like any mining activity, coal exploitation causes various negative effects on the environment. The subsidence phenomenon represents a significant issue associated with coal mining in the Jiu Valley. Underground extraction of mineral deposits induces displacement of the overburden strata. Such displacements result in ground subsidence and modifications of the surface topography. The larger the voids created following the exploitation of useful mineral deposits, the more they affect the surface of the land above the exploitation through sinking, displacement, deformation, and even cracks. Secondary deformations refer to post-mining surface movements induced by delayed rock mass adjustment, manifesting as ground collapse, localized subsoil failure, or uplift driven by groundwater rebound after drainage cessation. In this paper, we aim to study the subsidence phenomenon produced by coal mining at the Lupeni Mining Exploitation using the COMSOL simulation software and applying the Barcelona Basic Model (BBM) and Modified Cam-Clay (MCC) models. Following the simulation, the behavior of the rocks could be observed in order to improve prediction accuracy to support sustainable land management in post-mining areas. Full article

Crops nitrogen supply through the in situ mineralization of soil organic matter is a critical process for plant nutrition. However, accurately estimating the contribution of mineralization remains challenging. The complexity of biological, chemical, and physical processes in the soil, influenced by environmental conditions, makes it difficult to precisely quantify the amount of nitrogen available for crops. In this study, we created a database by collecting results from 121 mineralization monitoring experiments carried out between 2015 and 2021 on different experimental plots across Wallonia, Southern Belgium, and assessed the efficiency of predictive mineralization methods. The most impactful analytical parameters on in situ mineralization (ISM), determined using LIXIM program, appeared to be potentially mineralizable nitrogen (PMN) (r = 0.79). PMN, estimated by anaerobic soil incubation, also allowed the effective consideration of the after-effects of grassland termination and manure inputs. A multiple linear regression (MLR) combining PMN, POxC, pH, TOC:N, and TOC:clay significantly improved the prediction of soil nitrogen mineralization available for crops, achieving r = 0.87 (vs. r = 0.58 for the current method), while reducing dispersion by 41% (RMSE 56.35 → 33.13 kg N·ha⁻¹). The use of a more flexible Bootstrap Forest model (BFM) further enhanced performance, reaching r = 0.92 and a 50.8% reduction in dispersion compared to the current method (RMSE 56.35 → 27.76 kg N·ha⁻¹), i.e., about 16% lower RMSE than the MLR. Those models provided practical and efficient tools to better manage nitrogen resources in temperate agricultural systems. Full article

Sulfate minerals (SMs), such as BaSO₄, SrSO₄, and CaSO₄, precipitate when incompatible solutions from the oil industry, such as seawater (SW) and high-salinity brine solutions (HSBSs), are mixed during the oil production process. To investigate the potentiality to extract SM by mixing three different brine solutions, such as HSBS-1, -2, and -3, with SW, at different temperatures and pressures, a practical simple model was used to predict the saturation index (SI), the quantity of precipitated minerals (Y), and the induction time (t_ind) required for precipitation. From the results, it was found that CaSO₄ hemihydrate and SrSO₄ yield lower amounts of precipitate. BaSO₄ precipitation ranges from 20 to 60 mg/L and 1500 mg/L of CaSO₄ anhydrous under ambient conditions. These findings suggest that recovering low-solubility minerals is technically feasible and environmentally preferable to direct disposal. Full article

The continuous growth in global demand for mineral resources and the increasing difficulty of mineral exploration have created bottlenecks for traditional mineral prediction methods in handling complex geological information and large amounts of data. This review aims to explore the latest research progress in machine learning technology in the field of large-scale mineral prediction from 2016 to 2025. By systematically searching the Web of Science core database, we have screened and analyzed 255 high-quality scientific studies. These studies cover key areas such as mineral information extraction, target area selection, mineral regularity modeling, and resource potential evaluation. The applied machine learning technologies include Random Forests, Support Vector Machines, Convolutional Neural Networks, Recurrent Neural Networks, etc., and have been widely used in the exploration and prediction of various mineral deposits such as porphyry copper, sandstone uranium, and tin. The findings indicate a substantial shift within the discipline towards the utilization of deep learning methodologies and the integration of multi-source geological data. There is a notable rise in the deployment of cutting-edge techniques, including automatic feature extraction, transfer learning, and few-shot learning. This review endeavors to synthesize the prevailing state and prospective developmental trajectory of machine learning within the domain of large-scale mineral prediction. It seeks to delineate the field’s progression, spotlight pivotal research dilemmas, and pinpoint innovative breakthroughs. Full article

