Search Results (141,990)

Soil salinity is a major constraint on global agricultural productivity. This study evaluated the efficacy of a cell-free extract from Trichoderma hamatum (designated BEYF) in enhancing salt stress tolerance in lettuce (Lactuca sativa). Lettuce plants under normal and salt-stressed conditions exposed to 200 mM NaCl were treated with either water or YF (the working solution of BEYF) at concentrations of 0.05, 0.10, and 0.25 mg/L. Compared to the control, YF application significantly improved plant growth under salt stress, as indicated by increased plant height, biomass, leaf area, and other agronomic traits. Physiologically, YF mitigated oxidative membrane damage, as indicated by reduced electrolyte leakage and malondialdehyde (MDA) content, while promoting the accumulation of the osmoprotectant proline. Histochemical staining further confirmed that YF effectively suppressed hydrogen peroxide (H₂O₂) accumulation and preserved cell viability under salt stress. At the molecular level, YF significantly up-regulated the expression of key stress-responsive genes, including those involved in abscisic acid biosynthesis (NCED1, NCED2), signaling (WRKY58), and proline synthesis (P5CSs). Collectively, our findings demonstrate that BEYF enhances lettuce salt tolerance through integrated physiological, cellular, and transcriptional adaptations, supporting its potential as a sustainable biostimulant for improving crop cultivation in saline soils. Full article

Rapid assessment of existing reinforced concrete (RC) buildings is essential for effective seismic risk mitigation, particularly in highly active regions such as Bingol, Turkiye. This study evaluates the local performance of three Rapid Visual Screening (RVS) methods—RBTY-2019, FEMA-P154, and IITK-GSDMA—using verified post-earthquake damage data from the 2003 Bingol Earthquake (SERU-2003). To overcome the limitations of traditional RVS approaches, an Artificial Neural Network (ANN) model was developed and trained with the same dataset to predict building damage levels based on structural deficiency parameters. The ANN achieved a regression coefficient above 0.90 and 100% consistency in test predictions, demonstrating superior accuracy and adaptability to local construction characteristics. A Local Scaling Function (LSF) was also proposed to translate RBTY-2019 performance scores into empirical damage states, achieving 100% consistency with observed data. The findings highlight the reliability of locally trained AI models and the importance of adapting national screening regulations to regional seismic experiences. This integrated ANN–RVS framework provides a practical, data-driven tool for local authorities to prioritize urban building stock and strengthen disaster risk management strategies. Full article

The rapid integration of renewable energy into power systems has made voltage oscillations caused by the intermittency of wind and solar power a critical operational challenge. To mitigate these issues, this paper proposes a multi-mode coordinated reactive power control strategy to enhance voltage stability in renewable energy clusters. The approach integrates two key indicators: voltage sensitivity for steady-state regulation and an improved multi-renewable energy station short circuit ratio (MRSCR) that accounts for dynamic power interactions. Validation is conducted using a hardware-in-the-loop (HIL) platform combining real-time RMS-based simulation with physical controllers. Case studies on an offshore wind cluster demonstrate that the proposed method reduces voltage fluctuation amplitude more effectively than conventional automatic voltage control (AVC), successfully suppressing oscillations. The results confirm that the strategy exhibits stronger adaptability to varying grid conditions and offers a scalable solution for oscillation mitigation in large-scale renewable energy integration. Full article

Non-uniform rational B-spline (NURBS) surfaces are effective for accurately modeling curved geometries, and research in this area has recently increased. In this study, point cloud data obtained from two challenging test environments (a convex wooden object and the widely used Stanford Bunny dataset from the literature) were used to predict the u and v parameter values corresponding to positions in the knot vectors, to determine the knot points of NURBS surfaces. The u and v parameters were predicted with accuracies of 92.60% and 93.20% for the wooden object, and 85.50% and 84.40% for the Stanford Bunny. The models’ decision-making processes were analyzed using explainable artificial intelligence (XAI) methods, including SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). Predicted knot points were compared with the calculated knot points, which are considered as actual, yielding root mean square errors (RMSE) of 0.09 mm for the wooden object and 0.02 mm for the Stanford Bunny. This study fills a gap in the literature by predicting knot points and providing XAI-based analyses, demonstrating that the approach effectively preserves the characteristic features of NURBS surfaces across different geometries. Full article

