Search Results (66)

Leprosy is characterized by complex biological and cellular interactions, driven primarily by the interplay between Th₁ and Th₂ cells. In this study, we develop a deterministic mathematical model to investigate the interactions among healthy and infected Schwann cells, M. leprae bacteria, and Th₁–Th₂ immune responses. The positivity and boundedness of the proposed five-dimensional system are established, and the disease persistence condition is characterized in terms of the basic reproduction number (${\mathcal{R}}_0$). The existence conditions for the endemic equilibrium are derived, while the global asymptotic stability of the endemic state is established through the construction of an appropriate Lyapunov function. To evaluate the robustness of the proposed model, parameter sensitivity with respect to ${\mathcal{R}}_0$ is investigated using Latin Hypercube Sampling (LHS) and Partial Rank Correlation Coefficient sensitivity analysis. Furthermore, Monte Carlo uncertainty analysis (UA) is incorporated to account for the inherent uncertainty in the system, whereas a Sobol-based global sensitivity analysis quantifies the contribution of individual model parameters to the overall uncertainty. To further validate the dynamical behavior of the proposed system numerically, Lyapunov exponents are computed using the Benettin (renormalization) algorithm. The impact of combined multidrug therapy (MDT) and IL-10 inhibitor therapy is subsequently investigated within an optimal control framework, and the corresponding optimal treatment strategies are derived using Pontryagin’s maximum principle. Numerical simulations demonstrate that suppressing the Th₂-mediated weakening of the host immune response through IL-10 inhibitor therapy provides superior long-term control of leprosy. The proposed treatment strategy therefore identifies IL-10 inhibitor therapy as a promising adjunct immunomodulatory intervention alongside MDT for enhancing protective cellular immunity against M. leprae in a cost-effective manner. Full article

High-altitude wetlands in the Caucasus Anatolia transition zone remain among the least-documented avian habitats in the Western Palearctic. Standardised avian biodiversity assessment in such systems provides essential evidence for sustainable wetland management, supporting the conservation planning and long-term ecological monitoring needed to safeguard these ecosystems under increasing anthropogenic pressure. We report on the avifauna of Çalı Lake (2237 m a.s.l.; 391 ha; Kars Province, Türkiye), a nationally designated wetland located on the Central Asian Flyway, based on five systematic survey periods conducted from March 2024 to Spring 2026 using line transects and point counts, combined with a 25 × 25 m grid-based GIS analysis encompassing 498 cells. Approximately 31 ha of the core open-water and marsh perimeter within the 391 ha designated boundary is covered; upland steppe and pasture zones beyond the active survey perimeter were excluded. A total of 154 species belonging to 18 orders and 41 families were recorded, representing approximately 30.5% of Turkey’s national checklist. IUCN status assessment identified two Endangered species, Neophron percnopterus and Oxyura leucocephala, two Vulnerable, five Near Threatened, and 145 Least Concern species. Grid-level species richness averaged 1.47 ± 1.20 per cell per period; cumulative richness per grid reached 7.62 ± 2.73 across all five survey periods. Spearman rank correlation between per-grid richness (S) and abundance (N) was consistently strong across all five periods (ρ = 0.52–0.60; all p < 0.001). A Friedman test indicated significant overall variation across periods (χ²(4) = 127.73, p < 0.001, Kendall’s W = 0.064, a negligible effect size by conventional benchmarks, indicating that the statistically significant variation reflects trivially small per-cell richness differences at this block size). Bonferroni-corrected post hoc Wilcoxon tests revealed that all significant contrasts involved the 2024 Spring–Summer period or the 2026 partial Spring window, while the four fully comparable 2024 Autumn–2025 periods showed no significant differences. A Lorenz concentration curve yielded a Gini coefficient of 0.351, with the top 10% of grid cells concentrating 24.0% of all individual detections in the central and south-western lake zones. Collectively, these findings document Çalı Lake as a species-rich high-altitude wetland with significant conservation value, and establish a reproducible spatial and temporal baseline for long-term ornithological monitoring. These results demonstrate the value of standardised biodiversity assessment as a practical tool for sustainable wetland governance and align with international sustainability frameworks, including the UN Sustainable Development Goals on life on land and clean water and sanitation. Full article

