Search Results (25,040)

In order to obtain a gradient coating with excellent performance, novel titanium-modified plasma nitriding was primarily used as a support layer for the PVD coating of 38CrMoAl steel. The samples were subjected to titanium-modified plasma nitriding by placing sponge titanium around the samples, resulting in a thicker ductile diffusion layer and a thinner and denser compound layer. The research results showed that this thinner, denser compound layer formed by titanium-modified plasma nitriding provides stronger support for the AlCrN coating and thus bring about better performance compared to a conventional plasma nitrided layer, with the adhesion strength increasing from 16.8 N to 29.4 N, which is 42.8% higher than the conventional PN compound layer; the surface hardness increasing from 3650 HV_0.05 to 3780 HV_0.05; the friction coefficient and wear rate reducing from 0.64 and 5.4849 × 10⁻⁶ mm³/(N·m) to 0.61 and 2.3060 × 10⁻⁶ mm³/(N·m), respectively; and the wear performance improving by 137.85%. Additionally, the corrosion potential increased from −979.2 mV to −711.51 mV, and the value of impedance increased from 1.5515 × 10⁴ Ω·cm² to 9.4518 × 10⁴ Ω·cm², resulting in a significant improvement in corrosion resistance. In all, the novel support layer by titanium-modified plasma nitriding can provide much better support for AlCrN coating and thus bring about excellent enhanced performances, including adhesion strength and wear and corrosion resistance. Therefore, it is of great value in the PVD coating field, and it can provide valuable insights into gradient coating technology. Full article

Isorhapontigenin (trans-3,5,4′-trihydroxy-3′-methoxystilbene; ISO), a dietary derivative of resveratrol (trans-3,5,4′-trihydroxystilbene; RES), exhibits diverse health-promoting properties. To facilitate its potential development as a nutraceutical, a simple and reliable high-performance liquid chromatography (HPLC) method was developed and validated for the quantification of ISO in various murine biological matrices. Chromatographic separation was achieved with a reversed-phase HPLC column through a 17 min gradient delivery of a mixture of acetonitrile and formic acid (0.1% v/v) at a flow rate of 1.5 mL/min at 50 °C. Quantification was performed using ultraviolet (UV) detection at 325 nm, with a lower limit of quantification (LLOQ) of 15 ng/mL in both plasma and tissue homogenate samples. The method demonstrated excellent selectivity, accuracy, and precision, and ISO remained stable under the tested conditions. This method was subsequently employed to investigate the tissue distribution of ISO in mice following oral administration at a dose of 200 µmol/kg (equivalent to 51.7 mg/kg). ISO was rapidly absorbed and extensively distributed across major pharmacologically relevant organs. Despite its limited aqueous solubility, its oral absorption was not significantly compromised. Given its oral bioavailability and broad tissue distribution, ISO represents a promising candidate for further nutraceutical development. Full article

Compressor blades are subjected to multiaxial loads during operation. Using uniaxial life prediction formulas to predict their fatigue life can result in significant errors. Therefore, by analyzing the loading conditions of the blades, a fatigue life prediction model suitable for compressor blades was developed. This model was established by applying the load of a specific engine type to a notched bar specimen and considering the gradient and strengthening effects. Firstly, the parameters of the SWT model were used as the damage parameters to determine the critical plane location based on the principle of coordinate transformation, and these results were compared with the actual fracture angles. Additionally, the physical mechanisms of multiaxial fatigue crack initiation and propagation were investigated at the microscopic level. Secondly, the non-uniform stress field on the critical plane was obtained using the finite element method. The stress distribution from the critical point to the specimen’s principal axis was extracted and normalized to calculate the equivalent stress gradient factor. Finally, the results of the comprehensive fatigue life prediction model were computed. Comparisons between the calculated results of the proposed model, the SWT model, and the Shang model with the experimental fatigue life showed that the prediction accuracy of the proposed model is higher than that of the SWT model and the Shang Deguang model. Full article

