Search Results (1,387)

Background/Objectives: In Japan, well-balanced meals composed of staple grains, protein-rich main dishes, and vegetable sides are recommended. However, issues such as infrequent breakfast consumption and poor vegetable intake persist. Obesity and non-communicable disease (NCD) rates from age 40 have also begun rising. Therefore, we investigated the structural associations between healthy diets and weight control for NCD prevention, including the potential associations with rice consumption and eating out/home meal replacement use in middle-aged Japanese individuals. Methods: This study was a cross-sectional survey based on data from 577 respondents to the 2016 Hyogo Diet Survey, Hyogo Prefecture, Japan, aged 40–59 years. A healthy diet was defined as including at least two well-balanced meals daily, eating breakfast regularly, and eating five or more vegetable dishes daily. A hypothetical model included factors associated with healthy diets and maintaining a healthy weight (energy, salt, fat, and sugar intake; using nutritional fact labels; and regular exercise), and the frequencies of rice consumption and eating out/home-meal replacement. A simultaneous multi-population analysis by sex was performed. Results: Simultaneous multi-population analysis showed acceptable goodness-of-fit. Maintaining appropriate weight and eating rice were positively associated with healthy diet scores in both sexes. However, for men, using home meal replacements was negatively associated. Conclusions: Among middle-aged Japanese in Hyogo Prefecture, weight control for NCD prevention and rice consumption were linked to healthy diets. In men, using home meal replacements was associated with worse diet quality. Full article

Flange bolts are vital fasteners in civil, mechanical, and aerospace structures, where preload stability directly affects overall safety. Conventional methods for bolt loosening detection often suffer from missed detections, weak feature representation, and insufficient cross-scale fusion under complex backgrounds. This paper presents an integrated detection and angle estimation framework using a lightweight deep learning detection network. A MobileViT backbone is employed to balance local texture with global context. In the spatial pyramid pooling stage, large separable convolutional kernels are combined with a channel and spatial attention mechanism to highlight discriminative features while suppressing noise. Together with content-aware upsampling and bidirectional multi-scale feature fusion, the network achieves high accuracy in detecting small and low-contrast targets while maintaining real-time performance. For angle estimation, the framework adopts an efficient training-free pipeline consisting of oriented FAST and rotated BRIEF feature detection, approximate nearest neighbor matching, and robust sample consensus fitting. This approach reliably removes false correspondences and extracts stable rotation components, maintaining success rates between 85% and 93% with an average error close to one degree, even under reflection, blur, or moderate viewpoint changes. Experimental validation demonstrates strong stability in detection and angular estimation under varying illumination and texture conditions, with a favorable balance between computational efficiency and practical applicability. This study provides a practical, intelligent, and deployable solution for bolt loosening detection, supporting the safe operation of large-scale equipment and infrastructure. Full article

Biofuels represent a sustainable alternative that supports global energy development without compromising environmental balance. This work introduces a novel hardware–software platform for the experimental characterization of biomass solid yield during the slow pyrolysis process, integrating physical experimentation with advanced computational modeling. The hardware consists of a custom-designed pyrolizer equipped with temperature and weight sensors, a dedicated control unit, and a user-friendly interface. On the software side, a two-step kinetic model was implemented and coupled with three optimization algorithms, i.e., Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Nelder–Mead (N-M), to estimate the Arrhenius kinetic parameters governing biomass degradation. Slow pyrolysis experiments were performed on wheat straw (WS), pruning waste (PW), and biosolids (BS) at a heating rate of 20 °C/min within 250–500 °C, with a 120 min residence time favoring biochar production. The comparative analysis shows that the N-M method achieved the highest accuracy (100% fit in estimating solid yield), with a convergence time of 4.282 min, while GA converged faster (1.675 min), with a fit of 99.972%, and PSO had the slowest convergence time at 6.409 min and a fit of 99.943%. These results highlight both the versatility of the system and the potential of optimization techniques to provide accurate predictive models of biomass decomposition as a function of time and temperature. Overall, the main contributions of this work are the development of a low-cost, custom MATLAB-based experimental platform and the tailored implementation of optimization algorithms for kinetic parameter estimation across different biomasses, together providing a robust framework for biomass pyrolysis characterization. Full article