Background: Balance is an essential motor skill that enables individuals to maintain a stable posture and perform daily activities safely. Dual-task assessments are widely used to evaluate the integration of motor and cognitive functions in paediatric populations, but their effects on postural control in children with haemophilia (CwH) remain poorly understood. The objective was to analyse and compare static balance performance under single-task and cognitive dual-task conditions between CwH and age-matched healthy controls (HC). Methods: This comparative cross-sectional study included 34 CwH and 32 HC aged 8–12 years. Balance was assessed using the Wii Balance Board^® under six conditions: bipedal, bipedal with cognitive task, and dominant and non-dominant unipedal (with and without cognitive task). Primary outcome variables included overall stability index, total velocity, and sway area. Physical activity habits were also recorded in both groups with an ad hoc questionnaire. Among CwH, joint health was measured with the Haemophilia Joint Health Score (v2.0), and joint effusion/synovial hypertrophy was evaluated by ultrasound. Results: CwH showed significantly poorer performance in all variables, primarily under unipedal dominant/non-dominant dual-task conditions (p < 0.05) with percentage differences ranging from approximately 15% to over 60%. CwH reported lower-intensity and shorter-duration physical activity than HC. Conclusions: CwH demonstrates impaired balance, especially under unipedal and cognitive demands. Assessment of balance alongside dual-task performance may help detect subtle deficits not captured by only static balance assessment. These findings may suggest the incorporation of dual-task training into balance training programmes for this population. Full article

Hemodiafiltration (HDF) is increasingly used because of its enhanced theoretical clearance of diverse uremic toxins, particularly middle molecules and inflammatory cytokines, relative to conventional hemodialysis (HD), yet evidence on its biochemical benefits remains conflicting. Therefore, this meta-analysis was performed to evaluate the effects of HDF versus HD on uremic toxins, inflammation, anemia, and nutritional parameters. A systematic literature search was conducted using PubMed, Scopus, and the Cochrane Central Register of Controlled Trials to identify relevant studies. Only randomized controlled trials (RCTs) were included. Random-effects meta-analyses were performed to evaluate changes in the prespecified outcomes. Twenty-four RCTs involving 6072 dialysis patients were included. Compared with conventional HD, HDF was associated with significant reductions in serum phosphorus (weighted mean difference [WMD] −0.28 mg/dL; 95% CI −0.44 to −0.12) and β2-microglobulin (WMD −4.84 mg/dL; 95% CI −6.13 to −3.54). HDF also significantly reduced serum urea and C-reactive protein (CRP) levels, along with weekly erythropoietin requirements. Serum albumin levels were slightly but significantly lower in the HDF group than in the conventional HD group (WMD –0.06 g/dL; 95% CI −0.10 to −0.01); however, the clinical significance of such a difference remains uncertain. Higher convective volumes were identified as a key determinant of greater reductions in β2-microglobulin and CRP. Compared with conventional HD, HDF demonstrated superior reductions in several surrogate endpoints, including serum phosphorus, urea, β2-microglobulin, CRP, and weekly erythropoietin requirements. Reduced need for phosphate binders and anemia management may lower treatment-related costs. Full article

