Search Results (5,716)

To reduce the high computational cost and lengthy design cycles of traditional solid rocket motor (SRM) development, this paper proposes an efficient surrogate-assisted multi-objective optimization approach. A comprehensive performance model was first established, integrating internal ballistics, grain structural integrity, and cost estimation, to enable holistic assessment of the coupled effects of key motor components. A parametric analysis framework was then developed to automate the model, facilitating seamless data exchange and coordination among sub-models through chain coupling. Leveraging this framework, a large-scale, high-fidelity dataset was generated via uniform sampling of the design space. The Kriging surrogate model with the highest global fitting accuracy was subsequently employed to replicate the integrated model’s complex responses and reveal underlying design principles. Finally, an enhanced NSGA-III algorithm incorporating a phased hybrid crossover operator was applied to improve global search performance and guide solution evolution along the Pareto front. Applied to a specific SRM, the proposed method achieved a 4.72% increase in total impulse and a 6.73% reduction in cost compared with the initial design, while satisfying all constraints. Full article

This study examined how young learners’ (YLs’) views of teachers’, peers’, and parents’ roles influence their motivation and task engagement in learning English, as well as their parents’ perspectives on their children’s motivation and task engagement, using a quantitative cross-sectional design. The study draws on self-determination theory, social cognitive theory, YLs’ language learning motivation, and task engagement. Data from surveys were analyzed using structural equation modeling with IBM SPSS AMOS Version 26. The sample included 598 YLs and their parents from Indonesian elementary schools. The model examines direct and indirect effects of four independent variables (YLs’ and parents’ perceptions of teachers’, peers’, and parents’ roles) on a dependent variable (task engagement) with a mediator (motivation). The model fit was adequate (Chi-Square = 67.452, CMIN/DF = 3.895, RMSEA = 0.07, RMR = 0.014, CFI = 0.934, and TLI = 0.976). Both children’s and parents’ perceptions positively influenced children’s motivation and engagement. Motivation significantly influenced task engagement and mediated the impact of children’s and parents’ views on it. The findings recommend engaging parents, encouraging peer collaboration, and training teachers to build a supportive environment for young English learners. Full article

Background: Borderline personality disorder (BPD), a significant personality trait frequently observed in young adults, is associated with challenges in mental health and academic performance. Screening for BPD symptoms is essential. The Screening Instrument for Borderline Personality Disorder (SI-Bord) is widely used to assess general BPD symptoms. However, despite being translated and culturally adapted, the psychometric properties of the Chinese version of the SI-Bord have not been thoroughly investigated in a Chinese population. Objectives: The aim of the study was to evaluate the psychometric properties of the Chinese version of the Screening Instrument for Borderline Personality Disorder (SI-Bord) among university students using confirmatory factor analysis (CFA). Methods: Participants completed the SI-Bord along with the Perceived Stress Scale (PSS), the Meaning in Life Questionnaire (MLQ), the Experiences in Close Relationships–Revised (ECR-R), and the Rosenberg Self-Esteem Scale (RSES). Results: A total of 715 Chinese university students (mean age = 20.33 years; age range = 18–25), including 385 males (54.2%) and 325 females (45.5%), participated in this study. The unidimensional model demonstrated adequate fit indices. The SI-Bord showed significant correlations with the PSS and ECR-R (attachment anxiety), alongside smaller correlations with the MLQ, supporting its convergent and discriminant validity. The Chinese version of the SI-Bord exhibited good reliability. Invariance testing confirmed at least metric invariance across various groups. Conclusions: The Chinese version of the SI-Bord demonstrates strong validity and reliability as a tool for screening for core BPD symptoms among Chinese university students. Further studies are encouraged to evaluate the validity of the SI-Bord across diverse groups, including age, socioeconomic status, and geographic regions. Applying it in clinical BPD samples will further enhance its utility across Chinese populations. Full article

