The sudden outbreak of the COVID‐19 epidemic forced healthcare workers to use all their professional and personal skills to battle it. The unexpected onset of the disease has led to extraordinary pressure on healthcare workers and has challenged their resilience. The study aimed to explore the subjective experiences of 18 Israeli nurses who are directly treating COVID‐19 patients, and to identify the sources of resilience used by nurses to address national health crises. The data were gathered via semi‐structured interviews and thematically analyzed. The analysis yielded three central analytic themes that described the nurses’ experiences during the pandemic: maneuvering between professional demands and personal‐family life; the nurses’ coping strategies and resilience; and nurses' use of metaphorical military language as a way of coping with the difficulties. The findings show that in a time of severe health crisis, and despite the fear of infection, nurses adhere to the values of the profession and are willing to fight the virus to save lives. The nurses' extensive use of military metaphorical language reflected their experiences, strengthened them, and provided them with a source of empowerment in the face of a common enemy that needed to be overcome.

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Generating asset-specific trading signals based on the financial conditions of the assets is one of the challenging problems in automated trading. Various asset trading rules are proposed experimentally based on different technical analysis techniques. However, these kind of trading strategies are profitable, extracting new asset-specific trading rules from vast historical data to increase total return and decrease the risk of portfolios is difficult for human experts. Recently, various deep reinforcement learning (DRL) methods are employed to learn the new trading rules for each asset. In this paper, a novel DRL model with various feature extraction modules is proposed. The effect of different input representations on the performance of the models is investigated and the performance of DRL-based models in different markets and asset situations is studied. The proposed model in this work outperformed the other state-of-theart models in learning single asset-specific trading rules and obtained a total return of almost 262% in two years on a specific asset while the best state-ofthe-art model get 78% on the same asset in the same time period.