Search Results (113)

With the growing requirements for multi-particle process simulation, improving computational accuracy, efficiency, and scalability has become a critical challenge. This study generally focused on comprehensive analyses of existing numerical methods for simulating particle–substrate interactions in gas–thermal spraying (including gas–dynamic spraying processes), covering both single-particle and multi-particle models to develop practical recommendations for the optimization of modern coating spraying processes. First of all, this paper systematically analyzes the key limitations of current approaches, including their inability to handle high deformations effectively or high computational complexity and their insufficient accuracy in dynamic scenarios. A comparative evaluation of four numerical methods (Lagrangian, Arbitrary Lagrangian–Eulerian (ALE), Coupled Eulerian–Lagrangian (CEL), and Smoothed Particle Hydrodynamics (SPH)) revealed their strengths and weaknesses in modeling of real gas–thermal spraying processes. Furthermore, this study identifies the limitations of the widely used Johnson–Cook (JC) constitutive model under extreme conditions. The authors considered the Zerilli–Armstrong (ZA), Mechanical Threshold Stress (MTS), and Preston–Tonks–Wallace (PTW) models as more realistic alternatives to the Jonson–Cook model. Finally, comparative analyses of theoretical and realistic deformation and defect-generation processes in gas–thermal coatings emphasize the critical need for fundamental changes in the simulation strategy for modern gas–thermal spraying processes. Full article

Wet milling of aluminum alloys involves complex interactions among thermal, fluid, and mechanical fields that strongly affect cutting temperature, stress distribution, and surface integrity. To achieve reproducible and physics-based predictions of these coupled phenomena, this study develops a three-dimensional thermo–fluid–solid-coupled Eulerian–Lagrangian (CEL) framework for the wet milling of Al6061. The model system in this study evaluated the effects of milling cutter feed rate and spindle speed, feed per tooth of the milling cutter, axial cutting depth, and coolant flow rate on equivalent stress and peak milling temperature., as well as their correlation with surface roughness metrics (Ra, Sa). Simulation results reveal that higher feed rates significantly raise T_peak (+12.9%) while reducing σ_eq (−22.7%) and deteriorating surface quality (Ra +104.2%, Sa +29.9%). Increasing spindle speed lowers both T_peak (−2.2%) and σ_eq (−8.5%) and improves surface finish (Ra −39.3%, Sa −16.6%). A greater depth of cut amplifies mechanical and thermal loads, increasing T_peak (+10.3%) and σ_eq (+17%). Enhanced coolant flow reduces T_peak (−23.5%) and σ_eq (−6.1%) and markedly improves surface quality (Ra −88.8%, Sa −51.3%). Research findings indicate that coolant coverage is the dominant factor determining surface integrity. Although experimental data for T_peak and σeq were not directly validated, this framework clearly articulates modeling assumptions, quantifies parameter sensitivities, and provides a reproducible methodology for future experimental-numerical verification. Full article

While community energy initiatives and pilot projects have demonstrated technical feasibility and economic benefits, their site-specific nature limits transferability to systematic, scalable investment models. This study addresses this gap by proposing a modular framework for Renewable Energy Valleys (REVs), developed from real-world Community Energy Lab (CEL) demonstrations in Crete, Greece, which is an island with pronounced seasonal demand fluctuation, strong renewable potential, and ongoing hydrogen valley initiatives. Four modular business schemes are defined, each representing different sectoral contexts by combining a baseline of 50 residential units with one representative large consumer (hotel, rural households with thermal loads, municipal swimming pool, or hydrogen bus). For each scheme, a mixed-integer linear programming model is applied to optimally size and operate integrated solar PV, wind, battery (BAT) energy storage, and hydrogen systems across three renewable energy penetration (REP) targets: 90%, 95%, and 99.9%. The framework incorporates stochastic demand modeling, sector coupling, and hierarchical dispatch schemes. Results highlight optimal technology configurations that minimize dependency on external sources and curtailment while enhancing reliability and sustainability under Mediterranean conditions. Results demonstrate significant variation in optimal configurations across sectors and targets, with PV capacity ranging from 217 kW to 2840 kW, battery storage from 624 kWh to 2822 kWh, and hydrogen systems scaling from 65.2 kg to 192 kg storage capacity. The modular design of the framework enables replication beyond the specific context of Crete, supporting the scalable development of Renewable Energy Valleys that can adapt to diverse sectoral mixes and regional conditions. Full article

