The virus SARS-CoV2, which causes coronavirus disease (COVID-19) has become a pandemic and has spread to every inhabited continent. Given the increasing caseload, there is an urgent need to augment clinical skills in order to identify from among the many mild cases the few that will progress to critical illness. We present a first step towards building an artificial intelligence (AI) framework, with predictive analytics (PA) capabilities applied to real patient data, to provide rapid clinical decision-making support. COVID-19 has presented a pressing need as a) clinicians are still developing clinical acumen to this novel disease and b) resource limitations in a surging pandemic require difficult resource allocation decisions. The objectives of this research are: (1) to algorithmically identify the combinations of clinical characteristics of COVID-19 that predict outcomes, and (2) to develop a tool with AI capabilities that will predict patients at risk for more severe illness on initial presentation. The predictive models learn from historical data to help predict who will develop acute respiratory distress syndrome (ARDS), a severe outcome in COVID-19. Our results, based on data from two hospitals in Wenzhou, Zhejiang, China, identified features on initial presentation with COVID-19 that were most predictive of later development of ARDS. A mildly elevated alanine aminotransferase (ALT) (a liver enzyme), the presence of myalgias (body aches), and an elevated hemoglobin (red blood cells), in this order, are the clinical features, on presentation, that are the most predictive. The predictive models that learned from historical data of patients from these two hospitals achieved 70% to 80% accuracy in predicting severe cases.

A binary nanoporous‐SixSb alloy is prepared successfully as electrode for lithium‐ion batteries (LIBs) by a one‐step chemical dealloying method. The sample morphology can be controlled by adjusting the content of Al and the molar ratio of Si and Sb in the alloy precursors. Structural and morphological characterizations reveal the presence of uniformly‐distributed nanopores which can effectively accommodate the volume change and provide massive diffusion channels for Li‐ions during charging/discharging. Electrochemical experiments show that the nanoporous Si15Sb15 (np‐Si15Sb15) anode can deliver excellent performance with a specific capacity of 647.40 mAh g−1 after 90 cycles at a current density of 100 mA g−1. This simple approach may be further extended to the design of novel nanoporous materials, providing a guideline for mass production of high‐performance electrochemical energy storage devices.

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FeB@SiO amorphous particles were firstly introduced into GaIn alloys to prepare metal-based magnetic fluids. The morphology of the FeB amorphous particles is spherical with an average particle size of about 190 nm. The shape of the particles is regular and the particle size is homogeneous. Stable core-shell structure SiO modified FeB amorphous particles are obtained and the thickness of the SiO coatings is observed to be about 40 nm. The results of VSM confirm that the saturation magnetization of the FeB amorphous particles is 131.5 emu g, which is almost two times higher than that of the FeO particles. The saturation magnetization of the FeB@SiO amorphous particles is 106.9 emu g, an approximate decrease of 18.7% due to the non-magnetic SiO coatings. The results from the torsional oscillation viscometer show that the metal-based magnetic fluids with FeB amorphous particles exhibit a desirable high temperature performance and are ideal candidates for high temperature use.