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This paper explores the development of a smart Structural Health Monitoring (SHM) platform tailored for long-span bridge monitoring, using the Forth Road Bridge (FRB) as a case study. It discusses the selection of smart sensors available for real-time monitoring, the formulation of an effective data strategy encompassing the collection, processing, management, analysis, and visualization of monitoring data sets to support decision-making, and the establishment of a cost-effective and intelligent sensor network aligned with the objectives set through comprehensive communication with asset owners. Due to the high data rates and dense sensor installations, conventional processing techniques are inadequate for fulfilling monitoring functionalities and ensuring security. Cloud-computing emerges as a widely adopted solution for processing and storing vast monitoring data sets. Drawing from the authors' experience in implementing long-span bridge monitoring systems in the UK and China, this paper compares the advantages and limitations of employing cloud- computing for long-span bridge monitoring. Furthermore, it explores strategies for developing a robust data strategy and leveraging artificial intelligence (AI) and digital twin (DT) technologies to extract relevant information or patterns regarding asset health conditions. This information is then visualized through the interaction between physical and virtual worlds, facilitating timely and informed decision-making in managing critical road transport infrastructure. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewable energy resources, their variable, intermittent and unpredictable characteristics bring new challenges to the power system. These challenges require micro-meteorological data and techniques to provide more support for the power systems, including planning, dispatching, operation, and so on. This paper aims to provide readers with insights into the effects of micro-meteorology on power systems, as well as the actual improvement brought by micro-meteorology in some power system scenarios. This paper provides a review including the relevant micro-meteorological techniques such as observation, assimilation and numerical techniques, as well as artificial intelligence, presenting a relatively complete overview of the most recent and relevant micro-meteorology-related literature associated with power systems. The impact of micro-meteorology on power systems is analyzed in six different forms of power generation and three typical scenarios of different stages in the power system, as well as integrated energy systems and disaster prevention and reduction. Finally, a case study in China is provided. This case takes wind power prediction as an example in a power system to compare the performance when applying micro-meteorological data or not. The experimental results demonstrated that using the micro-meteorological reanalysis dataset with high spatial--temporal resolution for wind power prediction performed better, verifying the improvement of micro-meteorology to the power system to some extent. [ABSTRACT FROM AUTHOR]

In the globalization trend, China's cultural heritage is in danger of gradually disappearing. The protection and inheritance of these precious cultural resources has become a critical task. This paper focuses on the Miao batik culture in Guizhou Province, China, and explores the application of knowledge graphs, natural language processing, and deep learning techniques in the promotion and protection of batik culture. We propose a dual-channel mechanism that integrates semantic and visual information, aiming to connect batik pattern features with cultural connotations. First, we use natural language processing techniques to automatically extract batik-related entities and relationships from the literature, and construct and visualize a structured batik pattern knowledge graph. Based on this knowledge graph, users can textually search and understand the images, meanings, taboos, and other cultural information of specific patterns. Second, for the batik pattern classification, we propose an improved ResNet34 model. By embedding average pooling and convolutional operations into the residual blocks and introducing long-range residual connections, the classification performance is enhanced. By inputting pattern images into this model, their categories can be accurately identified, and then the underlying cultural connotations can be understood. Experimental results show that our model outperforms other mainstream models in evaluation metrics such as accuracy, precision, recall, and F1-score, achieving 94.46%, 94.47%, 93.62%, and 93.8%, respectively. This research provides new ideas for the digital protection of batik culture and demonstrates the great potential of artificial intelligence technology in cultural heritage protection. [ABSTRACT FROM AUTHOR]

The Communist Party of China's 19th National Congress underlined the necessity of speeding the development of a manufacturing powerhouse and advanced manufacturing sector by supporting the deep integration of the Internet, big data, artificial intelligence, and the real economy. This study employed principal component analysis to extract the prominent risk factors from questionnaire data in order to manage the risks connected with the Internet strategic transformation of manufacturing firms. To confirm the major risk factors, a structural equation modeling was created using Amos-24 software. The findings revealed that risk factors of Internet strategic transformation in manufacturing businesses are mostly expressed in equipment flexibility risks, organizational versatility risks, smart technology risks, Internet technology risks, flexible management risks, and financing management risks. The paper offers useful theoretical and practical insights into the risks of China's manufacturing businesses' Internet strategic transformation. The findings can assist manufacturing firms in better identifying and managing these risks, supporting their smooth transition to the Internet economy. [ABSTRACT FROM AUTHOR]

Introduction: The emergence of artificial intelligence (AI) chat programs has opened two distinct paths, one enhancing interaction and another potentially replacing personal understanding. Ethical and legal concerns arise due to the rapid development of these programs. This paper investigates academic discussions on AI in medicine, analyzing the context, frequency, and reasons behind these conversations. Methods: The study collected data from the Web of Science database on articles containing the keyword "ChatGPT" published from January to September 2023, resulting in 786 medically related journal articles. The inclusion criteria were peer-reviewed articles in English related to medicine. Results: The United States led in publications (38.1%), followed by India (15.5%) and China (7.0%). Keywords such as "patient" (16.7%), "research" (12%), and "performance" (10.6%) were prevalent. The Cureus Journal of Medical Science (11.8%) had the most publications, followed by the Annals of Biomedical Engineering (8.3%). August 2023 had the highest number of publications (29.3%), with significant growth between February to March and April to May. Medical General Internal (21.0%) was the most common category, followed by Surgery (15.4%) and Radiology (7.9%). Discussion: The prominence of India in ChatGPT research, despite lower research funding, indicates the platform's popularity and highlights the importance of monitoring its use for potential medical misinformation. China's interest in ChatGPT research suggests a focus on Natural Language Processing (NLP) AI applications, despite public bans on the platform. Cureus' success in publishing ChatGPT articles can be attributed to its open-access, rapid publication model. The study identifies research trends in plastic surgery, radiology, and obstetric gynecology, emphasizing the need for ethical considerations and reliability assessments in the application of ChatGPT in medical practice. Conclusion: ChatGPT's presence in medical literature is growing rapidly across various specialties, but concerns related to safety, privacy, and accuracy persist. More research is needed to assess its suitability for patient care and implications for non-medical use. Skepticism and thorough review of research are essential, as current studies may face retraction as more information emerges. [ABSTRACT FROM AUTHOR]

China communicates strategic narratives about artificial intelligence (AI) in digital media productions to create a shared meaning about its actions and its image in the global race to develop AI. Building upon the literature in emotions and strategic narratives, this study seeks to clarify which emotions are discursively used in China's international AI narratives, and their function and significance. Specifically, the study investigates emotion discourses in AI-focused videos disseminated on China's international broadcasting (CGTN YouTube channel). The analysis reveals that pride, hope and fear discourses have multiple functions in China's international AI narratives on CGTN. Hope is used to represent China as a key competitor in the global AI race, who is catching up to the US. China uses pride to showcase its advances in AI applications, to highlight its transformation of traditional industries, and to identity itself as equal to the US. Fear is used to frame US's perception of China's AI developments, to suggest their crisis of confidence, but also to refute the "China threat". Additionally, the fear discourse is used to deliberate anticipated risks, particularly on job loss and data privacy. These emotion discourses forge China's identity as a future global AI power. The paper then discusses how these findings contribute to key debates about Chinese discourse and media strategy. [ABSTRACT FROM AUTHOR]

