Search Results (81,785)

The Duanfengshan deposit, located in the Mufushan area of the middle Jiangnan Orogen, is a large-scale pegmatite-type Nb-Ta deposit. Previous studies have explored the genetic linkage between regional magmatic evolution and Nb-Ta mineralization; however, the specific roles played by magmatic and hydrothermal processes in rare-metal mineralization remain poorly constrained. Focusing on the newly discovered No. 124 dyke with Li-Nb-Ta mineralization, this study systematically divides it into four internal zones, including the graphic texture zone (GT), coarse-grained albite zone (CGA), fine-grained albite zone (FGA), and lepidolite–quartz core (LQ). Monazite U-Pb age of 134.6 ± 1.1 Ma precisely constrains the emplacement age of the dyke to the Early Cretaceous. Coltan in the CGA and FGA exhibit primary homogeneous or oscillatory zoning and are identified as columbite–(Mn), whereas those in the LQ display patchy texture or Ta-enriched overgrowth rims. Mica compositions exhibit continuous evolutionary trends from GT to LQ, with progressive decreases in K/Rb and K/Cs ratios and synchronous increases in F, Li, Cs, Rb, and Ta contents. Such compositional variations drive the transformation of mica species from muscovite, via Li–phengite and zinnwaldite (Li–muscovite), to metasomatic lepidolite. Integrated magmatic–hydrothermal evolution controls overall Li-Nb-Ta mineralization of Dyke No. 124. Early Nb-Ta-Li pre-enrichment within GT and CGA zones is dominated by magmatic fractional crystallization. Mica-based Li-Cs-Rb Rayleigh fractionation evidence demonstrates that the FGA may represent a transitional stage governed by volatile-rich, fluid-unsaturated melts. Massive albite crystallization elevates melt ASI values, lowers coltan solubility, and thus induces extensive Nb-Ta mineral precipitation. Subsequent metasomatism by Ta-Li-F-rich hydrothermal fluids further causes prominent secondary Li and Ta enrichment. Compared with the global typical LCT pegmatites, Dyke No. 124 possesses highly evolved magmatic differentiation. Zone-specific mica geochemical signatures are well coupled with Nb-Ta and Li mineralization, highlighting the favorable integrated Li-Nb-Ta mineralization potential of the dyke. This study improves regional Li-Nb-Ta mineralization theories and supplies reliable geological constraints for further rare-metal prospecting in the Duanfengshan area. Full article

Two-dimensional metallography cannot be used to determine how graphite volume is partitioned among three-dimensional connected structures in compacted graphite iron (CGI), even when the conventional graphite fractions are similar. This study therefore uses archived micro-CT data to determine whether 75FeSi additions in thermal analysis specimens alter the three-dimensional distribution of graphite volume. Three spherical specimens were cast from an industrial CGI melt with 0, 0.15, and 0.30 wt.% 75FeSi placed at the bottom of the specimen cavity. Center coupons were examined using a ZEISS Xradia 620 Versa X-ray microscope, and the retained reconstructed volumes had 3.0 µm isotropic voxels. We decoded the archived Dragonfly volumetric datasets, removed inactive historical labels, and calculated number-weighted and volume-weighted size, concentration, and shape descriptors. Graphite volume fraction remained within 8.56–8.83%, whereas connected object number density changed from 9764 mm⁻³ without additional 75FeSi to 5739 and 6854 mm⁻³ at 0.15 and 0.30 wt.%, respectively. Number-weighted median diameter remained near 13 µm, but volume-weighted D90 increased from 185.0 to 454.7 and 501.9 µm. The largest connected object contained 3.42%, 20.30%, and 31.39% of graphite volume, respectively. The main contribution is a specimen-level three-dimensional graphite volume-partitioning signature that separates graphite amount from graphite connectivity and upper-tail concentration. The results show that similar total graphite fractions can correspond to different internal graphite architectures, which conventional planar measurements cannot resolve. Full article