Gamba grass (Andropogon gayanus Kunth) is a promising forage alternative for Brazil’s Cerrado regions, attracting increasing research interest due to its potential to complement or replace widely planted species such as Urochloa and Megathyrsus. Despite the release of three cultivars, significant improvements in dry matter (DM) yield and forage quality are needed to fully realize its agronomic potential. This study aimed to evaluate genetic variability, estimate narrow sense heritability, and predict expected genetic gains for DM yield and key forage quality traits in two gamba grass populations derived from the cultivars BRS Sarandi and Planaltina. Trials were established in spring 2017 in Planaltina, DF, and evaluated during February–March 2018 and January–March 2019. Crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), cellulose (CEL), and hemicellulose (HEMIC) were quantified alongside DM yield. BRS Sarandi exhibited higher CP (12.3% vs. 9.8%) and lower NDF (57.1% vs. 63.4%), ADF (36.2% vs. 41.5%), CEL (20.8% vs. 23.7%), and HEMIC (20.9% vs. 21.9%) compared to Planaltina, while DM yield did not differ significantly between populations (4.57 t·ha⁻¹ vs. 4.50 t·ha⁻¹ per harvest, p > 0.05). Heritability estimates for individual harvests ranged from 0.31 to 0.68 for DM yield and 0.28 to 0.62 for quality traits, whereas multi-harvest models across years yielded lower estimates (0.07–0.27). Expected annual genetic gains were modest, with the highest predicted increase for CP (0.45% per year) and the largest decrease for NDF (−0.78% per year), reflecting the quantitative nature of trait inheritance and strong environmental influence. This study provides novel insights by simultaneously comparing two populations for multiple harvests and quantifying both yield and detailed forage quality traits, offering practical guidance for gamba grass breeding strategies. Results indicate that breeding programs should prioritize multiple selection cycles, precise phenotyping, genotypic and potentially genomic selection to accelerate improvement in both DM yield and forage quality, overcoming the constraints of low heritability and multi-trait selection. Full article

Laboratory incubation experiments were conducted to investigate the effects of coal fly ash (FA) amendment (0%, 2.5%, 7.5%, and 15%) and moisture regimes (40%, 70%, and 100% water holding capacity (WHC)) on the mineralization of carbon (C) in an acidic agricultural soil. The results showed that the soil C mineralization intensity initially increased and subsequently decreased throughout the incubation period, with the mineralization dynamics well described by the first-order kinetic model (0.9633 ≤ R² ≤ 0.9972). Carbon mineralization increased with the application rate of FA, while moisture effect followed the order 70% WHC > 100% WHC > 40% WHC. Indicators showing highly significant correlations with total C mineralization amount included FA application rate, pH, water-soluble organic carbon, (WSOC) and cellulase (CEL) activity. Specific bacterial (Acidobacteriota, Gemmatimonadota, Pseudomonadota, and Actinobacteriota) and fungal phyla (Chytridiomycota, Glomeromycota, and Olpidiomycota) exhibited stronger correlations with C mineralization. The microbial taxa exhibiting significant responses to FA and moisture conditions were not consistent. Although the addition of high proportions of FA, especially with adequate moisture conditions, can enhance soil microbial activity and C mineralization, the potential risks of soil C loss and the accumulation of toxic elements necessitate the prudent implementation of elevated FA application rates in practical scenarios. Full article