Flammulina velutipes (enoki mushroom) is a functional edible mushroom rich in antioxidants, polysaccharides, mycosterols, fiber, and minerals. Accumulating evidence highlights its therapeutic potential across diverse pathological contexts, including boosting cognitive function. However, its role in neuromodulation has not been systematically explored. This study examined the effects of methanolic and ethanolic extracts of F. velutipes on primary hippocampal neurons. Neurons were treated with different extract concentrations, followed by assessments of cell viability, cytoarchitecture, neuritogenesis, maturation, and neuroprotection under oxidative stress. The extracts were further characterized by GC-MS to identify bioactive metabolites, and molecular docking combined with MM-GBSA binding energy analysis was employed to predict potential modulators. Our results demonstrated that the methanolic extract significantly enhanced neurite outgrowth, improved neuronal cytoarchitecture, and promoted survival under oxidative stress, whereas the ethanolic extract produced moderate effects. Mechanistic studies indicated that these neuroprotective and neurodevelopmental benefits were mediated through activation of the NTRK receptors, as validated by both in vitro assays and molecular docking studies. Collectively, these findings suggest that F. velutipes extracts, particularly methanolic fractions, may serve as promising neuromodulatory agents for promoting neuronal development and protecting neurons from oxidative stress. Full article

Background/Objectives: Psoriasis is a chronic immune-mediated disease frequently accompanied by systemic inflammation and metabolic disturbances. Nutrition plays a crucial role in modulating inflammatory pathways, yet the impact of baseline dietary status on systemic therapy outcomes remains underexplored. Methods: A total of 37 patients (20 men, 17 women; mean age 47.8 ± 4.87 years) scheduled for cyclosporine A (CsA) therapy underwent dietary assessment using 24 h recall and food frequency questionnaires. Intake was compared with dietary reference values. Psoriasis severity was measured by using the Psoriasis Area and Severity Index (PASI) and Body Surface Area (BSA) at baseline, day 42, and day 84. Mixed-effects regression models adjusted for body mass index (BMI), age, and sex assessed associations between nutrient adequacy and clinical outcomes. Results: Participants exhibited frequent dietary imbalances, including low polyunsaturated fatty acids, fiber, vitamin D, folate, and minerals such as magnesium and zinc, alongside excess saturated fat and sodium. Adequate intake of fiber, eicosapentaenoic acid (EPA)+ docosahexaenoic acid (DHA), and vitamins A and D, folate, magnesium, and zinc was independently associated with a lower baseline PASI/BSA and faster improvement during CsA therapy (p < 0.05). Higher BMI, older age, and male sex predicted poorer outcomes. Conclusions: Pre-treatment nutritional inadequacies are common in psoriasis and independently predict diminished therapeutic response to CsA. Early nutritional optimization may enhance treatment efficacy and support long-term disease control. Integrating dietary assessment in psoriasis management represents a feasible, impactful adjunct to pharmacotherapy. Full article

This study examines the impact of physicochemical and geological factors on radon concentrations in groundwater throughout the Mexican Altiplano. Geological diversity, uranium deposits, seismic zones, and geothermal areas with high heat flow are all potential factors contributing to the presence of radon in groundwater. To move beyond local-scale assessments, this research employs spatial prediction methodologies that incorporate geological and geochemical variables recognized for their role in radon transport and geogenic potential. Certain properties of radon enable it to serve as an ideal tracer, viz., short half-life, inertness, and higher incidence in groundwater than surface water. Twenty-five variables were analyzed in samples from 135 water wells. Geostatistical techniques, including inverse distance weighted interpolation and kriging, were used in conjunction with multivariate statistical analyses. Salinity and geothermal heat flow are key indicators for determining groundwater origin, revealing a dynamic interplay between geothermal activity and hydrogeochemical evolution, where high temperatures do not necessarily correlate with increased solute concentrations. The occurrence of toxic trace elements such as Cd, Cr, and Pb is primarily governed by lithogenic sources and proximity to mineralized zones. Radon levels in groundwater are mainly influenced by geological and structural features, notably rhyolitic formations and deep hydrothermal systems. These findings underscore the importance of site-specific groundwater examination, combined with spatiotemporal models, to account for uranium–radium dynamics and flow paths, thereby enhancing radiological risk assessment. Full article

