Search Results (538,143)

Under the intensifying pressures of climate change and human activities, the characteristics of land-use types and landscape patterns are widely recognized to exert significant influences on river water quality. Nevertheless, in dry-hot valley basins characterized by fragile ecological conditions and frequent geological hazards, the responses of river water quality to changes in landscape characteristics under the combined effects of natural disasters and anthropogenic disturbances remain poorly understood. In the present study, the Xiaojiang River Basin, a typical dry-hot valley basin subjected to intensive anthropogenic activities and frequent geological hazards, was selected. Through the integration of landscape pattern indices analysis and redundancy analysis, the spatial and temporal variations in river water quality in the Xiaojiang River Basin were quantified, and the effects of land-use types and landscape patterns on river water quality were systematically elucidated. Results showed that (1) the key water quality indexes such as total phosphorus, total nitrogen, ammonia nitrogen and COD in the Xiaojiang River Basin were shown as flood season > non-flood season; for example, the average TN increased from 1.37 mg/L (non-flood season) to 2.90 mg/L (flood season), and the average COD increased from 3.24 mg/L to 15.98 mg/L. In contrast, DO decreased from 8.07 mg/L (non-flood season) to 6.72 mg/L (flood season), and conductivity decreased from 561.4 µs/cm to 480.90 µs/cm. (2) Spatially, these key water quality indicators were shown as hazard-prone area > residential area > cultivated land area. (3) The larger the area of the debris flow trace areas, the greater the fluxes of nitrogen and phosphorus in the tributaries and the main stream in the flood season, and the worse the water quality of the river; after heavy rainfall, the fluxes of key water quality indicators generally showed a geometric multiple increase, with average growth rates of 1.95 (TP), 2.41 (TN), 2.34 (NH₃-N) and 4.74 (COD), respectively. (4) The ability of landscape patterns in flood season to explain the change in water quality is better than that in non-flood season. On different spatial scales, in the down-stream hazard-prone areas, upstream residential areas and cultivated land areas, the changes in river water quality indicators were mainly affected by landscape pattern indicators such as PD_hazard-influenced areas, IJI_residential areas and DIV_cultivated land. Our results can provide scientific guidance for the soil and water conservation practice, ecological restoration, and land-use management in the dry-hot valley of Southwest China and the water environment protection of the Baihetan Reservoir area. Full article

Advance hydraulic supports are widely applied in deep coal mine roadways; however, inappropriate initial support force often leads to either insufficient roof control or over-support, weakening the effectiveness of bolt–cable systems. To clarify the interaction mechanism between advance passive support and active bolt–cable reinforcement, an advance roadway support model was developed using FLAC3D based on the geological conditions of the 1432 working face in the Dongtan Coal Mine. Numerical simulations were conducted by varying the initial support force from 0 to 14 MPa, and the corresponding roof displacement, bolt stress, and cable axial force responses were systematically analyzed. The results indicate that roof subsidence decreases nonlinearly with increasing support force, exhibiting a rapid suppression stage (0–10 MPa) and a stable coordination stage (10–12 MPa). Within this optimal range, load transfer from the roof to the passive support is significantly enhanced, leading to effective stress relief and homogenization in the bolt–cable system. When the support force exceeds 12 MPa, further deformation control becomes marginal, indicating a transition from cooperative load sharing to over-support. These findings reveal the staged interaction mechanism between advance passive support and active reinforcement systems, providing a quantitative basis for selecting appropriate initial support force in deep roadway engineering. Full article

Conventional tribological materials such as metals, ceramics, and synthetic polymers demand energy-intensive processing and create end-of-life waste. This motivates the search for more sustainable alternatives. Recent research demonstrates that agricultural residues, industrial by-products, post-consumer waste, and recycled polymers can be engineered into tribological systems that provide competitive wear resistance, stable friction, and multifunctional benefits, including thermal dissipation and vibration damping. This review summarizes progress across these material categories, highlighting how fillers like rice husk ash, fly ash, tire-derived carbon black, and reprocessed plastics transition from low-value waste into high-performance tribomaterials. System-level strategies such as interface engineering, hybrid reinforcement, and advanced processing are essential for overcoming material variability and achieving reliable tribological performance. In parallel, optimization approaches, including predictive modeling and smart material design, are increasingly enabling improved consistency, reproducibility, and scalability. Applications in automotive braking systems, recycled carbon black composites, acoustic damping structures, coatings, and reinforced polymers confirm the industrial viability of waste-derived materials. While challenges remain in feedstock variability, standardization, and long-term durability, these developments point to waste-based tribology as a practical pathway toward circular economy solutions that unite sustainability with engineering performance. Full article

