An outbreak of a novel coronavirus disease (i.e., COVID-19) has been recorded in Wuhan, China since late December 2019, which subsequently became pandemic around the world. Although COVID-19 is an acutely treated disease, it can also be fatal with a risk of fatality of 4.03% in China and the highest of 13.04% in Algeria and 12.67% Italy (as of 8th April 2020). The onset of serious illness may result in death as a consequence of substantial alveolar damage and progressive respiratory failure. Although laboratory testing, e.g., using reverse transcription polymerase chain reaction (RT-PCR), is the golden standard for clinical diagnosis, the tests may produce false negatives. Moreover, under the pandemic situation, shortage of RT-PCR testing resources may also delay the following clinical decision and treatment. Under such circumstances, chest CT imaging has become a valuable tool for both diagnosis and prognosis of COVID-19 patients. In this study, we propose a weakly supervised deep learning strategy for detecting and classifying COVID-19 infection from CT images. The proposed method can minimise the requirements of manual labelling of CT images but still be able to obtain accurate infection detection and distinguish COVID-19 from non-COVID-19 cases. Based on the promising results obtained qualitatively and quantitatively, we can envisage a wide deployment of our developed technique in large-scale clinical studies.

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With the wide-ranging and ever-increasing applications of lithium-ion batteries in electric vehicles (EV), thermal runaway (TR)-induced safety issues, such as fires and explosions, are raising more and more concerns. In this work, cylindrical 21700 batteries were externally heated to conduct the TR experiment, and the casing rupture in the form of melting holes and tearing cracks was found to be one of the key factors that caused cell-to-cell TR propagation. The appearance and the cross-section microstructure of the ruptured casing showed that the melting hole is formed because of a large current short circuit and the tearing crack is due to a decrease in the mechanical strength at high temperatures. Experimental simulations were conducted to further demonstrate the casing rupture mechanism. In addition, new designs with increased casing thickness were implemented to inhibit the occurrence of casing rupture, and their effectiveness was analyzed by performing numerous TR experiments. The improved casing was used in a commercial battery pack, and no TR propagation occurred during operation. Thus, in this study, the casing rupture mechanism was first elucidated, and guidance for the design of lithium-ion batteries with improved safety was provided.

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