With technological transformations such as big data, blockchain, artificial intelligence, and cloud computing, digital techniques are infiltrating the field of finance. Digital finance (DF) is a resource-saving and environmentally friendly innovative financial service. It shows great green attributes and can drive the flow of financial resources towards environmentally-friendly enterprises, thereby promoting green low-carbon circular development (GLCD). However, few studies have explored the coupling mechanism between DF and GLCD. To fill this gap, this paper explores the effect of DF on GLCD, and established a mediating effect model to investigate the mechanism of DF in promoting GLCD. Additionally, this paper established a random forest model and a CatBoost model based on machine learning to examine the relative importance of DF and the factors affecting GLCD. The results show that DF has significant positive effects on GLCD, and technological innovation plays a key role in the effect of DF on GLCD; meanwhile, the effect of DF on GLCD shows nonlinear features with an increasing "marginal effect"; moreover, both DF and conventional factors have significant impacts on GLCD. Our study highlights the effect of DF on GLCD and underscores the importance of developing policies for DF and GLCD. This study provides an empirical basis and path reference for DF to achieve "carbon peak, carbon neutralization" in China. [ABSTRACT FROM AUTHOR]

The coronavirus first appeared in China in 2019, and the World Health Organization (WHO) named it COVID-19. Then WHO announced this illness as a worldwide pandemic in March 2020. The number of cases, infections, and fatalities varied considerably worldwide. Because the main characteristic of COVID-19 is its rapid spread, doctors and specialists generally use PCR tests to detect the COVID-19 virus. As an alternative to PCR, X-ray images can help diagnose illness using artificial intelligence (AI). In medicine, AI is commonly employed. Convolutional neural networks (CNN) and deep learning models make it simple to extract information from images. Several options exist when creating a deep CNN. The possibilities include network depth, layer count, layer type, and parameters. In this paper, a novel Xception-based neural network is discovered using the genetic algorithm (GA). GA finds better alternative networks and parameters during iterations. The best network discovered with GA is tested on a COVID-19 X-ray image dataset. The results are compared with other networks and the results of papers in the literature. The novel network of this paper gives more successful results. The accuracy results are 0.996, 0.989, and 0.924 for two-class, three-class, and four-class datasets, respectively. [ABSTRACT FROM AUTHOR]

The mining of open pit mines is widespread in China, and there are many cases of landslide accidents. Therefore, the problem of slope stability is highlighted. The stability of the slope is a factor that directly affects the mining efficiency and the safety of the entire mining process. According to the statistics, there is a 15 percent chance of finding landslide risk in China's large-scale mines. And due to the expansion of the mining scale of the enterprise, the problem of slope stability has become increasingly obvious, which has become a major subject in the study of open-pit mine engineering. In order to better predict the slope stability coefficient, this study takes a mine in China as a case to deeply discuss the accuracy of different algorithms in the stability calculation, and then uses a deep learning algorithm to study the stability under rainfall conditions. The change of the coefficient and the change of the stability coefficient before and after the slope treatment are experimentally studied with the displacement of the monitoring point. The result shows that the safety coefficient calculated by the algorithm in this paper is about 7% lower than that of the traditional algorithm. In the slope stability analysis before treatment, the safety factor calculated by the algorithm in this paper is 1.086, and the algorithm in this paper is closer to reality. In the stability analysis of the slope after treatment, the safety factor calculated by the algorithm in this paper is 1.227, and the stability factor meets the requirements of the specification. It also shows that the deep learning algorithm effectively improves the efficiency of the slope stability factor prediction and improves security during project development. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has made our surroundings more innovative and responsive, which has improved our lives. This research aims to provide a comprehensive overview by describing the descriptive parameters of publications, visualizing co-authorship and citation patterns, extracting the authors' keywords, and determining the impact and research performance of the IoT in education. Subsequently, this paper provides an orientation for researchers to understand better the main progress and gaps in this scientific publication. From 2006 to 2021, a comprehensive inspection of 2503 documents in the Scopus database was accomplished. Based on the findings, the number of publications increased steadily, with a hike in publication numbers in 2020. Scholars from China contributed approximately 467 of the total global publications among 103 other countries. In addition, of the other 160 outlets, Advances in Intelligent Systems and Computing was the most prolific source title. Generally, there is sustained interest and increased research in this field, apart from establishing novel methods and directions. Accordingly, an in-depth examination of the IoT in education research may assist researchers and practitioners in advancing prospective knowledge in this subject by identifying gaps. [ABSTRACT FROM AUTHOR]

Bats are a crucial component within ecosystems, providing valuable ecosystem services such as pollination and pest control. In practical conservation efforts, the classification and identification of bats are essential in order to develop effective conservation management programs for bats and their habitats. Traditionally, the identification of bats has been a manual and time-consuming process. With the development of artificial intelligence technology, the accuracy and speed of identification work of such fine-grained images as bats identification can be greatly improved. Bats identification relies on the fine features of their beaks and faces, so mining the fine-grained information in images is crucial to improve the accuracy of bats identification. This paper presents a deep learning-based model designed for the rapid and precise identification of common horseshoe bats (Chiroptera: Rhinolophidae: Rhinolophus) from Southern China. The model was developed by utilizing a comprehensive dataset of 883 high-resolution images of seven distinct Rhinolophus species which were collected during surveys conducted between 2010 and 2022. An improved EfficientNet model with an attention mechanism module is architected to mine the fine-grained appearance of these Rhinolophus. The performance of the model beat other classical models, including SqueezeNet, AlexNet, VGG16_BN, ShuffleNetV2, GoogleNet, ResNet50 and EfficientNet_B0, according to the predicting precision, recall, accuracy, F1-score. Our model achieved the highest identification accuracy of 94.22% and an F1-score of 0.948 with low computational complexity. Heat maps obtained with Grad-CAM show that our model meets the identification criteria of the morphology of Rhinolophus. Our study highlights the potential of artificial intelligence technology for the identification of small mammals, and facilitating fast species identification in the future. [ABSTRACT FROM AUTHOR]

Introduction: To investigate the prevalence of fundus tessellation (FT), and the threshold for screening FT using an artificial intelligence (AI) technology in Chinese children. Methods: The Nanjing Eye Study was a population-based cohort study conducted in children born between September 2011 and August 2012 in Yuhuatai District of Nanjing. The data presented in this paper were obtained in 2019, when these children were 7 years old and underwent 45° non-mydriatic fundus photography. FT in whole fundus, macular area, and peripapillary area was manually recognized from fundus photographs and classified into three grades. Fundus tessellation density (FTD) in these areas was obtained by calculating the average exposed choroid area per unit area using artificial intelligence (AI) technology based on fundus photographs. The threshold for screening FT using FTD was determined using receiver operating characteristic (ROC) curve analysis. Results: Among 1062 enrolled children (mean [± standard deviation] spherical equivalent: − 0.28 ± 0.70 D), the prevalence of FT was 42.18% in the whole fundus (grade 1: 36.53%; grade 2: 5.08%; grade 3: 0.56%), 45.57% in macular area (grade 1: 43.5%; grade 2: 1.60%; grade 3: 0.50%), and 49.72% in peripapillary area (grade 1: 44.44%; grade 2: 4.43%; grade 3: 0.85%), respectively. The threshold value of FTD for screening severe FT (grade ≥ 2) was 0.049 (area under curve [AUC] 0.985; sensitivity 98.3%; specificity 92.3%) in the whole fundus, 0.069 (AUC 0.987; sensitivity 95.5%; specificity 96.2%) in the macular area, and 0.094 (AUC 0.980; sensitivity 94.6%; specificity 94.2%) in the peripapillary area, respectively. Conclusion: Fundus tessellation affected approximately 40 in 100 children aged 7 years in China, indicating the importance and necessity of early FT screening. The threshold values of FTD provided by this study had high accuracy for detecting severe FT and might be applied for rapid screening. [ABSTRACT FROM AUTHOR]

