This paper will discuss how the combination of digital media and immersive art exhibition can bring new interaction and participation experience to the audience under the reform of art media, as well as the significance of this combination in artistic expression, exhibition design and audience perception.Using a case study of teamLab in Japan. This paper evaluates the immersive exhibition of digital media intervention from four aspects: transformation, integration, cases, and reflection. It is found that while the emergence of digital media technology provides a new way of display and creative means for art exhibitions, there is also a certain contradiction between artistry and technicality in such exhibitions. It is necessary to balance the relationship between art and technology in order to achieve mutual promotion and common development of the two.

As the adoption of Radio Frequency Identification (RFID) technology continues to expand, concerns about its security have grown in significance. And the study of RFID security protocols aims to enhance system security and to provide solutions that can be applied to a variety of scenarios. Therefore, this paper delves into the security protocols employed in RFID technology, investigating their applications across a range of domains such as logistics tracking, financial payments, and access control systems. Besides, through a combination of literature review and comparative analysis, the study provides an in-depth exploration of the working principles, strengths, weaknesses, and suitable use cases of protocols based on cryptography, challenge-response, and time-based mechanisms. The results indicate that cryptography-based protocols perform well in high-security environments such as financial payments, albeit with high computational complexity. Challenge-response-based protocols enhance authentication in access control systems, while time-based protocols successfully mitigate replay attacks through timestamping mechanisms, making them ideal for intelligent transportation systems. In short, each security protocol presents distinctive strengths, and the selection or integration of these protocols should be tailored to specific application scenarios to create more secure and reliable RFID systems.

Unmanned aerial vehicles (UAVs) are characterized by safety, flexibility, and economy, and are widely used in many fields. At present, due to the limitation of UAV loads and the bottleneck of battery technology, UAVs can't carry out missions for a long time. The wireless charging system for UAVs has the advantages of safety, flexibility, automation, and environmental adaptability, so it has gained wide attention. Electromagnetic induction wireless energy transmission system is a system that transmits electrical energy through a magnetic field, and in electromagnetic induction wireless energy transmission system, it is necessary to reach the goals of lightweight, high efficiency, anti-skewing, and modularization. In order to achieve the goals of high efficiency and anti-deviation, this paper adopts three sets of series-connected rounded rectangular coils as the transmitting stage to improve the charging efficiency and anti-deviation ability; in order to achieve the goal of lightweight, this paper reduces the weight by changing the material of the magnetic core, and using the annulus core amorphous alloy core; in order to achieve the goal of modularity, this paper designs to carry multiple sets of receiving coils, which can be switched on and off according to the demand in order to satisfy the different power requirements. Finally, a bilateral LCC compensation topology network is established to control the input and output currents to achieve the design goal. The method proposed in this paper helps to realize the automatic charging of UAV and improve the endurance of UAV, so as to enhance the working hours and inspection range of UAV, and make UAV capable of doing more work.

China’s economic expansion during the last 30 years has been mostly driven by the real estate sector. However, since 2020, the Chinese market has experienced a lack of energy, particularly within the real estate industry. Therefore, it’s imperative to reanalyze the connection between them at this point in time. A clear understanding of the relationship between real estate investment and economic growth is essential for the maintenance of sustainable economic growth in the future. his essay quantitatively examines the relationship between real estate investment and economic growth in Chongqing Municipality over a 30-year period, utilizing co-integration theory, the Augmented Dickey-Fuller Test, Granger causality test, and regression analysis.The regression analysis demonstrates that real estate investment in Chongqing is a substantial catalyst for GDP growth and is essential for fostering economic development. However, in recent years, the housing market has become saturated, which means that it can no longer be used as a major factor to drive economic growth..At this point, there is an urgent need to explore new ways to maintain sustained economic growth.

The rapid evolution of mobile communication technologies, particularly with the transition from 5G to 6G, has heightened the demand for higher data capacity, lower latency, and improved network reliability. Traditional Distributed Radio Access Networks (D-RAN) struggle to meet these requirements, creating a gap that has led to the adoption of Cloud Radio Access Network (C-RAN) as the mainstream deployment model. The approach involves a combination of literature review and technological evaluation to assess the scalability and security challenges posed by C-RAN. Simulation models were employed to evaluate the performance of Wavelength Division Multiplexing (WDM) and Free Space Optical (FSO)- millimeter-wave (mmWave) integration in terms of network resilience and resource optimization. Despite its advantages, such as enhanced spectral efficiency and reduced energy consumption, C-RAN faces significant challenges related to high fiber-optic infrastructure demands and complex data processing requirements. The findings highlight C-RAN’s potential to meet the needs of 6G networks by optimizing resource allocation and enhancing network resilience. However, further advancements are required to overcome existing scalability and security issues. These insights underline C-RAN’s pivotal role in the future of wireless communication, driving the evolution toward autonomous and highly adaptive network architectures in 6G, capable of meeting future communication demands.

