Volume 140

In the context of the increasingly severe global energy crisis and environmental issues, new energy vehicles have gradually become a sustainable transportation solution and a development trend in the automotive industry. Against this backdrop, BYD has attracted widespread attention with its outstanding performance in the field of new energy vehicles. As a globally influential company, BYD has been committed to research and innovation in new energy technology. After years of relentless efforts, it has achieved significant success in the field of new energy vehicles. This study aims to explore in depth the reasons behind BYD's increasing sales after successfully manufacturing new energy vehicles. It focuses on analyzing where the significant advantages of new energy vehicles lie by conducting a detailed comparison of BYD's advantages and sales volume analysis, as well as comprehensively analyzing relevant research findings from various aspects such as domestic and international markets for new energy vehicles, consumer behavior, and policy influences. This research is of great significance. On one hand, it helps deepen understanding of the development dynamics and trends in the new energy vehicle industry, providing useful references for other companies; on the other hand, it provides a decision-making basis for policymakers to better promote the development of the new energy vehicle industry and optimize energy structure while protecting the environment. The ultimate conclusion drawn from this study is that BYD's sales growth benefits from practical characteristics and cost-effectiveness of new energy vehicles, while subsidies for these vehicles also play an important role.

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With the dramatic speed of developing the advanced technology and the huge demand for the traffic. In order to minimize the cost and also to use the human resources more efficiently, autonomous driving was encouraged. There are plenty of embedded systems which are associated in the driving operating system and sensor technique is one of the most important appliance in the whole system. This essay is going to focus on all kinds of applications of sensors used in the autonomous driving technique and also the improvements that the sensors can have.The advancement of sensor technology is pivotal in the development of intelligent driving systems, significantly enhancing vehicle safety, navigation, and automation. This research paper explores various sensor technologies, including LiDAR, radar, cameras, and ultrasonic sensors, and their applications in intelligent driving. By examining the functions, advantages, and limitations of each sensor type, this study highlights their roles in critical driving tasks such as obstacle detection, lane keeping, and adaptive cruise control. Furthermore, the integration of these sensors within vehicle systems is discussed, emphasizing the importance of data fusion and machine learning algorithms in processing sensor data for real-time decision-making. The paper concludes with a discussion on future trends and challenges in sensor technology for intelligent driving, suggesting areas for further research and development.

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With the increasingly serious global environmental problems and the depletion of fossil fuel resources, new energy vehicles, especially electric vehicles, have become a key way to achieve sustainable development. However, the main challenges limiting the wide application of new energy vehicles remain the diversity of charging methods and their efficiency. This paper based on existing literature and data research systematically analyzes the technical characteristics, efficiency and application scenarios of DC fast charging, AC home charging and other charging methods. By discussing the energy efficiency of charging equipment, the energy loss during charging, and the factors affecting the charging mode and efficiency, this paper aims to provide methods and strategies to improve the charging efficiency. In addition, the study also assesses the impact of different charging methods on the development of the new energy vehicle market, revealing the importance of efficient charging methods in driving market growth. Through the comparison of DC fast charging and AC home charging, the existing problems and future optimization direction of current charging methods are revealed.

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The integration of multimodal artificial intelligence (AI) has shown immense promise in enhancing cancer detection and diagnosis by leveraging diverse medical data, such as imaging, genomic, and clinical records. Traditional diagnostic methods, while effective in certain contexts, are limited by their inability to comprehensively capture the complex characteristics of diseases. Multimodal AI addresses these limitations by synthesizing data from multiple sources, leading to more precise and early-stage detection of cancer. This paper provides an in-depth analysis of key multimodal fusion methods, including feature-level fusion, decision-level fusion, and dataset-level fusion, each offering distinct advantages and challenges. By reviewing the current state of multimodal AI applications in cancer diagnostics, this paper highlights the strengths of these methods, explores their limitations, and discusses potential solutions for improving data privacy, evaluation standards, and explainability. Furthermore, the paper outlines future directions for multimodal AI, emphasizing its transformative potential in revolutionizing personalized cancer treatment and early intervention strategies.

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As an important application direction of urban computing, traffic flow prediction plays an important role in modern traffic management, urban planning and sustainable development. In recent years, many cutting-edge studies in the field of traffic flow prediction have had a significant impact and promoted the development of practical applications in this field. This paper mainly focuses on the research results of various traffic prediction directions. According to the actual environment and the functional characteristics of the research results, the research is classified into three aspects: data acquisition, feature engineering, and prediction model optimization. It also summarizes the optimization effects of research on traffic flow prediction in sensor data acquisition, data outlier processing, neural network prediction technology, etc. This paper first proposes three important aspects that affect traffic flow prediction and classifies recent research results. Then, the functions and impacts are analyzed from various aspects, and the advantages and progress of the research results are analyzed by comparing most mainstream methods. Then, the problems and limitations of the research are analyzed and discussed in combination with the actual road environment. Finally, the future research direction and development trend of this field are prospected, and the full text is summarized.