Leadership literature suggests that a transformational leadership style can reduce negative employee outcomes, even in challenging work environments such as the education sector, where teachers play a key role in social development. This study aimed to analyze the association between transformational leadership and job satisfaction: the role of gratitude toward the organization in the Peruvian context. A cross-sectional study with an explanatory design was conducted considering 457 men and women who declared themselves teachers, aged between 18 and 73 years (M = 38.63; SD = 10.61), recruited through non-probability convenience sampling. The theoretical model was evaluated using the Partial Least Squares method (PLS-SEM). An adequately fitting measurement model was obtained (α = between 0.893 and 0.969; CR = between 0.897 and 0.971; AVE = between 0.757 and 0.845), demonstrating that transformational leadership is positively associated with the components of gratitude toward the organization and job satisfaction, as well as the association of the components of gratitude toward the organization and job satisfaction. In turn, it was evident how gratitude toward the organization plays a mediating role in these relationships. In this sense, the study provides valuable information for Peruvian educational leaders seeking to improve indicators of satisfaction, gratitude, and leadership in their work environment. These findings enrich educational management, given that it is the first empirical study to demonstrate these links in a challenging sector of an emerging country, offering a solid foundation for the development of more humanized, effective, and sustainable management strategies. Full article

Background/Objectives: Vitamin D deficiency is a global epidemic. In certain populations, such as the United Arab Emirates (UAE), low nutritional intake of vitamin D, inadequate exposure to sunlight, and cultural dress codes can lead to deficiencies in blood vitamin D levels, predisposing them to musculoskeletal disorders, diabetes, and cardiovascular diseases. There are also notable associations between vitamin D deficiency, physical inactivity, and lower cognitive performance. The aim of this study was to determine how vitamin D status may affect physical inactivity and cognitive performance in a young UAE population. Methods: Primary data were obtained on vitamin D status, cardiorespiratory fitness, body composition, and blood profiles of students in the UAE. Following initial assessment, a cohort of vitamin D-deficient/insufficient individuals participated in a 10-week physical activity intervention (Group A), whilst another cohort was supplemented with 5000 IU vitamin D3 daily and an exercise intervention (Group B). Both groups underwent physiological and biochemical profiling, and the effects of vitamin D supplementation on cognitive function were assessed. Statistical analysis included paired samples t-tests between pre- and post-intervention values and the Wilcoxon signed rank test for within-group comparisons and the Mann–Whitney U test for between-group comparisons. Results: The findings suggest that physical exercise alone improves overall cardiorespiratory fitness, as shown by an increased VO₂ max (p < 0.05), while vitamin D supplementation combined with physical exercise did not significantly improve fitness over a 10-week period (p > 0.05). However, vitamin D combined with physical exercise significantly improved cognitive performance in Group B only, specifically in working memory, verbal memory, and cognitive flexibility (p < 0.05). Conclusions: This study highlights the need for targeted interventions such as physical exercise and vitamin D supplementation to be conducted at an early stage in order to improve physical and cognitive function and reduce the risk of disease. Full article

Financial time-series data often exhibit statistically significant skewness and heavy tails, and numerous flexible distributions have been proposed to model them. In the context of the Log-linear Realized GARCH model with Skew-t (ST) distributions, our objective is to explore how the choice of prior distributions in the Adaptive Random Walk Metropolis method and initial parameter values in the Generalized Reduced Gradient (GRG) Solver method affect ST parameter and log-likelihood estimates. An empirical study was conducted using the FTSE 100 index to evaluate model performance. We provide a comprehensive step-by-step tutorial demonstrating how to perform estimation and sensitivity analysis using data tables in Microsoft Excel. Among seven ST distributions—namely, the asymmetric, epsilon, exponentiated half-logistic, Hansen, Jones–Faddy, Mittnik–Paolella, and Rosco–Jones–Pewsey distributions—Hansen’s ST distribution is found to be superior. This study also applied the GRG method to estimate new approaches, including Realized Real-Time GARCH, Realized ASHARV, and GARCH@CARR models. An empirical study showed that the GARCH@CARR model with the feedback effect provides the best goodness of fit. Out-of-sample forecasting evaluations further confirm the predictive dominance of models incorporating real-time information, particularly Realized Real-Time GARCH for volatility forecasting and Realized ASHARV for 1% VaR estimation. The findings offer actionable insights for portfolio managers and risk analysts, particularly in improving volatility forecasts and tail-risk assessments during market crises, thereby enhancing risk-adjusted returns and regulatory compliance. Although the GRG method is sensitive to initial values, its presence in the spreadsheet method can be a powerful and promising tool in working with probability density functions that have explicit forms and are unimodal, high-dimensional, and complex, without the need for programming experience. Full article