Against the backdrop of the continuous growth in global demand for mineral resources, efficient and accurate mineral exploration technologies are of paramount importance. Therefore, utilizing remote sensing technology, which features wide coverage, a non-contact nature, and multi-source data acquisition, is of great significance for conducting mineral resource exploration and prospecting research. This study focuses on the Tsagaankhairkhan copper–gold mining area in Mongolia and proposes a structural-alteration anomalies coupling mining prediction method based on the collaboration of multi-source remote sensing data. By comprehensively utilizing multi-source image data from Landsat-8, GF-2, and Sentinel-2, and employing methods such as principal component analysis (PCA), band ratio, and texture analysis, we effectively extracted structural information closely related to mineralization, as well as alteration anomaly information, including hydroxyl alteration anomalies and iron-staining alteration anomalies. Landsat-8 and Sentinel-2 data were employed to extract and mutually validate hydroxyl and iron-staining alteration anomaly information in the study area, thereby delineating alteration anomaly zones. By integrating the results of structural interpretation, the distribution of alteration anomaly information, and their spatial coupling characteristics, we explored the spatial coupling relationship between structural and alteration anomalies, analyzed their mineral control patterns, and identified 7 prospecting target areas. These target areas exhibit abundant mineral anomalies and favorable structural settings, indicating high metallogenic potential. The research findings provide crucial clues for the exploration of the Tsagaankhairkhan copper–gold mine in Mongolia and can guide future mineral exploration and development efforts. Full article

Strategic lithium resources are critical to national security and have attained heightened importance in contemporary geopolitical, economic, and military contexts. Persistent geochemical anomalies of lithium were first identified in coal-bearing claystones of the Middle Jurassic Xishanyao Formation at the Liuhuanggou Coal Mine in the southern Junggar Basin, Xinjiang. In this study, a suite of analytical techniques, including X-ray fluorescence spectrometry, inductively coupled plasma mass spectrometry, X-ray diffraction, scanning electron microscopy-energy dispersive spectroscopy, time-of-flight secondary ion mass spectrometry, and sequential chemical extraction, was employed to investigate the provenance, depositional environment, and modes of lithium occurrence in the claystone. Results indicated that the claystone at the Liuhuanggou Coal Mine was dominated by moderately felsic rocks. The notable enrichment of lithium in the Liuhuanggou coal mine claystone indicates favorable metallogenic potential. Lithium was primarily hosted in the aluminosilicate-bound fraction with inorganic affinity and was structurally incorporated within clay minerals, such as kaolinite, illite, and Fe-rich chlorite (chamosite). Lithium-rich claystone was deposited under intense chemical weathering conditions in a transitional, slightly brackish environment characterized by elevated temperatures and low oxygen levels. These findings advance our understanding of sedimentary lithium mineralization mechanisms and offer direct practical guidance for lithium resource exploration and metallogenic prediction in the Xinjiang region, thereby supporting the development of efficient extraction technologies. Full article

Shale oil and gas reservoirs are characterized by low porosity and low permeability. The development of ultra-long horizontal wells can significantly increase reservoir contact area and enhance single-well production. Shale formations exhibit distinct bedding structures, high formation pressure, high rock hardness, and strong anisotropy. These characteristics result in poor drillability, slow drilling rates, and high costs when drilling horizontally, severely restricting efficient development. Therefore, accurately predicting the drillability of shale gas wells has become a major challenge. Currently, most scholars rely on a single parameter to predict drillability, which overlooks the coupled effects of multiple factors and reduces prediction accuracy. To address this issue, this study employs drillability experiments, mineral composition analysis, positional analysis, and acoustic transit-time tests to evaluate the effects of mineral composition, acoustic transit time, bottom-hole confining pressure, and formation drilling angle on the drillability of horizontal well reservoirs, innovatively integrating multiple parameters to construct a nonlinear model and introducing three intelligent optimization algorithms (PSO, AOA-GA, and EBPSO) for the first time to improve prediction accuracy, thus breaking through the limitations of traditional single-parameter prediction. Based on these findings, a nonlinear regression prediction model integrating multiple parameters is developed and validated using field data. To further enhance prediction accuracy, the model is optimized using three intelligent optimization algorithms: PSO, AOA-GA, and EBPSO. The results indicate that the EBPSO algorithm performs the best, followed by AOA-GA, while the PSO algorithm shows the lowest performance. Furthermore, the model is applied to predict the drillability of Well D4, and the results exhibit a high degree of agreement with actual measurements, confirming the model’s effectiveness. The findings support optimization of drilling parameters and bit selection in shale oil and gas reservoirs, thereby improving drilling efficiency and mechanical penetration rates. Full article

