Search Results (7,604)

Resilience refers to an enduring and yet fluid characteristic that enhances children’s adaptation. It is a dynamic developmental process that is highly promoted by individuals’ internal characteristics, such as self-efficacy and locus of control. The present study examined whether self-efficacy mediates the relationship between locus of control and resilience among Greek primary school students. Participants were 690 students aged 9–12 years who were enrolled at primary schools in Greece in Grades 4, 5, and 6. Participants completed a questionnaire including measures assessing resilience, locus of control, and self-efficacy. Structural equation modeling using AMOS 26.0 was used to analyze the data. The results indicated that locus of control and self-efficacy function as significant predictors for all dimensions of resilience, while demographic characteristics such as gender and grade only predict some dimensions of resilience. The hypothesized model was a good fit to the data, and self-efficacy partially mediates the relationship between locus of control and resilience. Psychologists, instructors, and practitioners can develop and apply intervention programs in order to strengthen children’s resilience by enhancing their self-efficacy and helping them adopt an internal locus of control. Full article

This study investigates the influence of chromatic dispersion on the performance of Brillouin optical time-domain analysis (BOTDA) sensors, particularly under high-pump-power conditions, where nonlinear effects become significant. By incorporating dispersion terms into the coupled amplitude equations of stimulated Brillouin scattering (SBS), we theoretically analyzed the dispersion-induced pulse broadening effect and its impact on the Brillouin gain spectrum (BGS). Numerical simulations revealed that dispersion leads to a moderate broadening of pump pulses, resulting in slight changes to BGS characteristics, including increased peak power and reduced linewidth. To explore the interplay between dispersion and nonlinearity, we built a gain-based BOTDA experimental system and tested two types of fibers, namely standard single-mode fiber (SMF) with anomalous dispersion and dispersion-compensating fiber (DCF) with normal dispersion. Experimental results show that SMF is more prone to modulation instability (MI), which significantly degrades the signal-to-noise ratio (SNR) of the BGS. In contrast, DCF effectively suppresses MI and provides a more stable Brillouin signal. Despite SMF exhibiting narrower BGS linewidths, DCF achieves a higher SNR, aligning with theoretical predictions. These findings highlight the importance of fiber dispersion properties in BOTDA design and suggest that using normally dispersive fibers like DCF can improve sensing performance in long-range, high-power applications. Full article

Purposes: This study aims to construct a research framework integrating the theory of human values and Self-Determination Theory (SDT) to examine whether parents’ sport values influence their support for children’s continued participation in balance bike activities in terms of the mediation of participation motivation. Methods: Data were collected from 439 parents whose children participated in balance bike activities using a snowball sampling method. Descriptive statistics and structural equation modeling (SEM) were employed to analyze the relationships among parents’ sport values, participation motivation, and continued participation intention. Results: The findings revealed that parents’ sport values significantly predicted participation motivation, which, in turn, remarkably influenced continued participation intention. Participation motivation fully mediated the relationship between sport values and continued participation intention, supporting SDT’s assumption of motivational internalization and highlighting the crucial role of intrinsic motivation. Full article

For a significant fluid model and the truncated M-fractional (1 + 1)-dimensional nonlinear generalized water wave equation, distinct types of truncated M-fractional wave solitons are obtained. Ocean waves, tidal waves, weather simulations, river and irrigation flows, tsunami predictions, and more are all explained by this model. We use the improved $(G^{\prime }/G)$ expansion technique and a modified extended direct algebraic technique to obtain these solutions. Results for trigonometry, hyperbolic, and rational functions are obtained. The impact of the fractional-order derivative is also covered. We use Mathematica software to verify our findings. Furthermore, we use contour graphs in two and three dimensions to illustrate some wave solitons that are obtained. The results obtained have applications in ocean engineering, fluid dynamics, and other fields. The stability analysis of the considered equation is also performed. Moreover, the stationary solutions of the concerning equation are studied through modulation instability. Furthermore, the used methods are useful for other nonlinear fractional partial differential equations in different areas of applied science and engineering. Full article

Marine coral sand–clay mixtures (MCCM) are promising green fill materials in civil engineering projects, where their strength characteristics play a vital role in ensuring structural safety and stability. To investigate these properties, a series of triaxial shear tests were performed under diverse conditions, including variations in asperity spacing, asperity height, the number of reinforcement layers, confining pressure, and axial strain. This experimental campaign yielded a robust strength dataset for MCCM. Utilizing this dataset, several predictive models were developed, including a standard Support Vector Machine (SVM), an SVM optimized via Genetic Algorithm (GA-SVM), an SVM enhanced by Particle Swarm Optimization (PSO-SVM), and a hybrid model incorporating Logical Development Algorithm preprocessing a SVM model (LDA-SVM). Among these models, the LDA-SVM model exhibited the best performance, achieving a test RMSE of 1.67245 and a correlation coefficient (R) of 0.996, demonstrating superior prediction accuracy and strong generalization ability. Sensitivity analyses revealed that asperity spacing, asperity height, and confining pressure are the most influential factors affecting MCCM strength. Moreover, an explicit empirical equation was derived from the LDA-SVM model, allowing practitioners to estimate strength without relying on complex machine learning tools. The results of this study offer practical guidance for the optimized design and safety evaluation of MCCM in civil engineering applications. Full article

