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With the deepening of aging in China, the demand of intelligent care system for the elderly is increasing day by day. The rapid development of information technology and artificial intelligence technology have gradually promoted the transformation of traditional care services from artificial to intelligent. Based on knowledge graph technology, this study built a knowledge graph model for elderly chronic disease smart care. The specific process includes ontology design, knowledge extraction, knowledge graph construction and question answering system design. The uniqueness of the knowledge graph in this paper is that it uses specific information about the elderly and data from professional care books. It is well guaranteed in practicality and reliability.With the help of the knowledge map constructed in this paper, primary care staff can effectively query high-quality and customized chronic disease care knowledge. [ABSTRACT FROM AUTHOR]

Green finance can effectively support industrial upgrading and technology development, such as artificial intelligence (AI). In this paper, we attempt to pursue the possibility of a win-win situation for achieving an eco- and tech-friendly society. By asking where and how we could develop green finance and AI innovation simultaneously, this paper conducts an empirical investigation on the influence of green finance on the development of AI in China from 2011 to 2020. Based on the panel data from the 30 provinces, we introduce spatial measurement, policy effect, heterogeneity, and threshold analyses to present deeper insights into the impact of green finance on AI innovations. Indeed, green finance could promote the progress of AI innovation. China's green finance pilot policy is verified to promote the progress of local AI innovations. Furthermore, the spatial spillover effect and regional heterogeneity are observed as well. The promotion effect is most significant in the western area, where the green finance index is relatively low. Besides, the threshold analysis also considers how to increase the marginal effect of green finance in different areas. Finally, several policy recommendations are proposed, which contribute to providing specific directions for the policymakers to improve AI innovation and achieve sustainable development at the same time. [ABSTRACT FROM AUTHOR]

This study explores the role of Artificial Intelligence (AI) orientation in facilitating the internationalization speed of enterprises from emerging economies. Based upon a knowledge management perspective, the paper proposes that AI orientation positively affects internationalization speed via broadening the scope of new knowledge, promoting knowledge creation, application, and dissemination within organizations, and hastening the redundancy of existing knowledge to increase adaptability to global dynamics. Moreover, while online local knowledge search facilitates, offline local knowledge search constrains the acceleration effect of AI orientation on internationalization. Using a dataset from China, the paper finds that AI orientation has a significant positive effect on accelerating the internationalization processes of emerging market firms, and home country embeddedness negatively while regional digital development positively moderates this relationship. The findings contribute to the literature by elucidating how AI orientation expedites the process of an emerging market firm's internationalization from a knowledge management perspective, thereby providing insights for the future scholarly investigation and practical applications in this field. • AI orientation expedites internationalization speed. • Offline local knowledge search negatively affects the acceleration effect of AI orientation on internationalization. • Online local knowledge search positively affects the acceleration effect of AI orientation on internationalization. • A knowledge management perspective of AI orientation and internationalization speed is developed. [ABSTRACT FROM AUTHOR]

This paper utilizes the skill-biased technological change and routine-biased technological change hypotheses, along with the theory of firms' innovation behavior, to establish a comprehensive analytical framework. This framework aims to elucidate the mechanisms through which technological innovation, industrial evolution, and education affect the labor skill structure in China manufacturing. The empirical results that have been gathered can be succinctly described in the following manner: (1) Research and development innovation stimulates the demand for high-and low-skilled labor, and, the rising of total factor productivity reduces the demand for low-skilled labor, confirming the labor-friendly effect of product innovation and the labor-saving effect of process innovation. (2) The cyclical pattern of fluctuating demand for low-skilled labor can be attributed to the industrial concentration, driven by technological advancements. Nevertheless, the phenomenon of cyclicality has not been observed for high-skilled labor. (3) There exists a paradoxical relationship between higher education and industrial demand, wherein education and skill are mismatched. (4) There exists the SBTC in China manufacturing resulted from process innovation. However, the SBTC does not explain the cause or mechanism behind the relative productivity shift and the higher demand for educated workers. This paper examines several policy implications including R&D innovation, employment, and education. • SBTC/RBTC and firms' innovation behavior theories are incorprated in a unified framework. • R&D inputs promote the demands for high- and low-skilled labor, and rising TFP reduces the demand for low-skilled labor. • Industrial concentration leads to a cyclical pattern of the demand for low-skilled labor rather than for high-skilled labor. • There exists a paradoxical relationship between higher education and skill demand. [ABSTRACT FROM AUTHOR]

The prime objective of this study is to analyze the spatio-temporal evolution of the relationship between digital economy and green innovation in China. Based on the panel data of 26 cities in the Yangtze River Delta (YRD) urban agglomeration from 2012 to 2019, this paper measures the digital economy and green innovation in each city of the YRD urban agglomeration using the entropy weight model and the projection tracking model, analyzes the characteristics of the spatio-temporal evolution, and explores the impact of the digital economy on green innovation and the spatial spillover effect. The results show that in the spatial dimension, there is a significant spatial correlation between digital economy and green innovation in each city, and the regional heterogeneity of digital economy is higher than that of the west in the east, and the regional heterogeneity of green innovation is high and high agglomeration in the east of the city cluster, and high and low agglomeration in the west of the city cluster; there is a trend of incremental increase of the level of digital economy and green innovation in the temporal dimension, and the regional imbalance of the development of green innovation among cities is decreasing. The regional imbalance in the development of green innovation between cities is narrowing, and it tends to be more towards the synergistic development of a unified big market; the spatial measurement model shows that the digital economy not only has a significant impact on the level of green innovation in its own region, but also has a positive spatial spillover effect on the neighboring regions. The research in this paper helps to clarify the connection between digital economy and green innovation among regions, and provides decision-making reference for building a modernized innovation system and efficiently promoting the construction of beautiful China in the new period. • Spatial and Temporal Evolution of Digital Economy and Green Innovation. • 26 cities in the Yangtze River Delta city cluster from 2012 to 2020. • Entropy power model and projection tracing models are used. • The influence of the digital economy on green innovation in Chinese cities is very significant. • Sustainability based implications are discussed and suggested. [ABSTRACT FROM AUTHOR]

Accurate and stable ultra-short-term wind speed prediction is very valuable for the dispatch planning and operational security for the wind power system, however it's very difficult to obtain satisfactory forecasting results in the wind power system due to the complexity and non-linearity of the wind speed series. In this paper, a novel hybrid model combined multi-objective optimization, data preprocessing technology and Elman neural network was proposed to forecast ultra-short-term wind speed, including 30min and 10min wind speed. To obtain better forecasting results with high accuracy and strong stability, multi-objective optimization target was utilized to balance the variance and bias of the forecasted series. Complementary ensemble empirical mode decomposition was used to remove the noise in the original data and several IMFs were obtained. This paper proposed a new optimization algorithm combined adaptive wind driven optimization and modified simulated annealing to optimize initial weights and thresholds of ENN. Wind speed data from two observation sites in China was involved in this paper to verify the forecasting performance of the proposed model. The simulation results illustrate that the proposed hybrid model has the best forecasting results at all step among all related models. • A new hybrid is proposed to forecast wind speed in this paper. • Data preprocessing technology is introduced to improve the forecasting performance. • Multi-objective optimization target is utilized to obtain stability and accuracy forecasting results. • The results are validated well in China. [ABSTRACT FROM AUTHOR]