Agricultural productivity is closely linked to the spatial variability of soil physical properties. However, high variability makes it difficult to implement effective management strategies, and the constant expansion of eucalyptus plantations in certain areas alters the soil’s physical properties. This study conducted a geostatistical analysis of the physical properties of a soil in Sogamoso, Boyacá (Colombia), which contains areas with different management practices and vegetation cover, among which the presence of Eucalyptus globulus stands out. Ninety-seven points were sampled in an area of 29.1 ha, with multiple land uses. The data were analyzed using descriptive statistics and geostatistical analysis, which determined the semivariogram parameters, the degree of spatial dependence, and the best-fitting interpolation model for mapping. A correlation analysis between variables was also performed. Analysis of variance showed no significant differences among vegetation covers (dense forest, grass-crop mosaic, weedy grassland, and crop mosaic), indicating structural homogeneity. The hydraulic conductivity (K_sat) had the highest coefficient of variation (CV), at 141.9%, while particle density had the lowest CV, at 9.25%. K_sat (exponential model, range = 207 m) and porosity (spherical model, range = 98 m) showed a strong spatial dependence. K_sat was lower in areas with eucalyptus (0.01 to 0.2 m day⁻¹), attributed to hydrophobicity induced by organic compounds emitted by these plantations. Soil moisture contents showed lower values in areas with eucalyptus, corroborating their high water consumption. Soil aggregates were lower when eucalyptus plantations were on slopes greater than 15%. Porosity showed an inverse correlation with apparent density (r² = −0.86). Full article

Lubricating oil reflects mechanical component aging and wear. Accurate quantification of its wear metals is essential for equipment safety and intelligent maintenance. This study introduces a rapid, non-destructive method for detecting wear metal content in lubricating oil using hyperspectral technology to overcome limitations such as bulky, expensive instruments and destructive testing in current spectroscopic techniques. Absorption spectra of 98 marine gearbox oil samples were acquired using Hach UV-Vis and GLT optical fiber spectrometers. We propose a multi-head attention mechanism enhanced genetic algorithm (MHA-GA) for deep feature extraction, integrating attention weights into band selection and fitness evaluation to identify key features under multi-element interference. Wear metal prediction models were constructed using random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost). Results demonstrate MHA-GA outperformed traditional genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) in feature selection. The MHA-GA-XGBoost model performed best. Fe prediction R² reached 0.96 (Hach) and 0.93 (GLT), with RPDs of 5.33 and 3.90. For Cu, R² reached 0.91 and 0.83, with RPDs of 3.35 and 2.42. The results indicate that hyperspectral technology combined with machine learning enables effective non-destructive wear metal quantification, offering a promising strategy for intelligent maintenance and condition monitoring of lubricating oil. Full article

The aerodynamic characteristics of the axisymmetric bullet determine the effectiveness of the final damage purpose. To solve the contradiction problem between the aerodynamic drag coefficient and the objective volume in the design and selection of oval head bullet, four design parameters, namely, head length, body length, and tail length and tail angle, are selected as response factors. They have significant impacts on the aerodynamic performance and volume of the rotating stable axisymmetric bullet. The optimization boundary for these four parameters is established, and the sample space of 30 optimization groups is created by using the response surface method. Through the finite volume simulation of these 30 groups of samples, the corresponding drag coefficient and bullet volume are obtained. The function between the drag coefficient, bullet volume and sample parameters is fitted. Then, the NSGA-II algorithm is combined to conduct optimization analysis on the fitting function, and four response factor combinations on the Pareto boundary are obtained, where the drag coefficient is decreased and the bullet volume is increased. The second derivative of the solution set is analyzed to determine the optimal shape of the oval head axisymmetric bullet. The comparison shows that the drag coefficient of the optimized bullet shape is 13.1% lower than the average drag coefficient of 30 original samples, and the volume is increased by 39.5%. The method, combined the response surface method with NSGA-II algorithm, effectively improves the design efficiency of oval head axisymmetric bullet optimization design. Full article

