Search Results (14,988)

Introduction: Clear aligner therapy has become a mainstream alternative to fixed orthodontics due to its versatility. However, the variability in thermoforming and the limited validation of digital workflows remain major barriers to reproducibility and predictability. Methods: This study addresses that gap by presenting a proof-of-concept digital workflow for clear aligner manufacturing by integrating additive manufacturing (AM), thermoforming simulation, and finite element analysis (FEA). Dental models were 3D-printed and thermoformed under clinically relevant pressures (400 kPa positive and −90 kPa negative). Results and Discussion: Geometric accuracy was quantified using CloudCompare v2.13.0, showing that positive-pressure thermoforming reduced maximum deviations from 1.06 mm to 0.4 mm, with all deviations exceeding the expanded measurement uncertainty. Thickness simulations of PETG sheets (0.5 and 0.75 mm) showed good agreement with experimental values across seven validation points, with errors <10% and overlapping 95% confidence intervals. Stress analysis indicated that force transmission was localized at the aligner–attachment interface, consistent with expected orthodontic mechanics. Conclusion: By quantifying accuracy and mechanical behavior through numerical and experimental validation, this framework demonstrates how controlled thermoforming and simulation-guided design can enhance aligner consistency, reduce adjustments, and improve treatment predictability. Full article

To address the uncertainty in selecting the optimal spherical base curvature radius (SR) for tapered roller bearings, this study develops dynamic and friction torque models under combined loading conditions to evaluate three SR configurations—0.85ρ_p, 0.90ρ_p, and 0.95ρ_p, where ρ_p represents the curvature radius of the inner rib—in terms of load capacity, friction losses, and operational precision. The results indicate that (1) the 0.85ρ_p configuration minimizes friction by optimizing the contact zone’s f_V parameter under combined loads, making it ideal for low-friction applications; (2) the 0.95ρ_p design achieves superior operational accuracy; (3) the intermediate value of 0.90ρ_p offers an optimal compromise, balancing friction torque reduction with operational precision. These findings establish quantitative guidelines for SR selection based on specific bearing performance requirements. Full article

Urban industrial complexes have been expanding worldwide, reducing the spatial separation between agricultural, residential, and industrial zones, particularly in developing nations. Urban road dust contamination, a sensitive indicator of urban environmental quality, primarily originates in urbanization and industrialization. Its detrimental impacts on human health arise not only from particulate matter itself but also from toxic and harmful substances embedded within dust particles. Toxic metals in road dust can pose health risks through inhalation, ingestion and contact. To investigate the seasonal patterns, bioaccessibility levels and the potential human health risks linked to toxic metals (Cadmium (Cd), Nickel (Ni), Arsenic (As), Lead (Pb), Zinc (Zn), Copper (Cu), and Chromium (Cr)), 34 dust samples were collected from key roads in proximity to representative industrial facilities in Wuhan’s Qingshan District. The study found that the concentrations of Cd, Pb, and Cu in road dust were within the limits set by the national standard (GB 15618-2018), while Ni and As were not. Seasonally, Ni, As, Pb, Zn, and Cr exhibited higher concentrations during the summer than in other seasons, whereas Cd levels were lowest in spring and highest in autumn, the opposite of Cu. According to the Simplified Bioaccessibility Extraction Test (SBET), the average bioaccessibility rates of toxic metals were Cd > Zn > Cu > Ni > Cr > As > Pb. An improved health risk assessment model was developed, integrating metal enrichment, bioaccessibility, and parameter uncertainty. Results indicated that Cd, Ni, Zn, Cu, As, and Cr posed no significant non-carcinogenic risk. However, for children, the carcinogenic risks of Cd and As were relatively high, identifying them as priority control metals. Therefore, it is recommended to periodically monitor As and Cd and regulate their potential emission sources, especially in winter and spring. Full article

In this study, the modeling of rough surfaces by eddy-viscosity-based roughness models is investigated, specifically focusing on surfaces representative of deterioration in aero-engines. In order to test these models, experimental measurements from a rough T106C blade section at a Reynolds number of 400 K are adopted. The modeling framework is based on the k–ω–SST with Dassler’s roughness transition model. The roughness model is recalibrated for the k–ω–SST model. As a complement to the available experimental data, a high-fidelity test rig designed for scale-resolving simulations is built. This allows us to examine the local flow phenomenon in detail, enabling the identification and rectification of shortcomings in the current RANS models. The scale-resolving simulations feature a high-order flux-reconstruction scheme, which enables the use of curved element faces to match the roughness geometry. The wake-loss predictions, as well as blade pressure profiles, show good agreement, especially between LES and the model-based RANS. The slight deviation from the experimental measurements can be attributed to the inherent uncertainties in the experiment, such as the end-wall effects. The outcomes of this study lend credibility to the roughness models proposed. In fact, these models have the potential to quantify the influence of roughness on the aerodynamics and the aero-acoustics of aero-engines, an area that remains an open question in the maintenance, repair, and overhaul (MRO) of aero-engines. Full article

