Learning Social Meta-knowledge for Nowcasting Human Mobility in Disaster

Human mobility nowcasting is a fundamental research problem for intelligent transportation planning, disaster responses and management, etc. In particular, human mobility under big disasters such as hurricanes and pandemics deviates from its daily routine to a large extent, which makes the task more challenging. Existing works mainly focus on traffic or crowd flow prediction in normal situations. To tackle this problem, in this study, disaster-related Twitter data is incorporated as a covariate to understand the public awareness and attention about the disaster events and thus perceive their impacts on the human mobility. Accordingly, we propose a Meta-knowledge-Memorizable Spatio-Temporal Network (MemeSTN), which leverages memory network and meta-learning to fuse social media and human mobility data. Extensive experiments over three real-world disasters including Japan 2019 typhoon season, Japan 2020 COVID-19 pandemic, and US 2019 hurricane season were conducted to illustrate the effectiveness of our proposed solution. Compared to the state-of-the-art spatio-temporal deep models and multivariate-time-series deep models, our model can achieve superior performance for nowcasting human mobility in disaster situations at both country level and state level. © 2023 ACM.

A public health mission in Canada in response to the coronavirus disease 2019 (COVID-19) pandemic.

Many governments in the world reacted to the coronavirus disease 2019 pandemic by swiftly offering stimulus packages to their populations. While public unpreparedness was dramatic, it was not unexpected: many alarms had been sounded. Strategies by the federal and various provincial governments of Canada in core sectors predisposed the country to the current situation and weakened its capacity to respond adequately. This paper reviews the cumulative effects of these strategic orientations: a deficient investment strategy in fundamental science; disconnect between laboratory work and the country's capacity to produce vaccines and antiviral drugs; the priority of cost efficiency that led to overwhelming dependency on foreign production of medical supplies; and dramatic spending cuts in public health. We will discuss a Mission strategy to exit the crisis that takes a long-term perspective, in which public interest and public health, combined with a strong State leadership, stimulate innovation and collaboration between national and international actors.

Short-term passenger volume forecast and model analysis of Beijing public transport

The prediction of bus passenger volume is the fundamental research content of bus transfer optimization. In order to get more accurate passenger volume data and improve the utilization efficiency of urban traffic resources, according to randomness, time-varying and uncertainty of public transport passenger volume in Beijing, combined with the current new coronavirus pneumonia epidemic, this paper collected the relevant data of Beijing in the past 40 years, and predicted and analyzed them from four dimensions of public transport, urban scale and residents' economic level, taxi and sudden health events by BP neural network and regression analysis. The results show that BP neural network has good prediction results, and BP neural network is suitable for large sample size, which needs to fit or predict complex nonlinear relationships. © 2021 SPIE

Digital Earth: Prism of 2020

Through the prism of the 8th Digital Earth Summit and within the context of the disastrous 2020, this article addresses some of the issues and challenges that need to be addressed in Digital Earth Vision 2030. Some significant scientific aspects of the Digital Earth development are described and briefly discussed: the problem of providing a satisfactory definition of the Digital Earth and typology of different geospatial visualizations, the ambiguity between the concepts of Digital Earth and Digital Twins, the problems arising from the use of Digital Earth in sustainable development as well as the problem of using Digital Earth for natural and technological disasters prevention and mitigation. Attention is given to the use of Digital Earth in exploring the key challenge of 2020 – the COVID-19 pandemic. The possibility of using the Digital Earth as a tool for fundamental research is declared. © 2021 Copyright for this paper by its authors.

Phototrophic Co-cultures From Extreme Environments: Community Structure and Potential Value for Fundamental and Applied Research.

Cyanobacteria are found in most illuminated environments and are key players in global carbon and nitrogen cycling. Although significant efforts have been made to advance our understanding of this important phylum, still little is known about how members of the cyanobacteria affect and respond to changes in complex biological systems. This lack of knowledge is in part due to our dependence on pure cultures when determining the metabolism and function of a microorganism. We took advantage of the Culture Collection of Microorganisms from Extreme Environments (CCMEE), a collection of more than 1,000 publicly available photosynthetic co-cultures maintained at the Pacific Northwest National Laboratory, and assessed via 16S rRNA amplicon sequencing if samples readily available from public culture collection could be used in the future to generate new insights into the role of microbial communities in global and local carbon and nitrogen cycling. Results from this work support the existing notion that culture depositories in general hold the potential to advance fundamental and applied research. Although it remains to be seen if co-cultures can be used at large scale to infer roles of individual organisms, samples that are publicly available from existing co-cultures depositories, such as the CCMEE, might be an economical starting point for such studies. Access to archived biological samples, without the need for costly field work, might in some circumstances be one of the few remaining ways to advance the field and to generate new insights into the biology of ecosystems that are not easily accessible. The current COVID-19 pandemic, which makes sampling expeditions almost impossible without putting the health of the participating scientists on the line, is a very timely example.