Search Results (64,649)

Inference after model selection is a very important problem. Model selection algorithms for ARIMA time series, with criteria such as AIC and BIC, tend to select an inconsistent model with positive probability, making data-splitting inference for testing and confidence intervals unreliable. One technique was fairly reliable for sample sizes greater than 600, and a modification also worked. Model selection is often useful for prediction, since the selected submodel tends to have fitted values and residuals that are highly correlated with those of the full model. A few prediction intervals perform fairly well even after model selection. A useful technique for handling outliers is to replace the outliers with missing values. Full article

Background: Inherited kidney diseases represent a genetically and clinically heterogeneous group of disorders affecting both pediatric and adult populations. Advances in next-generation sequencing (NGS) have improved diagnostic precision; however, genotype–phenotype correlations and diagnostic yield vary substantially across disease entities. Methods:We retrospectively evaluated 165 patients referred for genetic testing due to suspected inherited kidney disease. Patients were classified into three clinical groups: polycystic kidney disease, Alport syndrome, and other syndromic patients with inherited kidney diseases. Genetic analysis was performed using NGS with Human Phenotype Ontology–based gene filtering and included evaluation of both single-nucleotide variants and copy number variations. Results: Overall diagnostic yield differed markedly between groups. A molecular diagnosis was achieved in 71.4% of Alport patients, 41.0% of PKD patients, and 70.2% of patients in the Other syndromic group. In the Alport group, variants were identified exclusively in COL4A3, COL4A4, and COL4A5, with pathogenicity and gene involvement correlating with disease severity and the presence of extrarenal manifestations. The PKD group showed predominant involvement of PKD1, followed by PKHD1 and PKD2, while a substantial proportion of patients remained genetically negative, reflecting technical and biological complexity. The Other group exhibited pronounced genetic heterogeneity, with variants distributed across multiple genes involved in tubular, glomerular, metabolic, and ciliopathy-related pathways. Computational assessments demonstrated that several variants of uncertain significance (VUS) were located in functionally critical domains and were predicted to disrupt protein stability, intermolecular interactions, or conserved structural motifs, thereby supporting the biological plausibility of their potential pathogenic impact. Conclusions: Phenotype-driven NGS enables effective molecular diagnosis across diverse inherited kidney diseases while revealing disease-specific differences in diagnostic yield and genotype–phenotype correlations. Systematic inclusion of variants of uncertain significance and careful integration of genetic and clinical data are essential for accurate interpretation and long-term patient management. Collectively, this study enhances understanding of inherited kidney diseases and underscores the value of integrating comprehensive genomic and computational approaches into routine nephrogenetic practice. Full article

Accurate wind flow prediction is essential for various applications, including the placement of wind turbines and a multitude of environmental assessments. Traditionally this can be achieved by using time-consuming computational fluid dynamics (CFD) simulations on reanalysis data. This study explores the performance of an autoencoder (AE) and a variational autoencoder (VAE) in approximating downscaled wind speed and direction using real-world reanalysis data and reference geo- and vegetation data. The AE model was trained for 2000 epochs and demonstrates the ability to replicate wind patterns with a mean absolute error (MAE) of approximately −0.9. However, the AE model exhibited a consistent underestimation of wind speeds and a directional shift of approximately 10 degrees compared to CFD reference simulations. The VAE model produced visually improved results, capturing complex wind flow structures more accurately than the AE model. It mainly achieves better local accuracy and a reduced variance of the results. The overall result suggests that while autoencoders can approximate wind flow patterns, challenges remain in capturing the full variability of wind speeds and directions with sufficient precision. The study highlights the importance of balancing reconstruction accuracy and latent space regularization in VAE models. Future work should focus on optimizing model architecture and training strategies to enhance accuracy, prediction reliability and generalizability across diverse wind conditions and various locations. Full article