Sustainable machining of composite materials requires optimizing conflicting responses influenced by limited experimental datasets, trade-offs, nonlinear process variables, and response variability. This study proposes a hybrid framework (Gaussian Process Regression—Method based on the Removal Effects of Criteria—Criteria Reliability—Momentum-Based Optimization Algorithm: GPR–MEREC-CR–MOA) to address these challenges in turning composite materials (PA66, PA66 + GF30, and PA66 + MoS₂). The GPR model learns from small datasets to capture nonlinear relationships between machining variables (workpiece material, tool approach angle, tool nose radius, cutting speed, feed rate, depth of cut) and performance characteristics (surface roughness, cutting force, vibration, tool wear rate, temperature, sound pressure level, specific cutting energy, and material removal rate). The MEREC-CR method considers experimental dispersion and response variability to enhance the robustness of the multi-response aggregation model. The weighted responses determined by MEREC were optimized by exploring the operating ranges of machining variables using MOA. The GPR model accurately predicts eight performance characteristics (R² ≥ 0.973). The GPR–MEREC-CR–MOA model identified optimal conditions for PA66 + MoS₂ and composite material (tool angle = 93°, nose radius = 0.40 mm, cutting speed = 200 m/min, feed rate = 0.300 mm/rev, depth of cut = 1.08 mm), resulting in a composite performance index (CPI) of 0.9265 and a 30.2% improvement over the best experimental datasets from Taguchi L₂₇ design. The tool wear rate, specific cutting energy, and vibration have a significant impact on overall machining performance. Feed rate has the strongest influence on CPI, as confirmed by Partial Rank Correlation Coefficients analysis. Monte Carlo-driven uncertainty analysis validates the optimal solution with a 95% confidence level for CPI between 0.8859 and 0.9451. External validation with nine independent cases confirmed the GPR model’s strong generalizability (R² = 0.811–0.998). Benchmarking showed that MOA achieves solution quality comparable to GA, PSO, and GWO while reducing computational time by 66–86%, making it suitable for real-time optimization. The proposed hybrid framework provides an alternative data-driven decision support approach for evaluating sustainable machining parameters using limited experimental datasets of polymer composites. Full article

Background and Objectives: Efficient landing shock attenuation is important for both athletic performance and lower-extremity injury prevention in youth soccer players. Although landing biomechanics and change-of-direction (COD) performance have been widely investigated, the relationship between field-based COD performance and landing impact characteristics remains unclear. This study aimed to examine the relationships between linear sprint performance, COD performance, and landing impact during a standardized single-leg forward drop jump landing task in junior soccer players. Methods: Thirty-six junior soccer players (22 boys and 14 girls; 10–12 years) participated in this cross-sectional study. Linear sprint performance and COD performance were assessed using a 30 m sprint test and a 30 m zigzag run test, respectively. Landing impact was evaluated during a standardized single-leg forward drop jump landing task using a force plate. Body weight-normalized peak vertical ground reaction force (peak vGRF, %BW) was calculated for each trial, and the average of the final five successful trials was used for analysis. Relationships between performance measures and body weight-normalized peak vGRF were examined using Spearman’s rank correlation coefficients and partial Spearman correlation analyses adjusted for sex. Results: No significant correlation was observed between 30 m sprint performance and body weight-normalized peak vGRF (ρ = 0.106, p = 0.537). In contrast, 30 m zigzag run performance showed a moderate positive correlation with body weight-normalized peak vGRF (ρ = 0.401, p = 0.015), indicating that poorer COD performance was associated with greater landing impact. This association remained significant after adjustment for sex (partial ρ = 0.474, p = 0.004). Conclusions: These findings suggest that landing impact characteristics may represent one biomechanical factor associated with COD performance. Although the observed association was moderate, field-based COD assessments may provide practical information regarding landing impact characteristics. Future studies should incorporate biological maturation, COD deficit, and additional biomechanical variables to further clarify these relationships. Full article

