Since the outbreak of COVID-19, many COVID-19 research studies have proposed different models for predicting the trend of COVID-19. Among them, the prediction model based on mathematical epidemiology (SIR) is the most widely used, but most of these models are adapted in special situations based on various assumptions. In this study, a general adapted time-window based SIR prediction model is proposed, which is characterized by introducing a time window mechanism for dynamic data analysis and using machine learning method predicts the basic reproduction number and the exponential growth rate of the epidemic. We analyzed COVID-19 data from February to July 2020 in seven countries–––China, South Korea, Italy, Spain, Brazil, Germany and France, and the numerical results showed that the framework can effectively measure the real-time changes of the parameters during the epidemic, and error rate of predicting the number of COVID-19 infections in a single day is within 5%.

Feedback from software users, such as bug reports, is vital in the management of software projects. In GitHub, the feedback is typically expressed as new issues. Through filing issue reports, users may help identify and fix bugs, document software code, and enhance software quality via feature requests. In this paper, we aim at investigating some characteristics of issues to facilitate issue management and software management. We investigate the important degrees of behaviors that are related to issues in popular projects to assess the importance of issues in GitHub and analyze the effectiveness of issue labeling for issue handling. Then, we explore the patterns of issue commits over time in popular projects based on visual analysis and obtain the following results: we find that the behaviors that are related to issues play important roles in the GitHub. We also find that the time distribution of issue commits follows a three-period development model, which approximately corresponds to the project life cycle. These results may provide a new knowledge about issues that can help managers manage and allocate project resources more effectively and even reduce software failures.INDEX TERMS Open-source software community, project development model, visual analysis, issue commit, software management.

With the rapid development of social networks, it has become extremely important to evaluate the propagation capabilities of the nodes in a network. Related research has wide applications, such as in network monitoring and rumor control. However, the current research on the propagation ability of network nodes is mostly based on the analysis of the degree of nodes. The method is simple, but the effectiveness needs to be improved. Based on this problem, this paper proposes a method that is based on Tsallis entropy to detect the propagation ability of network nodes. This method comprehensively considers the relationship between a node’s Tsallis entropy and its neighbors, employs the Tsallis entropy method to construct the TsallisRank algorithm, and uses the SIR (Susceptible, Infectious, Recovered) model for verifying the correctness of the algorithm. The experimental results show that, in a real network, this method can effectively and accurately evaluate the propagation ability of network nodes.

The excellent thermoelectric performance of monolayer KCuTe is discovered by first-principles study for the first time.

With the development of open source community, through the interaction of developers, the collaborative development of software, and the sharing of software tools, the formation of open source software ecosystem has matured. Natural ecosystems provide ecological services on which human beings depend. Maintaining a healthy natural ecosystem is a necessity for the sustainable development of mankind. Similarly, maintaining a healthy ecosystem of open source software is also a prerequisite for the sustainable development of open source communities, such as GitHub. This paper takes GitHub as an example to analyze the health condition of open source ecosystem and, also, it is a research area in Symmetry. Firstly, the paper presents the healthy definition of GitHub open source ecosystem health and, then, according to the main components of natural ecosystem health, the paper proposes the health indicators and health indicators evaluation method. Based on the above, the GitHub ecosystem health prediction method is proposed. By analyzing the projects and data collected in GitHub, it is found that, using the proposed evaluation indicators and method, we can analyze the healthy development trend of the GitHub ecosystem and contribute to the stability of ecosystem development. Symmetry 2019, 11, 144 3 of 16 sustainability in natural ecosystems. Liao et al. [20] measured the sustainability of open source software ecosystem from the aspects of openness, stability, activity, and extensibility, and applied the assessment method of open source software ecosystem sustainability from the target level, criterion level, and measurement level to stack overflow.At present, the research of software ecosystem mainly focuses on system structure, participants, and ecosystem characteristics. The research on ecosystem health is mainly based on its theoretical research, and there is less quantitative analysis of the health indicators. Therefore, it is worthwhile to get into the questions of how to analyze the GitHub ecosystem health quantitatively, and how to predict the healthiness of the ecosystem. GitHub Ecosystem Health Prediction MethodIn this section, the working process of GitHub ecosystem and the various behaviors of users in the GitHub ecosystem will be introduced in Section 3.1, and then followed by the Health Prediction Model of GitHub Ecosystem (HPGE model). Figure 1 is an overview of the HPGE model. Symmetry 2019, 11, x FOR PEER REVIEW 3 of 16 sustainability in natural ecosystems. Liao et al. [20] measured the sustainability of open source software ecosystem from the aspects of openness, stability, activity, and extensibility, and applied the assessment method of open source software ecosystem sustainability from the target level, criterion level, and measurement level to stack overflow.At present, the research of software ecosystem mainly focuses on system structure, participants, and ecosystem characteristics. The research on ecosystem health is mainly based on its theoretical research, and there is less quantitative analysis o...