Search Results (67,840)

The large-scale integration of photovoltaic (PV) generation and electric vehicles (EVs) into distribution networks introduces significant operational challenges, including voltage fluctuations, increased energy losses, and feeder congestion. While previous studies have addressed distribution system reconfiguration (DSR), PV scheduling, or EV intelligent parking lot (IPL) management separately, no unified framework exists that simultaneously optimizes all three flexibility tools. This research therefore aims to develop a coordinated scheduling framework that minimizes both energy losses and voltage deviations over a 24 h horizon. For solving the mathematical formulation, an Enhanced Grey Wolf Optimizer (EGWO) is developed using the concepts of dynamic neighborhood influence and self-adaptive convergence factor to prevent the issue of premature convergence and dynamic balancing of the algorithm during the search process. Simulation results on the IEEE 33-bus system across five scenarios quantify the benefits of each control layer. DSR alone reduces daily energy loss by 30.41%. Photovoltaic scheduling alone reduces loss by 15.40%. When combined, PV scheduling and DSR achieve a 38.29% loss reduction, demonstrating strong synergy. Full integration including IPL further improves voltage deviation by 40.26% compared to the base case, while maintaining loss reduction at 36.20%. Full article

Existing studies on trust are mainly based on rational choice theory or the relational logic of the “differential mode of association,” while neglecting the contextuality of trust and the interaction of multiple factors. This study used a within-subjects situational experiment involving 252 participants, manipulating three variables: relationship type (kin, acquaintance, general other), entrusted matter (loans of 2000 yuan, 20,000 yuan, and 200,000 yuan), and trustee attributes (high ability and integrity vs. low ability and integrity). The Friedman test and Generalized Estimating Equations (GEE) were used to examine the effects of these three factors on trust intention and the mechanisms of their interaction. The results indicate that trust intention is influenced by relationship type, the importance of the entrusted matter, and trustee attributes, with significant interactions among the three. This indicates that trust is a contextual outcome shaped by multiple interacting factors rather than a linear result. This study provides contextualized evidence that relationship type, entrusted matter, and trustee attributes jointly shape trust intention. Full article

Against the backdrop of intensifying global climate change, climate policy uncertainty (CPU) and investor sentiment have become critical factors influencing the stability of financial markets. In this study, a quantitative index of investor sentiment is constructed using stock trading volume, turnover rate, price-to-earnings ratio, circulating market value, and the consumer confidence index. The QVAR-DY model is employed to analyze the risk contagion mechanisms among CPU, investor sentiment, and China’s financial sub-markets across different quantiles. Furthermore, five machine learning models—LSTM, BiLSTM, CNN, XGBoost, and LightGBM—are used to forecast risk spillover indices, and their performance is compared with three benchmark models (ARIMA, Persistence, and HistMean) to systematically evaluate the advantages of machine learning models in capturing tail risk spillover effects. The findings reveal significant cross-market risk contagion in financial markets, characterized by asymmetry. The level of risk spillover under extreme conditions is substantially higher than under normal conditions, indicating high sensitivity to extreme events and major policies. CPU exhibits the most pronounced spillover effect on the money market, while investor sentiment has the greatest impact on the stock market. The stock, real estate, and commodity markets act simultaneously as sources of risk and receivers of shocks. In terms of forecasting performance, LightGBM performs best under normal conditions, whereas LSTM achieves the highest prediction accuracy under extreme conditions. Full article