This research examines the behavioral shifts within a discrete-time predator–prey framework, constructed by applying the forward Euler discretization to a continuous model. The system incorporates Smith’s growth dynamics for the prey population alongside a Holling type III functional response to characterize predator behavior. Through bifurcation analysis, it is demonstrated that the interior fixed point undergoes stability loss via Neimark–Sacker and period-doubling transitions, leading to the emergence of quasiperiodic oscillations and chaos. Furthermore, the application of normal-form theory verifies the nondegeneracy of these bifurcations and establishes the direction of the resulting orbits. We use phase portraits, Lyapunov exponents, and bifurcation diagrams to confirm the model’s rich dynamics. These numerical tools demonstrate how the system moves from stable equilibria to more intricate behaviors. The application of partial rank correlation coefficients reveals the most influential parameters governing the system’s asymptotic population levels, providing a global perspective on parameter sensitivity. The Ott–Grebogi–Yorke (OGY) chaos control strategy is employed to suppress unwanted bifurcations and stabilize chaotic oscillations within the system. These results underscore the role of nonlinear interactions and discrete-time frameworks in precipitating unpredictable population fluctuations while simultaneously offering a suite of mechanisms for enhancing the stability of ecological networks. Full article

In this study, we propose a mathematical model for the dynamics of atmospheric carbon dioxide (CO₂) with human population growth and an age-structured vehicle population. The positivity and boundedness of the model’s solutions are verified, and the global stability of an interior equilibrium point is proved. We calibrate the model parameters using the least-squares method. Simulations of the model show good alignment with the reported atmospheric CO₂ data. Moreover, the sensitivity analysis shows that human-related emission factors and emissions from old vehicles are the main contributors to CO₂ accumulation. The results of this study recommend that reducing anthropogenic emissions, accelerating the retirement of old vehicles, and strengthening carbon sequestration initiatives be considered to mitigate future atmospheric CO₂ accumulation. Full article

Background: First-trimester screening for preterm preeclampsia relies on validated competing-risks models integrating maternal characteristics with biochemical and biophysical markers to generate individualized risk estimates. Large language models (LLMs) are increasingly used for pregnancy-related health information, yet their alignment with established clinical risk models remains unclear. Objective: to perform an exploratory, local perturbation-based assessment of how LLM-generated numerical risk estimates reproduce the direction and relative magnitude of established preeclampsia predictor effects compared with the Fetal Medicine Foundation (FMF) competing-risks model. Methods: A total of 129 synthetic clinical scenarios were generated from a low-risk reference pregnancy using a one-factor-at-a-time perturbation approach across 16 risk factors represented as 22 predictors. Eight LLMs (Claude, GPT, DeepSeek, Gemini, Copilot, Meta, Mistral, and Grok) estimated the probability of preeclampsia requiring delivery before 37 weeks. Corresponding risks were calculated using the FMF model. Model behavior was analyzed in logit space using a local perturbation-based modeling approach inspired by Local Interpretable Model-Agnostic Explanations (LIME) to derive feature-effect coefficients. Agreement with the FMF model was assessed using correlation, directional concordance, cosine similarity, and normalized root mean squared error, summarized using an exploratory composite score. Results: The FMF model identified mean arterial pressure, placental growth factor, parity, uterine artery pulsatility index, and chronic hypertension as dominant predictors. Alignment between LLM outputs and the FMF model was heterogeneous, with composite scores ranging from 0.59 to 0.82. Models with higher descriptive scores preserved predictor directionality (up to 90.9%) and rank ordering, but agreement in magnitude and scaling was limited (R²: 0.44–0.59). Intermediate models showed preserved directionality with reduced magnitude agreement, while lower-scoring models demonstrated more frequent sign inconsistencies and minimal variance explained. Conclusions: LLMs demonstrated partial, prompt-specific alignment with the FMF model in this local perturbation analysis, particularly for predictor direction and relative importance, but did not consistently reproduce quantitative effect sizes. This approach was intended to characterize local model behavior around a predefined reference case rather than evaluate clinically realistic combinations of interacting risk factors or global clinical prediction performance. Given the evolving nature of LLMs, ongoing reassessment using standardized approaches is required. Full article