Sediment accumulation in irrigation channels poses a significant challenge to water resource management, impacting hydraulic efficiency and agricultural sustainability. This study introduces an innovative multidisciplinary framework that integrates advanced image analysis (FIJI/ImageJ 1.54p), statistical validation (RStudio), and vector field modeling with a novel Sinusoidal Morphodynamic Bedload Transport Equation (SMBTE) to predict sediment deposition patterns with high precision. Conducted along the Malacatos River in La Tebaida Linear Park, Loja, Ecuador, the research captured a natural sediment transport event under controlled flow conditions, transitioning from pressurized pipe flow to free-surface flow. Observed sediment deposition reduced the hydraulic cross-section by approximately 5 cm, notably altering flow dynamics and water distribution. The final SMBTE model (Model 8) demonstrated exceptional predictive accuracy, achieving RMSE: 0.0108, R²: 0.8689, NSE: 0.8689, MAE: 0.0093, and a correlation coefficient exceeding 0.93. Complementary analyses, including heatmaps, histograms, and vector fields, revealed spatial heterogeneity, local gradients, and oscillatory trends in sediment distribution. These tools identified high-concentration sediment zones and quantified variability, providing actionable insights for optimizing canal design, maintenance schedules, and sediment control strategies. By leveraging open-source software and real-world validation, this methodology offers a scalable, replicable framework applicable to diverse water conveyance systems. The study advances understanding of sediment dynamics under subcritical (Fr ≈ 0.07) and turbulent flow conditions (Re ≈ 41,000), contributing to improved irrigation efficiency, system resilience, and sustainable water management. This research establishes a robust foundation for future advancements in sediment transport modeling and hydrological engineering, addressing critical challenges in agricultural water systems. Full article

Spatiotemporal correlations between meteo-inputs and wind–solar outputs in an optimal regional scale are crucial for developing robust models, reliable in mid-term prediction time horizons. Modelling border conditions is vital for early recognition of progress in chaotic atmospheric processes at the destination of interest. This approach is used in differential and deep learning; artificial intelligence (AI) techniques allow for reliable pattern representation in long-term uncertainty and regional irregularities. The proposed day-by-day estimation of the RE production potential is based on first data processing in detecting modelling initialisation times from historical databases, considering correlation distance. Optimal data sampling is crucial for AI training in statistically based predictive modelling. Differential learning (DfL) is a recently developed and biologically inspired strategy that combines numerical derivative solutions with neurocomputing. This hybrid approach is based on the optimal determination of partial differential equations (PDEs) composed at the nodes of gradually expanded binomial trees. It allows for modelling of highly uncertain weather-related physical systems using unstable RE. The main objective is to improve its self-evolution and the resulting computation in prediction time. Representing relevant patterns by their similarity factors in input–output resampling reduces ambiguity in RE forecasting. Node-by-node feature selection and dynamical PDE representation of DfL are evaluated along with long-short-term memory (LSTM) recurrent processing of deep learning (DL), capturing complex spatio-temporal patterns. Parametric C++ executable software with one-month spatial metadata records is available to compare additional modelling strategies. Full article

Machine Learning methods are exploited to extract a universal approach for self-diffusion coefficient calculation in molecular fluids. Analytical expressions are derived through symbolic regression for fluids both in bulk and confined nanochannels. The symbolic regression framework is trained on simulation data from molecular dynamics and correlates the values of the self-diffusion coefficients with macroscopic properties, such as density, temperature, and the width of confinement. New expressions are derived for nine different molecular fluids, while an all-fluid universal equation is extracted to capture molecular behavior as well. In such a way, a highly computationally demanding property is predicted by easy-to-define macroscopic parameters, bypassing traditional numerical methods based on mean squared displacement and autocorrelation functions at the atomistic level. To achieve generalizability and interpretability, simple symbolic expressions are selected from a pool of genetic programming-derived equations. The obtained expressions present physical consistency, and they are discussed in terms of explainability. The accurate prediction of the self-diffusion coefficient both in bulk and confined systems is important for advancing the fundamental understanding of fluid behavior and leading the design of nanoscale confinement devices containing real molecular fluids. Full article

