Search Results (1,335)

A method is developed for integrating stochastic differential equations (SDEs) with Poisson (PWN) and Gaussian (GWN) white noises interpreted as the formal derivatives of the compound Poisson and Brownian motion processes. In contrast to the current integration schemes, which solve discrete time versions of the posed SDEs, the proposed method solves the posed SDEs for finite dimensional (FD) models of the compound Poisson and Brownian motion processes, i.e., finite sums of deterministic functions of time weighted by random coefficients. Paths of the resulting solutions, referred to as FD solutions, can be generated by standard ordinary differential equation (ODE) solvers since the paths of the FD input models are smooth. We also establish conditions under which the distributions of extremes and other continuous functionals of the solutions of the posed SDEs can be approximated by those of their FD solutions. This is essential in applications since the distributions of functionals of FD solutions can be estimated while those of actual solutions are rarely available analytically and cannot be obtained numerically. Full article

To address trajectory-tracking degradation of unmanned surface vehicles (USVs) in constrained waters caused by model uncertainty, strong environmental disturbances, and actuator limitations, this paper proposes a robust disturbance-observer-based optimization model predictive control method. First, a nonlinear tracking error model is established for a 3-DOF USV by incorporating environmental loads, parametric perturbations, and unmodeled dynamics into the kinematic and dynamic equations. Based on this model, a prediction model suitable for model predictive control is derived through linearization and discretization. Then, to estimate complex unknown disturbances online, a robust disturbance observer integrating a radial basis function neural network (RBFNN) with an adaptive sliding-mode mechanism is developed, enabling real-time approximation and compensation of lumped disturbances in the surge and yaw channels. Furthermore, to overcome actuator saturation caused by the direct superposition of feedforward compensation and feedback control in conventional composite strategies, a dynamic constraint reconstruction mechanism is introduced. By feeding the observer-generated compensation signal back into the MPC optimizer, the feasible control region is updated online so that the total control input satisfies both magnitude and rate constraints of the propulsion system. Theoretical analysis based on Lyapunov theory proves the uniform ultimate boundedness of the observation errors and neural-network weight estimation errors, while input-to-state stability theory is employed to establish closed-loop stability. Comparative simulations under sinusoidal trajectories, time-varying curvature paths, and large-maneuver turning conditions demonstrate that the proposed method significantly improves tracking accuracy, disturbance rejection capability, and control feasibility under severe disturbances and parameter mismatch. Full article

Background and Objectives: represents a major public health challenge in rapidly aging societies. While lifestyle behaviors are established modifiable risk factors for frailty, the longitudinal impact of composite lifestyle trajectories—particularly by sex—remains poorly understood. This study examined sex-stratified associations between Healthy Lifestyle Score Trajectories (HLSTs) and frailty among community-dwelling middle-aged and older adults in South Korea. Using 19 years of nationally representative panel data from the Korean Longitudinal Study of Aging (2006–2024), we analyzed 6603 participants (2684 males; 3919 females). Materials and Methods: Group-Based Trajectory Modeling was applied to Waves 1–6 to derive sex-specific HLSTs based on smoking, alcohol consumption, physical activity, and body mass index. Generalized Estimating Equations were used to assess longitudinal associations between HLSTs and Frailty Index (FI) scores across Waves 6–10, adjusting for sociodemographic covariates. Results: Five distinct HLSTs were identified in both sexes. In both males and females, persistently poor or deteriorating trajectories were independently associated with higher FI scores relative to the Favorable HLST reference group. The effect size for Poor HLST was more than twice as large in females (B = 0.039) than in males (B = 0.018), consistent with the sex-frailty paradox. Among females, the Improving HLST group did not demonstrate a statistically significant frailty benefit (B = 0.014, p = 0.091). Stratified analyses revealed that the lifestyle–frailty association among males was significant only in rural-dwelling participants, whereas in females the association was consistent across both urban and rural settings. Conclusions: Persistently unfavorable composite lifestyle trajectories were independently associated with higher frailty burden, with disproportionately greater impact in women. Late-life lifestyle improvement was not significantly associated with reduced frailty in women, reinforcing the importance of early and sustained behavioral maintenance. The rural-specific association in men highlights the role of structural disadvantage in amplifying lifestyle-related frailty risk. However, given the observational design of this study, the possibility of reverse causality cannot be excluded, and these findings should be interpreted as associative rather than causal. These findings support sex-sensitive, trajectory-based, and geographically tailored frailty prevention strategies. Full article