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Rising global vehicle numbers lead to traffic congestion, which is influenced by temperature. Nowadays, temperature forecasting has reached a high level of sophistication, so it is feasible to predict traffic volume based on the forecasting temperatures. To predict the traffic volume, collected the traffic and weather data of I-94 Interstate highway. Based on the data, the simple linear regression model (LR), polynomial regression model (PR) and random forest (RF) were applied. With the comparison between simulation performances, RF demonstrated the highest accuracy, with a correlation coefficient (R2) of 0.8364. To bolster the simulation performances of regression models, more constraints should be involved such as rainfall, snowfall, and cloud cover, should also be considered. Besides, more sophisticated algorithms, such as regression-enhanced random forests (RERFs) and improved random forest classifiers (RFC), should be applied as well. In conclusion, as more related factors are involved, the performance of regression models will be better, which serve for traffic management effectively, with accurate traffic flow prediction.

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Weather plays a broad and decisive role in many areas. Its volatility can disrupt traffic and endanger lives. It is therefore imperative to accurately predict its impact. Improved forecasting accuracy can aid multi-industry decision making. Traffic authorities can control traffic in advance; Agriculture can adjust its strategy in time; Resource allocation can be optimized in the energy sector. The rise of machine learning and deep learning technologies has opened up new prospects for weather-related forecasting. This article takes a methodical look at the application of machine learning and deep learning to traffic and weather forecasting, dissecting the details of model construction, data processing processes, and performance evaluation metrics. By comparing the advantages and disadvantages of each model, it provides ideas for model improvement, powerfully guides future research direction, and lays the foundation for building a more accurate prediction framework. The research direction of this thesis has far-reaching theoretical and practical value.

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The application of 3D printing technology in organ regeneration is one of the hot topics in medical research today. As well as, the researchers found that the technology has made significant progress in personalized organ production, tissue engineering, biological scaffolds, and cell printing, but there are still many challenges in terms of production costs, manufacturing efficiency, quality control, safety verification, and ethical laws. Therefore, the research theme of this paper is to explore the application, prospect and challenge of 3D printing technology in organ regeneration. Through a literature review, this paper summarizes and analyzes the existing research results. The results of the study found that 3D printing technology has great application potential in organ regeneration, but it also faces some technical and non-technical challenges. Future development directions include improving technical performance, developing intelligent bio-inks, realizing high-precision multi-material bio-3D printing, etc., to promote the wide application of 3D printing technology in the medical field.

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This article provides a detailed examination of the principles and applications of radar technology within autonomous driving systems, with a particular focus on its integration into Advanced Driver Assistance Systems (ADAS). It highlights the essential roles of radar sensors in critical technologies including Adaptive Cruise Control (ACC), Blind Spot Monitoring (BSM), Automatic Emergency Braking (AEB), and Automated Parking Assistance (APA). The discussion extends to address the challenges and opportunities associated with improving radar resolution, the fusion of multiple sensors, the design of multi-modal radars, and the optimization of data processing platforms. These advancements are shown to substantially enhance driving safety and comfort, reduce traffic accidents, and protect lives and property. The synergistic integration of high-frequency radar, 5G communication, and multi-modal radar technologies significantly boosts the sensing capability and environmental adaptability of autonomous driving systems. The article emphasizes the pivotal role of radar technology in achieving safer and more efficient autonomous driving. Future technological advancements are expected to further expand and refine the application of radar sensors in ADAS, enhancing radar resolution, multi-modal integration, and exploring more efficient computation and data processing methods, thereby driving the comprehensive development of autonomous driving technology.

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The present paper provides a comprehensive review of the application of 3D printing in the automotive industry, which is an emerging field with promising development prospects. Furthermore, this technology possesses exceptional characteristics that have the potential to revolutionize and steer the current automotive industry toward new horizons. This article provides a concise overview of the disparities between 3D printing and conventional manufacturing technologies, while also offering a succinct introduction to prevalent methodologies in 3D printing. This paper focuses on the application of 3D printing in four areas: automotive weight reduction, maintenance, manufacturing material diversification, and research and development. Additionally, it highlights that 3D printing technology faces challenges such as a lack of standardized specifications, limited theoretical models, and lengthy single-piece production cycles. This paper aims to provide the latest advancements and challenges in the application of 3D printing in the automotive industry, catering to the needs of researchers and practitioners in related fields, thus offering valuable support for their scholarly endeavors.

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