Background/Objectives: The aim of this study was to systematically synthesize and quantitatively estimate the mean serum 25-hydroxyvitamin D concentrations across populations in Kazakhstan and to examine variations according to age group, health status, and geographic region. In addition, we specifically evaluated healthy subgroups to establish reference estimates that may be useful for future epidemiological surveillance and public health planning. Methods: A systematic review and meta-analysis was conducted in accordance with the PRISMA 2020 guidelines. PubMed, Scopus, ScienceDirect, and Google Scholar were searched through March 2026 without date restrictions. Studies reporting the mean serum vitamin D concentrations among Kazakhstani populations were included. Random-effects meta-analysis was performed in R. Subgroup analyses were conducted by age group, health status, and geographic region. Meta-regression, influence diagnostics, publication bias assessment, JBI risk-of-bias evaluation, and GRADE certainty-of-evidence assessment were performed. Results: Sixteen studies comprising 28 groups and 5771 participants were included. The pooled mean serum 25(OH)D concentration in the overall cohort was 22.3 ng/mL (95% CI: 19.3–25.3), while the healthy cohort demonstrated a slightly higher pooled mean of 24.4 ng/mL (95% CI: 20.3–28.4). Adolescents had the lowest vitamin D levels among all age groups. Significant regional variability was observed, and meta-regression identified male participant proportion as a significant moderator (p = 0.03). Heterogeneity was extremely high across analyses (I² ≈ 99.9%). Conclusions: Mean serum 25(OH)D concentrations were generally within the insufficient range across the included study groups in Kazakhstan, including healthy subgroups. However, because the certainty of evidence was very low and between-study heterogeneity was extreme, the findings should be interpreted cautiously. These results support the need for standardized national surveillance and locally evaluated public health policy considerations, including targeted supplementation for high-risk groups, screening strategies where clinically indicated, and assessment of food fortification options. Full article

As the first generation in their families to pursue higher education, the mental health of first-generation college students has attracted significant attention from the academic community. Self-esteem and academic performance are significant factors influencing anxiety and mental health among first-generation college students. However, longitudinal research evidence specific to this group in China remains scarce. This study utilized two waves data, selecting a sample of 1024 first-generation college students (mean age 21.73; 55.18% male). Through follow-up surveys conducted at one-year intervals, a cross-lagged model was employed to systematically examine the longitudinal predictive relationships among self-esteem, academic performance, and anxiety. The results indicate significant negative correlations among self-esteem, academic performance, and anxiety. Cross-lagged analysis further indicated that self-esteem at T1 (β = −0.098, p < 0.05) and academic performance at T1 (β = −0.067, p < 0.05) were prospectively associated with lower anxiety at T2. This study reveals the longitudinal predictive associations among self-esteem, academic performance, and anxiety among China’s first-generation college students, providing empirical evidence for universities to improve their mental health support systems by focusing on the self-esteem development of this group and offering targeted academic support. Full article

The rapid integration of artificial intelligence (AI) technologies into educational contexts has introduced innovative instructional approaches, particularly within Science, Technology, Engineering, and Mathematics (STEM) education. Although an increasing number of empirical studies have examined AI-supported instruction, existing findings remain heterogeneous, making it difficult to draw firm conclusions about its overall effectiveness. This study aims to systematically synthesize experimental and quasi-experimental research on AI-supported instructional interventions in STEM education, quantify their overall effects on student learning outcomes, and examine potential moderating factors, including educational level, STEM discipline, and intervention duration. A comprehensive systematic literature search was conducted across Web of Science, Scopus, ERIC, ScienceDirect, and Google Scholar, covering studies published between 2005 and 2025. A total of 35 studies meeting predefined inclusion criteria were included in the meta-analysis. Effect sizes were calculated using Hedges’ g, and a Random Effects Model (REM) was employed to account for heterogeneity among studies. Moderator analyses were conducted for educational level, STEM discipline, and intervention duration. Publication bias was assessed using multiple diagnostic methods. The meta-analysis revealed a statistically significant overall positive effect of AI-supported instruction on student learning outcomes in STEM education (g = 0.67, 95% CI [0.49, 0.85], p < 0.001). Moderator analyses indicated that AI interventions were most effective at the high school level. Although Science and Mathematics disciplines showed slightly higher effect sizes, the between-group difference was not statistically significant (Q = 4.85, df = 2, p = 0.088). Regarding intervention duration, the highest effect size was observed in interventions lasting more than one month and up to two months, though no consistent pattern of increasing effectiveness with longer durations was found. Publication bias analyses suggested minimal influence on the overall findings. AI-supported instructional interventions demonstrate a moderately to highly positive impact on student learning outcomes in STEM education. The effectiveness of these interventions varies according to educational level, disciplinary context, and intervention duration. These findings provide robust empirical evidence supporting the pedagogical value of AI in STEM education and offer guidance for educators and policymakers regarding effective implementation. Full article

