To explore the handedness effects on phase asymmetries in deep gray matter of healthy adults by using magnetic susceptibility-weighted imaging (SWI) phase. Thirty left-handed (16 men, 14 women; age range, 20 to 57 years) and 30 age- and sex-matched right-handed (16 men, 14 women; age range, 20 to 58 years) healthy adults were examined at 3.0 T MRI. For each subject, phase values were detected in bilateral frontal white matters (FWM), caudate nucleus (CA), putamen (PU), globus pallidus (GP), thalamus (TH), red nucleus (RN) and substantia nigra (SN) on phase images. Statistical analysis was performed with paired-samples t-test and independent-samples t-test. In both handedness groups, the corrected phase values in the left hemisphere were significantly lower than those in the right one in FWM, CA, PU, GP (P < 0.05) and there was no significant hemispheric asymmetry in TH, RN and SN (P > 0.05). Differences in corrected phase values in corresponding brain regions of the same hemisphere between left-handed and right-handed groups were not statistically significant (P > 0.05). Hemispheric asymmetry of SWI phase in deep gray matter may not associate with handedness in adult brain.

In recent years, tennis has become a popular sport in China, whether in competitive sports or in the daily fitness of ordinary people is widely welcomed. However, due to the late development of tennis in China, the corresponding tennis teaching resources are relatively scarce, and there are problems such as unbalanced allocation. In view of this situation, this paper puts forward the research on the optimal allocation of tennis teaching resources based on big data. This paper makes an in-depth investigation on the current situation of Tennis Teaching under the background of big data, and analyzes various reasons for the backward allocation of tennis teaching resources. In view of these shortcomings, in order to improve the quality of tennis teaching in China, this paper combines the core concept of tennis teaching and big data technology, puts forward measures to strengthen the optimal allocation of tennis teaching resources, and creatively constructs a new basic principle of tennis teaching based on the background of big data. This paper investigates the current situation of tennis teaching development in 30 colleges and universities in China. Through the analysis results, this paper believes that using big data technology to optimize the allocation of tennis teaching resources can effectively improve the imbalance of existing resources and low learning efficiency.

No abstract

The article aims at dynamically fusing sports images to obtain the constraint items of the edge highlight model. At the same time, we combine the RGB feature decomposition technology to extract the feature mark point features. The article combines the steady-state matching technology to complete the automatic tracking of the landmark points of the sports image sequence. Preliminary application results show that this method has low requirements on the experimental environment, strong robustness, and high recognition rate. It can not only be used for human motion analysis, but also has great application potential for automatic tracking of moving targets in other free backgrounds.

The paper proposes remote sensing on the monitoring of mine geological environment in important mining areas, the risk assessment of landslides in the mining area, the feasibility study of land reclamation in the mining area, the monitoring of tailing ponds and the safety assessment, etc. Monitoring and comprehensive evaluation methods provide new technical means for mine geological environment supervision, which can further strengthen the timeliness of mine geological environment supervision in China, and thus promote remote sensing technology to play a greater role in mine geological environment monitoring.