Bats are a crucial component within ecosystems, providing valuable ecosystem services such as pollination and pest control. In practical conservation efforts, the classification and identification of bats are essential in order to develop effective conservation management programs for bats and their habitats. Traditionally, the identification of bats has been a manual and time-consuming process. With the development of artificial intelligence technology, the accuracy and speed of identification work of such fine-grained images as bats identification can be greatly improved. Bats identification relies on the fine features of their beaks and faces, so mining the fine-grained information in images is crucial to improve the accuracy of bats identification. This paper presents a deep learning-based model designed for the rapid and precise identification of common horseshoe bats (Chiroptera: Rhinolophidae: Rhinolophus) from Southern China. The model was developed by utilizing a comprehensive dataset of 883 high-resolution images of seven distinct Rhinolophus species which were collected during surveys conducted between 2010 and 2022. An improved EfficientNet model with an attention mechanism module is architected to mine the fine-grained appearance of these Rhinolophus. The performance of the model beat other classical models, including SqueezeNet, AlexNet, VGG16_BN, ShuffleNetV2, GoogleNet, ResNet50 and EfficientNet_B0, according to the predicting precision, recall, accuracy, F1-score. Our model achieved the highest identification accuracy of 94.22% and an F1-score of 0.948 with low computational complexity. Heat maps obtained with Grad-CAM show that our model meets the identification criteria of the morphology of Rhinolophus. Our study highlights the potential of artificial intelligence technology for the identification of small mammals, and facilitating fast species identification in the future. [ABSTRACT FROM AUTHOR]

China's economic growth has reached a new plateau. It is no longer appropriate to use the old economic growth model, which relied on labor, land resources, mineral resources, and other economic considerations. Under the background of artificial intelligence, high-quality economic development is an inevitable trend. A new financial paradigm called "digital finance" integrates financial services with information technologies. Digital financial technology is thought to be a crucial foundation for fostering high-quality and sustainable economic and social development since it may offer more economic entities reduced cost of capital and more realistic financial service skills than in traditional financial models. In the era of artificial intelligence, how to reasonably release the momentum of digital finance for China's sustained economic growth has become a hot topic of discussion at this stage. This paper studies the impact of digital finance on the economic efficiency of the energy industry in the context of artificial intelligence. Relevant metrics were also calculated. The findings revealed that: The benchmark regression result of digital finance on the efficiency of the green economy was 0.4685 before adding the main restrictions; the benchmark regression result of digital finance on the efficiency of the green economy was 0.2243 after adding the main constraints. As a result, data finance had a favorable impact on the effectiveness of the green economy. [ABSTRACT FROM AUTHOR]

This paper explores the impact of artificial intelligence and industrial robots on firms' export behaviour and divides the impact mechanism into the productivity effect and labour substitution effect. It examines the effect of industrial robots on firms' export value by using Chinese Customs data, Chinese Industrial Firm data and robot data from the International Robot Federation (IRF). The main findings are as follows: Firstly, the impact of artificial intelligence and industrial robots on Chinese firms' export value is generally negative, which means the negative labour substitution effect dominates the positive productivity effect. Secondly, the impact of artificial intelligence varies significantly by industry, and the export value of firms from high-tech industries benefits from the use of industrial robots. Thirdly, the impact of artificial intelligence on firms' export value also varies by time; before 2003, the use of industrial robots showed mainly an inhibiting effect on firms' exports, which turned into a driving effect thereafter, and after 2006, industrial robots began to significantly promote firms' export. Finally, the higher the quality of export products, the more likely the use of industrial robots will be to promote firms' export value, and the higher the capital–labour ratio is, the more likely firms' export value will be to benefit from the use of artificial intelligence and industrial robots. On the basis of these findings, this study proposes promoting the productivity effect to dominate the labour substitution effect through technological progress and the improvement of export product quality. [ABSTRACT FROM AUTHOR]

With the development of The Times, social events are increasing, and emergency management has gradually become the main helper to solve the crisis in the public domain. By observing the current situation of many countries and regions, we can find that various types of public crises often occur in many countries and regions in the world, which have severely affected people's daily life, lives, and property. Through long-term research and analysis, it can be known that the emergency management mechanism currently established in China has certain shortcomings. The communication problem of emergency information is likely to cause the emergency work to not proceed smoothly. In addition, problems in the communication channels of emergency information are likely to cause problems in the cooperation of various departments when people carry out emergency management work, and the efficiency of the government in dealing with problems will also be reduced in real scenarios. In order to improve the efficiency of emergency information management, this paper aims at the various problems existing and facing in the construction of emergency management system. On this basis, the integration of various relevant emergency information management plan models is analyzed and sorted out, and based on the research and integration of the development of artificial intelligence algorithms. The main research results of emergency information management at home and abroad are comprehensively studied and evaluated. Finally, a QG algorithm based on more model fusion is developed. In the process of analysis, this article uses artificial intelligence algorithms to build a prediction model of multiple modes and collects the data needed to build the model by random extraction. Through the analysis of different data sets, it is used as the basic training data for prediction. Through comprehensive analysis, the model constructed in this paper can promote the sharing of emergency information among departments to a certain extent. [ABSTRACT FROM AUTHOR]

With the rapid development of modern science and technology, the speed of digital media to disseminate information is also accelerating, and the forms of communication become more and more diversified. In order to make digital media art better innovate and develop, designers should actively explore, so that art under digital media has the characteristics of intelligence, networking, and content diversification, so that it can be better applied in modern digital media communication and let visual art reach new development heights in the context of digital media. The current rapid development of artificial intelligence technology is being gradually applied to all fields of society. From the current point of view, China's digital media art will inevitably remain in the early development stage for a long time to face many difficulties and problems. In the context of the era of artificial intelligence, there will also be a large number of art and design talents for continuous research and exploration, and the emergence of these talents will certainly prompt the further development and enhancement of digital media art and artificial intelligence technology. In this process, it is especially worth noting that China's excellent cultural connotations should not be abandoned. Committed to digging more beneficial digital media art elements from traditional culture to make digital media art always develop in a more meaningful way. This paper takes the artificial intelligence era as the background, discusses the core of the development of the mutual integration of artificial intelligence technology and digital media art, analyzes the current development status of digital media art and technology, as well as the innovative development direction and future trends, proposes a digital media art design algorithm based on the convolutional neural network, and finally proves the effectiveness of the method in the relevant data set. [ABSTRACT FROM AUTHOR]

Deep changes have taken place in China over the past ten years. The debate increasingly revolves around its new dynamics of accumulation, even questioning under which typology to frame the current Chinese model. In this paper, we propose to correlate the Chinese national system of technological innovations, reemerged in the first years of this century, disruptive technologies such as 5G internet, Big Data and Artificial Intelligence, and the emergence of new and superior forms of economic planning in that country, sowing the seeds for what we call "New Projectment Economy". [ABSTRACT FROM AUTHOR]