Background: Pediatric Intensive Care Unit (PICU) outcome prediction is challenging, and machine learning (ML) can enhance it by leveraging large datasets. Methods: We built an ML model to predict PICU outcomes (“Death vs. Survival”, “Death or Morbidity vs. Survival without morbidity”, and “New Morbidity vs. Survival without new morbidity”) using the Trichotomous Outcome Prediction in Critical Care (TOPICC) study dataset. The model used the Light Gradient-Boosting Machine (LightGBM) algorithm, which was trained on 85% of the dataset and tested on 15% utilizing 10-fold cross validation. Results: The model demonstrated high accuracy across all dichotomies, with 0.98 for “Death vs. Survival”, 0.92 for “Death or New Morbidity vs. Survival without New Morbidity”, and 0.93 for “New Morbidity vs. Survival without New Morbidity.” The AUC-ROC values were also strong, at 0.89, 0.79, and 0.74, respectively. The precision was highest for “Death vs. Survival” (0.92), followed by 0.45 and 0.30 for the other dichotomies. The recalls were low, at 0.26, 0.31, and 0.34, reflecting the model’s difficulty in identifying all positive cases. The AUC-PR values (0.43, 0.37, and 0.20) highlight this trade-off. Conclusions: The LightGBM model demonstrated a predictive performance comparable to previously reported logistic regression models in predicting PICU outcomes. Future work should focus on enhancing the model’s performance and further validation across larger datasets to assess the model’s generalizability and real-world applicability. Full article

This study investigated the influence of physicochemical water parameters on benthic macroinvertebrate communities across 11 sampling stations located in the Western, Antalya, and Eastern Mediterranean Basins of Türkiye. Field studies were conducted in April, July, and October of 2018–2019. Water quality variables, such as temperature, pH, electrical conductivity, salinity, dissolved oxygen (DO), biological oxygen demand, ammonia nitrogen, total nitrogen, and total phosphorus, were measured. A total of 177 taxa and 5331 individuals were identified, with Insecta being the most dominant class, especially the order Diptera. Statistical analyses, including detrended correspondence analysis (DCA), revealed clear relationships between environmental gradients and species distribution. Species such as Paratendipes albimanus, Microtendipes pedellus, and Potamanthus luteus showed strong correlations with DO and other water quality parameters. This study emphasizes the importance of specific macroinvertebrate taxa as indicators of environmental conditions and suggests that certain species may serve as bioindicators for ecological monitoring and management in Mediterranean freshwater ecosystems in the context of ongoing global climate change. Full article

Ecology and adaptive differentiation of Epimedium are central to understanding both its taxonomic complexity and medicinal value. In this study, we integrate transcriptomic and plastid data from four natural populations of E. brevicornu (HZ, QLH, TS, WD) to reconstruct their phylogenetic relationships, estimate divergence times, and identify candidate genes associated with local adaptation. Nuclear gene-based phylogenies provide higher resolution and greater topological consistency than plastid data, underscoring the utility of nuclear data in lineages affected by hybridization and incomplete lineage sorting. Molecular dating indicated that major intraspecific divergence occurred during the mid-Quaternary (0.61–0.45 Ma), coinciding with climatic oscillations and montane isolation. Population structure showed strong correlations with temperature and precipitation gradients, suggesting environmentally driven selection. Signatures of positive selection and accelerated evolutionary rates revealed population-specific enrichment of genes involved in stress response, protein modification, signaling, and carbohydrate metabolism—key pathways linked to high-elevation adaptation. Protein–protein interaction networks further indicated a two-tier adaptation mechanism: ancestral network rewiring combined with population co-evolution of interacting genes. Together, these findings advance our understanding of alpine plant adaptation and provide candidate genes for further functional and breeding studies in Epimedium. Full article

