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International Conference on Geospatial Science for Digital Earth Observation (GSDEO 2021)The international conference on "Geospatial Science for Digital Earth Observation” (GSDEO) 2021 was successfully held on a virtual platform of Zoom on March 26th and 27th, 2021. The conference was jointly organized by the Indian Society of Remote Sensing (ISRS), Kolkata chapter, and the Department of Geography, School of Basic and Applied Sciences, Adamas University. Due to the non-predictable behaviour of the COVID-19 second wave, which imposed restrictions on organizing offline events, the GSDEO (2021) organizing committee decided to organize the conference online, instead of postponing the event.Remotely sensed data and geographic information systems have been increasingly used together for a vast range of applications, which include land use/land cover mapping, water resource management, weather forecasting, environmental monitoring, agriculture, disaster management, etc. Currently, intensive research is being carried out using remotely sensed data on the geoinformatics platform. New developments have led to dynamic advances in recent years. The objective of the international conference on Geospatial Science for Digital Earth Observation (GSDEO 2021) was to bring the scientists, academicians, and researchers, in the field of geo-environmental sciences on a common platform to exchange ideas and their recent findings related to the latest advances and applications of geospatial science. The call for papers received an enthusiastic response from the academic community, and over 100+ participants from 50+ colleges, universities, and institutions participated in the conference. In total 50+ research papers had been presented through the virtual Zoom conference platform in GSDEO 2021.The conference witnessed the presentation of research papers from diverse applied fields of geospatial sciences, which include the application of geoinformatics in geomorphology, hydrology, urban science, land use planning, climate, and environmental studies. There were four sessions namely, TS 1: Geomorphology and Hydrology, TS 2: Urban Science, TS 3: Social Sustainability and Land Use Planning, and TS 4: Climate and Environment. Each session was further subdivided, into two parts, namely Technical Session 1-A and 1-B. Each sub-session had been designed with one keynote speech and 5 oral presentations. Oral sessions were organized in two parts and offered through live and pre-recorded components based on the preference of the presenters. The presentation session was followed by a live Q&A session. The session chairs moderated the discussions. Similarly, poster sessions were organized in three parts and offered e-poster, live, and pre-recorded components. The best presenter of each sub-session received the best paper award.Dr. Prithvish Nag, Ex-Director of NATMO & Ex Surveyor General of India delivered the inaugural speech, and Dr. P. Chakrabarti, Former Chief Scientist of the DST&B, Govt. of West Bengal delivered a special lecture after the inaugural session. Eight eminent keynote speakers, Prof. S.P. Agarwal from the Indian Institute of Remote Sensing, Prof. Ashis Kumar Paul from Vidyasagar University, Prof. Soumya Kanti Ghosh from the Indian Institute of Technology, Kharagpur, Prof. L. N. Satpati from the University of Calcutta, Prof. R.B. Singh from the University of Delhi, Dr. A.K. Raha, IFS (Retd), Prof. Gerald Mills from the University College Dublin and Prof. Sugata Hazra from Jadavpur University enriched the knowledge of participants in the field of geoinformatics by their informative lectures. The presentations and discussions widely covered the various spectrums of geoinformatics and its application in monitoring natural resources like vegetation mapping, agricultural resource monitoring, forest health assessment, water, and ocean resource management, disaster management, land resource management, water and climate studies, drought vulnerability assessment, groundwater quality monitoring, accretion mapping and the use of geospatial sci nce in studying morphological, hydrological, and other biophysical characteristics of a region etc. Application of geoinformatics in predicting urban expansion, urban climate, disaster management, healthcare accessibility, anthropogenic resource monitoring, spatial-interaction mapping, and, sustainable regional planning were well-discussed topics of the conference.List of Committees, photos are available in the pdf.
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PurposeThis study aims to examine the uncertainty spillover among eight important asset classes (cryptocurrencies, US stocks, US bonds, US dollar, agriculture, metal, oil and gold) using weekly data from 2014 to 2020. This study also examines the US macro uncertainty and US financial stress spillover on these assets. Design/methodology/approachThe authors use time-frequency connectedness method to study the uncertainty spillover among the asset classes. FindingsThis study's findings revealed that the uncertainty spillover is time-varying and peaked during the 2016 oil supply glut and COVID-19 pandemic. US stocks are the highest transmitter of uncertainty to all other assets, followed by the US dollar and oil. US stocks (US dollar and oil) transmit uncertainty in long (short) term. Furthermore, US macro uncertainty is the net transmitter of uncertainty to the US stocks, industrial metals and oil markets. In contrast, US financial stress is the net transmitter of uncertainty to the US bonds, cryptocurrencies, the US dollar and gold markets. US financial stress (US macro uncertainty) has long (short)-term effects on asset price volatility. Originality/valueThis study complements the studies on volatility spillover among the important asset classes. This study also includes recently financialized asset classes such as cryptocurrencies, agricultural and industrial commodities. This study examines the macro uncertainty and financial stress spillover on these assets.