Species distribution models were built for three short-palped crane fly species of the genus Dicranomyia: Dicranomyia affinis, Dicranomyia chorea, and Dicranomyia mitis. The main objective of this study was to assess potential habitat suitability in undersampled regions and explore differences in environmental space. Dicranomyia affinis was historically considered a variety of Dicranomyia mitis due to their morphological similarity. In contrast, Dicranomyia chorea is a widespread species. The biology and ecology of these species remain poorly understood. Models were developed using Maxent, a widely used tool. Our results indicate that Dicranomyia affinis and Dicranomyia chorea share highly similar predicted habitat suitability, with high suitability across the Mediterranean, Central, and Northern Europe, moderate suitability in Eastern Europe, and low suitability in Central Asia. In contrast, Dicranomyia mitis is predicted to have greater habitat suitability in Eastern Europe and Scandinavia, with lower suitability in Mediterranean regions. Analysis of variable importance revealed possible ecological differences between the species. While climatic factors primarily influenced the models for Dicranomyia affinis and Dicranomyia chorea, Dicranomyia mitis was more strongly influenced by the variable pH. These findings may provide insights into potential distributions in undersampled areas and improve our understanding of the species’ ecology. Full article

Lake Qinghai, the largest closed-basin lake on the Qinghai–Tibet Plateau, plays a crucial role in maintaining regional ecological stability through its hydrological functions. In recent decades, the lake has exhibited a continuous rise in water level and lake area expansion, sparking growing interest in the mechanisms driving these changes and their future evolution. This study integrates the Soil and Water Assessment Tool (SWAT), simulations under future Shared Socioeconomic Pathways (SSPs) and statistical analysis methods, to assess runoff dynamics and lake level responses in the Lake Qinghai Basin over the next 30 years. The model was developed using a combination of meteorological, hydrological, topographic, land use, soil, and socio-economic datasets, and was calibrated with the sequential uncertainty fitting Ver-2 (SUFI-2) algorithm within the SWAT calibration and uncertainty procedure (SWAT–CUP) platform. Sensitivity and uncertainty analyses confirmed robust model performance, with monthly R² values of 0.78 and 0.79. Correlation analysis revealed that runoff variability is more closely associated with precipitation than temperature in the basin. Under SSP 1-2.6, SSP 3-7.0, and SSP 5-8.5 scenarios, projected annual precipitation increases by 14.4%, 18.9%, and 11.1%, respectively, accompanied by temperature rises varying with emissions scenario. Model simulations indicate a significant increase in runoff in the Buha River Basin, peaking around 2047. These findings provide scientific insight into the hydrological response of plateau lakes to future climate change and offer a valuable reference for regional water resource management and ecological conservation strategies. Full article

This research introduces a hybrid framework that integrates stochastic modeling and machine learning for predicting postprandial glucose levels in individuals with Type 1 Diabetes (T1D). The primary aim is to enhance the accuracy of glucose predictions by merging a biophysical Glucose–Insulin–Meal (GIM) model with advanced machine learning techniques. This framework is tailored to utilize the Kaggle BRIST1D dataset, which comprises real-world data from continuous glucose monitoring (CGM), insulin administration, and meal intake records. The methodology employs the GIM model as a physiological prior to generate simulated glucose and insulin trajectories, which are then utilized as input features for the machine learning (ML) component. For this component, the study leverages the Light Gradient Boosting Machine (LightGBM) due to its efficiency and strong performance with tabular data, while Long Short-Term Memory (LSTM) networks are applied to capture temporal dependencies. Additionally, Bayesian regression is integrated to assess prediction uncertainty. A key advancement of this research is the transition from a deterministic GIM formulation to a stochastic differential equation (SDE) framework, which allows the model to represent the probabilistic range of physiological responses and improves uncertainty management when working with real-world data. The findings reveal that this hybrid methodology enhances both the precision and applicability of glucose predictions by integrating the physiological insights of Glucose Interaction Models (GIM) with the flexibility of data-driven machine learning techniques to accommodate real-world variability. This innovative framework facilitates the creation of robust, transparent, and personalized decision-support systems aimed at improving diabetes management. Full article