Background: Dental age (DA) estimation, which is widely used in orthodontics, pediatric dentistry, and forensic dentistry, predicts chronological age (CA) by assessing tooth development and maturation. Most methods rely on radiographic evaluation of tooth mineralization and eruption stages to assess DA. With the increasing adoption of large language models (LLMs) in medical sciences, use of ChatGPT has extended to processing visual data. The aim of this study, therefore, was to evaluate the performance of ChatGPT-4 in estimating DA from panoramic radiographs using three conventional methods (Nolla, Haavikko, and London Atlas) and to compare its accuracy against both orthodontist assessments and CA. Methods: In this retrospective study, panoramic radiographs of 511 Turkish children aged 6–17 years were assessed. DA was estimated using the Nolla, Haavikko, and London Atlas methods by both orthodontists and ChatGPT-4. The DA–CA difference and mean absolute error (MAE) were calculated, and statistical comparisons were performed to assess accuracy and sex differences and reach an agreement between the evaluators, with significance set at p < 0.05. Results: The mean CA of the study population was 12.37 ± 2.95 years (boys: 12.39 ± 2.94; girls: 12.35 ± 2.96). Using the London Atlas method, the orthodontists overestimated CA with a DA–CA difference of 0.78 ± 1.26 years (p < 0.001), whereas ChatGPT-4 showed no significant DA–CA difference (0.03 ± 0.93; p = 0.399). Using the Nolla method, the orthodontist showed no significant DA–CA difference (0.03 ± 1.14; p = 0.606), but ChatGPT-4 underestimated CA with a DA–CA difference of −0.40 ± 1.96 years (p < 0.001). Using the Haavikko method, the evaluators underestimated CA (orthodontist: −0.88; ChatGPT-4: −1.18; p < 0.001). The lowest MAE for ChatGPT-4 was obtained when using the London Atlas method (0.59 ± 0.72), followed by Nolla (1.33 ± 1.28) and Haavikko (1.51 ± 1.41). For the orthodontists, the lowest MAE was achieved when using the Nolla method (0.86 ± 0.75). Agreement between the orthodontists and ChatGPT-4 was highest when using the London Atlas method (ICC = 0.944, r = 0.905). Conclusions: ChatGPT-4 showed the highest accuracy with the London Atlas method, with no significant difference from CA for either sex or the lowest prediction error. When using the Nolla and Haavikko methods, both ChatGPT-4 and the orthodontist tended to underestimate age, with higher errors. Overall, ChatGPT-4 performed best when using visually guided methods and was less accurate when using multi-stage scoring methods. Full article

In response to the recent meta-analysis by Tassi et al. on hormonal contraception and bone metabolism, we raise critical concerns regarding the interpretation of bone turnover markers as surrogates for bone mineral density (BMD). While bone turnover markers can offer early insights into bone remodeling, they do not directly predict long-term BMD changes, which require 12–24 months to detect. The assumption that equivalent percentage changes in bone formation and resorption markers reflect stable BMD is not supported by current evidence. Bone metabolism varies significantly across life stages, particularly during adolescence and early adulthood, when peak bone mass is still accruing—underscoring the need for age-specific analyses. Additionally, biomarker interpretation is limited by assay variability, inconsistent sampling protocols, and uncertain clinical implications, especially for formation markers. Mechanistically, estrogen supports bone integrity by inhibiting resorption and promoting formation; thus, combined hormonal contraceptives (CHCs) containing estrogen may help preserve bone health. In contrast, progestin-only methods can suppress endogenous estrogen production, potentially compromising skeletal development. We advocate for longitudinal studies incorporating both BMD and turnover markers, stratified by age and contraceptive formulation, to guide safer and more informed contraceptive choices that protect long-term bone health. Full article