Background/Objectives: Pharmaceutical preparation technologies can enhance the bioavailability of poorly water-soluble drugs. Ursolic acid (UA) has been found to possess anti-cancer and hepatoprotective properties, demonstrating its potential as a therapeutic agent; however, its hydrophobicity and low solubility present challenges in the development of drug formulations. This study investigates the preparation of a nano-UA suspension by wet grinding, researches the influence of process parameters on particle size, and explores the rules of particle breakage and agglomeration by combining model fitting. Methods: Wet grinding experiments were conducted using a laboratory-scale grinding machine. The particle size distributions (PSDs) of UA suspensions under different grinding conditions were measured using a laser particle size analyzer. A single-factor experimental design was employed to optimize operational conditions. Model parameters for a population balance model considering both breakage and agglomeration were determined by an evolutionary algorithm optimization method. By measuring the degree to which UA inhibits the colorimetric reaction between salicylic acid and hydroxyl radicals, its antioxidant capacity in scavenging hydroxyl radicals was indirectly evaluated. Results: Wet grinding process conditions for nano-UA particles were established, yielding a UA suspension with a D50 particle size of 122 nm. The scavenging rate of the final grinding product was improved to three times higher than that of the UA raw material (D50 = 14.2 μm). Conclusions: Preparing nano-UA suspensions via wet grinding technology can significantly enhance their antioxidant properties. Model regression analysis of PSD data reveals that increasing the grinding mill’s stirring speed leads to more uniform particle size distribution, indicating that grinding speed (power) is a critical factor in producing nanosuspensions. Full article

Rapid urbanization and the proliferation of heterogeneous urban data have intensified the challenges of semantic interoperability and integrated urban governance. To address this, we propose the Smart City Ontology Framework (SMOF), a standards-driven ontology that unifies Building Information Modeling (BIM), Geographic Information Systems (GIS), Internet of Things (IoT), and relational data. SMOF organizes five core modules and eleven major entity categories, with universal and extensible attributes and relations to support cross-domain data integration. SMOF was developed through competency questions, authoritative knowledge sources, and explicit design principles, ensuring methodological rigor and alignment with real governance needs. Its evaluation combined three complementary approaches against baseline models: quantitative metrics demonstrated higher attribute richness and balanced hierarchy; LLM as judge assessments confirmed conceptual completeness, consistency, and scalability; and expert scoring highlighted superior scenario fitness and clarity. Together, these results indicate that SMOF achieves both structural soundness and practical adaptability. Beyond structural evaluation, SMOF was validated in two representative urban service scenarios, demonstrating its capacity to integrate heterogeneous data, support graph-based querying and enable ontology-driven reasoning. In sum, SMOF offers a robust and scalable solution for semantic data integration, advancing smart city governance and decision-making efficiency. Full article

A structural model of health and mental well-being among academic staff and students in Thailand was constructed and validated through confirmatory factor analysis (CFA). Data were obtained from 600 online questionnaires, equally distributed between staff (n = 300) and students (n = 300). Statistical analyses were undertaken in SPSS. Descriptive statistics were generated, internal reliability was assessed, and correlations were examined. The factor structure was first extracted through exploratory factor analysis (EFA). Model fit was subsequently assessed using CFA in LISREL. Five constructs were derived and validated: mental well-being (18 items), social participation (12 items), health literacy (28 items), work–life balance (10 items), and health behaviour (30 items). Convergent validity was demonstrated across all constructs. The final CFA model was found to exhibit a robust fit (χ² = 145.14, df = 62, p < 0.001, RMSEA = 0.047). Strong convergent validity and excellent fit indices were confirmed. Empirical evidence was therefore provided to support the model’s application in assessing health and mental well-being within Thai academic contexts. Full article

