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The application of support vector machine to the aircraft power supply system breakdown diagnosis is one of the research focuses in the tomorrow. Support vector machine (SVM) belongs a new type of machine learning technique, which uses structural hazard minimization rule to substitute for the conventional empirical hazard minimization according to great specimen. The powerful performance of the least squares support vector machine (LSSVM) needs to be reflected in the optimal selection of appropriate parameters, and the quality of parameters greatly affects the accuracy and efficiency of breakdown diagnosis. The traditional LSSVM parameters selection method is inefficient, the calculation is huge, and it takes expensive time to select the satisfactory solution. Aiming at the problem of parameters selection of LSSVM, the way of optimizing the parameters of LSSVM by apply simulated annealing genetic algorithm (SAGA) is proposed. SAGA uses the parallel sampling process of genetic algorithm to improve the time performance of optimization, and makes use of the simulated annealing algorithm to dominate the constriction of majorization to prevent precocity. On the one hand, genetic algorithm has strong ability to grasp the overall search process, on the other hand, simulated annealing algorithm is used to control the convergence of the algorithm to prevent premature phenomenon. The automatic optimal selection of LSSVM parameters is achieved. The Iris system classification and recognition in the UCI machine learning database and the breakdown diagnosis of the autopilot flight control box are used as experimental platforms to obtain data samples for simulation research. The simulation results show that LSSVM optimized by SAGA improves both the accuracy and efficiency of classification recognition. It not only effectively overcomes the problem of low efficiency caused by searching optimal parameters by experience, but also effectively improves the accuracy of classification recognition. The false alarm rate and redundant breakdowns are effectively reduced. [ABSTRACT FROM AUTHOR]

Flexible aqueous zinc batteries (FAZBs) with high safety and environmental friendliness are promising smart power sources for smart wearable electronics. However, the bare zinc anode usually suffers from damnable dendrite growth and rampant side reaction on the surface, greatly impeding practical applications in FAZBs. Herein, a composite polymer interface layer is artificially self‐assembled on the surface of the zinc anode by graft‐modified fluorinated monomer (polyacrylic acid‐2‐(Trifluoromethyl)propenoic acid, PAA‐TFPA), on which an organic–inorganic hybrid (PAA‐Zn/ZnF2) solid electrolyte interface (SEI) with excellent ionic conductivity is formed by interacting with Zn2+. Both the pouch cell and fiber zinc anode exhibit excellent plating/stripping reversibility after protecting by this organic–inorganic SEI, which can be stably cycled more than 3000 h in symmetric Zn||Zn cells or 550 h in fiber Zn||Zn cells. Additionally, this interface layer preserves zinc anode with excellent mechanical durability under various mechanical deformation (stably working for another 1200 h after bending 100 h). The corresponding PAA‐Zn/ZnF2@Zn||MnO2 full cell displays an ultra‐long life span (79% capacity retention after 3000 cycles) and mechanical robustness (85% of the initial capacity for another 3000 cycles after bending 100 times). More importantly, the as‐assembled cells can easily power smart wearable devices to monitor the user's health condition. [ABSTRACT FROM AUTHOR]

This paper studies the causal effects of air pollution on individual-level health care expenditure in China. We employ a nationally-representative panel dataset of the China Health and Nutrition Survey from 1991 to 2015 to estimate the impacts of air pollution on various types of health care expenditures. Using thermal inversions as an instrumental variable for air pollution, we fnd that exposure to air pollution in the past year (12 months) leads to an increase in total health care expenditure and health care utilization. In particular, with a 1 µg/m3 increase in average PM2.5 concentrations, total health care spending would rise by 5.1% and the probability of health care utilization would rise by 0.98% point. The increases are mainly driven by changes in hospital care and preventive care costs. Our mechanism evidence suggests that air pollution might affect health care expenditure by increasing weight and the probability of getting sick while reducing sleep time and the chances of walking to work. [ABSTRACT FROM AUTHOR]

This study utilizes data from China's 31 provinces, collected from 2007 to 2021, to establish a green finance (GF) index and investigate its impact on industrial structure upgrading (IS). In addition to the direct impact, this study investigates the indirect influence of GF on industrial structure upgrading through technological innovation (Tech) and foreign direct investment (FDI). Furthermore, this study analyzes the moderating role of environmental regulation intensity (ERI) and government intervention on the relationship between GF and industrial structure upgrading. The empirical findings demonstrate a positive relationship between GF and industrial structure upgrading, which remains robust after conducting a robustness analysis and stability tests. Moreover, GF positively impacts industrial structure upgrading by stimulating Tech and attracting FDI. Furthermore, the ERI is observed to positively and significantly moderate the impact of GF on industrial structure upgrading, while high levels of government intervention hinder the promoting effect of GF on industrial structure upgrading. Lastly, the association between GF development and industrial structure upgrading exhibits regional and market heterogeneity, with the most notable impact observed in coastal areas and regions with higher levels of market orientation. This study presents comprehensive suggestions for facilitating the improvement of GF and the upgrading of industrial structures. [ABSTRACT FROM AUTHOR]