Introduction: With the adoption of the rural rehabilitation strategy in recent years, China’s rural tourist industry has entered a golden age of growth. Due to the lack of management and decision-support systems, many rural tourist attractions in China experience a “tourist overload” problem during minor holidays or Golden Week, an extended vacation of seven or more consecutive days in mainland China formed by transferring holidays during a specific holiday period. This poses a severe challenge to tourist attractions and relevant management departments. Objective: This study aims to summarize the elements influencing passenger flow by examining the features of rural tourist attractions outside China’s largest cities. Additionally, the study will investigate the variations in the flow of tourists. Method: Grey Model (1,1) is a first-order, single-variable differential equation model used for forecasting trends in data with exponential growth or decline, particularly when dealing with small and incomplete datasets. Four prediction algorithms—the conventional GM(1,1) model, residual time series GM(1,1) model, single-element input BP neural network model, and multi-element input BP network model—were used to anticipate and assess the passenger flow of scenic sites. Result: The multi-input BP neural network model and residual time series GM(1,1) model have significantly higher prediction accuracy than the conventional GM(1,1) model and unit-input BP neural network model. A multi-input BP neural network model and the residual time series GM(1,1) model were used in tandem to develop a short-term passenger flow warning model for rural tourism in China’s outskirts. Conclusion: This model can guide tourists to staggered trips and alleviate the problem of uneven allocation of tourism resources.

This study explores the impact of technology effectiveness, social development, and opportunities on higher education accessibility in Myanmar, focusing on private higher education institutions. Utilizing a sample of 199 respondents, with an average age of X (SD = Y), the research employs standardized questionnaires and descriptive statistics, correlation analysis, and multiple regression analysis to examine the relationships between these variables. The findings indicate that technology effectiveness significantly enhances higher education accessibility, with strong positive correlations (r = 0.752, p < 0.001) and substantial impacts on educational outcomes (β = 0.334, p = 0.001). Social development also plays a crucial role, demonstrating that supportive social norms and community engagement significantly improve accessibility (β = 0.405, p < 0.001). Opportunities provided by technological advancements further contribute to enhanced accessibility (β = 0.356, p < 0.001), although socio-political and economic challenges pose significant barriers. The study highlights the interconnectedness of these factors and their collective influence on educational accessibility. Practical implications include the need for strategic investments in technological infrastructure, promotion of supportive social environments, and innovative solutions to leverage opportunities. Future research directions suggest longitudinal studies, broader demographic scopes, and in-depth analyses of specific technological and infrastructural challenges. By addressing these areas, stakeholders can develop effective strategies to improve higher education accessibility, ultimately contributing to the socio-economic development of Myanmar.

It is well known that determining the exact values of crossing number for circulant graphs is very difficult. Even so, some important results in this field are still proved. D.J. Ma was proved that the crossing number of C(2m + 2, m) is m + 1[8]. Then such problem for C(n, 3) was further solved [7]. Pak Tung Ho and X. Lin obtained accurate values for the crossover numbers of C (3m, m) and C (3m + 1, m)[4][5]. In this paper, as a complement, we show that the edges from the principal cycle of C(9, 3) do not cross each other in an optimal drawing.

With the continuous promotion and deepening of quality education, new teaching goals have been proposed for major universities and teachers, requiring teachers not to blindly pursue the academic performance of college students as the goal, but to achieve the comprehensive development of college students as the main teaching goal. Therefore, teachers need to actively transform educational concepts, transform educational methods, enrich classroom content, and provide high-quality teaching classrooms for college students, Help college students improve in all aspects. For college students, it is not only necessary to cultivate correct worldviews and values, establish positive life goals and attitudes, but also to enhance their resistance to pressure when facing society. Therefore, when teaching, teachers not only need to explain knowledge, but also serve as guides on the life path of college students, helping them guide and improve their ideological and moral character, Thus achieving significant development of ideological and political education in universities.

Imagining people’s functions in everyday life and work without the use of ICT, seems difficult. Their application is ubiquitous everywhere, regardless of which aspect it is viewed from, because it has a strong function in ensuring the competitiveness of various systems at the micro and macro levels. Numerous national and multinational strategies try to encourage educational systems to put a greater focus on ICT to more efficiently acquire skills, competencies, and knowledge, which should represent added value to all generations in the future. This article analyzes the progress of the ICT development index (IDI) in Scandinavian countries by comparing these countries in the European region. It is known that the Scandinavian countries belong to that part of the countries that have recognized the importance of involving ICT in education programs, which improves the economy of a certain country. Given this, the research reveals how ICTs play a key role in improving socio-economic development in Scandinavian countries.

In this Data science research on Education, it analyses the alcohol consumption, parent’s education, study time and other factors may influence on student performance.