Background/Objectives: Patient education materials (PEMs) in ophthalmology often exceed recommended readability levels, limiting accessibility for many patients. While organizations like the AAO provide relatively easy-to-read resources, topics remain limited, and other associations’ PEMs are too complex. AI chatbots could help clinicians create more comprehensive, accessible PEMs to improve patient understanding. This study aims to compare the readability of patient education materials (PEMs) written by the American Academy of Ophthalmology (AAO) with those generated by large language models (LLMs), including ChatGPT-4o, Microsoft Copilot, and Meta-Llama-3.1-70B-Instruct. Methods: LLMs were prompted to generate PEMs for 15 common diagnoses relating to cornea and anterior chamber, which was followed by a follow-up readability-optimized (FRO) prompt to reword the content at a 6th-grade reading level. The readability of these materials was evaluated using nine different readability analysis python libraries and compared to existing PEMs found on the AAO website. Results: For all 15 topics, ChatGPT, Copilot, and Llama successfully generated PEMs, though all exceeded the recommended 6th-grade reading level. While initially prompted ChatGPT, Copilot, and Llama outputs were 10.8, 12.2, and 13.2, respectively, FRO prompting significantly improved readability to 8.3 for ChatGPT, 11.2 for Copilot, and 9.3 for Llama (p < 0.001). While readability improved, AI-generated PEMs were on average, not statistically easier to read than AAO PEMs, which averaged an 8.0 Flesch–Kincaid Grade Level. Conclusions: Properly prompted AI chatbots can generate PEMs with improved readability, nearing the level of AAO materials. However, most outputs remain above the recommended 6th-grade reading level. A subjective analysis of a representative subtopic showed that compared to AAO, there was less nuance, especially in areas of clinical uncertainty. By creating a blueprint that can be utilized in human–AI hybrid workflows, AI chatbots show promise as tools for ophthalmologists to increase the availability of accessible PEMs in ophthalmology. Future work should include a detailed qualitative review by ophthalmologists using a validated tool (like DISCERN or PEMAT) to score accuracy, bias, and completeness alongside readability. Full article

The growing complexity of electric vehicle charging station (EVCS) operations—driven by grid constraints, renewable integration, user variability, and dynamic pricing—has positioned reinforcement learning (RL) as a promising approach for intelligent, scalable, and adaptive control. After outlining the core theoretical foundations, including RL algorithms, agent architectures, and EVCS classifications, this review presents a structured survey of influential research, highlighting how RL has been applied across various charging contexts and control scenarios. This paper categorizes RL methodologies from value-based to actor–critic and hybrid frameworks, and explores their integration with optimization techniques, forecasting models, and multi-agent coordination strategies. By examining key design aspects—including agent structures, training schemes, coordination mechanisms, reward formulation, data usage, and evaluation protocols—this review identifies broader trends across central control dimensions such as scalability, uncertainty management, interpretability, and adaptability. In addition, the review assesses common baselines, performance metrics, and validation settings used in the literature, linking algorithmic developments with real-world deployment needs. By bridging theoretical principles with practical insights, this work provides comprehensive directions for future RL applications in EVCS control, while identifying methodological gaps and opportunities for safer, more efficient, and sustainable operation. Full article

With the widespread adoption of space–air–ground integrated networks (SAGINs) in next-generation wireless communications, intelligent reflecting surfaces (IRSs) have emerged as a key technology for enhancing system performance through passive link reinforcement. This paper addresses the prevalent issue of channel state information (CSI) uncertainty in practical systems by constructing an IRS-assisted multi-hop SAGIN communication model. To capture the performance degradation caused by channel estimation errors, a norm-bounded uncertainty model is introduced. A simulated annealing (SA)-based phase optimization algorithm is proposed to enhance system robustness and improve worst-case communication quality. Simulation results demonstrate that the proposed method significantly outperforms traditional multiple access strategies (SDMA and NOMA) under various user densities and perturbation levels, highlighting its stability and scalability in complex environments. Full article

Tactical production–distribution planning in paper manufacturing involves hierarchical decision-making under hybrid uncertainty, where aleatory randomness (demand fluctuations, machine variations) and epistemic uncertainty (expert judgments, market trends) simultaneously affect operations. Existing approaches fail to address the bi-level nature under hybrid uncertainty, treating production and distribution decisions independently or using single-paradigm uncertainty models. This research develops a bi-level dependent-chance goal programming framework based on uncertain random theory, where the upper level optimizes distribution decisions while the lower level handles production decisions. The framework exploits structural symmetries through machine interchangeability, symmetric transportation routes, and temporal symmetry, incorporating symmetry-breaking constraints to eliminate redundant solutions. A hybrid intelligent algorithm (HIA) integrates uncertain random simulation with a Reinforcement-Learning-enhanced Arithmetic Optimization Algorithm (RL-AOA) for bi-level coordination, where Q-learning enables adaptive parameter tuning. The RL component utilizes symmetric state representations to maintain solution quality across symmetric transformations. Computational experiments demonstrate HIA’s superiority over standard metaheuristics, achieving 3.2–7.8% solution quality improvement and 18.5% computational time reduction. Symmetry exploitation reduces search space by approximately 35%. The framework provides probability-based performance metrics with optimal confidence levels (0.82–0.87), offering 2.8–4.5% annual cost savings potential. Full article

