Search Results (475)

Multivariate space–time datasets are often collected at discrete, regularly monitored time intervals and are typically treated as components of time series in environmental science and other applied fields. To effectively characterize such data in geostatistical frameworks, valid and practical covariance models are essential. In this work, we propose several classes of multivariate spatio-temporal covariance matrix functions to model underlying stochastic processes whose discrete temporal margins correspond to well-known autoregressive and moving average (ARMA) models. We derive sufficient and/or necessary conditions under which these functions yield valid covariance matrices. By leveraging established methodologies from time series analysis and spatial statistics, the proposed models are straightforward to identify and fit in practice. Finally, we demonstrate the utility of these multivariate covariance functions through an application to Kansas weather data, using co-kriging for prediction and comparing the results to those obtained from traditional spatio-temporal models. Full article

The widespread adoption of electric buses represents a major step forward in sustainable transportation, but also brings new operational challenges, particularly in terms of improving their efficiency and controlling costs. Therefore, battery energy consumption management is a key approach for addressing these issues. Accurate prediction of energy consumption and interpretation of the influencing factors are essential for improving operational efficiency, optimizing energy use, and reducing operating costs. Although existing studies have made progress in battery energy consumption prediction, challenges remain in achieving high-precision modeling and conducting a comprehensive analysis of the influencing features. To address these gaps, this study proposes a two-layer stacking framework for estimating the energy consumption of electric buses. The first layer integrates the strengths of three nonlinear regression models—RF (Random Forest), GBDT (Gradient Boosted Decision Trees), and CatBoost (Categorical Boosting)—to enhance the modeling capacity for complex feature relationships. The second layer employs a Linear Regression model as a meta-learner to aggregate the predictions from the base models and improve the overall predictive performance. The framework is trained on 2023 operational data from two electric bus routes (NO. 355 and NO. W188) in Changsha, China, incorporating battery system parameters, driving characteristics, and environmental variables as independent variables for model training and analysis. Comparative experiments with various ensemble models demonstrate that the proposed stacking framework exhibits superior performance in data fitting. Furthermore, XGBoost (Extreme Gradient Boosting, version 2.1.4) is introduced as a surrogate model to approximate the decision logic of the stacking framework, enabling SHAP (SHapley Additive exPlanations) analysis to quantify the contribution and marginal effects of influencing features. The proposed stacked and surrogate models achieved superior battery energy consumption prediction accuracy (lowest MSE, RMSE, and MAE), significantly outperforming benchmark models on real-world datasets. SHAP analysis quantified the overall contributions of feature categories (battery operation parameters: 56.5%; driving characteristics: 42.3%; environmental data: 1.2%), further revealing the specific contributions and nonlinear influence mechanisms of individual features. These quantitative findings offer specific guidance for optimizing battery system control and driving behavior. Full article

Background/Objectives: Dental practitioners widely use dental implants to treat traumatic cases. Titanium implants are currently the most popular choice among dental practitioners and surgeons. The discovery of newer polymeric materials is also influencing the interest of dental professionals in alternative options. A comparative study between existing titanium implants and newer polymeric materials can enhance professionals’ ability to select the most suitable implant for a patient’s treatment. This study aimed to investigate material property advantages of high-performance thermoplastic biopolymers such as PEEK and PEKK, as compared to the time-tested titanium implants, and to find the most suitable and economically fit implant material. Methods: Three distinct implant material properties were assigned—PEEK, PEKK, and commercially pure titanium (CP Ti-55)—to dental implants measuring 5.5 mm by 9 mm, along with two distinct titanium (TI6AL4V) abutments. Twelve three-dimensional (3D) models of bone blocks, representing the mandibular right molar area with Osseo-integrated implants were created. The implant, abutment, and screw were assumed to be linear; elastic, isotropic, and orthotropic properties were attributed to the cancellous and cortical bone. Twelve model sets underwent a three-dimensional finite element analysis to evaluate von Mises stress and total deformation under 250 N vertical and oblique (30 degree) loads on the top surface of each abutment. Results: The study revealed that the time-tested titanium implant outperforms PEEK and PEKK in terms of marginal bone preservation, while PEEK outperforms PEKK. Conclusions: This study will assist dental practitioners in selecting implants from a variety of available materials and will aid researchers in their future research. Full article

