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This review article highlights the critical role of nurses in disaster management, with a specific focus on addressing blood tumors in disaster-affected populations. Disasters have a significant impact on healthcare systems and populations, and nurses play a crucial role in disaster preparedness, response, and recovery. The article provides case studies and successful examples of nursing interventions in disaster settings and tumor management, emphasizing the challenges and opportunities in providing cancer care in disaster settings. Recommendations for future research and practice in disaster nursing and blood tumor care are also presented. This information is essential for healthcare professionals and policymakers involved in disaster management, as well as researchers and clinicians working in the field of cancer care.

Online action detection (OAD) is a practical yet challenging task, which has attracted increasing attention in recent years. A typical OAD system mainly consists of three modules: a frame-level feature extractor which is usually based on pre-trained deep Convolutional Neural Networks (CNNs), a temporal modeling module, and an action classifier. Among them, the temporal modeling module is crucial which aggregates discriminative information from historical and current features. Though many temporal modeling methods have been developed for OAD and other topics, their effects are lack of investigation on OAD fairly. This paper aims to provide an empirical study on temporal modeling for OAD including four meta types of temporal modeling methods, i.e. temporal pooling, temporal convolution, recurrent neural networks, and temporal attention, and uncover some good practices to produce a state-of-the-art OAD system. Many of them are explored in OAD for the first time, and extensively evaluated with various hyper parameters. Furthermore, based on our empirical study, we present several hybrid temporal modeling methods. Our best networks, i.e. , the hybridization of DCC, LSTM and M-NL, and the hybridization of DCC and M-NL, which outperform previously published results with sizable margins on THUMOS-14 dataset (48.6% vs. 47.2%) and TVSeries dataset (84.3% vs. 83.7%).