Search Results (129)

Diabetes develops through a mix of clinical, metabolic, lifestyle, demographic, and environmental factors. Most current classification models focus on traditional biomedical indicators and do not include environmental exposure biomarkers. In this study, we develop and evaluate a supervised machine learning classification framework that integrates heterogeneous demographic, anthropometric, clinical, behavioral, and environmental exposure features to classify physician-diagnosed diabetes using data from the National Health and Nutrition Examination Survey (NHANES). We analyzed NHANES 2017–2018 data for adults aged ≥18 years, addressed missingness using Multiple Imputation by Chained Equations, and corrected class imbalance via the Synthetic Minority Oversampling Technique. Model performance was evaluated using stratified ten-fold cross-validation across eight supervised classifiers: logistic regression, random forest, XGBoost, support vector machine, multilayer perceptron neural network (artificial neural network), k-nearest neighbors, naïve Bayes, and classification tree. Random Forest and XGBoost performed best on the balanced dataset, with ROC AUC values of 0.891 and 0.885, respectively, after imputation and oversampling. Feature importance analysis indicated that age, household income, and waist circumference contributed most strongly to diabetes classification. To assess out-of-sample generalization, we conducted an independent 80/20 hold-out evaluation. XGBoost achieved the highest overall accuracy and F1-score, whereas random forest attained the greatest sensitivity, demonstrating stable performance beyond cross-validation. These results indicate that incorporating environmental exposure biomarkers alongside clinical and metabolic features yields improved classification performance for physician-diagnosed diabetes. The findings support the inclusion of chemical exposure variables in population-level diabetes classification and underscore the value of integrating heterogeneous feature sets in machine learning-based risk stratification. Full article

Beer fermentation is a critical process that directly influences product quality and flavor. However, traditional fermentation practices often rely on empirical methods, leading to prolonged production cycles and inconsistent product quality. This study presents a multiphysics-coupled simulation model that integrates computational fluid dynamics (CFD) with fermentation reaction kinetics to address challenges in temperature control and monitoring in large-scale fermenters. The model incorporates the Navier–Stokes equations for fluid flow, energy equations for heat transfer, fermentation kinetics for sugar metabolism, and a yeast cell proliferation model based on population balance theory. The model is validated through experiments at both lab scale (0.3 m³) and industrial scale (375 m³). Statistical analysis shows excellent agreement, with coefficients of determination (R²) for alcohol and sugar content reaching up to 0.99 and 0.96 at the lab scale, and 0.93 and 0.85 at the industrial scale, respectively. Key quantitative results from the industrial-scale validation demonstrate that the model accurately predicts the primary fermentation dynamics: within a 100 h period, alcohol concentration increased from 0% to approximately 6%, while sugar content decreased from 13 °P to 2 °P, closely matching experimental data. Crucially, the simulation captures a significant temperature overshoot at approximately 48 h, where the peak temperature at the top of the fermenter reached 16.01 °C (a 3 °C overshoot above process requirements). This pronounced vertical temperature gradient, arising from symmetry-breaking thermal conditions on the fermenter walls, was found to induce strong, asymmetric natural convection with flow velocities up to 13.2 mm·s⁻¹, revealing spatial heterogeneities that are critical for optimizing fermenter design. At the lab scale, the simulation also accurately captures the observed quadratic temperature rise, further confirming the model’s robustness. This study provides a theoretical foundation for optimizing cooling jacket configurations and control strategies, ultimately improving fermentation efficiency and ensuring consistent product quality. Full article

This study advances the literature on digital entrepreneurship by examining how Information Technology Culture (ITC) and Technology Orientation (TO) influence entrepreneurial intentions through the mediating role of Entrepreneurial Self-Efficacy (ESE) and the moderating role of Social Support (SS) within the context of Saudi Arabia’s Vision 2030. By integrating psychological, cultural, and technological constructs, the research offers a comprehensive framework for understanding the internal drivers of digital venture creation in youth. Data were collected via an online survey targeting Saudi youth and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Validated scales measured ITC, TO, ESE, SS, and Digital Entrepreneurship Intention (DEI), with a sample of 372 participants predominantly under age 30. Findings reveal that while ITC and TO do not directly predict DEI, both exert significant indirect effects through ESE, underscoring the central role of psychological self-belief in entrepreneurial motivation. The moderating effect of SS on the ESE–DEI relationship was non-significant, suggesting that internal efficacy may outweigh external validation in this context. The sample’s demographic skew90.9% male and 99.5% under 30limits generalizability, though it aligns with the most digitally active segment of the population. The cross-sectional design restricts causal inference, and future research should explore longitudinal and gender-balanced samples to validate and extend these findings. This study provides actionable insights for policymakers and educators aiming to foster digital entrepreneurship by enhancing ESE through targeted training, cultural alignment, and strategic technology exposure, especially among youth populations driving Saudi Arabia’s innovation agenda. Full article