It has become a global challenge to realize sustainable regional development in the context of demographic transition. Based on the panel data of the Beijing-Tianjin-Hebei region from 2017 to 2022, this paper analyzes in depth the impact mechanism of labor market integration on export quality and its sustainable development effect by using various econometric methods. It is found that labor market integration enhances regional export quality, and every 1% increase in the integration level can bring 0.184% improvement in export quality. Mechanism analysis shows that labor market integration works mainly through two channels: innovation synergy effect (27%) and labor cost effect (8%). Heterogeneity analysis shows that the elasticity coefficients of general trade and high-income nations are 0.155 and 0.208, respectively, but the elasticity coefficients for processing trade, low-income, lower-middle-income and upper-middle-income nations are not significant. Furthermore, feature fact analysis reveals that the three regions of Beijing, Tianjin, and Hebei have varying degrees of labor market integration: Beijing (0.038) > Tianjin (0.037) > Hebei (0.034); nevertheless, the export product quality gradient is reversed: Beijing (0.617) < Tianjin (0.665) < Hebei (0.669). The evaluation of sustainable development impacts reveals that labor market integration not only mitigates internal labor shortages but also effectively counteracts the external shock of U.S. tariff increases on China. This study provides important theoretical support and policy insights for building a sustainable regional development model in the context of demographic transition. Full article

Salinity, a critical environmental factor for fish survival, remains poorly understood in terms of how Triplophysa strauchii, a characteristic fish in Northwest China, physiologically responds to salinity stress. This study aimed to determine its salinity tolerance threshold and explore the associated physiological damage mechanisms. Six salinity gradients (11, 11.7, 12.5, 13.3, 14.3, 15.1 ppt) and a freshwater control group were established. Acute toxicity tests recorded mortality and behavior, while physiological–biochemical assays measured ion concentrations and enzyme activities in gills, kidneys, liver, intestines, and plasma over 96 h. The results showed a 96-hour median lethal concentration of 13.31 ppt and a safe concentration of 4.05 ppt. Gills and kidneys, as primary osmoregulatory organs, responded rapidly, whereas the liver and intestine lagged. Salinity ≤ 13.3 ppt allowed the fish to maintain homeostasis via physiological adjustments, but ≥14.3 ppt caused ion imbalance, immune function was significantly suppressed, and irreversible damage. These findings clarify the species’ salinity adaptation strategies, providing a basis for further research on chronic salinity stress. Full article

This paper presents a thermal study of a lithium-ion traction battery with different cooling configurations during simulated city driving and high-power charging. Four liquid cooling configurations—single or triple plates with straight or U-shaped tubes—were evaluated using finite element models in the Q-Bat Toolbox for MATLAB. Simulations were conducted using the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) and a high-current charging profile based on the CHAdeMO standard (up to 400 A). The results indicate that while cooling is not strictly necessary under typical driving conditions, it significantly improves thermal stability and reduces peak temperatures. The best configuration reduced peak cell temperatures by 1.96% during driving and by 16% during fast charging. The cooling system also minimized temperature gradients within the battery, reducing the risk of degradation. Box-plot analysis confirmed that an efficient cooling system stabilizes the temperature distribution and smooths out extreme values. The results highlight the importance of thermal management for extending battery life and ensuring safe operation, particularly during fast charging conditions. Full article

