Search Results (846)

This paper addresses the optimal management of photovoltaic (PV) systems and distribution static synchronous compensators (D-STATCOMs) in modern electrical distribution networks. A mixed-integer nonlinear programming (MINLP) model is formulated which co-optimizes device placement, sizing, and multi-period dispatch to minimize the total annualized system costs while satisfying AC power flow and operational constraints. To solve this challenging problem, a decomposition methodology is proposed, wherein the binary location decisions for the PVs and D-STATCOMs are treated as predefined inputs, upon the basis of site selections commonly reported in the literature. With the integer variables fixed, the problem is reduced to a continuous nonlinear programming (NLP) subproblem for optimal capacity sizing and operational scheduling, which is solved using the interior point optimizer (IPOPT) via the Julia/JuMP environment. The core contribution of this work lies in its comprehensive demonstration of the economic superiority of variable reactive power injection over conventional fixed compensation schemes. Through numerical validation on standard 33- and 69-bus test systems, it is shown that a variable D-STATCOM operation yields substantial and consistent economic gains. Compared to optimized fixed-injection solutions, variable injection provides additional annual savings averaging USD 120,516 (33-bus feeder) and USD 125,620 (69-bus grid), corresponding to a further 3.4% reduction in total costs. These benefits prove robust across different device location sets identified by various metaheuristic algorithms, and they scale effectively to larger network topologies. The results demonstrate that transitioning to variable power injection is not merely an incremental improvement but a fundamental advancement for achieving techno-economic optimality in distribution system planning. The proposed methodology provides utilities with a computationally efficient framework for determining near-optimal PV and D-STATCOM management strategies by first fixing deployment locations based on established planning insights and then rigorously optimizing sizing and dispatch, in order to maximize economic returns while ensuring reliable network operation. Full article

The study aims to analyze comprehensive effects of site quality class (SQC), stand density index (SDI), and species composition (SC) on biomass growth. Based on 5872 observations from 2040 permanent sample plots of Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) plantations from successive national forest resource inventories, five classical growth equations were employed and nonlinear regression and dummy variables were used for modeling. A dominant height (DH) growth model was first developed to determine SQC, followed by a series of stand biomass (SB) growth models incorporating SQC, SDI, and SC (pure vs. mixed stands). Growth differences among different classes or categories were analyzed using inflection age and optimal rotation age. The results show that Korf equation performed best for both DH and SB growth models; SDI contributed the most to SB growth, followed by SQC, with their interaction accounting for over half of the total contribution. Mixed stands grew faster than pure stands; higher SQC was associated with faster growth and earlier attainment of inflection age and optimal rotation age. The productivity increased with rising SDI, but the rate of increase gradually diminished. Different optimal rotation ages should be determined for pure and mixed stands across different SQCs. Reasonable adjustment of harvesting age and control of stand density represent the greatest potential for improving forest productivity. Full article

Optimization has played a fundamental role in the evolution of chemical engineering, enabling systematic decision-making under technical, economic, and environmental constraints. This review presents a structured and comparative analysis of the historical development and current state of optimization methodologies applied to chemical engineering, covering the transition from early linear and nonlinear programming approaches to advanced data-driven and artificial intelligence-based frameworks. A systematic literature review was conducted following the PRISMA guidelines, through which a total of 101 articles were retained for analysis. The results indicate that mixed-integer programming and decomposition-based methods remain widely adopted for structured industrial problems, while metaheuristic and hybrid data-driven approaches have experienced significant growth in recent years. In particular, a clear trend toward the integration of machine learning and surrogate modeling techniques is observed, driven by the need to address large-scale, non-convex, and highly nonlinear systems. The analysis reveals a clear methodological shift from classical linear optimization frameworks toward hybrid optimization strategies capable of addressing large-scale, non-convex, and highly nonlinear problems. Finally, current challenges and future research directions are identified, emphasizing the need for robust hybrid approaches that combine mathematical programming and intelligent algorithms to effectively manage complexity in next-generation chemical systems. Full article