This research examines the role of preserving the vernacular cultural landscape in sustainable tourism, with a focus on the Stara Planina area in Serbia. The goal was to determine how the perception of cultural heritage preservation, the use of resources and the impact of European regulations shape attitudes about sustainable tourism among tourists and local residents. Using structural equation modeling (SEM), multiple group analysis (MGA), and machine learning (Random Forest and Gradient Boosting), clear differences in perception were revealed: tourists more strongly associate the preservation of the vernacular landscape with sustainability, while local residents prioritize economic benefits. Generational analyses indicated that younger groups are more supportive of European regulations and environmental measures, while older populations show a more pragmatic approach. The findings confirm that EU regulations have a significant moderating effect in shaping attitudes about sustainability and balancing cultural and economic goals. This work expands the understanding of the relationship between the preservation of vernacular architecture and tourism policy in non-EU countries, offering a theoretical and practical framework that can contribute to shaping sustainable development models not only for Serbia but also for other rural regions of Europe and the world that strive to preserve an authentic cultural landscape. Full article

Experimental and theoretical techniques have been developed to quantify foamy oil behaviour of solvent-heavy oil systems at bubble level during a gas exsolution process. During constant composition expansion (CCE) tests, we artificially induced foamy oil dynamics for solvent-heavy oil systems by gradually reducing pressure and recorded the changed pressures and volumes in an isolated PVT setup at a given temperature. By discretizing gas bubbles on the basis of the classical nucleation theory, we theoretically integrated the population balance equation (PBE), Fick’s law, and the Peng–Robinson equation of state (PR EOS) to reproduce the experimental measurements. Pseudo-bubblepoint pressure for a given solvent-heavy oil system can be increased with either a lower pressure depletion rate or a higher temperature, during which gas bubble growth is facilitated with a reduction in viscosity and/or an increase in solvent concentration, but gas bubble nucleation and mitigation is hindered with an increase in solvent concentration. Compared to CO₂, CH₄ is found to yield stronger and more stable foamy oil, indicating that foamy oil is more stable with a larger amount of dispersed gas bubbles at lower temperatures. Using the PR EOS together with the modified alpha functions at T_r = 0.7 and T_r = 0.6, the absolute average relative deviation (AARD) is reduced from 4.58% to 2.24% with respect to the predicted pseudo-bubblepoint pressures. Full article

A nonlocal transport–reaction system is proposed to model the coupled dynamics of stem and differentiated cell populations, structured by a continuous damage variable. The framework incorporates bidirectional transitions via differentiation and dedifferentiation, with nonlocal birth operators encoding damage redistribution upon division and Hill-type feedback regulation dependent on total populations. Global well-posedness of solutions in $C([0,\infty );L^1([0,\infty )\times L^1\left([0,\infty )\right))$ is established by combining the contraction mapping principle for local existence with a priori $L^1$ bounds for global existence, ensuring uniqueness and nonnegativity. Integration yields balance laws for total populations, reducing to a finite-dimensional autonomous ordinary differential equation (ODE) system under constant death rates. Linearization reveals a bifurcation threshold separating extinction, homeostasis, and unbounded growth. Under compensatory feedback, Dulac’s criterion precludes periodic orbits, and the Poincaré–Bendixson theorem confines bounded trajectories to equilibria or heteroclinics. Uniqueness implies global asymptotic stability. A scaling invariance for steady states under uniform feedback rescaling is identified. The analysis extends structured population theory to feedback-regulated compartments with nonlocal operators and reversible dedifferentiation, providing explicit stability criteria and linking an infinite-dimensional structured model to tractable low-dimensional reductions. Full article

