Search Results (4,142)

Background: Acute high-intensity exercise induces transient inflammatory and oxidative stress responses, mediated by redox-sensitive signaling pathways and reflected by elevations in interleukin-6 (IL-6) and lipid peroxidation products. Modulation of these responses through hydration-based redox interventions remains insufficiently characterized at the biochemical level. Objective: This randomized controlled trial investigated whether regular consumption of redox (alkaline) water influences exercise-induced inflammatory and oxidative stress markers in physically active adults. Methods: Forty physically active adults were randomized into an experimental group (EG; n = 20) and consumed redox water subjected to molecular-level modification, yielding alkaline hydrogen-enriched water (pH 9.2–9.4), or a control group (CG; n = 20) that consumed standard water. After eight weeks of intervention, participants performed a standardized maximal aerobic exercise test. Plasma IL-6 and malondialdehyde (MDA) concentrations were measured at baseline and immediately post-exercise. Statistical analyses included two-way repeated measures ANOVA and ANCOVA. Results: A pronounced group × time interaction was observed for IL-6 (F(1,38) = 36.89, p < 0.001). The EG exhibited a significant post-exercise reduction in IL-6, whereas the CG demonstrated a robust increase. A significant group × time interaction was also detected for MDA (F(1,38) = 4.98, p = 0.029), reflecting stable lipid peroxidation levels in the EG and increased levels in the CG; however, baseline-adjusted analyses indicated that post-exercise MDA differences were largely attributable to initial variability. Hematological and coagulation parameters remained within physiological ranges in both groups. Conclusions: Redox water intake was associated with lower immediate post-exercise IL-6 compared with controls after baseline adjustment; however, pronounced baseline imbalance limits causal interpretation and warrants confirmation in larger trials with balanced inflammatory profiles. These findings highlight a potential biochemical mechanism linking hydration redox properties with inflammatory regulation during physical stress. Full article

The design of multifunctional biomaterials that offer both structural support and biochemical cues is essential for enhancing tissue regeneration. In this study, hybrid nanofibrous scaffolds composed of poly(L-lactide-co-ε-caprolactone) (PLCL) and bioactive factors secreted by Wharton’s jelly-derived mesenchymal stem cells (WJ-MSCs) were fabricated via co-electrospinning. Nanofibers were produced in aligned and random configurations following an optimized protocol developed at the Institute for Bioengineering of Catalonia (IBEC). Their morphology and topography were characterized by light microscopy, scanning electron microscopy (SEM), and atomic force microscopy (AFM), and fiber orientation was quantified via Fast Fourier Transform (FFT) analysis. The scaffolds showed fiber diameters of 542.9 ± 62.3 nm, with aligned fibers predominantly oriented within 20° of the principal axis. Human AD-MSCs were used to assess biocompatibility and cell–material interactions. Aligned and random nanofiber architectures elicited distinct cellular responses. AD-MSCs on aligned fibers exhibited smaller spreading areas (~320 μm²) vs. on random nanofibers (~500 μm²) and substantially higher proliferation, resulting in a shorter cell-doubling time (~25 h) than those on random nanofibers (~130 h) or control substrates (~70 h). In addition, aligned nanofibers promoted markedly faster migration, reaching rates of ~5000 μm²/h surface coverage, compared with random nanofibers (~770 μm²/h) and controls (~1800 μm²/h). Together, the results show that nanofiber alignment and biochemical functionalization jointly influence MSC behavior and improve regeneration, highlighting the potential of these PLCL-based hybrid secretome/PLCL nanofibers for advanced wound healing. Full article

The growing emphasis on 21st-century competencies highlights the need to develop students’ systems thinking and problem-solving, particularly in science education, where many concepts involve complex, dynamic relationships. This study examined differences in fifth-grade students’ systems thinking performance and problem-solving perceptions associated with simulation-supported science instruction within the unit Electricity in Our Lives. A quasi-experimental pretest–posttest design was used with two intact classes, in which the experimental group received PhET-supported instruction and a control group followed the national curriculum. Data were collected through a systems thinking test (multiple-choice and open-ended items) and a problem-solving perception scale. The results showed that, after adjusting for baseline scores, the simulation-supported group demonstrated higher posttest systems thinking scores than the control group, with a large effect size. For problem-solving perceptions, the simulation-supported group also showed higher posttest scores compared to the control group. In addition, a moderate positive correlation was observed between systems thinking performance and problem-solving perceptions. Although causal inferences are limited due to the use of two intact classes and the absence of individual-level random assignment, the findings suggest that interactive simulations may support students’ holistic reasoning and engagement in problem-solving processes. The study highlights the potential value of integrating interactive simulations into science curricula to promote deeper cognitive competencies. Full article