With the growing demand for energy, oil and gas exploration and development are progressively moving into deep and ultra-deep formations, where extreme temperatures and pressures create complex challenges for drilling operations. While drilling fluids are critical for controlling bottom-hole pressure, cooling drill bits, and removing cuttings, accurately characterizing their rheological behavior under high-temperature and high-pressure (HTHP) conditions remains a key focus, as existing research has limitations in model applicability and parameter prediction range under extreme downhole environments. To address this, the study aims to determine the optimal rheological model and establish a reliable mathematical prediction model for drilling fluid rheological parameters under HTHP conditions, enhancing the precision of downhole temperature and pressure calculations. Rheological experiments were conducted on eight field-collected samples (4 water-based and four oil-based drilling fluids) using a Chandler 7600 HTHP rheometer, with test conditions up to 247 °C and 140 MPa; nonlinear fitting via a hybrid Levenberg–Marquardt and Universal Global Optimization algorithm and multivariate regression were employed for model development. Results showed that oil-based and water-based drilling fluids exhibited distinct rheological responses to temperature and pressure, with the Herschel–Bulkley model achieving superior fitting accuracy (coefficient of determination > 0.999). The derived prediction model for Herschel–Bulkley parameters, accounting for temperature-pressure coupling, demonstrated high accuracy (R² > 0.95) in validation. This research provides an optimized rheological modeling approach and a robust prediction tool for HTHP drilling fluids, supporting safer and more efficient deep and ultra-deep drilling operations. Full article

Post-exertional malaise (PEM) is a defining symptom of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), yet its molecular underpinnings remain elusive. This study investigated the temporal–longitudinal DNA methylation changes associated with PEM using a structured two-day maximum repeated effort cardiopulmonary exercise testing (CPET) protocol involving pre- and two post-exercise blood samplings from five ME/CFS patients. Cardiopulmonary measurements revealed complex heterogeneous profiles among the patients compared to typical healthy controls, and VO₂ peak indicated all patients had poor normative fitness. The switch to anaerobic metabolism occurred at a lower workload in some patients on Day Two of the test. Reduced Representation Bisulphite Sequencing followed by analysis with Differential Methylation Analysis Package-version 2 (DMAP2) identified differentially methylated fragments (DMFs) present in the DNA genomes of all five ME/CFS patients through the exercise test compared with ‘before exercise’. With further filtering for >10% methylation differences, there were early DMFs (0–24 h after first exercise test) and late DMFs between (24–48 h after the second exercise test), as well as DMFs that changed gradually (between 0 and 48 h). Of these, 98% were ME/CFS-specific, compared with the two healthy controls accompanying the longitudinal study. Principal component analysis illustrated the three distinct clusters at the 0 h, 24 h, and 48 h timepoints, but with heterogeneity among the patients within the clusters, highlighting dynamic methylation responses to exertion in individual patients. There were 24 ME/CFS-specific DMFs at gene promoter fragments that revealed distinct patterns of temporal methylation across the timepoints. Functional enrichment of ME-specific DMFs revealed pathways involved in endothelial function, morphogenesis, inflammation, and immune regulation. These findings uncovered temporally dynamic epigenetic changes in stress/immune functions in ME/CFS during PEM and suggest molecular signatures with potential for diagnosis and of mechanistic significance. Full article

