A series of novel soluble and thermoplastic polyurethane/polyaniline (TPU/PANI) composites doped with a compound acid, which was composed of an organic acid (p-toluene sulfonic acid) and an inorganic acid (phosphoric acid), were successfully prepared by in situ polymerization. The effect of aniline (ANI) content, ratio of organic acid/inorganic acid, and different preparation methods on the conductivity of the TPU/PANI composites were investigated by using conductivity measurement. Lithium bisoxalato borate (LiBOB) was added to the prepared in situ TPU/PANI to coordinate with the ether oxygen groups originating from the soft molecular chains of TPU, and thus the conductivity of the composites was further enhanced. The molecular structure, thermal properties, and morphology of the TPU/PANI composites were studied by UV-visible spectroscopy, differential scanning calorimetry, and scanning electron microscopy, respectively. The results show that the in situ TPU/PANI composites doped with the compound acid can be easily dissolved in normal solvents such as dimethylformamide (DMF) and 1,4-dioxane. The conductivity of the TPU/ PANI composites increases with the increase of the ANI content, in the ANI content range of 0-20 wt %; however, the conductivity of the composites reduces with further increment of ANI content. The conductivity of the TPU/ PANI composites prepared by in situ polymerization is about two orders of magnitude higher than that prepared by solution blending method. LiBOB can endow the in situ TPU/PANI composites with an ionic conductivity. The dependence of the conductivity on temperature is in good accordance with the Arrhenius equation in the temperature range of 20-80 C.

This study aims to explore the influence of industry leaders' behavior on common enterprise leaders' decisions in enterprise clustering by recognizing top executives' cognitive processes of brains. Methods: Twenty-one real top executives from twelve textile enterprises were recruited in the lab experiment, and decisions about whether entering an industrial zone under two conditions of following an industry leader or a common enterprise were designed as the experiment task. Throughout the formal experimental task, participants' electroencephalograms were recorded. Results: The behavioral results preliminarily proved the effect of industry leaders' behaviors on the real top executives' decisions in common enterprises: participants had a higher acceptance rate with a shorter reaction time in the condition of following an industry leader rather than that in the condition of following a common enterprise. Event-related potential results indicated that choices of following an industry leader led to a more positive perception of emotional valence (reflected by a smaller P2 amplitude) and better evaluation categorization and greater decision confidence (reflected by a larger late positive potential amplitude) than choices of following a common enterprise. Conclusion: Top executives from common enterprises tend to evaluate industry leaders' behaviors better than other common enterprises' behaviors, and they tend to make a similar business decision to keep their enterprises consistent with these industry leaders.

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The structure of wind turbine is complicated and the operating environment is harsh, which leads to the high probability of failure of wind turbine, and the maintenance is more difficult. Once an accident occurs, it will force the wind turbine to shut down, causing huge economic losses. In addition, it will affect the safe and reliable operation of the whole power system. Therefore, fault diagnosis of wind power generation system is very important. The traditional fault diagnosis algorithm can only identify the known types of faults in wind turbines. If a new fault category appears in the wind turbine generator, the traditional fault diagnosis algorithm can only identify it as a known fault category. To solve this problem, a new classification fault diagnosis method based on semi-supervised deep learning is proposed. The benchmark model of offshore wind turbine is built in FAST software, and 10 different types of faults such as sensors and systems are simulated through model simulation. Different responses of fault signals are analyzed, and 15 parameter signals are selected as input. Firstly, fault features are extracted through multi-scale convolutional self-coding network. Secondly, the initial model is established by using the compressed feature and the error feature map as the input of the classifier and the detector respectively. Finally, the detector judges and puts the new category of fault instances into the buffer. When the buffer reaches the maximum value and starts to overflow, the algorithm starts to update, so as to realize the diagnosis task of new types of faults. Compared with the experimental results of isolated forest, local anomaly factor and single-class support vector machine, the accuracy of our proposed method and other indicators are significantly better, with an accuracy of 99.2%, which can effectively solve the problem of new class fault identification of wind turbines. It can effectively solve the problem of fault identification of the new type of fan, and is conducive to avoiding the occurrence of shutdown accidents, thus maintaining the safe and reliable operation of the power system.

Individual aquaculture farmers in developing countries play an important role in ensuring food security. This study uses survey data from aquaculture households in Rongcheng and Xiangshan cities in China to explore the impact of cooperative participation on the benefits to the aquaculture households. The empirical results show that the participation of aquaculture farmers in cooperatives has effectively increased their net profit and output per unit area. On average, participating in cooperatives increased the net profit and output per unit area of farmers by approximately 15.55% and 11.47%, respectively. The test results of the mechanism show that the information services, technical training, and product sales guidance provided by the cooperatives have increased the net profit of the farmers. At the same time, the information services and product sales guidance provided by cooperatives are important reasons for the increase in the output per unit area.