Background/Objectives: Neutrophil-to-lymphocyte ratio (NLR) may predict outcomes after organ transplantation. This study evaluated the peri-transplant prognostic value of NLR in lung transplantation (LTx). Methods: This retrospective study included 282 LTx recipients (2012–2020). NLR measured on PODs 1, 3, 7, and 28 predicted 6-month mortality. Generalized estimating equations analyzed serial trends. Multivariable regression and ROC analysis identified predictors for a composite model, assessing discrimination and calibration. Results: Among 282 recipients (mean age, 54.2 years; male, 65.2%; idiopathic pulmonary fibrosis, 54.3%), 24.1% died within 6 months, most commonly from infection. Median NLR increased sharply after LTx (pre-LTx, 5.4; POD 1, 23.1; POD 3, 31.2), then decreased (POD 7, 18.8; POD 28, 8.7). Non-survivors had significantly higher preoperative and postoperative NLRs, particularly on POD 28. POD 28 NLR independently predicted 6-month mortality (multivariable analysis: OR, 1.05 per unit; 95% CI, 1.02–1.07; p < 0.001), alongside age and donor lung PaO₂/FiO₂ (P/F) ratio. Notably, a composite model combining these variables demonstrated significantly superior discrimination (area under the curve [AUC], 0.742; p = 0.001) compared to the NLR-only model (AUC, 0.698; p < 0.05). GEE demonstrated significantly steeper post-transplant NLR decline among survivors than non-survivors after adjusting for age (p = 0.02). Patients with NLR > 9.20 at POD 28 (area under the curve, 0.698; 95% CI, 0.615–0.782; sensitivity, 71.4%; specificity, 59.8%)—showed significantly lower survival on Kaplan–Meier analysis (p < 0.001, log-rank). Conclusions: Persistent NLR elevation on POD 28 independently predicts early mortality post-LTx and may support routine post-transplant risk stratification. Full article

Background/Objectives: To develop and internally validate multiple linear regression models to predict estimated VO2max from anthropometric variables and easily obtainable physical fitness tests in active university students and to compare model performance when estimated VO2max was derived from the Rockport Walk Test versus the 20-m Shuttle Run (Course Navette). Methods: Anthropometric variables and physical fitness indicators, including body mass index (BMI), Ruffier index, and burpee repetitions, as well as sex and age, were evaluated. Estimated VO2max was obtained separately from the Rockport Walk Test and the 20-m Shuttle Run using their respective field test equations. For each test, a multiple linear regression model was fitted using the same set of predictors. Model performance was assessed using apparent metrics and internal validation with optimism correction based on repeated cross-validation. Results: The Rockport walk test model showed better predictive performance, explaining 55.2% of the variability in estimated VO2max (R² = 0.552; adjusted R² = 0.498) with a lower prediction error (RMSE = 3.54 mL·kg⁻¹·min⁻¹). In contrast, the 20-m shuttle run model showed lower explanatory capacity (R² = 0.319; adjusted R² = 0.256) and a substantially higher prediction error (RMSE = 11.93 mL·kg⁻¹·min⁻¹). Internal validation reduced performance in both models, more markedly in the 20-m shuttle run, where the corrected R² fell to 0.163 and the corrected RMSE increased to 13.18 mL·kg⁻¹·min⁻¹, compared with 0.338 and 4.37 mL·kg⁻¹·min⁻¹ in the Rockport walk test. Conclusions: Estimated VO2max can be predicted pragmatically using low-cost models based on simple variables in a university setting; however, model performance depends on the field test used. The Rockport walk test appears more suitable for prediction using general-purpose predictors, whereas the 20-m shuttle run may require more test-specific predictors and external validation before application beyond the development sample. Full article

Accurate State of Charge (SOC) estimation is critical for the safety and efficiency of Battery Management Systems (BMS). While data-driven methods have shown promise, they often exhibit limited generalization capability due to the lack of physical constraints. Incorporating symmetry in the battery, such as through the use of Physics-Informed Neural Networks (PINNs), can mitigate this issue. However, PINNs typically rely on integer-order equivalent circuit model differential equations, which fail to accurately describe the complex electrochemical relaxation processes. To bridge this gap, we propose a novel Fractional Differential Physics-Informed Neural Network (FDE-PINN) framework. Unlike traditional approaches, this method embeds a Fractional-Order Equivalent Circuit Model (FO-ECM) into the Gated Recurrent Unit (GRU) architecture to explicitly capture the anomalous diffusion and long-memory effects inherent in battery polarization. Specifically, the network is trained by minimizing a composite loss function that integrates the data fitting error with residuals from fractional-order governing equations, including Coulomb counting and fractional voltage dynamics. Extensive experiments on the Panasonic 18650PF dataset and CALCE A123 dataset verify the method’s superiority. Results demonstrate that the proposed FDE-GRU model achieves an average MSE of $14.29\times {10}^{-4}$ (with an MAE of 2.43% and RMSE of 3.23%) on the NCA chemistry and $26.24\times {10}^{-4}$ (with an MAE of 3.75% and RMSE of 5.09%) on the LiFePO₄ chemistry, significantly outperforming traditional methods by reducing the estimation error by 35.6% and 26.2% compared to the standard GRU, respectively. Full article