The double hidden-layer neural network has increasingly been applied in tree height modeling due to its superior performance. To improve the precision of tree height estimation, this study compared the performance of a double hidden-layer neural network with that of a nonlinear mixed-effects model, aiming to provide a new method for tree height prediction. Taking the Larix olgensis forest plantation in Jilin Province as the research object, a double hidden-layer back propagation (BP) neural network was established for tree height prediction by adopting trial and error, k-fold cross-validation, and near-domain optimization strategies. In constructing the nonlinear mixed-effects model, the overall and local differences in forest growth data, as well as the autocorrelation among the various levels of data, were considered. Accordingly, after determining the base model, random effects were introduced, the correlation variance–covariance matrix was calculated, and random parameters were estimated to compare the predictive performance of the two aforementioned models. For the mixed-effects model, the coefficient of determination R² was 0.8590, the root mean square error (RMSE) was 1.6230, and the mean absolute error (MAE) was 2.2658. For the double hidden-layer BP neural network, the R² reached 0.9068 (an increase of 5.56%), the RMSE was 1.3197 (a decrease of 18.69%), and the MAE was 1.2736 (a decrease of 43.79%). The results demonstrate that the double hidden-layer BP neural network is superior to the nonlinear mixed-effects model for tree height prediction. Therefore, the results provide a more accurate method for tree height prediction. Full article

Modeling and forecasting nonstationary and nonlinear economic time series remain fundamentally challenging due to structural breaks, volatility clustering, and noise contamination that distort the intrinsic stochastic structure. To address these limitations, this study proposes a novel three-stage hybrid statistical framework that systematically integrates multi-level signal decomposition with structured parametric modeling to enhance predictive accuracy. The proposed hybrid architectures—EMD–EEMD–ARIMA, EMD–EEMD–GMDH, and EMD–EEMD–ETS—employ a hierarchical decomposition–reconstruction strategy before forecasting. In the first stage, Empirical Mode Decomposition (EMD) decomposes the observed series into intrinsic mode functions (IMFs) and a residual component. In the second stage, Ensemble Empirical Mode Decomposition (EEMD) is applied to further refine the extracted components, mitigating mode mixing and improving signal separability. In the final stage, each reconstructed component is modeled using ARIMA, Exponential Smoothing State Space (ETS), and Group Method of Data Handling (GMDH) frameworks, and the individual forecasts are aggregated to obtain the final prediction. Empirical evaluation based on a recursive one-step-ahead forecasting scheme demonstrates consistent numerical improvements across all standard accuracy measures. In particular, the proposed EMD–EEMD–ARIMA model achieves the lowest forecasting error, reducing the root-mean-square error (RMSE) by approximately 6–7% relative to the best-performing single-stage model and by about 3–4% relative to the two-stage EMD-based hybrids. Similar improvements are observed in mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), indicating enhanced stability and robustness of the three-stage architecture. The results provide strong numerical evidence that multi-level decomposition combined with structured statistical modeling yields superior predictive performance for complex nonlinear and nonstationary time series. The proposed framework offers a mathematically coherent, computationally tractable, and systematically structured hybrid modeling strategy that effectively integrates noise-assisted decomposition with parametric and data-driven forecasting techniques. Full article

