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Under the continuous development of China's social economy, the Internet and multimedia technology have been developed and innovated and are widely used in all walks of life and all fields. Because of their powerful interactivity, effectiveness, and dynamism, combining them with colleges and universities' English teaching plays an essential role in enriching teaching resources and improving teaching efficiency and quality. This paper proposes an improved LSTM model based on the web and multimedia environment, which improves the prediction effect of neural networks by adding an attention mechanism to the LSTM layer. The attention mechanism is based on a decision support system that automates the availability of various teaching resources to ensure an interactive teaching and learning environment in the colleges and universities. The decision support system of attention mechanism enhances the model's generalization ability by tuning the model parameters with the genetic algorithm (GA). Finally, this paper proposes some suggestions on improving the effectiveness of teaching English as a foreign language in the online and multimedia environment, taking into account the popular MU teaching format and English APP. The results show that the improved LSTM model outperforms the existing models in terms of various performance metrics. [ABSTRACT FROM AUTHOR]

This paper uses patterned structure to study the efficiency improvement of the practical application of BIM technology, and innovates the patterned structure method based on this. The research focus of this paper is to build a new assembly component library based on BIM model and algorithm optimization, improve its use and management efficiency, and use the core technology to design the simulation analysis model of BIM in construction, and give timely feedback of experimental information. Analyze the existing assembly structure design process, design standards and design methods, propose a BIM-based assembly structure design method, change the design idea from the whole to the component elements, and use the design method for prefabricated components. Taking the W prefabricated component library as a starting point, select the prefabricated components of the component library for assembly design to form a BIM model with a complete structure. Adjust and optimize the BIM model, and finally form a reasonable design plan. This BIM type of product includes production, transportation and assembly construction. Prefabricated buildings have obvious advantages, its comprehensive benefits are higher than other buildings, and the technical content is relatively high, through innovative prefabricated buildings, the modernization level of the building can be improved. Therefore, on the basis of in-depth investigation and study of the development status of China's construction industry, this paper studies the impact of different factors on engineering costs, including engineering schemes, engineering construction modes, etc., based on a large number of actual engineering operations and application cases, so as to achieve the purpose of reducing cost costs, improving project income, and improving project quality. [ABSTRACT FROM AUTHOR]

Tourism is the pillar industry of many cities, and it is also an important key point to promote urban development and maintain urban vitality. At present, the analysis of urban tourism activity in China can better assist the research of regional economic development and promote the orderly development of regional economy. Many scholars have carried out the analysis in this respect. As a new and growing field, artificial intelligence also plays an important role in urban tourism. With the continuous development of science and technology, and the human intelligence field of human research is also developing. New artificial intelligence products continue to emerge. The workload of most artificial intelligence may exceed the manual workload. In order to continuously update artificial intelligence, individuals effectively combine data mining and artificial intelligence, and combine many knowledge disseminated by the network with artificial intelligence technology to create an advanced knowledge network model. This paper uses the OPTICS-based clustering algorithm to analyze the clustering of photographs on the Flickr website and obtain information about tourism activities in Chinese cities. With the help of visualization software to visualize the experimental data and verify the experimental results introduced in this article, city tourism activities can be recommended to the destination. At present, many scholars have studied the application of improved density clustering algorithm in the field of biology and image analysis, but there are still some gaps in the development of tourism. This paper can make some contributions to the related fields. [ABSTRACT FROM AUTHOR]

At present, the development of value chain mainly shows that the added value is positive; otherwise, the industrial value chain will be eliminated. Therefore, the construction and measurement of the industrial value chain height can effectively avoid the collapse of the industrial value chain caused by the low system value of the value chain. In this paper, the mobile virtualization channel allocation algorithm is used to construct the multi-objective industrial value chain height index, and the actual industrial value chain height in China is measured. This paper first describes the channel allocation algorithm of mobile virtualization, extracts the allocation rules of the algorithm, constructs the model of multi-objective industrial value chain, then measures the value chain of eight regions in China combined with the elements of technology, production, and marketing in the height of value chain, and quantitatively compares the results of value chain height of different regions according to the embeddedness and depth. Finally, it puts forward the improvement scheme for the measurement results and the problems faced by the value chain. The research shows that the average value chain height of South China is about 70.86, while the average value chain height of Northwest China is about 46.58, ranging from 40.58 to 60.45. Through quantitative data, the value chain height of South China is generally higher than that of Northwest China by more than 50%, which may be related to the innovation of technology and the imitation form of production. Therefore, this paper proposes to strengthen the technological innovation in the upstream of the value chain, so as to drive the development of the regional value chain. [ABSTRACT FROM AUTHOR]

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]

Cold-chain logistics presents significant challenges to sustainable development in terms of economics, society, and the environment. To address this issue, this paper focuses on solving a real-world problem—the multipath location-routing problem-based cold-chain logistics (MPLRPCCL), which considers soft time windows and heterogeneous fleets. The mathematical model aims to optimize the total logistics costs, including fuel consumption, carbon emissions, cargo deterioration, penalties, operating depots, and renting fleets. To effectively solve this problem, the paper proposes three versions of a memetic algorithm (MA) that incorporate control modules for monitoring the MA's performance and improving its global optimization performance. Computational evaluations were conducted, and the results show that the proposed algorithms are effective and outperform the classic MA framework. Furthermore, the proposed model and algorithms were applied to real-world cases in Hangzhou city, China, demonstrating their practical applicability. Overall, this paper offers a promising approach to tackling the complex challenges posed by cold-chain logistics and contributes to the sustainable development of this important field. [ABSTRACT FROM AUTHOR]