Landslides with step‐like deformation features are widely distributed in the Three Gorges Reservoir area (TGR) of China, posing a severe hazard to the inhabitants of this region. This paper proposes a multi‐input and multi‐output intelligent integrated displacement prediction model for landslides with step‐like displacement patterns. In this new model, three interconnected and information‐transmitted functional sub‐models are integrated. Unsupervised learning is used to identify different landslide deformation states automatically, and the imbalance classification and explainable artificial intelligence techniques are introduced for qualitative prediction and information filtering. Probability theory and deep machine learning are adopted to provide deterministically predicted values and quantify their uncertainty. The case study of the Baijiabao landslide in the TGR region proves that the proposed model performs satisfactorily in both point and interval predictions. The intelligent integrated model can also provide the forecast of landslide deformation states, visual input information filtering and back analysis of influencing factors, which are valuable to landslide early warning and risk management. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is an interdisciplinary field that encompasses a wide range of computer science disciplines, including image recognition, machine learning, human−computer interaction, robotics and so on. Recently, AI, especially deep learning algorithms, has shown excellent performance in the field of image recognition, being able to automatically perform quantitative evaluation of complex medical image features to improve diagnostic accuracy and efficiency. AI has a wider and deeper application in the medical field of diagnosis, treatment and prognosis. Nasopharyngeal carcinoma (NPC) occurs frequently in southern China and Southeast Asian countries and is the most common head and neck cancer in the region. Detecting and treating NPC early is crucial for a good prognosis. This paper describes the basic concepts of AI, including traditional machine learning and deep learning algorithms, and their clinical applications of detecting and assessing NPC lesions, facilitating treatment and predicting prognosis. The main limitations of current AI technologies are briefly described, including interpretability issues, privacy and security and the need for large amounts of annotated data. Finally, we discuss the remaining challenges and the promising future of using AI to diagnose and treat NPC. [ABSTRACT FROM AUTHOR]

This work addresses a soft-clustered vehicle routing problem that extends the classical capacitated vehicle routing problem with one additional constraint, that is, customers are partitioned into clusters and all customers of the same cluster must be served by the same vehicle. Its potential applications include parcel delivery in courier companies and freight transportation. Due to its NP-hard nature, solving it is computationally challenging. This paper presents an efficient bilevel memetic search method to do so, which explores search space at both cluster and customer levels. It integrates three distinct modules: a group matching-based crossover (to generate promising offspring solutions), a bilevel hybrid neighborhood search (to perform local optimization), and a tabu-driven population reconstruction strategy (to help the search escape from local optima). Extensive experiments on three sets of 390 widely used public benchmark instances are conducted. The results convincingly demonstrate that the proposed method achieves much better overall performance than state-of-the-art algorithms in terms of both solution quality and computation time. In particular, it is able to find 20 new upper bounds for large-scale instances while matching the best-known upper bounds for all but four of the remaining instances. Ablation studies on three key algorithm modules are also performed to demonstrate the novelty and effectiveness of the proposed ideas and strategies. Funding: This work was supported by the Macau Young Scholars Program [Grant AM2020011], Fundo para o Desenvolvimento das Cienciase da Tecnologia (FDCT) [Grant 0047/2021/A1], the National Natural Science Foundation of China [Grants 61903144, 71871142, and 71931007], and the Open Project of the Shenzhen Institute of Artificial Intelligence and Robotics for Society [Grant AC01202005002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.1186. [ABSTRACT FROM AUTHOR]

In recent years, the development of intelligent iteration technology and the use of big data processing technology have set off an upsurge, and the analysis and application of artificial intelligence algorithms have been paid more and more attention. In order to face the challenges of Arab migration and international trade, this paper constructs the basic structure of the Arab migration action imitation model. In this paper, a simulated servo clustering algorithm based on big data and intelligent iteration is used. Then, through the analysis of pseudo servo clustering algorithm, an optimization model is established, and a big data analysis system is formed. This paper focuses on the wide application of big data statistics to solve the construction of Arab immigration and entrepreneurship data system. This paper studies and applies the big data statistics and intelligent iterative algorithm of Arab immigration behavior, focuses on the annual ladder degree of Arab immigrants, and constructs a pseudo servo cluster system based on Intelligent iterative algorithm. Finally, the simulation experiment verifies whether the clustering model can accurately retrieve the behavior of Arab immigrants in China. The era of big data provides good development opportunities for Arab immigrants and international trade, but it also faces severe challenges. The study provides a reference for strengthening the analysis of international trade, and puts forward perfect countermeasures in combination with the actual situation, so as to improve the efficiency of international trade management and promote the better implementation of international trade. [ABSTRACT FROM AUTHOR]

The digitalization of the automobile industry must depend on the information technology industry. The coupling development of the two creates conditions for the realization of industry 4.0 and intelligent manufacturing. The purpose of this paper is to make an empirical study on the possibility of coupling and the coupling degree, correlation degree, and innovation ability of China's automobile industry and information technology industry. This paper takes the intelligent manufacturing of the automobile industry as the research object. According to the current situation of China's automobile industry, this paper uses data to analyze and predict the development level of China's automobile industry and the gap with the foreign automobile industry. Firstly, it summarizes and analyzes the development of China's automobile industry and information technology industry. Then, the gray correlation analysis method is used to explore the factors affecting the innovation of China's automobile industry. It is found that the relationship between capital investment and innovation is the most significant in "industrial innovation investment." Finally, the coupling development model of the automobile industry and information technology industry is established, and the coupling degree of the two is analyzed by using the analytic hierarchy process and the Delphi method. The research of this paper mainly focuses on the automobile industry. Under the wave of internet, other industries such as the commerce and service industry also have a strong coupling relationship with the information technology industry, which will provide a new research direction for the follow-up research of experts and scholars. [ABSTRACT FROM AUTHOR]

As China's economic level and industrial volume continue to develop and expand, the traditional methods of business management and resource scheduling demonstrate significant limitations and misalignments with China's daily economic activities. Traditional industrial and commercial management resource scheduling is highly reliant on manual labor. When confronted with the requirements of large-scale project management, the pure manual mode cannot track the real-time progress of the project flexibly, effectively, and in a timely manner, resulting in the inability to complete the corresponding resource-scheduling work efficiently, which will affect the overall operation progress of the project. Deep neural networks have a high-application potential for solving extremely complex and highly nonlinear optimization problems. Artificial intelligence and deep learning research have advanced at a rapid pace over the past few decades, thanks to the efforts of numerous researchers. Combined with GPU technology, the deep learning framework can provide an extremely complex optimization problem with a practical and feasible optimization scheme and corresponding solution path in a very short amount of time. Therefore, this paper explores the potential application of deep learning technology to industrial and commercial resource-scheduling management. By analyzing the benefits of deep learning technology and the bottleneck issues of existing industrial and commercial resource scheduling, a real-time optimized industrial and commercial resource-scheduling model based on deep learning technology is developed. The model is evaluated using the respective data set. The test results demonstrate that the resource-scheduling model proposed in this paper has strong real-time and high-operation efficiency and can assist engineers in completing the corresponding resource-scheduling tasks. [ABSTRACT FROM AUTHOR]