Cylindrical roller bearings are widely used in industrial machinery, automotive systems, and aerospace applications, where their reliability directly affects the performance and safety of mechanical systems. The fatigue life of cylindrical roller bearings is significantly affected by their elastohydrodynamic lubrication condition, with variations potentially reaching multiple times. However, conventional quasi-static models often neglect lubrication effects. This study establishes a quasi-static analysis model for cylindrical roller bearings that incorporates the effects of elastohydrodynamic lubrication by integrating elastohydrodynamic lubrication theory with the Lundberg–Palmgren life model. The isothermal line contact elastohydrodynamic lubrication equations are solved using the multigrid method, and the contact load distribution is determined through nonlinear iterative techniques to calculate bearing fatigue life. Taking the N324 support bearing on the main shaft of an SFW250-8/850 horizontal hydro-generator as an example, the influences of radial load, inner race speed, and lubricant viscosity on fatigue life are comparatively analyzed. Experimental validation is conducted under both light-load and heavy-load operating conditions. The results demonstrate that elastohydrodynamic lubrication markedly increases contact loads, leading to a reduced predicted fatigue life compared with that of the De Mul model (which ignores lubrication). The proposed lubrication-integrated model achieves an average deviation of 5.3% from the experimental data, representing a 16.1% improvement in prediction accuracy over the De Mul model. Additionally, increased rotational speed and lubricant viscosity accelerate fatigue life degradation. Full article

This study introduces a novel reflective–formative hierarchical model specification for the classic push–pull tourist motivation construct, aligning its measurement with the theoretical distinction between intrinsic “push” drives and external “pull” attributes. Unlike the traditional reflective-reflective structuring of tourist motivation we defied the higher order factors (novelty, knowledge and facilities as formative. Using partial least squares structural equation modeling (PLS-SEM) on a purposive sample of 319 international tourists, we empirically validate the reflective–formative (reflective first-order, formative second-order) model. The reflective–formative model showed a superior fit and predictive power: it explained substantially more variance in key outcome constructs (social motives (R² = 53.60) and self-actualization (R² = 23.10)) than the traditional reflective–reflective specification (social motives (R² = 49.30) and self-actualization (R² = 21.70)), which is consistent with best-practice guidelines for theoretically grounded models. In contrast, the incorrectly specified reflective–reflective model showed stronger effects between unrelated constructs, supporting concerns that choosing the wrong type of measurement model can lead to incorrect conclusions. By reconciling the push–pull theory with measurement design, this work’s main contributions are a theoretically justified reflective–formative model for tourist motivation, and evidence of its empirical benefits. These findings highlight a methodological innovation in motivation modeling and underscore that modeling push–pull motives formatively yields more accurate insights for theory and practice. Full article

Background/Objectives: Proper management of food services aimed at preventing malnutrition and weight loss among residents of long-term care facilities is a critical priority. Accordingly, accurate prediction of energy intake requirements is necessary. This study aimed to estimate the energy intake required to prevent weight loss in residents of Japanese long-term care facilities. Methods: Body weight and 12-day dietary intake were measured from residents aged ≥75 years with a body mass index (BMI) < 25.0 kg/m² who were consuming a regular or chopped diet. In the survey, individuals with oral intake were included, while those with swallowing problems, serious illnesses, dietary restrictions, or medications causing appetite loss were excluded. The rate of body-weight loss and the energy intake per kilogram of body weight (kcal/kg BW) during each 6-month period were calculated. The energy intake per kilogram of body weight corresponding to the rate of body-weight loss of 0% was estimated from the regression line between the rate of body-weight loss and energy intake per kilogram of body weight. Results: The data was analyzed for 99 residents (15 men and 84 women, age 89.3 ± 5.0 years, BMI 20.3 ± 2.6 kg/m²). From the regression results in all participants, the energy intake per kilogram of body weight corresponding to the rate of body-weight loss of 0% was 31.4 kcal/kg BW overall and 33.4 kcal/kg BW for those with a BMI < 18.5 kg/m². Conclusions: The calculation of energy intake using a regression line may be able to predict the energy intake required for weight maintenance without using instrumental measurements or estimation equations, especially in the case of underweight individuals. Full article

The Industrial Internet of Things (IIoT) offers transformative potential for supply chain management by enabling automation, real-time monitoring, and predictive analytics. However, fragmented standardization, interoperability challenges, and cybersecurity risks hinder its sustainable adoption. This study aims to develop and validate an ISO-based framework to optimize IIoT networks for sustainable supply chain operations. A quantitative time-series research design was employed, analyzing 150 observations from 10–15 industrial firms over five years. Analytical methods included ARIMA, structural equation modeling (SEM), and XGBoost for predictive evaluation. The findings indicate a 6.2% increase in system uptime, a 4.7% reduction in operational costs, a 2.8% decrease in lead times, and a 55–60% decline in security incidents following ISO standard implementation. Interoperability improved by 40–50%, and integration cost savings ranged from 35–40%, contributing to a 25% boost in overall operational efficiency. These results underscore the critical role of ISO frameworks such as ISO/IEC 30141 and ISO 50001 in enhancing connectivity, energy efficiency, and network security across IIoT-enabled supply chains. While standardization significantly improves key performance indicators, the persistence of lead time variability suggests the need for additional optimization strategies. This study offers a structured and scalable methodology for ISO-based IIoT integration, delivering both theoretical advancement and practical relevance. By aligning with internationally recognized sustainability standards, it provides policymakers, practitioners, and industry leaders with an evidence-based framework to accelerate digital transformation, enhance operational efficiency, and support resilient, sustainable supply chain development in the context of Industry 4.0. Full article