In this study, a mathematical model was proposed for a continuous, multistage, industrial-scale alcoholic fermentation process, comprising four vats in series with volumes equal to 600 m³, with separation, acid treatment, and cell recycling from the fourth to the first vat. The system was operated daily under variable volumetric flow rates and substrate concentrations in the feed stream, i.e., F₀ = 93–127 m³/h and S₀ = 210–238 g/L. The mathematical model consisted of mass balance equations for cells, substrate, and product in the vats, the separator, and the acid treatment unit. An unsegregated and unstructured approach was used to describe the microbial population, with the kinetics of cell growth, substrate consumption, and product formation represented by equations generally adopted for alcoholic fermentation. The model parameters were estimated by nonlinear regression, providing typical values for alcoholic fermentation. Model predictions agreed well with both the experimental data used in the parameter estimation step and those used in the model validation step. Full article

We study how small harmful mutations spread in populations that reproduce asexually. This process is known as Muller’s ratchet—it means that even though these mutations are damaging, they can still build up over generations. To explore this, we use a mathematical model that describes how such mutations move through a population living in an environment with limited resources. We model Muller’s ratchet deterministically using differential equations, incorporating modifications that account for extinction risk of small mutation classes. We analyze two modifications: a published cutoff modification and a more flexible exponential modification. We show that the exponential modification better matches stochastic simulations over specific parameter ranges. Full article

This paper develops a dynamic optimal growth model integrating population, economic activity, and environmental constraints to investigate sustainable long-run development. The model incorporates capital accumulation, consumption, pollution abatement, and an endogenous demographic equation in which population growth responds negatively to pollution. A critical environmental threshold is imposed beyond which population growth collapses. Calibrating the model with plausible parameter values indicates that a sustainable steady state can support a global population of approximately 5 billion, a level consistent with high per capita consumption and stable environmental conditions. The optimal policy entails devoting roughly one-third of output to pollution abatement, which is sufficient to stabilize pollution below the safe threshold without imposing excessive economic cost. In this equilibrium, the economy achieves high consumption per person, a stable capital stock, and environmental balance, thereby avoiding overshoot and collapsing scenarios. The results highlight the trade-off between economies of scale and environmental limits. Larger populations can stimulate production and innovation but risk unsustainable pollution levels, whereas smaller populations allow higher per capita welfare within ecological boundaries. These findings suggest that achieving global sustainability requires balancing population size, consumption, and ecological limits through effective pollution abatement. 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

An analysis was conducted to investigate how reaction system turbulence affects the butadiene-isoprene copolymerization in the presence of the TiCl₄ + Al(i-Bu)₃ catalytic system. A model was developed, which integrates CFD simulations of TiCl₄ + Al(i-Bu)₃ particle breakage based on population balance equations with the kinetic modeling of the butadiene-isoprene copolymerization. It was established that an increase in turbulent kinetic energy leads to a reduction in catalyst particle size, an increase in active site concentration, an acceleration of the copolymerization process, and a decrease in the average molecular weights of the copolymer. Furthermore, catalytic activity correlates with both the average and maximum values of turbulent kinetic energy in the reaction system, whereas the effect of the average residence time of catalytic particles under turbulent conditions is insignificant. Based on these results, recommendations were provided for optimizing the impact of reaction system turbulence on TiCl₄ + Al(i-Bu)₃ particles to enhance the butadiene-isoprene copolymerization rate and achieve precise control over the molecular weight characteristics of the copolymer. The findings of this study can be applied to optimize the synthesis technology of the cis-1,4 butadiene-isoprene copolymer, which is used in the production of frost-resistant rubber. Full article

Locating all roots of nonlinear equation of systems (NESs) in a single computational procedure remains a fundamental challenge in computational mathematics. The Dynamic Neighborhood Particle Swarm Optimization algorithm based on Euclidean Distance (EDPSO) is proposed to address this issue. First, a dynamic neighborhood strategy based on Euclidean distance is proposed, to facilitate particles within the population with forming appropriate neighborhoods. Secondly, the Levy flight strategy is integrated into the particle velocity-update mechanism to balance the global search capability and local search capability of particles. Furthermore, integrating a discrete crossover strategy into the PSO algorithm enhances its capability in solving high-dimensional nonlinear equations. Finally, to validate the effectiveness and feasibility of the proposed algorithms, the EDPSO algorithm, along with its comparative counterparts, is applied to solve 20 NESs problems and the forward kinematics equations of a 3-RPS parallel mechanism. Experimental results demonstrate that for the 20 NESs, the EDPSO algorithm achieved the highest success rate (SR = 0.992) and root rate (RR = 0.999) among all compared methods, followed by LSTP, NSDE, KSDE, NCDE, HNDE, and DR-JADE. In solving the forward kinematics of the 3-RPS parallel mechanism, the EDPSO algorithm achieved the highest SR of = 0.9975 and RR = 0.9800, followed by LSTP, KSDE, DR-JADE, NCDE, NSDE, and HNDE, based on these metrics. Full article

