Autophagy is a conserved recycling process with important functions in plant growth, development, and stress responses. Phytohormones also play key roles in the regulation of some of the same processes. Increasing evidence indicates that a close relationship exists between autophagy and phytohormone signaling pathways, and the mechanisms of interaction between these pathways have begun to be revealed. Here, we review recent advances in our understanding of how autophagy regulates hormone signaling and, conversely, how hormones regulate the activity of autophagy, both in plant growth and development and in environmental stress responses. We highlight in particular recent mechanistic insights into the coordination between autophagy and signaling events controlled by the stress hormone abscisic acid and by the growth hormones brassinosteroid and cytokinin and briefly discuss potential connections between autophagy and other phytohormones.

Marine ranching plays an integral role in providing fishery resources. Given the critical importance of protecting the marine ecological system, the ecological security evaluation of marine ranching is significant. However, the existing research rarely involves marine ranching ecological security (MRES) evaluation and does not provide a complete evaluation index system. The purpose of the present study was to estimate MRES through several steps and evaluate it scientifically by fully considering the relationship between the factors. First, the evaluation index system was structured using the Driver-Pressure-State-Impact-Response (DPSIR) framework by following certain principles. Second, the study applied the Decision Making Trial and Evaluation Laboratory (DEMATEL) method and fuzzy comprehensive evaluation (FCE) method to assess MRES. Third, a case study of a marine ranching in the Shandong Province was discussed to demonstrate the applicability of the proposed method. This also illustrated the steps of the evaluation. In this case study, the result of the MRES evaluation was graded as excellent. The results demonstrate that using these methods to evaluate MRES can account for the complex relationships between the factors and the cognitive ability of the experts and thus integrate the experts’ information comprehensively, which adds to the credibility of the evaluation results.

Reciprocating compressors play a vital role in oil, natural gas, and general industrial processes. Their safe and stable operation directly affects the healthy development of the enterprise economy. Since the valve failure accounts for 60% of the total failures when the reciprocating compressor fails, it is of great significance to quickly find and diagnose the failure type of the valve for the fault diagnosis of the reciprocating compressor. At present, reciprocating compressor valve fault diagnosis based on deep neural networks requires sufficient labeled data for training, but valve in real-case reciprocating compressor (VRRC) does not have enough labeled data to train a reliable model. Fortunately, the data of valve in laboratory reciprocating compressor (VLRC) contains relevant fault diagnosis knowledge. Therefore, inspired by the idea of transfer learning, a fault diagnosis method for reciprocating compressor valves based on transfer learning convolutional neural network (TCNN) is proposed. This method uses convolutional neural network (CNN) to extract the transferable features of gas temperature and pressure data from VLRC and VRRC and establish pseudolabels for VRRC unlabeled data. Three regularization terms, the maximum mean discrepancy (MMD) of the transferable features of VLRC and VRRC data, the error between the VLRC sample label prediction and the actual label, and the error between the VRRC sample label prediction and the pseudolabel, are proposed. Their weighted sum is used as an objective function to train the model, thereby reducing the distribution difference of domain feature transfer and increasing the distance between learning feature classes. Experimental results show that this method uses VLRC data to identify the health status of VRRC, and the fault recognition rate can reach 98.32%. Compared with existing methods, this method has higher diagnostic accuracy, which proves the effectiveness of this method.

Cognitive radio (CR) has been proposed to mitigate the spectrum scarcity issue to support heavy wireless services on sub-3GHz. Recently, broadband spectrum sensing becomes a hot topic with the help of compressive sensing technology, which will reduce the high-speed sampling rate requirement of analog-to-digital converter. This paper considers sequential compressive spectrum sensing, where the temporal correlation information between neighboring compressive sensing data will be exploited. Different from conventional compressive sensing, the previous compressive sensing data will be fused into prior knowledge in current spectrum estimation. The simulation results show that the proposed scheme can achieve 98.7% detection probability under 3.5% false alarm probability and performs the best compared with the typical BPDN and OMP schemes.

Vehicle to Grid (V2G) refers to the optimal management of the charging and discharging behavior of electric vehicles through reasonable strategies and advanced communication. In the process of interaction, there are three stakeholders: the power grid, operators (charging stations), and EV users. In real life, the impact of peak-valley difference caused a lot of power loss when charging. At the same time, the loss of current is also a loss for power grid companies and EV users. In this paper, we propose a multiobjective optimization method to reduce the current loss and determine the relationship between the parameters and the objective function and constraints. This optimization method uses a genetic algorithm for multiobjective optimization. Through the analysis of the number of vehicles and load curve of AC class I and AC class II electric vehicles before and after optimization in each period, we found that the charging load of electric vehicles played a role of valley filling in the low valley price stage and played a peak-cutting role in a peak price period.

With the popularity of neural networks and the maturity of network technology, fully functional intelligent terminals have become indispensable devices for people’s lives, research, and entertainment. However, in the badminton teaching of people’s daily exercise, the old traditional teaching mode is still used, which cannot achieve good teaching effects. In order to study the best of badminton teaching, this article is based on the previous research, by introducing neural network, using literature data method, questionnaire survey method, interview method, experimental method, and other research methods to conduct research. The intelligent learning of the network is connected, experiments are designed to be applied, and then, data analysis is conducted. The research results show that with the use of smartphone mobile learning teaching methods, the experimental group students’ technical movements, theoretical knowledge, learning interest, and learning enthusiasm are about 20% higher than those of the control group, and the badminton intelligent teaching system based on neural network is better than the control group’s traditional teaching methods. The satisfaction of the students in the experimental group was also higher than that of the students in the control group. Based on what network, the satisfaction of badminton teaching can reach more than 90%. This student recognizes and accepts the teaching methods of intelligent teaching.