The continued development of artificial intelligence (AI) has changed production methods but may also pose challenges related to energy consumption; in addition, the effectiveness of AI differs across industries. Thus, to develop efficient policies, it is necessary to discuss the effect of AI adoption on energy intensity and to identify industries that are more significantly affected. Using data on industrial robots installed in 16 Chinese industrial subsectors from 2006 to 2016, this paper investigates both the effect of AI on energy intensity and the channel through which this effect is transmitted. The empirical results show, first, that boosting applications of AI can significantly reduce energy intensity by both increasing output value and reducing energy consumption, especially for energy intensities at high quantiles. Second, compared with the impacts in capital-intensive sectors (e.g., basic metals, pharmaceuticals, and cosmetics), the negative impacts of AI on energy intensity in labor-intensive sectors (e.g., textiles and paper) and technology-intensive sectors (e.g., industrial machinery and transportation equipment) are more pronounced. Finally, the impact of AI on energy intensity is primarily achieved through its facilitation of technological progress; this accounts for 78.3% of the total effect. To reduce energy intensity, the Chinese government should effectively promote the development of AI and its use in industry, especially in labor-intensive and technology-intensive sectors. [ABSTRACT FROM AUTHOR]

The paper explores the regional cooperation model and the differences among artificial intelligence research teams. It is helpful to reveal the status and strategies of scientific cooperation models across regions or within regions. We identified the world of artificial intelligence research teams with co-authorship network, and then identified the leading team based on the Number of Publications, Number of Citations, H-index, Weighted Degree Centrality, Betweenness Centrality, and Closeness Centrality. Based on the identified artificial intelligence research leading teams, this paper divides different types of cooperation models by region and comprehensively analyzed the three aspects of geographical distribution, cooperation indicators, and cooperation topics in the research teams from the perspective of comparisons. In order to find the international gap between China and other countries, we still highlight the difference between China's participation and non-participation in cooperation. The research results show that Chinese and their foreign research maintain close ties with major scientific research countries; international cooperation is widespread and is conducive to crossing into the leading team; China's domestic cooperation is higher than in other countries, and their domestic cooperation research is mainly manifested in the data processing and application level, while the core technology and basic algorithm levels need to cooperate with foreign countries. [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]

Accurate prediction of the Particulate Matter 2.5 (PM 2.5) plays a crucial role in the accurate management of air pollution and prevention of respiratory diseases. However, PM 2.5 as a time series is extremely difficult to accurately predict. In this paper, a Hybrid Integration (HIG) algorithm that combines data pre-processing, time-series decomposition, signal decomposition, a prediction module, a matching strategy, and a hybrid integrated optimization algorithm is proposed. First, the optimal parameters for the four individual models were selected by integrating multiple evaluation perspectives. Additivity was then determined by Seasonal and Trend decomposition using LOWESS (STL), followed by refinement decomposition using signal decomposition. The four new sequences were reconstructed using Range Entropy (RangeEn) and mapped to the models. Additionally, Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) methods were optimized using the HIG algorithm. The results show that the HIG-RNN and HIG-LSTM are more advantageous than the ordinary method in terms of reasonable weight assignment. Finally, an innovative confusion test method was developed to test the stability of the prediction direction. To ensure generalizability, validation was performed using PM 2.5 data from two regions of China. The results show that the method significantly improves the prediction performance and provides a powerful tool for policy formulation and management. [Display omitted] • A hybrid integration (HIG) algorithm is proposed. • Prediction results were evaluated using the original COI and improved ICOI. • Creative time-frequency analysis bypasses traditional time series constraints. • RangeEn can assess entropy series for regularity to avoid excessive decomposition. • A pioneer confusion test for model direction consistency check was developed. [ABSTRACT FROM AUTHOR]

Exploring the key influencing factors (KIFs) of Urban Low-Carbon Innovation (ULCI) from urban characteristics is essential for devising customized promotion strategies. However, existing studies are hampered by methodological limitations that lead to an inability to effectively discern KIFs among urban characteristics or unravel complex, non-linear relationships, and interaction effects. To address these gaps, this paper adopts a synthetic approach based on interpretable Machine Learning (ML). Firstly, the influencing factors are identified through the Delphi method and a systematic literature review. Subsequently, three single AI models (KNN, SVR, and CART) and three ensemble models (RF, XGBoost, and LBGM) are employed to fit the data. Finally, the SHapley Additive exPlanations (SHAP) algorithm is integrated to identify the KIFs and disentangle their impact effects. The findings indicate that (1) 41 influencing factors are identified, from which 10 KIFs, such as Expenditure on Research, Carbon Emissions, Local General Budgetary Revenue, and Education Expenditure, are determined, (2) the developed interpretable ML model tailored for ULCI's KIFs analysis demonstrates high precision and effectively capturing non-linear relationships (R2 = 0.841, RMSE = 0.591, MAE = 0.463), and (3) the global impact, interactive effects, and individual sample impact of the KIFs are explained, and two categories of KIFs dominated by positive and negative influences are revealed respectively. Results of the KIFs identification can provide policy-makers with insight for designing ULCI enhancement paths and consequently promote emission mitigation in China. • 41 influencing factors of ULCI is identified from urban characteristics. • An interpretable ML model is developed to quantitatively explore KIFs. • 10 KIFs are determined and the comprehensive impact, interactive effects, and individual sample impact of the them are explained. [ABSTRACT FROM AUTHOR]

Traditional patent statistics cannot accurately reflect the real development level of technology. In order to overcome this deficiency, this study adopts quantitative research methods to conduct empirical research on global artificial intelligence technology-related patent applications, and on this basis, provides suggestions for the government and enterprises on how to improve my country's innovation capabilities in the field of artificial intelligence. First, this paper analyzes patent application trends, technology application areas and innovation trends using the incoPat patent search database and information visualization tools. On this basis, the Herfindahl-Hirschman index is further used to measure and analyze the patent concentration of artificial intelligence technology patent applicants in various regions of my country. The analysis results show that my country has the largest number of patent applications in the area of artificial intelligence technology, but the proportion of high-value patents is relatively low, which needs to be further improved. Patents in the eastern region are relatively scattered, while patents in the western region are relatively concentrated. The government should introduce policies according to local conditions to avoid excessive concentration and dispersion of patents. [ABSTRACT FROM AUTHOR]

In recent years, artificial intelligence (AI) has developed rapidly in the field of medical imaging. However, the collaborations among hospitals, research institutes and enterprises are insufficient at the present, and there are various issues in technological transformation and value landing of products in this area. To solve the core problems in the developmental path of medical imaging AI, the Chinese Innovative Alliance of Industry, Education, Research and Application of Artificial Intelligence for Medical Imaging compiled the White Paper on Medical Image AI in China. This article introduces the current status of collaboration, the clinical demands for medical imaging AI technique, and the key points in AI technology transformation: robustness, usability and security. We are facing challenges of lacking industry standards, data desensitization standard, assessment system, as well as corresponding regulations and policies to realize the application values of AI products in medical imaging. Further development of AI in medical imaging requires breakthroughs of the core algorithm, deep involvement of doctors, input from capitals, patience from societies, and most importantly, the resolutions from government for multiple difficulties in links of landing the technology. [ABSTRACT FROM AUTHOR]

Tackling climate change crises needs intelligent manufacturing and effective business models. This paper uses the adaptive structuration theory (AST) and configuration perspective to investigate the effects of digital infrastructure, digital orientation, top management team heterogeneity, servitization, government support, and customer demand uncertainty on the business model of intelligent manufacturing enterprises in China. The fuzzy set qualitative comparative analysis (fsQCA) was employed to analyze the data. The study found that there were five configurations of business model formation, which classified business models into five types: executive-led enhanced, digital leadership-enhanced, adaptive, extended, and complex business models. There was an intrinsic relationship between the five models with respect to the dimensions of digitalization and servitization. Further analysis revealed that executive-led, digital leadership-enhanced, and adaptive business models were positively associated with enterprise performance. The paper discusses the potential implications of these findings. • There are five formation pathways of the business model. • An interrelated relationship among the models regarding the digitalization and servitization • Business models influenced by digitization make enterprises more sensitive to climate change. • Enhanced and adaptive business models are associated with enterprise performance. [ABSTRACT FROM AUTHOR]