Glucocorticoid-induced hypertension affects over 30% of treated patients, yet its underlying mechanisms remain unclear, particularly how glucocorticoids regulate renin within the renin-angiotensin-aldosterone system (RAAS). Modeling these dynamics is difficult because only four dose-response measurements are available at a single 24-h timepoint (36 observations total), while the system depends on roughly eleven biochemical parameters spanning minutes-long receptor interactions to days-long protein secretion. Classical parameter estimation becomes unreliable in this extremely underdetermined setting, and purely data-driven methods offer limited biological interpretability. In this paper, we introduce a physics-informed neural network (PINN) framework that integrates ELISA measurements from As4.1 juxtaglomerular cells, ordinary differential equations describing glucocorticoid receptor signaling and renin transcription, and automatic differentiation to enforce mechanistic constraints. By systematically tuning synthetic-data weights (SW in {0.2, 0.3, 0.5}), we identify an intermediate value of SW = 0.3 that provides the best overall balance between predictive accuracy, accepted ensemble size, and biologically plausible parameter estimates among the tested configurations. The framework uses adaptive constraint scheduling with a plateau ramp to reduce premature convergence and introduces calibrated plausibility thresholds reflecting experimental noise. The accepted PINN ensemble (n = 5, 50% success rate) achieved R² = 0.803, compared with 0.759 for the SW = 0.5 baseline and −0.220 for the ODE-only baseline, with RMSE = 0.024. Key learned parameters (IC₅₀ = 2.925 ± 0.012 mg/dL, Hill = 1.950 ± 0.009) are biologically plausible within the model assumptions, and the best single accepted model attained R² = 0.891. Information criteria favored the PINN over the ODE model, with improvements of approximately 77× (AIC) and 5.9× (BIC). Despite training on a single timepoint, the PINN also infers full 48-h trajectories and reproduces non-monotonic dose-response behavior. This work presents, to our knowledge, the first PINN framework for glucocorticoid-mediated renin regulation and should be interpreted as a proof-of-concept approach for integrating sparse biomedical data with mechanistic constraints. The inferred parameters and temporal dynamics are best viewed as model-dependent, hypothesis-generating estimates rather than validated biological quantities. Full article

Colmation phenomena play a critical role in long-term gas flow through porous media, significantly influencing methane migration, mine ventilation efficiency, and emission control in both active and abandoned coal mines. In colmation modeling, three fundamental kinetic types are commonly distinguished, with the third kinetic providing a generalized nonlinear formulation capable of describing state-dependent and spatially variable permeability degradation. However, the strong nonlinearity of the coupled transport–colmation equations prevents the derivation of closed-form solutions, which necessitates the application of linearization techniques. In this study, gas flow with colmation governed by third-kinetics is analyzed with particular emphasis on methane migration in underground mining environments. Linearization of nonlinear kinetic terms is applied at the level of the coupled mass balance and colmation equations, resulting in an approximate form of Darcy’s law and an explicit analytical solution describing the evolution of the porous medium state. The primary objective of the study is to quantify the error introduced by the adopted linearization and to analyze its spatial and temporal propagation with respect to the nonlinear reference solution. A rigorous error estimation based on Taylor series truncation is developed, yielding an explicit criterion that defines the validity range of the linearized solution. The results demonstrate that the approximation remains reliable within the regime of weak colmation, while the associated error is locally generated and propagates through transport mechanisms without exhibiting uncontrolled growth. Full article