Kynurenic acid (KynA), a tryptophan metabolite that regulates immune homeostasis via G protein-coupled receptor 35 (GPR35), has an undefined role in post-myocardial infarction (MI) immune responses. To clarify this role, we established a murine MI model and administered KynA intraperitoneally to evaluate cardiac function and ventricular remodeling. Macrophage infiltration was assessed, and macrophages were depleted via clodronate liposomes to confirm their contribution to KynA-mediated cardioprotection. In bone marrow-derived macrophages (BMDMs), GPR35-targeted siRNA verified the receptor-dependent action of KynA. KynA improved cardiac function, reduced infarct scarring and fibrosis, and suppressed pro-inflammatory macrophage infiltration in MI mice, with these cardioprotective effects abrogated by macrophage depletion. Mechanistically, KynA inhibited voltage-dependent anion channel 1 oligomerization, prevented mitochondrial DNA leakage, and downregulated the cGAS/STING/TBK1/IκBα/P65 pathway in macrophages, while exogenous mitochondrial DNA counteracted this inhibition. Collectively, the KynA/GPR35 axis exerts cardioprotective effects against MI by attenuating macrophage pro-inflammatory responses, highlighting its potential as a novel therapeutic target. Full article

Reinforced concrete (RC) columns, vital components of urban infrastructure, are highly vulnerable to severe damage from contact explosions, posing significant threats to structural integrity and occupant safety. This study presents a rigorous experimental investigation into the dynamic blast response of RC columns and the efficacy of externally bonded Carbon Fiber Reinforced Polymer (CFRP) wraps as a retrofitting solution. Three series of scaled RC columns were subjected to controlled contact explosions using RDX charges of 50 g, 30 g, and 20 g. For each charge level, three configurations were tested: unretrofitted, single-layer unidirectional CFRP (hoop direction), and dual-layer orthogonal CFRP (hoop and longitudinal). A comprehensive instrumentation system, including high-speed cameras, accelerometers, and pressure transducers, captured blast overpressure, crack evolution, and dynamic acceleration. The results demonstrate that CFRP retrofitting substantially enhances blast resistance and structural performance. Peak accelerations were reduced by up to 68%, with the dual-layer configuration achieving the highest mitigation across all charge levels. In terms of damage control, a single CFRP layer reduced spalling height by 65%, while the dual-layer system achieved up to a 75% reduction. Damage depth was also mitigated by up to 60%, highlighting the superior energy dissipation and containment provided by multi-layered CFRP. These findings underscore CFRP’s significant potential as a robust, practical, and scalable retrofitting solution for enhancing the blast resilience of critical infrastructure, contributing directly to improved urban safety and structural protection in blast-prone environments. Full article

Accurate tool wear monitoring plays a decisive role in machining efficiency, product quality and reliability in modern manufacturing systems. Existing deep learning methods struggle to balance the high-frequency transient features and low-frequency evolution trends in tool wear signals, often losing key temporal evolution details when processing long-range degradation data. Therefore, this paper proposes an online prediction method of tool wear value that combines multi-scale convolution and dual-attention temporal features. This method extracts local mutation and trend features in wear signals through multi-scale convolution, captures wear evolution features through bidirectional cyclic network, and adaptively fuses local detail information and global trend through dual attention mechanism SWGC-DA to generate a multi-scale time series feature-driven prediction model. The ablation experiment based on the PHM2010 public data set verifies the effectiveness of the network structure design and demonstrates the model’s superior predictive ability. Experiments on the self-built TiAl alloy milling dataset achieved a stable prediction of R² up to 99.1%, with MAE and RMSE of 2.29 and 2.47, respectively. The results show that this method significantly improves the accuracy and robustness of wear prediction. Full article

