Addressing the problem of two-dimensional steady-state thermal boundary recognition, a hybrid algorithm of conjugate gradient method and social particle swarm optimization (CGM-SPSO) algorithm is proposed. The global search ability of particle swarm optimization algorithm and local search ability of gradient algorithm are effectively combined, which overcomes the shortcoming that the conjugate gradient method tends to converge to the local solution and relies heavily on the initial approximation of the iterative process. The hybrid algorithm also avoids the problem that the particle swarm optimization algorithm requires a large number of iterative steps and a lot of time. The experimental results show that the proposed algorithm is feasible and effective in solving the problem of two-dimensional steady-state thermal boundary shape.
Based on the boundary element method and the decentralized fuzzy inference algorithm, the thermal conductivity in the two-dimensional unsteady-state heat transfer system changing with the temperature is deduced. The more accurate inversion results are obtained by introducing the variable universe method. The concrete method is as follows: using experimental means to obtain the instantaneous temperature in the material or on the boundary, to determine the thermal conductivity of the material by solving the inversion problem. The boundary element method is used to calculate the regional boundary and internal temperature in the direct problem. With the inversion problem, the decentralized fuzzy inference algorithm is used to compensate for the initial guess of the thermal conductivity by using the difference between the temperature measurement and the temperature calculation. In the inversion problem, the influence of the initial guess of different thermal conductivities, different numbers of measuring points, and the existence of measurement errors on the results is discussed. The example calculation and analysis prove that, with different initial guesses, existence of measurement errors, and the number of boundary measurements decrease, the methods adopted in this paper still maintain good validity and accuracy.
The compound variable inverse problem which comprises boundary temperature distribution and surface convective heat conduction coefficient of two-dimensional steady heat transfer system with inner heat source is studied in this paper applying the conjugate gradient method. The introduction of complex variable to solve the gradient matrix of the objective function obtains more precise inversion results. This paper applies boundary element method to solve the temperature calculation of discrete points in forward problems. The factors of measuring error and the number of measuring points zero error which impact the measurement result are discussed and compared with L-MM method in inverse problems. Instance calculation and analysis prove that the method applied in this paper still has good effectiveness and accuracy even if measurement error exists and the boundary measurement points' number is reduced. The comparison indicates that the influence of error on the inversion solution can be minimized effectively using this method.
The introduction and scale-up of antiretroviral therapy (ART) have contributed to significantly improved patients with acquired immune deficiency syndrome (AIDS) quality of life and prolongs their survival. This has occurred by suppressing viral replication and recovering the CD4 cell count. However, some patients do not normalize their CD4 cell count, despite suppression of the viral load (VL). Patients with suboptimal immune recovery (SIR), as defined by a VL < 400 copies/ml with a CD4 cell count of<200 cells/μl, after ART initiation, exhibit severe immune dysfunction and have a higher risk of AIDS and non-AIDS events. In recent years, People living with HIV/AIDS (PLWHA) with first-line ART failure began to gradually switch to secondline ART. This study aimed to examine the prevalence and factors affecting SIR among PLWHA who switch to second-line ART in rural China. A 1-year retrospective cohort study was conducted among PLWHA who switched to second-line ART between January 2009 and December 2018. All patients with a VL < 400 copies/ml after 1 year of second-line ART were included. SIR was defined as a CD4 cell count <200 cells/μl and a VL < 400 copies/ml after 1 year of second-line ART. The data collected from medical records were analyzed by univariate and multivariate analyses. A total of 5294 PLWHA met the inclusion criteria, 24 died, and 1152 were lost to follow-up after 1 year of second-line ART. Among 4118 PLWHA who were followed up, 3039 with a VL < 400 copies/ml had their data analyzed, and the prevalence of SIR was 13.1%. The patients' mean age at recruitment was 47.6 ± 8.1 years and 45.3% were men. A total of 30.7% of patients were HIV-positive for >8 years and 88.2% were receiving ART before starting second-line ART for >3 years. The mean CD4 cell count was 354.8 ± 238.2 cells/μl. A multivariable analysis showed that male sex, single status (unmarried or divorced), and a low CD4 cell count were risk factors for SIR among PLWHA with second-line ART. The prevalence of SIR among PLWHA who switched to second-line ART in this retrospective cohort study is lower than that in most other
An inverse algorithm on boundary element method and conjugate gradient method is proposed to solve the problem of thermal conduction inverse of geometric shape. The direct problem is solved with the boundary element method, while the solution to the inverse problem is obtained through optimizing the objective function in the conjugate gradient method. Taking into account the identification of different material specimens when the unknown boundary is sinusoidal, step function, or circular shape, the influence of initial value, temperature error, thermal conductivity, and thermal intensity on the precision of inversion solution is discussed. The experimental results show that the method can recognize various irregular boundaries and is insensitive to initial values, measurement errors, and heat intensity. The thermal conductivity has a certain effect on this method. The inversion accuracy is higher on the condition that the thermal conductivity is smaller.
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