This paper addresses the limitations of existing bivariate generalized exponential (GE) distributions for modeling lifetime data, which often exhibit rigid dependence structures or non-GE marginals. To overcome these limitations, we introduce four new bivariate GE distributions based on the Farlie–Gumbel–Morgenstern, Gumbel–Barnett, Clayton, and Frank copulas, which allow for more flexible modeling of various dependence structures. We employ a Bayesian framework with Markov Chain Monte Carlo (MCMC) methods for parameter estimation. A simulation study is conducted to evaluate the performance of the proposed models, which are then applied to a real-world dataset of electrical treeing failures. The results from the data application demonstrate that the copula-based models, particularly the one derived from the Frank copula, provide a superior fit compared to existing bivariate GE models. This work provides a flexible and robust framework for modeling dependent lifetime data. Full article

(1) Background: This study aimed to evaluate the marginal and internal fit of ceramic crowns produced by various digital methods using microcomputed tomography (MCT) imaging. (2) Methods: The ceramic crown preparation was performed on typodont maxillary first premolar. The crown preparation was scanned with an intraoral scanner and a model scanner, and cone-beam computed tomography (CBCT) scans were performed with three different voxel sizes (0.075 mm, 0.1 mm, and 0.15 mm). The space between the crown and prepared teeth was measured at nine different points in both coronal and sagittal sections. Three different digital model acquisition techniques, namely, intraoral scanning, model scanning, and CBCT-based standard tessellation language (STL) reconstruction, were compared in terms of marginal and internal fit. (3) Results: Quantitative analyses revealed that model scanners exhibited the lowest marginal and internal gap values, indicating superior fit compared to intraoral scanners and CBCT-based models. The highest gap values were observed in the CBCT group with a voxel size of 0.15 mm. Overall, crowns obtained from model scanners demonstrated the highest success rates in both marginal and internal fit. (4) Conclusions: In conclusion, this study highlights the critical role of digital scanning accuracy in achieving clinically acceptable prosthetic fits and emphasizes the need for continued technological advancement. Full article

Broader use of multioperational machines in forestry requires efficient methods for determining various timber parameters. Here, we present a novel approach to model the bark thickness (BT) as a function of stem diameter. Stem diameter (D) is any diameter measured along the bole, not a specific one. The following four regression models were tested: marginal model (MM; reference), classical nonlinear regression with independent residuals (M1), nonlinear regression with residuals correlated within a single tree (M2), and nonlinear regression with the correlation of residuals and random components, taking into account random changes between the trees (M3). Empirical data consisted of larch (Larix sp. Mill.) BT measurements carried out at two sites in northern Poland. Relative root square mean error (RMSE%) and adjusted R-squared (R2adj) served to compare the fitted models. Model fit was tested for each tree separately, and all trees were combined. Of the analysed models, M3 turned out to be the best fit for both the individual tree and all tree levels. The fit of the regression function M3 for SITE1 (50-year-old, pure stand located in northern Poland) was 87.44% (R2adj), and for SITE2 (63-year-old, pure stand situated in the north of Poland) it was 80.6%. Taking into account the values of RMSE%, at the individual tree level the M3 model fit at location SITE1 was closest to the MM, while at SITE2 it was better than the MM. For the most comprehensive regression model, M3, it was checked how the error of the bark thickness estimate varied with stem diameter at different heights (from the base of the trees to the top). In general, the model’s accuracy increased with greater tree height. Full article

