Search Results (263)

Microbial safety in low-moisture foods (LMFs) has attracted widespread public attention due to the multiple outbreaks and recalls in recent years. Dry cleaning methods are typically used in LMFs production environments. However, there is no standardized and consistent method for controlling normal force and measuring the shear force of cleaning tool applied on food contact surfaces during dry cleaning. A dry-cleaning platform with the normal force controllable feature was custom-designed, and its performance was evaluated as the primary objective of the study. Effects of various factors (bacterial type, surface material, surface roughness, and normal force) on the shear force and removal of Salmonella enterica Enteritidis PT 30 (S. PT 30) and Enterococcus faecium NRRL B2354 (E. faecium) during dry wiping were investigated using the developed platform. The performance evaluation indicated that the platform was adequately stable during standardized and consistent dry cleaning. Surface roughness, normal force, and surface material significantly affected shear force (p < 0.05) applied on food contact surfaces. The bacterial type significantly affected the shear force on stainless steel (p < 0.05). No significant difference (p > 0.05) was observed in removing S. PT 30 from inoculated surfaces after dry wiping under all investigated conditions. Surface material significantly affected the removal of E. faecium (p < 0.05). The removal of E. faecium was numerically higher than that of Salmonella under the same conditions. Thus, E. faecium may not be a suitable surrogate for S. PT 30 removal at the end of dry cleaning under the wiping conditions tested. The potential applications of the platform were also discussed for future studies on how to enhance dry cleaning efficiency. Shear force can guide the disruption of cohesion and adhesion in surface microorganisms/residues, thereby enhancing cleaning efficiency. The custom-designed dry-cleaning platform with the controllable normal force feature has potential applications in further laboratory dry cleaning studies. Full article

This review paper analyzes the recent applications of computational mechanics methods in combination with machine learning (ML) and neural network (NN) techniques, as found in the literature published between 2015 and 2024. We present how ML and NNs are enhancing traditional computational methods, such as the finite element method, enabling the solution of complex problems in material modeling, surrogate modeling, inverse analysis, and uncertainty quantification. We categorize current research by considering the specific computational mechanics tasks and the employed ML/NN architectures. Furthermore, we discuss the current challenges, development opportunities, and future directions of this dynamically evolving interdisciplinary field, highlighting the potential of data-driven approaches to transform the modeling and simulation of mechanical systems. The review has been updated to include pivotal publications from 2025, reflecting the rapid evolution of the field in multiscale modeling, data-driven mechanics, and physics-informed/operator learning. Accordingly, the timespan is now 2015–2025, with a focused inclusion of high-impact contributions from 2024 to 2025. Full article

Background: Due to the wide host range of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), vaccination has been recommended for susceptible species in zoological collections, particularly to protect endangered species. The Zoetis^® Experimental Mink Coronavirus Vaccine (Subunit) was temporarily authorized in 2021–2024 for emergency use in North America for this purpose. However, there are limited data regarding its safety or efficacy in non-domestic mammals. The present study was conducted to assess the ability of this vaccine to elicit serum neutralizing titers against SARS-CoV-2 in selected animals from the Toronto Zoo (TZ) vaccinated during 2022. Methods: Serum samples were collected from 24 individuals across four families (Cervidae, Felidae, Ursidae, and Hyaenidae) and tested using a surrogate virus neutralization test (sVNT) and a plaque-reduction neutralization test (PRNT). Results: The results showed that all species developed some neutralizing titers after at least one vaccine dose, except for polar bears, which showed no seroconversion. Felids and hyenas had the highest neutralizing titers, which peaked at 3 and declined between 4 and 6 months after boost. These differences may stem from species-specific immune responses or lack of vaccination protocols tailored to individual species. Conclusions: While natural infection with SARS-CoV-2 could not be ruled out in the cohort of this study, insights from our results have the potential to inform future vaccine recommendations for non-domestic species. Furthermore, our study highlighted the value of competitive assays in assessing serological responses across a broad range of exotic species, for which reagents, such as anti-isotype antibodies, are often unavailable. Full article

