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Finite strip method (FSM) is a classical method to analyze the buckling of thin-walled members. The traditional FSM adopting trigonometric functions longitudinally can hardly analyze the members with spaced ribs along the longitudinal direction, while the compound strip method (CSM) can compensate for this shortcoming. Based on the CSM, the influence of utilizing plane elements and shell elements to respectively imitate stiffeners on buckling is investigated. Compared with the shell-element ribs, the plane-element ribs are prone to assembling the stiffener matrices with fewer degrees of freedom. But the shell-element ribs are more comprehensive as the out-plane displacement of ribs are taken into consideration. It is found that plane element ribs and shell element ribs have little difference on the buckling capacity of members. The buckling capacity has a small difference of mean absolute error (MAE) underneath 0.75% between the two types of CSMs, and the buckling capacity and modes are in good agreement with the finite element results. The buckling loads of the two types of CSMs are close to the FEM with a MEA less than 5%. The accuracy of the plane elements satisfies the predicted requirements, which helps to reduce the program computation and simplify the analysis complexity. The efficiency of analysis can be dramatically improved for fine meshing elements.

Mastering the high strain characteristics at cryogenics and constructing an accurate constitutive model are critical for establishing a cryogenic cutting simulation of Ti-6Al-4V alloy. The traditional constitutive model is inadequate for accounting for high and cryogenic conditions. In this paper, cryogenic split Hopkinson pressure bar (SHPB) experiments are conducted, specifically for Ti-6Al-4V alloy, to develop a modified Johnson–Cook (J–C) constitutive model that incorporates the cryogenic effect. A coupled two-dimensional orthogonal finite element simulation involving temperature displacement was conducted for the cryogenic cutting of Ti-6Al-4V alloy. This simulation was performed using ABAQUS, based on a modified J–C constitutive model subroutine. It was observed that the smallest discrepancy between the simulation results and the data from ice-fixed milling experiments was just 2.12%. The modified J–C constitutive model is more adept at simulating the chip morphology under cryogenic conditions. It was also found that cryogenic machining enhances the distribution of temperature and stress within the chip. For every 5°C reduction in processing temperature, the tool temperature diminishes by more than 2%. [ABSTRACT FROM AUTHOR]

Background: To identify risk factors for acquiring HAIs in COVID-19 patients and establish visual prediction model. Methods: Data was extracted from Xinglin Hospital Infection Monitoring System to analyze COVID-19 patients diagnosed between December 1, 2022, and March 1, 2023. Univariate and multivariate analyses were conducted to identify risk factors. Predictive signature was developed by selected variables from lasso, logistic regression, and their intersection and union. Models were compared using DeLong's t-tests. Likelihood ratio (LR) and Youden's index was used to evaluate the predictive performance. Nomogram was constructed using optimal variables ensemble, prediction accuracy was evaluated using AUC, DCA and calibration curve. Results: Total of 739 patients met the criteria, of which 53 (7.2%) were HAIs. NSAIDs, surgery, fungi and MDRO detected, hormone drugs and LYMR were independent risk factors. Lasso model screened seven variables, and logistic model identified six risk factors. Union model performed the best with the maximum of the Youden's index is 0.703, the sensitivity is 95.6%, the specificity is 74.7%, the LR is 3.778. The best AUC of union model is 0.953 (0.928– 0.978), and the accuracy is 87.5%. DCA indicated that the union model provided the best net benefits and calibration curve demonstrated good predictive agreement. Conclusions: HAIs prediction in COVID-19 patients is feasible and beneficial to improve prognosis. Physicians can use this nomogram to identify high-risk COVID-19 populations for HAIs and tailor follow-up strategies. [ABSTRACT FROM AUTHOR]