The most direct effect on the efficiency is the temperature of solar modules. I discussed the reasons why the temperature of modules can affect the efficiency of solar cells and the explanation of how can we improve the solar system to decrease the influence of high cell temperature. The scarcity of traditional energy increases, so the scale of use of renewable energy increases. The efficiency of the energy-generating system is the most important factor that users consider. The extent of change in the temperature of solar modules is the highest, so the temperature of solar modules can cause an influential decrease in the efficiency of energy generation. I have used the data based on experiments set by other authors to illustrate the extent of influences of four main variables on the efficiency of the solar cells. I used of the properties of air and water to design two schemes to minimize the effects of high temperatures of solar cells. I compared the change in efficiency under one unit increase in variables. All of the data used in calculating or referring are from different experiments done by different authors. I connected the results with real conditions to determine the most influential variables-temperature of modules. Based on the theoretical result, the shading effect has the largest value of decrease in efficiency, so the effects of shading are considered by manufacturers a lot. Based on real-life situations, the scale of change in the temperature of modules and the actual temperature of modules are always higher than the standard operating temperature, 25℃, so the temperature of modules is the most influential variable. Monocrystalline silicon solar cells have different extents of influence of variables to polycrystalline silicon solar cells, these two types of solar cells are suitable to be installed in regions with low humidity. However polycrystalline solar cells are more sensitive to increases in the temperature of modules, so they need to pay more attention and avoid the effects of high temperature of modules.

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Deep learning-based object detection algorithm is becoming more and more important in autonomous driving area with an increasing amount and trending these days. This article first provides definitions and introduces an autonomous driving and object detection. Subsequently, a detailed discussion is conducted on object detection, comparing traditional object detection methods with deep learning object detection algorithms. The shortcomings of traditional methods highlighted the advantages of deep learning-based object detection algorithms, laying the groundwork for the use of deep learning object detection algorithms in the following text. Finally, several detection objects and detection scenarios are introduced. The detection objects are divided into different parts, including moving targets, stationary targets, and infrared targets. Moving targets such as pedestrians and vehicles, while stationary targets include traffic signs and lanes. The detection scenarios are classified into ordinary scene detection and complex scene detection. In the discussion section, the commonly used datasets for autonomous driving target detection training are listed first, such as the KITTI dataset, the COCO dataset, and so on. Subsequently, a discussion was conducted on algorithms, mainly focusing on the models and features in one stage and two stages. For different types of algorithms, this article discusses the advantages and disadvantages of the algorithms. When judging the superiority or inferiority of algorithms, there are usually two aspects: detection accuracy and detection speed. FPS is a commonly used indicator for detection speed, and detection accuracy mainly covers five aspects, namely accuracy, precision, recall, AP (average precision), and MAP (mean average precision). Finally, how the improved algorithms are applied and solve the existing problems is discussed.

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The paper aims to investigate whether electric cars are environmentally friendly. With the growing global awareness of environmental issues, the impact of automobile emissions on the environment has become a major concern. Many countries are implementing measures to replace fuel cars with new energy-efficient electric vehicles, which will have a significant impact on both the environment and the international economy. This paper discusses the environmental impact of the shift towards electric vehicles, focusing on two aspects: the energy materials used and the evaluation standards for automobiles.

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This paper provides an overview of AI and ChatGPT, covering their definitions, the evolution of AI, ethical concerns surrounding their interaction with humans, and their extensive applications in everyday life. The advantages and disadvantages of AI and ChatGPT are examined. Furthermore, the essay delves into the technical aspects by explaining the algorithm and presenting a graph illustrating the functioning of ChatGPT. A historical background of ChatGPT is also included. The paper specifically focuses on the impact of AI in four industries: education, finance, healthcare, and computing. It explores their applications within these sectors and makes predictions regarding the future implications of AI on human life and work. In medical industry, AI can help human a lot in new drug research, medical imaging, medical services innovation and patient health management. In education realm, the application of AI can help people on intelligence process support, intelligence teacher assistant intelligence education and management, and intelligence environment building. Also, the application of AI in finance realm has many benefits in bank, insurance, capital markets business and financial support industry. What is more, the application of it in computer industry helps human on network security, systematic reviews and data analysis.

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