Search Results (9,663)

In most commercial lithium-ion batteries, graphite remains the primary anode material. However, its theoretical specific capacity is only 372 mAh∙g⁻¹, which falls short of meeting the demands of high-performance electronic devices. Silicon anodes, despite boasting an ultra-high theoretical specific capacity of 4200 mAh∙g⁻¹, suffer from significant volume expansion (>300%) during cycling, leading to severe capacity fade and limiting their commercial viability. Currently, silicon-based polymer-derived ceramics have emerged as a highly promising next-generation anode material for lithium-ion batteries, thanks to their unique nano-cluster structure, tunable composition, and low volume expansion characteristics. The maximum capacity of the ceramics can exceed 1000 mAh∙g⁻¹, and their unique synthesis routes enable customization to align with diverse electrochemical application requirements. In this paper, we present the progress of silicon oxycarbide (SiOC), silicon carbonitride (SiCN), silicon boron carbonitride (SiBCN) and silicon oxycarbonitride (SiOCN) in the field of LIBs, including their synthesis, structural characteristics and electrochemical properties, etc. The mechanisms of lithium-ion storage in the Si-based anode materials are summarized as well, including the key role of free carbon in these materials. Full article

To ensure water source security and sustainability of the national major strategic project “South-to-North Water Diversion”, this study aims to evaluate the spatio-temporal evolution characteristics of the ecosystem service value (ESV) in its water source area from 2002 to 2022. This study reveals its changing trends and main influencing factors, and thereby provides scientific support for the ecological protection and management of the water source area. Quantitative assessment of the ESV of the region was carried out using the Equivalence Factor Method (EFM), aiming to provide scientific support for ecological protection and resource management decision-making. In the past 20 years, the ESV has shown an upward trend year by year, increasing by 96%. The regions with the highest ESV were Garzê Prefecture and Aba Prefecture, which increased by 130.3% and 60.6%, respectively. The ESV of Xinlong county, Danba county, Rangtang county, and Daofu county increased 4.8 times, 1.5 times, 12.5 times, and 8.9 times, respectively. In the last two decades, arable land has decreased by 91%, while the proportions of bare land and water have decreased by 84% and 91%, respectively. Grassland had the largest proportion. Forests and grasslands, vital for climate regulation, water cycle management, and biodiversity conservation, have expanded by 74% and 43%, respectively. It can be seen from Moran’s I index values that the dataset as a whole showed a slight positive spatial autocorrelation, which increased from −0.041396 to 0.046377. This study reveals the changing trends in ESV and the main influencing factors, and thereby provides scientific support for the ecological protection and management of the water source area. Full article

The size of the gluteus medius muscle (GM) in swine significantly impacts both hindlimb conformation and carcass yield, while little is known about the genetic architecture of this trait. This study aims to estimate genetic parameters and identify candidate genes associated with this trait through a genome-wide association study (GWAS). A total of 439 commercial crossbred pigs, possessing both Landrace and Yorkshire ancestry, were genotyped using the Porcine 50K chip. The length and width of the GM were directly measured, and the area was then calculated from these values. The heritabilities were estimated by HIBLUP (V1.5.0) software, and the GWAS was conducted employing the BLINK model implemented in GAPIT3. The heritability estimates for the length, width, and area of the GM were 0.43, 0.40, and 0.46, respectively. The GWAS identified four genome-wide significant SNPs (rs81381267, rs697734475, rs81298447, and rs81458910) associated with the gluteus medius muscle area. The PDE4D gene was identified as a promising candidate gene potentially involved in the regulation of gluteus medius muscle development. Our analysis revealed moderate heritability estimates for gluteus medius muscle size traits. These findings enhance our understanding of the genetic architecture underlying porcine muscle development. Full article

Green innovation is a critical driver of sustainable development, yet it often faces efficiency challenges in rapidly industrializing economies. This study investigates the effect of China’s Circular Economy Demonstration Policy (CEDP) on urban green innovation efficiency (GIE) using city-level panel data from 2010 to 2021. Employing a difference-in-differences (DID) approach, we find that CEDP significantly enhances GIE, with the policy effect becoming statistically significant after a three-year lag and accumulating over time. Robustness tests, including placebo analyses, alternative dependent variables, and propensity score matching, confirm the validity of the results. Mechanism analysis reveals that the policy improves green innovation primarily by reducing capital distortion, promoting market integration, and enhancing resource allocation efficiency. Further heterogeneity analyses show that the positive effects are stronger in central cities, capital cities, and eastern regions, reflecting the role of local economic and institutional conditions. The study concludes with policy implications emphasizing regionally tailored implementation, capacity building, and long-term commitment to maximize green innovation outcomes. Full article

