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In the grey prediction, the GM(1, 1) model is an important type, but it sometimes shows big prediction errors. To improve the prediction precision of GM(1, 1) model, the paper makes improvements from the following three aspects: (1) to improve the data's adaptability to the model, the paper transforms the accumulated generating sequence of original time sequence; (2) to make the model meet the variation characteristics of data, the paper extends the grey action of traditional GM(1, 1) model; (3) to avoid big average simulation or prediction relative error of model, the paper considers the minimum of the maximum of the two errors as the optimization objective function. The new extended grey model is called the GM(1, 1, exp × sin, exp × cos) model. The paper uses an improved particle swarm optimization (PSO) algorithm for the parameter optimization of GM(1, 1, exp × sin, exp × cos) model and thus improves the model's convergence rate and precision. According to the model and method proposed, the paper builds a GM(1, 1, exp × sin, exp × cos) model for China's GDP per capita. Results show that the model has high precision. [ABSTRACT FROM AUTHOR]

In recent years, the focus on modernization and advanced communication technologies has shifted to smart cities, where society and families, particularly, are engaged with various means of entertainment, e.g., cinema. In the context of cinema, movies have a pivotal role, where cultural heritage, along with urban and rural images, have gradually been marginalized from the commercial field to an elite position. The real picture of rural life has been narrowed down to explore the local cultural heritage. In China, rural communities have existed for centuries, where the rural images of farmers depict their standard of living, civilization and the way in which the ecological environment is protected. This paper studies the factors relevant to the transmission of rural images for a better understanding of cultural heritage conducive to rural culture's inheritance and development. This paper first constructs water yield and soil conservation models for rural eco-environmental protection. Next, the paper analyzes the main factors that influence the communication of rural images in these models, followed by an analysis of the communication mode used by each factor. Moreover, the proposed study disseminates rural images by creating an imaging action framework. Wuzhen, a famous historic town in China, is taken as a case study for this paper. The study analyzed the representation of Wuzhen's rural pictures over time, with 77% of the content dominated by bridges and rivers. The highest number of shootings, involving 82 households, was recorded in 2018. Therefore, 2019 showed a slight decline in shootings, with 67 households involved, indicating potential fluctuations in interest or external factors affecting video production that year. However, in 2019, bridges and rivers still constituted 75% of the content, underscoring their enduring significance in portraying the rural beauty of Wuzhen. These findings highlight the appeal and importance of these natural features in the region's visual narrative. [ABSTRACT FROM AUTHOR]

In recent years, the convergence of the Internet of Things (IoT) and machine learning technologies has instigated advancements in IoT, and machine learning has revolutionized SMEs, reshaping their role in regional economic growth. These technologies provide SMEs with innovative tools, fostering connectivity within regional ecosystems. This transformation is crucial for China's economic realignment in its 'new normal' phase, with enterprises driving social and economic progress. This study proposed the XGBoost algorithm and IoT data enhance collaboration among SMEs in regional economies, fostering innovation and driving economic development. The role of IoT and machine learning in promoting sustainable regional economic prosperity. Technological innovation is essential for enterprise survival and growth in a competitive landscape. This paper examines the influence of environmental factors and geography on economic development across China's regions. It focuses on SME coupling and coordination, contributing to regional economic progress through performance evaluation. IoT integration provides SMEs with abundant real-time data, enabling deep insights into customer behavior, supply chains, and production. Simultaneously, the XGBoost algorithm efficiently processes and extracts actionable insights from this data. Data from 11 provinces in the Yangtze River economic belt highlights Jiangsu Province's leadership in regional innovation performance for 2015 and 2020. The empirical results, grounded in datasets amalgamating information from these provinces, attest to the potential of this IoT and XGBoost-driven approach. Boasting an impressive accuracy rate of 91.7%, surpassing alternative machine learning techniques like SVM, KNN, DT, RF, and LR, this research underscores the effectiveness of this integrated strategy in optimizing SME operations. Additionally, it computes the coupling coordination degree, innovation environment ranking, mean value, and numerical changes from 2010 to 2020, with an average innovation coupling coordination degree of 0.1624 across 30 Chinese provinces. [ABSTRACT FROM AUTHOR]