Objectives: To evaluate the efficacy of resin infiltrants (RIs) in controlling non-cavitated occlusal carious lesions (NCOCLs) in primary and permanent teeth. Methods: This systematic review followed PRISMA guidelines. Randomized clinical trials (RCTs) and in vitro/ex vivo studies comparing RI with placebo or other materials were included. Searches were conducted in five databases and gray literature up to December 2025. Risk of bias was assessed using the RoB 2.0 tool for RCT and an adapted instrument for in vitro/ex vivo studies. Certainty of evidence was evaluated using the GRADE tool, and data were synthesized qualitatively. Results: Eight studies were included, of which only two were RCTs, and six were in vitro and ex vivo studies. RCTs showed reduced caries progression in infiltrant-treated groups compared with controls, with efficacy comparable to conventional sealants. In vitro studies demonstrated improved resin penetration and sealing ability with optimized protocols. Risk of bias ranged from low to moderate. Certainty of clinical evidence was low, primarily due to the limited number of RCTs and methodological limitations. Conclusions: RIs may be effective in managing NCOCLs, with performance comparable to conventional preventive approaches. However, the limited number of clinical trials and short follow-up periods reduce the strength of the evidence. Long-term clinical studies are needed to confirm the sustained effectiveness and durability of RIs. Full article

Achieving thickness uniformity is a critical challenge in superplastic forming (SPF) of hemispherical shells, as standard constant-thickness blanks suffer from excessive thinning at the pole. While the literature suggests using variable thickness blanks to mitigate this issue, existing solutions often rely on complex, non-linear profiles that are expensive and difficult to manufacture. This work proposes a cost-effective, truncated conical blank design (linearly variable thickness) to optimize material distribution. The approach combines Finite Element Method (FEM) analysis and experimental validation on AZ31 magnesium alloy. The study demonstrates that the optimized truncated conical profile (α = 0.2) yields superior structural quality, drastically reducing the thinning factor to 9%. This represents a significant improvement compared to the ~14% thinning observed with conical profile (α = 0) blanks and outperforms constant-thickness blanks (30%). These results demonstrate that a simplified, easily machinable blank geometry can effectively address the thinning problem, providing a practical solution for industrial SPF applications. Full article

The exceptional adjustability and ambipolar behavior of graphene offer significant potential for next-generation optoelectronics, where the conductivity of graphene is primarily modulated by the interface field of heterojunction. However, interface defects, which are inevitably introduced during fabrication, severely limit the effectiveness of gate voltage modulation. Although the layer-by-layer transfer method can effectively enhance conductivity, it also raises the carrier concentration and impairs the symmetry of ambipolar characteristics. This work presents a stacked multi-gate graphene transistor in which synergistic modulation enables efficient regulation of channel conductivity while maintaining low carrier concentration. Simulations are carried out to analyze how mobility, doping concentration, and the number of stacking layers influence the modulation of conductivity. Experimentally, a three-layer stacked graphene structure with distributed source and drain electrodes is fabricated. The device exhibits pronounced ambipolar transfer characteristics and demonstrates a clear improvement in transconductance compared to its conventional one-layer graphene counterpart. This research offers a feasible design strategy for high-performance, vertically integrated graphene-based electronic devices. Full article

Current wind energy planning in the China Seas and adjacent waters generally focuses on wind speed or wind power density (WPD), yet lacks sufficient understanding of the long-term climatic evolution patterns and climatic driving mechanisms of effective wind speed occurrence (EWSO) and its correlation with climate oscillations. Based on the ERA5 10 m sea surface wind reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and multiple key climate index datasets from 1941 to 2020, this study systematically analyzed spatiotemporal distribution characteristics, long-term variation trends, and correlations with climate oscillations of EWSO in the China Seas and adjacent waters. The results indicated the following: (1) There are discrepancies between the distribution of EWSO and mean wind speed. (2) Over the past 80 years, EWSO across the study area has shown an overall significant increasing trend with pronounced regional disparities, among which the Yellow–Bohai Sea area has exhibited a significant decreasing trend. (3) The interannual variability of EWSO is regulated by climate oscillations such as ENSO. This study demonstrates that incorporating EWSO as an independent indicator separate from wind speed into the wind energy resource assessment system is crucial for identifying offshore wind power generation risks and more accurately evaluating the actual operational duration of wind farms in China’s offshore waters and adjacent sea areas. The correlation between EWSO and climate oscillations such as ENSO provides an important scientific basis for improving seasonal prediction models of wind energy resources. Full article