In Fusion Deposition Modeling-based manufacturing, process parameters affect the mechanical and physical properties of the print. Considering these properties, accurately predicting print quality is essential. This is where machine learning (ML) models for three-dimensional (3D) print quality prediction come to the forefront. In this study, a dataset was prepared under strict operational measurement standards—utilizing the Interquartile Range (IQR) method for data sanitization—encompassing 10 material types, 2 printer types, and 4 printing parameters. Five hundred different sample combinations were prepared and printed in sets of three according to ISO 527-2 Type 4 standard dimensions. Tensile, hardness, and surface roughness tests were applied to the prepared samples. Using this validated dataset, a Hybrid Multi-Material Quality–Ensemble System–Stacking–Gradient Boosting Regressor (HMQ-ES-Stack-GBR) architecture is proposed as a diagnostic framework for multi-output quality prediction. Particularly in terms of quality outputs such as tensile strength, hardness, and surface roughness, while also providing a quantitative analysis of the effect of material type on print quality. Furthermore, a multi-objective optimization pipeline integrating three distinct meta-heuristic algorithms—Non-dominated Sorting Genetic Algorithm II (NSGA-II), Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO)—was coupled with the framework to systematically derive material-specific optimal processing parameter configurations. Furthermore, the study shows that open-system printers exhibit higher prediction errors than closed-system printers. Reflecting system-induced variability rather than full hardware independence. Although the study is limited to internal validation within the current experimental design and includes material imbalance across filament groups, the findings suggest that the proposed framework provides a promising diagnostic decision-support tool for pre-print quality estimation within the studied dataset. By accurately reflecting rather than physically overcoming manufacturing variability, it supports decision-making processes through pre-print quality estimation, thereby enabling proactive interventions that reduce raw material, time, and energy losses. Full article

To address the cleaning requirements for marine growth on offshore platform jackets, an underwater cleaning robot featuring a combined “chassis + thruster-assisted adhesion + magnetic adhesion” mode is designed. The robot is equipped with four thrusters and a magnetic-adhesion wheeled chassis, enabling stable attachment and movement on varying-diameter pipes. Kinematic models in both inertial and body-fixed coordinate systems are established, and six-degree-of-freedom (6-DOF) dynamic equations are derived. These equations systematically incorporate key factors including added-mass forces, damping forces, hydrostatic restoring forces, ocean current disturbances, and thruster torques. Based on CFD simulations employing overset grids, moving reference frames, and simple harmonic motion techniques, the damping, added-mass, and thruster thrust and torque coefficients for each degree of freedom are identified. The obtained parameters demonstrate reasonable consistency with the CFD internal validation and preliminary external verification, providing a complete theoretical model and simulation data to support the motion control and operational stability analysis of the underwater cleaning robot. The established dynamic model addresses the free-navigation condition of the robot without cleaning operation. The additional hydrodynamic effects during cleaning operations will be considered in future work. Full article

Despite societal benefits derived from the many uses of plastics in our daily lives, plastic pollution has become another One Health crisis that has resulted in microplastic (MP) and nanoplastic (NP) contamination of all aspects of our global food production system and requires urgent attention. According to Plastic Europe, 99% of all plastics are made from fossil fuels but only 9% are recycled, which has created a major environmental injustice problem associated with geographic disparities for disposal. Plastic polymers slowly degrade to MPs and NPs in the environment and adsorb and transport other toxic environmental contaminants that compound their detrimental effects on all components of ecosystems and human health. Our global food system represents the culmination of MP contamination in the environment and is the major route of human exposure to MPs and NPs, but there are no comprehensive guidelines to avoid unhealthy consumption of contaminated food and drinking water; numerous analytical, data interpretation, and risk assessment challenges; and no international treaties to end plastic pollution. This comprehensive review is intended to provide a synthesis of disparate threads of vast amounts of scientific information across multiple disciplines to improve our understanding and action for addressing this complex One Health problem. Full article

In the context of pavement management, conventional data collection methods for the evaluation of pavement functional condition are limited by relatively slow acquisition speeds, that prevent fast-lane motorway surveying at 120–130 km/h, and by survey frequency, which on vast networks typically occurs twice a year. Given these limitations, continuous pavement condition monitoring from moving vehicles offers an attractive solution to move towards real-time digital road assessment. In particular, such a result is achieved by making use of “intelligent” or “smart” tires, which by means of appropriate arrays of sensors can capture contact patch information, thereby providing quantitative information related to pavement roughness and macrotexture. In this study, smart tire data functional condition indicators, Dynamic Index (DI) and Pr index, were collected over several segments of a motorway network, with a total length of 405 km. Correlations were investigated between such parameters and the results of measurements coming from a traditional pavement monitoring technique, expressed in terms of international roughness index (IRI) and mean profile depth (MPD). Furthermore, the ability of smart tire indicators to identify time-dependent trends and to rank different motorway segments was assessed. Obtained results, which were generated by adopting different data processing and homogenization techniques, showed that DI displays a moderate correlation with IRI, while Pr exhibits a strong correlation with MPD. Pavement-age analysis highlighted the existence of meaningful trends for both dense-graded and open-graded asphalt-wearing courses. Motorway rankings based on average DI and Pr values were found to be in agreement with those obtained from average IRI and MPD values, thereby confirming the potential of smart tire technology as a complementary network-level monitoring tool for pavement asset management systems. Full article