Fractional laser (FL) and fractional radiofrequency (FRF) are effective treatments for atrophic acne scars, yet comparative data in Asian populations with darker skin types remain limited. This retrospective cohort study compared the clinical effectiveness and safety of FL and FRF in Thai patients aged 18–60 years with Fitzpatrick skin types III–IV who underwent at least two treatment sessions between 2012 and 2023. Baseline characteristics were balanced using propensity score stratification, and missing data were addressed through multiple imputation with chained equations. The primary endpoint was the proportion of patients achieving ≥25% improvement in scarring at 6 months, with equivalence testing performed using a 20% margin. A total of 397 patients (254 FL, 143 FRF) were included, with balanced baseline characteristics after stratification. At 6 months, 88.1% of FRF-treated and 71.9% of FL-treated patients achieved the primary endpoint. FRF showed numerically greater mean improvement at all time points, though differences were not statistically significant. FL met the non-inferiority criterion but not equivalence. FRF was associated with significantly higher pain scores (p < 0.001), while adverse events, including post-inflammatory hyperpigmentation, were rare and similar between groups. Both modalities demonstrated meaningful clinical benefit and acceptable safety, although statistical equivalence could not be established and FRF was associated with greater procedural discomfort. Full article

Magnetic components serve as critical energy conversion elements in power conversion systems, with their performance directly determining overall system efficiency and long-term operational reliability. The development of accurate core loss frameworks and multi-objective optimization strategies has emerged as a pivotal technical bottleneck in power electronics research. This study develops an integrated framework combining physics-informed modeling and multi-objective optimization. Key findings include the following: (1) a square-root temperature correction model (exponent = 0.5) derived via nonlinear least squares outperforms six alternatives for Steinmetz equation enhancement; (2) a hybrid Bi-LSTM-Bayes-ISE model achieves industry-leading predictive accuracy (R² = 96.22%) through Bayesian hyperparameter optimization; and (3) coupled with NSGA-II, the framework optimizes core loss minimization and magnetic energy transmission, yielding Pareto-optimal solutions. Eight decision-making strategies are compared to refine trade-offs, while a crow search algorithm (CSA) improves NSGA-II’s initial population diversity. UFM, as the optimal decision strategy, achieves minimal core loss (659,555 W/m³) and maximal energy transmission (41,201.9 T·Hz) under 90 °C, 489.7 kHz, and 0.0841 T conditions. Experimental results validate the approach’s superiority in balancing performance and multi-objective efficiency under thermal variations. Full article

Infectious diseases can spread through virus-laden respiratory droplets exhaled into the air. Ventilation systems are crucial in indoor settings as they can dilute or eliminate these droplets, underscoring the importance of understanding their efficacy in the management of indoor infections. Within the field of fluid dynamics methods, the dispersed droplets may be approached through either a Lagrangian framework or an Eulerian framework. In this study, various Eulerian methodologies are systematically compared against the Eulerian–Lagrangian (E-L) approach across three different scenarios: the pseudo-single-phase model (PSPM) for assessing the transport of gaseous pollutants in an office with displacement ventilation (DV), stratum ventilation (SV), and mixing ventilation (MV); the two-fluid model (TFM) for evaluating the transport of non-evaporating particles within an office with DV and MV; and the two-fluid model-population balance equation (TFM-PBE) approach for analyzing the transport of evaporating droplets in a ward with MV. The Eulerian and Lagrangian approaches present similar agreement with the experimental data, indicating that the two approaches are comparable in accuracy. The computational cost of the E-L approach is closely related to the number of tracked droplets; therefore, the Eulerian approach is recommended when the number of droplets required by the simulation is large. Finally, the performances of DV, SV, and MV are presented and discussed. DV creates a stratified environment due to buoyant flows, which transport respiratory droplets upward. MV provides a well-mixed environment, resulting in a uniform dispersion of droplets. SV supplies fresh air directly to the breathing zone, thereby effectively reducing infection risk. Consequently, DV and SV are preferred to reduce indoor infection. Full article