This paper examines how Chinese university students negotiate their second language (L2) motivational dynamics, including their ideal and ought-to L2 selves, to participate in informal digital learning of English (IDLE) mediated by generative artificial intelligence (AI). It demonstrates the extent to which enjoyment, the most observable positive emotion in L2 learning, influences their involvement in AI-mediated IDLE (AI-IDLE) activities. Employing an explanatory sequential mixed-method design, this study surveyed 690 Chinese undergraduate students and conducted 12 post-survey interviews. Using a structural equation modeling approach, the quantitative analysis reveals that participants' ideal L2 self can significantly predict both their sense of enjoyment and AI-IDLE, while the ought-to L2 self is only able to directly predict enjoyment. The quantitative results also demonstrate that enjoyment can partially mediate the relationship between the ideal L2 self and AI-IDLE and simultaneously fully channel the indirect impact of the ought-to L2 self on AI-IDLE. Supplementing these quantitative findings, the interview data provides a nuanced understanding of how motivation and enjoyment shift and interact with learning contexts as participants engage in AI-IDLE. Drawing on these quantitative and qualitative insights, this study identifies implications for pedagogy, particularly in terms of motivating Chinese university students to engage in IDLE while maintaining emotional well-being in the age of generative AI. • Ideal L2 self significantly predicts AI-mediated Informal Digital Learning of English (IDLE). • Ought-to L2 self fails to directly predict AI-mediated IDLE. • Enjoyment partially mediates the relationship between Ideal L2 self and AI-mediated IDLE. • Enjoyment fully channels the indirect impact of Ought-to L2 self on AI-mediated IDLE. • Motivation and enjoyment shift and interact with learning contexts to shape AI-mediated IDLE. [ABSTRACT FROM AUTHOR]

Forecasting petroleum consumption is a complicated and challenging task because many parameters affect the oil consumption. Whereas a highly accurate prediction model can help one utilize data resources reasonably, an inaccurate model will lead to a waste of resources. Thus, choosing an optimization model with the best forecasting accuracy is not only a challenging task but also a remarkable problem for oil consumption forecasting. However, a single model cannot always satisfy time series forecasting and the variations in oil consumption. In this paper, a total of 26 combination models using traditional combination method were developed to increase the prediction accuracy and avoid the problem of individual risk prediction methods "over-fitting", which would reduce the accuracy. Our conclusion is that the proposed combination models provide desirable forecasting results compared to the traditional combination model, and the combination method of TCM-NNCT is the most feasible and effective one. This paper also discussed the factors related to the statistical models and the results can be used by policy makers to plan strategies. Numerical results demonstrated that the proposed combined model is not only robust but able to approximate the actual consumption satisfactorily, which is an effective tool in analysis for the energy market. [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]

Research on artificial intelligence for brain injury is currently a prominent area of scientific research. A significant amount of related literature has been accumulated in this field. This study aims to identify hotspots and clarify research resources by conducting literature metrology visualization analysis, providing valuable ideas and references for related fields. The research object of this paper consists of 3000 articles cited in the core database of Web of Science from 1998 to 2023. These articles are visualized and analyzed using VOSviewer and CiteSpace. The bibliometric analysis reveals a continuous increase in the number of articles published on this topic, particularly since 2016, indicating significant growth. The United States stands out as the leading country in artificial intelligence for brain injury, followed by China, which tends to catch up. The core research institutions are primarily universities in developed countries, but there is a lack of cooperation and communication between research groups. With the development of computer technology, the research in this field has shown strong wave characteristics, experiencing the early stage of applied research based on expert systems, the middle stage of prediction research based on machine learning, and the current phase of diversified research focused on deep learning. Artificial intelligence has innovative development prospects in brain injury, providing a new orientation for the treatment and auxiliary diagnosis in this field. • Bibliometric analysis of research on artificial intelligence(AI) in brain injury. • Describe the spatial and temporal distribution of research resources of brain injury. • Result shows the research topic of AI in brain injury is divided into three stages. [ABSTRACT FROM AUTHOR]

Technological progress, represented by artificial intelligence, has a dual impact on employment structure. In order to explore the impact of artificial intelligence development on employment skill structure, this paper, based on the panel data of 30 provinces and cities in China from 2003 to 2017, used the mediating effect regression model and the threshold regression model to conduct empirical tests. The results show that the development of artificial intelligence will significantly affect the structure of employment skills, and regional innovation has a significant mediating effect. From the threshold characteristics, there is a threshold effect of regional innovation. With the improvement of innovation environment, industrial upgrading and technological progress, the influence of artificial intelligence on middle-skill employment is gradually weakened. Under the influence of innovation environment and technological progress, the high-skilled employment shows a U-shaped change. [ABSTRACT FROM AUTHOR]

• The co-authorship networks constructed from the bibliometric data of research published in the area of Artificial Intelligence and Machine Learning are investigated. • The dichotomous networks are highly assortative and the corresponding strength-coupled networks are highly cohesive. • The distribution profile, not for the degrees but for the weighted degrees, is smooth and well described by power-law with exponential cut-off. • Dominance of affiliations from domestic institutions of the authors with high closeness centrality is observed for highly productive cases. • This study suggests that the dominance of domestic affiliations for central authors influences and catalyses the collaborative research. Extant studies suggest that the proximity between the researchers and their structural positioning in the collaboration network may influence productivity and performance in collaboration research. In this paper, we analyze the co-authorship networks of the three countries, viz. the USA, China, and India, constructed in consecutive non-overlapping 5-year long time windows from bibliometric data of research papers published in the past decade in the rapidly evolving area of Artificial Intelligence and Machine Learning (AI&ML). Our analysis relies on the observations ensued from a comparison of the statistical properties of the evolving networks. We consider macro-level network properties which describe the global characteristics, such as degree distribution, assortativity, and large-scale cohesion etc., as well as micro-level properties associated with the actors who have assumed central positions, defining a core in the network assembly with respect to closeness centrality measure. For the analysis of the core actors, who are well connected with a large number of other actors, we consider share of their affiliations with domestic institutes. We find dominant representation of domestic affiliations of the core actors for high productivity cases, such as China in the second time window and the USA in the first and second both. Our study, therefore, suggests that the domestic affiliation of the core actors, who could access network resources more efficiently than other actors, influences and catalyzes the collaborative research. [ABSTRACT FROM AUTHOR]

As population aging becomes the new demographic norm in China, its workforce structure is changing, and its demographic dividend is about to disappear. Artificial intelligence (AI) has been experiencing rapid progress in the last few years, and it is becoming an important tool to address the impact and challenges of an aging population. Therefore, this research introduces population aging and artificial intelligence into agricultural production, focusing on the effects of population aging on food security and the function played by artificial intelligence in it. From an empirical study conducted based on provincial panel data, the following conclusions arise. First, population aging in rural China has not negatively impacted food security, so there is no need to be overly pessimistic about the inevitable aging of the rural population. Second, AI has a favorable moderating function on the effects of population aging on food security. Third, the moderating effect of AI is heterogeneous. Compared with other provinces, AI can play a strongly positive moderating effect in central and west regions and major grain-selling areas. Based on the above findings, this paper proposes targeted policy recommendations on protecting food security in the context of artificial intelligence and population aging. • Artificial intelligence is introduced into the field of agricultural production. • Food security is investigated considering the aging of the agricultural workforce. • The positive moderating role of AI on the influence of aging population on food security. • This study contributes to ensuring food security in the context of aging population and artificial intelligence. [ABSTRACT FROM AUTHOR]