Uncertain regression model is a powerful analytical tool for exploring the relationship between explanatory variables and response variables. It is assumed that the errors of regression equations are independent. However, in many cases, the error terms are highly positively autocorrelated. Assuming that the errors have an autoregressive structure, this paper first proposes an uncertain regression model with autoregressive time series errors. Then, the principle of least squares is used to estimate the unknown parameters in the model. Besides, this new methodology is used to analyze and predict the cumulative number of confirmed COVID-19 cases in China. Finally, this paper gives a comparative analysis of uncertain regression model, difference plus uncertain autoregressive model, and uncertain regression model with autoregressive time series errors. From the comparison, it is concluded that the uncertain regression model with autoregressive time series errors can improve the accuracy of predictions compared with the uncertain regression model. [ABSTRACT FROM AUTHOR]

In the analysis of agricultural product price time series, the detection of abnormal fluctuations is the primary task. Accurately judging the abnormal fluctuations of agricultural product prices can help farmers avoid potential economic losses in a timely manner and improve the circulation efficiency of agricultural products. This paper introduces a convolutional neural network (CNN) classification model based on time series images (TSI), and identifies abnormal fluctuations in agricultural product prices using improved standard deviation–slope judgment. First, a standard deviation–slope (SDS) time series abnormal fluctuation criterion is proposed. Second, the Markov Transfer Field (MTF) method is used to convert the sparse one-dimensional time series of agricultural product prices into a two-dimensional dense image. Third, the CNN model is used to automatically extract features from time series images containing abnormal fluctuations, and they are divided into two categories: "normal" and "abnormal"; Finally, the performance of the proposed model was evaluated using China's corn and wheat price dataset. Comparing with other abnormal fluctuation judgment methods, the accuracy of the proposed algorithm is about 20% higher on average. This confirms the applicability of the standard deviation–slope time series Image-Resnet-34 (SDS-TSI-Resnet34) model in practical scenarios. At the end of the paper, some feasible suggestions for the efficient development of agricultural economy are proposed based on the abnormal fluctuation judgment method. [ABSTRACT FROM AUTHOR]

English language translation teaching plays a crucial role in today's globalized world, particularly in countries like China, which has seen an increased emphasis on English language proficiency. However, effective teaching and assessment of students' translation abilities remain significant challenges for educators. To address the challenges of English language translation and teaching, this paper proposes a novel approach that integrates Internet of Things (IoT), Particle Swarm Optimization (PSO), and Neural Network algorithms to assist teachers in evaluating and assessing students' English translation abilities by enabling them to better fulfill their teaching responsibilities. This paper first describes the computational mechanism of Neural Network algorithm using PSO, which includes the particle coding approach. An application model is designed to assess students' capacity for learning using English translational instruction. The PSO algorithm gathers information on their learning progress, and fully utilize the analyzed results to develop learning strategies and create teaching materials for various learning types, which is helpful for the quick advancement of English translation teaching. Secondly, this paper develops a translational application model for English language as a practical framework. The proposed method utilizes the upgraded PSO-enabled Neural Network to process English language translation and teaching data by achieving network training in a global optimal state of PSO by reducing training errors. Thirdly, this paper evaluates the effectiveness of the model by comparing the average errors of the training and test samples with different numbers of particles such as 5, 10, and 20. Finally, the results demonstrate high accuracy in measuring students' translation abilities. Furthermore, the study collects and examines three data sets, specifically English-Foreign Language Translation Corpus (EFLT), English Language Learners' Corpus (ELLC), and English-Multilingual Parallel Corpus (EMPC), with a detection accuracy of 0.84, 0.97, and 0.65, respectively. [ABSTRACT FROM AUTHOR]

Accurately predicting the performance in various sports is crucial in comprehending training characteristics and enhancing physical education and training processes. The increasing significance of sports in China has enhanced the interest for developing prediction models to assess athletes' sports performance, accurately. These models will greatly contribute to improving sports performance and implementing scientific training methods. This paper proposes a prediction model for athletes' sports performance using Neural Networks as the underlying framework. This research aims to enhance sports performance and scientific training by developing a reliable and effective prediction model. First, the proposed model utilizes neural network algorithms, including error backpropagation and genetic algorithms, to train and optimize the prediction model. Second, it analyzes temporal patterns, extracts statistical features, and adapts to new data by ensuring reliability and effectiveness of the proposed model. Third, this paper conducts an error analysis by comparing the prediction errors between our proposed method and traditional models to evaluate the accuracy of the prediction model. In addition, this paper conducts an error analysis by comparing the prediction errors between our proposed method and traditional models. Finally, the results demonstrate that our proposed method achieves a maximum error of 36.12%, with the highest error rate in the BP network prediction being 6.76%. Furthermore, we compared our predicted results with multiple linear regression and other existing prediction methods, and our proposed method demonstrated superior accuracy with an overall prediction accuracy of 97.6%. Our suggested solution surpasses previous approaches with much higher accuracy, reduced latency, improved recall, and increased scalability. [ABSTRACT FROM AUTHOR]

In recent years, there have been several sports-related injuries and even unexpected fatalities due to the rapid growth of China's sports industry and increasing fitness issues. The prevalence of sports injuries is rapidly rising because of the intense competition among athletes and the increased physical load caused by players' long-term training. The difficulty in the sports' world is the identification and evaluation of risk factors associated with athletes. To address these issues, this paper develops a cognitive evaluation model for risk assessment of injuries in sports using big data to understand and determine the likelihood of injuries. The model is used to prevent the issues faced by running athlete's injury and thereby lowers the risk of overall risk. This paper also analyzes the injury factors while running by establishing an estimation model based on the radial basis function (RBF) neural network and explains how to compare RBF neural networks and risk level evaluation using certain criteria. The estimation model helps to identify and judge the risk factors of athletes. It designs an algorithm for Gaussian distribution, which predicts the output of the model. It discusses how the error may be calculated, which measures the neural network model's overall performance in forecasting sports' injury outcomes using a particular dataset. Finally, it designs the RBF Neural Network Learning algorithm, which generates the leaned network parameters to evaluate the reliability of risk cognitive model for athletes based on big data. The Lagrange and Bayesian models are selected for comparison. The results indicate that this model has a higher evaluation effect and accuracy as compared to other models. For this purpose, the comparison is made using 25 athletes. The proposed model's evaluation time is the lowest in comparison to the existing approaches. After examining the impact of cognitive evaluation, it has been reflected that the proposed model can be utilized to assess the different injury types for different parts of athletes. The highest single injury degree is 38.76%, while the highest composite injury degree is 40.05%. [ABSTRACT FROM AUTHOR]