Background/Objectives: Recent evidence challenges the classical chemiosmotic theory, suggesting that proton movement along membrane surfaces—not bulk-phase gradients—drives bioenergetic processes. Proton accumulation on membranes like the myelin sheath and endoplasmic reticulum (ER) may represent a universal mechanism for cellular energy storage. This study investigates whether phospholipids from these membranes, combined with anionic bee venom proteins, enhance proton absorption, potentially elucidating a novel bioenergetic pathway. Methods: Five phospholipids (phosphatidylethanolamine, phosphatidylserine, phosphatidylinositol, sphingomyelin, phosphatidylcholine) from rat liver were isolated to model myelin/ER membranes. Anionic proteins (pI 5.65–5.80) were purified from bee venom via cation exchange chromatography. Liposomes (with/without proteins) were prepared, and proton absorption was quantified by pH changes in suspensions versus pure water. Statistical significance was assessed via ANOVA and t-tests. Results: All phospholipid liposomes examined in this study absorbed protons under the tested conditions, with phosphatidylethanolamine showing the highest capacity (pH increase: 7.00 → 7.18). Liposomes enriched with anionic proteins exhibited significantly greater proton absorption (e.g., phosphatidylserine + proteins: pH 8.15 vs. 7.15 alone; p < 2.43 × 10⁻⁶). Sphingomyelin-protein liposomes absorbed the most protons, suggesting that protein–phospholipid interactions modulate surface proton affinity. Conclusions: Anionic bee venom proteins amplify proton absorption by phospholipid membranes, supporting the hypothesis that lipid–protein complexes act as “proton capacitors”. This mechanism may underpin extramitochondrial energy storage in myelin and ER. Pharmacologically, targeting these interactions could mitigate bioenergetic deficits in aging or disease. Further research should define the structural basis of proton capture by membrane-anchored proteins. Full article

Deep neural networks (DNNs) are highly effective across many domains but are sensitive to noisy or corrupted training data. Existing noise mitigation strategies often rely on strong assumptions about noise distributions or require costly retraining, limiting their scalability. Inspired by machine unlearning, we propose a novel framework that integrates attribution-guided data partitioning, neuron pruning, and targeted fine-tuning to enhance robustness. Our method uses gradient-based attribution to probabilistically identify clean samples without assuming specific noise characteristics. It then applies sensitivity-based neuron pruning to remove components most susceptible to noise, followed by fine-tuning on the retained high-quality subset. This approach jointly addresses data and model-level noise, offering a practical alternative to full retraining or explicit noise modeling. We evaluate our method on CIFAR-10 image classification and keyword spotting tasks under varying levels of label corruption. On CIFAR-10, our framework improves accuracy by up to 10% (F-FT vs. retrain) and reduces retraining time by 47% (L-FT vs. retrain), highlighting both accuracy and efficiency gains. These results highlight its effectiveness and efficiency in noisy settings, making it a scalable solution for robust generalization. Full article

Lithology identification is a critical technology for geological resource exploration and engineering safety assessment. However, traditional methods suffer from insufficient feature representation and low classification accuracy due to challenges such as weathering, vegetation cover, and spectral overlap in complex sedimentary rock regions. This study proposes a hierarchical multi-feature random forest algorithm based on Feature-Preserved Compressive Sampling (FPCS). Using 3D laser point cloud data from the Manas River outcrop in the southern margin of the Junggar Basin as the test area, we integrate graph signal processing and multi-scale feature fusion to construct a high-precision lithology identification model. The FPCS method establishes a geologically adaptive graph model constrained by geodesic distance and gradient-sensitive weighting, employing a three-tier graph filter bank (low-pass, band-pass, and high-pass) to extract macroscopic morphology, interface gradients, and microscopic fracture features of rock layers. A dynamic gated fusion mechanism optimizes multi-level feature weights, significantly improving identification accuracy in lithological transition zones. Experimental results on five million test samples demonstrate an overall accuracy (OA) of 95.6% and a mean accuracy (mAcc) of 94.3%, representing improvements of 36.1% and 20.5%, respectively, over the PointNet model. These findings confirm the robust engineering applicability of the FPCS-based hierarchical multi-feature approach for point cloud lithology identification. Full article

