Search Results (263)

Sulfotransferase (SULT) enzymes contribute significantly to drug metabolism in pediatric patients. The purpose of this study was to develop a PBPK model for acetaminophen (APAP) in pediatric populations that accounts for the ontogeny of SULT isozymes that play a critical role in APAP metabolism. PBPK modeling and simulation were performed using the Simcyp^® Simulator. The model incorporated the developmental ontogeny of three key hepatic SULT enzymes: SULT1A1, SULT1A3, and SULT2A1 using “best-fit” ontogeny equations for each isozyme as determined by nonlinear regression analysis of enzyme abundance versus age. PBPK model-simulated pharmacokinetic profiles for APAP captured observed clinical data for systemic exposure (Cmax, AUC) in neonates, infants, and children. SULTS accounted for ~60% APAP metabolism in neonates, with decreased contributions to infants and children. Model sensitivity analysis highlighted the potential for APAP metabolic DDIs, primarily through SULT1A1. The study demonstrates that the impact of SULT enzymes on drug metabolism is significant in neonates, which is an important clinical consideration for APAP. A PBPK model that incorporates SULT ontogeny has the potential to help inform dosing decisions in this special patient population. Full article

Understanding nonlinear fluid flow in fractured rocks is critical for various geoengineering and geosciences. This study investigates the evolution of seepage behavior under varying fracture surface roughness, confining pressures, and shear displacements. A total of four sandstone fracture specimens were prepared using controlled splitting techniques, with surface morphology quantified by Joint Roughness Coefficient (JRC) values ranging from 2.8 to 17.7. Triaxial seepage tests were conducted under four confining pressures (3–9 MPa) and four shear displacements (0–1.5 mm). Experimental results reveal that permeability remains stable under low hydraulic gradients but transitions to nonlinear regimes as the flow rate increases, accompanied by significant energy loss and deviation from the cubic law. The onset of nonlinearity occurs earlier with higher roughness, stress, and displacement. A critical hydraulic gradient Jc was introduced to define the threshold at which inertial effects dominate. Forchheimer’s equation was employed to model nonlinear flow, and empirical regression models were developed to predict coefficients A, B, and Jc using hydraulic aperture and JRC as input variables. These models demonstrated high accuracy (R² > 0.92). This work provides theoretical insights and predictive approaches for assessing nonlinear fluid transport in rock fracture. Future research will address mechanical–hydraulic coupling and incorporate additional factors such as scale effects and flow anisotropy. Full article

For the calculation of thermal processes of canned food, the original formula method of Ball is still widely used for its accuracy and safety. However, it requires the consultation of tables that Ball prepared and the relative interpolation of the data. This is due to the exponential integral function (Ei) resulting after the integration of the thermo-bacteriological and heat transfer differential equations. Mathematical modelling that replaces the Ball tables is useful for speeding up the thermal process calculations and for being prospectively implemented in process control systems. Stoforos had already proposed a simple and accurate mathematical model based on the regression of the table data. However, Stoforos’ equations do not contain the influence of the temperature difference between the steam and the cold water (m+g) when this is different from the two values of the tables (180 and 130 °F). This approximation leads, in some cases, to over-sterilization with a consequent loss of quality. To overcome these limitations, in this work a nonlinear regression of the values of the exponential integral function (Ei) has been developed. However, this is performed by using the regression on the ratio between the function and its derivative and replacing the hyperbola of the initial cooling imposed by Ball with an appropriate exponential function. The overall mean relative error, MRE, compared to Ball’s tables was less than 1%. Full article