With the continuous growth in the scale of engineering simulation and intelligent manufacturing workflows, more and more problem-solving tasks are migrating to cloud computing platforms to obtain elastic computing power. However, a core operational challenge for cloud platforms lies in the difficulty of stably obtaining high-quality scheduling solutions that are both efficient and free of symmetric redundancy, due to the coupling of multiple constraints, partial resource interchangeability, inconsistent multi-objective evaluation scales, and heterogeneous resource fluctuations. To address this, this paper proposes a Dual-Improved Whale Optimization Algorithm (DI-WOA) accompanied by a modeling framework featuring discrete–continuous divide-and-conquer modeling, a unified monetization mechanism of the objective function, and separation of soft/hard constraints; its iterative trajectory follows an augmented Lagrangian dual-rollback mechanism, while being rooted in a three-layer “discrete gene–real-valued encoding–decoder” structure. Scalability experiments show that as the number of tasks J increases, the DI-WOA ranks optimal or sub-optimal at most scale points, indicating its effectiveness in reducing unified billing costs even under intensified task coupling and resource contention. Ablation experiment results demonstrate that the complete DI-WOA achieves final objective values (OBJ) 8.33%, 5.45%, and 13.31% lower than the baseline, the variant without dual update (w/o dual), and the variant without perturbation (w/o perturb), respectively, significantly enhancing convergence performance and final solution quality on this scheduling model. In robustness experiments, the DI-WOA exhibits the lowest or second-lowest OBJ and soft constraint violation, indicating higher controllability under perturbations. In multi-workload generalization experiments, the DI-WOA achieves the optimal or sub-optimal mean OBJ across all scenarios with H = 3/4, leading the sub-optimal algorithm by up to 13.85%, demonstrating good adaptability to workload variations. A comprehensive analysis of the experimental results reveals that the DI-WOA holds practical significance for stably solving high-quality scheduling problems that are efficient and free of symmetric redundancy in complex and diverse environments. Full article

Diaphragm walls are widely used for deep foundation pit support and permanent underground structures. The joints between adjacent panels are critical weak points, significantly influencing the overall deformation and stress distribution of the structure. To address the insufficient shear and tensile capacity of existing diaphragm wall joints, this study proposes a novel rigid joint incorporating retractable shear studs. The joint features a straightforward and constructible design, primarily comprising retractable shear studs, H-section steel, and shear stud pop-out limit plates. By withdrawing the limit plates inserted into the H-section steel, the retractable shear studs mounted on the web automatically extend along their axis, penetrating into the adjacent reinforcement cage to form an intrusive lap joint. This mechanism effectively enhances the integrity and load-bearing capacity at the joint. To validate its mechanical performance, large-scale specimens featuring this new joint were fabricated and subjected to shear and tensile tests. The experimental results demonstrate that, compared to traditional H-section steel joints, the peak shear and tensile strengths of the proposed joint are increased by approximately 10 times and 16 times, respectively. These findings fully verify the excellent mechanical performance of the novel diaphragm wall joint structure. Full article

In this article, an adaptive neural network (ANN) controller based on feature augmentation (FA) is designed for quadrotors. The proposed controller consists of two components: a position sub-controller and an attitude sub-controller. We use the ANN to estimate unknown internal and external disturbance terms within quadrotors. To improve the learning accuracy of the ANN, we design an FA structure, which enables networks to more effectively learn the characteristics in the data. To increase the learning rate of the ANN, a state predictor (SP) is proposed to anticipate the state errors, which subsequently updates the learning rate of the ANN. Based on stability analysis, we prove that the closed-loop system is input-to-state stable (ISS). Finally, the effectiveness of our proposed control algorithm is demonstrated by comparing it with related control algorithms on both the MATLAB R2020a/Simulink simulation platform and a quadrotor experimental platform. Full article