In this paper, we discuss the prediction of the delivery efficiency of magnetic carriers based on their properties and field parameters. We developed a theory describing the behavior of magnetic capsules in the capillaries of living systems. A partial differential equation for the spatial distribution of magnetic capsules has been obtained. We propose to characterize the interaction between the magnetic field and the capsules using a single vector, which we call “specific magnetic force”. To test our theory, we performed experiments on a model of a capillary bed and on a living organism with two types of magnetic capsules that differ in size and amount of magnetic material. The experimental results show that the distribution of the capsules in the field correlated with the theory, but there were fewer actually accumulated capsules than predicted by the theory. In the weaker fields, the difference was more significant than in stronger ones. We proposed an explanation for this phenomenon based on the assumption that a certain level of magnetic force is needed to keep the capsules close to the capillary wall. We also suggested a formula for the relationship between the probability of capsule precipitation and the magnetic force. We found the effective value of a specific magnetic force at which all the capsules attracted by the magnet reach the capillary wall. This value can be considered as the minimum level for the field at which it is, in principle, possible to achieve a significant magnetic control effect. We demonstrated that for each type of capsule, there is a specific radius of magnet for which the effective magnetic force is achieved at the largest possible distance from the magnet’s surface. For the capsules examined in this study, the maximum distance where the effective field can be achieved does not exceed 1.5 cm. The results of the study contribute to our understanding of the behavior of magnetic particles in the capillaries of living organisms when exposed to a magnetic field. Full article

In recent years, significant changes in food consumption habits have emerged due to various factors, including climate change, population growth, urbanization, and the depletion of natural resources. These changes pose a threat to the stability of global food systems and raise serious concerns about food security. Although this process encourages innovative and sustainable food consumption, it also makes individuals more skeptical and concerned about new foods. In this context, understanding consumer intentions regarding behaviors such as purchasing genetically modified (GM) foods is critical for predicting consumer responses and promoting responsible consumption patterns within the scope of sustainability. This study examined the effects of food technology neophobia and perceived information on attitudes and purchase intentions toward genetically modified (GM) foods. Survey data were collected from 324 participants across Turkey and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that food technology neophobia reduces perceived benefits and increases perceived risks, whereas perceived information enhances perceived benefits and lowers perceived risks. Additionally, attitudes were found to influence the intention to purchase GM foods significantly. Global issues, such as climate change and the depletion of natural resources, highlight the importance of innovations in food technology for sustainable food production. Understanding consumer concerns and perceived knowledge levels regarding genetically modified (GM) foods is critical to ensuring that these products are accepted at the societal level in an informed and conscious way. This study contributes to the promotion of sustainable food technologies and responsible consumer behavior, in line with the objectives of Sustainable Development Goal 12 (Responsible Consumption and Production). Full article

This paper primarily investigates the response and failure behavior of 6063-T5 aluminum alloy square tubes with varying outer side lengths under symmetric curvature-controlled cyclic bending in different bending directions. The response is characterized by the moment–curvature relationship and the variation in the outer side length with respect to curvature, whereas failure is characterized by the relationship between the controlled curvature and the number of cycles required to initiate buckling. The outer side lengths studied are 20 mm, 30 mm, 40 mm, and 50 mm, and the bending directions considered are 0°, 22.5°, and 45°. The moment–curvature curves exhibited cyclic hardening, and stable loops were formed for all outer side lengths and bending directions. An increase in the outer side length resulted in a higher peak bending moment, while a greater bending direction led to a slight increase in the peak bending moment. For a fixed bending direction, the curves representing the variation of the outer side length (defined as the change in length divided by the original length) with respect to curvature displayed symmetry, serrated features, and an overall increasing trend as the number of cycles increased, irrespective of the specific outer side length. In addition, increasing either the outer side length or altering the bending direction led to a larger variation in the outer side length. As for the relationship between curvature and the number of cycles required to initiate buckling, the data for each bending direction and each of the four outer side lengths formed distinct straight lines on a double-logarithmic plot. Based on the experimental observations, empirical equations were developed to characterize these relationships. These equations were then used to predict the experimental data and showed excellent agreement with the measured results. Full article