ABSTRACT
The U.S. National Institute of Health (NIH) 3D Print Exchange is a public, open-source repository for 3D printable medical device designs with contributions from clinicians, expert-amateur makers, and people from industry and academia. In response to the COVID-19 pandemic, the NIH formed a collection to foster submissions of low-cost, locally manufacturable personal protective equipment (PPE). We evaluated the 623 submissions in this collection to understand: what makers contributed, how they were made, who made them, and key characteristics of their designs. We found an immediate design convergence to manufacturing-focused remixes of a few initial designs affiliated with NIH partners and major for-profit groups. The NIH worked to review safe, effective designs but was overloaded by manufacturing-focused design adaptations. Our work contributes insights into: the outcomes of distributed, community-based medical making;the features that the community accepted as "safe"making;and how platforms can support regulated maker activities in high-risk domains. © 2023 Copyright held by the owner/author(s).
ABSTRACT
The increasingly remote workforce resulting from the global coronavirus pandemic has caused unprecedented cybersecurity concerns to organizations. Considerable evidence has shown that one-pass authentication fails to meet security needs when the workforce work from home. The recent advent of continuous authentication (CA) has shown the potential to solve this predicament. In this paper, we propose NF-Heart, a physiological-based CA system utilizing a ballistocardiogram (BCG). The key insight is that the BCG measures the body's micro-movements produced by the recoil force of the body in reaction to the cardiac ejection of blood, and we can infer cardiac biometrics from BCG signals. To measure BCG, we deploy a lightweight accelerometer on an office chair, turning the common chair into a smart continuous identity "scanner". We design multiple stages of signal processing to decompose and transform the distorted BCG signals so that the effects of motion artifacts and dynamic variations are eliminated. User-specific fiducial features are then extracted from the processed BCG signals for authentication. We conduct comprehensive experiments on 105 subjects in terms of verification accuracy, security, robustness, and long-term availability. The results demonstrate that NF-Heart achieves a mean balanced accuracy of 96.45% and a median equal error rate of 3.83% for CA. The proposed signal processing pipeline is effective in addressing various practical disturbances. © 2023 ACM.
ABSTRACT
AMICA is an argument mining-based search engine, specifically designed for the analysis of scientific literature related to COVID-19. AMICA retrieves scientific papers based on matching keywords and ranks the results based on the papers' argumentative content. An experimental evaluation conducted on a case study in collaboration with the Italian National Institute of Health shows that the AMICA ranking agrees with expert opinion, as well as, importantly, with the impartial quality criteria indicated by Cochrane Systematic Reviews. © 2022 International Joint Conferences on Artificial Intelligence. All rights reserved.
ABSTRACT
Many Italian universities had numerous nursing students attending hospital wards for administrating anti SARS-COV 2/COVID-19 vaccines. The training of nursing students was necessary to facilitate good practices, disseminate knowledge about anti SARS-COV 2/COVID-19 vaccines. On 22 December 2021, the Italian National Institute of Health (NIH) created a course that aimed to promote the anti-SARS-CoV-2/COVID-19 vaccination strategy in the country, providing the basic skills, tools and technical-scientific contents necessary to guarantee all phases of the vaccination campaign, including the safe administration of vaccines and counteract vaccination hesitation through the involvement and informed participation of health and social health personnel towards the population. The purpose of this paper was to describe the method used by the Sapienza University of Rome in delivering the Italian NIH course nursing students at Italian universities. The research group in charge of delivering the course decided to use the Google Classroom platform. From the 03/02/2022 to 25/03/2022, 3154 students from 46 Italian universities attended the course. This paper represents a clear advantage in the field of e-learning, not only because it describes an effective method for delivering a course to many students but also because it demonstrates how health professions students can be protected while allowing them to continue or restart internships in health facilities more safely and with more awareness. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.