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Genetic lineages of severe acute respiratory syndrome corona virus-2 (SARS-CoV-2) have continued to emerge and circulate around the world since the onset of the COVID-19 pandemic. There are numerous variants of SARS-CoV-2, the virus that causes corona virus disease 2019 (COVID-19). Like other viruses, SARS-CoV-2 evolves over time. Most mutations in the SARS-CoV-2 genome have no impact on viral function, but certain variants have gained worldwide attention because of their rapid emergence within populations, evidence of transmission, and clinical implications. During the pandemic, most parts of India were affected, including Odisha, leading to high rates of morbidity and mortality. For the present study, 368,303 samples were received by the COVID-19 lab i.e., medical college level (Virus Research Diagnostic Laboratory) VRDL from six districts of western Odisha, including approximately 25,000 COVID-19-positive samples. The diagnostic method of the quantitative RT-PCR cannot be used to distinguish among the variants created by mutation of the genes initially, therefore selected positive clinical samples were sent in cold chain for whole genome sequencing (WGS), using the Illumina Seq. at ILS, BBSR for variant detection. The reported observation from the next generation sequencing (NGS) based sequenced samples of western Odisha updated in the INSACOG-WGS portal confirms the presence of Delta (B.1.617.2) and Delta sublineages, Omicron (BA.2), and Omicron (B.1.1.529). Maximum infection was caused by Delta sublineages (83.5%) irrespective of age, sex, and geographic area followed by Delta and Omicron. Molecular diagnosis and WGS based study reveal the widespread transmission of the fatal virus, significantly affecting every corner of the globe. Copyright © 2023 Wolters Kluwer Medknow Publications. All rights reserved.
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Genetic lineages of severe acute respiratory syndrome corona virus-2 (SARS-CoV-2) have continued to emerge and circulate around the world since the onset of the COVID-19 pandemic. There are numerous variants of SARS-CoV-2, the virus that causes corona virus disease 2019 (COVID-19). Like other viruses, SARS-CoV-2 evolves over time. Most mutations in the SARS-CoV-2 genome have no impact on viral function, but certain variants have gained worldwide attention because of their rapid emergence within populations, evidence of transmission, and clinical implications. During the pandemic, most parts of India were affected, including Odisha, leading to high rates of morbidity and mortality. For the present study, 368,303 samples were received by the COVID-19 lab i.e., medical (Virus Research Diagnostic Laboratory) VRDL from six districts of western Odisha, including approximately 25,000 COVID-19-positive samples. The diagnostic method of the quantitative RT-PCR cannot be used to distinguish among the variants created by mutation of the genes initially. Therefore, selected positive clinical samples were sent in cold chain for whole genome sequencing (WGS), and disease severity was sequenced using the Illumina Seq at ILS, BBSR for variant detection. The reported observation from the next generation sequencing (NGS) based sequenced samples of western Odisha updated in the INSACOG-WGS portal confirms the presence of Delta (B.1.617.2) and Delta sub lineages, Omicron (BA.2), and Omicron (B.1.1.529). Maximum infection was caused by Delta sub lineages 83.5%) irrespective of age, sex, and geographic area followed by Delta and Omicron. Molecular diagnosis and WGS based study reveal the widespread transmission of the fatal virus, significantly affecting every corner of the globe. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.
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With the evolution of technology and the availability of advanced service delivery approaches, usage preference regarding the healthcare industry is changing. Technology has converted patient's roles from treatment seeker to health information contributor. Looking at the vast population of India, it is adequate to incorporate technology and use advanced technological features, which will make it easy, quick and transparent for users. Integration between biology and technology with the help of internet features changes the face of traditional treatment practices. It enables users to collaborate to share information, seek consultation, develop social bonding and carry research to fight critical health conditions. The current COVID-19 pandemic has forced the world to change the way it has been operating and be more technology-friendly. Wide usage of information systems with the evolution of internet technology is continuously adding new outlooks to the legacy treatment practices. These new outlooks are the combined reflection of certain key factors, which are essential from both users as well as service provider perspective. This study aims to systematically evaluate all these vital factors and understand their roles in effective healthcare delivery. Copyright © 2022 Inderscience Enterprises Ltd.
