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Space Industry and COVID-19: An Insight into Their Shared Relation
Abstract and Figures
Space and its technologies have always been used primarily for beyond-earth explorations. Secondarily, society has benefitted from space technologies concerning communications, navigations, earth observations, disaster response. Additionally, during COVID-19, space-based technologies have helped bring about innovative solutions to global health by tracking the spread of the pandemic, mapping hotspots, and providing location-based services. Today, we are all aware that the virus has adversely affected funding and human resources for the space industry. This, in turn, has led to the delay of many pre-scheduled space missions. The present article, thus, aims to lay down the negative and positive implications of the COVID-19 on the space sector and, at the same time, highlight the innovative uses of space-based technologies in different societal aspects and in combating the impact of the pandemic.
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... From a more humane perspective, Subhrajit Barua believed that space is one of the key mediums that unite the world and teach us that 'We are EARTHLINGS FIRST, and then citizens of our respective nations". This is indeed true and has been cited in previously published manuscripts [4][5][6]. ...
For ages, the world has primarily depended on the rich diversity of flora and fauna for therapeutic lead molecules. Once such flora of biological importance is plants included in the genus , as evidenced in Ayurveda, Charaka Samhita, and Sushruta Samhita. With this backdrop, the present review aims to scrutinize the work done between the time frame of 1999-2021 on the genus based on bibliometric parameters by analyzing manuscripts indexed in one of the most popular databases, the Web of Science. The bibliometric review was carried out using VOSviewer software to demonstrate the research gap in this thematic area. In this, the various analyses carried out included co-occurrence analysis, citations analysis, and analysis of funding agencies. From the result, it could be seen that most researchers focused on the pharmacognostic/phytochemical evaluation of . while at the same time leading to a shortage of research on their pharmacological and pharmaceutical aspects.
Contaminated surface water poses a risk to human populations and is a challenge for public health authorities. Climatic change, intensification of agriculture, urban development of coastal areas, and declining freshwater sources may contribute significantly to the risk of surface water contamination and increase incidence of waterborne diseases. Monitoring of surface water quality requires early detection of problems in order to minimize any negative impact on public health. Tele-epidemiology uses remote sensing and geospatial technologies to characterize the spatial and temporal variability of environmental determinants involved in the epidemiology of some diseases. By offering a systematic and integrated approach to water and risk management in public health, tele-epidemiology can be an efficient tool to assess surface water quality and any associated health risks.
Urban monitoring implies multi-temporal observation and measurement of transformations or consistencies within cities. The urban context is highly complex, as cities consist of a large number of people living in close proximity and conditions of relative density and diversity in dynamically interrelated processes. Most of the conditions and processes are related to space. Thus, for measuring, analyzing and understanding the urban context, the dynamic interrelations and its permanent changes, spatial information are crucial. As one data source, remotely sensed data are inherently suited to provide information on urban land cover characteristics, and their changes over time, at various spatial and temporal scales. This paper gives an overview and examples on current applications at DLR to demonstrate capabilities and limitations for monitoring in cities based on remotely sensed data.
The quality of poverty alleviation programmes relies heavily on appropriate targeting and priority setting. Major problems in assessing poverty include identification of the indicators of poverty and the methods used for its assessment. Nutritional status, expressed by anthropometric indices, has been proposed as a poverty indicator because of its validity, objectivity, reliability and feasibility. This study was conducted to explore the application of remote sensing to poverty mapping based on nutritional status at the community level. Relationships between the nutritional status within a community and the ecological characteristics of the community were investigated. Multiple linear regression tests were executed, and the resultant equations were tested for their validity in predicting communities with poor nutritional status. Among geographical and ecological indicators used, distance to the nearest market, main soil type, rice field area, and perennial cultivation area were found to be most useful predictors for the ranking of the communities by nutritional status. Among non-ecological determinants, food consumption, health service status and living conditions were also found as predictors. The highest correlation was found if total population was also taken into account in the regression model (R2 = 0.69; p < 0.0001). In the assessment of the sensitivity and specificity of the eight models studied, 'undernutrition' was defined as a condition where a community belongs in the first quartile for nutritional status (highest prevalence of undernutrition), and the baseline nutritional survey was considered as a standard method for final diagnosis. Most models which included only ecological factors in the equations had lower sensitivity and specificity than models which included all determinant factors in the equations. All models which took into account the total population had higher sensitivity and specificity than those that did not take total population into account. The best model of those that took into account only the geographical and ecological characteristics of the community's living environment had similar sensitivity and specificity (80% and 94.1%, respectively) as the models that considered non-geographical and non-ecological variables in addition to geographical and ecological variables. In the case of West Sumatra, only four ecological and geographic characteristics were sufficient to predict poverty in village. Since these characteristics could be surveyed by remote sensing, it may well be possible to use remote sensing for a rapid method for poverty mapping.
PIP
The quality of poverty alleviation programs depends heavily upon appropriate targeting and priority setting. However, to assess poverty, the indicators of poverty and the methods used for assessment must first be identified. Findings are presented from a study of the application of remote sensing to poverty mapping based upon nutritional status at the community level. Sampling 43 villages, relationships between the nutritional status within a community and the ecological characteristics of the community were investigated. Multiple linear regression tests were executed and the resultant equations tested for their validity in predicting communities with poor nutritional status. Of the geographical and ecological indicators used, distance to the nearest market, main soil type, rice field area, and perennial cultivation area were found to be the most useful predictors for ranking communities by nutritional status. Among non-ecological determinants, food consumption, health service status, and living conditions were also determined to be predictors. The strongest correlation was found when total population was also considered in the regression model. Most models which included only ecological factors in the equations had lower sensitivity and specificity than models which included all determinant factors in the equations. All models which accounted for total population had higher sensitivity and specificity than those which did not take such consideration. If not the nutritional status in absolute terms, at least the rank of communities can be predicted solely by geographical and ecological factors.
Space Science and Technology for the Benefit of Society
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Space Technology Applications in India with relevance to COVID-19
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