Background/Objectives: African Union (AU) guidance identifies decentralized diagnostics as central to epidemic preparedness. However, the epidemiological role of self-testing across epidemic-prone diseases remains underexplored. Drivers for the potential impact of self-testing were examined conceptually using a transmission model. Methods: A deterministic SEIR model compared standard-of-care testing with additional self-testing. Global sensitivity analysis using Latin Hypercube sampling and partial rank correlation coefficients (PRCCs) examined parameters influencing reductions in peak disease prevalence (mitigation). Dynamics were illustrated using AU pathogen archetypes (Ebola, Influenza A, Cholera, Coronavirus, and Mpox), estimating the number needed to self-test (NNST) to avert one death. Results: Epidemic mitigation was minimal (median 1.9%; IQR: 0.4–5.8%); this correlated with isolation adherence (PRCC = 0.784), self-testing intensity (PRCC = 0.617), lower R₀ (basic reproductive number; PRCC = −0.607) and greater duration of infectiousness (PRCC = 0.370). Conditional scenario exploration indicated 34 self-tests per 10,000 people per day to achieve a 10% reduction in peak prevalence at R₀ = 1.1, assuming self-test sensitivity 78.7%, specificity 99.3%. This exceeded the WHO Afro COVID-19 operational benchmark of 10 per 10,000 per week. High-mortality, moderate-transmission archetypes (e.g., Ebola) were most responsive to mortality reductions (median 1512 NNST/death averted) compared to Mpox (median 355,708 NNST/death averted). Adherence to post-test isolation exerted greater epidemiological impact than diagnostic accuracy. Conclusions: The epidemiological value of untargeted self-testing depends on pathogen characteristics and post-test behavioral adherence. Epidemic mitigation effects were limited under constrained health-system capacity. Future studies evaluating early decentralized self-testing deployment during Ebola-archetype outbreaks may identify operationally feasible deployment strategies to support mitigation and mortality reduction. Full article

The main objective of this study is to investigate smoking dynamics, identify the most influential factors governing smoking behavior, and develop effective intervention strategies through the integration of fractional-order modeling, sensitivity analysis, optimal control theory, and artificial neural networks (ANNs). A nonlinear fractional-order compartmental model is formulated by dividing the population into potential smokers, light smokers, heavy smokers, and quit smokers. The smoking reproduction number is derived to characterize the transmission and persistence of smoking behavior within the population. To determine the impact of model parameters on smoking dynamics, both normalized forward sensitivity analysis and global sensitivity analysis based on Latin Hypercube Sampling (LHS) with Partial Rank Correlation Coefficient (PRCC) are performed. The obtained results identify the most sensitive transmission and progression parameters and demonstrate their important role in shaping smoking prevalence within the community. Furthermore, the classical integer-order model is compared with the fractional-order formulation, where the fractional model provides a more realistic description due to its ability to incorporate memory and hereditary effects associated with smoking behavior. An optimal control framework involving awareness and treatment strategies is further introduced to investigate effective smoking reduction policies. The numerical results demonstrate that awareness campaigns reduce smoking initiation, while treatment interventions increase smoking cessation, and the combined implementation of both strategies produces the most significant reduction in smoking prevalence. The consistency between the sensitivity analysis and optimal control results further supports the reliability of the proposed framework. Numerical simulations are carried out to analyze the qualitative and quantitative behavior of the system under different epidemiological scenarios. In addition, an ANN-based computational framework is employed as an efficient numerical tool to accurately approximate the complex dynamics of the proposed fractional-order smoking model with very low prediction error. Overall, the present study provides a comprehensive mathematical and computational framework for understanding, analyzing, and controlling smoking behavior within a population. Full article

Background: Objective performance assessment is essential in surgical skill training, yet current methods are labor-intensive and focus on observing the trainee rather than the end-product of the procedure. Machine learning (ML) methods offer reproducible feedback but have mainly relied on kinematic or video data, often reducing assessment to binary or ternary classification. Our objective was to compare ML regression models predicting expert-assigned scores of vascular anastomoses from computational fluid dynamics (CFD) features of the final product. Additionally, we aimed to assess biomechanical plausibility of predictions. Methods: A total of 146 participants performed 419 end-to-side anastomoses on case-specific three-dimensional (3D) printed simulators. Anastomoses were digitized via 3D scanning, ranked by experts, and characterized using CFD-derived hemodynamic features. These served as input for linear models (Ridge, Partial Least Squares), support vector machines, and tree-based ensembles (Random Forest, Extremely Randomized Trees, and Extreme Gradient Boosting [XGBoost]), evaluated using 10-fold nested cross-validation with genetic hyperparameter optimization. Results: Inter-rater reliability of expert indicated strong agreement (intraclass correlation coefficient ICC3k = 0.846). XGBoost achieved the lowest mean root mean squared error of 0.758 (95% bootstrap CI: 0.722–0.799) and a coefficient of determination (R²) of 0.673 (0.617–0.725), with the most stable performance across folds. Shapley additive explanations (SHAP) identified the wall shear stress gradient, transverse wall shear stress, and maximum pressure as the most influential features—variables associated with intimal hyperplasia and atherosclerotic remodeling. Conclusions: Tree-based ensemble methods, particularly XGBoost, effectively modeled biomechanical properties against expert scores. Combining CFD and ML can provide reproducible, mechanistically relevant feedback in vascular surgical skill training. Full article

