Atmospheric smog modeling, using EOS Satellite ASTER Image Sensor, with feature extraction for pattern recognition techniques and its correlation with In-situ ground sensor data

Atmospheric pollution was previously considered as a \u27Brown Cloud’ phenomenon restricted to industrialized urban regions. Studies in field stations and satellite observations made since the last decade revealed that it now spans continents and ocean basins world wide. The objective of this research investigation is to assess atmospheric pollutants in the troposphere and their spectral characteristic signatures by using highspectral and spatial resolution Earth Observation System (EOS) satellite imaging sensor Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and to find correlations with ground sensor observations. Ground sensor data are imported into a geodatabase for spatial reference. Raw ASTER data are georegistered and geocorrected by image-to-image registration with a known geo-corrected image. Data Fusion, Principal Component Analysis (PCA), Density Slicing, Band Ratioing, and Bandpass Filtering techniques are applied to extract features in the ASTER datasets. Spectral signatures in graphical form of the atmospheric features are obtained in ER-Mapper 7.1 geospatial software (ER-Mapper, 2006) and compared both in short wave infra-red (SWIR) and thermal infra-red (TIR) bands. It is observed that the impact of air pollutants from polluting sources are not just confined to the areas under investigation but extend further as pollutants are transported by wind to greater distances. Correlation between ground sensor pollution level and ASTER image pollutants pixel digital numbers are obtained by creating a general linear model in the PROC-GLM program in Statistical Analysis System (SAS) user software. Despite broader bandwidth of ASTER as compared to hyperspectral satellite systems, an excellent high correlation is observed in spectral response of all TIR bands and moderate correlation with SWIR bands of ASTER with ground sensor monitoring in all the three areas, i.e. San Francisco Bay area and Los Angeles, in California, and in Charleston in West Virginia. Future investigation is envisioned to study the subtle differences in spectral signatures of air pollutants by using hyperspectral satellite data and nanotechnology based sensors

Measuring the open access friendliness of the state universities in India through data carpentry

The present research aims to investigate the open access (OA) friendliness of the twenty-six (26) state universities that have been ranked in the National Institutional Ranking Framework’s (2021) ‘Overall Category’ (Rank Band 1: Top 100). The open-access ranking framework, as designed, is based on four major OA areas: i) proportion of OA publications by a given state university; ii) use of a license in sharing OA publications; iii) citations received by OA publications; and iv) popularity of OA publications in socio-academic web-space as indicated by altmetric attention scores.Ten years(2012–2021) primary publication data for the 26 state universities from Scopus was collected. We analyzed a total of 1,57,971 publications; 1,60,723 citations received by these publications; and altmetric attention scores received by 26,996 publications from these 26 state universities listed in the NIRF 2021 overall category. It applies an open-source data wrangling tool (OpenRefine) and a number of ODbL-based (Open Database License) data sources to gather the necessary data elements required for this large-scale study. The study finds that almost 98% of the state universities enlisted in the top 100 overall category passed the 50 marks on a 100-point scale, and the newly established state universities’ open access friendliness is greater than the older ones. The final open access friendliness ranking places King Georges Medical University at the top, with leading scores in areas I (share of OA publications) and III (OA citation share), while Panjab University has the highest value in Area II (use of license in OA publications) and Delhi Technological University has the highest value in Area IV (OA altmetric attention score)

Theoretical Backbone of Library and Information Science: A Quest

This study primarily aims to identify unique theories and specific uses of theories in the library and information science (LIS) domain. It provides a comprehensive list of the theories used in LIS journal articles indexed by Scopus (an abstract and citation database) from 1970–2021. It expands on the most common theories and highlights the areas and purposes for which used theories in the LIS domain. Our goal is to demonstrate the usages and applications of various borrowed theories from complementary disciplines in the LIS domain. A systematical methodology is applied, following a few open-source AI-based software packages (such as ASReview, and OpenRefine), to analyse the theories against different parameters, keeping in mind the drawbacks of the previous studies. The study's findings show that the LIS domain's theoretical foundations are understudied. Researchers mainly borrowed theories from social sciences such as sociology, psychology, and management studies to solidify their domain. The paper provides a clear road map for the theoretical development of LIS research. And the resulting outputs may help policymakers, academicians, and researchers, irrespective of disciplines in general and information science in particular, understand the foundations and theoretical and methodological trends of theories that may lead to a better understanding of the theories before their selection and applications

Global Repository Movement in the Domain of Library and Information Science Discipline

Provides an overview of Subject Repositories (SRs) throughout the World in response to the open access movement (OAM). It mainly highlights the current trends of repository development in Library and Information Science (LIS) field. This paper covers all repositories in LIS field as registered in OpenDOAR (Directory of Open Access Repository) database. The main objective of the paper is to select a set of parameters for evaluation of LIS repositories with other disciplinary repositories taking into consideration global recommendations and best practice guidelines. The paper also shows the growth of selected LIS repositories in terms of volume and number of objects, contents type, software pattern, subjects coverage etc. Lastly points out lacunas of LIS repositories in compare to other disciplinary repositories as well as recommends possible directions which can make the repository sustainable and will change the culture of information exchange pattern in the social science disciplines as a whole.