Rapid antigenic drift in the coronavirus spike protein motivates alternative antiviral strategies. We target the conserved nucleocapsid (N) protein—central to RNA binding, genome packaging, and replication—and perform a comparative, cross-species 3D structure-based in silico evaluation. A library of 494 compounds (natural, phytochemical, synthetic) was docked with AutoDock Vina against the MERS-CoV N–terminal RNA–binding domain (NTD; PDB 7DYD) and the C–terminal dimerization domains (CTD) of SARS-CoV (2CJR) and SARS-CoV-2 (8R6E), reflecting the availability of high-resolution, functionally relevant domain structures for each virus. Top-ranked poses underwent ADME profiling and 100 ns GROMACS molecular-dynamics (MD) simulations. Myricetin 3-O-β-D-Galactopyranoside (myricetin) showed the most favorable predicted docking scores across targets (−8.9 kcal/mol, MERS–NTD; −10.1, SARS–CTD; −9.8, SARS-CoV-2 CTD). Curcumin showed moderate predicted affinity (−7.1 to −8.1), while MCC950 achieved consistently favorable docking score (−7.9 to −9.0). ADME results highlighted a trade-off: glycosylated flavonoids offered rich interaction networks but violated oral drug-likeness criteria (e.g., high TPSA), whereas MCC950 met Lipinski/Veber guidelines, supporting translational potential. MD analyses revealed ligand- and target-specific stability: myricetin maintained persistent binding over 100 ns in the SARS-CoV-2 CTD with lower RMSD than comparators; curcumin exhibited transient stability (~30 ns) in MERS- and SARS-bound complexes; MCC950 showed intermittent interactions. Collectively, these findings suggest that the conserved N protein RNA-binding groove represents a resistance-resilient target for broad-spectrum antiviral discovery. Natural flavonoids provide promising scaffolds for optimization, and MCC950 warrants further exploration given its drug-like profile. As this study is purely computational, the results are hypothesis-generating and should be validated via RNA-binding disruption assays, antiviral cell studies, and in vivo models. Full article

As the oil price has skyrocketed, the attraction towards electric vehicles has gone up. This scenario has also increased the demand for charging infrastructure. This paper proposes a novel charging infrastructure for electric vehicles which is energized by a solar photovoltaic unit, integrated with a distribution static compensator. The output of the photovoltaic array is regulated by a DC–DC converter, which uses maximum power point tracking to support optimal solar energy conversion. The compensator is integrated into the grid through a zigzag-star transformer, which helps with neutral current compensation, promoting balanced and distortion-free operation. The control algorithm is designed to ensure superior power quality during grid synchronization and sustainable energy management. This novel architecture ensures bidirectional power flow, enabling the charge–discharge dynamics of the electric vehicles, which can be termed Grid-to-Vehicle and Vehicle-to-Grid modes. Better grid flexibility and resilience are ensured by this dynamic power exchange. The control strategy based on the Linear Kalman Filter provides reactive power balance and maintains steady voltage at the point of common coupling, and it ensures enhanced power quality during power flow, resulting in efficient and reliable grid operations. The effectiveness of the control algorithm is tested and validated under Grid-to-Vehicle, Vehicle-to-Grid, nonlinear, unbalanced, and isolated solar conditions. Analytical tuning of the gains in the controller, by using the conventional methods, is not efficient under dynamic conditions and nonlinear loads. An optimization technique is used to estimate the proportional–integral control gains, which avoids the difficulty of tuning the controllers. Simulation of the system is carried out using MATLAB 2022b/SIMULINK. Simulation results under diverse operating scenarios confirm the system’s capability to sustain superior power quality, maintain grid stability, and support a robust and reliable charging infrastructure. By enabling regulated bidirectional energy exchange and autonomous operation during grid disturbances, the charger operates as a resilient grid-support asset rather than as a passive electrical load. Full article

This paper proposes a dual-modal monitoring system combining visible and infrared imaging to enhance overlap defect detection in wire arc additive manufacturing (WAAM) based on cold metal transfer (CMT) welding for multi-pass builds. Traditional single-modal approaches, primarily relying on melt pool imagery, are often hindered by arc light and spatter interference, which can compromise detection accuracy. In this work, overlap defect refers to insufficient overlap between adjacent tracks, and the dataset is created by inducing overlap defects through inter-track spacing in multi-pass deposition. The proposed dual-modal strategy mitigates these challenges and significantly improves detection precision. A dual-input convolutional neural network model named Multimodal Mutual Fusion Network (MMFNet) was designed, fusing visible and infrared data at the feature level to achieve a prediction accuracy of 98.34%. Comparative experiments with single-modal models demonstrate the superiority of the proposed approach, with single-modal accuracies of only 95.76% (infrared) and 92.85% (visible light). The proposed system provides a robust solution for monitoring of overlap defects in WAAM in the studied multi-pass setting, highlighting the potential of dual-modal systems for improving quality control in additive manufacturing processes. Full article