Spatial heterogeneity in soil deposition poses a significant challenge to accurate geotechnical characterization, which is essential for sustainable infrastructure development. This study presents an advanced geotechnical data-driven mapping framework, based on a monotonized and augmented formulation of Shepard’s inverse distance weighting (IDW) algorithm, implemented through the Google Earth Engine (GEE) platform. The approach is rigorously evaluated through a comparative analysis against the classical IDW and Kriging techniques using standard key performance indices (KPIs). Comprehensive field and laboratory data repositories were developed in accordance with international geotechnical standards (e.g., ASTM). Key geotechnical parameters, i.e., standard penetration test (SPT-N) values, shear wave velocity (Vs), soil classification, and plasticity index (PI), were used to generate high-resolution geospatial models for a previously unmapped region, thereby providing essential baseline data for building infrastructure design. The results indicate that the augmented IDW approach exhibits the best spatial gradient conservation and local anomaly detection performance, in alignment with Tobler’s First Law of Geography, and outperforms Kriging and classical IDW in terms of predictive accuracy and geologic plausibility. Compared to classical IDW and Kriging, the augmented IDW algorithm achieved up to a 44% average reduction in the RMSE and MAE, along with an approximately 30% improvement in NSE and PC. The difference in spatial areal coverage was found to be up to 20%, demonstrating an improved capacity to model spatial subsurface heterogeneity. Thematic design maps of the load intensity (LI), safe bearing capacity (SBC), and optimum foundation depth (OD) were constructed for ready application in practical design. This work not only establishes the inadequacy of conventional geostatistical methods in highly heterogeneous soil environments but also provides a scalable framework for geotechnical mapping with accuracy in data-poor environments. Full article

Phosphogypsum represents a gypsum-based solid waste originating from phosphoric acid production, which can be exploited for road filling after cement modification. This study delved into the composition design of high-volume phosphogypsum road base materials, aiming to ascertain their feasibility for subgrade filling, and refine the mix ratio. The main content of phosphogypsum was set at three high-proportion intervals of 86%, 88% and 90%, while the total content of inorganic curing agent was fixed at 0.5% of the total material. Within such a total amount, the proportion of bentonite was preserved at 20%, whereas the proportion of waterproofing agent was configured at three gradients of 20%, 25% and 30%, with the remaining part supplemented by powdered sodium silicate. Merged with trace amounts of inorganic curing agents, particularly the waterproofing agent component, the composite cementitious system comprising cement and ground granulated blast-furnace slag (GGBS) was leveraged to augment the key road performance and water stability of high-volume phosphogypsum-based materials. Material strengths were observed to be distinguishable under an array of phosphogypsum contents, which could be explained by the varying proportions of cement, GGBS and waterproofing agent. The test samples and microscopic products were dissected via XRD and SEM, demonstrating that the hydration products of the materials were predominantly C-S-H gel and ettringite crystals. Full article

To foster the performance of wireless communication while saving energy, the integration of Intelligent Reflecting Surfaces (IRS) into autonomous vehicle (AV) communication networks is considered a powerful technique. This paper proposes a novel IRS-assisted vehicular communication model that combines Lagrange optimization and Gradient-Based Phase Optimization to determine the optimal transmission power, optimal interference transmission power, and IRS phase shifts. Additionally, the proposed model help increase the Signal-to-Interference-plus-Noise Ratio (SINR) by utilizing IRS, which leads to maximizes energy efficiency and the achievable data rate under a variety of environmental conditions, while guaranteeing that resource limits are satisfied. In order to represent dense vehicular environments, practical constraints for the system model, such as IRS reflection efficiency and interference, have been incorporated from multiple sources, namely, Device-to-Device (D2D), Vehicle-to-Vehicle (V2V), Vehicle-to-Base Station (V2B), and Cellular User Equipment (CUE). A Lagrangian optimization approach has been implemented to determine the required transmission interference power and the best IRS phase designs in order to enhance the system performance. Consequently, a one-dimensional convolutional neural network has been implemented for the optimized data provided by this framework as training input. This deep learning algorithm learns to predict the required optimal IRS settings quickly, allowing for real-time adaptation in dynamic wireless environments. The obtained results from the simulation show that the combined optimization and prediction strategy considerably enhances the system reliability and energy efficiency over baseline techniques. This study lays a solid foundation for implementing IRS-assisted AV networks in real-world settings, hence facilitating the development of next-generation vehicular communication systems that are both performance-driven and energy-efficient. Full article