Artificial Intelligence (AI) significantly revolutionizes and transforms the global healthcare industry by improving outcomes, increasing efficiency, and enhancing resource utilization. The applications of AI impact every aspect of healthcare operation, particularly resource allocation and capacity planning. This study proposes a multi-step AI-based framework and applies it to a real dataset to predict the length of stay (LOS) for hospitalized patients. The results show that the proposed framework can predict the LOS categories with an AUC of 0.85 and their actual LOS with a mean absolute error of 0.85 days. This framework can support decision-makers in healthcare facilities providing inpatient care to make better front-end operational decisions, such as resource capacity planning and scheduling decisions. Predicting LOS is pivotal in today’s healthcare supply chain (HSC) systems where resources are scarce, and demand is abundant due to various global crises and pandemics. Thus, this research’s findings have practical and theoretical implications in AI and HSC management.
China actively broadens its channels for environmental protection and limits pollutant emissions through industrial structure adjustment and technical progress. Based on panel data of 30 provinces in China from 2003 to 2017, this study investigated the effects of industrial structure adjustment and technical progress on environmental pollution using spatial Dubin models. The findings show the following. (1) As the economy develops, the situation of environmental pollution in various regions deteriorates; moreover, spatio-temporal dependence is an aspect of environmental pollution. (2) Industrial structure adjustment and technical progress are beneficial to environmental improvement. Furthermore, there are spillover effects in factors such as industrial structure and technical progress to varying degrees. Thus, this study suggests that the path of coupling between industrial structure and technical progress should be explored to establish a pollution filtering mechanism, thereby improving environmental quality.
Abundant natural resources are the basis of urbanisation and industrialisation. Citizens are the key factor in promoting a sustainable supply of natural resources and the high-quality development of urban areas. This study focuses on the co-production behaviours of citizens regarding urban natural resource assets in the age of big data, and uses the latent Dirichlet allocation algorithm and the stepwise regression analysis method to evaluate citizens’ experiences and feelings related to the urban capitalisation of natural resources. Results show that, firstly, the machine learning algorithm based on natural language processing can effectively identify and deal with the demands of urban natural resource assets. Secondly, in the experience of urban natural resources, citizens pay more attention to the combination of history, culture, infrastructure and natural landscape. Unique natural resource can enhance citizens’ sense of participation. Finally, the scenery, entertainment and quality and value of urban natural resources are the influencing factors of citizens’ satisfaction.
This paper aims to study the Countermeasures of big data security management in the prevention and control of computer network crime in the absence of relevant legislation and judicial practice. Starting from the concepts and definitions of computer crime and network crime, this paper puts forward the comparison matrix, investigation and statistics method and characteristic measure of computer crime. Through the methods of crime scene investigation, network investigation and network tracking, this paper studies the big data security management countermeasures in the prevention and control of computer network crime from the perspective of criminology. The experimental results show that the phenomenon of low age is serious, and the number of Teenagers Participating in network crime is on the rise. In all kinds of cases, criminals under the age of 35 account for more than 50%.
With the rise of cloud computing, big data and Internet of Things technology, intelligent manufacturing is leading the transformation of manufacturing mode and industrial upgrading of manufacturing industry, becoming the commanding point of a new round of global manufacturing competition. Based on the literature review of intelligent manufacturing and intelligent supply chain, a total factor production cost model for intelligent manufacturing and its formal expression are proposed. Based on the analysis of the model, 12 first-level indicators and 29 second-level indicators of production line, workshop/factory, enterprise and enterprise collaboration are proposed to evaluate the intelligent manufacturing capability of supply chain. This article also further studies the layout superiority and spatial agglomeration characteristics of intelligent manufacturing supply chain, providing useful reference and support for enterprises and policy makers in the decision-making.
Distance and space are important factors affecting international trade, but they have different effects on cross-border e-commerce (CBE) due to the creation of the Internet. This study utilizes spatial autocorrelation, the multi-dimension gravity model and the Spatial Durbin model to conduct an comparative analysis of international trade and CBE within one-belt one-road (BR) countries. Our study obtained several key findings. Firstly, the spatial autocorrelation effect which exists in international trade does not exist in CBE. Secondly, the geographical distance effect of CBE is not significant, which is different from that of international trade. Thirdly, CBE is affected by GDP, culture, policy and institution distances which is not entirely consistent with international trade. Finally, the Spatial Durbin model shows that the spillover effect of CBE and international trade are both significant in the inverse distance weight matrix. These findings provide not only important theoretical contributions but also a practical guide for Government policy makers of the BR and CBE.
Product country-of-origin (COO) is now playing a central role in consumers’ purchase behavior. Previous studies have investigated several factors that impact COO. However, little attention has been paid to the impact of COO on consumers’ product evaluation on Chinese products, especially in the cross-border e-commerce context. Using a multi-methods design, this study first unearthed the antecedents of COO image towards Chinese products from the qualitative data in Study 1 by drawing on the legitimacy theory and then develops a contextual model of consumers’ product evaluation and purchase intention, integrating the role of a product with a different level of involvement. Using quantitative survey data from 252 foreign consumers, the study tests the research model in Study 2. The findings provide empirical evidence to support the model and highlight the importance of COO cues on foreign consumers’ purchase intention towards Chinese products. The results also enhance our understanding of consumers’ purchase decision in cross-border e-commerce.
Purpose- The aim of this study is to analyze the effect of corporate social responsibility (CSR) and social preference on quality improvement of the agricultural products supply chain composed of agricultural products producer and processor (A3P) and supermarket by theoretical analysis and empirical evidence. Methodology- This paper sets Stackelberg game model under A3P’s CSR by considering supermarket’s altruistic reciprocity and A3P’s fairness conern, respectively. By comparative analysis, we study the effect of CSR, altruistic reciprocity and fairness conern on the quality improvement of the agricultural products supply chain. Then, we adopt the empirical evidence to analyze the correlation between CSR, altruistic reciprocity (fairness concern) and quality improvement and the mediating effect of altruistic reciprocity (fairness concern) by investigating the agricultural enterprises.
This paper aims to expand the acceptance of the AI Virtual Assistant model from the perspective of user’s cognition. Based on the 240 samples, we used multi-layer regression analysis to investigate the influencing factors and differential effects of users' acceptance of AI Virtual Assistant. The results show that functional cognition and emotional cognition of users are important influencing factors for an artificial intelligence virtual assistant. This provides a new perspective for user acceptance processes of the AI Virtual Assistant. We also examined the moderating effect of social norms between user cognition and AI Virtual Assistant. At last, a new AI acceptance model of AI Virtual Assistant was established.
BACKGROUND: With the gradual improvement of market economy, people' s consumption level is constantly improving, and the quality requirements are getting higher and higher. OBJECTIVES: In order to study the management accounting information analysis platform based on Artificial Intelligence (AI) and realize the goal of accounting computerization, the application of AI in expert system is applied to the field of accounting information analysis. METHODS: The combination of subsystems is applied to the construction of AI accounting information Web system, and the feasibility analysis of its theory and technology is carried out. RESULTS: The results show that its effect is obvious: accelerating the flow of all information and promoting the change of enterprise management mode. Moreover, compared with the traditional system algorithm, the accuracy of the system model is improved by 6% and the time delay is reduced by 9ms, which makes the overall management level of the enterprise further improved, the scope of enterprise competition further expanded, the cost of enterprise saved