This study presents a detailed electrical analysis of AlGaN/GaN/Si HEMTs grown by molecular beam epitaxy, using direct and pulse current, small-signal microwave, and deep-level transient spectroscopy (DLTS) techniques to investigate transport characteristics and defect-related effects. DC measurements revealed self-heating effects and leakage currents, while RF analysis highlighted the devices’ high-frequency capabilities alongside parasitic effects linked to deep-level traps. Pulsed I–V characterization demonstrated gate-lag and drain-lag behaviors associated with dynamic charge trapping. DLTS identified electron traps, emphasizing their critical role in device degradation and switching performance. The strong correlation between trap states and electrical behavior underlines the importance of defect control for enhancing efficiency and reliability. Full article

Malaria is a major global health concern caused by Plasmodium parasites, among which Plasmodium falciparum is responsible for the most severe and fatal cases. The emergence of drug resistance to existing antimalarial therapies necessitates the discovery of novel molecular targets and chemically distinct inhibitors. Current study employed an integrated in silico drug discovery pipeline combining high-throughput structure-based virtual screening of 1549 deep-sea marine microbial metabolites with MM-GBSA binding free-energy estimation, QikProp-based ADME/Tox profiling, and 100 ns molecular dynamics (MD) simulations to link rapid screening with dynamic verification of binding stability. Molecular docking against Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH; PDB ID: 7KZ4) yielded five top-ranked compounds with Glide scores ranging from −12.02 to −10.61 kcal·mol⁻¹, which is higher than the Primaquine (−6.920 kcal·mol⁻¹; a clinically approved antimalarial reference compound). MM-GBSA analysis further refined hit selection, producing binding free energies (ΔG_bind) between −63.28 and −31.37 kcal·mol⁻¹. The selected lead compounds included (±)-puniceusine P, aspergilol F, tersaphilone C, 4-carbglyceryl-3,3′-dihydroxy-5,5′-dimethyldiphenyl ether, and 15-O-methyl ML-236A. The top hits were subjected to 100 ns MD simulations in Desmond, demonstrating stable protein–ligand complexes, particularly for (±)-puniceusine P and 15-O-methyl ML-236A (protein backbone root mean square deviation (RMSD; ~0.8–1.0 Å). ADME profiling indicated acceptable predicted physicochemical and pharmacokinetic properties. Overall, these in silico findings highlight deep-sea marine microbial metabolites as promising PfDHODH inhibitor candidates requiring experimental validation. Full article

Food safety, quality management, and ecologically conscious practices must be successfully integrated throughout supply chains to guarantee the sustainability of organic food systems. There is little empirical data on how these factors interact in various institutional and regulatory contexts, despite the growing significance of the organic sector for sustainable food transitions. This study examines sustainability-focused compliance procedures in Italian and Indian organic food processing businesses. A systematic questionnaire was used to gather primary data from 300 certified organic businesses (150 per nation), and non-parametric statistical methods were used to examine firm-size and cross-country variations. The results show notable differences between the two contexts in terms of food safety rules and sustainability performance. Because of a more developed regulatory framework and more robust enforcement mechanisms, Italian businesses demonstrate greater and more consistent sustainability performance. On the other hand, Indian businesses, especially small ones, show more unpredictability, which suggests that they have limited ability to implement sustainable practices and environmental management. The performance of quality management systems in both nations is similar, indicating the contribution of international certification standards to the harmonisation of quality governance. In contrast to Italy, where they operate as separate operational domains, correlation research shows that sustainability, food safety rules, and quality management are more closely interwoven in Indian businesses. The study emphasises the importance of the company’s size and the regulatory environment in determining how sustainability is integrated into organic food chains. The findings present beneficial guidance for small and medium-sized businesses by highlighting important areas where targeted capacity building and regulatory assistance can improve sustainability and compliance performance and it is one of the first enterprise-level empirical evaluations of food safety rules, quality management, and sustainability in organic food processing across different regulatory contexts. The findings provide actionable insights for small organic processors by highlighting priority areas for targeted regulatory support, technical help, and capacity building to improve the incorporation of sustainability practices. Full article