To address the operational demands of irregular farmland with fixed obstacles, this study proposes a full-coverage path planning framework that integrates UAV-based 3D perception and angle-adaptive optimization. First, digital orthophoto maps (DOMs) and digital elevation models (DEMs) were reconstructed from low-altitude aerial imagery. The feasible working region was constructed by shrinking field boundaries inward and dilating obstacle boundaries outward. This ensured sufficient safety margins for machinery operation. Next, segmentation angles were scanned from 0° to 180° to minimize the number and irregularity of sub-regions; then a two-level simulation search was performed over 0° to 360° to optimize the working direction for each sub-region. For each sub-region, the optimal working direction was selected based on four criteria: the number of turns, travel distance, coverage redundancy, and planning time. Between sub-regions, a closed-loop interconnection path was generated using eight-directional A* search combined with polyline simplification, arc fitting, Chaikin subdivision, and B-spline smoothing. Simulation results showed that a 78° segmentation yielded four regular sub-regions, achieving 99.97% coverage while reducing the number of turns, travel distance, and planning time by up to 70.42%, 23.17%, and 85.6%. This framework accounts for field heterogeneity and turning radius constraints, effectively mitigating path redundancy in conventional fixed-angle methods. This framework enables general deployment in agricultural field operations and facilitates extensions toward collaborative and energy-optimized task planning. Full article

Background/Objectives: Digital prosthodontics increasingly utilize both additive (3D printing) and subtractive Computer-Aided Design/Computer-Aided Manufacturing (CAD/CAM), yet comprehensive comparisons remain limited. This scoping review evaluates their relative performance across prosthodontic applications. Methods: Systematic searches (PubMed, Scopus, Web of Science, Embase, 2015–2025) identified 28 studies (27 in vitro, 1 retrospective). Data were extracted on accuracy, efficiency, materials, and outcomes. Results: CAD/CAM milling demonstrated superior accuracy for fixed prostheses, with marginal gaps for milled zirconia (123.89 ± 56.89 µm), comparable to optimized 3D-printed interim crowns (123.87 ± 67.42 µm, p = 0.760). For removable prostheses, milled denture bases achieved a trueness of 65 ± 6 µm, while SLA-printed dentures post-processed at 40 °C for 30 min showed the lowest root mean square error (RMSE) (30 min/40 °C group). Three-dimensional printing excelled in material efficiency (<5% waste vs. milling > 30–40%) and complex geometries, such as hollow-pontic fixed dental prostheses (FDPs) (2.0 mm wall thickness reduced gaps by 33%). Build orientation (45° for crowns, 30–45° for veneers) and post-processing protocols significantly influenced accuracy. Milled resins exhibited superior color stability (ΔE00: 1.2 ± 0.3 vs. 3D-printed: 4.5 ± 1.1, p < 0.05), while 3D-printed Co-Cr frameworks (SLM) showed marginal fits of 8.4 ± 3.2 µm, surpassing milling (130.3 ± 13.8 µm). Digital workflows reduced chairside time by 29% (154.31 ± 13.19 min vs. 218.00 ± 20.75 min). All methods met clinical thresholds (<120 µm gaps). Conclusions: Milling remains preferred for high-precision fixed prostheses, while 3D printing offers advantages in material efficiency, complex designs, and removable applications. Critical gaps include long-term clinical data and standardized protocols. Future research should prioritize hybrid workflows, advanced materials, and AI-driven optimization to bridge technical and clinical gaps. Full article

Objectives: This systematic review aimed to evaluate the cost, production time, clinical performance, and patient satisfaction of 3D printing workflows in prosthodontics compared to conventional and subtractive methods. Methods: Following PRISMA guidelines, a systematic search of electronic databases was performed to identify studies published between 2015 and 2025 that directly compared digital additive workflows with analogue or subtractive workflows. Studies were eligible if they included prosthodontic treatments such as dentures, crowns, or implant-supported prostheses and reported at least one relevant outcome. The primary outcomes were cost, time efficiency, clinical accuracy (e.g., marginal adaptation, fit), and patient satisfaction. Included studies were methodologically evaluated using MINORS scale and the risk of bias was assessed using ROBINS-I and RoB 2 tools. Results: Seven studies met the inclusion criteria. Overall, 3D printing workflows demonstrated reduced production time and cost in comparison to conventional or subtractive methods. Clinical outcomes were generally comparable or superior, particularly regarding adaptation and fit. Patient satisfaction was favourable in most studies, although reporting varied. Long-term follow-up was limited, which constrains the interpretation of sustained clinical performance. Conclusions: These findings suggest that 3D printing can serve as an efficient and cost-effective alternative in prosthodontic fabrication, with clinical results comparable to those already established. Further research is needed to assess long-term clinical performance and cost-effectiveness in various clinical scenarios. Full article