As the presentation form of the web, web pages contain a lot of important information in web design. As many information carriers, such as sound, graphics, and text, web pages have been integrated and have played a good role in visual transmission. It inherits many features of traditional graphic design, it can be said that it extends graphic design, and web design is richer in visual performance. At present, the research on web design in China is mainly background research. There is little research on the front-end aspects of web pages, especially in the aspect of plane vision. The lack of research in this area limits the development of web design. This paper introduces the relevant situation of web design and then analyzes the relevant elements in web design, such as visual elements, and their innovative applications. In addition, it focuses on the presentation form of visual elements in web design and summarizes the development trend of visual elements in contemporary web interface design. It proposes the strategy of innovation and application of visual elements in web design. The author hopes that, through the research of this paper, it will lay a theoretical foundation for the development of web design in the future. At the same time, this paper uses the NCMF algorithm in artificial intelligence to identify the visual elements of web pages, thereby improving the efficiency of web page design. Experiments show that the Chaos accuracy rate of the NCMF algorithm in the CETD dataset is 73.3482%. [ABSTRACT FROM AUTHOR]

With the widespread application of IoT technology in the world, the new industry of IoT finance has emerged. Under this new business model, commercial banks and other financial institutions can realize safer and more convenient financial services such as payment, financing and asset management through the application of IoT technology and communication network technology. In the cloud computing model, the local terminal device of IOT will transmit the collected data to the cloud server through the network, and the cloud server will complete the data operation. Cloud computing model can well solve the problem of poor performance of IoT devices, but with the increasing number of IoT terminal devices and huge number of devices accessing the network, cloud computing model is constrained by network bandwidth and performance bottleneck, which brings a series of problems such as high latency, poor real-time and low security. In this paper, based on the new industry of IoT finance which is developing rapidly, we construct a POT (Peaks Over Threshold) over threshold model to empirically analyze the operational risk of commercial banks by using the risk loss data of commercial banks, and estimate the corresponding ES values by using the control variables method to measure the operational risk of traditional commercial banks and IoT finance respectively, and compare the total ES values of the two. This paper adopts the control variable method to reduce the frequency of each type of loss events of operational risk of commercial banks in China respectively. [ABSTRACT FROM AUTHOR]

Toll-gates are crucial points of management and key congestion bottleneck for the freeway. In order to avoid traffic deterioration and alleviate traffic congestion in advance, it is necessary to predict and evaluate the congestion in toll-gates scattering in large-scale region of freeway network. In this paper, traffic volume and operational delay time are selected from various traffic indicators to evaluate congestion considering the particular characteristics of the traffic flow within the toll-gate area. The congestion prediction method is designed including two modules: a deep learning (DL) prediction and a fuzzy evaluation. We propose a modified deep learning method based on graph convolutional network (GCN) structure in the fusion of dilated causal mechanism and optimize the method for spatial feature extraction by constructing a new adjacency matrix. This new AI network could process spatiotemporal information of traffic volume and operational delay time, that extracted from large-scaled toll-gates spontaneously, and predict key indicators in 15/30/60 min future time. The evaluation module is proposed based on these predicted results. Then, fuzzy C-means algorithm (FCM) is further modified by determining coupling weight for these two key indicators to detect congestion state. Original traffic data are obtained from the current 186 toll-gates served on the freeway network in Shaanxi Province, China. Experimental tests are carried out based on historical data of four months after preprogressing. The comparative tests show the proposed CPT-DF (congestion prediction on toll-gates using deep learning and fuzzy evaluation) outperforms the current-used other models by 6-15%. The successful prediction could extend to the real-time prediction and early warning of traffic congestion in the toll system to improve the intelligent level of traffic emergency management and guidance on the key road of disasters to some extent. [ABSTRACT FROM AUTHOR]

With the continuous advancement of urbanization, the contradiction between urban development and environmental resources has become increasingly prominent, and environmental pollution has become increasingly serious. To fundamentally solve the problems of environment, energy, and low carbon, we must rely on the intelligence of energy. This paper aims to study the sustainable development of China's intelligent energy industry based on artificial intelligence and low‐carbon economy. In view of the problems existing in the optimization of power generation industry, this paper uses the annual load, electricity price, weather, and climate data of a southern power grid, uses the statistical variation particle swarm optimization algorithm, uses the historical runoff and rainfall data to optimize it, and studies the analysis methods, characteristics and laws of short‐term load, electricity price and runoff, as well as the uncertain factors affecting their changes. The experimental results show that the predicted price is close to the actual price, and the median error of each period is <1% in statistical analysis, so the forecast value can be used to replace the actual value for scheduling. This method makes full use of the adaptive mutation in the late stage of particle optimization, and introduces the mechanism of particle size selection, which fully ensures the diversity of particles and improves the search ability of particles. [ABSTRACT FROM AUTHOR]

The rise of FinTech has been meteoric in China. Investing in mutual funds through robo-advisor has become a new innovation in the wealth management industry. In recent years, machine learning, especially deep learning, has been widely used in the financial industry to solve financial problems. This paper aims to improve the accuracy and timeliness of fund classification through the use of machine learning algorithms, that is, Gaussian hybrid clustering algorithm. At the same time, a deep learning-based prediction model is implemented to predict the price movement of fund classes based on the classification results. Fund classification carried out using 3,625 Chinese mutual funds shows both accurate and efficient results. The cluster-based spatiotemporal ensemble deep learning module shows better prediction accuracy than baseline models with only access to limited data samples. The main contribution of this paper is to provide a new approach to fund classification and price movement prediction to support the decision-making of the next generation robo-advisor assisted by artificial intelligence. [ABSTRACT FROM AUTHOR]

This umbrella review is motivated to understand the shift in research themes on brain-computer interfacing (BCI) and it determined that a shift away from themes that focus on medical advancement and system development to applications that included education, marketing, gaming, safety, and security has occurred. The background of this review examined aspects of BCI categorisation, neuroimaging methods, brain control signal classification, applications, and ethics. The specific area of BCI software and hardware development was not examined. A search using One Search was undertaken and 92 BCI reviews were selected for inclusion. Publication demographics indicate the average number of authors on review papers considered was 4.2 ± 1.8. The results also indicate a rapid increase in the number of BCI reviews from 2003, with only three reviews before that period, two in 1972, and one in 1996. While BCI authors were predominantly Euro-American in early reviews, this shifted to a more global authorship, which China dominated by 2020–2022. The review revealed six disciplines associated with BCI systems: life sciences and biomedicine (n = 42), neurosciences and neurology (n = 35), and rehabilitation (n = 20); (2) the second domain centred on the theme of functionality: computer science (n = 20), engineering (n = 28) and technology (n = 38). There was a thematic shift from understanding brain function and modes of interfacing BCI systems to more applied research novel areas of research-identified surround artificial intelligence, including machine learning, pre-processing, and deep learning. As BCI systems become more invasive in the lives of "normal" individuals, it is expected that there will be a refocus and thematic shift towards increased research into ethical issues and the need for legal oversight in BCI application. [ABSTRACT FROM AUTHOR]

Air-quality monitoring and forecasting are crucial for atmosphere pollution control and management. We propose an innovative data-driven framework for air quality index (AQI) prediction by integrating GNSS radio occultation (GNSS-RO) observation and weather modeling. Empowered by the state-of-the-art machine learning approach, our method can effectively predict regional AQI with a comparable accuracy much more quickly than the traditional numerical modeling and simulation approach. In a real case study using a representative region of China, our data-driven approach achieves a 2000 times speedup; meanwhile, the prediction error measured by rRMSE is only 2.4%. We investigate further the effects of different models, hyperparameters, and meteorological factors on the performance of our AQI prediction framework, and reveal that wind field and atmospheric boundary-layer height are important influencing factors of AQI. This paper showcases a direct application of GNSS-RO observation in assisting in forecasting regional AQI. From a machine learning point of view, it provides a new way to leverage the unique merits of GNSS atmospheric remote sensing technology with the help of the more traditional weather forecasting modeling approach. [ABSTRACT FROM AUTHOR]