Electron behaviors—including how electrons interact with energy, matter, and magnetic fields—form the foundation of many biomedical imaging modalities, including X-ray imaging, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), optical imaging, electron microscopy, atomic force microscopy, and electron paramagnetic resonance (EPR). These interactions allow visualization of internal structures, molecular processes, tissue composition, oxygenation, redox biology, and high-resolution cellular or surface features. This narrative review provides an overview of key electron-associated mechanisms and their applications in biomedical imaging. Advanced MRI methods, including magnetic resonance spectroscopy, chemical exchange saturation transfer, relayed nuclear Overhauser effect imaging, dynamic nuclear polarization, and hyperpolarized 13C MRI, are highlighted as examples of molecular and metabolic imaging. By comparing modalities across contrast mechanism, spatial and temporal scale, penetration depth, sensitivity, clinical utility, and technological maturity, this review provides a framework for understanding established imaging approaches and contextualizing emerging biomedical imaging technologies. Full article

High-resolution projections of near-surface winds are crucial for ocean circulation and sea level studies in Southeast Asia, a region characterized by complex coastlines and monsoon variability. This study evaluates the added value of dynamical downscaling using the Weather Research and Forecasting (WRF) model at 9 km resolution, driven by two CMIP6 global climate models (EC-Earth3 and MPI-ESM1-2-HR), to simulate 10 m wind climatology over the Southeast Asian seas. Comparisons were made against ERA5 reanalysis and the parent CMIP6 GCMs, focusing on seasonal mean patterns, interannual variability, and the annual cycle. The WRF simulations demonstrate substantial improvement in capturing the spatial structures of monsoonal winds and regional circulation features. Future wind projections under SSP2-4.5 and SSP5-8.5 scenarios reveal seasonally and spatially heterogeneous trends. The downscaled models project strengthening of winter monsoon winds over the Southeast Asian seas and a weakening of summer monsoon flows, with implications for upper ocean dynamics and regional sea level patterns. The leading modes of variability from EOF analysis indicate basin-wide wind anomalies modulated by periodic signals at ~1 year and ~2–7 years, likely driven by ENSO and the Asian monsoon. The power spectra of principal components reveal that internal variability persists across scenarios, though with increased signal-to-noise ratios (SNRs) in the WRF projections toward the end of the 21st century. Full article

Analyzing the long-term evolution of meso-scale urban morphological types is constrained by the “small-sample, high-dimensional” nature of historical data, which weakens the robustness and interpretability of conventional data-driven methods and limits the use of morphological evidence in digital urban analysis and built environment governance. To address this, we propose a theory-guided modular principal component analysis (TG-MPCA) framework for sample-constrained morphological research. By embedding domain knowledge into dimensionality reduction, the framework extracts compact, low-dimensional morphological indices that are both morphologically interpretable and internally stable within the selected sample set. Applied to Harbin’s representative districts through unsupervised hierarchical clustering, evolutionary lineage analysis, and transition node identification, it reveals a “layered response pattern” among morphological modules, an asymmetrical mapping between morphological types and historical periods, a structural breakpoint around 1946 that parallels post-war Western urban restructuring, and anomalous deceleration and premature convergence in typological evolution during transitional periods. These observations offer a meso-scale morphological perspective for understanding both the spatial transformation of Chinese cities and the developmental challenges of Northeast China’s old industrial bases, while demonstrating the value of theory-guided quantitative analysis for transforming fragmented historical spatial information into interpretable morphological evidence in data-constrained contexts. Full article