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Introduction: The present study aims to assess the knowledge and attitude among the patients attending a dental hospital in Bhubaneswar, Odisha, India. Materials and Methods: A cross-sectional questionnaire-based survey was conducted among the general population from July 2020 to September 2020. It included 205 patients attending the outpatient department of Kalinga Institute of Dental Sciences, Bhubaneswar. A self-structured 17 item questionnaire regarding antibiotic resistance was used to assess the knowledge and attitude of the patients. Data were entered into Microsoft Excel sheet and analyzed using SPSS version 25.0. Results: The present study comprised 47.3% males and 52.7% females. Comparison of the knowledge and attitude domain scores was made across the educational levels of the participants and a significant difference was observed in the attitude domain scores. Conclusion: The present study stresses on the dire need for educating the general public about the rational use of antibiotics, thereby reducing further abuse leading to a global problem. The following core competencies are addressed in this article: Medical knowledge, Systems-based practice, Practice-based learning and improvement.
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This present study has used the long-short-term memory (LSTM) network-based deep learning architecture to analyze the influence of the current widespread COVID-19 on the Indian stock market. The major contribution of this work is as follows: (1) Designing LSTM-based deep neural network is used to study the effect of the COVID-19 outbreak and Lockdown on the Indian stock exchange (Nifty 50), and (2) designing a prediction model to capture the effect of various COVID-19 waves in India on Indian Stock exchange. The outcomes of the analysis show that the increase in daily new confirmed cases, recovered cases, and death cases have a significant adverse impact on the trend of the stock market. Moreover, the results of the work have also analyzed the impact of government policy such as ‘lockdown city’ with a reaction to increased Pandemic cases. This work is briefly summarized as follow: (1) LSTM-based deep neural network is used for this study to analyze the effect of the COVID-19 outbreak on the Indian stock exchange. (2) The Indian Stock exchange affected by the COVID-19 pandemic has been studied. Here, the analysis is based on the impact of COVID-19 including the effect of lockdown. (3) A prediction model has been proposed for the study of the behavior of the Indian stock index (Nifty 50) during the COVID-19 pandemic. (4) Comparison of the efficacy of the suggested approach with other existing baseline regression models. © 2022, The Institution of Engineers (India).
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Rapid urbanisation has led to degradation in air quality index in past decades caused by pollutants generated by factories, industries and transportation. Designing an automated system for air quality tracking and monitoring is essential for generating awareness. Restrictions imposed by COVID 19 lockdown has resulted in the degradation of pollutants in air and having a great impact on air pollution management. An analysis of air pollution index based on vehicular pollutants and industrial pollutants is done, depicting the most polluted cities in India. Various machine learning models are compared so, as to figure out a better model for classification and analysis. Results delineate that Delhi was one of the most polluted cities before lockdown but shown a tremendous decrease in air pollution index after lockdown. Further boosting models proved to outperform other models in the prediction and forecasting of air quality index.
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The low-pressure system developed in the Bay of Bengal and the Andaman Sea during March-October, often forms tropical cyclones, depending upon the intensity widespread destruction occurs in the areas where landfall takes place along the Indian coastal region. On 20 May, 2020, tropical cyclone Amphan hit the Indian coast at Bakkhali, West Bengal, in the afternoon (1330 IST). On 19 May, 2020, the intensity strengthened into a super cyclonic storm, with a strong wind speed up to 220 km/h. This cyclone affected a large population of India and Bangladesh. More than twenty-two thousand houses were damaged and millions of people were shifted to a safe place and due to the spread of COVID-19, the rescue missions were quite challenging. The cyclone affected most of the eastern states of India, heavy rainfall occurred causing floods along the track of cyclones. Using multi-satellite, ground and Argo floats data, we have analyzed meteorological and atmospheric parameters during May 2020. Our detailed analysis shows pronounced changes in atmospheric (CO mole fraction, total ozone column) and ocean parameters (chlorophyll concentration, dissolved oxygen, salinity, sea surface and sub-surface temperature) before and after the cyclone. Changes in ocean parameters such as caused by the cyclone Amphan along its track and the atmospheric and meteorological parameters change as the cyclone moves over the land.