Background: Personalizing adjuvant chemotherapy (ACT) after curative resection in early-stage NSCLC remains unmet because prior ACT-biomarker findings rarely reproduce across studies. Key barriers are platform and preprocessing heterogeneity, dominant batch effects, and incomplete ACT annotations. As a result, many signatures that perform well in a single cohort fail during external validation. We created an open, harmonized meta-database linking gene expression with curated ACT exposure and survival to enable fair benchmarking and modeling. Methods: A PRISMA-guided search of 999 GEO studies (through January 2025) used LLM-assisted triage of titles, clinical tables, and free text to identify datasets with explicit ACT status and patient-level survival. Eight Affymetrix microarray cohorts (GPL570/GPL96) met eligibility. Raw CEL files underwent robust multi-array average; probes were re-annotated to Entrez IDs and collapsed by median. Covariate-preserving ComBat adjusted platform/study while retaining several clinical factors. Batch structure was quantified by principal-component analysis (PCA) variance, silhouette width, and UMAP. Two quality-control (QC) filters, median M-score deviation and PCA leverage, flagged and removed technical outliers. Results: The final meta-database comprises 1340 patients (223 (16.6%) ACT; 1117 (83.4%) observation), 13,039 intersecting genes, and 594 overall-survival events. Batch-associated variance (PC1 + PC2) decreased from 63.1% to 20.1%, and mean silhouette width shifted from 0.82 to −0.19 post-correction. Seven arrays (0.5%) were excluded by QC. Event depth supports high-dimensional survival and heterogeneity-of-treatment modeling, and the multi-cohort design enables internal–external validation. Conclusions: This first open, rigorously harmonized NSCLC transcriptomic database provides the sample size, demographic diversity, and technical consistency required to benchmark ACT-benefit markers. By making these data openly available, it will accelerate equitable precision-oncology research and enable data-driven treatment decisions in early-stage NSCLC. Full article

This study analyzed the dynamic behavior of EN C45 structural steel under impulse loading generated by a pressure wave. The experiments were conducted on a special test rig using two load configurations: (I) direct contact of the load with the sample surface and (II) detonation at a distance of 30 mm. Depending on the loading conditions, the specimens were fragmented or developed extensive internal cracks and plastic deformations. To complement the experimental program, hybrid numerical simulations were performed using the finite element method (FEM), smoothed particles hydrodynamics (SPH), and coupled Euler–Lagrange (CEL) approach. A modified Johnson–Cook (JC) model was used to account for dynamic damage and cracks. Computed tomography (CT) and metallographic analyses provided detailed information on the formation of cracks in MnS inclusions, brittle cracks near the sample axis, and shear deformation zones away from the axis. These observations allowed direct correlation with the predicted numerical deformation and damage fields. The innovative nature of this work lies in the combination of three complementary computational techniques with computed tomography analysis and microstructure analysis, providing a comprehensive framework for describing and confirming the mechanisms of damage and fragmentation of structural steels under explosive loading. Full article

Recently, considerable research has been conducted on wet shot peening (WSP), but a detailed investigation of this process is still lacking. For a systematic study, four three-dimensional models of WSP and shot peening (SP) were developed using the finite element method (FEM), based on the coupled Eulerian–Lagrangian (CEL) method. Micron-scaled water film is directly observed during WSP processing. Simulation results indicate that the water film has a significant impact on the dimple size and residual stress distribution. Compared with SP, WSP can produce (a) a dimple with a larger curvature radius, (b) greater compressive residual stress in the surface layer with a larger area, and (c) more uniformly distributed surface residual stress. This work reveals the mechanism underlying the changes mentioned above, which provides rationales for the promotional applications of WSP. Full article

Ensuring the safe landing of deep-sea mining vehicles (DSMVs) on soft seabed sediments is critical for the stability and operational reliability of subsea mineral extraction. However, deep-sea sediments, particularly in polymetallic nodule regions, are characterized by low shear strength, high compressibility, and rate-dependent behavior, posing significant challenges for full-scale experimental investigation and predictive modeling. To address these limitations, this study develops a high-fidelity finite element simulation framework based on the Coupled Eulerian–Lagrangian (CEL) method to model the landing and penetration process of full-scale DSMVs under various geotechnical conditions. To overcome the high computational cost of FEM simulations, a data-driven surrogate model using the random forest algorithm is constructed to predict the normalized penetration depth based on key soil and operational parameters. The proposed hybrid FEM–ML approach enables efficient multiparameter analysis and provides actionable insights into the complex soil–structure interactions involved in DSMV landings. This methodology offers a practical foundation for engineering design, safety assessment, and descent planning in deep-sea mining operations. Full article