Objectives: While Pilates exercise is commonly prescribed for chronic low back pain (CLBP), its effect on normalizing the lumbar flexion–relaxation ratio (FRR) remains unclear. This trial examined whether an 8-week Pilates exercise program (PEP) modifies FRR magnitude and side-to-side asymmetry in women with CLBP and explored associations with trunk kinematics, pain, and functional capacity. Methods: In a randomized controlled pre-test–post-test training design, ninety-six women with CLBP (55.8 ± 5.4 y) were allocated to a PEP group (n = 49) or a usual-care control group (n = 47). The PEP included two supervised 60-minute mat sessions per week over eight weeks. Surface electromyography of the right and left erector spinae and trunk flexion range of motion (TFRoM), measured via inertial sensors, were recorded during the standardized flexion–extension task pre- and post-intervention. Pain intensity (Visual Analog Scale) and functional capacity (Low Back Outcome Score, LBOS) were assessed concurrently. Results: Two-way repeated-measures ANOVA revealed no group × time interaction for global FRR (p = 0.454) or TFRoM (p = 0.745). FRR asymmetry increased by 11% in the PEP group (p = 0.033), with no change observed in the controls (p = 0.143). Compared to the controls, the PEP group exhibited a 30% reduction in pain (p = 0.003) and a 13.4% improvement in LBOS (p < 0.001) compared to the control group (all ps > 0.228). Conclusions: An 8-week Pilates intervention reduces pain and improves functional capacity in women with CLBP but does not restore lumbar extensor relaxation. The observed increase in FRR asymmetry may reflect compensatory or maladaptive redistribution. Full article

This study explores the effects of integrating virtual reality (VR) into task-based learning (TBL) to support Chinese language learning among Thai university students enrolled in a basic Chinese course. A total of fifty first-year students were selected using simple random sampling and assigned to either a VR-supported experimental group or a traditional control group. Both groups received instruction on the same vocabulary and writing content, delivered by the same instructor, and were assessed using identical pre- and post-tests. The findings indicate that students in the VR-supported group significantly outperformed their peers in the control group. Large effect sizes suggest substantial improvements in both vocabulary knowledge and Chinese character writing, while the control group demonstrated only minimal progress. Survey responses also revealed that students found VR-based tasks highly engaging, closely connected to real-life communication, and strongly motivating. Most participants reported a better understanding of vocabulary and noticeable advancement in learning Chinese characters. However, some students encountered technical difficulties and mild discomfort while interacting with the VR environment. These observations underscore the need for careful instructional design and the importance of implementing VR in a user-friendly and accessible manner. Overall, the study highlights the potential of VR-supported TBL to enhance learning outcomes in beginner-level Chinese courses, provided that technological and pedagogical considerations are carefully addressed. Full article

River basins are not merely geographical spaces but also cultural-historical ecosystems, where the spatial patterns of Intangible Cultural Heritage (ICH) profoundly reflect the long-term interaction between human and environment, as well as contemporary transformations. While international research on ICH has evolved from conceptual clarification to interdisciplinary theory-building, and spatial quantitative methods have been widely applied to cultural heritage analysis, the spatial patterns, multi-scale structures, and “natural-human” driving mechanisms of ICH in continental arid river basins—particularly in the Tarim River Basin (TRB, China’s largest inland river and a key corridor of the Silk Road)—remain underexplored. To address this gap, this study takes 313 ICH items in the TRB as the research object. It uses ArcGIS 10.8.1 to visualize their spatial distribution and employs an integrated methodology—including global Moran’s I, kernel density estimation (KDE), DBSCAN spatial clustering, and geographical detector (Geodetector)—to systematically reveal their spatial characteristics and influencing factors. The findings indicate that: (1) The distribution of ICH exhibits a multi-scale feature of “global randomness with local clustering”: spatial autocorrelation is not significant at the county level, while at the micro-geographical scale, a dendritic structure characterized by “one axis, three cores, denser in the north and sparser in the south” emerges, which is highly coupled with the river network. DBSCAN clustering further identifies a “mainstem axis–tributary node” cluster system and a relatively high proportion of peripheral “noise” heritage points. (2) Agglomeration patterns vary significantly across different ICH categories, with traditional craftsmanship showing high clustering, while traditional sports, entertainment, and acrobatics display highly fragmented distributions. (3) The study reveals and validates a ternary “Water–Tourism–Urbanization” driving framework that predominantly shapes the spatial heterogeneity of ICH: water resources constitute a fundamental ecological threshold, whereas tourism development and urbanization have emerged as more explanatory social driving forces, with widespread nonlinear enhancement interactions between natural and human factors. This research moves beyond the traditional view of river basins as static cultural “containers,” providing empirical evidence for their dynamic nature as “cultural-ecological co-evolutionary systems.” The proposed ternary framework not only offers a new perspective for understanding the spatial resilience of ICH in arid regions and the potential risks of “spectacularization” and “spatial polarization” amid rapid changes, but also provides a scientific basis for spatial governance, culture-tourism integration, and the formulation of conservation strategies for ICH at the basin scale. Full article