A scientometric approach is utilized to investigate the dynamic maps of relationships among researchers, institutes, and countries in the field of Artificial Intelligence for Fluid-flow and Heat-transfer (AIFH). The Web of Science database was searched for related publications during the last 40 years (1982 and 2022). A total of 6151 articles were discovered, which were analyzed in detail. Using a bibliometric analysis, the most relevant and most cited sources of publications were identified. The most active researchers, institutions, and countries leading AIFH were reported. Then, the worldwide dynamic collaboration maps and coupling maps of relationships were reported. The Islamic Azad University (1893 T.C.), the Chinese Academy of Sciences (1374 T.C.), and Beihang University (1266 T.C.) were the most influential institutes in AIFH. The most influential countries were China, the USA, and Iran. The dynamic map of collaborations shows a good worldwide collaboration distribution. The USA and China established the most connection with the rest of the world. ANNs are the most studied topic (19.5% of publications), followed by Machine Learning (17.9%) and Neural Networks (15.4%). Support Vector Machines lag behind at 1.4%. ANNs boast the highest total citations (17,064) and H-index (63). Most ANIF papers were published by Medical Physics (119 T.P.). Half of the articles in AIFH were published by five journals of Medical Physics, Neurocomputing, International Journal of Heat and Mass Transfer, International Journal of Radiation Oncology Biology Physics, and IEEE Access. The International Journal of Heat and Mass Transfer received the most citations in AIFH. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is revolutionizing the medical industry. This paper first investigates the mechanism of patients' medical AI acceptance after experiencing AI service failure from the perspective of responsibility attribution. Using data from 249 patients in China, findings reveal that patients' self-responsibility attribution is positively related to human–computer trust (HCT) and sequentially enhances the acceptance of medical AI for independent diagnosis and treatment. This paper also investigates the moderating role of personality traits. Specifically, conscientiousness and openness strengthen the association between HCT and acceptance of AI for independent diagnosis and treatment; agreeableness and conscientiousness weaken the association between HCT and acceptance of AI for assistive diagnosis and treatment. • Medical AI diagnosis consisted of independent and assistive diagnosis. • This paper discusses the medical AI acceptance after experiencing AI service failure. • Attribution theory was considered as a theoretical framework. • Personality affects the association between human–computer trust and AI acceptance. [ABSTRACT FROM AUTHOR]

Accurate and timely carbon emission accounting (CEA) is vital to industrial corporations, especially those who participate in the carbon market. With the rapid development of artificial intelligence and power systems, the power data-based method provides a new way for real-time CEA. However, the extensive installation of distributed photovoltaics (PV) significantly increases the accounting difficulty of corporate carbon emissions. This paper proposes a non-intrusive method of real-time CEA for industrial corporations from the perspective of modern power systems. First, a device operation state (DOS) estimation model based on a modified Informer algorithm is proposed to calculate corporate direct carbon emissions. Wherein, an equivalent distributed PV output estimation model is used to decrease the impact of invisible PVs on direct emission accounting. Second, an improved carbon emission flow model is proposed to calculate corporate indirect carbon emissions, which considers "prosumers" arising from the installation of distributed PVs. Finally, the total corporate carbon emissions, including direct and indirect parts, are obtained by using the CEA model. Case studies based on four typical high‑carbon-emission factories in Zhejiang province, China demonstrate that the proposed method can make accurate CEA for industrial corporations by effectively lessening the impact of distributed PVs. • A non-intrusive carbon emission accounting method for industrial corporations. • A novel perspective of modern power systems for carbon emission accounting. • Calculating carbon emissions considering distributed photovoltaics' impacts. • The method becomes superior as the photovoltaic output proportion increases. [ABSTRACT FROM AUTHOR]

Xi Jinping's judicial reforms have placed the concepts of 'fairness' and 'justice' at the forefront, coinciding with the integration of information technology and AI into all aspects of China's court system through smart court reform. According to official Chinese discourse, smart court reform is supposed to make the justice system 'fairer'. However, research has not yet clearly established how 'fairness' and automation are connected in the Chinese context. This article is interested in how smart court and automation fit into Chinese interpretations of 'fairness'. Therefore, we ask what notions of 'fairness' drive and justify smart court reform? The main argument is that SCR allegedly reinforces elements of procedural fairness, i.e., internal accountability, external visibility, and due process in a way that they are conducive to substantive goals of legitimation, social stability, and user convenience. Most noteworthy, there is a strong emphasis on procedural consistency. This article conducts a systematic qualitative analysis of the foundational texts and discourse about smart courts in China, such as judicial policy documents, development and reform plans, white papers, and regulations. In our analysis we find that smart courts promote procedural and substantive components of 'fairness' that strengthen legal rationality while keeping open channels of control. Our findings help explain the rapid embrace of automation and technology in China's justice administration: they fit perfectly within the ruling party's worldview and perpetuate it in turn. [ABSTRACT FROM AUTHOR]

Accurate estimation of ride-hailing travel mobility is significant for demand management, and transportation planning. Although existing intervening opportunities models based on individual destination selection behavior can estimate travel mobility patterns (e.g., commuter flow, and migration flow), they usually ignore the substitutability of candidate destinations. In the context of ride-hailing travel, people tend to have strong destination preferences, and candidate destinations should be related to individual travel needs. Meanwhile, artificial intelligence offers powerful tools to extract complex nonlinear dependencies of mobility data, which are difficult to capture by traditional intervention opportunities models. This paper proposes a novel ensemble model with conditional intervening opportunities to improve the accuracy of ride-hailing travel mobility estimation by considering the substitutability of candidate destinations, that is, only the location related to people's trip purpose will likely affect people's travel behavior. The proposed ensemble model employs a stacking strategy to integrate six advanced machine learning and deep learning algorithms to extract complex nonlinear dependencies from ride-hailing travel mobility data, and achieve accurate mobility estimation. Furthermore, datasets from two major cities in China with more than 25 million ride-hailing trips are used for model training and experimental comparison. The results indicate that the proposed model outperforms other baseline models in ride-hailing travel mobility estimation tasks. It accurately predicts trip flows and the trip distance distribution, and can capture mobility patterns with strong interpretability. The proposed model can be applied to analyze the travel behavior of ride-hailing passengers, as well as the mobility patterns between different regions. • Propose a novel ensemble model for ride-hailing travel mobility estimation. • Develop conditional intervening opportunities model with substitutability of candidate locations. • Extract nonlinear dependencies from ride-hailing data using artificial intelligence algorithm. • Verify the proposed prediction model using over 25-million ride-hailing trips. • Interpret ride-hailing travel mobility patterns using Shapley additive explanations method. [ABSTRACT FROM AUTHOR]