The growth of global environmental concerns has prompted a worldwide movement towards sustainable development. In recent years, China's rapid economic growth has been reflected in increased environmental degradation, exacerbated by rapid urbanization, the urban–rural divide, and a government commitment to urban ecological preservation at the expense of rural areas. At the same time, rural communities require increased cultural awareness of environmental conservation and sustainable practices. To address the indiscriminate depletion of natural resources endangering China's rural landscapes, this paper presents a novel framework that combines deep convolutional neural networks (CNNs) with rural ecological governance. A thorough understanding of rural ecological dynamics is made possible by the integration of cutting-edge CNN technology, which facilitates complex data processing and image recognition from satellite and sensor data. Utilizing the Eurostat dataset, it carries out an extensive sustainability analysis of rural ecological governance by applying fundamental theories of environmental governance, such as polycentricity and symbiosis, that are adapted for rural environments. The pastoral environmental governance issues are systematically addressed by means of a multi-stage PPS hierarchical approach. The framework also suggests a novel approach to household waste management for isolated communities, focusing on natural sewage treatment and waste recycling as ways to reduce environmental impact. This all-encompassing strategy seeks to transform rural ecological management by providing customized solutions to protect resources and promote sustainable development in China's rural areas. The experimental results show a fantastic accuracy rate of 98.5% of the CNN model. This compares favorably to recognized architectures such as ResNet16, ResNet32, and ResNet64, demonstrating CNN's ability to handle complex features of rural landscapes. A comparative analysis with RNN and LSTM highlights CNN's capacity to extract hierarchical features and capture spatial dependencies. In addition, the work reveals that rural residents have an overall satisfaction rate of 85.2% with environmental governance, of which 45.6% provide high levels of satisfaction. [ABSTRACT FROM AUTHOR]

With the opening of the Stock Connect programs, the mainland China and Hong Kong stock markets are becoming more closely linked. In this paper, we develop a China's stock market risk early warning system. The proposed early warning system consists of three components. First, we use value at risk (VaR) to identify the stock market risk in which stock market risk is divided into multiple categories instead of two categories. Second, we construct a comprehensive indicator system in which basic indicators, technical indicators, overseas return rate indicators, and macroeconomic indicators are considered simultaneously. Third, we use four machine learning models, namely long short-term memory (LSTM), gate recurrent unit (GRU), multilayer perceptron (MLP), and EXtreme Gradient Boosting algorithm (XGBoost), to predict China's stock market risk. Experimental results show that: (1) Considering the macroeconomic indicators and basic indicators of Shanghai Composite Index (SSEC), ShenZhen Component Index (SZCZ) and Hang Seng Index (HSI) can significantly improve the performance of predicting China's stock market risk. (2) The opening of SH-HK Stock Connect program improves the predictive performance, but the opening of SZ-HK Stock Connect program decreases the predictive performance. (3) The indicators related to Hong Kong become more important after the SZ-HK Stock Connect program. [ABSTRACT FROM AUTHOR]

In recent years, technological advancements have been replicated in various industries, including sports medicine. Recent developments, such as big data analytics and data mining, which have revolutionized medical services in sports, are apparent in this transformation. This technological shift is motivated by the need to enhance athletic performance, prevent injuries, and offer individualized health advice. Modern lifestyles have simultaneously increased people's attention to their health, creating a demand for better medical services. However, China's ability to provide superior medical care needs to be improved due to a lack of medical resources and an ever-increasing patient population. To address these challenges, this research paper presents an integrated framework that leverages Spark-based big data analytics and the XGBoost algorithm. The framework aims to provide a robust sports medical service encompassing real-time health monitoring and data-driven insights. Powered by the formidable distributed computing platform Spark, it adeptly manages extensive sports data generated during training and events, facilitating instant health evaluations. Incorporating the XGBoost algorithm for data mining amplifies health prediction and recommendation capabilities. Renowned for its predictive prowess, XGBoost excels in discerning intricate sports data patterns and trends. Its proficiency in tackling intricates feature selection and modeling tasks ensures precision and actionable insights. Empirical findings underscore substantial enhancements in sports medical services. When applied to chronic disease datasets, the XGBoost algorithm garnered an impressive 93% trust rate. In contrast to conventional methods like K-Nearest Neighbors (KNN), Random Forest (RF), Decision Trees (DT), Support Vector Machines (SVM), Naïve Bayes (NB), and Logistic Regression (LR), the proposed framework consistently outperforms these established techniques. This remarkable performance underscores the transformative potential of the integrated framework in revolutionizing sports medical services. [ABSTRACT FROM AUTHOR]