This study presents a comprehensive assessment of the energy systems in eight Eastern European countries—Bulgaria, Croatia, the Czech Republic, Hungary, Poland, Romania, Slovakia, and Slovenia—focusing on their energy transition, security of supply, decarbonisation, and energy efficiency. Using principal component analysis (PCA) and clustering techniques, we identify three different energy profiles: countries dependent on fossil fuels (e.g., Poland, Bulgaria), countries with a balanced mix of nuclear and fossil fuels (e.g., the Czech Republic, Slovakia, Hungary), and countries focusing mainly on renewables (e.g., Slovenia, Croatia). The sectoral analysis shows that industry and transport are the main drivers of energy consumption and CO₂ emissions, and the challenges and policy priorities of decarbonisation are determined. Regression modelling shows that dependence on fossil fuels strongly influences the use of renewable energy and electricity consumption patterns, while national differences in per capita electricity consumption are influenced by socio-economic and political factors that go beyond the energy structure. The Decarbonisation Level Index (DLI) indicator shows that Bulgaria and the Czech Republic achieve a high degree of self-sufficiency in domestic energy, while Hungary and Slovakia are the most dependent on imports. A typology based on energy intensity and import dependency categorises Romania as resilient, several countries as balanced, and Hungary, Slovakia, and Croatia as vulnerable. The projected investments up to 2030 indicate an annual increase in clean energy production of around 123–138 TWh through the expansion of nuclear energy, the development of renewable energy, the phasing out of coal, and the improvement of energy efficiency, which could reduce CO₂ emissions across the region by around 119–143 million tons per year. The policy recommendations emphasise the accelerated phase-out of coal, supported by just transition measures, the use of nuclear energy as a stable backbone, the expansion of renewables and energy storage, and a focus on the electrification of transport and industry. The study emphasises the significant influence of European Union (EU) policies—such as the “Clean Energy for All Europeans” and “Fit for 55” packages—on the design of national strategies through regulatory frameworks, financing, and market mechanisms. This analysis provides important insights into the heterogeneity of Eastern European energy systems and supports the design of customised, coordinated policy measures to achieve a sustainable, secure, and climate-resilient energy transition in the region. Full article

Binarizing continuous metaheuristics to solve challenging NP-hard binary optimization problems is a fundamental step in adapting continuous algorithms for discrete domains. Binary optimization problems, such as the Set Covering Problem and the 0–1 Knapsack Problem, demand tailored approaches to efficiently explore and exploit the solution space. The process of binarization often introduces complexities, as it requires balancing the transformation of continuous populations into binary solutions while preserving the algorithm’s capability to navigate the search space effectively. In this context, we explore the performance of the Reptile Search Algorithm (RSA), a continuous metaheuristic, applied to these two benchmark problems. To address the binary nature of the problems, a two-step binarization process is implemented, utilizing combinations of transfer functions with binarization rules. This framework enables the RSA to generate binary solutions while leveraging its inherent strengths in exploration and exploitation. Comparative experiments are conducted with Particle Swarm Optimization and the Grey Wolf Optimizer to benchmark the RSA’s performance under similar conditions. These experiments analyze critical factors such as fitness values, convergence behavior, and exploration–exploitation dynamics, providing insights into the effectiveness of different binarization approaches. The results demonstrate that the RSA achieves competitive performance across both problems, highlighting its flexibility and adaptability, which are attributed to its diverse movement equations. Notably, the Z4 transfer function consistently enhances performance for all algorithms, even when paired with less effective binarization rules. This indicates the potential of Z4 as a robust transfer function for binary optimization. The findings underscore the importance of selecting appropriate binarization strategies to maximize the performance of continuous metaheuristics in binary domains, paving the way for further advancements in hybrid optimization methodologies. Full article

Educational institutions face complex challenges when allocating limited teaching resources to specialized seminars, where budget, capacity, and balanced disciplinary representation must all be satisfied simultaneously. We address this for the first time in the educational domain by formulating the teacher seminar selection problem as a multi-dimensional knapsack variant with category-specific benefit multipliers. To solve it, we design a constraint-aware genetic algorithm that incorporates smart initialization, category-sensitive operators, adaptive penalties, and targeted repair mechanisms. In experiments on a realistic dataset representing multiple academic categories, our method achieved an 11.5% improvement in solution quality compared to the best constraint-aware greedy baseline while maintaining perfect constraint satisfaction (100% feasibility) vs. 0–30% for baseline methods. Statistical tests confirmed significant and practically meaningful advantages. For comprehensive benchmarking, we also implemented binary particle swarm optimization (PSO) and Tabu Search (TS) solvers with standard parameterizations. While PSO consistently produced feasible solutions with high budget utilization, its optimization quality was substantially lower than that of the GA. Notably, Tabu Search achieved the highest performance, with a mean fitness of 1557.3 compared to GA’s 1533.2, demonstrating that memory-based local search can be highly competitive for this problem structure. These findings show that metaheuristic approaches, particularly those integrating constraint-awareness into evolutionary or memory-based search, provide effective, scalable decision-support frameworks for complex, multi-constraint educational resource allocation. Full article

