Application Research of Internet of Things Technology in the Causes of Dragon Boat Sports Injury

In recent years, the Internet of Things technology can effectively innovate applications and services. The Internet of Things technology has become more and more popular. It provides an effective and direct bridge between the physical world and virtual objects in cyberspace. With the increase in the intensity of dragon boat training and the increasingly fierce competition, the possibility of injury is increasing. Dragon boat racing is a noncontact team sport based on strength and technology. The purpose of this paper is to solve the problem of people's lack of understanding of the sports injuries and causes of dragon boat athletes. We used the data fusion algorithm and cluster maintenance optimization algorithm to study the application of Internet of Things technology in the cause of dragon boat sports injury. In order to save energy, extend the network life cycle, shorten service interruption time, and increase data packet transmission, the cluster maintenance optimization algorithm in this paper mainly improves and optimizes the startup time of cluster maintenance, which depends on the maintenance cost. The experiment result shows that the etiological detection system proposed in this paper matches the actual sports injury results well. The experiment result shows that the research on the cause of injury in dragon boat sports based on Internet of Things technology can detect the damage law well and can have a more comprehensive understanding for the cause of injury, which helps to prevent injuries better and take effective treatments. In the analysis part, it can be concluded that the detection system is very accurate in detecting the cause, and the accuracy rate is basically 100%.

The impact of ‘cluster maintenance TMS’ on irritability occurring in major depressive disorder

Objective: To determine the impact of clustered maintenance transcranial magnetic stimulation (TMS) on irritability occurring in treatment-resistant major depressive disorder (MDD). Method: A naturalistic study of 106 courses that includes pre- and posttreatment assessments of subjective and objective depression and a subjective measure of irritability developed for this study. Results: Forty-six participants (35 females), mean age 43.2 years (14.3), completed 106 courses. There was a significant reduction in irritability and depression scores ( p < .001). The change in irritability scores was significantly correlated with the change in depression scores, r = .40, p < .001. Conclusion: TMS has the capacity to reduce the irritability co-occurring with treatment-resistant MDD, known to be responsive to TMS. This increases the possibility of using TMS in the treatment of irritability co-occurring with other disorders or standing alone (should irritability be categorized as a stand-alone disorder).

Mechanistic insights into EGFR membrane clustering revealed by super-resolution imaging

We investigate the distribution of membrane EGFR by direct stochastic optical reconstruction microscopy (dSTORM). Our results illustrate the clustering distribution pattern of EGFR in polarized cells and uncover the essential role of lipid rafts in EGFR cluster maintenance.

Mobile Prediction Idea Based Clustering Algorithm and Related Mathematical Quantitative Description

Mobile prediction idea refers to predicting link expiration time with relative velocity and relative position between different nodes. In ad hoc networks, mobile prediction idea is adopted in MSWCA, and cluster stability is measured by link expiration time. MSWCA only considers on intra-cluster stability, and neglects inter-cluster stability. Aiming at the above problem, MPICA (Mobile Prediction Idea based Clustering Algorithm) was proposed. Firstly, involved concepts were given with mathematical quantitative description. Secondly, the realization process of MPICA was described. Lastly, the complexity of MPICA was analyzed. MPICA considers intra-cluster and inter-cluster stability at the same time, which is in favor of improving cluster stability and reducing cluster maintenance overheads.

MANET-Based Stable Clustering Algorithm and its Performance Analysis

In MANET, MSWCA is a typical algorithm in clustering algorithms with consideration on motion-correlativity. Aiming at MSWCA’s problem that “it only considers on intra-cluster stability, and neglects the inter-cluster stability”, a MANET-based stable clustering algorithm (MSCA) was proposed. Firstly, MSCA clustering algorithm and its cluster maintenance scheme were designed. Secondly, the theoretical quantitative analyses on average variation frequency of clusters and clustering overheads were conducted. The results show that MSCA can improve cluster stability and reduce clustering overheads.

A New Clustering Algorithm and Relevant Theoretical Analysis for Ad-Hoc Networks

In ad-hoc networks, MSWCA is a typical algorithm in clustering algorithms with consideration on motion-correlativity. Aiming at MSWCA’s problem that “it only considers on intra-cluster stability, and neglects the inter-cluster stability”, a new clustering algorithm (NCA) was proposed. Firstly, NCA clustering algorithm and its cluster maintenance scheme were designed. Secondly, the theoretical quantitative analyses on average variation frequency of clusters and clustering overheads were conducted. The results show that NCA can improve cluster stability and reduce clustering overheads.