Deep soft-rock tunnels are prone to large deformations and structural damage. This study used the Guanyinping Tunnel as a prototype and conducted 1/50-scale progressive loading model tests under three support configurations: rock-bolt-free, equal-length rock bolts, and mixed long–short rock bolts. Rock stress, radial rock displacement (u), and rock bolt axial force (F_N) at the vault, arch shoulders, sidewalls, and wall feet were monitored to reveal reinforcement mechanisms and mechanical response. The results indicated that stress evolution in the bolt-free case exhibited significant spatial heterogeneity. The vault experienced horizontal stress concentration, while the arch shoulder underwent vertical stress concentration. u underwent a three-stage nonlinear progression: elastic linear growth, plastic linear growth, and plastic-accelerated growth. Displacement at the vault was markedly larger than that at other locations. Equal-length rock bolts substantially improved the rock mass stability by delaying stress concentration and fracture propagation. This reinforcement raised the elastic response threshold to 96 kPa and substantially reduced u. F_N at the vault and shoulder followed linear growth, accelerated growth, and then gradual decline, whereas F_N at the sidewalls and wall feet exhibited a steady linear trend. Combined long and short rock bolts produced a multi-level anchoring effect. Short bolts induced a shallow arching action, while long bolts provided deep suspension. This synergy raised the elastic response threshold to a maximum of 120 kPa and moderated the stress reduction process. Deep residual stresses increased to 74.3–88.4% of peak values. The displacement gradient between shallow and deep rock masses was significantly reduced. The coordinated deformation capacity within the anchoring zone was markedly enhanced. F_N distribution exhibited spatial differentiation: short bolts carried the load initially, followed by the activation of long bolts. Both anchoring schemes increased residual stress and mitigated rock mass deformation. The deformation control effect was stronger in shallow rock mass than in deep rock mass. Improvements at the vault and arch shoulders exceeded those at the sidewalls and wall feet. The mixed short–long bolt configuration was superior because it maximized the self-bearing capacity of the deep rock mass. The findings provide experimental data and theoretical guidance for the design and optimization of rock-bolt support in deep soft-rock tunnels. Full article

The global restriction of antimicrobial growth promoters has intensified the search for alternative strategies to sustain poultry health and productivity. One proposed mechanism underlying the historical efficacy of antibiotic performance enhancers is the modulation of intestinal inflammation. In this context, meloxicam (MLX), a preferential COX-2 inhibitor and non-steroidal anti-inflammatory drug, has emerged as a potential candidate for investigation. However, pharmacokinetic data in broiler chickens remain limited, particularly for practical oral formulations intended for production systems. This study aimed to characterize the pharmacokinetic profile of a novel granulated MLX formulation in male Cobb 500 broiler chickens following single-dose administration. Seventy-two 21-day-old broilers received MLX granulate (19.24% m/m) via oral gavage at 3.6 mg/kg body weight. Plasma samples were collected over 48 h post administration. MLX concentrations were quantified using validated high-performance liquid chromatography, and pharmacokinetic parameters were estimated using nonlinear mixed-effects modelling (NLME). Mean pharmacokinetic parameters included AUC_0–∞ of 79.97 μg·h/mL, C_max of 14.43 μg/mL, and T_max of 1 h, indicating rapid absorption and substantial systemic exposure. These findings provide novel insights into MLX disposition from the granulated formulation in broilers and provide pharmacokinetic information to support future investigations evaluating its potential biological effects in poultry production systems. Full article

State-of-charge (SOC) estimation for lithium-ion batteries in smartphones is complicated by nonlinear load variation, electro-thermal coupling, aging effects, and heterogeneous user behaviors. This study proposes a multi-physics coupled SOC estimation framework, termed the Multi-Physics Thermo-Electrochemical Coupled SOC Model (MTEC-SOC), to characterize battery behavior under representative user-load conditions within controlled ambient thermal boundaries. The model combines system-level power profiling, thermal evolution, voltage dynamics, and aging-related capacity correction within a unified framework. To support model development and validation, a dual-source dataset is established using laboratory battery characterization data and real-world smartphone behavioral data, from which users are classified into light, heavy, and mixed usage patterns. Comparative results against four benchmark models (M1–M4) show that MTEC-SOC achieves the highest overall accuracy, with average MAE, RMSE, and TTE error values of 0.0091, 0.0118, and 0.08 h, respectively. The results suggest distinct degradation tendencies across user types: calendar aging dominates under prolonged high-voltage dwell in light-use scenarios, whereas, within the tested thermal range, heavy-use scenarios exhibit stronger voltage sag, relative temperature rise, and polarization-related stress; mixed-use scenarios are characterized by transient responses induced by abrupt load switching. Sensitivity analysis further indicates that the predictive behavior of the model is strongly scenario-dependent, with higher-load operation within the calibrated range amplifying parameter perturbations. Overall, the proposed MTEC-SOC framework provides accurate SOC estimation and physically interpretable insight within the evaluated dataset and operating conditions, offering potential guidance for battery management and energy optimization in intelligent mobile terminals. Full article