An appropriate protein content in the diet can effectively increase the growth rate, muscle quality, and environmental stress resistance of fish. In this study, juvenile Yellow River carp (51.56 ± 0.17 g) were fed isofat diets with different protein concentrations (22%, 25%, 28%, 31%, 34%, and 37%). The results showed that, compared with other protein content groups, when the protein content was 34%, the WGR, SGR, and FCR were significantly higher, while the FCR was significantly lower (p < 0.05). Among them, the SGR and FCR were positively correlated with the dietary protein content, with the regression equations being y₁ = −32.208x² + 21.897x − 1.4001 (R² = 0.8622) and y₂ = 97.027x² − 68.428x + 13.269 (R² = 0.9663), respectively. When x was 33.99% and 35.26%, the SGR and FCR had extreme values. The contents of CP, Lys, EAA, and EAA/TAA were significantly greater in the 34% protein group, reflecting muscle quality (p < 0.05). In addition, the activities of α-AMS, LPS, TPS, SOD, CAT, and GSH-Px were significantly greater in the 34% protein group (p < 0.05). Similarly, the relative expression levels of GH, IGF-I, TOR, 4EBP2, Rhag, Rhbg, and Rhcg1 were significantly greater in the 34% protein group compared to the other protein groups (p < 0.05). The above results indicated that when the protein content in the diet was 34%, it significantly improved the growth and stress resistance of juvenile Yellow River carp. Based on the nonlinear regression equations for the SGR and FCR, the optimal dietary protein content of juvenile Yellow River carp (51.56 ± 0.17 g) was determined to be 33.99–35.26%. Full article

The durability of reinforced concrete (RC) structures is strongly influenced by carbonation, a phenomenon governed by material and environmental interactions. This study applied machine learning (ML) techniques—Random Forest (RF), Support Vector Regression (SVR), and Artificial Neural Networks (ANNs)—to predict carbonation depth using a synthetic dataset of 20,000 instances generated from the validated Possan equation. Model performances were evaluated across multiple scenarios, with compressive strength and exposure time identified as the most influential features, while relative humidity and exposure conditions had intermediate effects. SVR consistently captured linear and nonlinear trends, the ANN achieved the highest R² values but showed minor overestimations, and RF exhibited lower adaptability to feature variations. The results highlight the applicability of ML models for durability assessments, particularly under complex conditions where traditional approaches are limited. Moreover, this study reinforces the strategic value of synthetic datasets in developing predictive models when experimental data collection is time-consuming or impractical. The methodologies developed here can be extended beyond carbonation modeling to other deterioration processes, supporting data-driven strategies for maintenance planning and resilience design in RC structures. Full article

The prediction of soil moisture conditions using multispectral data from unmanned aerial vehicles (UAVs) has advantages over ground measurements in terms of costs and monitoring range. However, the prediction accuracy for moisture conditions using spectral data alone is low. In this study, relationships between water deficits and phenotypic characteristics in oats were evaluated and used to develop a UAV multispectral-based water prediction model. The vegetation indices NDRE (Normalized Difference Red Edge), CIG (Chlorophyll Index), and MCARI (Modified Chlorophyll Absorption in Reflectance Index) were highly correlated with oat yield. Based on a multipath analysis in the structural equation modeling framework, irrigation (p < 0.01), leaf area index (LAI) (p < 0.001), and SPAD (p < 0.001) had direct positive effects on NDRE. Three distinct machine learning approaches—linear regression (LR), random forest (RF), and artificial neural network (ANN) were employed to establish predictive models between the Normalized Difference Red Edge Index (NDRE) and soil water content (SWC). The linear regression model showed moderate correlation (R² = 0.533). Machine learning approaches demonstrated markedly superior performance (RF: R² = 0.828; ANN: R² = 0.810). Nonlinear machine learning algorithms (RF and ANN) significantly outperform conventional linear regression in estimating SWC from spectral vegetation indices. Full article

With accelerating climate change and urbanization, regional carbon balance faces increasing uncertainty. Terrestrial carbon sinks play a crucial role in advancing China’s sustainable development under the dual-carbon strategy. This study quantitatively modeled China’s terrestrial carbon sink capacity and analyzed the multidimensional relationships between impact factors and carbon sinks. After preprocessing multi-source raster data, we introduced kernel normalized the difference vegetation index (kNDVI) to the Carnegie–Ames–Stanford approach (CASA) model, together with a heterotrophic respiration (R_h) empirical equation, to simulate pixel-level net ecosystem productivity (NEP) across China. A light gradient-boosting machine (LightGBM) model, optimized via Bayesian algorithms, was trained to regress NEP drivers, categorized into atmospheric components (O₃, NO₂, and SO₂) and subsurface properties (a digital elevation model (DEM), enhanced vegetation index (EVI), soil moisture (SM)), and human activities (land use/cover change (LUCC), POP, gross domestic product (GDP)). Shapley Additive Explanation (SHAP) values were used for model interpretation. The results reveal significant spatial heterogeneity in NEP across geographic and climatic contexts. The pixel-level mean and total NEP in China were 268.588 gC/m²/yr and 2.541 PgC/yr, respectively. The north tropical zone (NRZ) exhibited the highest average NEP (828.631 gC/m²/yr), while the middle subtropical zone (MSZ) and south subtropical zone (SSZ) demonstrated the most stable NEP distributions. LightGBM achieved high simulation accuracy, further enhanced by Bayesian optimization. SHAP analysis identified EVI as the most influential factor, followed by SM, NO₂, DEM, and POP. Additionally, LightGBM effectively captured nonlinear relationships and variable interactions. Full article

