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Laminated bamboo is a novel green building material, understanding its mechanical properties at service temperatures is essential for structural safety and optimal design. However, currently there is no constitutive model capable of effectively predicting the compressive stress–strain relationship of laminated bamboo under the influence of service temperatures. This paper examines the influence of service temperature on the compressive stress–strain relationship in laminated bamboo. The compressive properties of laminated bamboo severely decreased as the temperature rises. Based on the fundamental form of the Weibull cumulative distribution, a constitutive model is proposed. Additionally, four constitutive models previously successfully applied to laminated bamboo or other bamboo composites were modified and analyzed for their capability to predict the compressive stress–strain relationship of bamboo-based materials under different temperature. Among all the models, the proposed Weibull model and the modified RA mode have higher accuracy, and both can simulate strain hardening and thermal softening characteristics of laminated bamboo. The findings of this study not only guide the application of bamboo-based engineered materials in actual engineering structures, enhancing the precision and safety of structural designs but also provide valuable references for the research and application of other bamboo composite materials. [ABSTRACT FROM AUTHOR]

This study addresses the critical challenges faced in cancer care, particularly in predicting survival times for patients with lung cancer and acute myeloid leukemia. Despite recent advancements in medical science, existing models often fall short in accurately capturing disease progression, leading to less effective clinical decision-making, and compromised patient outcomes. The need for advanced predictive models is urgent to improve survival time forecasts and enhance treatment strategies. In response to this, we introduce a novel probabilistic approach, the New Weibull (NEWE) model, which is part of a newly generated class designed to model cancer patient data more effectively. Our methodology includes using seven well-known estimation methods, each rigorously evaluated for consistency through Monte Carlo simulation studies focused on key metrics such as absolute bias, mean square error, and mean relative error. The datasets analyzed include survival times for twenty acute myeloid leukemia patients, 121 breast cancer patients from 1929 to 1938, 33 patients with acute myelogenous leukemia, data from eighteen individuals who died from causes unrelated to cancer, and survival times of advanced lung cancer patients undergoing standard chemotherapy. The NEWE model outperformed competing models, particularly in Anderson-Darling, Cramer-von Mises, and Kolmogorov-Smirnov tests, with significantly higher p-values. These findings highlight the NEWE model’s potential to transform predictive oncology by offering more precise survival time predictions, improving the quality of care and decision-making in cancer treatment.

• A Weibull-based model incorporating the effect of stress ratio was developed, enhancing existing models. • The model was validated, showing superior accuracy and reliability compared to traditional probabilistic fatigue models. • A sensitivity analysis identified key factors influencing fatigue life for each model parameter. • The model utilizes non-destructive testing data to assess the remaining service life of corroded steel wires. Fatigue behavior of high-strength steel wires in bridge stay cables is critical to bridge reliability, as these wires experience varying stress ratios, stress ranges, and corrosion levels that significantly impact their fatigue life. Existing models typically predict fatigue life under different stress ranges and corrosion conditions but often neglect the critical influence of stress ratio, leading to potential inaccuracies. This paper introduces an innovative R-C-S-N Weibull model that integrates stress ratio, stress range, and corrosion effects to improve fatigue life predictions. Model parameters are determined through maximum likelihood estimation (MLE) and validated using both experimental data and literature sources. The model achieves a Mean Absolute Error (MAE) of 0.10, indicating minimal deviation from observed values, while a Skewness of 0.13 reflects a balanced yet slightly conservative distribution. Additionally, the 0.4–0.6 Probability Interval Coverage of 0.6 shows that 60% of predictions fall within this central range, underscoring model stability. Results indicate that corrosion has a more pronounced impact on fatigue life at lower stress ranges, with stress ratio playing a secondary but significant role. In the model, parameter D quantifies the effect of corrosion, and the shape parameter β governs variability. Sensitivity analysis highlights stress range and corrosion as the most influential factors in determining fatigue life. The model's reliable predictive capabilities make it valuable for assessing the remaining life of corroded steel wires in bridge structures, particularly when combined with non-destructive testing methods, providing a robust and partially conservative tool for structural health monitoring. [ABSTRACT FROM AUTHOR]

Nowadays, wood stands as one of the foremost used construction materials, owing largely to its exceptional physical and mechanical properties. Ensuring the safety of timber structures necessitates thorough investigations into the influential phenomena that significantly affect their strength and longevity. The aim of this paper is to study the coupled creep-recovery behavior of tropical wood from the Entandrophragma cylindricum species by evaluating the influence of stress levels on the performance of rheological models. Hence, the Burger and Weibull classic models were introduced to elucidate these phenomena. These models have been compared with the fractional Maxwell and Zener models. Following the simulations, the Burger classic model effectively characterized creep and recovery, comprising elastic, viscoelastic, and viscous elements arranged in series, as well as the classic Weibull model. During the recovery phase, the four-parameter Weibull model demonstrated a satisfying description, achieving 99% accuracy compared to 97% for the four-parameter Burger classic model. Three-parameter fractional Maxwell model fit all phases of the process for all deformations with an average accuracy of 98% for creep and 95% for recovery. These results provide valuable information on the material's ability to recover from deformation and offer essential insights for materials characterization, engineering design, and quality assurance processes in materials engineering. • Investigation of coupled creep-recovery behavior of tropical wood from the Entandrophragma cylindricum species. • Burger and Weibull classic models considered to elucidate the related creep phenomena end compared to fractional Zener and Burger models. • The simulation proves the ability of the classic Burger model to effectively characterize creep and recovery. • The five-parameters Weibull model demonstrates a satisfying description, achieving 98% accuracy compared to 97% for the Burger classic model. • Fractional models fit all phases of the recovery process for positive deformations with an average accuracy of 99.5%. [ABSTRACT FROM AUTHOR]

Hepatitis E virus (HEV) is a zoonotic virus that infects humans when virus-containing pork products are consumed. This study aimed to explore MNV (murine norovirus) and HEV inactivation during cold smoking and ripening/fermentation treatments used for salami-like sausages (mettwurst). MNV inactivation was monitored in culture medium solution and in sausage while being subjected to a salami-like sausage manufacturing process. The inactivation of MNV in the solution was also monitored at room temperature (RT) for four weeks. HEV inactivation was monitored in solution during the cold smoking process and at RT. A TCID 50 assay was used to calculate the infectious MNV and HEV titres. MNV survival was modelled using Bayesian inference. MNV load in solution decreased by as much as 4.7 (SD 0.9) log 10 TCID 50 /ml when it was subjected to the cold smoking process. Modelling revealed that 99.999% MNV might be inactivated during the treatment when observed at a level of 95% CI (Bayesian Confidence Interval). On the contrary, MNV load decreased by only 1.8 (0.2) log 10 when stored at RT. The low-titre HEV in solution was inactivated (> 1.1 (0.2) log 10 ) when treated, and at RT. However, MNV was resistant in the sausage matrix during the cold smoking process (log 10 reduction of 1.9 (0.5) TCID 50 /ml). Based on modelling, a substantial amount of virus would remain in the product, even when the uncertainty was considered. Hence, viruses, here exemplified by MNV, may not be inactivated from salami-like pork sausages during manufacturing, which poses a risk for consumers in real-life situations., Competing Interests: Declarations. Conflict of interest: The authors declare no competing interests., (© 2025. The Author(s).)