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AIM: To study the efficacy of laser combined with ranibizumab in the treatment of proliferative diabetic retinopathy(PDR)and its effects on levels of plasminogen activator inhibitor(PAI), tissue plasminogen activator(t-PA)and vascular endothelial growth factor(VEGF)in aqueous humor. METHODS: Totally 76 cases of patients(76 eyes)with PDR were divided into observation group(n=41)and control group(n=35)according to the use of ranibizumab or not. Control group was given routine laser surgery, and observation group was added with ranibizumab for adjuvant treatment. The laser energy and density used in treatment, and the best corrected visual acuity(BCVA), central macular thickness(CMT)and levels of PAI, t-PA and VEGF in aqueous humor after treatment, postoperative complications and prognosis were compared between the two groups. RESULTS: The sunfleck quantity, laser energy and energy density in observation group were lower than those in control group(PPPPPPPP>0.05). CONCLUSION: Laser combined with ranibizumab for PDR can significantly reduce the expression levels of serum VEGF, t-PA and PAI in aqueous humor, decrease the CMT, and improve the BCVA. And combined therapy is beneficial to reduce the sunfleck quantity, laser energy and energy density during treatment, and lower the laser damage towards the retina.

The thermal management system is one of the important assemblies that ensure the secure operation of electric vehicles (EVs). Using intelligent algorithms to optimize the control strategy of the thermal management system can reduce energy consumption under the premise of effective heat dissipation of EVs. This paper attempts to construct the control strategy of EV thermal management system by coupling the modified genetic algorithm (MGA) and support vector regression (SVR). Firstly, the double-population adaptive mutation method and a novel optimization process are adopted to obtain MGA. Afterward, the performance of MGA is verified by four benchmark functions compared with three typical algorithms, which are genetic algorithm (GA), double-population genetic algorithm (DPGA), and quantum genetic algorithm (QGA). The results demonstrate that the accuracy and stability of MGA are obviously better than the other three algorithms. Secondly, MGA is applied to modify parameters of SVR kernel function, and the accuracy of MGA-SVR algorithm is verified by the Auto-MPG and Computer Hardware data sets. The mean square deviations of the SVR algorithm test set are 0.0186 and 0.0806, respectively, and the mean square deviations of the MGA-SVR algorithm test set are 0.0099 and 0.0054, respectively, which fully shows that MGA-SVR have more accurate forecasting capabilities. Finally, the thermal management system model of EV is built by the one-dimensional simulation software KULI. Under the Chinese working condition, fan speed which meets the cooling requirements of the motor and controller is obtained from the KULI model, and the database is constructed. Then, MGA-SVR is trained by database and employed to predict fan speed under the Chinese working condition and obtain control strategy of the thermal management system. Compared with traditional control strategy, the thermal management system based on MGA-SVR control strategy can not only meet the radiating requirements, but also effectively reduce the power consumption of fans. [ABSTRACT FROM AUTHOR]