This study aimed to propose a probability-guaranteed spectrum method to enhance the reliability of seismic building designs, thereby addressing the inadequacy of the current code-specified response spectrum based on mean fortification levels. This study systematically evaluated the fitting performance of dynamic coefficient spectra under normal, log-normal, and gamma distribution assumptions based on 288 ground motion records from type II sites. MATLAB(2010) parameter fitting and the Kolmogorov–Smirnov test were used, revealing that the gamma distribution optimally characterized spectral characteristics across all period ranges (p < 0.05). This study innovatively established dynamic coefficient spectra curves for various probability guarantee levels (50–80%), quantitatively revealing the insufficient probability assurance of code spectra in the long-period range. Furthermore, this study proposed an evaluation framework for load safety levels of spectral values over the design service period, demonstrating that increasing probability guarantee levels significantly improved safety margins over a 50-year reference period. This method provides probabilistic foundations for the differentiated seismic design of important structures and offers valuable insights for revising current code provisions based on mean spectra. Full article

Background/Objectives: Adverse effects of a sedentary lifestyle on an individual’s overall health are inevitable. With reference to primary-school-aged children, the establishment of effective postural hygiene is critical as it not only promotes optimal musculoskeletal development but also significantly influences their long-term well-being and productivity. This study aimed to develop and internally validate a regularized regression model to predict static postural loading (SPL) in primary school children. Methods: The outcome and predictors of SPL were shortlisted through a systematic review of the literature and expert panels. Data were derived from 258 primary school children. We developed regularized elastic net (EN) and used multiple linear regression (MLR) as a reference. Both models were fitted through five-fold cross-validation with 10 iterations. The grid search technique was used to find the optimal combination of hyperparameters α and λ for the EN. We conducted a permutation importance analysis to obtain and compare predictor rankings for each model. Results: Both models presented a good and comparable fit, with the EN marginally outperforming the MLR in error metrics. Postural risk, sedentary behavior, task duration, and BMI were the most important predictors of SPL in primary school children. Conclusions: The proof of a direct impact of a sedentary lifestyle on children’s overall health is both credible and alarming. Hence, proper identification and management of contributing factors to static postural loading in this age group is critical. In various clinical settings, where the objective is to develop a model that accurately forecasts the outcome, advanced regularized regression methods have evidently shown great performance. Full article

Campylobacteriosis has been described as an ever-changing disease and health issue that is rather dangerous for different population groups all over the globe. The World Health Organization (WHO) reports that 33 million years of healthy living are lost annually, and nearly one in ten persons have foodborne illnesses, including Campylobacteriosis. This explains why there is a need to develop new policies and strategies in the management of diseases at the intergovernmental level. Within this framework, an advanced stochastic fractional delayed model for Campylobacteriosis includes new stochastic, memory, and time delay factors. This model adopts a numerical computational technique called the Grunwald–Letnikov-based Nonstandard Finite Difference (GL-NSFD) scheme, which yields an exponential fitted solution that is non-negative and uniformly bounded, which are essential characteristics when working with compartmental models in epidemic research. Two equilibrium states are identified: the first is an infectious Campylobacteriosis-free state, and the second is a Campylobacteriosis-present state. When stability analysis with the help of the basic reproduction number $R_0$ is performed, the stability of both equilibrium points depends on the $R_0$ value. This is in concordance with the actual epidemiological data and the research conducted by the WHO in recent years, with a focus on the tendency to increase the rate of infections and the necessity to intervene in time. The model goes further to analyze how a delay in response affects the band of Campylobacteriosis spread, and also agrees that a delay in response is a significant factor. The first simulations of the current state of the system suggest that certain conditions can be achieved, and the eradication of the disease is possible if specific precautions are taken. The outcomes also indicate that enhancing the levels of compliance with the WHO-endorsed SOPs by a significant margin can lower infection rates significantly, which can serve as a roadmap to respond to this public health threat. Unlike most analytical papers, this research contributes actual findings and provides useful recommendations for disease management approaches and policies. Full article