Missense single nucleotide variants (SNVs) represent one of the most common forms of genetic variation and account for a substantial proportion of variants of uncertain significance in clinical databases. Accurate computational classification of these variants remains an important challenge in precision medicine and genomic research. In this study, we present PathoPredictor, an interpretable machine-learning framework designed to distinguish pathogenic from benign missense variants using curated clinical variant data and functional annotations. High-confidence variants were obtained from the November 2023 ClinVar release and annotated using dbNSFP v5.1 (GRCh37). After data filtering, imputation, and normalization, 59,302 expert-reviewed missense variants were retained for model development. Six machine-learning algorithms were evaluated under identical cross-validation conditions applied to the training set. Among the evaluated models, LightGBM demonstrated the strongest overall performance and was selected as the final PathoPredictor classifier, achieving a mean ROC–AUC of 0.93 ± 0.004, accuracy of 0.90 ± 0.006, and Matthew’s correlation coefficient of 0.80 ± 0.008 across five cross-validation folds. Model interpretability was examined using SHAP (SHapley Additive exPlanations), enabling both global feature ranking and variant-level explanation of predictions. Temporal validation using ClinVar variants submitted after November 2023 showed consistent predictive performance on previously unseen submissions within the same database ecosystem (ROC–AUC = 0.91). While the framework demonstrates strong discrimination and structured interpretability, potential limitations include training data bias and partial circularity associated with the inclusion of existing meta-predictors. Overall, PathoPredictor provides a reproducible and interpretable computational framework for integrating functional annotations in missense variant prioritization, supporting research and genomic analysis workflows. Full article

Essential oils (EOs) from combined plant materials offer a promising alternative to conventional extraction by enhancing chemical diversity and bioactivity. This study evaluated the chemical composition and insecticidal properties of individual and combined plant EOs from Cymbopogon citratus, Eucalyptus camaldulensis, Eucalyptus lehmannii, Salvia rosmarinus and Thymus vulgaris were evaluated against aphids. Binary and ternary combinations were prepared in equal proportions prior to hydrodistillation. GC-MS analysis revealed significant compositional shifts in EOs from combined plant materials. Major compounds in individual oils included citral (53.11%) and neral (29.14%) in C. citratus, thymol (70.84%) in T. vulgaris, and eucalyptol as the predominant compound in E. camaldulensis (66.51%), E. lehmannii (56.99%) and S. rosmarinus (46.56%), respectively. In the combined oils, the relative abundance of these constituents was altered, and in some cases new constituents were introduced. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) revealed that combined plant EOs clustered near their parental oils, indicating compositional inheritance. Contact toxicity assay against Aphis fabae demonstrated enhanced efficacy of the combined oils, with reduced LC₅₀ values (1.39 µL mL⁻¹ for E. camaldulensis + T. vulgaris) and synergistic interactions, indicated by a co-toxicity coefficient (CTC) of 221.58 and elevated synergistic factors. Pearson correlation analysis and Partial Least Squares (PLS) regression jointly identified Acorenone B and thymol as negatively, and caryophyllene as positively correlated compounds, all with relatively high contribution to insecticidal activity, ranking highest with a Variable Importance in Projection (VIP) scores > 1.0. While PLS model had modest predictive power, the integration of these statistical approaches supports the insecticidal potential of combined plant-derived EOS in laboratory bioassays and indicates their relevance to sustainable crop protection. Full article