These days, digital art has substantially improved painting art in China's new media period through modern technology and specialized software, which has generated various digital artworks with painting effects, demonstrating their aesthetic, commercial, and cultural worth. As a result, designers must actively investigate intelligently linked and content-diverse techniques to innovate and improve digital media art. This investigation will allow the extraction of pivotal role of digital media communication and drives visual art to new heights in the modern digitized era. Based on this motivation, this paper tries to analyze the application of new media digital technology in painting creation using big data. The major goal is to obtain insights into artists' behavior using behavioral habits and a morphological algorithm in digital painting. The study investigates the design process of digital painting by analyzing the habitual forms displayed by diverse painting user groups and integrating them with user experience design concepts. Two experimental methodologies are used to investigate painting user behavior: the interview method and the observation method. The observation approach collects comprehensive user behavior data to develop a habit form model that includes a both virtual and real activity. This model employs a hierarchical scheme emphasizing various behavior aspects and concepts throughout user interactions and painting system operations. The interview method, on the other hand, captures the psychological activities involved in user behavior decision-making. The study discovers and decomposes the traits specific to these characters by defining painted characters, developing their behavior sets, and making interactive decisions. The learned information and experience are then applied in interactive decision-making processes. Based on these findings, prototype development includes explicit and implicit interaction prototypes, with usability tests determining whether specific roles are included in the design. Throughout the study process, fuzzy mapping associations are built, and behavior mappings are labeled with varying relationship proportions, providing critical data for the habit form model's construction. The paper uses new digital media technology to create paintings and experiments with 50 randomly selected persons. Forty-four participants are well-known authors on major painting websites, with the remaining six being interviewed as professional painters. [ABSTRACT FROM AUTHOR]

In big data era, data has become an important part of human lives and work. At the same time, data plays an important role in information acquisition and dissemination. At present, the influence of data journalism is gradually increasing. However, unlike other countries, China data journalism started late. To study the problems of data journalism and the trend of future development, we use the method of combining data and news to explore the status quo and dig out the existing problems. This article first summarizes the research status, research methods, and theoretical basis of data journalism's propagation path. Next, it uses Lasswell's 5W model: a new model to analyze data news from five aspects, namely, disseminator, disseminating channel, dissemination content, audience, and dissemination effect. Finally, based on content analysis, searching, and data mining, an indicator system is constructed for the current new media's news dissemination effect evaluation, and the Delphi method is used to assign weights to various indicators and make decisions based on them. By analyzing the results, this paper identify the problems in the process of combining data journalism and new media platforms, and provide help for the future communication strategy of data journalism. [ABSTRACT FROM AUTHOR]

With the rapid development of economy, China's cities and villages have been changing with each passing day. The planning and updating of traditional settlements have become an important topic in the development of our country. By studying the theory of space syntax, the author extracted the technical method of updating traditional settlements. We chose a traditional settlement as the research object. Based on the regional function of traditional settlements, the spatial structure of the region has been used to plan and update the area. The method designed in this paper can effectively update and transform traditional settlements. This is of practical significance to the progress and construction of our society. [ABSTRACT FROM AUTHOR]

At this stage, there is a big gap between the cognition and practice of smart devices in universities and the theoretical development of universities. Theoretical and practical scientific research on university wisdom factors from the perspective of wisdom culture education can sort out the essential attributes of university wisdom in terms of actual practice and appearance and give references for the development of university theory and within the field of wisdom practice. In the age of the information explosion, modern education is developing toward an intelligent system approach. In this cultural education environment, universities in China are paying close attention to the construction of smart schools. Among them, the national Ministry of Education policy document emphasizes the construction of "intelligent education demonstration parks" and promotes the construction and implementation of smart campuses in all aspects. Given this, the paper takes university culture education as an example and explains how to build smart classroom teaching to promote the development of modernization and intelligence in China's education and improve its quality of teaching. [ABSTRACT FROM AUTHOR]

After more than 40 years of long-term development, as a pioneer of China's reform and opening up, the Yangtze River Delta region has made tremendous economic progress and strong scientific and technological innovation strength, which has made the economy flourish and scientific and technological innovation strength strong. However, in the process of financial growth in the Yangtze River Delta, there are also financial structures that do not match the optimization of industrial structure, thus hindering the quality and efficiency of industrial structure optimization, thus affecting the strategic function of industrial development. For the development of the financial field, this paper applies the real-time data processing system of the Internet of Things based on blockchain technology to analyze the financial industry of the Yangtze River Delta urban agglomeration. The Internet of Things, based on the technology of real-time data processing system, can store different types of financial assets. These assets are a common component of supporting the application of the Internet of Things. They mix and build a powerful back-end process that can support the data processing application of the Internet of Things. Therefore, the Internet of Things data network and other information are stored in the data structure, so that data analysis and consolidation can be carried out with consensus, Thus, the processing efficiency of relevant financing data in the Yangtze River Delta region has been greatly improved, and the development process of the financial sector in the region has been effectively accelerated. Therefore, the research on this issue will enable the financial industry to give full play to its potential as a major component of national economic development, so as to maximize the improvement of the industrial structure and promote the development of blockchain and Internet of Things technologies. [ABSTRACT FROM AUTHOR]

