A Participatory Action Research Study with Bi-ethnic Children in South Korea on Bi-ethnic Identity Development

The purpose of this research was to explore how bi-ethnic children in South Korea understand their identity using a participatory action research (PAR) method. The number of bi-ethnic/multicultural families and children is increasing in South Korea, matched with a rising xenophobia towards these groups. Thus, the need for research that captured the inner thoughts and feelings of children, through their own voices, seems of paramount importance for a more secure and authentic identity development. The findings from this research provided evidence through their own storybooks that bi-ethnic Korean children had individual identity experiences in different contexts through diverse development processes. The PAR methodology enabled children to make their voices heard in the academic field through reflections and dialogue, producing a new genuine knowledge connected to their own lives. There is a hope that this study will empower the underrepresented bi-ethnic Korean children to develop their consciousness, make their own voices heard, and change their status

Personalized Event Prediction for Electronic Health Records

Clinical event sequences consist of hundreds of clinical events that represent records of patient care in time. Developing accurate predictive models of such sequences is of a great importance for supporting a variety of models for interpreting/classifying the current patient condition, or predicting adverse clinical events and outcomes, all aimed to improve patient care. One important challenge of learning predictive models of clinical sequences is their patient-specific variability. Based on underlying clinical conditions, each patient's sequence may consist of different sets of clinical events (observations, lab results, medications, procedures). Hence, simple population-wide models learned from event sequences for many different patients may not accurately predict patient-specific dynamics of event sequences and their differences. To address the problem, we propose and investigate multiple new event sequence prediction models and methods that let us better adjust the prediction for individual patients and their specific conditions. The methods developed in this work pursue refinement of population-wide models to subpopulations, self-adaptation, and a meta-level model switching that is able to adaptively select the model with the best chance to support the immediate prediction. We analyze and test the performance of these models on clinical event sequences of patients in MIMIC-III database.Comment: arXiv admin note: text overlap with arXiv:2104.0178

Strategies for ultrahigh outputs generation in triboelectric energy harvesting technologies: from fundamentals to devices

Since 2012, a triboelectric nanogenerator (TENG) has provided new possibilities to convert tiny and effective mechanical energies into electrical energies by the physical contact of two objects. Over the past few years, with the advancement of materials' synthesis and device technologies, the TENGs generated a high instantaneous output power of several mW/cm(2), required to drive various self-powered systems. However, TENGs may suffer from intrinsic and practical limitations such as air breakdown that affect the further increase of the outputs. This article provides a comprehensive review of high-output TENGs from fundamental issues through materials to devices. Finally, we show some strategies for fabricating high-output TENGs