ABSTRACT
Presence of suspended particulate matter (SPM) in a waterbody or a river can be caused by multiple parameters such as other pollutants by the discharge of poorly maintained sewage, siltation, sedimentation, flood and even bacteria. In this study, remote sensing techniques were used to understand the effects of pandemic-induced lockdown on the SPM concentration in the lower Tapi reservoir or Ukai reservoir. The estimation was done using Landsat-8 OLI (Operational Land Imager) having radiometric resolution (12-bit) and a spatial resolution of 30 m. The Google Earth Engine (GEE) cloud computing platform was used in this study to generate the products. The GEE is a semi-automated workflow system using a robust approach designed for scientific analysis and visualization of geospatial datasets. An algorithm was deployed, and a time-series (2013-2020) analysis was done for the study area. It was found that the average mean value of SPM in Tapi River during 2020 is lowest than the last seven years at the same time.
Subject(s)
COVID-19 , Particulate Matter , Humans , Particulate Matter/analysis , Cloud Computing , Search Engine , Communicable Disease ControlABSTRACT
The outbreak of COVID-19 has raised concerns about the availability of health care facilities globally. Disruptive innovations in health care may impact a new system that provides a continuum of treatment tailored to each patient’s specific requirements. In light of this evolution, this study aimed to visualize global research output on disruptive innovation in health care between 2001 to 2021 as indexed in the Scopus database. The dataset was extracted on January 10, 2022, and 204 records were identified for data analysis. Various bibliometric indicators were used to identify publication trends. VOSviewer visualization software was also used to analyze data. The findings revealed the increasing pattern of publication growth with slight fluctuation over time. M. Friebe was the most prolific author having contributed four publications. The Harvard Medical School was the most productive institution with eight publications and the United States was the most productive country with 84 publications on disruptive innovation in health care. Furthermore, human, health care, and disruptive innovation were the top keywords in this field. These findings are expected to be useful to academics and administrators all across the world. This study also gives readers insight into this domain and will allow them to begin their research by selecting a topic of their choice. [ FROM AUTHOR] Copyright of Journal of Hospital Librarianship is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
The main goal of this present study was to access the global research trends in financial literacy. The data obtained from the Scopus database, one of Elsevier's largest bibliographic databases. The various scientometric indicators have been applied in this study, such as year-wise growth pattern with Citation, Annual growth rate (AGR), Relative growth rate (RGR), Authorship pattern, degree of collaboration (DC), Correlation coefficient (CC), Most prolific authors, highly cited documents, most collaborative institutes, highly preferred sources, top funding agencies, Subject wise distribution and types of papers, etc. The study comprises a review of 2000 research documents published with 22229 citations from 2001 to 2020. The most productive year during the study was 2019. It is apparent that Lusardi, A. was the most prolific author, with 33 publications. The most highly cited document as financial literacy's Economic importance: Theory and evidence published in 2014. The leading institution in Financial Literacy was the University of Pennsylvania, with 25 publications. The top source was the Journal of consumer affairs from the USA. The most funding agency was the National Institute of Aging funding to 21 publications. The top subjects were economics, Econometrics, and finance. The VOSviewer software version 1.6.16 is used for network visualization. The present study revealed that there a continuous increase in financial literacy research productivity during the study period. Keywords: Scientometric, Financial literacy, Financial education, Financial knowledge, Financial skills, Research trends, Annual Growth rate, Authorship pattern
ABSTRACT
BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), known to be the causative agent of COVID-19, has led to a worldwide pandemic. At presentation, individual clinical laboratory blood values, such as lymphocyte counts or C-reactive protein (CRP) levels, may be abnormal and associated with disease severity. However, combinatorial interpretation of these laboratory blood values, in the context of COVID-19, remains a challenge. METHODS: To assess the significance of multiple laboratory blood values in patients with SARS-CoV-2 and develop a COVID-19 predictive equation, we conducted a literature search using PubMed to seek articles that included defined laboratory data points along with clinical disease progression. We identified 9846 papers, selecting primary studies with at least 20 patients for univariate analysis to identify clinical variables predicting nonsevere and severe COVID-19 cases. Multiple regression analysis was performed on a training set of patient studies to generate severity predictor equations, and subsequently tested on a validation cohort of 151 patients who had a median duration of observation of 14 days. RESULTS: Two COVID-19 predictive equations were generated: one using four variables (CRP, D-dimer levels, lymphocyte count, and neutrophil count), and another using three variables (CRP, lymphocyte count, and neutrophil count). In adult and pediatric populations, the predictive equations exhibited high specificity, sensitivity, positive predictive values, and negative predictive values. CONCLUSION: Using the generated equations, the outcomes of COVID-19 patients can be predicted using commonly obtained clinical laboratory data. These predictive equations may inform future studies evaluating the long-term follow-up of COVID-19 patients.