In this work, we introduce our project on Dunhuang cultural heritage protection using artificial intelligence. The Dunhuang Mogao Grottoes in China, also known as the Grottoes of the Thousand Buddhas, is a religious and cultural heritage located on the Silk Road. The grottoes were built from the 4th century to the 14th century. After thousands of years, the in grottoes decaying is serious. In addition, numerous historical records were destroyed throughout the years, making it difficult for archaeologists to reconstruct history. We aim to use modern computer vision and machine learning technologies to solve such challenges. First, we propose to use deep networks to automatically perform the restoration. Through out experiments, we find the automated restoration can provide comparable quality as those manually restored from an archaeologist. This can significantly speed up the restoration given the enormous size of the historical paintings. Second, we propose to use detection and retrieval for further analyzing the tremendously large amount of objects because it is unreasonable to manually label and analyze them. Several state-of-the-art methods are rigorously tested and quantitatively compared in different criteria and categorically. In this work, we created a new dataset, namely, AI for Dunhuang, to facilitate the research. Version v1.0 of the dataset comprises of data and label for the restoration, style transfer, detection, and retrieval. Specifically, the dataset has 10,000 images for restoration, 3455 for style transfer, and 6147 for property retrieval. Lastly, we propose to use style transfer to link and analyze the styles over time, given that the grottoes were build over 1000 years by numerous artists. This enables the possibly to analyze and study the art styles over 1000 years and further enable future researches on cross-era style analysis. We benchmark representative methods and conduct a comparative study on the results for our solution. The dataset will be publicly available along with this paper. [ABSTRACT FROM AUTHOR]

With regard to the development of colleges and universities, ensuring the quality of education is the fundamental goal and main task of teaching daily management. With the continuous improvement of the application level of the Internet and other information technologies, the construction of smart campus in colleges and universities in China is rapidly advancing. This paper studies the construction and innovation strategy of the public sports quality monitoring system and discusses the changes in college students' sports quality after the introduction of smart campuses from the perspective of artificial intelligence and the creation of smart universities. In this paper, the field survey method and other research methods are combined to study, and in the process of data storage, SQL Server database platform is used to store the data. This study shows that the proportion of each element of physical state management has changed significantly before and after college entrance. According to the data, since the introduction of smart campus real name system identification, tracking data, and evaluation functions, the number of college students' physical exercise has increased significantly. The number of students with exercise plan in school 1 has increased from 70 to 222, and that of school 2 has increased from 49 to 199. Before the introduction, the students were very satisfied with the learning effect of physical education, which was 40.12% and increased to 45.70% after the introduction. Before the introduction, the students were very satisfied with the sports equipment, which was 30.12% before the introduction and increased to 35.24% after the introduction. Therefore, building a system for monitoring the quality of public sports in universities is very important for improving the quality of education in public sports in universities and plays an active role in promoting the physical and mental health of students. [ABSTRACT FROM AUTHOR]

This paper discusses the method of fossil digitization combining paleontology and art by using data mining. The aim of the study is to increase the creativity and vitality of museum exhibits and promote scientific exchange. The purpose of the study is to discuss how the interdisciplinary approach will benefit the communication of science and to realize the cooperative development of creative science popularization, art science popularization, dialogue science popularization, and communication science popularization through the cooperation between museums and institutions disseminating and researching science. The research method takes the Jehol Biota in western Liaoning Province of China as an example to explore the methods and approaches to transform museum resources into creative products. Through artificial intelligence data mining and digital reconstruction, data can be used for archaeological research but also for scientific popularization and scientific dissemination of the resources of art transformation. Research results: as the cornerstone to consolidate the digital development of society and economy, "the new infrastructure of artificial intelligence" will start a comprehensive digital transformation. Through the integration of multiple fields and disciplines, museums will change from resource dependence to focus more on market creation, which will promote museum's industry development. [ABSTRACT FROM AUTHOR]

Intercultural communication not only promotes the emergence and development of multiculturalism but also promotes cultural exchanges on a global scale. China has rich resources and a long history. Because of its unique national characteristics, it is even more famous and unique in the world today. However, with the transformation of society, cross-cultural dissemination and blending are inevitable. Intangible cultural heritage is an important cultural form, and its international dissemination is increasingly attracting people's attention. The spontaneous inheritance mode of intangible cultural heritage, which is mainly based on oral and heart teaching, has been unable to adapt to the development of society and has gradually moved away from people's sight. How to better protect and inherit intangible cultural heritage is an important topic in the field of current cultural exchanges. However, due to the particularity of intangible culture, especially its abstract nature, it is difficult to show it to the public, which makes it difficult to protect and promote intangible cultural heritage. The application of the advantages of artificial intelligence technology to the dissemination of intangible cultural heritage is to solve these problems, which can not only make it innovative in protection but also promote its sustainable development to a certain extent. This paper discusses the application of artificial intelligence technology in the protection and dissemination of intangible cultural heritage from multiple perspectives and points out that the advantage of artificial intelligence is that although it cannot be creatively inherited, it can be reproduced in a historical moment, so as to meet people's needs for intangible cultural heritage. It can also be found in the questionnaire for the visitors that 66% of the visitors prefer to learn about the intangible cultural heritage through experience and feel that the personal experience is more intuitive and interesting. Artificial intelligence technology has great advantages in disseminating intangible cultural heritage. [ABSTRACT FROM AUTHOR]

In recent years, with the advance of Artificial Intelligence, automatic music composition has been demonstrated. However, there are many music genres and music instruments. For a same piece of music, different music instruments would produce different effects. Invented some 2500 years ago, Guzheng is one of the oldest music instruments in China and the world. It has distinct timbres and patterns that cannot be duplicated by other music instruments. Therefore, it is interesting to see whether AI can compose Guzheng music or alike. In this paper we present a method that can automatically compose and play Guzheng music. First, we collect a number of existing Guzheng music pieces and convert them into Music Instrument Digital Interface format. Second, we use these data to train a Long Short-Term Memory (LSTM) network and use the trained network to generate new Guzheng music pieces. Next, we use the Reinforcement Learning to optimize the LSTM network by adding special Guzheng playing techniques. Comparing to the existing AI methods, such as LSTM and Generative Adversary Network, our new method is more effective in capturing the characteristics of Guzheng music. According to the evaluations from skilled Guzheng players and general audiences, our Guzheng music is very close to the real Guzheng music. The presented method can also be used to automatically compose the music of other Chinese music instruments. [ABSTRACT FROM AUTHOR]

In order to solve the problems of machine translation efficiency and translation quality, this paper proposes an English translation evaluation system based on the BP neural network algorithm. This method provides users with a more intelligent machine translation service experience. With the help of the BP neural network algorithm, taking English online translation as the research object, Google's translation quality is the best, with an error frequency of only 167, while Baidu translation and iFLYTEK translation in China have a high error rate of 266 and 301, respectively, which is much higher than Google translation. A model of machine translation evaluation based on the neural network algorithm is proposed to better solve the disadvantages of traditional English machine translation. The results show that the machine translation system based on the neural network algorithm can further optimize the problems existing in machine translation, such as insufficient use of information and large scale of model parameters, and further improve the performance of neural network machine translation. [ABSTRACT FROM AUTHOR]

