Multiple approaches to NFT rentals have surfaced this year as blockchain games continue to proliferate but a standard solution has not been adopted, leaving the game ecosystems independent of each other. This paper defines an auction-based system to decouple usage and ownership of a playable NFT, which functionally creates a decentralized mechanism to carry out scholarship for GameFi economies. Our system can be viewed as a proposal for standard interfaces between games, making rental a primitive instead of a product.1

CoViD-19 pandemic has shown that we have deep gaps in understanding this extremely infectious virus—not only both from a clinical diagnosis and treatment perspective—but also from a forecasting point of view, so that we are better prepared for the next onset of a similar pandemic, which, at this point, seems almost inevitable. In this paper, we present a novel approach towards modeling influenza, a closely related disease to CoViD-19, marrying clinical understanding with artificial intelligence, exploiting the Forest Deep Neural Network (fDNN) with accuracy rates in the 90% range.

This paper presents a targeted, machine learning based solution to model the phenomenon known as the `cytokine storm,' which is suspected to play a major role in explaining the highly variable severity of COVID-19 among patients. It describes how a Natural Language Processing (NLP) approach, augmented by biomedical knowledge databases, can extract pre-existing conditions and relevant clinical markers from Electronic Health Records (EHRs). These extracted variables can be modeled to demonstrate correlation with the severity of infection outcomes, the building blocks of a comprehensive risk assessment and stratification strategy to predict which patients have higher or lower risks in terms of the disease severity and likelihood of hospitalization, exclusively from insights taken from the natural language data. The model has been applied to a cohort of patients from a large database of real, anonymized patients and has displayed demonstrable results.

This work focuses on creating a smart Electronic Health Record (EHR) platform to collect and analyze doctor-patient interactions. It is an often cited fact that doctors are spending less and less time with patients, but two recent surveys have quantified what these numbers really are and the results are disturbing. In one survey [1], it was found that during the office day, physicians spent 27.0% of their total time on direct clinical face time with patients and 49.2% of their time on EHR and deskwork. In another survey [2], results “suggest that the physicians logged an average of 3.08 hours on office visits and 3.17 hours on desktop medicine each day,... Over time, log records from physicians showed a decline in the time allocated to face-to-face visits, accompanied by an increase in time allocated to desktop medicine It is precisely this problem that the University of Arizona's “Wired Room” project seeks to address.

This project seeks to use wearable sensors to develop a novel method for measuring respiratory activity in human subjects. This is the first stage of an ongoing project under the Arizona Center for Accelerated Biomedical Innovation (ACABI) [1]. The ultimate ambition of this effort is to develop a baseline digital breathing signature for a particular individual, so that medical professionals equipped with big-data analysis tools can use deviations from one's signature to differentiate between conventional breathing and abnormal breathing patterns, such as splinting and Kussmaul respirations.