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Due to the short-term large-scale access of renewable energy and residential electric vehicles in residential communities, the voltage limit in the distribution network will be exceeded, and the quality of power supply will be seriously reduced. Therefore, this paper introduces the mobile energy storage system (MESS), which effectively solves the problem of overvoltage limit caused by the large number of distributed power sources and household electric vehicles in the distribution network. This paper proposes an optimal scheduling model for distribution network based on mobile energy storage system. First, the space-time energy transfer model of mobile energy storage is established, and the transfer cost of MESS and the income of peak shaving and valley filling are considered. According to daily driving data of household electric vehicle (EV), a prosumer group electric vehicle charging and discharging model is established; After that, by establishing a target model for maximizing MESS operating income and penalizing voltage overshoot under different scenarios such as low peak load of electric vehicles and different initial capacities of MESS, a multi-scenario multi-objective collaborative optimization model for the distribution network is established; Finally, an improved IEEE33-bus system is used to analyze the calculation example. The results of the calculation example show that the optimal scheduling model in this paper can improve the photovoltaic consumption capacity and improve the voltage limit problem, which verifies the effectiveness and economy of the scheduling model. INDEX TERMS electric vehicle (EV),mobile energy storage system (MESS), distribution network, collaborative optimization

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Food quality detection is an important method for ensuring food safety. Efficient quality detection methods can improve the efficiency of food circulation and reduce storage and labor costs. Traditional methods use instrumentation, testing reagents, or manual labor. These methods take a long time to detect, are time-consuming and labor-intensive, and require professionals to operate. Fruit, as a high-value food that provides essential nutrition for human beings, is susceptible to spoilage during packaging, transportation, and sales, so the freshness and safety assurance of fruit are a hot and difficult area of current research. Therefore, for the detection of fruit freshness, this paper proposes an efficient and nondestructive way to detect fruit freshness by using the machine learning algorithm convolutional neural network (CNN). This paper shows that convolutional neural networks have good performance in identifying the freshness of fruits through extensive experimental results and discusses the overfitting of machine learning based on the experimental results.

Scope: This research work is designed and conducted to explore the joint effect of capsaicin and quercetin and the potential mechanism for the regulation of hyperglycemia. Methods and results: Insulin-resistance HepG2 cell model and high-fat diet combined with streptozotocin-induced type 2 diabetes mouse model are applied for the investigation. The results illustrate that capsaicin and quercetin can exert hypoglycemic effects via reducing the concentrations of serum lipids of total cholesterol, triglyceride and low-density lipoprotein cholesterol, decreasing hepatic enzyme activities of phosphoenolpyruvate, carboxykinase, and glucose-6-phosphatase, as well as improving the histopathological morphology of liver and pancreas tissues. After administration with capsaicin and quercetin, the relative protein expression abundance of Ras, Raf-1, MEK1/2, and ERK1/2 is obviously attenuated compared to diabetic mice. Capsaicin and quercetin at a ratio of 3:1 are generally determined as the optimal combination. Conclusion: Capsaicin and quercetin present in the chili pepper fruit can act in a synergistic way to alleviate hyperglycemia. This study provides supporting data for the discovery of novel anti-diabetic components and for the valorization of chili pepper industry.