The work presented here describes an approach that separates the viscous stress from the quasi-static counterpart for polycarbonate (PC) and its short glass fiber composite (GF-PC), with the aim to characterize the influence of short glass fiber on the viscous behavior of PC as the matrix of GF-PC. A multi-relaxation (MR) test was used for the mechanical testing and a three-branch spring–dashpot model for the data analysis, using a genetic algorithm to establish 100 sets of fitting parameter values that enabled the three-branch model to regenerate the measured stress decay during relaxation. Using the spring modulus $\left(K_{v,s}\right)$ of the short-term branch in the three-branch model, two groups for these fitting parameter values were established as a function of specimen displacement (named stroke) of GF-PC, one of which shows a trend that is similar to the trend of the corresponding fitting parameters for the pure PC, and thus is believed to reflect the influence of glass fiber on the PC matrix of GF-PC. The study concludes that the short glass fiber increases the short-term viscous stress, but its role on the long-term viscous stress is marginal. Full article

Offshore wind power is one of the primary forms of utilizing marine green energy in China. Currently, near-shore wind power predominantly employs monopile foundations, with designs typically being overly conservative, resulting in high construction costs. Precise characterization of the interaction mechanisms between marine piles and surrounding soils is crucial for foundation design optimization. Traditional p-y curve methods, with simplified fitting functions, inadequately capture the complex pile–soil behaviors, limiting predictive accuracy and model uncertainty quantification. To address these challenges, this research collected 1852 empirical datasets of offshore wind monopile foundation pile–soil interactions, developing p-y curve and horizontal displacement prediction models using artificial neural network (ANN) expressions and comprehensive uncertainty statistical analysis. The constructed ANN model demonstrates a simple structure with satisfactory predictive performance, achieving average error margins below 6% and low to moderate prediction accuracy dispersion (26%~45%). In contrast, traditional p-y curve models show 30%~50% average biases with substantial accuracy dispersion near 80%, while conventional finite element methods exhibit approximately 40% error and dispersion. By strictly characterizing the probability cumulative function of the neural network model factors, a foundation is provided for reliability-based design. Through comprehensive case verification, it is demonstrated that the ANN-based model has significant advantages in terms of computational accuracy and efficiency in the design of offshore wind power foundations. Full article

The present study analyzed the growth pattern of the stingray Urotrygon chilensis caught as bycatch by the shrimp fishery in the southeastern Mexican Pacific. From January to December 2012, the thoracic vertebrae of 491 females and 205 males were collected. Female ages ranged from 0 to 14 years, whereas male ages ranged from 0 to 12 years. The marginal increment and edge analyses suggested the annual formation of growth bands in the vertebrae. The size-at-age data were analyzed using the multimodel inference approach; six candidate growth models were compared, including models with a theoretical age-at-zero total length, mean size-at-birth, and generalized models. Based on Akaike’s information criterion, the best statistical fit to the size-at-age data was the two-phase Gompertz growth model (k = −0.13, G = 1.59, $L_0$ = 10.40) for males and the two-parameter Gompertz growth model (k = 1.42, α = 0.15, $L_0$ = 10.90) for females. In this study, we compare the growth parameters among batoid species, finding that U. chilensis has a relatively short lifespan, slower growth, and that females are larger than males. Full article