Finite element (FE) models are widely used in structural health monitoring to represent real structures and assess their condition, but discrepancies often arise between numerical and actual structural behavior due to simplifying assumptions, uncertain parameters, and environmental influences. Temperature variation, in particular, significantly affects structural stiffness and modal properties, yet it is often treated as noise in traditional model updating methods. This study treats temperature changes as valuable information for model updating and structural damage quantification. The Bayesian model updating approach (BMUA) is a probabilistic approach that updates uncertain model parameters by combining prior knowledge with measured data to estimate their posterior probability distributions. However, traditional BMUA methods assume mass is known and only update stiffness. A novel BMUA framework is proposed that incorporates thermal buckling and temperature-dependent stiffness estimation and introduces an algorithm to eliminate the coupling effect between mass and stiffness by using temperature-induced stiffness changes. This enables the simultaneous updating of both parameters. The framework is validated through numerical simulations on a three-story aluminum shear frame under uniform and non-uniform temperature distributions. Under healthy and uniform temperature conditions, stiffness parameters were estimated with high accuracy, with errors below 0.5% and within uncertainty bounds, while mass parameters exhibited errors up to 13.8% that exceeded their extremely low standard deviations, indicating potential model bias. Under non-uniform temperature distributions, accuracy declined, particularly for localized damage cases, with significant deviations in both parameters. Full article

With the high penetration of renewable energy integrated into the power grid, the system exhibits strong randomness and volatility. To balance these uncertainties, a large amount of flexible regulating resources is required. This paper proposes an optimization method based on a Seq2Seq Transformer model, which takes stochastic renewable energy and load data as inputs and outputs the allocation ratios of various regulating resources. The method considers renewable energy stochasticity, power flow constraints, and adjustment characteristics of different regulating resources, while constructing a multi-objective loss function that integrates ramping response matching and cost minimization for comprehensive optimization. Furthermore, a multi-feature perception attention mechanism for stochastic renewable energy is introduced, enabling better coordination among resources and improved ramping speed adaptation during both model training and result generation. A multi-solution optimization framework with Pareto-optimal filtering is designed, where the Decoder outputs multiple sets of diverse and balanced allocation ratio combinations. Simulation studies based on a regional power grid demonstrate that the proposed method effectively addresses the problem of regulating resource capacity optimization in new-type power systems. Full article

This study introduces a novel Fuzzy Piecewise Fractional Derivative (FPFD) framework to enhance epidemiological modeling, specifically for the multi-phasic co-infection dynamics of Omicron and malaria. We address the limitations of traditional models by incorporating two key realities. First, we use fuzzy set theory to manage the inherent uncertainty in biological parameters. Second, we employ piecewise fractional operators to capture the dynamic, phase-dependent nature of epidemics. The framework utilizes a fuzzy classical derivative for initial memoryless spread and transitions to a fuzzy Atangana–Baleanu–Caputo (ABC) fractional derivative to capture post-intervention memory effects. We establish the mathematical rigor of the FPFD model through proofs of positivity, boundedness, and stability of equilibrium points, including the basic reproductive number (R0). A hybrid numerical scheme, combining Fuzzy Runge–Kutta and Fuzzy Fractional Adams–Bashforth–Moulton algorithms, is developed for solving the system. Simulations show that the framework successfully models dynamic shifts while propagating uncertainty. This provides forecasts that are more robust and practical, directly informing public health interventions. Full article

The assessment for the resistance of a new ship under design can be performed through the Experimental Fluid Dynamics (EFD) or the Computational Fluid Dynamics (CFD) approach; both have their own Uncertainty Assessment (UA). In CFD field, the Verification and Validation (V&V) procedures take into account the approximations for numerical issues and the assumptions adopted to describe the physical phenomena to assess UA. Different theoretical approaches have become available over time; nevertheless, a single comprehensive solution to achieve the UA remains still unknown because as the theoretical methodology varies, the outcome changes. In current work, four different literature approaches will be augmented to perform a V&V analysis for two kinds of model hulls, tested at different speeds and compared with the experimental data. The investigations performed among results lead to the division of all the approaches into the three and four solutions families and to define a robust procedure to identify a reasonable value for the numerical uncertainty assessment. Regarding the robustness and the UA of the approaches, the first family proved successful in only 55% of cases with a UA mean value below 2.01%, while the second one always provides a quantification but with a mean value of 6.65%. Full article