During the automatic leveling process of small low-cost unmanned aerial vehicles (UAVs) after airdrop, their state parameters and control surface efficiency undergo drastic changes. It is difficult to achieve good control effects using controllers with fixed parameters. To solve these problems, this study proposes a parameter adaptive PID controller based on indicated airspeed. When tuning the controller parameters, in order to ensure the successful pulling of the UAV and the safety of structure and flight, it is necessary to optimize the success rate of pulling up, normal overload, angle of attack (AOA), airspeed, and descent altitude simultaneously. These five indicators are of different importance to the UAV. To facilitate parameter tuning based on these differences, an improved second-generation non-dominated sorting genetic algorithm (NSGA-II) is proposed, which combines a comprehensive fitness mechanism based on target priority and segmented scoring and an adaptive genetic strategy. In this study, different priorities were set for all indicators, and segmented scores were given based on individual indicators to calculate the comprehensive fitness, which guided the evolutionary direction of the population. Then, while the genetic parameters were modified, elite individuals were retained to balance search ability and convergence. Finally, the effectiveness of this mechanism was confirmed through comparative simulation. The flight test results show significant differences from the simulation results of the controller designed in this study, but the basic trend remains consistent. The controller can effectively suppress the oscillations caused by the initial state. Full article

Background/Objectives: Poor sleep quality is a prevalent and burdensome concern among breast cancer survivors. However, its assessment relies heavily on the Pittsburgh Sleep Quality Index (PSQI), whose latent structure has shown inconsistent support across populations. This study aimed to examine the underlying factor structure and reliability of the PSQI among Korean breast cancer survivors using confirmatory factor analysis. Methods: A cross-sectional survey was conducted with 386 non-metastatic breast cancer survivors recruited from a university cancer center in South Korea. Ten competing one-, two-, and three-factor models were identified in previous studies and tested using confirmatory factor analysis with maximum likelihood estimation. Model fit was assessed with χ²/df, Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR), and model parsimony was compared using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Results: The mean global PSQI score was 7.46 (SD = 3.95), and 72.8% of participants were classified as poor sleepers. Among the tested model, a three-factor solution provided the best fit (χ²/df = 0.795, CFI ≈ 1.000, TLI ≈ 1.000, RMSEA ≈ 0.000, SRMR = 0.017) and achieved the lowest AIC and BIC values. This finding indicates the most favorable balance between fit and parsimony. This three-factor model delineates three distinct but related domains: perceived sleep quality, sleep efficiency, and daily disturbances. The global PSQI demonstrates acceptable reliability. Conclusions: These findings support the three-factor structure of the PSQI as the most valid representation of sleep quality among Korean breast cancer survivors. These results underscore the importance of population-specific validation of sleep measures and confirm the clinical utility of this measure as a multidimensional tool for assessing sleep in survivorship care. Full article

The wettability of falling film flow outside multi-row horizontal tubes is a core factor determining the heat and mass transfer performance of falling film heat exchangers, which is critical for their optimized design and stable operation. A visualization experimental platform for falling film flow over ten rows of horizontal tubes was constructed, with water as the working fluid. High-definition imaging technology and image processing methods were employed to systematically investigate the liquid film distribution and wettability under three tube diameters (d = 0.016, 0.019, 0.025 m), four tube spacings (s = 0.75d, 1d, 1.25d, 1.5d), and four inter-tube flow patterns (droplet, columnar, column-sheet, and sheet flow). Two parameters, namely the “total wetting length” and the “total wetting area”, were proposed and defined. The distribution characteristics of the wetting ratio for each row of tubes were analyzed, along with the variation laws of the total wetting area of the ten rows of tubes with respect to tube diameter, tube spacing, and liquid film Reynolds number (Re_l). The following results were indicated: (1) Increasing the fluid flow rate and the tube spacing both promote the growth of the wetting length. When Re_l ≤ 505, with the increase of tube diameter, the percentage of the wetting length of the tenth tube row relative to that of the first tube row decreases under the same fluid flow rate; when Re_l > 505, this percentage first decreases and then increases. (2) The total wetting area exhibits a trend of “first increasing then decreasing” or “continuous increasing” with the tube spacing, and the optimal tube spacing varies by flow pattern: s/d = 1 for droplet flow (d ≤ 0.016 m), s/d = 1.25 for columnar flow, and s/d = 1.25 (0.016 m), 1 (0.019 m), 1.5 (0.025 m) for sheet flow. (3) The effect of tube diameter on the total wetting area is a balance between the inhibitory effect (reduced inter-tube fluid dynamic potential energy) and promotional effect (thinner liquid film spreading). The optimal tube diameter is 0.016 m for droplet flow and 0.025 m for columnar/sheet flow (at s/d = 1.25). (4) The wetting performance follows the order 0.016 m > 0.025 m > 0.019 m when Re_l > 505, and 0.025 m > 0.019 m > 0.016 m when Re_l ≤ 505. Finally, an experimental correlation formula for the wetting ratio considering the Re_l, the tube diameter, and tube spacing was fitted. Comparisons with the present experimental data, the literature simulation results, and the literature experimental data showed average errors of ≤10%, ≤8%, and ≤14%, respectively, indicating high prediction accuracy. This study provides quantitative data and theoretical support for the structural optimization and operation control of multi-row horizontal tube falling film heat exchangers. Full article