This study evaluates the performance of count regression models in the presence of zero inflation, outliers, and overdispersion using both simulated and real-world maternal mortality dataset. Traditional Poisson and negative binomial regression models often struggle to account for the complexities introduced by excess zeros and outliers. To address these limitations, this study compares the performance of robust zero-inflated (RZI) and robust hurdle (RH) models against conventional models using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to determine the best-fitting model. Results indicate that the robust zero-inflated Poisson (RZIP) model performs best overall. The simulation study considers various scenarios, including different levels of zero inflation (50%, 70%, and 80%), outlier proportions (0%, 5%, 10%, and 15%), dispersion values (1, 3, and 5), and sample sizes (50, 200, and 500). Based on AIC comparisons, the robust zero-inflated Poisson (RZIP) and robust hurdle Poisson (RHP) models demonstrate superior performance when outliers are absent or limited to 5%, particularly when dispersion is low (5). However, as outlier levels and dispersion increase, the robust zero-inflated negative binomial (RZINB) and robust hurdle negative binomial (RHNB) models outperform robust zero-inflated Poisson (RZIP) and robust hurdle Poisson (RHP) across all levels of zero inflation and sample sizes considered in the study. Full article

This paper proposes optimal sliding mode fault-tolerant control for multiple robotic manipulators in the presence of external disturbances and actuator faults. First, a quantitative prescribed performance control (QPPC) strategy is constructed, which relaxes the constraints on initial conditions while strictly restricting the trajectory within a preset range. Second, based on QPPC, adaptive gain integral terminal sliding mode control (AGITSMC) is designed to enhance the anti-interference capability of robotic manipulators in complex environments. Third, a critic-only neural network optimal dynamic programming (CNNODP) strategy is proposed to learn the optimal value function and control policy. This strategy fits nonlinearities solely through critic networks and uses residuals and historical samples from reinforcement learning to drive neural network updates, achieving optimal control with lower computational costs. Finally, the boundedness and stability of the system are proven via the Lyapunov stability theorem. Compared with existing sliding mode control methods, the proposed method reduces the maximum position error by up to 25% and the peak control torque by up to 16.5%, effectively improving the dynamic response accuracy and energy efficiency of the system. Full article

To address limitations such as short forecast periods, data collection challenges, insufficient understanding of physical mechanisms, and single-scale constraints, forecasting factors and their characteristics were analyzed across astronomical, global, and watershed scales. Forecasting factors were selected based on astronomical observations, ocean current predictions, traditional calendars, and agricultural proverbs, and their characteristics were quantitatively processed. A BP neural network optimized by the Gray Wolf Optimizer (GWO) algorithm (GWO-BP) was constructed, and the dataset derived from sample division of the Fengman Reservoir Basin was used to train the model for secondary fitting. The model successfully fit and predicted the annual inflow of the Fengman Reservoir Basin from 2013 to 2017. Through a comparison with the GWO–Support Vector Machine (GWO-SVM) model, results showed that the GWO-BP model exhibited superior predictive performance. This method integrates multi-scale, easily accessible, and quantifiable forecasting factors, facilitating the extension of long-term runoff forecasting applications within the river basin. Full article

Thermogravimetric analysis (TGA) has been extensively applied in polymeric characterization and quality inspection, facilitating in-depth investigations of the microstructural thermal response characteristics of polymers, including thermal stability, composition analysis, and thermal decomposition mechanisms. Here, the impacts of six factors on the TG thermal analysis curves obtained during operation are systematically examined while analyzing their causes and recommending solutions. Furthermore, the thermal degradation kinetics of an ionomer formed by neutralizing an ethylene–methacrylic acid copolymer with metal ions (SGP membrane) used in laminated tempered glass is analyzed using the Arrhenius equation, Ozawa–Flynn–Wall hypothesis and Kissinger method. Kinetic parameters at 5% degradation are fitted and used to predict the service lifetime of the SGP membrane. The results indicate that the SGP membrane sample exhibits activation energy Ea = 136.90 kJ/mol, reaction order n = 1.65 and pre-factor A = e^25.93. It can be seen that the service lifetime of the SGP membrane sample is 16 years at 80 °C and 1.65 years at 100 °C. Full article