430 stainless steel exhibits soft magnetic properties, excellent formability, and corrosion resistance, making it widely used in industrial applications. This study investigates the effects of different cold working rates on the properties of 430 stainless steel subjected to various magnetic annealing atmospheres (F-1.5Si, F-1.5Si-10%, F-1.5Si-40%, F-1.5Si-10% (MA), F-1.5Si-40% (MA), F-1.5Si-10% (H₂), and F-1.5Si-40% (H₂)). The results indicate that increasing the cold working rate improves the material’s mechanical properties; however, it negatively impacts its magnetic and corrosion resistance properties. Additionally, the magnetic annealing process improves the mechanical properties, while atmospheric magnetic annealing optimizes the overall magnetic performance. In contrast, magnetic annealing in a hydrogen atmosphere does not enhance the magnetic properties as effectively as atmospheric magnetic annealing. Still, it promotes the formation of a protective layer, preserving the mechanical properties and providing better corrosion resistance. Furthermore, regardless of whether magnetic annealing is conducted in an atmospheric or hydrogen environment, materials with 10% cold work rate (F-1.5Si-10% (MA) and F-1.5Si-10% (H₂)) exhibit the lowest coercive force (286 and 293 A/m in the 10 Hz test condition), making them ideal for electromagnetic applications. Full article

Landslides are destructive geological hazards, making accurate landslide susceptibility assessment essential for disaster prevention and mitigation. However, existing studies often lack scientific rigor in negative sample construction and have unclear model applicability. This study focuses on Ya’an City, Sichuan Province, China, and proposes an innovative approach to negative sample construction using Geographically Weighted Regression (GWR), which is then integrated with Tabular Network (TabNet), a deep learning architecture tailored to structured tabular data, to assess landslide susceptibility. The performance of TabNet is compared against Random Forest, Light Gradient Boosting Machine, deep neural networks, and Residual Networks. The experimental results indicate that (1) the GWR-based sampling strategy substantially improves model performance across all tested models; (2) TabNet trained using the GWR-based negative samples achieves superior performance over all other evaluated models, with an average AUC of 0.9828, exhibiting both high accuracy and interpretability; and (3) elevation, land cover, and annual Normalized Difference Vegetation Index are identified as dominant predictors through TabNet’s feature importance analysis. The results demonstrate that combining GWR and TabNet substantially enhances landslide susceptibility modeling by improving both accuracy and interpretability, establishing a more scientifically grounded approach to negative sample construction, and providing an interpretable, high-performing modeling framework for geological hazard risk assessment. Full article

Manganese (Mn) excess and low pH often coexist in some citrus orchard soils. Little information is known about the underlying mechanism by which raising pH reduces Mn toxicity in citrus plants. ‘Sour pummelo’ (Citrus grandis (L.) Osbeck) seedlings were treated with 2 (Mn2) or 500 (Mn500) μM Mn at a pH of 3 (P3) or 5 (P5) for 25 weeks. Raising pH mitigated Mn500-induced increases in Mn, iron, copper, and zinc concentrations in roots, stems, and leaves, as well as nitrogen, phosphorus, potassium, calcium, magnesium, sulfur, copper, iron, and zinc distributions in roots, but it mitigated Mn500-induced decreases in nitrogen, phosphorus, potassium, calcium, magnesium, sulfur, and boron concentrations in roots, stems, and leaves, as well as nutrient imbalance. Raising pH mitigated Mn500-induced necrotic spots on old leaves, yellowing of young leaves, decreases in seedling growth, leaf chlorophyll concentration, and CO₂ assimilation (A_CO2), increase in root dry weight (DW)/shoot DW, and alterations of leaf chlorophyll a fluorescence (OJIP) transients and related indexes. Further analysis indicated that raising pH ameliorated Mn500-induced impairment of nutrient homeostasis, leaf thylakoid structure by iron deficiency and competition of Mn with magnesium, and photosynthetic electron transport chain (PETC), thereby reducing Mn500-induced declines in A_CO2 and subsequent seedling growth. These results validated the hypothesis that raising pH reduced Mn toxicity in ‘Sour pummelo’ seedlings by (a) reducing Mn uptake, (b) efficient maintenance of nutrient homeostasis under Mn stress, (c) reducing Mn excess-induced impairment of thylakoid structure and PEPC and inhibition of chlorophyll biosynthesis, and (d) increasing A_CO2 and subsequent seedling growth under Mn excess. Full article