Background/Objectives: Tablet development requires simultaneous optimization of multiple quality attributes under limited experimental budgets, yet formulation–property relationships are highly nonlinear in mixture systems. To support pre-formulation decision-making prior to extensive tablet prototyping, this study proposes an AI framework that organizes formulation and process data together with raw-material property records into a reusable database, and enriches conventional composition/process features with physically motivated mixture descriptors derived from raw-material properties and formulation/process settings. Methods: Mixture-level scalar descriptors are constructed by composition-weighted aggregation of material properties, and particle size distribution (PSD) is incorporated via a compact set of summary statistics computed from composition-weighted mixture PSDs. Three feature sets are compared: (i) Materials + Processes (MP), (ii) MP with scalar Descriptors (MPD), and (iii) MPD with PSD summaries (MPDD). Five target properties are modeled: hardness, disintegration time, flow function, cohesion, and thickness. We train and evaluate Random Forest, Extra Trees Regressor, Lasso, Partial Least Squares, Support Vector Regression, and a multi-branch neural network that processes the three feature blocks separately and concatenates them for prediction. For interpolation assessment, repeated Train/Dev/Test splitting (5:3:2) across multiple random seeds is used, and the effect of feature augmentation is quantified by paired RMSE improvements with bootstrap confidence intervals and paired Wilcoxon signed-rank tests. To assess robustness under practical formulation updates, rolling-origin time-series splits are employed and Applicability Domain indicators are computed to characterize out-of-distribution coverage. Results: Across interpolation evaluations, mixture-descriptor augmentation (MPD/MPDD) improves hardness and disintegration time in most settings, whereas gains for flow function are smaller and cohesion/thickness show mixed effects under limited sample sizes. Conclusions: Under extrapolation-oriented evaluation, the descriptors can improve hardness but may degrade disintegration-time prediction under covariate shift, emphasizing the need for careful descriptor selection and dimensionality control when deploying pre-formulation predictors. Full article

Background: The rising prevalence of childhood overweight/obesity (OWOB) and elevated blood pressure (EBP) parallels a global decline in muscular fitness. However, evidence linking whole-body muscular strength to the comorbidity of these cardiometabolic risks remains scarce. Methods: Data were obtained from five nationally representative waves of the Chinese National Survey on Students’ Constitution and Health (CNSSCH, 2000–2019), including 1,072,404 children and adolescents aged 7–18 years. A novel Muscle Strength Index (MSI) was developed by integrating handgrip strength (HGS) and standing broad jump (SBJ), standardized for body weight and height, respectively. Generalized linear mixed-effects models (GLMMs) with restricted cubic splines (RCS) were first applied to characterize dose–response associations. Subsequently, categorical analyses and forest plots were conducted to quantify risks of OWOB, EBP, and their comorbidity across five waves and subgroups. Sex-specific normative reference curves were established using the LMS method, and population-attributable fractions (PAFs) were estimated to assess the potential public health benefits of improving muscular strength. Results: Between 2000 and 2019, the prevalence of OWOB, EBP, and comorbidity increased markedly, reaching 25.80%, 12.23%, and 4.83% in 2019, and are projected to rise further to 37.88%, 20.16%, and 10.01% by 2030. Over the same period, mean MSI increased from 2000, peaked in 2005, and subsequently declined by 2019 with the values for boys and girls, being 1.73, 1.75, 1.63 and 1.46, 1.49, 1.41, respectively. Dose–response analyses revealed consistent L-shaped associations, with the greatest risk reductions observed when moving from low to moderate MSI levels. In 2019, participants with low MSI had higher odds of OWOB (OR 4.81, 95% CI 4.65–4.97), EBP (OR 1.42, 95% CI 1.36–1.49), and comorbidity (OR 3.49 95% CI 3.26–3.73) compared with those at middle levels. PAF analyses indicated that improving MSI to at least the 40th percentile could potentially avert 43.5% of OWOB cases, 12.3% of EBP cases, and 48.2% of comorbidity cases. The highest potential benefits were observed in northern and northeastern provinces, particularly Tianjin and Heilongjiang. Conclusions: Chinese children and adolescents face a dual burden of rising cardiometabolic comorbidity and declining muscular strength. Muscular strength demonstrates a strong nonlinear protective association with OWOB, EBP, and their co-occurrence. Targeted improvement among those with low muscular strength may substantially reduce future cardiometabolic burden. Full article

