Search Results (2,771)

Soil salinity remains a major constraint to rice productivity, particularly during early developmental stages when plants are highly sensitive to osmotic and ionic stress. In this study, we evaluated 201 genetically diverse rice genotypes from the 3K Rice Diversity Panel to investigate stage-specific mechanisms of salinity tolerance and develop machine learning-based predictive models for rapid phenotypic screening. Morphological and physiological traits were measured under control and saline conditions at germination and early seedling stages to derive Stress Tolerance Indices (STIs). The average membership function value (AMFV), calculated from multi-trait STI profiles, effectively captured variation in salinity responses and enabled classification of genotypes into five tolerance categories. Genome-wide association analysis using high-density SNP markers identified 36 significant marker–trait associations, including potentially novel SNPs on chromosomes 1 and 12. Several loci co-localized with candidate genes (LTR1, LGF1, OsCPS4, OsNCX7, and OsNHX4), while functional SNPs within genes (OsDRP2C, RLCK168, and OsMed37_2) and non-synonymous variants (qSVII11.1 and qSNaK3.1) further supported their candidacy in salinity tolerance. Mining favourable SNPs of causal genes identified superior multilocus combinations consistent with STI-based phenotypic patterns, with genotype 91-382 emerging as the strongest performer, exhibiting enhanced Na⁺ exclusion, K⁺ retention, and biomass resilience across developmental stages. To address multicollinearity among STI traits, we applied cross-validated LASSO (germination) and Elastic Net (early seedling) models, achieving high predictive accuracy and revealing a developmental shift from biomass-driven tolerance at germination to ion-regulatory processes at the seedling stage. Independent validation showed strong agreement between predicted and observed AMFVs. By integrating physiological indices, GWAS-derived SNP signals, and regularized machine learning approaches, this study provides a robust framework for identifying elite donors and accelerating breeding for salt-tolerant rice. Full article

Heavy metal contamination associated with mining activities is a major source of abiotic stress for plants, strongly affecting plant physiology, growth and survival in contaminated environments. Due to their non-biodegradable nature and long-term bioavailability, heavy metals persist in soils affected by mining activities, exposing plants to chronic stress conditions that require the activation of coordinated cellular and molecular response mechanisms to limit toxicity and maintain internal homeostasis. This review synthesises and critically analyses current knowledge on the molecular and cellular mechanisms governing plant responses to heavy metal stress in mining-affected environments. Key processes involved in metal uptake and transport, redox imbalance and oxidative stress generation, antioxidant defence systems, and molecular detoxification mechanisms, including metal chelation, subcellular compartmentalisation, and gene expression regulation, are discussed. Particular attention is paid to cellular signalling pathways that mediate plant adaptation to prolonged exposure to complex metal mixtures. Emphasis is placed on integrating molecular-level knowledge with the specific context of mining sites, highlighting the limitations of extrapolating results obtained under controlled experimental conditions to naturally contaminated environments. This perspective integrates molecular mechanisms with the geochemical realities of mining sites, providing a solid basis for the development of effective phytoremediation strategies and the optimisation of plant species selection. Full article

Archaeological evidence for prehistoric placer tin mining is rare due to the ephemeral nature of the workings and the associated tools in the dynamic setting of active river systems. Here, we report an additional line of evidence for metallurgical activities at stream tin mining in Serbia at Mt. Cer and Bukulja. Rivers at these locations contain Pb-rich-glass grains, many of which are also enriched in Cu and Sn. Compositionally, the detrital grains of glass are similar to the vitreous infillings on a bleached ceramic sherd found at Spasovine, an archaeological site situated on the bank of the tin-rich Milinska River. The high-Pb-bearing (average 42 wt%) and Sn-bearing (average 0.7 wt%) composition of the glass, along with the inclusions of secondary cassiterite, indicate that the slag was derived from the refinement of leaded bronze (i.e., lead removal). Although the detrital glass slag grains lack direct archaeological context, broader archaeological observations limit their production to either the Roman or Medieval Periods. The presence of Pb-Cu-Sn metallurgical glass grains in a river at Bukulja provides the first concrete evidence of prehistoric tin mining at this locality, which demonstrates that sluicing for crushed glassy residues is a viable means to prospect for as yet undiscovered sites of ancient metallurgical activities. Full article