This article first describes the embedded voice processing process and uses the two-door restriction method for endpoint detection. Next, the wavelet + MFCC algorithm is proposed. The Fourier transform will replace it with ripple. The disadvantage of the ripple is that the size of the Fourier transform window cannot move with frequency. On this basis, the overall goal of the system is proposed, the overall structure is constructed, and the functions of the system are divided. Android mobile phone data collection technology has been extensively researched, including logical search, temporary access to permissions, JTAG search and ISP search. The design and implementation of the logical search engine based on root authority, the physical search engine based on Qualcomm processor, and the data analysis system have been completed, and the evidence collection of e-commerce applications has been completed. Based on the digital forensics system, this article studies and designs an Android device digital forensics system suitable for China's national conditions and laws from the three perspectives of data collection, analysis and storage. At the same time, brainstorming methods must be used between corporate financial management and cost management personnel. Finally, through research, scientifically construct the framework of the financial cost management system, distinguish the various requirements of the construction system, classify the importance, urgency, and feasibility of the requirements, establish the basic requirements and extension requirements, set the demand function to complete the schedule and Perform as planned. Through the research of embedded voice and digital forensics system, this paper applies it to the research of financial cost management, thereby promoting the development of enterprise financial management. [ABSTRACT FROM AUTHOR]

The development of science and technology has also led to the progress of rural digital economy. In China, the construction of new countryside has always been an important part of economic development. The application of big data technology in rural areas can not only promote the development of rural economy, but also improve the results of environmental protection. Using the matching coefficient method of agricultural water and soil environmental characteristics, it match the rural water and soil environmental characteristics and the impact of the water environment in the autonomous region. An indispensable driving force in the process, agricultural digitalization will update the rural economic development model, weaken the barriers to the dual structure of urban and rural areas, stabilize agricultural production, promote sustainable rural development, and promote rural economic development. The current government policy support, the urgent need for new development momentum for rural economic development, and the advancement of changes in the lifestyle of urban and rural residents are promoting high-quality development of the rural economy. At the same time, we are facing challenges such as the lack of local network coverage, the lack of logistics, sales systems, and the lack of talents with digital skills. Expanding rural network coverage, optimizing rural logistics and distribution, promoting the development of agricultural big data, and strengthening the development of digital human resources are important ways for agricultural digitalization to promote rural economic development. The research focus of this paper is the parallel development of rural digital economy and environmental protection under the big data economy, and its application in the current agricultural development can achieve a win–win situation between man and nature. [ABSTRACT FROM AUTHOR]

To establish and improve the theoretical framework and operation mechanism of emergency information sharing, and to establish the index system and evaluation method of the efficiency of emergency information sharing are the key work to improve the disaster prevention and mitigation capacity of coastal cities in China. In this paper, the synergetic theory is introduced into the construction of emergency information sharing framework. By clarifying the operation relationship between the participants and the information transmission, the framework and operation mechanism of emergency information sharing are constructed. Around the 3 dimensions of emergency information sharing mechanism construction, resource guarantee ability and collaborative driving force of emergency information sharing, the evaluation index system of emergency information sharing collaborative efficiency in coastal cities is proposed, and the evaluation method of emergency information sharing collaborative efficiency based on coupling degree model is proposed. The empirical results show that the efficiency of emergency information sharing in coastal cities in China is generally on the rise, and the contribution rate of the construction level of emergency information sharing mechanism is higher than that of the resource guarantee ability and the collaborative driving force of emergency information sharing, but the efficiency of emergency information sharing in coastal cities in China is still at the bottom level. The research results provide theoretical basis and methods for the emergency management departments of coastal cities in China. [ABSTRACT FROM AUTHOR]

The health organizations around the world are currently facing one of the greatest challenges, to overcome the current global pandemic, COVID-19. It erupted in December 2019, in Wuhan City, China. It spreads rapidly throughout the world within couple of months. In this paper, the data of the COVID-19 have been collected, organized, analyzed and interpreted using the discrete-time model of SIR epidemic model. Moreover, results for several countries from different regions of the world have been obtained. Furthermore, comparative study has been carried out for the countries under consideration. The comparison was performed for the data of different countries on same dates of each month. However, the calculations are carried out for thirteen consecutive weeks, to investigate the rate of spread and the control of the disease in these countries. This guides us to some important concepts like factors favoring the spread of virus and those resisting the spread. Different regions are studied and their data have been evaluated to know which regions are the most effected. This study helps to know the important factors about the behavior of the coronavirus in different environments, such as lockdowns, temperatures, humidity and other restrictions. The proposed concepts and equations can be used to project the upcoming behavior of the pandemic. [ABSTRACT FROM AUTHOR]

As a kind of particulate matter which is very detrimental to human health, the prediction of PM 2.5 has attracted increasing attention in air pollution forecasting and early warning. In this paper, density peaks clustering (DPC) is employed to select the individual models for combination forecasting, and a novel high-precision combination forecasting model named DPC-based combined model is proposed to predict PM 2.5 concentrations. Firstly, CEEMD is introduced to decompose the original P M 2.5 data into three intrinsic mode functions ( I M F s ) with different features. Then, 125 individual models are obtained by applying SVR and GRNN to these three I M F s , and the intelligent optimization algorithms are adopted to optimize the parameters of SVR. Finally, the 125 individual models are clustered by DPC based on five model evaluation indexes to select the individual model for combination forecasting. The experimental results of four Chinese cities demonstrate that the proposed model outperforms the optimal individual model and can greatly improve the prediction accuracy. Taking Dalian as an example, the MAE of the proposed model is 2.2 by clustering the 125 individual models into two groups, and the result reveals that the accuracy is increased by 65.73% compared with the minimum MAE (6.42) of the individual models. [ABSTRACT FROM AUTHOR]

There have been many recent fears of severe house-price decreases in some provinces in China causing a nationwide collapse of the housing market. Therefore, this paper aims to clarify the linkage structure of China's housing market and its risk contagion routes. Given monthly data of provincial housing and stock-market capital returns from 2001M01 to 2019M12, on the basis of graph theory, this paper first explores the linkage structure of provincial housing markets. Relying on the linkage structure, this paper then simulates the effect of unexpected negative shocks from the stock market on the probabilities of a housing-market collapse based on the epidemic model. The results show that (i) consistently with practical evidence, the probability of housing-market collapse is relatively high in the southwest of China and (ii) reducing housing-market linkage, such as through a blocking mechanism, to prevent collapse is helpful. [ABSTRACT FROM AUTHOR]