Background/Objectives: Perioperative risk stratification in cardiac surgery is based mainly on static preoperative variables and therefore does not fully capture dynamic multiorgan responses to surgical stress. The Model for End-Stage Liver Disease (MELD) score, which integrates bilirubin, creatinine, and the international normalized ratio (INR), reflects hepatorenal function, but its perioperative dynamics remain insufficiently explored. This study aimed to characterize perioperative MELD trajectories in patients undergoing off-pump coronary artery bypass grafting (OPCAB) and to assess the influence of sex and diabetes mellitus on these changes and their clinical relevance. Methods: This retrospective observational study included 111 patients undergoing elective OPCAB. MELD scores were assessed preoperatively (MELD₀), on postoperative day 1 (MELD₁), and on day 6 (MELD₆). Dynamic indices of MELD change were calculated, including the early postoperative increase (ΔMELD₀₁). The effects of sex and diabetes mellitus on MELD trajectories were analyzed using multivariable linear regression and generalized estimating equations. A high-surge phenotype was defined as the upper quartile of ΔMELD₀₁. Results: MELD increased significantly on postoperative day 1 and partially recovered by day 6 (p < 0.001). Female sex was independently associated with lower postoperative MELD values (β = −2.54, p < 0.001) and a smaller ΔMELD₀₁, whereas diabetes mellitus was associated with a reduced MELD rise (β = −1.07, p = 0.028). Patients with a high-surge MELD phenotype had significantly longer hospitalization than those with a lower MELD response (12.8 ± 2.1 vs. 9.2 ± 1.2 days, p < 0.001). Conclusions: Perioperative MELD trajectories reflect a dynamic hepatorenal stress response after OPCAB and may improve identification of clinically relevant physiological vulnerability. Full article

Background: Patients undergoing dental treatment often experience psychological distress and social discomfort, yet longitudinal data on these changes are limited. Existing studies rely on cross-sectional designs or lengthy tools, reducing feasibility in routine practice. This study explored psychological and social trajectories during dental care, highlighting challenges and implications for patient wellbeing and care delivery. Methods: A prospective cohort study with repeated measures across three dental visits (V1–V3) was conducted. Participants completed a 21-item binary (yes/no) questionnaire assessing psychological (Q1–Q6) and social dimensions (Q7–Q14 at all visits; extended social domain Q7–Q21 at V2–V3). Composite scores were calculated, and longitudinal changes were analyzed using generalized estimating equations or mixed-effects models. Item-level trajectories were examined with multiple comparison adjustments. Results: Of 120 enrolled patients, 100 completed all visits. Psychological well-being consistently improved, while social outcomes showed more complex, domain-specific patterns. Item-level analyses revealed gains in appearance and satisfaction, whereas stigma, fear, and social integration remained relatively stable, underscoring the need to monitor multiple psychosocial dimensions. Conclusions: Psychosocial monitoring during dental care is feasible and potentially beneficial. The 21-item questionnaire was practical and well-accepted, with composite scores serving as simple indicators for tracking patient wellbeing and supporting a holistic, patient-centered approach. Further validation in larger and more diverse populations is needed. Full article

Cardiac point-of-care ultrasound (cPOCUS) is used to obtain key information about the heart’s structure and function when an echocardiogram is not available. This prospective, cross-sectional study aimed to compare fractional shortening (FS%) and left-atrium-to-aorta ratio (LA:Ao) obtained by cPOCUS in different body positions to echocardiography. Thirty-nine dogs had cPOCUS performed in three different body positions: left lateral recumbency (RT), right lateral recumbency (LT), and standing or sternal recumbency (RST). The cPOCUS values from each body position for FS% and LA:Ao were tested for agreement with the echocardiogram by Bland-Altman plots, correct clinical assessment by generalized estimated equation models, and quality score of the cPOCUS images as a percentage. Bland-Altman analysis showed a positive bias for FS% (0.9% to 9.8%) and both positive and negative bias for LA:Ao (within 0.2) for the cPOCUS values. The correct clinical assessment was made in the majority of cases for FS% in RST (67%) and RT (67%), and for LA:Ao in RST (55%). The clinical assessment was more often correct when the image quality score was higher. Intraclass correlation showed good agreement (≥0.61) between investigators for FS% in all body positions and LA:Ao in RT and RST. This study showed that cPOCUS performed from the right hemithorax can provide estimates and correct clinical assessment of FS% and LA:Ao. Obtaining measurements in the LT position is not recommended. Full article