Introduction: Eating Disorders (ED) represent a significant health concern in the pediatric population due to high morbidity, prolonged hospital stays, and frequent readmissions. Scientific evidence regarding nutritional factors that may influence length of stay or risk of readmission is limited in this population. Objectives: To identify variables associated with longer hospital stays and readmission in pediatric patients with ED admitted to a reference unit in northern Spain. Methods: A retrospective observational study was conducted following STROBE guidelines, including patients under 18 years admitted for ED at a tertiary referral hospital between 2022 and 2025. Nutritional, anthropometric, clinical, evolution-related, and treatment variables were collected. Descriptive analyses, group comparisons according to length of stay and readmission, and logistic regression models were performed to identify associated factors. Results: The study included 75 patients, predominantly female (94.7%), with a mean age of 14.5 years. Twenty-eight percent of patients experienced at least one readmission during the study period. Multivariable regression identified that the use of a nasogastric tube and nutritional supplements was significantly associated with reduced length of stay. In addition, in patients with moderate to severe malnutrition, a recovery greater than 5% according to the Waterlow index was associated with a lower probability of readmission. Although anthropometric differences were observed between groups according to their need for readmission, most were not statistically significant. Conclusions: Nutritional support via nasogastric tube when indicated, the use of nutritional supplements, and a > 5% recovery in the Waterlow index in patients with moderate to severe malnutrition are key factors in reducing hospital stay and readmission risk in pediatric patients with ED in our cohort. Isolated laboratory analyses and anthropometric measures showed limited predictive value in this context. Full article

This paper investigates the electromagnetic performance of an outer-rotor spoke-type permanent magnet synchronous generator intended for small wind turbine applications below 5 kW. The study focuses on the influence of structural ferromagnetic components on magnetic flux distribution and overall machine performance. The generator was initially designed and optimized using 2D finite element analysis, followed by a comprehensive 3D model to account for axial flux leakage and structural details; particular attention was given to the fastening screws used. Experimental validation on a dedicated laboratory test bench confirms the accuracy of the 3D model, mainly at lower wind speeds. The results highlight the necessity of including structural components in three-dimensional electromagnetic modeling for accurate performance prediction of flux-concentrating wind turbine generators. Full article

Purpose: HER2-targeted therapy has been incorporated into the standard neoadjuvant treatment (NAT) regimen for HER2-positive early-stage breast cancer, yet a subset of patients have shown a limited pathological response. This study aimed to evaluate clinicopathological factors associated with NAT sensitivity and to develop a predictive model. Methods: This retrospective study included 13,004 HER2-positive breast cancer patients from the National Cancer Database (2010–2022) who received neoadjuvant chemotherapy plus HER2-targeted therapy. Pathological complete response (pCR) was defined as no residual invasive carcinoma in the breast and axillary lymph nodes (ypT0/is, ypN0). NAT sensitivity was additionally defined using clinical-to-pathologic stage migration according to the AJCC 8th edition criteria. Baseline characteristics and overall survival (OS) were compared between NAT-sensitive and NAT-insensitive groups. A multivariable logistic regression model was developed based on age, clinical T stage, clinical N stage, histologic subtype, tumor grade, and hormone receptor (HR) status. Model performance was assessed using the area under the receiver operating characteristic curve and calibration curves. Results: Among the patients included, 3660 (28.1%) achieved pCR. Based on the predefined stage-based criteria, 10,451 (80.4%) were classified as NAT-sensitive and 2553 (19.6%) as NAT-insensitive. NAT-insensitive patients were older and more likely to present with clinical T1c and node-negative disease, whereas NAT-sensitive patients more frequently had higher clinical T and N stages. HR-positive and lower tumor grades were significantly associated with treatment insensitivity. NAT-insensitive patients demonstrated significantly worse OS compared with NAT-sensitive patients (p < 0.001). The predictive model showed acceptable discrimination with AUCs of 0.762 in the training cohort and 0.776 in the validation cohort, demonstrating good calibration. Conclusions: NAT sensitivity in HER2-positive early-stage breast cancer exhibited substantial biological and clinical heterogeneity in real-world practice. A younger age, higher clinical stage, invasive ductal histology, higher tumor grade, and HR-negative status were associated with improved responses. A predictive model based on routinely available baseline variables demonstrated reasonable performance for estimating treatment sensitivity, supporting its potential utility for baseline risk stratification pending external validation. Full article