Understanding vehicle emissions is essential for developing effective carbon reduction strategies in the transport sector. Conventional emission models often assume homogeneity and linearity, overlooking real-world asymmetries that arise from variations in vehicle design and powertrain configurations. This study explores how machine learning and explainable AI techniques can effectively capture both symmetric and asymmetric emission patterns across different vehicle types, thereby contributing to more sustainable transport planning. Addressing a key gap in the existing literature, the study poses the following question: how do structural and behavioral factors contribute to asymmetric emission responses in internal combustion engine vehicles compared to new energy vehicles? Utilizing a large-scale Spanish vehicle registration dataset, the analysis classifies vehicles by powertrain type and applies five supervised learning algorithms to predict CO₂ emissions. SHapley Additive exPlanations (SHAPs) are employed to identify nonlinear and threshold-based relationships between emissions and vehicle characteristics such as fuel consumption, weight, and height. Among the models tested, the Random Forest algorithm achieves the highest predictive accuracy. The findings reveal critical asymmetries in emission behavior, particularly among hybrid vehicles, which challenge the assumption of uniform policy applicability. This study provides both methodological innovation and practical insights for symmetry-aware emission modeling, offering support for more targeted eco-design and policy decisions that align with long-term sustainability goals. Full article

Ecotribology focuses on both saving energy resources and reducing environmental pollution. Considering environmental concerns, water-based nanolubricants have gained significant attention over conventional oil-based ones. Non-ecotoxic and highly environmentally friendly nanoadditives were chosen for nanolubricant synthesis, especially considering their use at elevated temperatures. In this study, hexagonal boron nitride nanosheets (hBNNSs) and titanium dioxide nanoparticles (TiO₂ NPs) were used to prepare water-based lubricants with glycerol and surfactant sodium dodecyl benzene sulfonate (SDBS) in water under ultrasonication. An Rtec ball-on-disk tribometer was used to investigate the tribological performance of the synthesised water-based lubricants containing different nano-hBN/TiO₂ concentrations, with dry and water conditions used as benchmarks. The results indicated that the water-based nanolubricant containing 0.5 wt% hBN and 0.5 wt% TiO₂ exhibited the best tribological performance at both ambient (25 °C) and elevated (500 °C) temperatures. This optimal concentration leads to a reduction in the coefficient of friction (COF) by 72.9% and 37.5%, wear of disk by 62.5% and 49%, and wear of ball by 74% and 69% at ambient and elevated temperatures, respectively, compared to that of distilled water. Lubrication mechanisms were attributed to the rolling, mending, tribofilm, solid layer formation, and synergistic effects of hBNNSs and TiO₂ NPs. Full article

In order to increase the effectiveness of model training, data reduction is essential to data-centric Artificial Intelligence (AI). It achieves this by locating the most instructive examples in massive datasets. To increase data quality and training efficiency, the main difficulty is choosing the best examples rather than the complete datasets. In this paper, we propose an effective data reduction strategy based on Pointwise 𝒱-Information (PVI). To enable a static method, we first use PVI to quantify instance difficulty and remove instances with low difficulty. Experiments show that classifier performance is maintained with only a 0.0001% to 0.76% decline in accuracy when 10–30% of the data is removed. Second, we train the classifiers using a progressive learning strategy on examples sorted by increasing PVI, accelerating convergence and achieving a 0.8% accuracy gain over conventional training. Our findings imply that training a classifier on the chosen optimal subset may improve model performance and increase training efficiency when combined with an efficient data reduction strategy. Furthermore, we have adapted the PVI framework, which was previously limited to English datasets, to a variety of Chinese Natural Language Processing (NLP) tasks and base models, yielding insightful results for faster training and cross-lingual data reduction. Full article

In this study, steel fibers were used to improve the mechanical properties of high-strength self-compacting concrete (HSSCC), and its effect on the fracture mechanical properties was investigated by a three-point bending test with notched beams. Coupled with the digital image correlation (DIC) technique, the fracture process of steel-fiber-reinforced HSSCC was analyzed to elucidate the reinforcing and fracture-resisting mechanisms of steel fibers. The results indicate that the compressive strength and flexural strength of HSSCC cured for 28 days exhibited an initial decrease and then an enhancement as the volume fraction (V_f) of steel fibers increased, whereas the flexural-to-compressive ratio linearly increased. All of them reached their maximum of 110.5 MPa, 11.8 MPa, and 1/9 at 1.2 vol% steel fibers, respectively. Steel fibers significantly improved the peak load (F_P), peak opening displacement (CMOD_P), fracture toughness (K_IC), and fracture energy (G_F) of HSSCC. Compared with HSSCC without steel fibers (HSSCC-0), the F_P, K_IC, CMOD_P, and G_F of HSSCC with 1.2 vol% (HSSCC-1.2) increased by 23.5%, 45.4%, 11.1 times, and 20.1 times, respectively. The horizontal displacement and horizontal strain of steel-fiber-reinforced HSSCC both increased significantly with an increasing V_f. HSSCC-0 experienced unstable fracture without the occurrence of a fracture process zone during the whole fracture damage, whereas the fracture process zone formed at the notched beam tip of HSSCC-1.2 at its initial loading stage and further extended upward in the beams of high-strength self-compacting concrete with a 0.6% volume fraction of steel fibers and HSSCC-1.2 as the load approaches and reaches the peak. Full article

