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The 4th Asia Partnership Conference of Regenerative Medicine (APACRM) was held online on April 15, 2021, to promote regulatory harmonization of regenerative medicine products throughout Asia. Recognizing domestic regulatory guidelines within each country and region, and their underpinning rationales, is an important initial step toward a convergence of regulations. The 4th APACRM consisted of an open dialog with regulatory agencies regarding nonclinical and quality settings for cell therapy products (CTPs) through industry presentations and panel discussions with regulatory agencies. The latest updates on regenerative medicine fields in each country and region, and specific regulatory schematics in Japan, were also introduced. The objective of this paper is to summarize the proceedings of the 4th APACRM for public dissemination and to foster further discussion in the future. [ABSTRACT FROM AUTHOR]

The application of data mining technology to food services and the restaurant industry has certain social value. By predicting customer traffic and needs, a restaurant can prepare a reasonable amount of meals for customers according to predicted needs which is conducive to improving the dining experience of customers and also improving the quality of food preparation and making the restaurant itself operate more efficiently. In recent years, we have seen the use of collaborative robots for use in the fast food industry. In Asia and more specifically in Japan, we have seen many fast-food chains implement the use of robots to better serve their customers. By studying the linear regression algorithm and the random forest algorithm, this paper proposes a new interwoven novel fusion approach of combining both algorithms and applies the new model to restaurant data to assist in the prediction of customer traffic in the restaurant industry. This predictive algorithm using cognitive techniques can assist these newly place robots in the food industry better serve their client base and in doing so make the industry more efficient. Experimental, comparison, and analysis are reported in the paper. The error rate of the fusion solution is reduced by approximately 5.503% compared with the linear regression algorithm and is approximately 3.719% lower than the error rate of the random forest algorithm. Results show that the new fusion algorithm can achieve better prediction results of customer traffic prediction for the restaurant industry. Furthermore, we also provide a new take on the application of data mining technology in the restaurant industry itself. • The application of data mining technology to the restaurant industry has certain social value. • Propose a new interwoven novel fusion approach applied to restaurant data to assist in the prediction of customer traffic in the restaurant industry. • This predictive algorithm using cognitive techniques can assist these newly place robots in the food industry better serve their client base. [ABSTRACT FROM AUTHOR]