The valorization of salmon farming by-products is an essential strategy within the circular economy. This study optimized the enzymatic hydrolysis of salmon frame proteins using Alcalase 2.5L and response surface methodology (RSM). The effects of temperature (50–60 °C), substrate concentration (50–100% w/w), and protease dose (1–13 mAU/g salmon frames) were evaluated on two key responses: degree of hydrolysis (DH) and nitrogen recovery (NR). The 20 experimental assays showed that substrate concentration and enzyme dosage strongly influenced both responses, whereas temperature had a moderate effect. The fitted models exhibited R²_adjusted values above 70% and met statistical assumptions, confirming their predictive reliability. Optimal conditions for maximizing DH were 55 °C, 50% w/w substrate, and 13 mAU/g protease, yielding a predicted DH of 6.65%. In contrast, the highest NR (48.35%) was observed at 50 °C, 50% w/w substrate, and 13 mAU/g protease, indicating that solubilization does not depend solely on hydrolysis intensity. Validation experiments showed no significant differences between predicted and experimental values (p > 0.05), supporting the robustness of the models. These results demonstrate the usefulness of RSM for optimizing enzymatic hydrolysis and advancing sustainable valorization of salmon by-products. Full article

Background: Exercise plays a crucial role in the prevention and management of type 2 diabetes. During self-motion, optic flow provides visual information about heading direction and influences postural control. This study investigated postural responses and muscle activation in individuals with type 2 diabetes exposed to optic flow stimuli simulating self-motion, and examined whether these responses varied according to habitual physical activity over 12 months. Methods: Surface electromyographic (EMG) and stabilometric data were collected from 23 individuals during quiet standing under different visual motion conditions. Participants were classified as physically active or inactive based on standardized criteria. EMG activity was recorded bilaterally from the tibialis anterior and soleus muscles at baseline, 6, and 12 months. Center of pressure (COP) displacement was measured using two force platforms. Results: Stabilometric analysis revealed a significant effect of visual stimulus on COP displacement in both antero-posterior and medio-lateral directions, as well as on COP speed, indicating that optic flow modulates postural control. COP speed changes over time differed by sex, while medio-lateral sway showed time-dependent variations across sides and physical activity groups. EMG analysis showed a significant effect of visual stimulus on soleus activation, with no consistent effects for tibialis anterior. Conclusions: Optic flow significantly modulated postural control and lower-limb muscle activation in individuals with type 2 diabetes. Preliminary differences in response profiles associated with habitual physical activity level were observed, though these should be interpreted cautiously given the exploratory nature of the study. Larger, adequately powered studies are warranted to further investigate these associations. Full article

Surface modification of zinc oxide nanoparticles (ZnO NPs) with organosilane capping agents represents an effective strategy to control their physicochemical and biological properties. In this work, we report for the first time the use of halogenosilanes, namely (3-chloropropyl)trimethoxysilane (CPTMS), (3-bromopropyl)trimethoxysilane (BPTMS) and (3-iodopropyl)trimethoxysilane (IPTMS), for the surface functionalization of ZnO NPs obtained by chemical precipitation. Structural and morphological characterization (PXRD, TEM, SEM-EDX and FTIR) confirmed successful surface modification and revealed a significant particle size reduction from ~31 nm for unmodified ZnO to ~8 nm for BPTMS-modified ZnO (ZnO_b). The biological evaluation showed that halogenosilane-modified ZnO NPs exhibit enhanced cytotoxic activity against prostate cancer cell lines (PC3 and 22Rv1), with ZnO_b displaying the highest activity, likely associated with improved cellular uptake and increased reactive oxygen species (ROS) generation. In contrast, antimicrobial assays revealed only moderate bactericidal effects against Escherichia coli and Staphylococcus aureus at relatively high concentrations (≥1250 µg mL⁻¹), while no significant activity was observed against Pseudomonas aeruginosa, Burkholderia contaminans or Candida spp, within the tested range. These findings suggest that halogenosilane functionalization modulates the biological profile of ZnO nanoparticles by enhancing anticancer effects while also influencing microbiocidal activity, highlighting the role of surface chemistry in tuning biological selectivity. The present study supports the concept that rational surface engineering of ZnO-based nanoplatforms can be exploited to favor tumor-targeted activity over broad-spectrum antimicrobial effects, providing new perspectives for the design of application-oriented nanomaterials. Full article