This study focuses on the mathematical method developed by integrating the surrogate model as constraints for wall stability into the heuristic optimization algorithm to gain the optimum cost and ${\mathrm{CO}}_2$ emission value of the gabion retaining wall (GRW). This study also includes the comparison of optimum GRW results with optimum cantilever retaining wall (CRW) designs for different design cases. The Harmony Search Algorithm (HSA), which efficiently explores the design space and robustly reaches the optimum result in solving optimization problems, was used as the heuristic optimization algorithm. The primary construction scenario was considered as an optimization problem, which involved excavating the slope, constructing the wall, and compacting the backfill soil to minimize the cost and CO${}_2$ emissions for separate objective functions of GRW and CRW designs. Comparative results show that GRWs outperform CRWs in terms of sustainability and cost-efficiency, achieving 55% lower cost and 78% lower CO${}_2$ emissions on average, while the HSA–surrogate model provides a fast and accurate solution for geotechnical design problems. The surrogate models for sliding, overturning, and slope stability safety factors of GRW exhibited exceptional accuracy, characterized by minimal error values (MSE, RMSE, MAE, MAPE) and robust determination coefficients ($R^2\approx 0.99$), hence affirming their dependability in safety factor assessment. By integrating the surrogate model based on the statistical method into the optimization algorithm, a quick examination of the wall’s stability was performed, reducing the required computational power. Full article

To address the challenges of weight redundancy, low material utilization, and excessive performance margins in the design of electric cable-hauling machines, this study proposes a novel multi-objective optimization framework. The framework integrates Latin hypercube experimental design, Kriging surrogate modeling, a Non-dominated Sorting Genetic Algorithm III (NSGA-III), and a coupled TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach. A high-fidelity finite element model based on extreme operating conditions was established to simulate the performance of the electric towing winch. The Kriging model was employed to replace time-consuming finite element calculations, significantly improving computational efficiency. The NSGA-III algorithm was then utilized to search for the Pareto front, identifying a set of optimal solutions that balance multiple design objectives. Finally, the TOPSIS method was applied to select the most preferable solution from the Pareto front. The results demonstrate a 7.32% reduction in the overall mass of the towing winch, a 7.34% increase in the safety factor, and a 4.57% reduction in maximum structural deformation under extreme operating conditions. These findings validate the effectiveness of the proposed Kriging-NSGA-III-TOPSIS strategy for lightweight design of ship deck winch machinery. Full article

This study evaluated the effectiveness of photodynamic treatment (PDT) using riboflavin (Rbf) and blue light-emitting diode (BL) irradiation for microbial inactivation and quality preservation in fresh betel leaves (Piper betle L.). Non-pathogenic surrogates Escherichia coli K-12 and Listeria innocua were used to model Gram-negative and Gram-positive bacteria. The combined Rbf-BL treatment significantly reduced microbial populations by up to 5.3 log CFU/g for E. coli and 6.2 log CFU/g for L. innocua on leaf surfaces (p < 0.05) and 1.3–1.5 log CFU/mL in broth cultures. Treated samples showed significantly higher total soluble solids (12.0 ± 0.0 °Brix), total phenolic content (0.17 ± 0.02 mmol GAE/g, p < 0.05), and antioxidant activity (62.0 ± 3.1% DPPH inhibition, p < 0.05), with minimal color alteration after treatment (ΔE = 4.68). The total fluence measured at the leaf surface was approximately 11.72 J/cm². As a mild thermal treatment utilizing a GRAS photosensitizer, riboflavin-assisted PDT presents a promising strategy for enhancing microbial safety and promoting phytochemical quality in betel leaves. Full article

Traffic safety is undergoing a profound transformation, driven by advances in data science, sensing technologies, and computational modeling. Proactive approaches are enabling the early identification of potential hazards, real-time decision-making, and the development of smarter, safer transportation systems. This Special Issue summarizes recent progress in traffic safety assessment, highlighting the application of emerging tools such as machine learning, explainable artificial intelligence, and computer vision. These innovations are used to predict crash risks, evaluate surrogate safety measures, and automate the analysis of behavioral data, contributing to more inclusive and adaptive safety frameworks, particularly for vulnerable road users such as pedestrians and cyclists. The research also addresses key challenges, including data integration across diverse sources, aligning safety metrics with human perception, and ensuring the scalability of models in complex environments. By advancing both technical methodologies and human-centered evaluation, these developments signal a shift toward more intelligent, transparent, and equitable approaches to traffic safety assessment and policy-making. Full article