Transverse shear deformation plays a non-negligible role in lightweight periodic-core structures and motivates the use of shear-corrected reduced-order plate and beam models. However, the shear correction factor $k_s$ is often treated as a constant despite its strong dependence on cross-sectional heterogeneity and geometry. This work quantifies the global sensitivity of $k_s$ in corrugated paperboard by combining an energy-consistent pixel-based identification of the effective shear stiffness $\left(GA{)}_{\mathrm{e}\mathrm{f}\mathrm{f}}\right.$ with a space-filling exploration of the parameter domain. Representative three-ply (single-wall) and five-ply (double-wall) configurations are generated directly in the pixel domain using sinusoidal fluting descriptions and non-overlapping liner bands. The effective shear stiffness is obtained from a heterogeneous shear-energy equivalence, where a normalized two-dimensional shear-stress shape function is computed from pixel-based sectional descriptors and integrated with spatially varying shear moduli. Latin Hypercube Sampling is employed to explore wide ranges of flute period, height, and thickness, liner thicknesses, and liner–flute shear-modulus contrasts. Global sensitivity is reported using unit-free normalized indices, including log-elasticities (based on the slope of $\ln k_s$ versus $\ln x$) and partial rank correlation coefficients. The results demonstrate that flute geometry is the primary driver of $k_s$ variability, while material contrast significantly modulates shear-energy localization, particularly in double-wall boards with two distinct flutings. The proposed framework enables high-throughput shear correction assessment and supports robust parameterized reduced-order models for corrugated structures. Full article

Chalcopyrite leaching in sulfuric acid with chloride-based oxidizing agents (NaCl, NaClO, NaClO₃) was investigated to optimize copper recovery. The influence of sulfuric acid concentration, chloride concentration, and temperature on copper dissolution was systematically evaluated through experimental tests. A Gaussian Process Regression (GPR) model was developed to predict copper recovery, integrating experimental data with Partial Rank Correlation Coefficient (PRCC) analysis to assess the impact of key variables. The results showed that NaClO and NaClO₃ significantly improved copper recovery, with NaClO₃ achieving nearly 100% copper recovery in under 30 min at higher temperatures. Maximum recovery of 45.5% was achieved with NaCl at 1 M concentration, 3 M H₂SO₄, and 80 °C. The GPR model demonstrated superior predictive accuracy, achieving RMSE = 4.0028 and R² = 0.99, outperforming Support Vector Machine Regression (SVMR) and Ensemble Regression (ER) models. The GPR model accurately predicted recovery under conditions not tested experimentally, providing a robust tool for process optimization. The results confirm the effectiveness of chloride-based oxidizers in enhancing copper dissolution and demonstrate the practical application of GPR for optimizing leaching conditions, ensuring maximum copper recovery in hydrometallurgical processes. Full article

Accurate prediction of shrimp body weight is critical for optimizing harvest timing, feed management, and stocking density decisions in intensive aquaculture. While prior studies emphasize environmental factors, operational management variables—particularly harvesting metrics—remain understudied. This study quantified the predictive importance of harvesting-related variables using 5 years of industrial-scale operational data from 12 ponds (5479 cleaned records, 34.94% retention rate). We trained seven machine learning models and applied three independent feature importance methods: consensus importance ranking, SHAP explainability analysis, and Pearson correlations. Main findings: Operational variables (days of culture: 2.833 SHAP, stocking density: 1.871, cumulative feed: 1.510) ranked substantially above environmental variables (temperature: 0.123, pH: 0.065, dissolved oxygen: 0.077). Partial harvest frequency showed bimodal clustering, indicating two distinct viable operational strategies. The Weighted Ensemble model achieved the highest performance (R² = 0.829, RMSE = 4.23 g, MAE = 3.12 g). Model stability analysis via 10-fold GroupKFold cross-validation showed that the Artificial Neural Network (ANN) exhibited the tightest confidence bounds (0.708 g width, 27.7% coefficient of variation), indicating exceptional consistency. This is the first study to systematically analyze the importance of harvesting variables using SHAP explainability, revealing that operational management decisions may yield greater returns than marginal environmental control investments. Our findings suggest that operational optimization may be more impactful than environmental fine-tuning in well-managed systems. Full article