Lithium-ion batteries (LIBs) are subject to mechanical abuse both in electric vehicles and consumer electronic applications when dropped, which can lead to capacity degradation even if the cells survive the impact. This study investigates the impact of mechanical damage on the electrochemical performance of LIBs, focusing on capacity retention and internal resistance changes. The batteries were subjected to dynamic mechanical impact using varying impact energies (3J, 5J, and 7J) while measuring internal resistance and capacity before and after the impact. Hybrid Pulse Power Characterization (HPPC) was employed to assess internal resistance and capacity degradation across multiple cycles. Our results demonstrate that even minor mechanical damage can cause significant performance decay, especially after several cycles. The study also reveals that the state of charge (SOC) prior to impact has a minimal effect on the survival rate of the cells but influences the extent of damage observed. Post-impact analysis using optical microscopy indicates structural damage, including separator tears and delamination, contributing to capacity fade. This work highlights the importance of considering intermediate mechanical damage in LIB safety and performance assessments. Full article

Optimizing speed breeding protocols for biennial crops requires matching the vernalization regime with the genetic background. In this study, nine sugar beet genotypes were exposed to 12, 13, 14 or 15 weeks of vernalization and subsequently grown under controlled speed breeding conditions. Survival analysis revealed a threshold-like acceleration of bolting and flowering: 12 and 13 weeks were largely equivalent, whereas 14–15 weeks sharply increased the bolting and flowering hazard rates. Genotypic variation strongly influenced reproductive success and seed yield traits; genotype MARGARITA KWS combined early flowering with the highest seed number (361 seeds per plant) and total seed weight (5.26 g), while genotype 1K073 did not flower under any vernalization duration. A separate mini-steckling root architecture experiment with 11 genotypes showed that slow-release Osmocote fertilizer significantly increased mini-steckling fresh weight, length and width, with the strongest responses in genotypes 1K073, 1K139 and SMART LIENNA KWS. The interaction between genotype and nutrition was significant for mini-steckling fresh weight and width, indicating that optimal nutrition can modulate the expression of genotypic differences. Multivariate analyses (PCA, CVA, Mahalanobis distances) confirmed that vernalization duration had a threshold-type effect and that genotype was the dominant factor for seed traits, whereas nutrition was the main driver of mini-steckling architecture. Overall, these findings suggest that tailoring vernalization duration and nutrition to the genetic background may substantially improve the efficiency of sugar beet speed breeding. Full article

Biological thin-sheet systems, including leaves, insect wings, and flowering organs, achieve adaptive deformation through distributed compliance, segmentation, curvature, and controlled opening. Kirigami offers a bio-inspired route for translating such deformation logics into programmable thin-sheet surfaces; however, the geometric parameters that most strongly influence elastic displacement remain insufficiently quantified, especially across different loading regimes. This study investigates Bio-Inspired Regime-Dependent Parameter Selection in Parametric Kirigami through twenty-five laser-cut specimens spanning five boundary shapes and three thermoplastic substrates. Specimens were tested under two contrasting regimes: quasi-static tensile loading and gravity-drape loading. Elastic displacement was measured under eight-point boundary fixation and analyzed using regime-separated Pearson correlations, Bonferroni-corrected significance testing (α/18 = 0.0028), and shape-controlled partial correlations. Under tensile loading, the Number of Offsets (r = 0.807), Segments per Offset (r = −0.603), and outer-boundary void perimeter (r = 0.621) showed the strongest Bonferroni-robust associations with displacement. Under gravity-drape loading, effects were weaker and more curvature-sensitive, indicating that parameter relevance is not universal but regime-dependent. Within the tested parametric design space, the study provides an experimentally grounded basis for selecting Kirigami geometric parameters in thin-sheet structures whose adaptive deformation logic is analogous to compliant systems found in nature. Full article