The Mediterranean Sea represents a hotspot of non-indigenous fish species introductions. Here, we provide a comprehensive bibliographic synthesis on five Lessepsian fish invaders—Fistularia commersonii, Lagocephalus sceleratus, Pterois miles, Siganus luridus, and S. rivulatus—verifying whether international bibliographic databases (IBDs) and online biodiversity information facilities (BIFs) provide adequate information on their occurrence, biology, ecology, and potential or extant commercial use. To this end, we extended to non-indexed bibliographic (NIB) sources available on Google Scholar and compared the results on spatial expansion dynamics and bibliographic trends. We demonstrate that the information provided on occurrence records exclusively by IBD and BIF sources underestimated the total expansion area of invaders by up to 14.8% and determined artifactual temporal voids. Remarkable knowledge gaps were identified in the literature on the reproductive biology and commercial use of all the species, independent of the literature sources, while a relatively larger breadth of information was available on their morphometrics and trophic ecology. Noticeably, NIB sources provided more complete bio-ecological information, particularly for L. sceleratus. In addition, the majority of the studies on commercial uses were published in NIB sources, to the point that for some species, IBD sources provided negligible or null information on the topic. This study indicates that disregarding non-indexed literature sources might exacerbate knowledge voids exclusively related to linguistic barriers or geographic biases, weakening the scientific support necessary for identifying appropriate strategies of control and mitigation of bioinvasions in the Mediterranean Sea. Full article

Water significantly modifies rock mechanical performance and fracture characteristics through water content and water distribution. Nevertheless, the evolution laws of rock mechanical properties and underlying fracture mechanisms under variable water conditions remain incompletely clarified. In this study, uniaxial compression tests were carried out on sandstone samples with diverse water immersion durations. Experimental observations reveal that the uniaxial compressive strength (UCS) and elastic modulus of sandstone follow negative exponential attenuation with prolonged immersion time, with maximum reductions of 50.1% and 25.6%, respectively. Under equivalent water contents, samples featuring dry exteriors and wet interiors possess lower strength than those with wet exteriors and dry interiors. A self-developed numerical code incorporating humidity diffusion effects was subsequently adopted to interpret water-controlled sandstone fracture behaviors. Numerical outputs verify that water-induced softening and heterogeneous water distribution exacerbate rock material heterogeneity and internal stress non-uniformity, triggering tensile microcracks along dry–wet interfaces. As the immersion duration rises, the rock failure mode transitions from shear-dominated mixed failure to tension-dominated failure, and finally reverts to shear-dominated mixed failure. Macroscopic rupture is predominantly governed by the gradual coalescence of tension-generated microcracks. This study offers a theoretical foundation to advance the understanding of water-triggered mechanical degradation and fracture mechanisms in sandstone. Full article

Background/Objectives: We aimed to evaluate the association between early post-treatment eosinophil (Eo) elevation and survival outcomes in metastatic renal cell carcinoma (mRCC) treated with vascular endothelial growth factor receptor tyrosine kinase inhibitors (VEGFR-TKIs). We also assessed whether early post-treatment Eo elevation provided prognostic information beyond the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) model. Methods: This Turkish retrospective multicenter cohort study included 280 patients with mRCC who received first-line VEGFR-TKI monotherapy between 2015 and 2025. Early post-treatment Eo elevation was defined as a post-baseline Eo percentage >5% in the complete blood count obtained within 15 days before the first computed tomography assessment performed for response/progression evaluation. Progression-free survival (PFS) and overall survival (OS) were estimated using Kaplan–Meier methods and compared with log-rank tests. Cox regression models assessed independent prognostic associations. Time-dependent receiver operating characteristic (ROC) curve analyses were performed to evaluate the incremental prognostic value of early post-treatment Eo elevation beyond IMDC risk. Results: The median age was 61 years, and 205 patients (73.2%) were male. Early post-treatment Eo elevation was observed in 88 patients (31.4%). Median PFS was longer in patients with early post-treatment Eo elevation than in those without Eo elevation (15.1 vs. 10.1 months; p < 0.001). Median OS was also longer in the Eo elevation group (70.0 vs. 38.6 months; p < 0.001). In multivariable analysis, early post-treatment Eo elevation remained independently associated with improved PFS (hazard ratio [HR]: 0.60, 95% confidence interval [CI]: 0.44–0.81, p = 0.001) and OS (HR: 0.51, 95% CI: 0.33–0.80, p = 0.004). The combined IMDC plus Eo model showed higher time-dependent area under the curve values than the IMDC model alone. Conclusions: Early post-treatment Eo elevation was independently associated with improved PFS and OS, and the combined model showed greater prognostic discrimination than IMDC risk alone. Given that Eo status was assessed after treatment initiation, residual selection bias cannot be excluded, and prospective validation is required before clinical implementation. Full article

