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In this article,elastic-plastic curved crack tip opening displacement in hardened material under quasi-static loads has been mainly studied,and curved crack tip opening displacement has been calculated as a practical application of a second order perturbation method and theorem of surname KA,where the effects of quasi-static applied stresses and quasi-static normal and shear stresses on the boundaries of plasticity area are synthetically taken into considerations. Diagrams have been constructed to analyse the transformation relationships between curved crack tip opening displacement size and hardening exponents. Curved crack tip opening displacement will decrease when hardening exponents increase in hardened material. The decrease extent of curved crack tip opening displacement will be more and more severe when hardening exponents increase evenly. Curved crack tip opening displacement will decrease when external loads decrease with the same hardening exponents. A brand new domain of theoretical model where CTOD was calculated has been reclaimed in hardened material.

Background: The allostatic load (AL) refers to the cumulative weakening of multiple physiological systems caused by repeated adaptation of the body to stressors There are still no studies have focused on the association between AL and the prognosis of patients with heart failure with preserved ejection fraction (HFpEF). The present study aimed to investigate the association between AL and adverse outcomes, including mortality and HF admission, among elderly male patients with HFpEF. Methods: We conducted a prospective cohort study of 1111 elderly male patients with HFpEF, diagnosed between 2015 and 2019 and followed up through 2021. We constructed an AL measure using a combination of 12 biomarkers. The diagnosis of HFpEF was made according to the 2021 European Society of Cardiology guidelines. A Cox proportional hazards model was used to determine the associations between AL and adverse outcomes. Results: In multivariate analysis, AL was significantly associated with increased risk of all-cause mortality (medium AL: adjusted hazard ratio [HR] = 2.53; 95% confidence interval [CI] 1.37–4.68; high AL: HR = 4.21; 95% CI 2.27–7.83; per-score increase: HR = 1.31; 95% CI 1.18–1.46), cardiovascular mortality (medium AL: HR = 2.67; 95% CI 1.07–6.68; high AL: HR = 3.13; 95% CI 1.23–7.97; per-score increase: HR = 1.20; 95% CI 1.03–1.40), non-cardiovascular mortality (medium AL: HR = 2.45; 95% CI 1.06–5.63; high AL: HR = 5.81; 95% CI 2.55–10.28; per-score increase: HR = 1.46; 95% CI 1.26–1.69), and HF admission (medium AL: HR = 2.68; 95% CI 1.43–5.01; high AL: HR = 3.24; 95% CI 1.69–6.23; per-score increase: HR = 1.24; 95% CI 1.11–1.39). Consistent results were found in multiple subgroup analyses. Conclusions: A higher AL was associated with poor prognosis in elderly men with HFpEF. AL relies on information that is easily obtained in physical examinations and laboratory parameters and can be assessed in various care and clinical settings to help risk stratification of HFpEF patients. [ABSTRACT FROM AUTHOR]

Reliability analysis methods such as active learning Kriging surrogate model combined with simulation-based methods have been paid much attention in recent years. These techniques can reduce the computational cost to a certain extent. However, the computational burden may still be heavy for complex engineering problems. To address these issues, a Kriging-based important region sampling method is proposed for efficient reliability analysis. The new method is an improvement on the original active learning reliability method combining Kriging and Monte Carlo simulation (AK-MCS), and three strategies are developed to enhance the original method: 1) a new strategy, which is called the key point method, is utilized to define the initial design of experiment (DoE) instead of the Latin hypercube sampling; 2) the concept of dynamic important region/uncertain region and importance factor is proposed to avoid adding useless sample points to the DoE, which have little effect on the accuracy improvement of the Kriging model; 3) the redundant region is introduced to make the distance between the new and existed sample points be larger than a certain value and avoid the information redundancy caused by too close sample points. Five examples are utilized to demonstrate the efficiency and accuracy of this new method. [ABSTRACT FROM AUTHOR]