In recent years, the Yellow River Basin has experienced frequent extreme climate events, with an increasing intensity and frequency of droughts, exacerbating regional water scarcity and severely constraining agricultural irrigation efficiency and sustainable water resource utilization. The accurate estimation of reference crop evapotranspiration (ET₀) is crucial for developing scientifically sound irrigation strategies and enhancing water resource management capabilities. This study utilized daily scale meteorological data from 31 stations across the Yellow River Basin spanning the period 1960–2023 to develop various machine learning models. The study constructed four machine learning models—random forest (RF), a Support Vector Machine (SVM), Gradient Boosting (GB), and Ridge Regression (Ridge)—using the meteorological variables required by the Priestley–Taylor (PT) and Hargreaves (HG) equations as inputs. These models represent a range of algorithmic structures, from nonlinear ensemble methods (RF, GB) to kernel-based regression (SVR) and linear regularized regression (Ridge). The objective was to comprehensively evaluate their performance and robustness in estimating ET₀ under different climatic zones and drought conditions and to compare them with traditional empirical formulas. The main findings are as follows: machine learning models, particularly nonlinear approaches, significantly outperformed the PT and HG methods across all climatic regions. Among them, the RF model demonstrated the highest simulation accuracy, achieving an R² of 0.77, and reduced the mean daily ET₀ estimation error by 0.057 mm/day and 0.076 mm/day compared to the PT and HG models, respectively. Under drought-year scenarios, although all models showed slight performance degradation, nonlinear machine learning models still surpassed traditional formulas, with the R² of the RF model decreasing marginally from 0.77 to 0.73, indicating strong robustness. In contrast, linear models such as Ridge Regression exhibited greater sensitivity to changes in feature distributions during drought years, with estimation accuracy dropping significantly below that of the PT and HG methods. The results indicate that in data-sparse regions, machine learning approaches with simplified inputs can serve as effective alternatives to empirical formulas, offering superior adaptability and estimation accuracy. This study provides theoretical foundations and methodological support for regional water resource management, agricultural drought mitigation, and climate-resilient irrigation planning in the Yellow River Basin. Full article

Self-driving technology aims to minimize human error and improve safety, efficiency, and mobility through advanced autonomous driving algorithms. Among these, Model Predictive Control (MPC) is highly valued for its optimization capabilities and ability to manage constraints. However, its effectiveness depends on an accurate system model, as modeling errors and disturbances can degrade performance, making uncertainty management crucial. Learning-based MPC addresses this challenge by adapting the predictive model to changing and unmodeled conditions. However, existing approaches often involve trade-offs: robust methods tend to be overly conservative, stochastic methods struggle with real-time feasibility, and deep learning lacks interpretability. Sparse regression techniques provide an alternative by identifying compact models that retain essential dynamics while eliminating unnecessary complexity. In this context, the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm is particularly appealing, as it derives governing equations directly from data, balancing accuracy and computational efficiency. This work investigates the use of SINDy for learning and adapting vehicle dynamics models within an MPC framework. The methodology consists of three key phases. First, in offline identification, SINDy estimates the parameters of a three-degree-of-freedom single-track model using simulation data, capturing tire nonlinearities to create a fully tunable vehicle model. This is then validated in a high-fidelity CarMaker simulation to assess its accuracy in complex scenarios. Finally, in the online phase, MPC starts with an incorrect predictive model, which SINDy continuously updates in real time, improving performance by reducing lap time and ensuring a smoother trajectory. Additionally, a constrained version of SINDy is implemented to avoid obtaining physically meaningless parameters while aiming for an accurate approximation of the effects of unmodeled states. Simulation results demonstrate that the proposed framework enables an adaptive and efficient representation of vehicle dynamics, with potential applications to other control strategies and dynamical systems. Full article