With the development of electronic vehicles, accurately predicting the price of vehicles is essential for both consumers and business. Thus, this study aims to explore the application of machine learning in vehicle price prediction, specifically focusing on the use of linear regression, a widely adopted technique in this domain. Utilizing a comprehensive dataset containing variables such as make, model, year, and mileage, the research develops a predictive model through rigorous data cleaning, feature engineering, and model tuning processes. The model’s performance will be analyzed by R-squared value and Mean Squared Error(MSE). The model’s predicting result will be visualized by scatter plot. The study also addresses potential biases and applies regularization techniques to enhance the model’s accuracy. Additionally, a comparative analysis with a Decision Tree model evaluates the relative performance and nuances of each approach. This research not only underscores the practical value of predictive modeling in the automotive industry but also offers insights into integrating more advanced machine learning techniques to further improve prediction accuracy. Additionally, in this research, the drawbacks of linear regression model will be indicated and the future works to improve the performance of the model will be provided.

As the limitations of 5G in spectrum scarcity and massive data transmission capability become evident, the vision of 6G technology emerges, aiming for global coverage, abundant spectrum resources, ultra-high-speed data transmission, minimal latency, and high reliability. Terahertz (THz) communication technology, with its rich spectrum resources, is seen as a key to achieving 6G objectives. This paper explores the application and challenges of THz communication in 5G and future networks, analyzes its basic theory, key technologies, channel characteristics, and modeling, and discusses its prospects and challenges in 6G and beyond. The paper provides an overview of THz communication, including its position in the electromagnetic spectrum, bandwidth potential, and advantages of short wavelengths. It discusses THz-related technologies, particularly the roles of THz electronics, photonics, and plasmonics in device development, and their contributions to 6G advancements. Future research directions are outlined, including optimizing transmission windows, applying AI in wireless communication, and the potential of 6G as a new generation of intelligent digital infrastructure. While THz communication shows great potential in 6G networks, it faces technical and economic challenges. This paper provides a solid theoretical foundation for understanding THz technology, but future work requires deeper exploration in experimental validation, technical detail research, and cost-benefit analysis.

As multi-agent systems are increasingly applied in various environments, achieving stable control under communication interference has become a key challenge. This paper proposes a stability control algorithm based on a Markov model and reinforcement learning, which enables the multi-agent system to achieve its goals through a reward and punishment mechanism under interference. The results demonstrate that the algorithm effectively handles communication interference, converges well, and ultimately completes control tasks, showing promising application prospects for the proposed solution.

Heart disease has long been a major threat to human health, causing about one-third of all deaths each year. Therefore, there is a need for accurate and effective prediction of cardiac disease using machine learning techniques. This paper uses data from 1,319 patients with heart disease and applies several machine-learning methods to study the relationship between a total of eight factors. The algorithms used in this paper include neural networks, decision trees, random forests, and so on. The best model was established by the random forest method, with an accuracy of 96.97%, precision of 97.03%, recall of 96.97%, and f1-Score of 0.9. It was found that troponin and CK-MB indicators had the highest influence weights, and the sum of the weights of each model accounted for more than 75% of the total weight, which demonstrated their significance in the prediction of heart disease, and the results could be utilized for the future prediction of heart disease. In addition, it also plays an essential role in the prediction of heart disease. To sum up, this is really important for the prediction of future heart disease.

Visible light communication (VLC) is an efficient wireless communication with great development prospects, which is closely related to our daily lives especially for indoor applications. This paper first introduced the development process of VLC, and some significant milestones were emphasized. Then some key technologies, including OOK, VPPM, CSK, OFDM, PWM, were mentioned to provide readers with a deeper understanding of the working principle of VLC. In addition, the paper discussed some prospects of VLC, as well as its practical applications classified based on technical principles. Moreover, the author also pointed out the current difficulties and challenges, specifically mentioning the difficulties faced by OFDM technology in VLC system and its current development process. Some suggestions were proposed for future advancement. With continuous exploration and research, VLC will flourish in future wireless communications.