The molten salt reactor (MSR) is a prominent Generation IV nuclear reactor concept that offers substantial advantages over conventional solid-fueled systems, including enhanced fuel utilization, inherent passive safety features, and significant reductions in long-lived radioactive waste. Central to its safety strategy is a reliance on natural circulation (NC) mechanisms, which eliminate the need for active pumping systems and enhance system reliability during normal and off-normal conditions. However, the challenges associated with molten salts, such as their high melting points, corrosivity, and material compatibility issues, render experimental investigations inherently complex and demanding. Therefore, the use of high-Pr-number surrogate fluids represents a practical alternative for studying molten salt behavior under safer and more accessible experimental conditions. In this study, a single-phase natural circulation loop setup at the University of Idaho’s Thermal–Hydraulics Laboratory was employed to investigate NC behavior under various operating conditions. The RELAP5-3D code was initially validated against water-based experiments before employing Therminol-66, a high-Prandtl-number surrogate for molten salts, in the natural circulation loop for the first time. The RELAP5-3D results demonstrated good agreement with both steady-state and transient experimental results, thereby confirming the code’s ability to model NC behavior in a single-phase flow regime. The results also highlighted certain experimental limitations that should be addressed to enhance the NC loop’s performance. These include increasing the insulation thickness to reduce heat losses, incorporating a dedicated mass flow measurement device for improved accuracy, and replacing the current heater with a higher-capacity unit to enable testing at elevated power levels. By identifying and addressing the main causes of these limitations and uncertainties during water-based experiments, targeted improvements can be implemented in both the RELAP5 model and the experimental setup, thereby ensuring that tests using a surrogate fluid for MSR analyses are conducted with higher accuracy and minimal uncertainty. Full article

Accurate wind field modeling over hilly terrain is critical for wind energy, infrastructure safety, and environmental assessment, yet its inherent complexity poses significant simulation challenges. This paper systematically reviews this field’s major advances by analyzing 610 key publications from 2015 to 2024, selected from core databases (e.g., Web of Science and Scopus) through targeted keyword searches (e.g., ‘wind flow’, ‘complex terrain’, ‘CFD’, ‘hilly’) and subsequent rigorous relevance screening. We critique four primary modeling paradigms—field measurements, wind tunnel experiments, Computational Fluid Dynamics (CFD), and data-driven methods—across three key application areas, filling a gap left by previous single-focus reviews. The analysis confirms CFD’s dominance (75% of studies), with a clear shift from idealized 2D to real 3D terrain. Key findings indicate that high-fidelity coupled models (e.g., LES), validated against benchmark field experiments such as Perdigão, can reduce mean wind speed prediction bias to below 0.1 m/s; and optimized engineering designs for mountainous infrastructure can mitigate local wind speed amplification effects by 15–20%. Data-driven surrogate models, represented by FuXi-CFD, show revolutionary potential, reducing the inference time for high-resolution wind fields from hours to seconds, though they currently lack standardized validation. Finally, this review summarizes persistent challenges and outlines future directions, advocating for physics-informed neural networks, high-fidelity multi-scale models, and the establishment of open-access benchmark datasets. Full article

Highway congestion is a persistent issue, and dynamically activating emergency lanes offers a promising mitigation strategy. However, traditional fixed-time or single-threshold methods often fail to balance traffic efficiency and safety. This paper introduces a dynamic optimization framework that integrates a Kriging surrogate model with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to identify optimal activation strategies. By simultaneously minimizing total travel time (efficiency) and the duration vehicles spend in unsafe proximity (safety), our method generates a set of Pareto-optimal solutions. We calibrated and validated the model using real-world highway data. The results are compelling: the optimized compromise strategy reduced total travel time by 20.5% compared to having no activation, while keeping safety risks within an acceptable range. The use of a Kriging surrogate model sped up the optimization process by approximately 20 times compared to direct simulation, achieving a prediction accuracy of 97.8%. The optimal strategies characteristically involve opening the emergency lane at the downstream bottleneck during peak congestion and closing it promptly as traffic eases. This research provides a robust, efficient, and practical decision-support tool for intelligent traffic management, offering a clear pathway to safer and less congested highways. Full article

This study investigates the application potential of large language models (LLMs), particularly GPT-4o, in generating geometric parameter suggestions during the early stages of structural design. Design recommendations from the LLM are validated using a finite-element solver (FFE Plus solver), forming the core workflow of the proposed approach. To assess its effectiveness, the LLM’s performance is compared against traditional regression-based surrogate models, which serve as baseline references. A two-hole hanger bracket serves as the case study, evaluating prediction accuracy, data efficiency, generalization capability, and workflow complexity across three materials: 6061-T6, AISI 304, and AISI 1020. The key evaluation indicators include safety factor (SF) and Mass. The results show that the regression models offer high accuracy and interpretability but require extensive amounts of simulation data; in this study, each material required 252 samples to adequately cover the design space. In contrast, GPT-4o produced feasible design suggestions using only 18 initial samples, combining semantic prompting and finite-element analysis. Its prediction accuracy improved significantly with a small number of iterations, demonstrating superior data efficiency and cross-material adaptability. Overall, the findings suggest that, when paired with appropriate prompting strategies and validation mechanisms, LLMs hold great promise as an assistive tool in early-stage structural design optimization. Full article