Accurately predicting the permeability of coarse-grained soils is crucial for ensuring geotechnical safety and performance. In this study, 64 coarse-grained soil (CGS) samples were designed using a negative exponential gradation equation (NEGE), and computational fluid dynamics–discrete element method (CFD-DEM) coupled seepage simulations were conducted to generate a permeability coefficient (k) dataset comprising 256 entries under varying porosity and gradation conditions. Three machine learning models—a neural network model (BPNN), a regression model (GPR), and a tree-based model (RF)—were employed to predict k, with hyperparameters optimized via particle swarm optimization (PSO) and four-fold cross-validation applied to improve generalization. Gray relational analysis (GRA) revealed that all input parameters (α, β, d_max, n) significantly influence k (R > 0.6). The interquartile range (IQR) method confirmed data suitability for modeling. Among the models, BPNN achieved the best performance (R² = 0.99, MAE = 1.5, RMSE = 2.9, U₉₅ = 0.4), effectively capturing the complex nonlinear relationship between gradation and permeability. GPR (R² = 0.92) was hindered by kernel selection and noise sensitivity, while RF (R² = 0.97) was limited by its discrete regression nature. Compared to a traditional empirical model (R² = 0.9031), BPNN improved prediction accuracy by 10.13%, demonstrating the advantage of data-driven methods for evaluating CGS permeability. Full article

Structural damage detection is crucial for ensuring the safety and durability of engineering systems. Conventional detection methods based on the frequency response function (FRF) in linear systems tend to fail when small early damage occurs in engineered structures. Nonlinear output frequency response functions (NOFRFs), which are extensions of the FRF in linear systems to weak nonlinear systems, have been applied in nonlinear system analysis. In this study, we extended the structural damage detection method based on NOFRFs to multi-degree-of-freedom systems and beam structures. Due to the presence of multiple modal frequencies in these structures, the nonlinear characteristic frequencies exhibited by the system are often more complex than those of typical rotor systems, significantly increasing the difficulty of system identification and the feasibility of frequency-domain analysis. To improve the accuracy of the Nonlinear Auto-Regressive with eXogenous inputs (NARX) model and reduce the impact of noise interference, we proposed a Multi-input Multi-output Forward Regression Orthogonal Least Squares (MFROLS) algorithm for processing multi-input multi-output data to identify the NARX model of the same structural system. Next, a numerical simulation study was conducted using the combined NARX model and Generalized Associated Linear Equations (GALEs) method, taking a one-dimensional multi-degree-of-freedom (MDOF) system as an example. Nonlinear stiffness terms were introduced into the MDOF system to simulate structural damage, and a comparative study was performed with a least squares method (LSM). The results show that the proposed method can capture the trends of dynamic characteristic changes in the one-dimensional MDOF system under the influence of different nonlinear stiffnesses, whereas the LSM fails to do so. Finally, experimental research was carried out on simply supported beams with varying degrees of damage. The results demonstrate that the frequency-domain analysis method based on nonlinear systems can detect differences in damage levels in beam structures, providing a new approach for structural damage detection. Full article

This work aims to explore the magnetohydrodynamic mixed convection boundary layer flow (MHD-MCBLF) on a slanted extending cylinder using Eyring–Powell fluid in combination with Levenberg–Marquardt algorithm–artificial neural networks (LMA-ANNs). The thermal properties include thermal stratification, which has a higher temperature surface on the cylinder than on the surrounding fluid. The mathematical model incorporates essential factors involving mixed conventions, thermal layers, heat absorption/generation, geometry curvature, fluid properties, magnetic field intensity, and Prandtl number. Partial differential equations govern the process and are transformed into coupled nonlinear ordinary differential equations with proper changes of variables. Datasets are generated for two cases: a flat plate (zero curving) and a cylinder (non-zero curving). The applicability of the LMA-ANN solver is presented by solving the MHD-MCBLF problem using regression analysis, mean squared error evaluation, histograms, and gradient analysis. It presents an affordable computational tool for predicting multicomponent reactive and non-reactive thermofluid phase interactions. This study introduces an application of Levenberg–Marquardt algorithm-based artificial neural networks (LMA-ANNs) to solve complex magnetohydrodynamic mixed convection boundary layer flows of Eyring–Powell fluids over inclined stretching cylinders. This approach efficiently approximates solutions to the transformed nonlinear differential equations, demonstrating high accuracy and reduced computational effort. Such advancements are particularly beneficial in industries like polymer processing, biomedical engineering, and thermal management systems, where modeling non-Newtonian fluid behaviors is crucial. Full article