Uzbekistan has rapidly expanded solar photovoltaic (PV) capacity as part of its transition toward a low-carbon energy system, supported by guaranteed purchase tariffs and an evolving regulatory framework. This paper evaluates the economic feasibility of solar PV investments in Uzbekistan through a combined ex post and ex ante analysis, focusing on both commercial-scale rooftop installations and a proposed large utility-scale floating photovoltaic (FPV) project. Ex post performance data from four commercial rooftop PV systems in Tashkent (20–304 kW) over the period 2024–2025 are analyzed using lifecycle investment appraisal metrics, including the Equivalent Uniform Annual Cost (EUAC)/LCOE framework, Net Present Value (NPV), and Internal Rate of Return (IRR), to benchmark real operating outcomes against modeled expectations. These results are subsequently used to calibrate ex ante simulations for a 491 MW FPV installation planned on the Sardoba reservoir, assessed using RETScreen Expert and a bespoke three-statement financial model incorporating detailed tax, financing, and operational assumptions. The findings indicate that commercial-scale rooftop PV projects in Tashkent operate close to the financial break-even point, with EUAC-based levelized costs of energy broadly aligned with current guaranteed purchase prices for PV electricity, resulting in near-zero NPVs. In contrast, the large-scale Sardoba FPV project demonstrates moderate but positive financial viability, with nominal IRRs of approximately 14–16% and payback periods under ten years at the prevailing tariff levels. Importantly, the monetized value of environmental externalities—primarily avoided CO₂ emissions—amounts to roughly from one quarter to a third of initial capital expenditure, materially enhancing the project’s overall economic value. The results suggest that while large-scale solar projects in Uzbekistan generate limited private financial rents, their societal benefits justify continued policy support, stopping short of additional direct subsidy disbursements but conducive to lower cost-of-capital measures. Full article

Student stress is a significant mental health issue in educational settings; therefore, developing reliable, calibrated, and interpretable predictive models can support the classification of observed stress levels. This study analyzed the public Student Stress Factors dataset, comprising 1100 records, 20 predictors, and one target variable, using a supervised machine learning pipeline designed to reduce information leakage. The pipeline included stratified data partitioning, encapsulated preprocessing, nested cross-validation restricted to the training data, and independent holdout evaluation. Nine ensemble and boosting algorithms for tabular data were compared: AdaBoost, Gradient Boosting, Random Forest, Extra Trees, Bagging, Voting, Stacking, XGBoost, and LightGBM. Model performance was assessed using key discrimination and calibration metrics, together with the nonparametric Friedman test for statistical comparison. Gradient Boosting achieved the best average performance in nested cross-validation, with an accuracy of 89.55 ± 3.16%, F1-weighted of 89.54 ± 3.17%, MCC of 0.845 ± 0.047, and ROC-AUC weighted of 98.59 ± 0.92%. XGBoost and LightGBM showed comparable performance. In the independent holdout set, the final calibrated model maintained robust predictive performance, achieving an accuracy of 0.8818, F1-weighted of 0.8818, MCC of 0.8237, and ROC-AUC weighted of 0.9861. Although the overall results indicate stable and high predictive performance, the Friedman test did not identify statistically significant differences among the algorithms, χ² = 10.953, p = 0.204. Therefore, model selection should consider not only predictive accuracy but also computational efficiency, calibration, interpretability, and implementation feasibility. Despite the internal stability of the pipeline and satisfactory holdout performance, the public and cross-sectional nature of the dataset limits causal inference and model transferability. Consequently, external and prospective validation is required before integration into institutional early warning systems. Full article

Game-based learning (GBL) has increasingly been recognized as a valuable approach for supporting student engagement, motivation, and skill development in educational settings. However, comparatively little research has examined analog gameplay within informal, co-curricular higher education environments. Grounded in Self-Determination Theory (SDT), this exploratory study examined students’ perceptions of their experiences at a university’s Academic Skills Centre’s (ASC) Game Café (not course-based), with particular attention to motivation, social engagement, and perceived academic skill development. Using a descriptive, cross-sectional survey design, data were collected from 44 student participants through a researcher-developed survey consisting of Likert-scale and open-ended questions. Quantitative data were analyzed using descriptive statistics, while qualitative responses were thematically coded. Internal consistency analyses demonstrated acceptable to strong reliability across survey scales (Q6 α = 0.70; Q7 α = 0.89). Findings indicated that students perceived a consistent association between the Game Café and positive emotional experiences, collaboration, peer interaction, and the perceived development of transferable skills such as communication, critical thinking, and problem-solving. Participants also described the café as a low-pressure and socially supportive environment that fostered motivation and informal learning beyond the classroom. Overall, the findings suggest that analog, co-curricular game-based environments may support students’ perceived engagement, belonging, and learning within higher education contexts. Full article