The Training Theory of Chinese University Sports Teams Based on the Complexity Paradigm

Under the background of the integration of sports and education, it is of great significance to deepening the training theory of college sports teams. This research puts forward the necessity of using the complexity paradigm to find a more suitable training theory for college sports teams. The training theory of our country is deeply influenced by the simplicity paradigm. The simplicity paradigm is based on reductionism, which believes that the research object comprises simple addition of single elements, showing a linear relationship. There are many general pieces of training in the training arrangement, the preparation period is too long, the special training is not deepened, and the actual training effect is poor. Lack of a composite coaching team, in addition to training and competition tasks, but also team building and management, too exhausted, no time to delve into training theory. When using traditional sports staging theory to make training arrangements, the student-athletes’ particularity should be taken into consideration. University sports teams are affected by the economy and school development, and the strengths of various sports teams are uneven. Universities with superior resources are in a leading position in major competitions, resulting in a significant gap in competition. The development of training theory should be based on the particularity of student-athlete status. The integration and in-depth development will improve the overall level of competition and enhance the driving force of innovative sports team training theory innovation

Research on the Path of Training Athletes in Chinese Universities Under the Background of Integration of Sports and Education

The purpose of this study was to realize the cognition and practice of sports education concepts from all walks of life in China, and eliminate the barriers of traditional systems and mechanisms, it will point out the direction for the training of Chinese competitive sports reserve talents to get out of the predicament and move towards high-quality development. The cultivation of reserve talents for competitive sports in China has gone through three stages: “combination of sports and education,” “combination of education and sports,” and “integration of sports and education.” Through analysis, it is concluded that the problems encountered by high-level sports teams in Chinese colleges and universities in the process of talent training. First, the sports department mainly cultivated reserve talents in the past, and the evaluation of athletes paid more attention to medals and honors. However, the main focus of the work of the education department is to enhance the physical fitness of the students and promote the physical and mental health of the students. Secondly, the lack of investment in training by universities leads to low coaching level and ability of coaches, which is the main factor restricting the training of athletes. Finally, in actual campus life, athletes spend most of their time in daily competitions and training, and their cultural performance is not good, which leads to their inability to truly master the cultural knowledge of their chosen major during their studies, it is challenging to choose a career as an elite in related industries after graduation. First, the main body of implementing the training of competitive sports talents in the new era in the sports department and the education department. Therefore, it is necessary to strengthen the coordination, communication and cooperation between the two departments to realize the sharing and sharing of resources between them. Secondly, universities should provide athletes with diversified teaching projects and practice forms. The teachers’ team is divided into two categories: physical education teachers and physical education coaches. Teachers are responsible for daily classroom teaching work to achieve the goal of coordinated development of physical exercise and cultural learning. The coach is responsible for sports training, through scientific and systematic training to enable students to reach a specific level of competition. Finally, universities should adopt learning planning guidance for athletes to help them complete their learning tasks, improve their cultural literacy, and enable them to master sports skills without delaying their normal study life

Predictive Learning from Real-World Medical Data: Overcoming Quality Challenges

Randomized controlled trials (RCTs) are pivotal in medical research, notably as the gold standard, but face challenges, especially with specific groups like pregnant women and newborns. Real-world data (RWD), from sources like electronic medical records and insurance claims, complements RCTs in areas like disease risk prediction and diagnosis. However, RWD's retrospective nature leads to issues such as missing values and data imbalance, requiring intensive data preprocessing. To enhance RWD's quality for predictive modeling, this thesis introduces a suite of algorithms developed to automatically resolve RWD's low-quality issues for predictive modeling. In this study, the AMI-Net method is first introduced, innovatively treating samples as bags with various feature-value pairs and unifying them in an embedding space using a multi-instance neural network. It excels in handling incomplete datasets, a frequent issue in real-world scenarios, and shows resilience to noise and class imbalances. AMI-Net's capability to discern informative instances minimizes the effects of low-quality data. The enhanced version, AMI-Net+, improves instance selection, boosting performance and generalization. However, AMI-Net series initially only processes binary input features, a constraint overcome by AMI-Net3, which supports binary, nominal, ordinal, and continuous features. Despite advancements, challenges like missing values, data inconsistencies, and labeling errors persist in real-world data. The AMI-Net series also shows promise for regression and multi-task learning, potentially mitigating low-quality data issues. Tested on various hospital datasets, these methods prove effective, though risks of overfitting and bias remain, necessitating further research. Overall, while promising for clinical studies and other applications, ensuring data quality and reliability is crucial for these methods' success

Restricted phase space thermodynamics for black holes in higher dimensions and higher curvature gravities

The recently proposed restricted phase space thermodynamics is shown to be applicable to a large class of higher dimensional higher curvature gravity models coupled to Maxwell field, which are known as black hole scan models and are labeled by the spacetime dimension $d$ and the highest order $k$ of the Lanczos-Lovelock densities appearing in the action. Three typical example cases with $(d,k)=(5,1), (5,2)$ and $(6,2)$ are chosen as example cases and studied in some detail. These cases are representatives of Einstein-Hilbert, Chern-Simons and Born-Infield like gravity models. Our study indicates that the Einstein-Hilbert and Born-Infield like gravity models have similar thermodynamic behaviors, e.g. the existence of isocharge $T-S$ phase transitions with the same critical exponents, the existence of isovoltage $T-S$ transitions and the Hawking-Page like transitions, and the similar high temperature asymptotic behaviors for the isocharge heat capacities, etc. However, the Chern-Simons like $(5,2)$-model behaves quite differently. Neither isocharge nor isovoltage $T-S$ transitions could occur and no Hawking-Page like transition is allowed. This seems to indicate that the Einstein-Hilbert and Born-Infield like models belong to the same universality class while the Chern-Simons like models do not.Comment: 29 pages. v2: typo in the title correcte

Warfarin Dose Estimation on High-dimensional and Incomplete Data

Warfarin is a widely used oral anticoagulant worldwide. However, due to the complex relationship between individual factors, it is challenging to estimate the optimal warfarin dose to give full play to its ideal efficacy. Currently, there are plenty of studies using machine learning or deep learning techniques to help with the optimal warfarin dose selection. But few of them can resolve missing values and high-dimensional data naturally, that are two main concerns when analyzing clinical real world data. In this work, we propose to regard each patient’s record as a set of observed individual factors, and represent them in an embedding space, that enables our method can learn from the incomplete date directly and avoid the negative impact from the high-dimensional feature set. Then, a novel neural network is proposed to combine the set of embedded vectors non-linearly, that are capable of capturing their correlations and locating the informative ones for prediction. After comparing with the baseline models on the open source data from International Warfarin Pharmacogenetics Consortium, the experimental results demonstrate that our proposed method outperform others by a significant margin. After further analyzing the model performance in different dosing subgroups, we can conclude that the proposed method has the high application value in clinical, especially for the patients in high-dose and medium-dose subgroups