Prediction of Sports Aggression Behavior and Analysis of Sports Intervention Based on Swarm Intelligence Model

In the process of sports, athletes often have aggressive behaviors because of their emotional fluctuations. This violent sports behavior has caused many serious bad effects. In order to reduce and solve this kind of public emergencies, this paper aims to create a swarm intelligence model for predicting people's sports attack behavior, takes the swarm intelligence algorithm as the core technology optimization model, and uses the Internet of Things and other technologies to recognize emotions on physiological signals, predict, and intervene sports attack behavior. The results show the following: (1) After the 50-fold cross-validation method, the results of emotion recognition are good, and the accuracy is high. Compared with other physiological electrical signals, EDA has the worst classification performance. (2) The recognition accuracy of the two methods using multimodal fusion is improved greatly, and the result after comparison is obviously better than that of single mode. (3) Anxiety, anger, surprise, and sadness are the most detected emotions in the model, and the recognition accuracy is higher than 80%. Sports intervention should be carried out in time to calm athletes' emotions. After the experiment, our model runs successfully and performs well, which can be optimized and tested in the next step.

Attractin Participates in Schizophrenia by Affecting Testosterone Levels

Attractin (ATRN) is a widely expressed glycoprotein that is involved in energy homeostasis, neurodevelopment, and immune response. It is encoded by a gene spanning 180 kb on chromosome 20p13, a region previously implicated in schizophrenia by linkage studies. To address a possible role of ATRN in disorders of the central nervous system, we created an atrn knockout zebrafish line and performed behavioral tests. Adult atrn–/– zebrafish exhibited more pronounced attack behavior relative to wild-type control zebrafish in a tracking analysis. Biochemical analysis revealed elevated testosterone levels in atrn–/– zebrafish. At the gene expression level, we noted an upregulation of cyp51 and hsd17b7, key proteins in testosterone synthesis in the brains of both adult and larvae of atrn–/– zebrafish. In order to further elucidate the relationship between testosterone and behavioral syndromes, we then compared testosterone levels of 9,008 psychiatric patients and 247 healthy controls from the same catchment area. Of all subjects examined, male subjects with schizophrenia exhibited lower testosterone levels compared with controls. In contrast, female subjects with a diagnosis of schizophrenia or bipolar disorder featured higher testosterone levels than did same sex controls. Purposeful sampling of extreme groups showed reduced ATRN expression in a subset of these subjects. Finally, we identified 14 subjects with ATRN mutations. All of whom displayed abnormal testosterone levels. In summary, the interplay of ATRN and testosterone may help to explain sexual dimorphisms in selected behavioral phenotypes.

Cyberattacks Detection and Analysis in a Network Log System Using XGBoost with ELK Stack

The usage of artificial intelligence and machine learning methods on cyberattacks increasing significantly recently. For the defense method of cyberattacks, it is possible to detect and identify the attack event by observing the log data and analyzing whether it has abnormal behavior or not. This paper implemented the ELK Stack network log system (NetFlow Log) to visually analyze log data and present several network attack behavior characteristics for further analysis. Additionally, this system evaluated the extreme gradient enhancement (XGBoost), Recurrent Neural Network (RNN), and Deep Neural Network (DNN) model for machine learning methods. Keras was used as a deep learning framework for building a model to detect the attack event. From the experiments, it can be confirmed that the XGBoost model has an accuracy rate of 96.01% for potential threats. The full attack data set can achieve 96.26% accuracy, which is better than RNN and DNN models.

Targeting GluN2B/NO Pathway Ameliorates Social Isolation–Induced Exacerbated Attack Behavior in Mice

Exacerbated attack behavior has a profound socioeconomic impact and devastating social consequences; however, there is no satisfactory clinical management available for an escalated attack behavior. Social isolation (SI) is widespread during this pandemic and may exert detrimental effects on mental health, such as causing heightened attack behavior. To explore the therapeutic approaches that alleviate the SI-induced heightened attack behavior, we utilized pharmacological methods targeting the GluN2B/NO signaling pathway during the attack behavior. Ifenprodil and TAT-9C peptide targeting GluN2B showed that the inhibition of GluN2B mitigated the SI-induced escalated attack behavior and the SI-induced aberrant nitric oxide (NO) level in the brain. Additionally, the potentiation of the NO level by L-arginine reversed the effects of the inhibition of GluN2B. Moreover, we showed that high doses of L-NAME and 7-NI and subeffective doses of L-NAME in combination with ifenprodil or TAT-9C or subeffective doses of 7-NI plus ifenprodil or TAT-9C all decreased the SI-induced escalated attack behavior and reduced the NO level, further supporting the idea that GluN2B/NO signaling is a crucial modulator of the escalated attack behavior.