Student retention has become a major challenge for higher education institutions due to the influence that academic, socioeconomic, family, and motivational factors exert on students’ academic continuity. In this context, understanding the determinants that explain university persistence is essential for designing effective retention strategies. Based on the analysis of factors related to motivation, commitment, attitude, academic integration, and social and economic conditions, retention patterns were examined in a population of 532 university students, of whom 57.7% showed high retention, 38.2% medium retention, and 4.1% low retention. To identify the factors with the greatest influence on academic continuity, educational data mining techniques and supervised classification models were applied and evaluated using stratified 10-fold cross-validation. Tree-based ensemble models showed the most consistent predictive performance, with Random Forest achieving the best results (accuracy = 0.729 ± 0.058; F1-macro = 0.636 ± 0.136). Model interpretability was examined through SHAP analysis, which revealed that transportation conditions (0.249), task completion (0.170), absence of work obligations (0.168), and course completion (0.164) were the most influential predictors in the classification of retention levels. In addition, sensitivity analysis indicated that academic commitment accounts for 41.6% of the predictive impact, followed by motivation (23.5%). These findings demonstrate that student retention is shaped by the interaction of academic, motivational, and contextual factors and provide practical implications for the development of **early warning systems, personalized tutoring programs, psychosocial support initiatives, and financial assistance policies aimed at strengthening university retention. Full article

To investigate the mechanisms of coal seam reservoir modification and the efficient development of surface coalbed methane (CBM), the coal with different structural formations in the 13-1 coal seam of Huainan Mining Area was selected as the research object. Fracturing numerical simulation technology was employed to analyze the effect of hydraulic fracturing on tectonic coal reservoirs and explore the mechanism of fracturing-induced gas production. The results show that fragmented coal contains well-developed face and butt cleats, and distinct fracture models were constructed for the three tectonic coal types. Granulated and mylonitic structural coals exhibit larger total pore volumes and higher proportions of pores larger than 10 nm than fragmented coal. Both tectonic coal types exhibit a high proportion of methane flow space, with rapid methane desorption and diffusion under high pressure and stable behavior under low pressure. Pore volume compressibility calculations indicate that tectonic coal exhibits poor compressibility. Numerical simulations indicate that direct horizontal well fracturing produces short, wide fractures, whereas roof-strata horizontal well fracturing generates longer, more effective fractures, primarily due to large-scale depressurization and induced fracturing associated with horizontal well drilling and staged fracturing. Full article

Dahua in Guangxi is an important soft jade mining area in southern China. Despite this, research on the nephrite from this region, particularly on the coloring mechanism of black nephrite, remains limited. This study systematically investigates the gemological, mineralogical, and geochemical properties of black nephrite from Dahua. Petrographic analysis reveals that tremolite is the primary mineral, with clinochlore and apatite as associated minerals. Tremolite (SiO₂: 58.00 wt%; MgO: 24.75 wt%; CaO: 12.46 wt%) in Dahua nephrite is close to the theoretical values of tremolite. Chlorite thermometry indicates formation temperatures of 240 °C and 328 °C. Geochemical analysis of the samples shows enrichment in light rare earth elements (LREEs), flat heavy rare earth element (HREEs) patterns, and Ce and Eu anomalies. The Mg²⁺/(Mg²⁺ + Fe²⁺) ratio was below 0.06. In the c(Ca²⁺), c(Mg²⁺), and c(Fe²⁺ + Fe³⁺) ternary diagram, the amphibole plots close to the Dahua green nephrite, suggesting a similar genetic environment and supporting a contact metasomatic origin for the amphibole. Combined with the geological setting, mineralization was driven by hydrothermal fluids from diabase magma, which introduced Si and heat, with Ca and Mg being mobilized from the dolomitic limestone host rocks. These findings contribute to the understanding of nephrite formation in Dahua, distinguishing it from nephrite from other regions and providing a foundation for future studies on the geochemical and mineralogical characteristics of nephrite. Full article