This study systematically investigates 50 μm-diameter 631 stainless steel fine wires subjected to both sequential and simultaneous electrothermomechanical loading to simulate probe spring conditions in microelectronic test environments. Under cyclic current loading (~10⁴ A/cm²), the 50 μm 631SS wire maintained electrical integrity up to 0.30 A for 15,000 cycles. Above 0.35 A, rapid oxide growth and abnormal grain coarsening resulted in surface embrittlement and mechanical degradation. Current-assisted tensile testing revealed a transition from recovery-dominated behavior at ≤0.20 A to significant thermal softening and ductility loss at ≥0.25 A, corresponding to a threshold temperature of approximately 200 °C. These results establish the endurance limit of 631 stainless steel wire under coupled thermal–mechanical–electrical stress and clarify the roles of Joule heating, oxidation, and microstructural evolution in electrical fatigue resistance. A degradation map is proposed to inform design margins and operational constraints for fatigue-tolerant, electrically stable interconnects in high-reliability probe spring applications. Full article

Background: Ulcerative colitis (UC), characterized by chronic intestinal inflammation, epithelial barrier dysfunction, and immune imbalance demands novel ameliorative strategies beyond conventional approaches. Methods: In this study, the probiotic properties of Lactobacillus paracasei L21 (L. paracasei L21) and its ability to ameliorate colitis were evaluated using an in vitro lipopolysaccharide (LPS)-induced intestinal crypt epithelial cell (IEC-6) model and an in vivo dextran sulfate sodium (DSS)-induced UC mouse model. Results: In vitro, L. paracasei L21 decreased levels of pro-inflammatory cytokines (TNF-α, IL-1β, IL-8) while increasing anti-inflammatory IL-10 levels (p < 0.05) in LPS-induced IEC-6 cells, significantly enhancing the expression of tight junction proteins (ZO-1, occludin, claudin-1), thereby restoring the intestinal barrier. In vivo, both viable L. paracasei L21 and its heat-inactivated postbiotic (H-L21) mitigated weight loss, colon shortening, and disease activity indices, concurrently reducing serum LPS and proinflammatory mediators. Interventions inhibited NF-κB signaling while activating HIF1α/AhR pathways, increasing IL-22 and mucin MUC2 to restore goblet cell populations. Gut microbiota analysis showed that both interventions increased the abundance of beneficial gut bacteria (Lactobacillus, Dubococcus, and Akkermansia) and improved faecal propanoic acid and butyric acid levels. H-L21 uniquely exerted an anti-inflammatory effect, marked by the regulation of Dubosiella, while L. paracasei L21 marked by the Akkermansia. Conclusions: These results highlight the potential of L. paracasei L21 as a candidate for the development of both probiotic and postbiotic formulations. It is expected to provide a theoretical basis for the management of UC and to drive the development of the next generation of UC therapies. Full article

Timely and accurate monitoring of crop nitrogen (N) status is essential for precision agriculture. UAV-based hyperspectral remote sensing offers high-resolution data for estimating plant nitrogen concentration (PNC), but its cost and complexity limit large-scale application. This study compares the performance of UAV hyperspectral data (S185 sensor) with simulated multispectral data from DJI Phantom 4 Multispectral (P4M), PlanetScope (PS), and Sentinel-2A (S2) in estimating winter wheat PNC. Spectral data were collected across six growth stages over two seasons and resampled to match the spectral characteristics of the three multispectral sensors. Three variable selection strategies (one-dimensional (1D) spectral reflectance, optimized two-dimensional (2D), and three-dimensional (3D) spectral indices) were combined with Random Forest Regression (RFR), Support Vector Machine Regression (SVMR), and Partial Least Squares Regression (PLSR) to build PNC prediction models. Results showed that, while hyperspectral data yielded slightly higher accuracy, optimized multispectral indices, particularly from PS and S2, achieved comparable performance. Among models, SVM and RFR showed consistent effectiveness across strategies. These findings highlight the potential of low-cost multispectral platforms for practical crop N monitoring. Future work should validate these models using real satellite imagery and explore multi-source data fusion with advanced learning algorithms. Full article