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Simple Summary: In current pig farming, pigs are susceptible to stress-induced adverse emotions. Emotional contagion may propagate these emotions within a herd, potentially affecting overall welfare. Typically, treatments of individual pigs should be conducted separately from the group. Thus, after separation from treatment on an individual pig, the behavioral responses and heart rate changes of the companion pig were recorded to determine if the sensory avoidance was effective. It was found that peers were still affected by the treated pigs after sensory avoidance. Separation from treatment does not eliminate the effects of the treated pig on companion pigs, which can still be affected in post-treatment contact. In the pig farming industry, it is recommended to avoid groups when treating individuals to reduce adverse reactions in the group. However, can this eliminate the adverse effects effectively? Piglets were assigned to the Rewarding Group (RG), the Punishing Group (PG), and the Paired Control Group (PCG). There were six replicates in each group, with two paired piglets per replicate. One piglet of the RG and PG was randomly selected as the Treated pig (TP), treated with food rewards or electric shock, and the other as the Naive pig (NP). The NPs in the RG and PG were unaware of the treatment process, and piglets in the PCG were not treated. The behavior and heart rate changes of all piglets were recorded. Compared to the RG, the NPs in the PG showed longer proximity but less contact behavior, and the TPs in the PG showed more freezing behavior. The percentage change in heart rate of the NPs was synchronized with the TPs. This shows that after sensory avoidance, the untreated pigs could also feel the emotions of their peers and their emotional state was affected by their peers, and the negative emotions in the pigs lasted longer than the positive emotions. The avoidance process does not prevent the transfer of negative emotions to peers via emotional contagion from the stimulated pig. [ABSTRACT FROM AUTHOR]

Power dispatching systems currently receive massive, complicated, and irregular monitoring alarms during their operation, which prevents the controllers from making accurate judgments on the alarm events that occur within a short period of time. In view of the current situation with the low efficiency of monitoring alarm information, this paper proposes a method based on natural language processing (NLP) and a hybrid model that combines long short-term memory (LSTM) and convolutional neural network (CNN) for the identification of grid monitoring alarm events. Firstly, the characteristics of the alarm information text were analyzed and induced and then preprocessed. Then, the monitoring alarm information was vectorized based on the Word2vec model. Finally, a monitoring alarm event identification model based on a combination of LSTM and CNN was established for the characteristics of the alarm information. The feasibility and effectiveness of the method in this paper were verified by comparison with multiple identification models. [ABSTRACT FROM AUTHOR]