Background: Patient-perceived seasonality of psoriasis is frequently reported, yet the independent contribution of objectively quantified, individualized ultraviolet (UV) exposure remains insufficiently characterized. We evaluated seasonal variation in plaque psoriasis and its association with geocoded, phototype-adjusted ambient antipsoriatic radiant exposures (ARE) using mixed-effects modeling. Methods: This cross-sectional study included 119 adults with plaque psoriasis (476 seasonal observations). Participants rated seasonal disease courses using a 7-point scale. Ambient ARE was geocoded to residential postal codes and quantified as a behaviorally weighted dose normalized to individual minimal erythema dose (MED). Mixed-effects logistic regression models, adjusted for relevant confounders, estimated associations with seasonal improvement and worsening. Results: Seasonality was reported by 89.9% of participants (p < 0.001). Summer was the most favorable season, whereas winter was the most detrimental. The highest ARE quartile was independently associated with increased odds of improvement (OR 4.65, 95% CI 2.04–10.58, p < 0.001) and reduced odds of worsening (OR 0.16, 95% CI 0.08–0.33, p < 0.001). Crucially, continuous quadratic modeling revealed a significant inverted U-shaped relationship between UV exposure and improvement, with an estimated turning point of 3.85 (95% CI 1.88–5.82, p < 0.001) for the declared daily ARE (UV_decl) normalized by MED. Beyond this threshold, the probability of improvement attenuated. The protective effect against seasonal worsening remained linear. Conclusions: Psoriasis seasonality demonstrates a robust exposure–response association relationship with ambient UV. The estimated turning point (UV_decl/MED = 3.85) within our modeled exposure metric is exploratory and hypothesis-generating. It suggests an association where moderate UV exposure correlates with patient-perceived benefits, but these diminish at very high levels. This threshold requires external prospective validation before being considered a clinically actionable recommendation. Full article

Phosphorus availability is extremely limited in Florida’s sandy soils due to intense sorption by aluminum (Al), iron (Fe) oxides, and fertilizer retention. Current fertilization recommendations do not account for P-fixation, a defining characteristic of Florida’s soils. Site-specific and multi-year yield-based thresholds for snap bean under these conditions have not been established. This study is among the first to derive yield-based thresholds from a multi-year linear–plateau model using nonlinear mixed-effects modeling that accounts for stochastic variability across sites and years, thereby defining a threshold range for this crop in this soil system. This work assessed snap bean (Phaseolus vulgaris L.) pod yield responses to phosphorus fertilization from 2022 to 2025. Field experiments employing increasing P₂O₅ rates and fertilizer sources were conducted. Hastings and Citra were selected to represent sandy soil conditions across northeast and north-central Florida’s commercial snap bean production areas, where soil tests consistently indicated elevated extractable Al and Fe in the rhizosphere, key drivers of P fixation and fertilizer demand. At low-to-moderate P₂O₅ rates, yield increased linearly over site-years before plateauing. A breakpoint of 215.6 kg ha⁻¹ P₂O₅ was found in Hastings by the multi-year model. A single-year fit at Citra in 2025 revealed a breakpoint of 265.7 kg ha⁻¹ P₂O₅. Confidence intervals were wide due to year and plot variability, with values of 148.2–283 kg ha⁻¹ P₂O₅. When all site-years were pooled, the population-level breakpoint was estimated at 223.5 kg ha⁻¹ P₂O₅, with 90% and 95% model estimates of maximum yield obtained at about 164 and 194 kg ha⁻¹ P₂O₅, respectively. These findings provide a fertilizer range for snap bean production in Florida’s sandy soils under similar conditions, with implications for regional fertilizer guidelines. Full article