Coal preparation tailings from the K18 seam of the Abayskaya mine were evaluated as a potential secondary source of critical rare earth elements (REEs). The study showed that REEs are predominantly associated with the mineral fraction of coal; therefore, during beneficiation, approximately 70% of their total content is transferred to flotation tailings. The concentrations of valuable elements in the tailings are as follows (g/t): Li—65; Sc—16; Y—17; Yb—2.5; V—135; and Ti—2293. These values significantly exceed the Clarke values and are comparable to those of some low-grade primary ores, indicating the potential of coal preparation wastes as a technogenic raw material for critical elements. To extract REEs from the resistant aluminosilicate matrix, a fluorine–ammonium sulfate thermochemical activation method was proposed. Using a probabilistic–deterministic experimental design approach, a mathematical model of the process was developed and optimal parameters were determined (400 °C, 120 min, (NH₄)₂SO₄ consumption—140% relative to Al, NH₄HF₂ consumption—110% relative to Si), providing a feed liberation degree (by Al extraction) of up to 94%. Under optimal conditions, high leaching efficiencies of key elements were achieved: Sc (95%), Y (100%), Yb (100%), and Li (100%). The results demonstrate the significant potential of coal preparation tailings as a secondary resource of rare earth elements and confirm the efficiency of fluorine–ammonium sulfate technology for processing this type of technogenic waste. Full article

The increasing penetration of intermittent renewable energy demands innovative solutions to maintain grid stability, resilience, and security in the body of smart cities. This paper presents a novel framework that redefines Bitcoin mining as a form of virtual energy storage, a flexible and controllable load capable of delivering large-scale demand response services, positioning it as a competitive alternative to traditional energy storage systems, including electrical, mechanical, thermal, chemical, and electrochemical storage solutions. By strategically aligning mining activities with grid conditions, Bitcoin mining can absorb excess electricity during periods of oversupply, converting it into digital assets, and reduce operations during times of scarcity, effectively emulating the behavior of conventional energy storage systems without the associated capital expenditures and material requirements. Beyond its operational flexibility, this paper explores the cyber–physical benefits of integrating Bitcoin mining into the power transmission systems as a defensive mechanism against false data injection (FDI) cyberattacks in smart city infrastructure. To achieve this goal, a decentralized and adaptive control strategy is proposed, in which mining loads dynamically adjust based on authenticated grid-state information, thereby improving system observability and hindering adversarial efforts to disrupt state estimation. In addition, to handle the proposed approach, this paper introduces a high-performance algorithm, a combination of quantum-augmented particle swarm optimization and wavelet-oriented whale optimization (QAPSO-WOWO). Simulation results confirm that strategic deployment of mining loads improves grid sustainability by utilizing curtailed renewables, enhances resilience by mitigating load-generation imbalances, and bolsters cybersecurity by reducing the impacts of FDI attacks. This work lays the foundation for a transdisciplinary paradigm shift, positioning Bitcoin mining not as a passive energy consumer but as an active participant in securing and stabilizing the future power grid in smart cities. Full article