Facing the dual background of risk society and the fourth scientific and technological revolution, digital emergency management (DEM) research has attracted wide attention worldwide. Based on the comparative analysis of data from Web of Science (WOS) and China National Knowledge Infrastructure (CNKI) databases, the research draws the knowledge map by CiteSpace, in order to analyse the spatiotemporal distribution, hotspots, and trends of DEM research. The following are the results: (1) DEM research shows a spiral growth, with a significant increase in the number of papers. Existing highly cited references are mainly carried out from the perspective of management or technology. (2) The complex countries' cooperation network has been formed from the USA, China, and other countries; the complexity of the institutions' cooperation network must be strengthened, and the density of the authors' cooperation network is low; DEM research has significant interdisciplinary attributes. (3) The hotspots of DEM research can be divided into three categories: means, processes, and objects, but the research emphases between international and China are different. (4) The evolution of DEM research can be divided into three stages between international and China: embryonic, development, and deepening stages, and research contents in each stage show some differences. (5) The trends of DEM research between international and China are different. The former are information technology, artificial intelligence, and machine learning, whereas the latter are emergency decision making, scenario analysis, and public health. [ABSTRACT FROM AUTHOR]

"Autonomous weapon systems" (AWS) have been subject to intense discussions for years. Numerous political, academic and legal actors are debating their consequences, with many calling for strict regulation or even a global ban. Surprisingly, it often remains unclear which technologies the term AWS refers to and also in what sense these systems can be characterised as autonomous at all. Despite being feared by many, weapons that are completely self-governing and beyond human control are more of a conceptual possibility than an actual military reality. As will be argued, the conflicting interpretations of AWS are largely the result of the diverse meanings that are constructed in political discourses. These interpretations convert specific understandings of AI into strategic assets and consequently hinder the establishment of common ethical standards and legal regulations. In particular, this article looks at the publicly available military AI strategies and position papers by China and the USA. It analyses how AWS technologies, understood as evoking sociotechnical imaginaries, are politicised to serve particular national interests. The article presents the current theoretical debate, which has sought to find a functional definition of AWS that is sufficiently unambiguous for regulatory or military contexts. Approaching AWS as a phenomenon that is embedded in a particular sociotechnical imaginary, however, flags up the ways in which nation states portray themselves as part of a global AI race, competing over economic, military and geopolitical advantages. Nation states do not just enforce their geopolitical ambitions through a fierce realpolitik rhetoric but also play around with ambiguities in definitions. This especially holds true for China and the USA, since they are regarded and regard themselves as hegemonic antagonists, presenting competing self-conceptions that are apparent in their histories, political doctrines and identities. The way they showcase their AI-driven military prowess indicates an ambivalent rhetoric of legal sobriety, tech-regulation and aggressive national dominance. AWS take on the role of signifiers that are employed to foster political legitimacy or to spark deliberate confusion and deterrence. [ABSTRACT FROM AUTHOR]

The digital twin is considered the central component of modern industry. It has been adopted in many industrial fields. However, its application in nuclear engineering is very rare, especially for the radioactive waste deposits winch is an urgent and tricky issue. Motivated by this demand and considering China's research & development guidelines for geological disposal of high-level radioactive waste (a three-step strategy by 2050 to construct the radioactive waste repository), a framework of the radioactive waste repository digital twin is proposed. The digital twin uses the framework edge + cloud with a multi-layer structure. It can be adopted in the construction of the radioactive waste repository. It can significantly strengthen the management capability, reduce the operating cost, improve the safety level and deal with accidents more efficiently. The first step for the achievement for the digital twin development of radioactive waste repository based on the framework is also introduced in the paper. The proposed digital twin framework of the radioactive waste repository in this work could be widely used as a reference and easily extended to support management in other industrial fields. [ABSTRACT FROM AUTHOR]

Based on the development background of the interweaving and integration of computer technology and Internet technology, China's artificial intelligence industry is quietly rising. In the social life of the information age, the artificial intelligence industry represented by machine deep learning is playing a very important role. This study in combination with the background of the new health reform, in view of the reform of the medical industry, analyzes the connotation of financial wisdom based on the important role of the hospital financial development problems, puts forward the development direction of artificial intelligence hospital financial wisdom development measures, designed to meet the changing external environment demand, reduces human costs, and improves the overall efficiency of hospital financial fund management. Based on the evaluation results, this paper proposes the correct direction for the development of hospital intelligence finance by using the BP neural network model. After the analysis, it is found that the development of artificial intelligence is an important measure to promote the development of hospital intelligent finance. In other words, hospital intelligent financial management is the product of the continuous progress of artificial intelligence technology. At the present stage, the intelligent financial management problems of hospitals are mainly as follows: (1) lack of financial informatization, (2) lack of perfect financial risk early warning system, and (3) the phenomenon of "information island" in the financial system. After analyzing the above problems, the research believes that the development of hospital intelligent finance based on artificial intelligence needs to solve the above thorny problems, so as to improve the outcome of hospital intelligent finance development. The following work should be done: (1) strengthen the design of information sharing module, (2) intelligent control of the cost of hospitals, and (3) intelligent treatment of hospital accounting. Combining the development of artificial intelligence and hospital intelligent finance theory, combined with the actual trend of financial intelligence development under the background of artificial intelligence development in the new era, it provides scientific basis for the development of hospital intelligent finance. [ABSTRACT FROM AUTHOR]

Energy plays a vital part in stimulating economic progress, and the shift towards a cleaner energy system is highly significant for ensuring the sustainable development of the economy. China's energy structure urgently needs to be transitioned. The fast advancement and implementation of artificial intelligence (AI) has provided a new and important tool for promoting the transition of energy structure. So, what is the relationship between the application of artificial intelligence and the transition of the energy structure? This research introduces artificial intelligence into the energy sector, focusing on the relationship between artificial intelligence and energy transition. Since nonlinear models are better able to study the complex effects and phase differences of artificial intelligence. Using China's provincial panel data spanning from 2006 to 2019, this study employs nonlinear modeling to explore the stage differences in the process of AI in facilitating energy structure transformation. This paper derives the following findings based on empirical research. First, there is a U-shaped relationship between artificial intelligence and the transition of energy structure. Specifically, before the inflection point, the initial application of artificial intelligence, artificial intelligence may adversely impact energy transition. When the inflection point is passed, AI will help facilitate the energy transition. Second, the U-shaped relationship between AI and energy transition is more pronounced in coastal and non-resource-based regions. Third, energy intensity, government investment in science and technology, and informatization will moderate the U-shaped relationship between artificial intelligence and energy transition, changing the steepness of the original U-shaped relationship and even reversing it. Hence, it is imperative to effectively utilize the technological benefits of artificial intelligence through the development patterns and distinctive features of different regions, thereby facilitating the smooth transition of the energy structure. [Display omitted] • The role of artificial intelligence in the energy transition process is studied. • Stage differences in the role of AI are analyzed. • The nonlinear relationship between AI and energy transition is investigated. • Provide insights into the factors affecting the relationship between AI and energy transition. • This research helps the government to advance the energy structure transition. [ABSTRACT FROM AUTHOR]

