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Key research themes
1. How can cohesive subgroups within ethnic groups be identified and modeled in social networks?
This research theme focuses on the methodological identification and modeling of cohesive subgroups within ethnic groups in social network data, which is crucial for understanding intra-group interactions, shared beliefs, behaviors, and integration within larger social structures. Cohesive subgroup detection helps reveal community structures, influence patterns, and the internal dynamics of ethnic groups, providing insights into social integration and conflict resolution.
Key finding: This work advances the identification of cohesive subgroups using statistical and graph-theoretic modeling of social exchange data, specifically via loglinear and p1 network models. It critiques strict clique definitions and... Read more
2. What mathematical and algebraic frameworks can be applied to model structures related to ethnic groups?
This theme addresses the application of abstract algebraic structures such as hypergroups and HX-groups to represent complex substructures and interaction patterns relevant to ethnic group categorization and analysis. Using algebraic methods provides formal and rigorous tools to characterize group properties, symmetries, and subset operations, offering potential for modeling ethnic group relationships and their internal stratifications beyond classical set theory or network approaches.
Key finding: The paper establishes a correspondence between HX-groups and hypergroups, extending the algebraic framework to semigroups of subsets of finite groups such as Z2 × Z3. It introduces the concept of fuzzy grade to characterize... Read more
3. How can digital platforms and eHealth systems support ethnic group populations, especially aging citizens, through tailored interventions?
This research direction investigates the design, implementation, and evaluation of interoperable, secure, and person-centered digital health platforms that address the heterogeneous health needs of ethnic groups, particularly elderly populations. It focuses on setting selection, stratification by health complexity, and intervention intensity, emphasizing integrative and scalable solutions that respect ethnic diversity and socio-cultural specificities in health management.
Key finding: This study introduces a double stratification pyramid to classify populations by health complexity and intervention intensity, aiming to recruit diverse aging populations including ethnic minorities into the GATEKEEPER... Read more
Key finding: The openEHR Foundation presents an open-source, standards-based architecture for electronic health records designed to be culturally and jurisdictionally agnostic. Its architecture supports flexible modeling of patient data... Read more