With the development of recent economic globalization, international exchanges and cooperation are increasingly frequent and in-depth. In this process, there is a serious obstacle, that is, language differences. Therefore, the development of high-quality and practical machine translation system is of great significance. Japan has a close relationship with China, so it is necessary to acquire and process Japanese information. Japanese translation is the basis of Japanese information processing, which plays an important role in cross language information retrieval, machine translation, information extraction and other practical applications. In recent years, machine translation has made remarkable progress, but there is still much room for improvement in the quality of translation. Multimedia assisted instruction is an important application of computer technology. Therefore, this paper realizes the feature recognition and hardware structure of Japanese machine translation; for the efficient implementation of the model, the edge-driven model is combined. The proposed model is implemented through the edge devices and the performance of the model is validated through the testing. The recognition accuracy is much higher than the traditional models and the robustness is higher. [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]

In recent years, the rapid development of the Internet has promoted the continuous expansion of the scale of China's tourism industry, and the amount of tourism data has surged. However, tourists need help bringing personal interest and high-value data from the plethora of tourism information. The rise of artificial intelligence has transformed traditional tourism into an intelligent, data-driven industry. This shift has generated vast tourism data, offering both opportunities and challenges. The paper discusses an AI and IoT-based Intelligent Tourism Recommendation System (ITRS) that offers travelers predefined itineraries, personalized suggestions, and tourism insights. This system simplifies attraction discovery, unveiling hidden value within tourism data at the intersection of AI and IoT. The present study thoroughly investigates AI-based recommendation algorithms before delving into the system's architecture. It categorizes user-based, project-based, and article-based collaborative filtering methodologies tailored to specific goals. First, thoroughly examine AI-based recommendation algorithms before delving into the system architecture. Second, categorize collaborative filtering methods as user-based, project-based, and article-based, each tailored to specific objectives. Third, delve into the Apriori algorithm's complexity within the context of weighted association rules and introduce an enhanced iteration for improved efficiency. The proposed scheme encompasses an elaborate ITRS plan featuring a user interest model and a client module, crucial for the computation and analysis of users' long-term and short-term interests. Rigorous performance testing confirms the ITRS's superiority across varying support levels, with experimental results demonstrating the Apriori algorithm's exceptional accuracy, achieving a 94.3% improvement over other methods. The Apriori algorithm is better than traditional recommendation algorithms such as Linear Regression, Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, K-nearest neighbor, Naive Bayes, and XGBoost. [ABSTRACT FROM AUTHOR]

Forests play an essential role in ensuring ecological balance and providing valuable resources. Evaluating their ecological effectiveness and economic value is vital for sustainable forest management. Due to the ecological benefits of the forest ecosystem, China has introduced reforms in its ecological system and incorporated them into its national economic accounting system. This research study selects different types of forests based on their locality and species. It formulates a classifier using Convolution Neural networks (CNN) and Artificial Neural Networks (ANN) data mining techniques. First, this paper briefly discusses data mining methods used to determine the impact of different factors on forest health, quality, and its importance in terrestrial ecology. Second, this research study designs a classifier for labeling forests according to their ecological and economic worth. A confusion matrix was used to ascertain the performance of the classifier. Third, it illustrates an index system for evaluating forest ecosystem service function value and explores dynamic characteristics of forest resource variation and the change process of the forest ecosystem. Finally, a statistical analysis is performed among the proposed study and related studies. The proposed study outperforms its predecessors and depicts that the proposed technique is more accurate than traditional systems. The study showed that the amount of nutrients accumulated by trees and the amount of polluted gas absorbed have doubled, and the growth rate of other functions has also increased by more than 70%. The average growth of forest ecological functions is 97.93%, significantly improving people's awareness of forest resource protection. [ABSTRACT FROM AUTHOR]

Reducing users' online privacy concerns and encouraging users' privacy disclosure behaviors are the important prerequisite for online websites to use data to obtain economic value. Based on the theory of clue utilization and privacy calculation theory, this paper uses the APCO model to intermediate online privacy concerns and adjust privacy calculations to explore the influence mechanism of online website users' privacy disclosure behaviors in China. Using the questionnaire survey method, SPSS 20.0 and AMOS 23.0 were used to conduct structural equation path analysis on the collected 966 valid data to verify the proposed research hypothesis. Research results show that website reputation and website trust have a significant positive impact on privacy disclosure; users' online privacy concerns will play a mediating role in website reputation, website trust, and privacy disclosure; when users with online privacy concerns are doing privacy calculations of perceived risks and perceived benefits are carried out during privacy disclosure. However, privacy calculations are contextual. On the premise of website trust, the benefits of information disclosure can reduce users' online privacy concerns and increase users' privacy disclosure behaviors. This article exposes that users will adopt privacy disclosure decisions with different levels of cognitive effort based on the degree of online privacy concerns, enriches research on privacy disclosure behavior, and exposes the contextual nature of privacy calculus, which further enriches and expands the theory of privacy calculus. Promote online websites to obtain user data and provide relevant countermeasures and suggestions. [ABSTRACT FROM AUTHOR]

The management and development of human resources (HR) have become one of the core contents of enterprise management. Enterprises pay more attention to the importance of information technology which results in the development of many labor dispatch enterprises. The rapid development and limited market share of labor dispatch enterprises lead to more fierce competition among the enterprises. With the rapid development of machine learning techniques, more enterprises begin to pay attention to managing the completion of various decision-making activities in operation with the help of computer information systems to save human costs. The labor dispatch model is a valuable addition to China's core contractual employment paradigms. Based on the improved genetic algorithm, this paper studies the human resource labor dispatch model which is used to optimize the number of people required by the dispatch project and optimize its labor cost. The HR recruitment model is used to calculate the number of people to be recruited for the dispatch project to meet the needs of the project and optimize its project dispatch scheme. The experimental results show that the benefit of dispatching through the artificial selection method (1127) is less than the result calculated by the HR dispatching plan model (1162), which proves the effectiveness of this model. The experimental results of the study depict the usefulness of the research. [ABSTRACT FROM AUTHOR]