This study proposes CEL-DL-Bagging (Cross-Entropy Loss-optimized Deep Learning Bagging), a multi-model fusion framework that integrates cross-entropy loss-weighted voting with Bootstrap Aggregating (Bagging). First, we develop a lightweight recognition architecture by embedding a salient position attention (SPA) mechanism into four base networks (YOLOv5s-cls, EfficientNet-B0, MobileNetV3, and ShuffleNetV2), significantly enhancing discriminative feature extraction for disease patterns. Our experiments show that these SPA-enhanced models achieve consistent accuracy gains of 0.8–1.7 percentage points, peaking at 97.86%. Building on this, we introduce DB-CEWSV—an ensemble framework combining Deep Bootstrap Aggregating (DB) with adaptive Cross-Entropy Weighted Soft Voting (CEWSV). The system dynamically optimizes model weights based on their cross-entropy performance, using SPA-augmented networks as base learners. The final integrated model attains 98.33% accuracy, outperforming the strongest individual base learner by 0.48 percentage points. Compared with single models, the ensemble learning algorithm proposed in this study led to better generalization and robustness of the ensemble learning model and better identification of rice diseases in the natural background. It provides a technical reference for applying rice disease identification in practical engineering. Full article

High-viscosity stratified strata, characterized by complex geotechnical properties such as strong cohesion, low permeability, and pronounced layered structures, exhibit significant lateral friction resistance and high-end resistance during steel sheet pile installation. These factors substantially increase construction difficulty and may even cause structural damage. This study addresses two critical mechanical challenges during vibratory pile driving in Fujian Province’s hydraulic engineering project: prolonged high-frequency driving durations, and severe U-shaped steel sheet pile head damage in high-viscosity stratified soils. Employing the Coupled Eulerian–Lagrangian (CEL) numerical method, a systematic investigation was conducted into the penetration resistance, stress distribution, and damage patterns during vibratory pile driving under varying conditions of cohesive soil layer thickness, predrilled hole spacing, and aperture dimensions. The correlation between pile stress and penetration depth was established, with the influence mechanisms of key factors on driving-induced damage in high-viscosity stratified strata under multi-factor coupling effects elucidated. Finally, the feasibility of predrilling techniques for resistance reduction was explored. This study applies the damage prediction model based on the CEL method to U-shaped sheet piles in high-viscosity stratified formations, solving the problem of mesh distortion in traditional finite element methods. The findings provide scientific guidance for steel sheet pile construction in high-viscosity stratified formations, offering significant implications for enhancing construction efficiency, ensuring operational safety, and reducing costs in such challenging geological conditions. Full article

The widespread application of large underground water tank structures in urban areas necessitates reliable design guidelines to ensure their safety as critical infrastructure. This paper investigated the seismic response of large underground water tank structures considering fluid–structure interaction (FSI). Coupled Eulerian–Lagrangian (CEL) was employed to analyze the highly nonlinear FSI caused by intense fluid sloshing during earthquakes. The patterns of fluid sloshing amplitude observed from the finite element model were summarized based on analyses of fluid velocity, hydrodynamic stress components, and overall kinetic energy. In addition, the seismic response of the water tank structure was thoroughly assessed and compared with the simulation results of the empty tank structure. The results indicate that significant fluid sloshing occurs within the structure under seismic excitation. The amplitude of fluid sloshing increases horizontally from the center toward the edges of the structure, corresponding to higher hydrodynamic loads at the side area of the structure. By comparing the analysis results of the water tank structure with and without water, it was concluded that FSI is the primary cause of structural damage during an earthquake. The hydrodynamic loads on the roof, diversion walls, and external walls lead to significant localized damage. Full article

