Pain, as one of the major clinical and social problems, still lacks effective objective evaluation indicators. At present, nearly one in five adults suffer from different types and different parts of pain. An important feature of clinical pain is persistent pain, in addition to sensory discrimination processes, it involves a range of brain regions involved in top-down cognitive and emotional processing. However, clinical pain is often affected by multiple factors and is difficult to assess objectively. Recently, blood, urine, cerebrospinal fluid, and biopsy tissue have been identified using novel, sensitive, and specific protein analysis methods in an attempt to detect changes in biological markers associated with different pain types. This paper will summarize the biological markers related to different parts of pain in the latest relevant studies at home and abroad and focus on the application prospect of such biological markers in the diagnosis, treatment, and prognosis evaluation of the pain.

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Because of globalization and information technology (IT), e-commerce (EC) has changed business practices and had an impact on established market systems. Global EC sales are still increasing, which emphasizes how crucial it is to comprehend consumer purchasing behavior. Using a Kaggle dataset named "Exploring E-commerce Trends," which contains product data including price, rating, stock amount, and sales, this paper examines consumer purchasing behavior. The paper used Multiple Linear Regression and the Random Forest methods. The quantity of stock, price, and the number of reviews were found to be important predictors of sales using the Random Forest ensemble learning technique. However, the model exhibited overfitting, performing well on the training set but poorly on the testing set. Multiple Linear Regression results showed minimal explanatory power for sales, indicating limitations in the model's effectiveness. While the Random Forest model identified key factors influencing sales, its overfitting and the ineffectiveness of the Multiple Linear Regression model suggest the need for more robust methods to predict customer purchasing behavior in e-commerce accurately. Future research should integrate additional variables and employ more sophisticated models to enhance prediction accuracy and business decision-making.

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Regional economic disparities are widespread, especially in China. This is reflected not only in the differences between the four regions, but also in the differences between provinces. At the same time, the regional economic differences within each province are also very obvious. This paper studies the gap between the economic development levels in Zhejiang Province, which contributes to the balanced development and sustainable development of the overall economy of Zhejiang Province, in order to intuitively see the gap between the cities. This paper uses SPSS to reduce the dimension of factor analysis and calculate the comprehensive score through 10 indicators reflecting the level of economic development in 11 cities. Finally, this paper finds that the economic development of each city in Zhejiang Province has great potential. But at the same time, it exposes the imbalance of economic development in the province. The economic development level of Hangzhou, Ningbo and Zhoushan is far higher than that of other cities in the province, and the economic development level of some cities is general. The development level of some cities is poor. According to the research results, the causes and problems of the current situation of economic development are analyzed. Reasonable suggestions are put forward to further promote the coordinated development of the economy.

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With the development of driverless cars, intelligent driving technology is increasingly used in the automotive industry, monocular vision sensor plays an indispensable role in intelligent driving technology because of its simple structure, low cost and abundant information. This paper discusses and optimizes the application of the monocular vision sensor in intelligent driving. The basic principle and key technologies of the monocular vision sensor are described in detail. In the specific application of the monocular vision sensor, this paper focuses on the monocular vision sensor's depth learning network, multi-information fusion technology, improved target detection and tracking algorithm. Through in-depth research and analysis, a series of optimization strategies based on the monocular vision sensor, such as the FAST Region Convolutional Neural Network (FAST–RCNN) vehicle target detection method and improved Scale-Invariant Feature Transform (SIFT) algorithm, are proposed. Finally, this paper summarizes the intelligent driving technology based on the monocular vision sensor and suggests that the monocular vision sensor will play a more important role in intelligent driving technology. Future research shall focus on improving the accuracy of the algorithm, such as the development of end-to-end convolutional neural network fusion methods, the proposed depth multi-modal sensor fusion network, and so on.

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