To address the health monitoring and safety assessment challenges of telescopic cranes, this study proposes a comprehensive, online structural performance assessment method based on digital twins, applicable throughout the entire service life cycle of telescopic cranes. The modeling of the telescopic boom and turntable, key components of the target telescopic crane, was carried out using ANSYS Workbench. Working condition sample points were generated through a hierarchical Latin hypercube sampling method, and finite element analysis was conducted to construct a simulation stress database. The fatigue life of the target telescopic crane was analyzed using ANSYS nCode DesignLife to estimate its expected fatigue life. The BayeFsian optimization algorithm was employed to optimize the hyperparameters of BO-LightGBM, which serves as the surrogate model for stress calculations. A digital twin system for the structural performance assessment of telescopic cranes was developed, with a structural performance assessment module at its core. The research findings provide valuable insights for crane structural performance assessments based on digital twin technology. Full article

This study evaluates the safety impacts of upgrading traditional STOP signs to light-emitting diode (LED)-illuminated backlit STOP signs at urban intersections, aiming to address visibility and conspicuity concerns that affect driver behavior and intersection safety. STOP signs are critical for regulating traffic flow and minimizing conflicts, yet their effectiveness can diminish under low-visibility conditions. To assess the effectiveness of LED-enhanced signage, a before–after study was conducted using surrogate safety measures. Key performance indicators included vehicle speeds, driver compliance rates, and vehicle-to-vehicle interactions, recorded both prior to and following LED implementation. A multinomial logistic regression model was used to analyze driver behaviors, and a calibrated microscopic simulation model, optimized using a genetic algorithm (GA), was applied to estimate traffic conflict frequencies. Video data were processed to extract driver trajectories and reactions under varying signage conditions. Results showed LED STOP signs improved compliance rates from 60% to 85%, reduced average vehicle speeds by 25%, and increased post-encroachment times. Conflict analysis revealed significant reductions in vehicle-to-vehicle and pedestrian conflicts, particularly at night. These findings highlight the effectiveness of LED signage in enhancing intersection safety and offer important implications for urban traffic management and the adoption of advanced traffic control technologies. Full article

Ultra-high-voltage (UHV) transmission lines are prone to galloping and oscillations under ice and wind loads, posing risks to system reliability and safety. Accurate aerodynamic coefficients are essential for evaluating these effects, but conventional wind tunnel and CFD methods are costly and inefficient for practical applications. To address these challenges, this study develops a surrogate model for rapid and accurate prediction of aerodynamic coefficients for six-bundle conductors. Initially, a CFD model to calculate the aerodynamic coefficients of six-bundle conductors was proposed and validated against wind tunnel experimental results. Subsequently, Latin hypercube sampling (LHS) was employed to generate datasets covering wind speed, icing shape, icing thickness, and wind attack angle. High-throughput numerical simulations established a comprehensive aerodynamic database used to train and validate multiple tree-based surrogate models, including decision tree (DT), random forest (RF), extremely randomized trees (ERTs), gradient boosted decision tree (GBDT), and extreme gradient boosting (XGBoost). Comparative analysis revealed that the XGBoost-based model achieved the highest prediction accuracy, with an R² of 0.855 and superior generalization performance. Feature importance analysis further highlighted wind speed and icing shape as the dominant influencing factors. The results confirmed the XGBoost surrogate as the most effective among the tested models, providing a fast and reliable tool for aerodynamic prediction, vibration risk assessment, and structural optimization in UHV transmission systems. Full article

The assessment of ship dynamic stability in waves is crucial for navigation safety. To mitigate accidents, the International Maritime Organization (IMO) has formulated corresponding technical standards. However, evaluating the dynamic stability performance of ships involves complex numerical simulation or model experiments based on hydrodynamic methods, which demands professionalism, substantial time, and significant financial cost. This paper analyzes the feasibility of using the Generalized Regression Neural Network (GRNN) method to build a surrogate model for ship dynamic stability performance calculation. Comparisons with hydrodynamics-based simulations reveal that the surrogate model matches the trends well, yet the root-mean-square error (RMSE) remains non-negligible. Therefore, an improved GRNN surrogate model is proposed to solve this problem. By incorporating enhanced feature preprocessing and clustering techniques, the improved model not only increases predictive accuracy but also achieves significant efficiency gains, reducing the computational time from days or weeks for numerical simulations to seconds or minutes. Experimental results show that the improved surrogate model outperforms the baseline GRNN model, and this framework can serve as a practical surrogate for hydrodynamics-based numerical models to rapidly assess pre-voyage dynamic stability. Full article