Petroleum hydrocarbons remain major environmental contaminants, and understanding the mechanisms governing their biodegradation is essential for designing effective remediation plans. The strategy in this article is slightly different from other cases in the literature. Such literature models require, for their elaboration, a significant number of experiments; the number of experimental determinations is at least proportional to the square of the number of constants introduced in the mathematical expressions. For this reason, the strategy followed in this article is different—starting from a set of experiments carried out and presented in a coherent and published manner, a simple methodology for building specific and minimal models, which will allow solving specific problems, was effectively developed. This study develops a nonlinear mathematical structure, expressed as a system of coupled differential equations, that simultaneously describes the degradation of petroleum hydrocarbons and the dynamics of hydrocarbon-degrading bacteria and fungi in soil–sludge mixtures. The model was calibrated using experimental data obtained from biopiles prepared with different volumetric ratios of contaminated soil and sewage sludge. Approximate analytical solutions were derived and the distributed constants were evaluated. For a consistent discussion, the analytical solutions were assessed against numerical desk simulations performed with a classical fourth-order Runge–Kutta method, which accurately reproduced the nonlinear behavior of the specific system. This numerical approach was chosen in order to overcome the proper difficulties encountered in this strategy implementation. The results show that the soil–sludge ratio strongly influences biodegradation efficiency, while kinetic parameters determine whether microbial communities evolve toward a stationary regime or accelerated contaminant removal. The combined analytical–numerical framework provides a robust predictive tool for optimizing mixture composition and improving the design of bioremediation treatments for petroleum-contaminated soils. Full article

Background: Gamma Knife radiosurgery (GKS) delivers highly conformal intracranial irradiation, yet clinical decision-making still relies predominantly on physical dose metrics that do not account for fractionation, dose rate, treatment time, or DNA repair. Classical radiobiological models—including the linear–quadratic (LQ) formula and the Jones–Hopewell single-session repair model—do not extend naturally to 3- and 5-fraction GKS. Meanwhile, growing evidence suggests that biologically effective dose (BED) may better capture radiosurgical response in selected pathologies. A unified, biologically grounded, multi-fraction GKS framework has been lacking. Methods: We developed AI-BED-Fx, the first multi-fraction extension of the Jones–Hopewell radiobiological model capable of computing fraction-resolved BED for 1-, 3-, and 5-fraction GKS. The framework incorporates α/β ratio, dual-component repair kinetics, isocentre geometry, beam-on–time structure, and lesion-specific biological parameters. Four synthetic pathology-specific cohorts—arteriovenous malformation (AVM), meningioma (MEN), vestibular schwannoma (VS), and brain metastasis (BM)—were generated using distinct radiobiological signatures. Machine-learning models were trained to quantify the predictive value of physical dose versus BED for local control or obliteration. Additional experiments included Bayesian estimation of α/β and a neural-network surrogate for fast BED prediction. An exploratory comparison with a 60-lesion clinical brain–metastasis dataset was performed to assess whether key trends observed in the synthetic BM cohort were consistent with real radiosurgical outcomes. Results: AI-BED-Fx produced realistic pathology-specific BED distributions (AVM 60–210 Gy_2.47; MEN 41–85 Gy_3.5; VS 46–68 Gy₃; BM 37–75 Gy₁₀) and biologically coherent dose–response relationships. Predictive modeling demonstrated strong pathology dependence. In AVM, the three models achieved AUCs of 0.921 (Model A), 0.922 (Model B), and 0.924 (Model C), with corresponding Brier scores of 0.054, 0.051, and 0.051, with BED-based models performing best. In meningioma, BED was the dominant predictor, with AUCs of 0.642 (Model A), 0.660 (Model B), and 0.661 (Model C) and Brier scores of 0.181, 0.177, and 0.179, respectively. In vestibular schwannoma, the narrow BED range resulted in minimal BED contribution, with AUCs of 0.812, 0.827, and 0.830 and Brier scores of 0.165, 0.160, and 0.162, with physical dose and tumor volume determining performance. In brain metastases, outcomes were driven primarily by volume and physical dose, with AUCs of 0.614, 0.630, and 0.629 and Brier scores of 0.254, 0.250, and 0.253, showing negligible improvement from BED. AI-BED-Fx also accurately recovered the true α/β from synthetic outcomes (posterior mean 2.54 vs. true 2.47), and a neural-network surrogate reproduced full radiobiological BED calculations with near-perfect fidelity (R² = 0.9991). Conclusions: AI-BED-Fx provides the first unified, biologically explicit framework for modeling single- and multi-fraction Gamma Knife radiosurgery. The findings show that the predictive usefulness of BED is pathology-specific rather than universal, and that radiobiological dose provides additional predictive value only when repair kinetics and dose–response biology support it. By integrating mechanistic radiobiology with machine learning, AI-BED-Fx establishes the conceptual and computational foundations for biologically adaptive, AI-guided radiosurgery, and cross-pathology comparison of treatment response. This work uses large radiobiologically grounded synthetic cohorts for methodological validation; limited real-patient data are included only for exploratory consistency checks, and full clinical validation is planned. Full article