This paper is aimed to make sense of the real effect of implement of social healthcare insurance on one’s medical expense in China. Due to previous studies drew various and inconsistent conclusions on this issue, this works intend to apply meta-analysis to the problem. For 31 related studies, we first implement an advanced conditional Dirichlet-based Bayesian semi-parametric model specific to meta-analysis, and come to a primary conclusion that healthcare possesses little probability reducing one’s medical expense in China. Further, the authors conduct random effects meta-regression and find that heterogeneity exists among the observed effect sizes. Mixed effects model shows that the age variation may is actually the heterogeneity source. The coefficients for Non-old and Old are respectively 0.29 and 0.54, implying that when researching on the medical expense for the elderly, it is more likely to conclude the medical insurance could increase medical spending. The coefficients for IV and OLS are both remarkably negative at 90% confidence level. This suggests when directly using Instrument Variable (IV) approach and OLS method to assess the implementation effect for the healthcare insurance, it is inclined to result in the reduced impact on medical expense. We deduce this is because this two methods can’t solve the sample-selection bias when compared with the Two-part model and difference-in-difference (DID) model. Based on the results and discussion, we finally propose suggests for the government. [ABSTRACT FROM AUTHOR]

At present, there are still some obstacles in the development of China's construction projects, such as low information transmission efficiency, poor information coordination and sharing, and low value of information utilization. Based on this, the BIM architecture is described and applied. This paper uses the SURAIN model algorithm. The conceptual model of information integration management with data layer, information model layer and functional application layer as the core in distributed network environment is constructed and verified. The results show that the functional design of the BIM information integration management system platform for building information is conducive to the improvement of building information transmission efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Data play an increasingly crucial role in decision evaluation. However, the noise and redundant information in the data create confusion to the decision makers. To solve this problem, this paper creates a new attribute reduction model based on technique for order preference by similarity to an ideal solution (TOPSIS), grey correlation analysis and coefficient of variation approaches. First, we obtain the time weights of panel data in different years by TOPSIS and then transfer the panel data into a cross-sectional data matrix. Second, we delete the overlapping attributes by grey correlation analysis method. Third, we use the coefficient of variation to select the attributes with the highest information content. Finally, the proposed attribute reduction model has been varied based on the green economy evaluation data of 15 sub-provincial cities in China. The experimental findings provide decision-making reference for the local government policymakers to adjust or formulate green economic development strategies. [ABSTRACT FROM AUTHOR]

In the context of data eruption, the data often show a short-term pattern and change rapidly which makes it difficult to use a single real value to express. For this kind of small-sample and interval data, how to analyze and predict multi-factor sequences efficiently becomes a problem. By this means, grey system theory (GST) is developed in which the interval grey numbers, as a typical object of GST, characterize the range of data and the grey relational and prediction models analyze the relations of multiple grey numbers and forecast the future. However, traditional grey relative relational model has some limitations: the results obtained always show low resolution, and there are no extractions for the interval feature information from the interval grey number sequence. In this paper, the grey relational analysis model (GRA) based on effective information transformation of interval grey numbers is established, which contains comprehensive information of area differences and slope variances and optimizes the resolution of traditional grey degree. Then, according to the relational results, the multivariable GM model (GM (1, N)) is proposed to forecast the interval grey number sequence. To verify the effectiveness of this novel model, it is established to analyze the relationship between the degree of traffic congestion and its relevant factors in the Yangtze River Delta of China and predict the development of urban traffic congestion degrees in this area over the next 5 years. In addition, some traditional statistical methods (principal component analysis, multiple linear regression models and curve regression models) are established for comparisons. The results show high performances of the novel GRA model and GM (1, N) model, which means the models proposed in this paper are suitable for interval grey numbers from regional data. The strengths which recommend the use of this novel method lie in its high recognition mechanism and multi-angle information transformation for interval grey numbers as well as its characteristic of timeliness in information processing. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has gained significant popularity through wearable technologies. Several IoT devices are used in educational institutions, smart homes, and security management. Their widespread use in sports training applications using IoT (WS-IoT)-based wearable sensors is noteworthy. Meanwhile, artificial intelligence (AI) has evolved rapidly, and its applications have expanded across many diverse fields. Over the past few years, China has placed greater emphasis on physical education to enhance students' physical fitness and overall capabilities. A significant number of colleges and universities have adopted artificial intelligence to transform traditional single-mode sports training methods. This paper introduces a continuous monitoring system for sports individuals using the WS-IoT. The goal of coaches is to maximize the results of athlete training across various sports events using AI technology and their sports expertise. In this study, we examine the use of AI technology in sports and assess the practical implications of its implementation. To achieve the objective function, we used a Bayesian deep classifier and deep learning algorithms that utilize adaptive optimization. Utilizing artificial intelligence in Chinese educational institutions improves training efficiency and effectiveness. The system was then rigorously tested and analyzed in terms of the quality-of-service parameters, including communication collisions and packet loss rates. It is shown that packet loss rates increase with increasing packet numbers, but collision rates remain unchanged. Furthermore, a practical application of the system in colleges and universities showed that 14.4% of the students were extremely satisfied with the use of teaching materials, 24.5% were satisfied with the materials, 30.1% were satisfied with the training methods, and 17.9% were dissatisfied. [ABSTRACT FROM AUTHOR]