Previous studies have emphasized the role of task difficulty in decision performance while relatively neglecting the decision maker’s subjective initiative and intrinsic learning process during task execution. This study manipulated the rule hierarchy factor, which reflects external task difficulty, and the block factor, which reflects the accumulation of intrinsic learning, and used analysis of variance (ANOVA), the drift-diffusion model (DDM), and random forest algorithms to systematically examine how task difficulty and learning jointly influence decision behavior and its underlying mechanisms. A total of 40 participants were recruited, and after strict exclusion criteria were applied, 34 valid datasets were included in the final analysis. The results showed that although rule hierarchy had a significant impact on decision performance in the early stage of the task (the first two blocks), this effect gradually diminished as task repetitions increased. Furthermore, the results revealed a clear dissociation in predictive mechanisms: intrinsic cognitive factors (specifically, evidence accumulation efficiency and decision bias) were the primary predictors of decision accuracy, whereas external task difficulty (rule hierarchy) acted as the dominant predictor for decision speed (reaction time). These findings provide a new perspective for understanding the dynamic relationship between external task demands and intrinsic learning processes, highlighting the necessity of distinguishing between accuracy and speed metrics in personalized education, training, and human–computer interaction design. Full article

This study constructs a global transportation carbon emission spatial correlation network via a modified gravity model and explores its evolutionary characteristics and dynamic mechanisms by integrating three-dimensional evolutionary analysis (node, overall, structural) and temporal exponential random graph model (TERGM). The main findings are as follows: (1) Global transportation carbon emission spatial correlation intensity keeps rising, with improved connectivity and integration, forming three regionally agglomerated correlation poles centered on the United States (America), China (Asia) and major European countries (Europe). (2) Network centrality distributes asymmetrically: Switzerland, Norway and the United States remain core nodes, while China, Japan and other Asian economies with strong direct correlation radiation are not in the core tier. (3) Third, evolutionary dynamics stem from the synergistic interaction of multidimensional attributes. ① Economic level positively drives bidirectional connection emission and attraction; economic scale and openness curb emission but boost attraction, while tertiary industry structure inhibits both. ② Only economic level and government efficiency exert significant positive effects on absdiff, fostering network heterophilic attraction. ③ Spatial and institutional proximity in edgecov effectively facilitate connection formation. ④ Endogenous network variables present a collaborative mechanism of reciprocity and transmission, constrained by network density. ⑤ Temporal effects show early connection structure forms path dependence, resulting in low dynamic variability and overall network stability. Full article

Efficient and robust energy transfer is fundamental to quantum information processing and light-harvesting technologies. However, conventional systems are often limited by short interaction ranges and high susceptibility to environmental disorder. In this study, we propose and theoretically investigate a topologically protected tripartite energy transfer system based on photonic crystal nanocavities. By utilizing topological corner states as localized interaction nodes and edge states as robust transmission channels, we construct a platform that mediates energy exchange among three distinct quantum emitters. Using the Lindblad master equation formalism, we analyze the spectral dependence of coupling strengths and transfer dynamics. Our results demonstrate that coherent coupling between nearest neighbors is the dominant mechanism driving high-efficiency transport, whereas next-nearest-neighbor interactions can induce destructive interference. Furthermore, compared to bipartite systems, the tripartite configuration exhibits an enhanced cumulative probability for charge separation. Crucially, numerical simulations confirm that the energy transfer efficiency and time remain virtually unaffected by random structural disorder or sharp interface bends, unequivocally validating the topological protection of the system. These findings establish a robust blueprint for scalable quantum interconnects and integrated photonic circuitry. Full article