In recent years, transformative technological advances have reshaped countless sectors, including higher education, leaving a lasting and indelible mark. Artificial intelligence (AI) and the Internet of Things (IoT) coming together has proven to be an influential driving force for the development of creative solutions to the ever-evolving challenges faced by educational institutions. Traditional classroom teaching methods have encountered unprecedented hurdles with the widespread integration of the Internet into education. China's commitment to "governing the nation by law" has elevated the need for legal expertise. As society evolves, pedagogical approaches must adapt. Information and communication technology trends, combined with the rise of the IoT, are reshaping education and offering tools to bolster campus security, monitor vital assets, and enrich classroom accessibility. This paper proposes an AI-based hybrid model and an IoT-infused teaching approach personalized for legal education in colleges and universities, employing the decision tree algorithm. The study's focal point is the development of legal professionals possessing the robust capabilities and qualities society seeks, using teaching methods compatible with modern information technology. The foundation of this endeavor lies in the hybrid teaching mode, specifically Internet-based legal research. Within this platform, educators can disseminate instructional materials, videos, and exercises, with the option to enlist teaching assistants to aid in managing student learning. Comparing varied and traditional teaching classes, the passing rates were 96.75% and 95.36%, respectively. Data analysis using decision trees identifies factors influencing these rates. The study also evaluates students' accuracy in five in-class exercises; in the mixed class, correctness rates were 96.4%, 93.5%, 82.5%, 64.2%, and 62.8%. The proposed method is compared with other machine learning algorithms (SVM, KNN, LR, RF) and Decision Trees to evaluate the educational impact of the mixed teaching approach comprehensively. [ABSTRACT FROM AUTHOR]

In recent years, the escalating global concerns surrounding climate change have placed a growing emphasis on achieving dual objectives: reducing carbon emissions and achieving carbon neutrality. Businesses and organizations are under mounting pressure to align their operations with these crucial environmental goals. This paper introduces the concept of an enterprise carbon performance evaluation index system (ECPIS) to strike a balance between economic development and environmental protection and enhance overall enterprise management and development strategies. The ECPIS framework is constructed using machine learning and advanced data mining techniques, particularly support vector machines (SVM). Its core purpose is to provide enterprises with a systematic tool to gauge, analyze, and enhance their carbon performance, addressing dual carbon objectives. ECPIS development hinges on data mining techniques, extracting insights from diverse data sources to construct a comprehensive system that accommodates these dual carbon goals' intricacies. Its methodology includes data collection, preprocessing, feature selection, and data mining algorithms to unveil vital patterns and relationships within data. It conforms to international standards, establishing a tailored carbon performance index system aligned with China's national conditions. It validates carbon-related enterprise data and employs data mining's association rules to uncover pertinent carbon performance information. The results obtained from ECPIS are auspicious, boasting an experiential accuracy rate of 97.5%. This level of accuracy surpasses that achieved by other algorithms like K-Nearest Neighbors (KNN), Random Forest (RF), Decision Trees (DT), Naïve Bayes (NB), and Logistic Regression (LR). ECPIS stands out by considering various factors, including carbon emissions reduction, energy consumption, supply chain efficiency, and financial performance indicators. This multifaceted approach enables enterprises to gain a comprehensive understanding of their carbon performance and identify areas for improvement. [ABSTRACT FROM AUTHOR]