Timely, effective, and robust artificial intelligence (AI) technology is urgently needed to improve decision-making efficiency in the presence of renewable energy with high penetration by elevating the level of power grid intelligence. However, at this stage, AI technology lacks reliability and transparency, making it unable to play a full role in application areas with high-security requirements such as power systems. This paper presents a multi-hierarchical interpretable method for power system dispatch and operation based on the graph deep Q-network (GDQN) model to achieve active power corrective control. The multi-hierarchical interpretable method combined with an improved sample-balanced deep shapley additive explanation (SE-DSHAP) method and a subgraph explainer can promote a more intuitive and comprehensive explanation of decision-making for power systems with complex topology. Operators will obtain more noteworthy power grid areas through the proposed interpretable method as the basis of auxiliary decision-making to realize efficient and accurate control. Then, two cases studied in a modified 36-bus system in the IEEE 118-bus system and the 300-bus network of the China power grid validate the effectiveness of the proposed method. • A graph deep Q-network model achieve active power corrective in power systems. • The SE-DSHAP method improve the computational speed and accuracy. • A subgraph explainer bring a comprehensive understanding of the model predictions. • Two cases validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• ADP is proposed to avoid the redundant computation in DP's recursive equation. • LSTM-based response surface model reduces intensive power output calculations. • ADP uses less time to provide satisfying results for hydropower reservoir operation. Dynamic programming (DP) is a classical method developed to address the multi-stage hydropower reservoir operation problem, but still suffers from the serious dimensionality problem where the computational burden increases exponentially with the number of state variables. To improve the DP performance, this paper proposes a LSTM-based approximate dynamic programming (ADP) method for complex hydropower reservoir operation optimization. In ADP, the long short-term memory (LSTM) is treated as the response surface model to reduce redundant computations of power outputs in DP's recursive equation, making obvious improvements in the execution efficiency. To fully assess its feasibility, the ADP method is used to find the scheduling schemes of a real-world reservoir system in China. Simulation results show that compared with the standard DP method, ADP effectively reduces the execution time while guarantee the solution quality in different cases. In the 1000-state and wet-year scenario, the ADP method achieves approximately 86.7% and 85.8% reductions in DP's computation time for Longyangxia and Laxiwa reservoir with the goal of maximizing power generation. Thus, the LSTM-based response surface model is an effective tool to improve the DP performance in the hydropower reservoir operation problem. [ABSTRACT FROM AUTHOR]

• A new hybrid prediction system is proposed to predict wind speed. • Simplex and chaotic mapping optimization cuckoo algorithm are proposed. • An uncertainty prediction model based on Monte Carlo method is proposed. • The energy theory is proposed to optimize the decomposition mode number. • Theories proofs and experiments verify the effectiveness of the model. Wind energy has strong volatility and intermittent. Accurate wind speed prediction can not only improve the safety of the system, but also optimize dispatch and reduce economic losses. However, previous studies tend to ignore the influence of virtual components and lack effective identification of wind speed characteristics and a robust interval prediction scheme, resulting in poor results. To bridge these gaps, this paper proposes an energy theory method to solve the problem of modal over-decomposition. The study also combines effective modal recognition, uses different prediction methods according to modal characteristics and proposes a set of new optimization algorithms to improve nonlinear prediction capabilities. Finally, based on Monte Carlo theory, a set of interval prediction schemes that can adapt to different error characteristics are proposed. Under the verification of wind speed data in Changma, China and Sotavento, Spain. The mean absolute percentage error of wind speed deterministic prediction reaches 4.22% and 5.82%, respectively. The coverage rate of wind speed uncertainty prediction meets different confidence requirements, and the average interval width is still less than 2.5 m/s at 90% confidence. The results show that the forecasting system proposed in this paper is significantly better than all the comparative forecasting schemes, which can reduce the risk of fluctuations and improve the stability and safety of the wind power system. [ABSTRACT FROM AUTHOR]

Abstract: Recent advances in artificial intelligence, particularly in the field of multi-agent system theory and techniques, offer great promises in the development of decision support systems. This paper designs an agent-based regional agricultural economy decision support system (RAEDSS) to deal with complex decision problems. It introduces the architecture of the system, including interface agents, management agents, functional agents, model agents, information agents and knowledge agents and their interactions. Since dynamic analysis, evaluation, forecast, optimization and decision of regional agricultural economy are the central task of RAEDSS, this paper gives a detailed discussion on the decision processes and internal mechanisms in the system. Meanwhile, agent-based modeling is introduced to simulate and evaluate policy impact on rural development in different scenarios as an important part in RAEDSS. The simulation result shows that this agent-based agricultural development model is able to perform regeneration and is able to produce likely-to-occur projections of reality. The related issue such as building an agent based on the theory of classifier systems is also surveyed. [Copyright &y& Elsevier]

Shear wave velocity is a critical parameter for the characterization of hydrocarbon reservoirs. Compared with compressional wave velocity which almost exist in every well, shear wave velocity hasn't been recorded in those days for the older wells due to the lack of logging equipment or limited funds. Furthermore, measuring shear wave velocity is fairly time- and money-consuming as it can only be gained by the analysis of core samples conducted in the lab or from the dipole sonic imager (DSI). To cope with the above puzzles, a new methodology, by integrating extreme learning machines (ELM) and technique of mean impact value (MIV), is proposed in this paper with the support of log data collected from two wells located in unconventional shale gas reservoir in Ordos Basin, China. Based on mean impact value, a well-trained ELM model is taken to identify optimal well logs and five well logs are identified to provide the significant and valid information for the estimation of shear wave velocity. 3201 data points collected from well L4 are used in constructing the model. Compared with artificial neural network used in the former studies Levenberg-Marquardt algorithm (ANN-LM), the ELM model's prediction accuracy has been evaluated, the results indicates that the ELM model outperforms ANN-LM model with faster calculating speed and better performance. Additionally, the efficiency of the model proposed here is well investigated by using another well with 3201 data points. Through the comparison among ELM, support vector regression (SVR), convolutional neural network (CNN) and four widely known empirical formulas, what can be concluded is that ELM model is more efficient in fast calculation and high precision. From the result, it can be demonstrated that the use of ELM model, combined with the analysis of mean impact value (MIV), is a more efficient and promising method for shear wave velocity estimation process from conventional well log data, which can be recognized as a promising tool with an extended application. And this research can be applied into a software system for rapid acquisition of shear wave velocity logs. Image 1 • A powerful tool is proposed for shear wave velocity estimation. • The improvement in Vs estimation using AI methods. • It is proved that each intelligence model with selected well log inputs has higher accuracy. • ELM model is more efficient in fast calculation and high precision. [ABSTRACT FROM AUTHOR]

This paper investigates a large-scale vehicle routing and scheduling problem of B2C e-commerce urban logistics distribution in China. A qualitative and quantitative combined online intelligent scheduling approach is developed by means of incorporating operations research methods and artificial intelligence technologies. This approach aims to make good use of advantages of qualitative and quantitative methods by the complementarity of model-based computing in operations research and knowledge-based searching in artificial intelligence. A case study from a Chinese e-commerce company demonstrates its effectiveness. [ABSTRACT FROM AUTHOR]

Abstract: Although has been widely used to study geographical problems, case-based reasoning (CBR) method is far less than perfect and research is in great need of to improve CBR-based geographic data representation modeling, as well as spatial similarity computation and reasoning algorithm. This paper reports an improved CBR-based method for studying the spatially complex land use change. Based on a brief summary of advantages and challenges of current existing quantitative methods, the paper first proposes to introduce the CBR approach for land use change study. A three-component model (“problem”, “geographic environment”, and “outcome”) was proposed to represent the land use change cases among which there are complicated and inherent spatial relationships. This paper then presents an algorithm to retrieve the inherent spatial relationships, which are then introduced into the CBR similarity reasoning algorithm to predict land use change. The method was tested by examining the land use change in Pearl River Mouth area in China and yields a similar prediction accuracy of 80% as that derived by applying the Bayesian networks approach to the exact same data. As a result, the CBR-based method proposed in this study provides an effective and explicit solution to represent and solve the complicated geographic problems. [Copyright &y& Elsevier]