To reduce vehicular emission pollution in cold regions and maximize sustainable development of transportation, AC self-heating of electric vehicles is acknowledged as an efficient approach to mitigate the decline in Li-ion battery performance under low-temperature conditions. This paper introduces a novel battery self-heating approach based on reconfiguration of the drive circuit, which is compatible with both preheating and on-route heating. The undesired torque generated by the heating current can be inherently nullified regardless of the rotor position. The control of heating and driving currents is entirely decoupled, facilitating straightforward adaptation to a range of heating strategies. Furthermore, a battery electro-thermal model is proposed and integrated with the drive system model to estimate the battery temperature evolution. Comprehensive experiments are designed to validate the operating principle and the accuracy of battery temperature estimation under various working conditions. The results present a high fidelity between the experimental data and the simulation outcomes. The root mean square errors of the predicted battery temperature under all the constant and combined driving conditions are less than 1 °C. Full article

Understanding power consumption conditions is necessary for optimizing UAV energy use, particularly during flight under varying weather conditions and environmental factors. Maintaining UAV energy while accounting for multiple influencing variables and vulnerability to weather conditions provides an appropriate case study for advanced predictive modeling. This study investigates UAV power consumption during hovering flight by forecasting power usage using a MDFA network to improve prediction accuracy and better adapt to rapid weather-induced variations. To capture intricate temporal dependencies and recurrent oscillatory behavior, the integrated model combines multi-scale dilated convolutions with a Fourier-enhanced mechanism. According to the experimental results, this model achieves 3% error reductions under all tested flight conditions, indicating a significant improvement in performance. Overall, the MDFA model consistently showed better performance under high power consumption conditions than under low power consumption conditions, and it produced the lowest error in heavy flight compared to low and medium flight. Full article

Background/Objectives: The emergence of antimicrobial resistance necessitates the development of new and effective antimicrobial agents. In this study, three different series of imidazole-based chalcones (IBC1-25) were designed and synthesised using a sustainable approach, with the aim of identifying compounds with enhanced antimicrobial activity. Methods: A series of monoalkoxy, dialkoxy, and trialkoxy imidazole-based chalcones (IBC1–25) were synthesised and evaluated for their antimicrobial and antifungal activities against a range of microbial strains. Structure-activity relationships were analysed, and molecular docking studies were performed to investigate potential binding interactions with biofilm-associated regulatory proteins. In addition, ADME properties were predicted to assess drug-likeness. Results: Among the monoalkoxy derivatives (IBC1-14), IBC5 exhibited the broadest spectrum of activity, particularly against S. epidermidis. Several dialkoxy analogues (IBC17-21) demonstrated improved potency, with IBC20 showing notably high activity. While IBC22 and IBC25 were largely ineffective, IBC23 and IBC24 displayed significant antibacterial and antifungal activities. Overall, dialkoxy and trialkoxy derivatives exhibited enhanced efficacy, whereas monoalkoxy compounds with bulky or long-chain substituents were generally less active. The presence of multiple alkoxy substituents, such as methoxy and ethoxy groups, on the phenyl ring significantly improved activity, particularly against fungi and Gram-positive bacteria. Molecular docking studies revealed that IBC20 and IBC23 showed favourable binding to the biofilm-associated regulator TcaR, suggesting a potential allosteric inhibition mechanism, while weak interactions were observed with TagF. ADME predictions indicated good oral absorption and compliance with key drug-likeness criteria. Conclusions: The results demonstrate that both the number and type of alkoxy substituents play a critical role in antimicrobial activity. In particular, IBC20 and IBC23 emerge as promising candidates for further development as antimicrobial agents targeting biofilm-associated pathways. Full article

