Triboelectric Energy Generation for the Army

Our project team, partnering with the US Army Combat Capabilities Development Command (DEVCOM) Soldier Center, aims to explore the use of triboelectric nanogenerators (TENGs) for use in portable energy production and storage for the Army. By utilizing two different materials, each connected to separate electrodes, a voltage can be generated from electrostatic induction and the triboelectric effect. When these materials engage in cycles of contact and separation, mechanical energy is collected and converted to electrical power, which can be stored or used in self-powered electronics. In this project, the performance of eight different DEVCOM textiles is evaluated as triboelectric materials for implementation in wind and impact-based applications. After developing final prototypes for each application, the Stepping Tribo-Electric Power (STEP) TENG was created to simulate a realistic impact application by integrating the STEP in a shoe

Natural Language Processing to Identify Digital Learning Tools in Postgraduate Family Medicine: Protocol for a Scoping Review

BackgroundThe COVID-19 pandemic has highlighted the growing need for digital learning tools in postgraduate family medicine training. Family medicine departments must understand and recognize the use and effectiveness of digital tools in order to integrate them into curricula and develop effective learning tools that fill gaps and meet the learning needs of trainees. ObjectiveThis scoping review will aim to explore and organize the breadth of knowledge regarding digital learning tools in family medicine training. MethodsThis scoping review follows the 6 stages of the methodological framework outlined first by Arksey and O’Malley, then refined by Levac et al, including a search of published academic literature in 6 databases (MEDLINE, ERIC, Education Source, Embase, Scopus, and Web of Science) and gray literature. Following title and abstract and full text screening, characteristics and main findings of the included studies and resources will be tabulated and summarized. Thematic analysis and natural language processing (NLP) will be conducted in parallel using a 9-step approach to identify common themes and synthesize the literature. Additionally, NLP will be employed for bibliometric and scientometric analysis of the identified literature. ResultsThe search strategy has been developed and launched. As of October 2021, we have completed stages 1, 2, and 3 of the scoping review. We identified 132 studies for inclusion through the academic literature search and 127 relevant studies in the gray literature search. Further refinement of the eligibility criteria and data extraction has been ongoing since September 2021. ConclusionsIn this scoping review, we will identify and consolidate information and evidence related to the use and effectiveness of existing digital learning tools in postgraduate family medicine training. Our findings will improve the understanding of the current landscape of digital learning tools, which will be of great value to educators and trainees interested in using existing tools, innovators looking to design digital learning tools that meet current needs, and researchers involved in the study of digital tools. Trial RegistrationOSF Registries osf.io/wju4k; https://osf.io/wju4k International Registered Report Identifier (IRRID)DERR1-10.2196/3457

Use of Artificial Intelligence in the Identification and Management of Frailty: A Scoping Review Protocol

Introduction Rapid population ageing and associated health issues such as frailty are a growing public health concern. While early identification and management of frailty may limit adverse health outcomes, the complex presentations of frailty pose challenges for clinicians. Artificial intelligence (AI) has emerged as a potential solution to support the early identification and management of frailty. In order to provide a comprehensive overview of current evidence regarding the development and use of AI technologies including machine learning and deep learning for the identification and management of frailty, this protocol outlines a scoping review aiming to identify and present available information in this area. Specifically, this protocol describes a review that will focus on the clinical tools and frameworks used to assess frailty, the outcomes that have been evaluated and the involvement of knowledge users in the development, implementation and evaluation of AI methods and tools for frailty care in clinical settings.Methods and analysis This scoping review protocol details a systematic search of eight major academic databases, including Medline, Embase, PsycInfo, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Ageline, Web of Science, Scopus and Institute of Electrical and Electronics Engineers (IEEE) Xplore using the framework developed by Arksey and O’Malley and enhanced by Levac et al and the Joanna Briggs Institute. The search strategy has been designed in consultation with a librarian. Two independent reviewers will screen titles and abstracts, followed by full texts, for eligibility and then chart the data using a piloted data charting form. Results will be collated and presented through a narrative summary, tables and figures.Ethics and dissemination Since this study is based on publicly available information, ethics approval is not required. Findings will be communicated with healthcare providers, caregivers, patients and research and health programme funders through peer-reviewed publications, presentations and an infographic.Registration details OSF Registries (https://doi.org/10.17605/OSF.IO/T54G8)