The new type of coronavirus, COVID 19, appeared in China at the end of 2019. It has become a pandemic that is spreading all over the world in a very short time. The detection of this disease, which has serious health and socio-economic damages, is of vital importance. COVID-19 detection is performed by applying PCR and serological tests. Additionally, COVID detection is possible using X-ray and computed tomography images. Disease detection has an important position in scientific researches that includes artificial intelligence methods. The combined models, which consist of different phases, are frequently used for classification problems. In this paper, a new combined approach is proposed to detect COVID-19 cases using deep features obtained from X-ray images. Two main variances of the approach can be presented as single layer-based (SLB) and feature fusion-based (FFB). SLB model consists of pre-processing, deep feature extraction, post-processing, and classification phases. On the other side, the FFB model consists of pre-processing, deep feature extraction, feature fusion, post-processing, and classification phases. Four different SLB and six different FFB models were developed according to the number and binary combination of layers used in the feature extraction phase. Each model is employed for binary and multi-class classification experiments. According to experimental results, the accuracy performance for COVID-19 and no-findings classification of the proposed FFB3 model is 99.52%, which is better than the best performance accuracy (of 98.08%) in the literature. Concurrently, for multi-class classification, the proposed FFB3 model has an accuracy performance of 87.64% outperforming the best existing work (which reported an 87.02% classification performance). Various metrics, including sensitivity, specificity, precision, and F1-score metrics are used for performance analysis. For all performance metrics, the FFB3 model recorded a higher success rate than existing work in the literature. To the best of our knowledge, these accuracy rates are the best in the literature for the dataset and data split type (five-fold cross-validation). Composite models (SLBs and FFBs), which are generated in this paper, are successful ways to detect COVID-19. Experimental results show that feature extraction, pre-processing, post-processing, and hyperparameter tuning are the steps are necessary to obtain a higher success. For prospective works, different types of pre-trained models and other hyperparameter tuning methods can be implemented. • Using pre-trained CNN models is an effective way to extract deep features • Feature fusion-based model is more successful to detect COVID-19 in X-Ray images • Data pre-processing and post-processing techniques increase the performance of the models • Hyperparameter tuned SVM is more successful than without one • To the best of our knowledge, obtained accuracy rates are the best in the literature [ABSTRACT FROM AUTHOR]

• Develops a structure-agency analytical framework to examine the trajectories of emerging technologies in different national contexts. • Compares AI development and diffusion between a developed country and an emerging country based on 125 in-depth interviews. • Argues that the levels of institutionalisation exert different structural forces and leave different spaces for institutional work. • Yields practical implications for developing countries to address the trade-off between the opportunities and risks brought by emerging technologies. Development studies have paid less attention to the role of technological innovations and we are yet to understand how, and more importantly why, technological trajectories differ across countries. This gap becomes sharper as emerging technologies such as artificial intelligence (AI) are becoming increasingly important in addressing many world development challenges. Drawing on insights from institutional work literature, this paper develops a structure-agency interplay framework to unravel the various trajectories of emerging technologies at the national level and examines the development and diffusion of AI in Canada and China. The findings show that Canada's stable institutional environment, reinforced through institutional work by various actors, generated a national AI trajectory driven by technology development through a strong focus on scientific research and ethics, with slower organic commercialisation of AI. In China, a dynamic and loose institutional structure characterised by lax regulations, low entry barriers, and high openness to novelties has resulted in a market-driven AI trajectory focused on technology commercialisation, with domestic digital giants and the government as dominant players. National-level dynamics in formal institutions, informal institutions, technologies, and actor strategies determine heterogeneous approaches to technology development and diffusion, giving rise to varieties of technological trajectories. The levels of institutionalisation exert different forces and create different spaces for institutional work across different geographical contexts. [ABSTRACT FROM AUTHOR]

• Estimate the joint impact of environmental and digital policies (for designing smart cities) upon the generation of eco-innovations. • Apply negative binomial and quasi-natural experimental methods (i.e., Difference-in-Differences and Propensity Score Matching). • When digital policies (Artificial Intelligence and Internet of Things) are implemented in cities that have adopted strict environmental policies, the production of green patents increases. In this paper, we employ negative binomial and quasi-natural experimental methods (i.e., Difference-in-Differences and Propensity Score Matching), whereby we examine the joint impact of environmental and digital policies (for designing smart cities) upon the generation of eco-innovations in China. Using longitudinal data for the period 2006–2018, we examine the changes in green patents granted: (i) due to the implementation of various levels of stringency of environmental policies across all cities; and (ii) after the introduction of smart city policies in 2012 in China. The prior literature stresses the importance of environmental policies, yet less attention has been paid to digital policies required to drive eco-innovation and their spatial dimension in the context of a developing economy. Our results show that, when digital policies (artificial intelligence and internet of things) are implemented in cities that have adopted strict environmental policies, the production of green patents increases. We contribute to debates in the literature of policy mix for sustainability transitions in the context of a developing economy by illustrating the importance of both types of policy for eco-innovation, as they correct two market failures and, more importantly, address the systemic coordination problems that occur during the production of green patents. [ABSTRACT FROM AUTHOR]

Global climate change has led to large fluctuations in lake levels in recent years as meteorological and hydrological parameters have changed and water use has been intense. Water scientists use various computer models to analyze the hydrological variables recorded in the past and make projections for all future scenarios. Based on the technological progress, six different types of algorithms were studied in this review to predict the water level in lakes. The prediction results show that Deep Learning (DL) has the highest accuracy in terms of the evaluation metrics. Since the Artificial Intelligence (AI) field is still emerging and continue to improve, this study highlights better comprehension of current applications and the problems that need to be investigated more for LWL forecasting techniques. It reveals that the studies mainly focused on lakes either in USA or China and there is room for improvement for other locations that are scarcely investigated. • This paper presents a systematic literature review for lake and reservoir water level studies for the first time. • Researchers favored input parameters as lake water level, precipitation and temperature among others. • The studies can be extended towards the direction of least studied areas such as deep learning based algorithms. • Increasing water level prediction areas recently indicates it became a hot topic because of visible climate change effects. [ABSTRACT FROM AUTHOR]

Prof. Jian Wu graduated from Zhejiang University (ZJU) and obtained his Ph.D. degree at the College of Computer Science and Technology, ZJU. He is currently the vice president of National Academy of Health and Medical Big Data at ZJU, the director of the Real-doctor Artificial Intelligence (AI) Research Center of ZJU, and is also serving as members of China Computer Federation (CCF)'s Committee for Elite Young Professionals, and the Ministry of science and technology's key field innovation team. Prof. Wu's research focus on AI in Medicine. In recent years, he has presided one National Science and Technology Support project, five China's National Natural Science Foundation projects, and three National High Technology Research and Development Program (863 program) projects. He has received 32 licensed patents and published over 100 SCI/EI referenced papers in this field. His received honors and awards include the 100 Most Influential Domestic Academic Paper Award in 2008 and 2009, the CCF Excellent Paper Award in 2017, the First Prize of Science Technology Progress Award of Ministry of Education in 2007, the Second Prize of National Science Technology Progress Award in 2010, the First Prizes of Science Technology Progress Award of Zhejiang province in 2008 and 2014, and the Supreme prize of China General Chamber of Commerce's Science and Technology Award. [ABSTRACT FROM AUTHOR]