The rapid diffusion of generative artificial intelligence (GenAI) is reshaping organisational systems and digital transformation strategies, yet it remains unclear which organisational conditions are associated with long-term competitive performance in business-to-business (B2B) contexts. This study adopts a systems-informed perspective and examines how ethical governance, environmental dynamism, exploratory and exploitative innovation, and GenAI adoption are associated with long-term competitive performance in B2B firms. Using survey data from 104 Portuguese B2B managers and Partial Least Squares Structural Equation Modelling (PLS-SEM), the findings show that ethical governance is the strongest organisational correlate of long-term competitive performance, underscoring the central role of governance structures in responsible GenAI use. GenAI adoption is positively associated with performance, but its role is complementary rather than dominant. Exploratory innovation does not show a significant direct association with performance; instead, its association with performance operates through GenAI adoption in the estimated model, suggesting that experimentation becomes more performance-relevant when translated into digitally enabled routines. In contrast, exploitative innovation is directly associated with performance through incremental efficiency mechanisms. These findings challenge technology-deterministic assumptions and suggest that long-term competitive performance in B2B firms is more closely associated with the organisational alignment of governance structures, innovation capabilities, and GenAI adoption than with technology adoption alone. Full article

This study analyzes the long-run relationships between digitalization, investment, innovation, and economic growth in connection with the energy transition in the SPRING-F group (Spain, Poland, Romania, Italy, the Netherlands, Germany, and France) using annual data for the period of 2000–2024. The analysis starts from the premise that digitalization affects economic performance not only directly, but also through structural transmission mechanisms linked to investment and the energy transition. To capture these dynamics, this study employs three complementary panel ARDL models. The first model explains economic growth (GDP per capita) as a function of digitalization, capital accumulation, R&D expenditure, renewable energy consumption, trade openness, and foreign direct investment. The second model estimates gross capital formation (GCF) in order to assess the investment transmission channel. The third model explains renewable energy consumption (RNEC) in order to capture the sustainability dimension. The results show that trade openness and capital accumulation are the strongest long-run drivers of economic growth in the SPRING-F group. Internet use, R&D expenditure, and FDI also display positive long-run associations with GDP per capita, whereas fixed broadband subscriptions and renewable energy consumption enter the growth equation with negative coefficients, suggesting that digital infrastructure and the green transition do not automatically generate immediate growth gains. The GCF model confirms that investment acts as an important transmission mechanism, especially through the robust GDP–GCF linkage. The RNEC model indicates that the energy transition is positively associated with investment, innovation, and trade openness, while GDP and digital infrastructure remain negatively associated with the renewable energy share. Overall, the findings point to a conditional and nonlinear relationship between growth, digitalization, investment, and sustainability, with the sustainability channel remaining more specification-sensitive than the growth and investment equations. The long-run results for the GDP equation should also be interpreted with additional caution, given the comparatively weaker cointegration evidence for Model 1. Full article

Applications of Parametrized Relativistic Quantum Theory (PRQT) in curved spacetime are considered here. PRQT in curved spacetime is applied to the motion of free scalar particles in expanding universe metrics, including a generalized expanding universe (EU) metric and the Friedmann–Lemaître–Robertson–Walker (FLRW) metric. Governing equations are derived and solved through separation of variables. In addition, modern observational parameters and a rescaled Friedmann equation are used to estimate the age of the universe. Implications for cosmological models are discussed. Full article

Unreinforced masonry walls exhibit limited resistance to lateral loads and, therefore, frequently require strengthening interventions. Carbon fiber reinforced polymer (CFRP) systems provide an efficient retrofit solution; however, current design procedures defined in structural guidelines require repetitive trial calculations to determine the necessary reinforcement amount. This study introduces a hybrid computational process that integrates metaheuristic optimization with symbolic regression to generate direct analytical equations for the estimation of the required CFRP area. First, a comprehensive database containing 1300 optimal strengthening scenarios was generated using the Jaya optimization algorithm under the constraints specified in ACI 440.7R and ACI 530. The resulting dataset was subsequently processed through symbolic regression using the PySR platform to identify explicit mathematical relationships between structural parameters and the optimum CFRP area. Most traditional machine learning approaches operate as black-box predictors. In contrast, the proposed approach generates interpretable closed-form expressions that can be used directly in engineering calculations. Two models were derived from the Pareto-optimal solution set. The first model is a simplified equation emphasizing algebraic simplicity. The second model prioritizes prediction accuracy. The simplified formulation achieved a coefficient of determination of approximately 0.992. The accuracy-focused model achieved a value above 0.997 with very low prediction errors. Validation studies with independent test samples showed that the obtained equations are reliable. The average error for the simplified model is below 4%, and for the high-accuracy model, it is approximately 2%. The results demonstrate that combining the optimization-generated datasets with symbolic regression makes it possible to obtain transparent design equations. These equations eliminate iterative design processes and provide a fast and reliable estimation tool for CFRP strengthening of masonry walls. Full article