In this study, we investigate a class of mixed fractional partial integro-differential equations (FrPI-DE) involving symmetric singular kernels. The considered model problem involves Caputo fractional derivatives and integral operators that describe spatial interactions in a bounded domain. For the purpose of analysis, the original problem is reformulated in the form of a nonlinear Volterra–Fredholm integral equation (NV-FIE). The existence and uniqueness of the solution are established by the Banach fixed point theorem. To compute numerical solutions, a modified Toeplitz matrix method (TMM) is proposed to handle the singular kernel efficiently. The method transforms the integral equation to a system of nonlinear algebraic equations, which can be solved numerically. The convergence properties of the resulting numerical scheme are analyzed and illustrate the effectiveness of the method by providing numerical examples involving logarithmic, Cauchy-type, and weakly singular kernels. Numerical results indicate that the proposed method provides highly accurate approximations and exhibits stable convergence behavior for different parameter values. Furthermore, these results confirm the effectiveness and reliability of the proposed method for solving fractional integro-differential equations that include symmetric singular kernels. Full article

Accurate photovoltaic (PV) power forecasting is crucial for grid stability and the integration of renewable energy. To address the multiscale, nonlinear characteristics of PV power series and the limitations of traditional methods in dynamic error compensation, a novel hybrid forecasting framework is proposed, integrating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM), and a Simplified Deep Q-Network (SDQN). The framework first decomposes the power series into subcomponents across different frequency bands via CEEMDAN. Subsequently, dedicated CNN-BiLSTM sub-models are employed in parallel to extract spatiotemporal features from each component. Finally, an SDQN agent is introduced to perform real-time error compensation. Validation based on operational data from a PV plant in Ningxia, China, demonstrates that the proposed framework achieves RMSE, MAE, MAPE, and R² values of 0.4463, 0.1256, 1.2814%, and 92.58%, respectively, significantly outperforming benchmark models. Specifically, the CEEMDAN decomposition effectively mitigates mode mixing. The CNN-BiLSTM as the base predictor reduces RMSE by 25.04–65.68% compared to mainstream models. Furthermore, the SDQN compensation mechanism delivers an additional 24.5% reduction in prediction error. The proposed approach thus constitutes a high-precision, adaptive solution for PV power forecasting. Full article

This study introduces a modified computational scheme for handling linear and nonlinear fractal time-dependent partial differential equations. The method is constructed using three different stages that provide third-order accuracy in the fractal time variable. The stability of the approach is examined using scalar fractal models and Fourier analysis, while convergence is established for coupled convection–diffusion systems. The numerical algorithm is applied to analyze the mixed convective flow of a Carreau–Yasuda non-Newtonian fluid over stationary and oscillating plates under the influence of viscous dissipation and magnetic field effects. For spatial discretization, the incompressible continuity equation is handled by a first-order difference scheme, whereas higher-order compact schemes are implemented for the momentum, thermal, and concentration equations. The numerical findings show that increasing the Weissenberg number and magnetic field inclination reduces the velocity distribution. An accuracy assessment against existing numerical techniques demonstrates that the present method yields smaller computational errors, particularly when central difference schemes are used in space. In addition, a surrogate machine learning model is developed to predict the skin friction coefficient and local Nusselt number using Reynolds, Weissenberg, Prandtl, and Eckert numbers as input features. The predictive capability of the model is validated through Parity plots, bar charts for sensitivity analysis, scatter visualization, and Taylor Diagrams, confirming strong agreement with the numerical results. Full article