Cities in China suffer from severe smog and haze, and a forecasting system with high accuracy is of great importance to foresee the concentrations of the airborne particles. Compared with chemical transport models, the growing artificial intelligence models can simulate nonlinearities and interactive relationships and getting more accurate results. In this paper, the Kolmogorov–Zurbenko (KZ) filter is modified and firstly applied to construct the model using an artificial intelligence method. The concentration of inhalable particles and fine particulate matter in Dalian are used to analyze the filtered components and test the forecasting accuracy. Besides, an extended experiment is made by implementing a comprehensive comparison and a stability test using data in three other cities in China. Results testify the excellent performance of the developed hybrid models, which can be utilized to better understand the temporal features of pollutants and to perform a better air pollution control and management. [ABSTRACT FROM AUTHOR]

Beyond the mainstream discussion on the key role of China in the global AI landscape, the knowledge about the real performance and future perspectives of the AI ecosystem in China is still limited. This paper evaluates the status and prospects of China's AI innovation ecosystem by developing a Triple Helix framework particularized for this case. Based on an in-depth qualitative study and on interviews with experts, the analysis section summarizes the way in which the AI innovation ecosystem in China is being built, which are the key features of the three spheres of the Triple Helix -governments, industry and academic/research institutions-as well as the dynamic context of the ecosystem through the identification of main aspects related to the flows of skills, knowledge and funding and the interactions among them. Using this approach, the discussion section illustrates the specificities of the AI innovation ecosystem in China, its strengths and its gaps, and which are its prospects. Overall, this revisited ecosystem approach permits the authors to address the complexity of emerging environments of innovation to draw meaningful conclusions which are not possible with mere observation. The results show how a favourable context, the broad adoption rate and the competition for talent and capital among regional-specialized clusters are boosting the advance of AI in China, mainly in the business to customer arena. Finally, the paper highlights the challenges ahead in the current implementation of the ecosystem that will largely determine the potential global leadership of China in this domain. • The paper aims to both expanding the knowledge about the AI ecosystem in China and contributing to the innovation ecosystems theory. • It also stresses how a qualitative approach is useful to tackle the complexity in emerging environments of innovation. • In addition, it allows the extraction of insights and gaps that are not directly available from a quantitative analysis of the ecosystem. [ABSTRACT FROM AUTHOR]

Debris flow is a major geohazard in mountainous regions and poses a significant threat to life and property. The damage caused by debris flows have increased with the expansion of human settlements and activity into the mountainous regions of China. In regards to risks from debris flows, previously unrecognized low-frequency debris flow catchments constitute an especially significant threat. According to our investigation, only about 500 catchments have debris flow records in >2000 catchments of Bailong River basin. The main purpose of this paper is to introduce a new methodology using Artificial Intelligence (AI) that can simultaneously input parameters related to geomorphological conditions and material conditions to better distinguish low-frequency debris flow catchments (LFD s) from medium-high frequency debris flow catchments (MHFD s). A total of 449 prototypical debris flow catchments, 15 parameters, and 9 commonly used learning machines were used to build identification models. Debris flow catchments are divided into 4 cases (LO1-LO4) based on different sample ratios of LFD s and MHFD s, which are input into each classifier one by one. Based on model evaluation, the CHAID model in the case LO2 performs best, which only uses five parameters (formation lithology index, land use index, vegetation coverage index, drainage density and landslide density index) to predict LFD s. The results indicate that LFD s are mainly distributed in areas with less landslide distribution and better vegetation coverage compared with MHFD s. However, the distribution of LFD s is concentrated on FLI (formation lithology index) =4, which is the weak lithology area. The tree classifier seems to be better at classifying fluvial processes. The model developed in this paper can help us quickly find LFD s in similar areas, and help to assess the risk of debris flows. • A simple decision tree model was built to identify low-frequency debris flows. • Geomorphological and material parameters were used simultaneously for modeling. • Materials seem to be the main influencing factor of low-frequency debris flows. • The tree classifier seems to be better at classifying fluvial processes. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is becoming the engine of a new round of technological revolution and industrial transformation; as such, it has attracted much attention of scholars in recent years. Surprisingly, scarce studies have shed lights on the effects of AI on the environment, especially with respect to carbon intensity. Based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, we use Chinese industrial sector data from 2005 to 2016 to investigate how AI affects carbon intensity. The empirical results show that AI, as measured separately by the adoption of robotics by industry and the number of academic AI-related papers, significantly reduces carbon intensity. The results remain robust after addressing endogenous issues. We find that there are both stages and industrial heterogeneity in the effects of AI on carbon intensity. AI had a more decrease effect on carbon intensity during the 12th Five-Year Plan than the 11th. Compared with capital-intensive industries, AI tends to have a more decrease effect on carbon intensity in the labor-intensive and tech-intensive industries. To enlarge the effects of AI on reducing carbon intensity, the government should promote the development and application of AI and implement differentiated policies in line with the industry characteristics. • We investigate the effect of artificial intelligence on carbon intensity. • AI significantly reduces carbon intensity. • There are both the heterogeneities of developmental stages and industrial in the effects of AI on carbon intensity. [ABSTRACT FROM AUTHOR]

Motivated by the contradiction between a government's hard budget constraints and artificial intelligence, this study constructs a computable general equilibrium model embedded with various fiscal and tax policies to study the impact of artificial intelligence development on the Chinese economy under government budget constraints with different intensities. This paper seeks to find a reasonable policy that takes into account China's employment, income distribution, and budget constraints to achieve common prosperity. It finds that the softer the government's budget constraints, the smaller the negative impact of artificial intelligence on the economy. More specifically, allowing the government to increase its debts and spending is more effective than tax cuts. It is suggested that if the goal is to reconcile the contradiction between hard budget constraints and artificial intelligence, fiscal and tax policy combinations, together with an improvised soft budget constraint, are required to increase the taxation of capital to an appropriate degree. In order to resolve the contradiction between the government's hard budget constraints and the development of artificial intelligence in pursuit of common prosperity, a robot tax should be levied and automation capital taxed. • The softer the government budget constraints, the smaller the negative impact of artificial intelligence on the economy. • Allowing the government to increase its debts and spending is more effective than tax cuts. • A robot tax should be levied and automation capital taxed. [ABSTRACT FROM AUTHOR]

Different oil price shock incentives under different domestic and international environment will cause different oil price shocks and bring different impacts to China’s macroeconomy. However, there are few empirical studies on early warning prediction of the co-movements between oil price shocks and macroeconomy. This paper presents an incentive-oriented artificial intelligent (AI) early warning system (EWS) with ontology supported case based reasoning (CBR) method, called “relationship between oil price shocks and economy-an early warning system (ROSE 2 )”, to forecast the co-movements between macroeconomy and oil price shocks in China. Simultaneously, multi-galois lattice (MGL), which is more suitable for matching multiple attributes, is used to improve the recall and precision capability of ROSE 2 . Finally, several practical queries called Q1–Q4 are presented for verifying the validation and efficiency of the ROSE 2 system. [ABSTRACT FROM AUTHOR]

