This is a three-year-term (2018.8~2021.7) research project entitled “adaptive data analysis approaches for empirical data from complex systems”. The aim of the project is to study empirical data from complex systems using adaptive data analysis approaches, and from which to extract intrinsic properties of the systems, thereby to construct corresponding physics models. Specifically, this project plans to deal with the following problems: 1.Adaptive data analysis frameworks based on the maximum information content of measures2.Dynamics of protein mechanoactivation3.Empirical biomedical signal analysis4.Theoretical mechanics corresponding to the properties of financial data and its critical propertiesThis project will reach the following achievements:1.Adaptive data analysis approaches based on the maximum information content of measures for complex systems2.A general theory for identifying protein functional domains from nanomechanics of mechanoactivation, and its applications to molecular docking problems3.Applications of the developed adaptive data analysis approaches on empirical biomedical signals4.Better understanding of the properties of financial data, improving the experience-based models, and developing its mechanical correspondenceOur research results will provide useful references for fundamental researches of related fields. Two PhD students will involve in this project and join the study of the above-mentioned research problems. A number of research papers can be published in SCI Journals every year, based on the results of this project.
|Effective start/end date||1/08/18 → 31/08/19|
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):