The rapid development of urbanization in China has contributed to traffic events, such as traffic accidents and delays. It is difficult to detect and resolve highway traffic congestion in a timely manner using traditional methods because they are slow, require a large number of workers, and require the installation of a large amount of monitoring equipment. Therefore, it is imperative to introduce advanced technology to address these challenges. Recently, deep learning technology has made significant breakthroughs and has been widely applied to various fields with satisfactory results. Deep learning is among the most important technologies for the detection and evaluation of traffic congestion, enabling the accurate detection of the state of the expressway network's traffic congestion, the evaluation of traffic congestion, and the prediction of possible traffic congestion. This enables management to formulate traffic dredging strategies in advance to prevent the negative impact of traffic congestion on normal traffic flow. This paper proposes a framework based on deep learning for next-generation highway traffic management. This framework selects traffic congestion indicators to construct an index model, and then constructs a deep learning model based on self-coding. It predicts and classifies highway traffic environment data and excavates sample data based on the characteristics of traffic parameters. As soon as traffic data were classified, a prediction model based on SoftMax was established to detect and predict highway traffic congestion. We conducted a traffic congestion analysis of the Shanghai expressway network based on the speed performance data obtained from the China Traffic Management Bureau. As a result of their research, we developed an index to measure highway traffic congestion. For traffic control and management organizations to function effectively, it is crucial to have an accurate and clear picture of traffic network operations. We evaluated the proposed framework using data gathered from highway monitoring scenes, and the results indicated that 98.6% of the data could be correctly detected. Using the prediction model based on SoftMax for expressway vehicles during peak hours, the accuracy was 92%, and the misjudgment rate was 8%. This study demonstrates that detecting and evaluating the state of highway traffic environments using deep learning has high accuracy, can be applied to actual highway traffic systems, and is extremely useful for detecting highway congestion. This framework is a promising solution for next-generation highway traffic management and provides accurate and timely traffic congestion detection and evaluation. [ABSTRACT FROM AUTHOR]

The dramatic increase of the aging population is a current and primary livelihood issue in China. It is very important to accurately predict the aging population for policy-makers such as governments and insurance companies. To explore the future development trend of China's aging population, a novel grey prediction FPTGM(1,1,α) model is established in this paper. The data from 2003 to 2012 are used to build the model, and the data from 2013 to 2018 are used to assess the modeling accuracy. The results show that the fitting and forecasting accuracy of the proposed FPTGM(1,1,α) model are higher than those of other models. This indicates that the novel grey model is more suitable for predicting the aging population in China. Combined with the idea of metabolism, the FPTGM(1,1,α) model is applied to predict China's aging population from 2019 to 2042. Finally, some reasonable suggestions for dealing with the aging population are put forward to the government and other decision-makers. [ABSTRACT FROM AUTHOR]

With the development of quality of life, people pay more and more attention to the surrounding environmental factors, especially air pollution. The problem of air pollution in China is becoming more and more serious, which poses a great threat to people's health. Therefore, the prediction of air quality concentration is very important. PM2.5 is the primary indicator for evaluating the concentration of smog. Currently, studies have been proposed to predict the concentration of PM2.5. Before, most methods use traditional machine learning or real-time monitoring to predict pollution of PM2.5 value. However, the previous prediction methods cannot meet the requirement of the accuracy. For this end, this paper uses Convnet and Dense-based Bidirectional Gated Recurrent Unit to predict PM2.5 value which combined Convnet, Dense and Bi-GRU. The feature in air quality data was extracted from convnets without max-pooling instead another convolutional layer and Bi-GRU with additional Dense could provide a more accuracy result. Experiments show that the effectiveness of our method PM2.5 mass concentration prediction model provides a more superior method for PM2.5 mass concentration prediction. [ABSTRACT FROM AUTHOR]

In today's world, environmental problems are becoming increasingly serious, and countries and regions are attaching great importance to them. Low-carbon and circular economy have become a strategic choice for China's sustainable economic development. As the public's awareness of environmental protection becomes stronger and stronger, the managers of companies ought to consider the maximum economic benefits. Meanwhile, they are supposed to focus on the green image of enterprises, so as to win in the market competition. The probabilistic linguistic term sets (PLTSs) are useful for expressing uncertain and fuzzy cognitions of the DMs over attributes. In this paper, we extend the Evaluation based on Distance from Average Solution (EDAS) method to the multiple attribute group decision making (MAGDM) with PLTSs. Firstly, concept, comparative formula, and distances of PLTSs are introduced in a nutshell. Then, the extended EDAS method is used to cope with the problems of MAGDM in PLTSs. In addition, for the sake of verifying the applicability of the expanding method, a calculation example about the sorting of green supplier is utilized. Consequently, the example shows that the method is easy to understand and operate. This method can be employed to choose the appropriate solution in other problems of selecting. [ABSTRACT FROM AUTHOR]

The reliable optimization of multilevel urban distribution network research helps to adapt to the changeable demand. It also can guarantee service ability of urban distribution network, so as to promote the economic development. Using the method of fuzzy reliability, series system reliability, failure rate, relative failure rate and failure function, the paper aimed at defining the key factors, the measure model and the measurement method of the reliability of urban distribution network clearly. Then, with the simulated data, the analysis of measurement model process and the application method are presented. The method can clear the level of the reliability and the weaker aspect of the urban distribution network which can provide the basis for improving the whole reliability. The paper sets up the measure model for the reliability of two-layer urban distribution network and then verify the validity of the methods. Bayesian network analysis is used to examine the influence of each factor on the reliability of the system, and the importance of each influencing factor is calculated. [ABSTRACT FROM AUTHOR]

