Are Our Genes Important for Cooperation?

Social cooperation is a state in which people work together on a shared activity from which they both benefit, and the success of each person is dependent on everyone doing their part. Imagine, for example, a basketball game in which all team members make a shared effort and cooperate to win the game. To study this kind of social cooperation in the lab, we used rats. We created a special maze in which two rats must coordinate their behavior as a pair, moving together through the sections of the maze. Using this maze, we found that a rat’s genes are more important than its environment in determining its level of social cooperation.

The understanding of refers and trainers of basketball game rules

The background of this research is that there are still some basketball referees and coaches whose level of understanding is still lacking in the rules of the basketball game, seen during the game process, there are often debates between coaches and referees. The purpose of this study was to see the differences in the understanding of referees and basketball coaches throughout Riau on the rules in basketball games. This type of research is a comparative descriptive research, namely research that compares two different variables. The method used in this study is a survey method and data collection techniques using true-false tests. The population in this study were referees and coaches throughout Riau. The research sample was taken using a random sampling technique, totaling 25 trainers and 16 referees who were active throughout Riau Province. The results of this study obtained a sig value of 0.141 (p>0.05). So it can be concluded if there is no difference in the understanding of the referees and basketball coaches in Riau on basketball rules. If in hypothesis 1 it is said that there is no difference in understanding because the sig results obtained are p> 0.05. This happened because the average understanding of the coach and referee were both in the medium category. Research contributes to education, especially in the basketball branch, where this research can be known and understood by referees and coaches, so that in a basketball game a conducive game will be created.

First person: Watching the clock: Make it to the end of the year without messing up

Math coach Steven Goldman compares working the timer and scoreboard at a local basketball game to teaching. His mission as the clock guy is not to mess up. That means he avoids doing anything fancy or learning what any of the buttons do beyond the ones he needs. When he worked as a long-term sub for a colleague’s paternity leave, it would have been easy for him to have the same attitude. By trying to create new materials, Goldman risked making mistakes — and eventually he did. As a teacher who was new to the school, this mistake had the potential to carry higher consequences that it would for a veteran, making the temptation to simply watch the clock even greater.

Pengaruh Latihan Bench Dip Terhadap Kemampuan Passing Bola Basket Pada SMAN 1 Narmada Tahun 2020

The problem with this research is that the students' low shooting results are caused by their passing and shooting abilities that are still not optimal. This can be seen when students throw and catch, passing the ball often does not reach the given friend, deep passing is the key to the basketball game. The purpose of this study is to examine the significance of different samples with the formulation of the problem posed is to find out whether there is an effect of bench dip training on basketball passing abilities at SMAN 1 Narmada in 2020". The research method for taking subjects in this study is cluster random sampling, which is a sampling technique that selects an area or group as the sample. The samples used were male students who took part in basketball extracurricular with a total of 20 people. To obtain data in this study, the test method and the method of documentation were used. The data analysis method used is the t-test (t-test). The results of the study based on the results of the t-test (t-test) showed the calculated value of the t-test was 26.29, so the significance level was 5% and N was 20, it turned out that the number of rejection of the null hypothesis stated in the table was 2.069. This fact indicates that the value of t arithmetic from the results of data analysis of 26.29 is above the number of rejection of the null hypothesis which is 2.069. (T value = 26.29 > r table 2.069) it can be concluded that "There is an effect of bench dip training on basketball passing skills at SMAN 1 Narmada in 2020.

Development of Basketball Tactics Basic Cooperation Teaching System Based on CNN and BP Neural Network

The traditional basketball teaching mode cannot meet the needs of students for the basic cooperation of basketball tactics. Therefore, a basic cooperation teaching system of basketball tactics based on artificial neural network is studied and designed. The system has a professional basketball game video tactical learning module. The events in the basketball game video are classified through a convolutional neural network and combined with the explanation of teachers to make the students have an intuitive understanding of the basic cooperation of basketball tactics and then design the basketball game module based on a BP neural network to provide students with an online basketball tactics training platform. Finally, the teacher scores the performance of the actual on-site training students in the basic cooperation of basketball tactics through the tactical scoring module on the system. The results show that after the introduction of global and collective motion patterns, the classification accuracy of the convolutional neural network is improved by 22.48%, which has significant optimization. The average accuracy of basketball game video event classification is 62.35%, and the accuracy of snatch event classification is improved to 95.28%. The recognition rate of the BP neural network combined with momentum gradient descent method is 75%, the number of weight adjustment is less, and the memory is small while ensuring fast running speed. Students who accept the basic basketball tactics cooperation teaching system based on the artificial neural network for basketball teaching have an overall score of 27.99 ± 2.11 points The overall score of exchange defense cooperation was 24.12 ± 2.03, which was higher than that of the control group. The above results show that the basketball tactical basic cooperation teaching system based on the artificial neural network has a good teaching effect in improving students’ basketball tactical basic cooperation ability.