Optimizing nitrogen fertilization and planting date is essential for improving forage maize productivity under semi-arid conditions. This study evaluated the effects of nitrogen application rates and planting dates on growth, forage yield, and quality of maize (Zea mays L.) in Upper Egypt. A two-year field experiment (2024–2025) was conducted at the Experimental Farm of Assiut University using a strip-plot design arranged in a randomized complete block design with three replications. Four planting dates (15 April, 15 May, 15 June, and 15 July) were assigned horizontally, while three nitrogen rates (167, 238, and 309 kg N ha⁻¹) were applied vertically. Growth traits, fresh and dry forage yield, dry matter percentage, crude protein content, and protein yield were recorded at 60 days after sowing. Results showed that planting date, nitrogen rate, and their interaction significantly affected most measured traits in both seasons. Sowing in mid-May consistently produced the highest plant height, chlorophyll content, fresh and dry forage yield, and protein yield. Increasing nitrogen application enhanced biomass production and forage quality, with the highest values generally recorded at 309 kg N ha⁻¹. The strongest yield response to nitrogen occurred when maize was sown at the optimal planting date, indicating that nitrogen utilization was closely linked to favorable environmental conditions. Phenotypic correlation and multivariate analyses revealed strong associations among vegetative growth traits and forage yield, with a single dominant factor explaining more than 91% of the variation in yield-related traits across seasons. Overall, the results demonstrate that synchronizing planting date with appropriate nitrogen fertilization is critical for maximizing maize forage yield and quality under semi-arid conditions. Mid-May sowing combined with adequate nitrogen supply represents an effective management strategy for forage maize production in Upper Egypt, while further research is needed to optimize nitrogen-use efficiency and long-term sustainability. Full article

Employee turnover presents a significant challenge to modern organizations, often resulting in operational disruptions, substantial hiring costs, and a loss of institutional knowledge. While traditional human resource practices have historically been reactive, the emergence of machine learning has introduced a proactive capability to anticipate and mitigate attrition before it occurs. This research utilizes the IBM HR Analytics dataset, which contains 1470 employee records and 35 distinct features, to develop a hybrid machine learning model designed to enhance the accuracy of turnover predictions. To ensure the model’s effectiveness, the researchers employed a comprehensive preprocessing phase that included eliminating non-informative features, applying label encoding to categorical data, and using StandardScaler to normalize quantitative values. A critical component of the study addressed the common issue of class imbalance within HR data. To resolve this, a hybrid sampling strategy was implemented, combining Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) to create a more balanced learning environment for the algorithms. The core of the predictive engine is a soft voting ensemble that integrates three powerful algorithms: Random Forest, XGBoost, and logistic regression. Evaluated on an 80/20 train–test split, the tuned XGBoost model achieved an impressive 84% accuracy and an Area Under the Curve (AUC) of 0.80. Meanwhile, the logistic regression component contributed the highest F1-score, reinforcing the overall strength and balance of the ensemble approach. These metrics confirm that the hybrid model is both robust and reliable for identifying at-risk employees. Beyond simple prediction, the study prioritized interpretability by using SHapley Additive exPlanations (SHAP) to identify the primary drivers of attrition. The analysis revealed that the most significant variables influencing an employee’s decision to leave include the interaction between job level and experience, frequent overtime, monthly income, current job level, and total years spent at the company. By providing these data-driven insights, the model empowers HR teams to transition from reactive troubleshooting to proactive retention planning, ultimately securing the organization’s talent and stability. Full article

Assistive devices in recent years have transitioned from a passive mode of operation to the integration of smart solutions that enable humans to interact with active and robotic platforms. The main problems in the evolution of this kind of device are accessibility in terms of price and the functional limitations of the smart integrated solutions. This project proposes an armrest prototype for integration into smart walkers or wheelchairs that can detect the user’s intentions at a low development cost. The smart principle of operation is based on Hall-effect sensors, strategically positioned to measure the Center of Pressure (CoP) of the user’s forearm and to classify motor intention using machine learning algorithms such as Random Forest and Leave-One-Subject-Out (LOSO). The detection and correct classification of the user’s intention is a tool that can be integrated as a control system for both motorized and passive assistive devices. Full article