Deep learning has achieved impressive progress in automated electrocardiogram (ECG) analysis, yet its performance still relies heavily on large-scale labeled datasets. As ECG annotation requires cardiologists, this process is costly and time-consuming, limiting its scalability in clinical practice. Contrastive learning offers a promising alternative by enabling the extraction of generalizable representations from unlabeled ECG data. In this study, we propose Anatomy-Aware Contrastive Learning for ECG (ACL-ECG), a self-supervised method that incorporates cardiac anatomical relationships into contrastive learning. ACL-ECG employs a physiology-aware augmentation strategy to generate rhythm-preserving augmented views, including random scale cropping, cardiac-cycle masking, and temporal shifting. Furthermore, ECG leads are grouped into four anatomically meaningful regions—anterior, inferior, septal, and lateral—and region-level contrastive objectives are introduced to promote intra-region consistency while enhancing inter-region discriminability. Extensive evaluations of downstream tasks demonstrate that ACL-ECG consistently outperforms state-of-the-art contrastive baselines under linear probing, achieving improvements of up to 1.29% in the area under the receiver operating characteristic curve (AUROC) and 3.57% in the area under the precision–recall curve (AUPRC). Moreover, when fine-tuned using only 10% of labeled data, ACL-ECG attains a performance comparable to fully supervised training, effectively reducing annotation requirements by approximately 5∼8×. Ablation studies further confirm the contributions of both the physiology-aware augmentation strategy and the anatomy-aware contrastive objective. Overall, ACL-ECG enhances representation quality without increasing annotation burden, and provides a promising and anatomy-informed foundation for self-supervised ECG analysis in label-scarce settings. Full article

This study sought to evaluate the neuroprotective effects of YGD in an oxidative stress-induced Alzheimer’s disease (AD)-like cellular model and to elucidate the underlying molecular pathways, with a focus on tau phosphorylation, Aβ accumulation, and antioxidant defense mechanisms. Rat primary hippocampal neurons were exposed to hydrogen peroxide to induce oxidative stress. The effects of YGD on neuronal viability, neurite outgrowth, and synaptic integrity were assessed using the immunodetection of microtubule-associated protein 2 (MAP2), postsynaptic density protein 95 (PSD-95), and synapsin-1. Levels of phosphorylated tau and Aβ were quantified, and the involvement of extracellular signal-regulated kinase (ERK), glycogen synthase kinase 3β (GSK3β), and nuclear factor-erythroid 2-related factor-2 (Nrf2) pathways was examined. Additionally, in silico molecular docking studies targeting the ATP-binding site of GSK3β were conducted to screen major phytochemicals from the ten medicinal herbs constituting YGD. YGD markedly enhanced neuronal viability under oxidative stress, promoted neurite extension, and increased synaptic marker expression (MAP2, PSD-95, and synapsin-1). Treatment reduced phosphorylated tau by suppressing ERK and GSK3β activation and significantly decreased Aβ accumulation. YGD also upregulated antioxidant defenses via the activation of the Nrf2 pathway. Docking simulations identified oleanolic acid (from Cornus officinalis) as the most potent GSK3β binder (−9.86 ± 0.40 kcal/mol), forming stable interactions with ARG96, ASN95, and GLU97. Additional compounds, including alisol C, drypemolundein B, and friedelin, demonstrated favorable binding energies and engaged key ATP-binding site residues. YGD confers neuroprotection through the integrated modulation of tau phosphorylation, Aβ pathology, and oxidative stress, partly via the multi-target engagement of GSK3β by its constituent phytochemicals. These findings support that YGD attenuates oxidative stress-induced AD-like cellular alterations. Full article