Accurate prediction of thermo-mechanical behavior in Fused Deposition Modeling (FDM) is often limited by mismatches between idealized Computer-Aided Design (CAD) geometry and path-dependent material deposition. This paper presents a G-code-driven, filament-level modeling and process-simulation workflow for complex geometries and infill strategies, especially toolpaths with in-plane inclinations. Extrusion segments are parsed from slicing G-code to obtain endpoints and process parameters, and each filament is reconstructed as a path-aligned rectangular bead using a dedicated local coordinate system. Progressive deposition is simulated in ANSYS Parametric Design Language (APDL) via an element birth–death method, enhanced by a centroid-based element selection strategy that reduces dependence on strictly aligned hexahedral partitions and improves robustness for complex meshes. A nonlinear transient thermal analysis is performed, and temperatures are mapped to the structural model through an indirect thermo-mechanical coupling scheme to predict warpage and residual stresses. Case studies on square plates with triangular and hexagonal infills (with/without sidewalls and a bottom base) show that the high-temperature zone follows newly deposited paths with peak temperatures near 220 °C, while displacement and von Mises stress accumulate and are strongly affected by infill topology and boundary conditions. Full article

Accurate thermal conductivity measurement requires temperature control systems to establish stable operating conditions within a limited time. In practical thermal conductivity performance testing systems, large thermal inertia, complex heat transfer paths, and input time delays arising from thermal propagation and sensor placement often degrade dynamic response and control accuracy. To overcome these limitations, a composite PI–RLS–MPC control strategy is proposed for thermal systems with inertia and time delay. A proportional–integral (PI) controller serves as the baseline stabilizing controller, while model predictive control (MPC) is utilized to optimize the control input by explicitly considering system delay and input constraints. To enhance robustness against model uncertainty and parameter variations, recursive least squares (RLS) is adopted for online parameter identification and adaptive PI tuning, and a steady-state parameter freezing mechanism is introduced to suppress unnecessary parameter updates after convergence. Simulation studies are performed on an identified thermal process model with a 20 s input time delay. The results indicate that the proposed strategy reduces overshoot, shortens settling time, and improves disturbance rejection compared with conventional controllers. Overall, the proposed PI–RLS–MPC approach provides a practical solution for improving temperature control performance in thermal conductivity testing systems. Full article

Serbia’s agricultural budget has shifted repeatedly between production-linked market support and direct payments (MSDPs) and investment-oriented rural development support (RD). Using harmonized annual data for 2015–2025 from final amended incentive regulations, food-price indices, and yield statistics, we examine whether the composition of agricultural support is associated with short-run productivity and technical-efficiency dynamics. Descriptive analysis documents partial decoupling through 2017–2023, followed by an abrupt recoupling in 2024–2025 when MSDP absorbed over 92% of the incentive budget. Econometric results from OLS models with Newey–West standard errors indicate that food-price inflation is strongly associated with lower productivity growth, while the dominance of coupled instruments does not predict improved short-run performance. The political-importance indicator is not statistically significant once inflation is controlled for. The findings suggest that the structure and stability of agricultural spending matter at least as much as its volume, underscoring the importance of safeguarding investment-oriented measures as Serbia pursues EU policy alignment. Full article

Timely and accurate identification of learning disability (LD) severity is critical for early screening and for guiding appropriate clinical and educational interventions. This study developed a machine learning model with feature selection and hyperparameter optimization (MLFSHO) architecture to predict the severity of LD using heterogeneous clinical data with clinical expert labeling. Four machine learning models including eXtreme Gradient Boosting (XGB), Categorical Boosting (CAT), Light Gradient Boosting Machine (LGBM), and Multi-Layer Perceptron (MLP) were implemented within the MLFSHO architecture that integrates HSIC-based feature selection and Optuna-based joint optimization of feature-related parameters and model hyperparameters. Experiment results indicated all machine learning-based (ML-based) models can provide average accuracy of more than 85%. In addition, hyperparameter optimization consistently improved most predictive performance. Joint optimization of feature-related parameters and model hyperparameters achieved the best overall performance across models. These findings suggest that treating feature selection and hyperparameter tuning as a unified optimization problem can improve the reliability of severity prediction in learning disabilities and support early screening in clinical settings. The proposed MLFSHO architecture provides a systematic approach for modeling heterogeneous clinical data and improves the performance of LD severity prediction. Full article