Visibility is a critical meteorological factor for ensuring the safety of maritime and bridge transportation, and accurate identification of low-visibility levels is essential for early warning and operational scheduling. Traditional methods such as Random Forest often exhibit insufficient feature-modeling capability when dealing with high-dimensional, multi-source remote sensing data. Meanwhile, satellite observations used for visibility recognition are characterized by strong inter-channel correlations, complex nonlinear interactions, significant observational noise and outliers, and the scarcity of low-visibility samples that are easily confused with low clouds and haze. As a result, existing general deep learning methods (e.g., the Saint model) may still exhibit unstable attention weights and limited generalization under complex meteorological conditions. To address these limitations, this study constructs a visibility classification task for the Jiaxing–Shaoxing Cross-Sea Bridge region in China based on multi-channel visible and infrared spectral observations from the Fengyun-4A (FY-4A) and Fengyun-4B (FY-4B) satellites. We propose a visibility classification method using the LF-Transformer for the Jiaxing–Shaoxing Cross-Sea Bridge region in China, and systematically compare it with the Random Forest and Saint models. Experimental results show that the Precision of the LF-Transformer increases significantly from 0.47 (Random Forest) to 0.59, achieving a 13% improvement and demonstrating stronger discriminative ability and stability under complex meteorological conditions. Furthermore, a combination input of FY4A+FY4B outperform the single FY4A, with a 25.5% increased Macro F1-score. With an additional ensemble strategy, the LF-Transformer further improves its precision on the FY4A+FY4B fused dataset to 0.61, a 3% compared to the original LF-Transformer, indicating enhanced prediction stability. Overall, the proposed method substantially strengthens visibility classification performance and highlights the strong application potential of the LF-Transformer in remote-sensing-based meteorological tasks, particularly for low-visibility monitoring, early warning, and transportation safety assurance. Full article

Monitoring multi-temporal forest vertical structure in anthropogenically disturbed and topographically complex landscapes remains a major challenge, particularly when low-cost remote sensing technologies are used. This study aims to quantify forest vertical structure change and to determine whether these changes are systematically regulated by geomorphometric controls rather than occurring randomly. A multi-temporal unmanned aerial vehicle (UAV) photogrammetry workflow based on Structure from Motion (SfM) was applied to generate annual Canopy Height Models (CHMs) for 2023, 2024, and 2025. To ensure temporal robustness, the 95th percentile of canopy height (P₉₅) was adopted as the primary structural metric, and vertical change was quantified using a difference-based indicator (ΔP₉₅). Random Forest (RF) regression was used to model the relationship between canopy height change and terrain-derived predictors, including slope, aspect, and Topographic Wetness Index (TWI). The results reveal a consistent vertical growth signal across the study area, with a mean ΔP₉₅ increase of 0.65 m over the monitoring period, clearly exceeding the photogrammetric vertical error (RMSE = 0.082 m). Positive canopy height changes are concentrated on moisture-favored, moderately sloping and north-facing terrain, whereas negative changes (down to −1.20 m) are mainly associated with mining-disturbed and steep surfaces. The RF model achieved high explanatory performance (training R² = 0.919) and identified aspect (20%), slope (18%), and TWI (18%) as the dominant controls on forest vertical dynamics. These findings demonstrate that forest vertical structure evolution in disturbed landscapes is not stochastic but is systematically governed by terrain-driven hydro-morphological and microclimatic conditions. The main contribution of this study is the development of an interpretable, change-focused UAV–machine learning framework that moves beyond single-epoch canopy height estimation and enables process-oriented analysis of terrain–vegetation interactions. The proposed approach provides a cost-effective and transferable tool for forest monitoring and post-mining restoration planning in complex terrain settings. Full article

Traditional statistical approaches identify group-level associations between biomarkers and rupture status in intracranial aneurysms (IAs) but often miss nonlinear interactions at the patient level. Methods: The authors retrospectively analyzed 35 saccular IAs in 35 patients (57.1% ruptured) from a single center (2021–2023). Demographics, detailed morphology (e.g., neck width, aspect ratio, VERTI, irregular shape), and multi-site inflammatory/immune markers (CRP; complement C3/C4; IgA/IgG/IgM) were included. After preprocessing (min–max scaling; one-hot encoding), five algorithms (DT, AdaBoost, GBM, XGBoost, RF) were evaluated with stratified five-fold CV and class balancing via random oversampling. The primary model (Random Forest) was tuned with Optuna and explained using global feature importance and LIME. The results showed that baseline RF achieved CV ROC-AUC 0.81 and test ROC-AUC 0.92 (test accuracy 0.857). The tuned RF (with oversampling and Optuna) yielded a mean CV accuracy of 0.85 ± 0.09 and CV ROC-AUC of 0.98 ± 0.07 while maintaining test ROC-AUC of 0.92. The average precision on the test PR curve was 0.97. The most influential predictors combined inflammatory markers (CRP, C3, C4) with morphology (neck width, irregular shape). LIME revealed consistent local patterns: low A.CRP/C.CRP and lower C3/C4 favored Not-Broken, whereas higher CRP/complement with smaller neck and irregular shape pushed toward Broken classifications. It can be concluded that an interpretable machine learning (ML) pipeline captured clinically plausible, nonlinear interactions between inflammation and aneurysm geometry. Integrating explainable ML with conventional statistics may enhance rupture risk stratification, enable patient-level rationale, and inform personalized management. These results could significantly contribute to the quality of treatment for patients with intracranial aneurysms. Full article