Anxiety disorders represent a significant global health challenge, yet a substantial treatment gap persists, motivating the development of scalable digital health solutions. This study investigates the potential of integrating passive physiological data from consumer wearable devices with subjective self-reported surveys to predict state–trait anxiety. Leveraging the multi-modal, longitudinal LifeSnaps dataset, which captured “in the wild” data from 71 participants over four months, this research develops and evaluates a machine learning framework for this purpose. The methodology meticulously details a reproducible data curation pipeline, including participant-specific time zone harmonization, validated survey scoring, and comprehensive feature engineering from Fitbit Sense physiological data. A suite of machine learning models was trained to classify the presence of anxiety, defined by the State–Trait Anxiety Inventory (S-STAI). The CatBoost ensemble model achieved an accuracy of 77.6%, with high sensitivity (92.9%) but more modest specificity (48.9%). The positive predictive value (77.3%) and negative predictive value (78.6%) indicate balanced predictive utility across classes. The model obtained an F1-score of 84.3%, a Matthews correlation coefficient of 0.483, and an AUC of 0.709, suggesting good detection of anxious cases but more limited ability to correctly identify non-anxious cases. Post hoc explainability approaches (local and global) reveal that key predictors of state anxiety include measures of cardio-respiratory fitness (VO₂Max), calorie expenditure, duration of light activity, resting heart rate, thermal regulation and age. While additional sensitivity analysis and conformal prediction methods reveal that the size of the datasets contributes to overfitting, the features and the proposed approach is generally conducive for reasonable anxiety prediction. These findings underscore the use of machine learning and ubiquitous sensing modalities for a more holistic and accurate digital phenotyping of state anxiety. Full article

Developing classical three-dimensional consolidation theories considers the small-strain assumption. This small-strain assumption is inappropriate when studying the consolidation process of soft or very soft clay layers. Instead, this study derives a novel generalized mathematical model describing a three-dimensional finite-strain consolidation process and applies the deep Galerkin method to deduce its novel numerical solution. Developing this mathematical model uses the Reynolds transport theorem to describe mass and momentum balances for clay grain and pore water phases. The governing equation is the sum of the resulting mass and momentum balance equations. Next, the deep Galerkin method is applied to train a deep neural network to minimize the loss function defined by the governing equation and available initial and boundary conditions. The unknowns are the average velocity, effective stress, and pore water pressure. Predicting consolidation settlements is implemented by updating the problem domain using the resulting average velocity. Beneficial from the deep Galerkin method, two real-world examples demonstrate that the current mathematical model provides accurate predictions of consolidation settlements caused by the self-weight of two very soft clay layers. The deep Galerkin method helps resolve ill-posed problems by fitting a family of fields constrained by sampling/regularization rather than physics if the physics is under-determined. Full article

This study determined the influence of experimental factors such as current density, surface-to-volume ratio (S/V), and contact time on the removal of Chemical Oxygen Demand (COD) and energy consumption during electrocoagulation, aiming to optimize the efficiency of a coaxial electrocoagulator for the co-treatment of municipal wastewater and acid mine drainage. After identifying the optimal volumetric ratio between both types of effluents, a Box–Behnken design and response-surface methodology were employed to identify the conditions that maximize COD removal while minimizing energy consumption. Under optimal conditions (current density of 2.42 A·m⁻², S/V = 300 m²·m⁻³, 60 min), a COD removal of 91.13% was achieved with a specific energy of =2.59 kWh·kgCOD⁻¹. The statistical model for COD removal demonstrated a good fit (R² = 0.87), though its predictive power was limited (predicted R² = 0.53). In contrast, the model for energy consumption exhibited an outstanding fit (R² = 0.99) and high predictive consistency (predicted R² = 0.98), confirming the decisive influence of current density on energy demand. Additionally, the S/V ratio emerged as the most impactful factor in COD removal variability. Overall, the findings highlight the importance of balancing removal efficiency with the economic feasibility of the process, contributing to the design of more sustainable and effective strategies for integrated wastewater treatment. Full article