With the gradual formation of the open pattern of higher education, the issue of the cultural integration of international students has attracted increasing attention. This paper conducts a questionnaire survey of 2000 international students in East China and used factor analysis and structural equation modeling to determine the effects of life adaptation, interpersonal communication, and the training system on cultural integration, with possible moderation by study adaptation to explore the cultural integration of international students. The study found that the cultural integration of international students is jointly affected by multiple realistic factors, among which life adaptation and interpersonal communication are the primary factors. Furthermore, the training system has an important impact on the cultural integration of international students through study adaptation. Accordingly, we argue that universities should strengthen the informal organization and guidance of international students, enhance individual adaptability, promote international exchange, cooperation, and policy regulation of international students, and improve their training system. Moreover, efforts are needed to improve the study adaptability of international students so as to comprehensively address the problem of the cultural integration of international students and improve their education level.

The unsafe behavior of miners seriously affects the safety of deep mining. A comprehensive evaluation of miners’ unsafe behavior in deep coal mines can prevent coal mine accidents. This study combines HFACS-CM, SEM, and SD models to evaluate miners’ unsafe behaviors in deep coal mining. First, the HFACS-CM model identifies the risk factors affecting miners’ unsafe behavior in deep coal mines. Second, SEM was used to analyze the interaction between risk factors and miners’ unsafe behavior. Finally, the SD model was used to simulate the sensitivity of each risk factor to miners’ unsafe behavior to explore the best prevention and control strategies for unsafe behavior. The results showed that (1) environmental factors, organizational influence, unsafe supervision, and unsafe state of miners are the four main risk factors affecting the unsafe behavior of miners in deep coal mines. Among them, the unsafe state of miners is the most critical risk factor. (2) Environmental factors, organizational influence, unsafe supervision, and the unsafe state of miners have both direct and indirect impacts on unsafe behaviors, and their immediate effects are far more significant than their indirect influence. (3) Environmental factors, organizational influence, and unsafe supervision positively impact miners’ unsafe behavior through the mediating effect of miners’ unsafe states. (4) Mental state, physiological state, business abilities, resource management, and organizational climate were the top five risk factors affecting miners’ unsafe behaviors. Taking measures to improve the adverse environmental factors, strengthening the organization’s supervision and management, and improving the unsafe state of miners can effectively reduce the risk of miners’ unsafe behavior in deep coal mines. This study provides a new idea and method for preventing and controlling the unsafe behavior of miners in deep coal mines.

The coupling of and coordination between science and technology innovation (STI) and sustainable development (SD) is a basic requirement for Anhui Province’s economic high-quality development. According to panel data of 16 prefecture-level cities in Anhui Province from 2010 to 2021, the entropy method was applied to quantify the comprehensive development level of the two systems. The models of coupling coordination degree, grey GM (1, 1), and ARIMA prediction were constructed to analyze the spatiotemporal dynamic evolution features of the two systems’ coupling coordination. In the time series, the two systems’ comprehensive development showed a steady increase, a high level of coupling, and an increasing overall trend of coupling coordination. Moreover, the two systems’ coupling and coordination levels show the gradient spatial differentiation characteristics of “central > east > west.” The prediction shows that the two systems’ coupling coordination degree exhibits a monotonic increasing trend and reaches the optimal coupling coordination state around 2030. This study provides a decision-making reference for the implementation of the innovation-driven development strategy of Anhui Province.

High-energy-consuming industries (HECI) are the main sectors of energy consumption and carbon emissions. Technological innovation (TI) driving the energy conservation and emission reduction (ECER) of HECI is critical to achieving carbon neutrality. Using a system theory perspective, this study decomposed the system of ECER of HECI driven by TI into two stages, TI and ECER. Based on panel data of 30 provinces in China from 2015-2020, the network weighted stochastic block model (WSBM) and panel Tobit models were used to measure the efficiency of ECER of HECI driven by TI; the spatial and temporal differences and influencing factors were analyzed. The overall efficiency exhibited a V-shaped pattern of falling, then rising. From a regional perspective, the Northeast region shows a breakthrough growth from a low point to a high point. The East, Central and West regions show similar trends to the national overall, with the total efficiency of the East slightly higher than the West and a lower total efficiency in the Central region. In terms of two-stage efficiency, the efficiency of the ECER stage was higher than that of the TI stage. The rationalization level of industrial structure, green finance development level, and investment in education were the main forces enabling TI to drive the efficiency improvement of ECER in HECI.

In this study, the focus was on the development of green energy and future prediction for the consumption of current energy sources and green energy development using an improved deep learning (DL) algorithm. In addition to the analysis of the current energy consumption used for the natural gas and oil as fuel, deep neural network algorithm is used to train the system as well as to process the data obtained previously, ranging from literature from the year 2003 until the year 2019, for consumption of fuel. Also, using the proposed algorithm to predict the development of green energy consumption till 2030 is presented in terms of solar and wind generators. The resulting study also focuses on depletion of energy currently used or pollution caused because of it. The green energy controlling issue can take effect by using multiple layers of handling different features extracted from different sources and then learning the system to control it.This study aims to take advantage of carbon emissions to reduce their impact and dependence in the future on environmentally friendly renewable energies. Predicting the correct and precise amount of energy consumption and increasing the amount of environmentally friendly energy lead to a healthy ecosystem. The expected green energy consumption in the future is almost 78.25 EJ in 2030 and will be, in total energy average, 56% in 2045. The aim is to reduce dependency on costly and environmentally harmful fuels.