Anxiety disorders and chronic stress are the most common types of mental disorder. According to the WHO, more than 359 million people worldwide suffered from these conditions in 2021. The function of mastication and the masticatory muscles undergo significant changes under the influence of a disturbed psychoemotional state. This manifests as their parafunctional activity, accompanied by increased tone and damage to elements of the dentofacial system, including increased tooth wear, chipping, cracks, and fractures. Attention to this problem is growing annually among researchers in both dental and neurological fields. This is evidenced by a wide range of therapeutic and preventive interventions aimed at correcting chronic stress, muscle hypertonia, and pathology of the dentofacial system. Despite the aforementioned measures, it is often only possible to slow down the pathological process rather than completely resolve it. This is because knowledge regarding the biology and pathophysiology of how chronic stress affects muscle activity remains limited. Understanding such mechanisms and establishing precise interrelationships could help identify targets for effective therapeutic interventions and eliminate the problem. This review of the literature systematizes information on how chronic stress and various autonomic stimuli affect changes in the functional activity of the masticatory muscles and the pathology of hard dental tissues. Full article

Optimization of nitrogen (N) management is critical for enhancing maize (Zea mays L.) productivity while maintaining soil health. The present study investigated the impact of split-application fertilization strategies on soil chemical properties and grain yield across three distinct soil types (calcaric fluvisol, luvic phaeozem, and stagnic phaeozem) in Mureș County, Romania, over three cropping seasons (2022–2024). Three fertilization variants were evaluated: the first treatment, designated V1, involved the application of 300 kg/ha NPK 20-20-0 + 300 kg/ha urea, the second treatment V2 utilized 300 kg/ha NPK 20-20-0 + 300 kg/ha NAC 27 N-calcium ammonium nitrate, and the third treatment V3 served as the baseline control, receiving (300 kg/ha NPK 20-20-0). Results indicated that significant differences were observed among the three experimental sites representing contrasting soil types for soil chemical properties and maize productivity. Calcaric fluvisol exhibited the highest production potential, attaining a mean yield of 11,702.78 kg/ha. The impact of N supplementation on soil N levels and maize yield was found to be significant. The variant receiving urea supplementation (V1) achieved the highest median yield of 9560 kg/ha in comparison to the 7420 kg/ha obtained in the control. A strong positive correlation was observed between N index and yield across all soil types (ρ = 0.93 to 0.97, p < 0.001). Fertilization significantly influenced soil pH, CaCO₃ content, nitrogen index, phosphorus availability, and maize yield, whereas humus content remained relatively stable among treatments. These findings indicate that a split-fertilization regime combining NPK with urea provides a favorable balance between productivity and cost-effectiveness and maize output in the Transylvanian Plateau. Full article

Sperm capacitation is essential for fertilization and involves coordinated changes in membrane organization, ion fluxes, and intracellular signaling. However, commonly used detection methods may reflect different biological events, which can be strongly influenced by experimental methodology. This study critically evaluated fluorescence-based approaches for assessing capacitation in boar spermatozoa, focusing on their specificity, interpretative limits, and methodological sensitivity. Ejaculated boar spermatozoa were incubated under in vitro capacitating conditions in TALP medium. Selected samples were subsequently treated with calcium ionophore to induce the acrosome reaction (AR). Phosphotyrosine (PTyr) immunofluorescence was assessed using five fixation and labeling protocols, acrosin redistribution was evaluated with the ACR.2 antibody, calcium ion redistribution was assessed using chlortetracycline (CTC) fluorescence, and acrosomal responsiveness was monitored by peanut agglutinin (PNA) lectin labeling. PTyr immunofluorescence was highly dependent on fixation protocol, indicating marked methodological sensitivity. Acrosin immunodetection revealed a clear capacitation-associated redistribution from weak or diffuse staining to a well-defined acrosomal pattern, whereas ionophore treatment caused a pronounced signal loss consistent with acrosomal exocytosis. PNA labeling confirmed that capacitation alone did not increase spontaneous acrosome loss, whereas ionophore treatment induced a robust AR. CTC staining showed a significant shift from whole-head pattern to acrosome in TALP-treated spermatozoa, indicating capacitation-associated Ca²⁺ redistribution. Together with CTC and Western blot data, these findings show that sperm capacitation status should be evaluated using multiple complementary markers rather than a single gold-standard assay. Full article