Caffeic acid phenethyl ester (CAPE) is identified to be an efficacious bioactive polyphenol in propolis for ameliorating glucose and lipid metabolism disorders and inflammation. In this study, an alcohol-induced zebrafish inflammation model was established. CAPE treatments at different concentrations (0.04, 0.2, and 1.0 μg/mL) were administered to alcohol-exposed zebrafish to investigate the underlying mechanisms of alleviating alcohol-induced liver inflammation using transcriptomic analysis and 16S rRNA gene sequencing methods. The results indicated that CAPE decreased the expressions of TNF-α and IL-1β and significantly increased the expression of IL-10 (p < 0.0001). Based on the KEGG enrichment analysis of transcriptomic sequencing, CAPE effectively alleviated the inflammation in zebrafish mainly through pancreatic secretion, complement and coagulation cascades, and protein digestion and absorption. Molecular docking supported the potential of CAPE in targeting cholecystokinin (CCK) A Receptor (CCKAR) and mediating the regulation of pancreatic secretion and related inflammation pathways. Moreover, intestinal microbiota analysis demonstrated that CAPE could improve the alcohol-induced microbiota disorder. Additionally, there was a significant correlation between the key genes related to lipid and sterol metabolism among the KEGG-enriched pathways and the specific intestinal microbial communities in zebrafish. Flavobacterium from Bacteroidota was significantly positively correlated with CEL1, CEL2, and LPIN (p < 0.01), which suggested that the anti-inflammatory function of CAPE was closely associated with the intestinal microbiota improvement. In conclusion, our findings demonstrated that CAPE could ameliorate liver inflammation in alcohol-induced zebrafish, which was mainly associated with the regulation of pancreatic secretion and intestinal microbiota disorder. This study emphasized the anti-inflammatory mechanisms of CAPE based on targeting the pancreatic secretion pathway, which will broaden the application of natural antioxidants in improving metabolic and inflammatory problems. Full article

This research investigates the biochemical and microbiological characteristics of a composite comprising poly(lactide) (PLA) combined with polyethersulfone (PESf) and copper-complexed cellulose phosphate (CelP-Cu). The material was produced using the pneumothermic melt-blown method and then modified with polyethersulfone and cellulose phosphate, followed by complexation with copper ions using the dip-coating technique. Comprehensive physicochemical and biological evaluations were conducted to characterize the composite. The physicochemical assessments involved elemental analysis (C, O, Cu) and morphology examination. The biological evaluations encompassed microbiological testing and biochemical–hematological analysis, including activated partial thromboplastin time (aPTT) and prothrombin time (PT). Antimicrobial activity was assessed according to the EN ISO 20645:2006 and EN 14119:2005 standards, by placing material specimens on agar plates inoculated with representative microorganisms. The results revealed that the composites exhibited significant antimicrobial effects against model microorganisms: Staphylococcus aureus, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Bacillus atrophaeus, Candida albicans, Saccharomyces cerevisiae, Aspergillus niger, Chaetomium globosum. This study highlights the potential of PLA/PESf/CelP-Cu composites for novel biomedical applications, demonstrating their biocompatibility and their influence on hemostatic processes and antimicrobial properties. Full article

Friction Stir Welding (FSW) is widely used for high-strength aluminum alloys due to its solid-state bonding, which ensures superior weld quality and service stability. However, thermo-mechanical interactions during welding can induce complex residual stress distributions, compromising joint integrity. Previous studies have primarily focused on thermal load-driven stress evolution, often neglecting mechanical factors such as the shear force generated by the stirring pin. This study develops a three-dimensional thermo-mechanical coupled finite element model based on a moving heat source. The model incorporates axial pressure from the tool shoulder and torque-derived shear force from the stirring pin. A hybrid surface–volumetric heat source is applied to represent frictional heating, and realistic mechanical boundary conditions are introduced to reflect actual welding conditions. Simulations on AA6061-T6 aluminum alloy show that under stable welding, the peak temperature in the weld zone reaches approximately 453 °C. Residual stress analysis indicates a longitudinal tensile peak of ~170 MPa under thermal loading alone, which reduces to ~150 MPa when mechanical loads are included, forming a characteristic M-shaped distribution. Further comparison with a Coupled Eulerian–Lagrangian (CEL) model reveals stress asymmetry, with higher tensile stress on the advancing side. This is primarily attributed to the directional shear force, which promotes greater plastic deformation on the advancing side than on the retreating side. The consistency between the proposed model and CEL results confirms its validity. This study provides a reliable framework for residual stress prediction in FSW and supports process parameter optimization. Full article