The increasing availability of tourism information on digital platforms has improved tourists’ access to destination-related data. However, existing tourism information systems often lack effective integration between user preference information and geospatial data, limiting their ability to provide personalized and context-aware recommendations. This study proposes a personalized tourism recommendation system by integrating Geographic Information System (GIS) technology with association rule mining to analyze relationships between user preferences and spatial characteristics of tourist destinations. The proposed system provides map-based visualization, calculates distances between users and destinations, and generates personalized recommendations based on both user interests and spatial proximity. The implementation results demonstrate that the system can generate location-aware and personalized tourism recommendations, supporting users in identifying suitable destinations within their surrounding geographic context. The integration of geospatial processing with association rule mining improves recommendation relevance by incorporating both preference patterns and spatial proximity. Furthermore, the proposed framework has the potential to support more balanced spatial distribution of tourism activities by recommending geographically appropriate destinations rather than concentrating suggestions on highly popular locations. These findings highlight the value of combining geospatial technologies with data mining techniques to support tourism recommendation systems and spatially informed tourism planning. Full article

Approximately 20% of China’s land area is desertified or highly desertifiable, where loose sandy soil and low nutrient availability restrict plant growth. Microbial inoculants, as an emerging ecological restoration technology, play a key role in plant growth and soil nutrient activation in sandy regions. However, a systematic understanding of functional differences among microorganisms isolated from different stressed environments remains insufficient. Nine functional microbial strains from three stressed habitats, including sandy land, coastal saline-alkali soil, and heavy metal mining areas, were selected to conduct a three-month pot experiment, investigating their effects on soil nutrient activation, plant growth and microbial communities. Results showed that all inoculants increase plant biomass (by 4.15~25.59%), with KS-33, KS-36, SD-13 and SD-3 significantly promoting biomass in different plant parts (p < 0.05), and with YJ-15 remarkably enhancing root growth (root length increased by 70.83%, p < 0.01). Inoculation reduced bacterial Chao1 by 27.18~53.97%, but increased fungal Chao1 by 12.77~28.38% (except SD-30). Bacterial generalist species proportion increased from 61.12% to 83.78~93.99% after inoculation, higher than the variation degree of the fungal community. Mantel analysis revealed a reverse trend between soil nutrients, water content and plant growth. This may be associated with the increased consumption by plants and microorganisms. In summary, microbial inoculants enhance nutrient cycling processes and plant growth by reshaping soil microbial communities. Performance of microbial inoculants is more likely governed by their inherent ecological functions rather than being entirely determined by their original environments. Despite varying mechanisms, these inoculants can effectively enhance sandy soil microbial communities, providing a theoretical basis for regional ecological restoration. Full article

The intestinal microbiome plays a fundamental role in canine health and well-being, regulating functions, including digestion, immunity, metabolism, and behavior. Dysbiosis refers to the disruption of the balanced composition of resident commensal communities, and gut bacteria can influence behavior via neurological, metabolic, endocrine, and immune-mediated pathways. Growing evidence supports the existence of a bidirectional communication between the gut and the central nervous system, known as the gut–brain axis, through which intestinal microorganisms may influence behavior via neurological, metabolic, endocrine, and immune-mediated pathways. Despite the expanding interest in this field, the contribution of intestinal dysbiosis to the development and severity of behavioral and neurological disorders in companion dogs remains poorly understood. This review aims to critically analyze the literature from 2011 to 18 September 2025 concerning the association between dysbiosis, the gut–brain axis, and both gastrointestinal and non-gastrointestinal illnesses in dogs. To our knowledge, this review represents the first application of Text Mining (TM) in this domain: TM facilitates the identification and analysis of valuable information from extensive datasets, converting unstructured content into structured data, thereby enabling quantitative analysis. We used the following search terms on three bibliographic databases (PubMed, Scopus, and Web of Science): “dysbiosis” AND “canine” OR “dog” AND “gut–brain axis” AND “behavior”. Of the 1176 records retrieved, 35 studies were checked following the PRISMA guidelines, and they met the predefined inclusion criteria in the final analysis. Full article