Fishers' perception of the interaction between the South American sea lions and the Chinook salmon fishery in southern Chile

We studied how the South American sea lion (SASL, Otaria flavescens) interacts with the operation of an artisanal fishery of Chinook salmon, a non-native species in Chile, using a combination of biological and social approaches, including a valuation by fishers about this interaction. During austral summer of 2019, an observer onboard artisanal fishing boats characterized the attack behavior of SASLs to gillnet-captured Chinook salmon during 33 hauls and analyzed which factors may affect the intensity of attacks. To analyze the relationship between fishers and SASLs, a Likert scale about the perception and views about nature was applied. A total of 23 interviews—including 35 open and 16 closed questions—with fishers were conducted to describe how they perceived the interactions with SASLs. Interactions with SASLs were recorded in 35% of the fishing events and varied depending on both operational factors, such as the number of boats, as well as environmental factors, such as moon’s luminosity. Even though SASL interactions resulted in seven fish (~ 70 kg) damaged of a total catch of 2815 kg (2.5%) during the survey, boats with a damaged catch by SASL lost up to 11% of their revenue. This is consistent with 87% of the interviewed fishers who considered that the conflict with the SASL negatively impacts their activity and results in economic losses. A negative perception towards SASLs likely results from personal experience and revenue loss, even though impacts of SASL interactions at the scale of the entire fishery may be less important. While older fishers with less formal education have a productivist and instrumental focus, younger fishers with a more sustainable and conservationist view of fishing offer an opportunity to lead an improved local understanding of the relationship between salmon, SASLs, and humans.

Improved Seagull Optimization Algorithm Combined with an Unequal Division Method to Solve Dynamic Optimization Problems

The numerical solution of the dynamic optimization problem is often sought for chemical processes, but the discretization of control variables is a difficult problem. Firstly, based on the analysis of the seagull optimization algorithm, this paper introduces the cognitive part in the process of a seagull’s attack behavior to make the group approach the best position. Secondly, the algorithm adds the mechanism of natural selection, where the fitness value is used to sort the population, and the best half is used to replace the worst half, so as to find out the optimal solution. Finally, the improved seagull optimization algorithm (ISOA) is combined with the unequal division method to solve dynamic optimization problems. The feasibility of the method is verified by three practical examples of dynamic optimization in chemical industry.

Dynamic Scheduling Algorithm in Cyber Mimic Defense Architecture of Volunteer Computing

Volunteer computing uses computers volunteered by the general public to do distributed scientific computing. Volunteer computing is being used in high-energy physics, molecular biology, medicine, astrophysics, climate study, and other areas. These projects have attained unprecedented computing power. However, with the development of information technology, the traditional defense system cannot deal with the unknown security problems of volunteer computing . At the same time, Cyber Mimic Defense (CMD) can defend the unknown attack behavior through its three characteristics: dynamic, heterogeneous, and redundant. As an important part of the CMD, the dynamic scheduling algorithm realizes the dynamic change of the service centralized executor, which can enusre the security and reliability of CMD of volunteer computing . Aiming at the problems of passive scheduling and large scheduling granularity existing in the existing scheduling algorithms, this article first proposes a scheduling algorithm based on time threshold and task threshold and realizes the dynamic randomness of mimic defense from two different dimensions; finally, combining time threshold and random threshold, a dynamic scheduling algorithm based on multi-level queue is proposed. The experiment shows that the dynamic scheduling algorithm based on multi-level queue can take both security and reliability into account, has better dynamic heterogeneous redundancy characteristics, and can effectively prevent the transformation rule of heterogeneous executors from being mastered by attackers.