The water-conducting fracture zone (WCFZ) is a critical geological structure formed by the destruction of overburden during coal mining operations. Accurately predicting the height of the water-conducting fractured zone (HWCFZ) is essential for ensuring safe coal production. Based on more than 150 measured heights of fractured water-conducting zone samples from various mining areas in China, this study investigates the influence of five primary factors on the height: mining thickness, mining depth, length of the panel, coal seam dip, and the proportion coefficient of hard rock. The correlation degrees and relative weights of each factor are determined through grey relational analysis and principal component analysis. All five factors exhibit strong correlations with the height of the fractured water-conducting zone, with correlation degrees exceeding 0.79. Mining thickness is found to have the highest weight (0.256). A multiple nonlinear coordinated regression equation was constructed through regression analysis of the influencing factors. The prediction accuracy was compared with three other predictive models: the multiple nonlinear additive regression model, the BP neural network model, and the GA-BP neural network model. Among these models, the multiple nonlinear coordinated regression model was found to achieve the lowest error rate (7.23%) and the highest coefficient of determination (R² = 87.42%), indicating superior accuracy and reliability. The model’s performance is further validated using drill hole data and numerical simulations at the B-1 drill hole in the Fuda Coal Mine. Predictive results for the entire Fuda Coal Mine area indicate that as the No. 15 coal seam extends northwestward, the height of the fractured water-conducting zone increases from 52.1 m to 73.9 m. These findings have significant implications for improving mine safety and preventing geological hazards in coal mining operations. Full article

The identification of model parameters is a central challenge in the analysis of nonignorable nonresponse data. In this paper, we propose a novel penalized semiparametric likelihood method to obtain sparse estimators for a parametric nonresponse mechanism model. Based on these sparse estimators, an instrumental variable is introduced, enabling the identification of the observed likelihood. Two classes of estimating equations for the nonlinear regression model are constructed, and the empirical likelihood approach is employed to make inferences about the model parameters. The oracle properties of the sparse estimators in the nonresponse mechanism model are systematically established. Furthermore, the asymptotic normality of the maximum empirical likelihood estimators is derived. It is also shown that the empirical log-likelihood ratio functions are asymptotically weighted chi-squared distributed. Simulation studies are conducted to validate the effectiveness of the proposed estimation procedure. Finally, the practical utility of our approach is demonstrated through the analysis of ACTG 175 data. Full article

In Vietnam, models for estimating Above-Ground Biomass (AGB) to predict carbon stock are primarily based on diameter at breast height (DBH), tree height (H), and wood density (WD). However, remote sensing has increasingly been recognized as a cost-effective and accurate alternative. Within this context, the present study aimed to develop correlation equations between Total Above-Ground Carbon (TAGC) and vegetation indices derived from Sentinel-2 imagery to enable direct estimation of carbon stock for assessing emissions and removals. In this study, the remote sensing indices most strongly associated with TAGC were identified using principal component analysis (PCA). TAGC values were calculated based on forest inventory data from 115 sample plots. Regression models were developed using Ordinary Least Squares and Maximum Likelihood methods and were validated through Monte Carlo cross-validation. The results revealed that Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Near Infrared Reflectance (NIR), as well as three variable combinations—(NDVI, ARVI), (SAVI, SIPI), and (NIR, EVI — Enhanced Vegetation Index)—had strong influences on TAGC. A total of 36 weighted linear and non-linear models were constructed using these selected variables. Among them, the quadratic models incorporating NIR and the (NIR, EVI) combination were identified as optimal, with AIC values of 756.924 and 752.493, R² values of 0.86 and 0.87, and Mean Percentage Standard Errors (MPSEs) of 22.04% and 21.63%, respectively. Consequently, these two models are recommended for predicting carbon stocks in Evergreen Broadleaf (EBL) forests within Vietnam’s South Central Coastal Ecoregion. Full article