Applying artificial intelligence in education is relevant to addressing the current educational crises. Many available solutions apply Convolutional Neural Networks (CNNs) to help improve educational outcomes. Therefore, a series of works have been developed integrating techniques in different educational contexts, for instance, in online teaching practices. Given the various studies and the relevance of CNNs for educational applications, this paper presents a systematic literature review to discuss the state-of-the-art. We reviewed 133 papers from the IEEE Xplore, ACM Digital Library, and Scopus databases. Based on our revision, we discuss characteristics of studies such as publication venues, educational context, datasets, types of CNNs models, and performance of models. We evidence that the literature regarding CNNs still misses more studies discussing educational problems faced by Global South students, considering both teaching and learning perspectives. Such a population cannot be neglected during experiments due to specific educational weaknesses (for example, basic skills) demanding personalized solutions. • A systematic literature review of 133 published papers. • China covered most publications, followed by India. • There is a research gap regarding using CNNs in some regions of the Global South. • The main educational context with CNN applications is students' performance. [ABSTRACT FROM AUTHOR]

The research and application of artificial intelligence (AI) have changed the way people live and produce. This paper discusses the factors affecting the development of RE in the context of artificial intelligence, analyzes the industrial status of 31 provincial administrative regions in China from the three dimensions of industrial influence, industrial relevance, and industrial efficiency, and uses gray correlation analysis to evaluate the system and measure. [ABSTRACT FROM AUTHOR]

Since China's reform and opening up, the social economy has achieved rapid development, followed by a sharp increase in carbon dioxide (CO2) emissions. Therefore, at the 75th United Nations General Assembly, China proposed to achieve carbon peaking by 2030 and carbon neutrality by 2060. The research work on advance forecasting of CO2 emissions is essential to achieve the above-mentioned carbon peaking and carbon neutrality goals in China. In order to achieve accurate prediction of CO2 emissions, this study establishes a hybrid intelligent algorithm model suitable for CO2 emissions prediction based on China's CO2 emissions and related socioeconomic indicator data from 1971 to 2017. The hyperparameters of Least Squares Support Vector Regression (LSSVR) are optimized by the Adaptive Artificial Bee Colony (AABC) algorithm to build a high-performance hybrid intelligence model. The research results show that the hybrid intelligent algorithm model designed in this paper has stronger robustness and accuracy with relative error almost within ±5% in the advance prediction of CO2 emissions. The modeling scheme proposed in this study can not only provide strong support for the Chinese government and industry departments to formulate policies related to the carbon peaking and carbon neutrality goals, but also can be extended to the research of other socioeconomic-related issues. [ABSTRACT FROM AUTHOR]

Faced with the AI (artificial intelligence) era, it is both theoretically and practically important to examine the challenges and opportunities that vocational college education in China faces, as well as to actively explore how vocational college education can overcome the challenges and achieve a realistic path. This paper proposes and implements a wireless network-based vocational college talent management system. The main personnel management system primarily completes business operations related to daily personnel file management, while the data mining subsystem mines talent data using the DT (decision tree) classification algorithm to aid talent selection. At the same time, a new topology optimization algorithm based on the principle of minimum rigidity graph is proposed for talent management system wireless network optimization. The maximum-minimum balance criterion and the user-by-user optimization mechanism are designed to obtain the optimal relay selection and subchannel allocation strategy, ensuring the system's reliability and fairness. The optimized algorithm has a user security satisfaction of 0.93 in the range of 250 m, which is higher than other algorithms. It demonstrates that this algorithm's communication link is short, and it has good network connectivity and structural stability. [ABSTRACT FROM AUTHOR]

The development and spread of Internet technology have made it easier to find web servers. People can browse various websites to shop or pay for living expenses, which brings great convenience to life, but as a result, Internet security problems continue to appear. This article is based on a detailed theoretical analysis of mainstream algorithms, making an analysis of web logs which is of great significance and practical value. In addition, through reasoning analysis, technical support is provided for improving the weight factor of the KNN (K-nearest neighbor) algorithm, and the literature research method of the SVM-KNN hybrid algorithm and the KNN classifier is proposed. This paper conducts a detailed theoretical analysis based on the mainstream algorithms that are widely used in the current classification technology and integrates the mainstream classification algorithms in real-life applications and popularization, selecting the support vector machine and KNN calculation method. In the digital economy development model, although China has a large number of netizens, obvious late-comer advantages and institutional advantages as a guarantee, due to the constraints of two key factors, capital and technology, a series of social problems have also arisen. During the transformation of the digital economy, prominent digital security issues, high-risk vulnerabilities, and increasing number of cyber-attacks, along with uneven data quality levels and lagging laws and regulations, have brought many challenges and obstacles. [ABSTRACT FROM AUTHOR]

Fourth Industrial Revolution (4IR) technologies have elevated the capabilities and possibilities of improvement and efficiency in the energy sector. This paper interrogates how energy companies in South Africa, Germany and China apply 4IR technologies. A total of 26 energy companies in those countries were surveyed. An analysis was carried out using the Cronbach Alpha, Kruskal-Wallis and Mann-Whitney tests. Survey results indicate that 85% of companies acknowledge good levels of participation in the 4IR, and were clear about which 4IR technologies are important, although few companies develop these themselves. Technologies enabling access to big, real-time data (BRTD) and BRTD analysis software, are valued the most in measured importance, efficiency, reliability and ability to be integrated across the energy system. The transfer of data using the Internet of things ranked highly as a 4IR technology, whereas artificial intelligence, robotics and machine-human integration (also referred to as machine-human interaction) are considered less important, efficient, and reliable. China rates 4IR technologies as more important than South Africa and Germany do. For South Africa to be competitive in the global energy sector it needs to engage with and embrace 4IR technologies to a greater extent. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) assisted educational institutions extensively utilize electronic learning context to guarantee improved teaching and learning experiences accompanied by educational activities. E-learning or online learning plays a significant role in Chinese higher education. There is a challenge to implement e-learning in China's higher education to improve course resources, student learning style prediction, teaching quality, and service support. Hence in this paper, Artificial Intelligence based Efficient E-learning Framework (AI-EELF) has been proposed to overcome the challenges faced by China's higher education while implementing e-learning modules. The collected student data can be efficiently utilized and exploited to progress in an adaptive learning environment. The proposed AI-EELF method introduces multiple learning models to enhance teaching quality and predict the student learning style. The experimental results show that the proposed AI-EELF achieves high performance, prediction ratio in determining students' learning style and improves teaching quality compared to other existing methods. [ABSTRACT FROM AUTHOR]

Traditional marine pastures have made great contributions to human beings, but traditional marine pastures have also caused major harm to the marine natural environment. Modern technology represented by artificial intelligence has broad application prospects and significant potential and has the ability to innovate and enhance the conventional marine ranching business that is currently in recession. The particularity of China's marine ranching industry determines the combination of technologies such as underwater robots and the Internet of Things with the development of marine ranching to create a sustainable intelligent marine ranching industry in China. The growth of China's national economy is largely influenced by the development of the country's financial services industry. In order to better support the development of the real economy, it is necessary to establish a financial service system with controllable risks and complementary activities. This paper proposes to use the method of Internet of Things data collection combined with sensors to conduct economic analysis. By taking a number of data in recent years for research and analysis, the results show that financial service industry innovation has become an important part of improving the competitiveness of the industry and an important driving force for economic growth. The Internet of Things (IoT) business in China is a fast-growing industry with huge potential. From the perspective of international comparison, our country is currently in the early stage of the development of the Internet of Things, and a certain technology, industry, and application foundation has been established in the early stage of development. In the financial services business, the visual tracking provided by IoT technology has innovative potential. Introducing IoT technology into financial service business and identifying the driving force and process of financial innovation have practical significance for accelerating financial innovation. [ABSTRACT FROM AUTHOR]