Stress-induced changes in inflammatory and atherosclerosis markers during official basketball games in professional coaches

Objective: Top-level competitive sports coaches repeatedly cope with situations of acute stress in order to succeed and manage high team performance. Occupational stress-induced biochemical and immune system markers are not well studied for this specific group of people. The purpose of this study was to evaluate stress-induced alterations of inflammatory markers and atherosclerosis risk factors during an official basketball game in top-level professional basketball coaches (head and assistant). Methods: Blood samples and vital signs were obtained from 27 healthy coaches (Greek A1 Men National Basketball League), 30 minutes before and 30 minutes after the games. We performed a full blood count and measured inflammatory cytokines, atherosclerosis markers and cortisol levels. Data were statistically analysed using two-tailed paired and independent samples t-tests and Pearson Correlation. Results: Post-game neutrophils (NEU) and apolipoprotein B (ApoB) levels were significantly increased while lymphocytes (LYM) were significantly decreased in comparison to pre-game values. Blood pressure (systolic and diastolic) levels were considered as a pre-hypertension state at both measurements. We found significant alterations between head and assistant coaches in diastolic blood pressure and cortisol levels after the game. Cortisol was negatively correlated to inflammatory cytokine levels and positively correlated with ApoB levels. Conclusions: Game-induced acute psychological stress initiates an aseptic inflammatory response in top-level professional coaches and can be related to the atherosclerosis pathways posing as an acute as well as chronic health threat for top-level coaches who have to deal with long periods of stressful working conditions.

Image Recognition Method for Pitching Fingers of Basketball Players Based on Symmetry Algorithm

In the basketball game, the accuracy and standardization of pitching are directly related to the score. So it is very important to analyze the pitching figure movement to have a better positioning of the fingers. There are limited techniques to recognize the movement. The human motion recognition method is one of them. It utilizes the spatiotemporal image segmentation and interactive region detection to recognize images of pitching finger movement of basketball players. This method has a limitation that the symmetrical information of the human body and sphere cannot be excavated, which leads to certain errors in recognition effect. This paper presents a method of recognizing pitching finger movement of basketball players based on symmetry algorithm, constructs an acquisition model, carries out edge contour detection and adaptive feature segmentation of images of pitching finger movement of basketball players, and uses a fixed threshold to segment finger image to extract players’ hand contour and locate the middle axis of the finger. On this basis, the symmetry recognition method based on nematode recognition algorithm is used to recognize the symmetry of basketball pitching finger movement image and complete the accurate recognition of basketball pitching finger movement image. The experimental results show that the proposed method can effectively recognize the basketball player’s finger movement image. The average recognition accuracy is 98%, the growth rate of recognition speed is 98%, and the maximum recognition time is 12 s. The robustness of the proposed method is 0.45.

The Construction of Basketball Training System Based on Motion Capture Technology

Motion capture is a cross-cutting application field developed in recent years, which comprises electronics, communications, control, computer graphics, ergonomics, navigation, and other disciplines. The accurate application of basketball technical movements in the basketball game is very important. Therefore, it is of great significance to capture and standardize athletes’ movements and improve their training. Unfortunately, there are numerous issues in traditional classroom teaching that largely helps to train the athletes. To solve the issues of traditional basketball classroom teaching, a virtual simulation system for students’ sports training is designed in this paper. Firstly, the information of basketball dribbling movement is captured and simulated in three dimensions. Secondly, we compare it with the standard database to judge the irregularities of athletes’ movements, and carry out digital processing on athletes’ movements and skill improvements statistics in combination with system functions. Thirdly, we set up a gradual training cycle. Finally, the Kinect-based capture technology is adopted to obtain the activity information of different joints of the human body. Through processing the motion data, relevant motion analysis data are fed to the established motion model, to realize the comparative analysis of motion pictures. In our experiments, we observed better training of the physical education.

Optimization Research on Deep Learning and Temporal Segmentation Algorithm of Video Shot in Basketball Games

The analysis of the video shot in basketball games and the edge detection of the video shot are the most active and rapid development topics in the field of multimedia research in the world. Video shots’ temporal segmentation is based on video image frame extraction. It is the precondition for video application. Studying the temporal segmentation of basketball game video shots has great practical significance and application prospects. In view of the fact that the current algorithm has long segmentation time for the video shot of basketball games, the deep learning model and temporal segmentation algorithm based on the histogram for the video shot of the basketball game are proposed. The video data is converted from the RGB space to the HSV space by the boundary detection of the video shot of the basketball game using deep learning and processing of the image frames, in which the histogram statistics are used to reduce the dimension of the video image, and the three-color components in the video are combined into a one-dimensional feature vector to obtain the quantization level of the video. The one-dimensional vector is used as the variable to perform histogram statistics and analysis on the video shot and to calculate the continuous frame difference, the accumulated frame difference, the window frame difference, the adaptive window’s mean, and the superaverage ratio of the basketball game video. The calculation results are combined with the set dynamic threshold to optimize the temporal segmentation of the video shot in the basketball game. It can be seen from the comparison results that the effectiveness of the proposed algorithm is verified by the test of the missed detection rate of the video shots. According to the test result of the split time, the optimization algorithm for temporal segmentation of the video shot in the basketball game is efficiently implemented.

Semantic Extraction of Basketball Game Video Combining Domain Knowledge and In-Depth Features

The team sports game video features complex background, fast target movement, and mutual occlusion between targets, which poses great challenges to multiperson collaborative video analysis. This paper proposes a video semantic extraction method that integrates domain knowledge and in-depth features, which can be applied to the analysis of a multiperson collaborative basketball game video, where the semantic event is modeled as an adversarial relationship between two teams of players. We first designed a scheme that combines a dual-stream network and learnable spatiotemporal feature aggregation, which can be used for end-to-end training of video semantic extraction to bridge the gap between low-level features and high-level semantic events. Then, an algorithm based on the knowledge from different video sources is proposed to extract the action semantics. The algorithm gathers local convolutional features in the entire space-time range, which can be used to track the ball/shooter/hoop to realize automatic semantic extraction of basketball game videos. Experiments show that the scheme proposed in this paper can effectively identify the four categories of short, medium, long, free throw, and scoring events and the semantics of athletes’ actions based on the video footage of the basketball game.