Uranium mining generates complex multi-element contamination that affects interconnected ecosystem components, posing long-term ecological and sanitary risks; this review places these impacts in a broad environmental context and aims to synthesize current knowledge on the distribution, migration, and accumulation of uranium and associated heavy metals in water, soil, and plants. A structured analysis of international peer-reviewed literature was conducted, focusing on documented pathways of metal release from tailings and waste dumps, geochemical controls on mobility, and biological uptake by vegetation. The reviewed studies consistently show that tailings and disturbed ore-bearing strata act as persistent sources of uranium and heavy metals (e.g., Cd, Pb, Cr, Ni, Zn, Mn, As), which migrate through infiltration, acid mine drainage, and atmospheric dispersion, leading to elevated concentrations in surface and groundwater and long-term accumulation in soils. Soils function as the principal sink controlling metal bioavailability, while vegetation reflects the bioavailable fraction and exhibits pronounced species-specific accumulation patterns. These processes establish an active “soil–water–plant” transfer chain that facilitates entry of contaminants into food webs. The synthesis indicates that combined uranium and heavy metal contamination represents a sustained ecological and public health concern in uranium-mining regions and underscores the need for integrated monitoring of soils, waters, and vegetation, along with quantitative risk assessment and scientifically grounded remediation strategies. Full article

As a key metering device in the smart grid, the clock battery status of smart meters directly affects the operational efficiency and economy of the grid. In response to the limitations of current evaluation methods in feature correlation analysis and model interpretability, this study proposes a knowledge-and-data-driven low battery voltage status prediction method. We systematically dissected the physical mechanisms underlying battery undervoltage faults and constructed a status features knowledge graph comprising 17 state features across four dimensions. By employing Pearson correlation analysis and association rule mining techniques, we achieved a quantitative correlation analysis between multi-source heterogeneous features and battery status. Building on this foundation, we developed an interpretable model framework based on XGBoost-SHAP. Empirical studies utilized a dataset of 939,000 faulty meters recalled by a provincial power company in 2023, with 9.87% of outlier samples eliminated using the Isolation Forest algorithm during preprocessing. Results demonstrate that the proposed model achieved an R² of 0.851 and a Mean Squared Error (MSE) of 0.0088 on the test set. The prediction performance significantly surpassed that of Random Forest (R² = 0.692) and MLP+BP neural networks (R² = 0.583), thereby validating the effectiveness of the approach in combining predictive accuracy with decision transparency. Full article

Platinum group elements (PGE) are six rare highly siderophile metals which have similar chemical characteristics and occur together in mineral deposits: platinum (Pt), palladium (Pd), rhodium (Rh), ruthenium (Ru), iridium (Ir) and osmium (Os). In nature, they tend to exist in a metallic state or bond with sulfur and arsenic and occur as trace accessory minerals predominantly in mafic and ultramafic rocks. High industrial demand together with their scarcity in crustal rocks has been reflected in their inclusion in 2025 US Government’s List of Critical Minerals, European Union’s List of Critical Raw Materials and Mongolian List of 11 Critical Minerals. Although Mongolia is not currently a producer, it hosts four types of potentially economic PGE deposits: (1) Podiform chromitites associated with ophiolites; (2) Ni-Cu-PGE sulfide mineralization of rift-related mafic–ultramafic intrusions; (3) Alaskan–Uralian type arc related zoned mafic–ultramafic intrusions; and (4) Placers. Particularly promising are Permian Ni-Cu-PGE sulfide bearing mafic–ultramafic intrusions of the Khangai large igneous province which bear resemblance to mineralized Permian intrusions in Russia (e.g., Norilsk-Talnakh) and N.W. China (e.g., Kalatongke; Tarim basin). In addition, sub-economic ophiolite-hosted PGE mineralization can be extracted as a by-product during chromite mining. There is also the potential for PGE recovery as a by-product in existing gold placer operations in areas hosting ophiolitic massifs and Alaskan–Uralian type intrusions. Mongolia is a promising frontier for PGE exploration and mining. Full article