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This paper analyzes the spatiotemporal patterns of nitrogen dioxide (NO2) tropospheric vertical column densities (TVCDs) before and during the second wave of COVID-19 in India. The results indicate that the NO2 columns increase significantly in the reopening period before the second wave (Mar. 1 ∼ Apr. 20) in 2021, which exceed the levels of the same period in 2019. The relative difference from the mean of 2010–2019 is 18.76% higher in 2021 than that of 2019, during the reopening. The paper identifies Odisha, Madhya Pradesh, Chhattisgarh, Jharkhand and West Bengal as the five states with the largest increases in relative difference from 2019 to 2021, which are 33.81%, 29.83%, 23.86%, 30.01%, and 25.48% respectively. As illustrated by trends in the indices of industrial production (IIP), these unexpected increases in tropospheric NO2 can be attributed to reopening as well as elevated production across various sectors including electricity, manufacturing and mining. Analysis of NO2 TVCD levels alongside IIPs indicate a marked increase in industrial activity during the reopening period in 2021 than in the same time period in 2019. After the beginning of the second wave of COVID-19 (Apr. 21 ∼ Jun. 21), India re-implemented lockdown policies to mitigate the spread ofmore »the pandemic. During this period, the relative difference of total NO2 columns declined in India as well as in most individual study regions, when compared to 2019, due to the pandemic mitigation policies. The relative declines are as follows: 6.43% for the whole country and 14.25%, 22.88%, 4.57% and 7.89% for Odisha, Madhya Pradesh, Chhattisgarh and Jharkhan, respectively, which contain large industrial clusters. The change in relative difference in West Bengal from 2019 to 2021 is not significant during the re-lockdown period with a 0.04% increase. As with the first wave, these decreases in NO2 TVCD mainly due to the mitigation policies during the second wave.« less

The COVID-19 pandemic has been sweeping across the United States of America since early 2020. The whole world was waiting for vaccination to end this pandemic. Since the approval of the first vaccine by the U.S. CDC on 9 November 2020, nearly 67.5% of the US population have been fully vaccinated by 10 July 2022. While quite successful in controlling the spreading of COVID-19, there were voices against vaccines. Therefore, this research utilizes geo-tweets and Bayesian-based method to investigate public opinions towards vaccines based on (1) the spatiotemporal changes in public engagement and public sentiment; (2) how the public engagement and sentiment react to different vaccine-related topics; (3) how various races behave differently. We connected the phenomenon observed to real-time and historical events. We found that in general the public is positive towards COVID-19 vaccines. Public sentiment positivity went up as more people were vaccinated. Public sentiment on specific topics varied in different periods. African Americans’ sentiment toward vaccines was relatively lower than other races.

Space-time adaptive processing (STAP) is an effective method for multi-input multi-output (MIMO) radar systems to identify moving targets in the presence of multiple interferers. The idea of joint optimization in both spatial and temporal domains for radar detection is consistent with the symbol-level precoding (SLP) technique for MIMO communication systems, that optimizes the transmit waveform according to instantaneous transmitted symbols. Therefore, in this paper we combine STAP and constructive interference (CI)-based SLP techniques to realize dual-functional radar-communication (DFRC). The radar output signal-to-interference-plus-noise ratio (SINR) is maximized by jointly optimizing the transmit waveform and receive filter, while satisfying the communication quality-of-service (QoS) constraints and the constant modulus power constraint. An efficient algorithm based on majorization-minimization (MM) and nonlinear equality constrained alternative direction method of multipliers (neADMM) methods is proposed to solve the non-convex optimization problem. Simulation results verify the effectiveness of the proposed DFRC scheme and the associate algorithm.

Many previous studies have shown that open-source technologies help democratize information and foster collaborations to enable addressing global physical and societal challenges. The outbreak of the novel coronavirus has imposed unprecedented challenges to human society. It affects every aspect of livelihood, including health, environment, transportation, and economy. Open-source technologies provide a new ray of hope to collaboratively tackle the pandemic. The role of open source is not limited to sharing a source code. Rather open-source projects can be adopted as a software development approach to encourage collaboration among researchers. Open collaboration creates a positive impact in society and helps combat the pandemic effectively. Open-source technology integrated with geospatial information allows decision-makers to make strategic and informed decisions. It also assists them in determining the type of intervention needed based on geospatial information. The novelty of this paper is to standardize the open-source workflow for spatiotemporal research. The highlights of the open-source workflow include sharing data, analytical tools, spatiotemporal applications, and results and formalizing open-source software development. The workflow includes (i) developing open-source spatiotemporal applications, (ii) opening and sharing the spatiotemporal resources, and (iii) replicating the research in a plug and play fashion. Open data, open analytical tools and source code,more »and publicly accessible results form the foundation for this workflow. This paper also presents a case study with the open-source spatiotemporal application development for air quality analysis in California, USA. In addition to the application development, we shared the spatiotemporal data, source code, and research findings through the GitHub repository.« less