Nonlinear grey Bernoulli model, abbreviated as NGBM model, has been validly used in real applications due to its high accuracy in nonlinear time series forecasting. However, there remain technical challenges to explain the mechanism of the accumulative sum operator in nonlinear grey modelling process and estimate structural parameters independent from the initial values. This paper aims to reconstruct the modelling process of the NGBM model so as to explain the modelling mechanism better by utilizing the integral matching approach, which consists of an integral formula and the numerical discretization-based least squares. First, the integral formula is employed to investigate the accumulative sum operator and further reconstruct the NGBM model to a generalized form, referred as to INGBM model. Then, a novel parameter estimation strategy, estimating structure parameters and initial values simultaneously, is developed by utilizing the numerical discretization-based least squares approach. Next, Monte Carlo simulation studies are designed to evaluate the finite sample performance of both models. Comparisons show that the INGBM model outperforms to the original one in terms of parameter estimation accuracy, forecasting accuracy and robustness to noise. Finally, we apply the INGBM model at a coal consumption in China study to further illustrate the usefulness of this model. [ABSTRACT FROM AUTHOR]

A dynamical system for optimal path of energy and resources between two cities in China is considered in this paper. We have discussed dynamics of variables and parameters involved in mentioned system. Bifurcation analysis around non-hyperbolic equilibria is also explained for codimension 1 and 2 bifurcations. Furthermore, double-zero eigenvalue condition is calculated for the proposed model. We have adopted methodology of the generalized vectors for existence of Bogdanov–Takens bifurcation critical point and used analytical computations instead of center manifold theorem for Bogdanov–Takens bifurcation around zero equilibria. Further, with the aid of bifurcation diagram, phase portraits and time history, we discussed occurrence of period doubling, Hopf bifurcation and chaotic region of our proposed model. Based on Lyapunov function and robust control, optimal controllers are designed using Hamilton-Jacobi theorem for the stability of disturbance and aperiodic solution in optimal transportation system (3) due to energy imports from city A to city B. [ABSTRACT FROM AUTHOR]

Every year, millions of people worldwide (including a major portion in China) are suffering from lung cancer disease (Chinese report of Smoking and Health 2017). The aim of this paper is to develop a new fuzzy soft expert system which can be used to predict lung cancer disease. A prediction process using this fuzzy soft expert system is composed of four main steps: (1) Transform real-valued inputs into fuzzy numbers. (2) Transform fuzzy numbers of data into fuzzy soft sets. (3) Reduce, using normal parameter reduction method, the obtained family of fuzzy soft sets into a new family of fuzzy soft sets. (4) Use the proposed algorithm to get the output data. An experiment is conducted on forty five patients (thirty males, fifteen females, all are cigarette smokers) who endure treatment in the Respiratory Department of Nanjing Chest Hospital, China. The number of training data taken was 55 records, and the remaining 45 records were used for the testing process in our system by using weight loss, shortness of breath, chest pain, persistence a cough, blood in sputum, and age of patients. The quantized accuracies of the proposed system were found to be 100 % . In this work, we developed a fuzzy soft expert system based on fuzzy soft sets; we used a fuzzy membership functions and an algorithm to predict those patients who may suffer lung cancer. In this way, it is possible to conclude that the use of fuzzy soft expert system can produce valuable results for lung cancer detection. It is found that the fuzzy soft expert system developed is useful to the expert doctor to decide if a patient has lung cancer or not. Finally, we introduce comparison diagnosed between our proposed system and the fuzzy inference system. [ABSTRACT FROM AUTHOR]

In recent years, there has been a high prevalence rate of heart disease (HD) among 50-year-old people in China. It has become the first disease of old ages death. It is a very interesting and challenging work to have an effective early forecasting of the risk of HD according to the patients data. In this paper, we propose a novel method to analyze the factors with views of group features. Normalized mutual information based on entropies and information gain ratio are employed to select factors. Discriminant minimum class locality preserving canonical correlation analysis is presented to determine the effectiveness of the view of group factors. Moreover, a novel model is given to forecast the risks of New York Heart Association Functional Classification. To verify the effectiveness of the proposed method and model, we collected electronic health records of 1271 patients from 28 Chinese Level III-A hospitals in 2015. After the risk factors analysis, several results are concluded: (1) Patients with HD usually suffer from similar complications. For example, most patients with heart disease suffer from hypertension, diabetes and arrhythmia at the same time. (2) The risk forecasting has an accurate recognition rate. The risk value of the level of patients is impacted on the complications. (3) Hypertension, arrhythmia, chronic cardiac insufficiency and coronary disease are the highest concurrent diseases. There is a high reliability to have a decision of levels on the cardiac functional diseases according to the output of our proposed model. [ABSTRACT FROM AUTHOR]

Artificial intelligence has a very wide application in modern society. Deep learning algorithms promote the development of artificial intelligence industry. The development of artificial intelligence industry needs the support of capital market. As far as the relationship between the three is concerned, the artificial intelligence industry is in the middle position. In short, the computer deep learning algorithm is the technical basis for the development of artificial intelligence, and the capital market is an important guarantee for the development of the artificial intelligence industry. However, under the current development background of China, the capital market is far from meeting the improvement and development of high-tech industries. Therefore, in order to further clarify the important role of deep learning algorithms in the development of artificial intelligence and capital markets, this paper systematically analyzes these three relationships and proposes measures from the perspective of supporting theoretical research and development and improving capital markets. [ABSTRACT FROM AUTHOR]

The development of financial integration makes a country's economic links difficult. This is especially so for emerging market countries, which are more vulnerable to economic shocks. The objective of this paper is to explore whether the emerging market countries which adopt floating exchange rate system have realized, at the same time, in the aftermath of a crisis, free movement of capital flow and independence of monetary policy. This is done by introducing foreign exchange reserves into Mundell–Fleming model to do derivation. The approach is supported by empirical research based on 20 emerging countries including China, Brazil, Poland and South Africa and others. It was found that: (1) after the crisis, the emerging market countries that implemented floating exchange rate system did not achieve monetary policy independence. Rapid accumulation of foreign exchange reserves weakened the positive effect of the increase in money supply on output; (2) As a result of holding foreign exchange reserves, the independence of monetary policy in emerging market countries has been challenged, independently of the type of exchange rate system adopted, which proves the existence of dilemma phenomenon. Finally, this study puts forward policy recommendations on the exchange rate system and capital account convertibility for China. [ABSTRACT FROM AUTHOR]