A Cloud Computing-based Research on the Relationship between Educational Internship and Pre-Service English Teachers' Professional Development

Educational internships are a crucial component of teacher education programs, as they evaluated the experience practical opportunities for preschool teachers with the development of skills, experience of practical scenarios, and experience feedback. Through educational internships, pre-service teachers can also develop their professional identities and gain a deeper understanding of the complex challenges and rewards of teaching. This paper explores the relationship between educational internships and heuristic optimization cloud environments for the professional development of English pre-service teachers. The research presents a novel approach called Cloud Spider Wolf Optimization (CSWO) that utilizes cloud computing technology to enhance the effectiveness of educational internships. The study evaluates the impact of CSWO on pre-service teachers' professional development by examining their learning outcomes and perceptions of the internship program. The data for the analysis is collected through primary data among the pre-service English teachers. The data for analysis is collected from 200 pre-service teachers in academic schools in China. The results indicate that CSWO significantly improves pre-service teachers' professional development by providing them with opportunities to engage in authentic, real-world tasks that enhance their knowledge and skills in English language teaching. The study also suggests that the use of cloud computing technology can provide a valuable tool for enhancing the effectiveness of educational internships. The findings have important implications for teacher preparation programs and suggest that the integration of cloud computing technology and heuristic optimization techniques can be used to improve the quality of teacher education

Social contagions on interdependent lattice networks

Although an increasing amount of research is being done on the dynamical processes on interdependent spatial networks, knowledge of how interdependent spatial networks influence the dynamics of social contagion in them is sparse. Here we present a novel non-Markovian social contagion model on interdependent spatial networks composed of two identical two-dimensional lattices. We compare the dynamics of social contagion on networks with different fractions of dependency links and find that the density of final recovered nodes increases as the number of dependency links is increased. We use a finite-size analysis method to identify the type of phase transition in the giant connected components (GCC) of the final adopted nodes and find that as we increase the fraction of dependency links, the phase transition switches from second-order to first-order. In strong interdependent spatial networks with abundant dependency links, increasing the fraction of initial adopted nodes can induce the switch from a first-order to second-order phase transition associated with social contagion dynamics. In networks with a small number of dependency links, the phase transition remains second-order. In addition, both the second-order and first-order phase transition points can be decreased by increasing the fraction of dependency links or the number of initially-adopted nodes.This work was partially supported by National Natural Science Foundation of China (Grant Nos 61501358, 61673085), and the Fundamental Research Funds for the Central Universities. (61501358 - National Natural Science Foundation of China; 61673085 - National Natural Science Foundation of China; Fundamental Research Funds for the Central Universities)Published versio

Promoting information spreading by using contact memory

Promoting information spreading is a booming research topic in network science community. However, the exiting studies about promoting information spreading seldom took into account the human memory, which plays an important role in the spreading dynamics. In this paper we propose a non-Markovian information spreading model on complex networks, in which every informed node contacts a neighbor by using the memory of neighbor's accumulated contact numbers in the past. We systematically study the information spreading dynamics on uncorrelated configuration networks and a group of $22$ real-world networks, and find an effective contact strategy of promoting information spreading, i.e., the informed nodes preferentially contact neighbors with small number of accumulated contacts. According to the effective contact strategy, the high degree nodes are more likely to be chosen as the contacted neighbors in the early stage of the spreading, while in the late stage of the dynamics, the nodes with small degrees are preferentially contacted. We also propose a mean-field theory to describe our model, which qualitatively agrees well with the stochastic simulations on both artificial and real-world networks.Comment: 6 pages, 6 figure

Spectral gap for measure-valued diffusion processes

The spectral gap is estimated for measure-valued diffusion processes induced by the intrinsic/extrinsic derivatives on the space of finite measures over a Riemannian manifold. This provides explicit exponential convergence rate for these processes to approximate the Dirichlet and Gamma distributions arising from population genetics.Comment: 18 page