• A hybrid intelligent GA-NN approach is developed for uncertainty analysis. • A new prediction intervals based cost function is proposed. • It overcomes the limitations of the conventional prediction interval indicator. • A realistic tunnel case is used to testify the superiority of the proposed approach. • A comparison with the deterministic analysis by LSSVM and RFs is discussed. This paper constructs the prediction intervals (PIs) of the tunnels' settlement caused by the shielding steering process. The hybrid genetic algorithm -neural network (GA-NN) is developed to obtain the upper and lower bound of the settlement based on a series of shield operating parameters, geological condition parameters, tunnel geometric parameters, and anomalous conditions. An improved prediction interval-based cost function is proposed to enable the consideration of the uncertainty from model misspecification and noise variance. The genetic algorithm optimizes the weighted parameters in the neural network by minimizing the cost function value. This study adopts a metro tunnel construction case in China to verify the effectiveness of the proposed hybrid genetic algorithm-neural network approach. The results based on the study case illustrate the superiority of the proposed hybrid approach in (1) overcoming the limitations of the conventional prediction interval indicator; (2) achieving comparative results with the deterministic estimation based on the least squares support vector machine; (3) providing a probability prediction of the settlement only based on deterministic input multivariables. Overall, this study contributes to (1) the uncertainty assessment of tunnel settlement based on the deterministic variables, (2) developing a new PIs based cost function which is stable and reliable, (3) the engineering practice for a safer assessment based on the prediction intervals. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak has led to a substantial loss of human life throughout the world and has a tremendous impact on healthcare services. Industry 4.0 technologies have established effective supply chain management towards the fulfillment of customized demands in the healthcare field. In addition, the internet of things, artificial intelligence, big data analytics, and 3D printing have been extensively used to combat the COVID-19 pandemic and assist in providing value-added services in the healthcare sector. Henceforth, this paper presents a scientometric analysis on the literature of aforementioned Industry 4.0 technologies in the context of COVID-19. It provides extensive insights into co-citation and co-occurrence analysis of high cited publications, participating countries, influential authors, prolific journals, and keywords using the CiteSpace tool. The analyses reveal that China has produced the highest research outputs, although India is the most collaborative country in this field. The current research hotspots include supply chain, 4D printing, and social distancing technologies. Furthermore, it explores emerging trends, intellectual structure of publications, research frontiers, and potential research directions for further work in the Industry 4.0 assisted healthcare domain. [Display omitted] • Industry 4.0 technologies have emerged in the healthcare domain as an area of growing interest in academia and industry discipline. • Artificial intelligence, big data analytics, internet of things and 3D printing have been extensively used to control the COVID-19 spread and assist in providing value-added services in the healthcare sector. • It explores emerging trends, intellectual structure of publications, and potential research directions for further work in the Industry 4.0 assisted healthcare domain. [ABSTRACT FROM AUTHOR]

Oil production and consumption is of great importance for the sustainable development and management of energy and environment. The forecasting of oil imports dependence caused by the gap between production and consumption is particularly crucial in the strategic deployment of oil development. However, researches on oil import dependence forecasting are often limited by the small size of data samples and assumptions, and the previous single-objective optimization algorithms only focus on the improvement of forecasting accuracy but ignore the stability. Therefore, in order to overcome the shortcomings of researches, in this paper, a hybrid forecasting system for oil imports dependence forecasting based on fuzzy time series and multi-objective optimization algorithm is proposed considering the accuracy and stability simultaneously to achieve the balance and optimality and bridge the limitations of small sample forecasting. The proposed forecasting system is compared with other small sample forecasting models and the fuzzy times series model with traditional interval partition methods. The results show that the proposed system is superior to the traditional methods in all indicators for oil import dependence forecasting. Otherwise, the out of sample forecasting results provided in our research indicate that the oil import dependence will maintain an upward trend, but the rate of increase will slow down. The research results can not only provide the basis for the planning and control of oil import and safety, but also benefit the perceptions of oil price trend in the world energy market and the adjustment of energy market structure. • Develop a novel hybrid forecasting model for oil imports dependence forecasting. • Improve fuzzy time series model for the small sample forecasting. • Consider the forecasting accuracy and stability simultaneously in forecasting. • Search the optimal interval length and weight coefficient for fuzzy time series. [ABSTRACT FROM AUTHOR]

• An unexpected rapid spread of SARS-CoV-2, the agent of the coronavirus disease 2019 (COVID-19), had been observed in China since January 2020, which resulted in a worldwide pandemic and a high number of deaths. • A real-time acquisition, centralization, and constant update of a COVID-19 Data Mart with information collected in healthcare systems of patients affected by COVID-19, and the availability of user-oriented data visualization tools, is a valuable source of information to support clinical practice and research on the pandemic. • A detailed description of the structure and technologies used to construct the COVID-19 Data Mart architecture. • Several views are presented to demonstrate how a large hospital had faced the challenge of pandemic emergency by creating a strong retrospective knowledge base, a real-time environment and integrated information dashboard for daily practice and early identification of critical condition at patient level. The COVID-19 pandemic affected healthcare systems worldwide. Predictive models developed by Artificial Intelligence (AI) and based on timely, centralized and standardized real world patient data could improve management of COVID-19 to achieve better clinical outcomes. The objectives of this manuscript are to describe the structure and technologies used to construct a COVID-19 Data Mart architecture and to present how a large hospital has tackled the challenge of supporting daily management of COVID-19 pandemic emergency, by creating a strong retrospective knowledge base, a real time environment and integrated information dashboard for daily practice and early identification of critical condition at patient level. This framework is also used as an informative, continuously enriched data lake, which is a base for several on-going predictive studies. The information technology framework for clinical practice and research was described. It was developed using SAS Institute software analytics tool and SAS® Vyia® environment and Open-Source environment R ® and Python ® for fast prototyping and modeling. The included variables and the source extraction procedures were presented. The Data Mart covers a retrospective cohort of 5528 patients with SARS-CoV-2 infection. People who died were older, had more comorbidities, reported more frequently dyspnea at onset, had higher d -dimer, C-reactive protein and urea nitrogen. The dashboard was developed to support the management of COVID-19 patients at three levels: hospital, single ward and individual care level. The COVID-19 Data Mart based on integration of a large collection of clinical data and an AI-based integrated framework has been developed, based on a set of automated procedures for data mining and retrieval, transformation and integration, and has been embedded in the clinical practice to help managing daily care. Benefits from the availability of a Data Mart include the opportunity to build predictive models with a machine learning approach to identify undescribed clinical phenotypes and to foster hospital networks. A real-time updated dashboard built from the Data Mart may represent a valid tool for a better knowledge of epidemiological and clinical features of COVID-19, especially when multiple waves are observed, as well as for epidemic and pandemic events of the same nature (e. g. with critical clinical conditions leading to severe pulmonary inflammation). Therefore, we believe the approach presented in this paper may find several applications in comparable situations even at region or state levels. Finally, models predicting the course of future waves or new pandemics could largely benefit from network of DataMarts. [ABSTRACT FROM AUTHOR]

A large part of energy consumption in homes, offices and commercial spaces is related to Heating, Ventilation and Air-conditioning (HVAC) devices. The effective parameter on the consumption of HVAC systems is internal heat gains that arise from occupants, electric equipment and lighting. In order to reduce the energy consumption of these systems, internal heat gains should be predicted accurately. Since there are few investigations performed on the prediction of internal heat gains, in this paper, three predictive models, namely multiple regression model, Levenberg–Marquardt back-propagation (LM-BP) model and similar days method based on combined weights, have been deployed. By assessing the influential factors on internal heat gains, fundamental theories, structures, equations and parameters of these models are thoroughly proposed. To examine the prediction techniques, an office building in China was considered. It was found that all the proposed models have high accuracy; however, the LM-BP neural network showed the most precision among other models with RMSE = 15.59, MAE = 10.16 and MAPE = 6.35. This model had a higher agreement with the actual internal heat gains compared to the predetermined working programs in the ASHRAE standard 90.1. The proposed models used in this study can lead to providing a theoretical base for scholars and engineers to improve the predictive control of HVAC systems, which plays an important role in enhancing thermal comfort, saving energy of residential buildings. [ABSTRACT FROM AUTHOR]