The profound impact of China's concept of healthy development on various fields of society has influenced the mode of university education, and has gradually undergone changes in actual teaching modes, ways of thinking, and technologies. As one of the important educational courses in universities and sports departments, the reasonable introduction and implementation of functional training models in practical guidance can deepen students' learning of basic dance and skills, and further improve the performance of the dance stage. As a mentor, teachers should play the role of functional guidance essentials based on students' differentiated training abilities, optimize educational content from students' physical function training, and make adjustments. The professional functional training method is adopted to establish the practical application and promotion of functional training in university physical education.

This paper delves into the analysis of the physical flow patterns of users and its subsequent influence on their purchasing behavior. The research methodology encompassed surveying a substantial sample size of 400 users actively engaged with travel applications. The gathered data underwent meticulous analysis employing a combination of descriptive statistics and structural equation modeling techniques. The findings from this study have unveiled noteworthy insights into user behavior within travel applications. It is evident that the inclination to engage with the system has a substantial and positive impact on users’ purchase intentions. Moreover, the motivation behind users’ system usage has a direct bearing on their purchase intentions, primarily mediated by the enjoyment derived from the overall experience. This research underscores the pivotal role played by travel applications in the contemporary travel industry landscape. As travelers increasingly rely on digital platforms to plan their trips and make informed choices, understanding the intricate dynamics of user engagement, motivation, and subsequent purchasing decisions within these applications is paramount. This deeper comprehension not only sheds light on consumer behavior but also empowers businesses to tailor their offerings and enhance user experiences, thereby solidifying the indispensable position of travel applications in the ever-evolving travel sector.

This study investigated the relationship between telecommunications development, trade openness and economic growth in South Africa. It determined explicitly if telecommunications development and trade openness directly impact economic growth or whether telecommunications strengthen or weaken the link between trade openness and economic growth using the ARDL bounds test methodology. The findings reveal that both telecommunications development indicators and trade openness significantly and positively impact South Africa’s GDP in the short and long terms. The study also found that control variables like internet usage and gross fixed capital formation significantly and positively influence GDP. Conversely, inflation was found to consistently affect GDP negatively and significantly. The findings from the ARDL cointegration analysis affirm a long-run economic relationship between the independent variables and GDP. The study also established that telecommunications development slightly distorts trade in the foreign trade-GDP nexus in South Africa. Despite this, the negative interaction effect is not substantial enough to overshadow the positive impact of trade openness on economic growth. From a policy perspective, the study recommends that South African policymakers prioritise enhancing local goods’ competitiveness in global markets and reducing trade barriers. It also advocates for improving the accessibility and affordability of telecommunications technologies to foster economic development.

Infrared thermal imaging technology is another new branch for medical imaging after traditional medical imaging technologies such as X-ray, ultrasound and magnetic resonance (MRI). It has the advantages of noninvasive, nondestructive, simple and fast. Its application can radiate multiple clinical departments. This paper mainly expounds the principle, influencing factors of medical infrared thermography and its application in radiation protection and other medical fields.

Nanotechnology is recognized as one of the high and new technologies in the 21st century. Carbon nanotubes have been widely used in molecular sieve, drug transport and seawater desalination due to their unique mechanical, electrical, optical and other excellent properties. As the main representative of carbon nanotube macroscopic materials, carbon nanotube film not only retains the microscopic properties of carbon nanotube, but also has good mechanical properties and stable chemical properties. The preparation and application of carbon nanotubes (CNTS) have attracted extensive attention from scholars at home and abroad. In this paper, the research on carbon nanotube films in recent years is reviewed. Based on the preparation of carbon nanotube films, chemical vapor deposition, LB (Langmuir-Blodgett) film and electrostatic layer-by-layer self-assembly techniques are briefly described. In addition, the applications of carbon nanotubes in biological field, photoelectric nano devices, water treatment, seawater desalination and other fields are also described.

Abrupt changes in environmental temperature, wind and humidity can lead to great threats to human life safety. The Gansu marathon disaster of China highlights the importance of early warning of hypothermia from extremely low apparent temperature (AT). Here a deep convolutional neural network model together with a statistical downscaling framework is developed to forecast environmental factors for 1 to 12 h in advance to evaluate the effectiveness of deep learning for AT prediction at 1 km resolution. The experiments use data for temperature, wind speed and relative humidity in ERA-5 and the results show that the developed deep learning model can predict the upcoming extreme low temperature AT event in the Gansu marathon region several hours in advance with better accuracy than climatological and persistence forecasting methods. The hypothermia time estimated by the deep learning method with a heat loss model agrees well with the observed estimation at 3-hour lead. Therefore, the developed deep learning forecasting method is effective for short-term AT prediction and hypothermia warnings at local areas.