Digitizing grey portions of e-governance

Purpose: The purpose of this research paper is to assess e-governance efficacy in various sectors of India. The paper develops on Grey System Theory (GST) methodology and enlightens grey portions of e-governance in select sectors. Research study identifies few grey criteria which affect implementation of information and communication technology (ICT) applications to support sustainable e-governance. Such criteria are related to information security breaches, information technology (IT) policy implementation, investments and strategic advantages for the various sector developments. Design/methodology/approach: Considering “information” as a sensitive element to security for administration and part of dark portion to Indian economy, GST-based COmplex PRroportional ASsessment (COPRAS-G) method is adopted to assess the e-governance efficacy. The method provides flexible multicriteria decision-making (MCDM) approach to assess e-governance in prioritizing the sector alternatives of future strategic development. Priority order of select sectors is estimated, and COPRAS-G method is used in the research study to support decision-making on e-governance. Study compares ten major gross domestic product-dependent sectors based on few grey criteria. These criteria are chosen based on authors’ perspective on this study and feedback received from government officials of district levels under the Digital India-training programme. To address the subjectivity that lies in e-governance grey areas of sector, criteria are also weighted using fuzzy scale. Later methodology-based results are presented to draw a strategic road map for strategic development of the country. Findings: On applying COPRAS-G method to predict pessimistic, optimistic and realistic scenarios of e-governance implementation across the ten sectors, high priory order in realistic scenario of results shows that implementation of ICT applications for e-governance should be in the sectors such as environment, climate change and in the railways. Industrial sector is also ranked as the preferred one over the other sectors on the basis of e-governance efficacy assessment. Research limitations/implications: Here COPRAS-G method is used as MCDM techniques. However, few other MCDM techniques such as GRA, DRSA, VIKOR, SMAA, SWARA and SAW can be also explored to outrank various Indian sectors to deal with subjectivity in decision-making. Practical implications: Implementation of ICT applications to support e-governance varies from sector to sector. ICT-based governance involves high degree of complexity in driving the operations for development of respective sectors. Therefore, government and policymakers need more flexibility to overcome present barriers of sector development. Such research can support decision-making where GST-based COPRAS-G method is able to capture and address the breaches of information security. Moreover, management concern for sector development has been presented on the basis of pessimistic, optimistic and realistic scenarios more precisely. Social implications: The results can provide guidance to the academicians, policymakers and public sectors highlighting various possible measures to handle the security breaches in multi-facet intention of sustainable development. The outcomes from MCDM framework can also help in drawing a rough trajectory of strategy, i.e. development of ICTs applications and e-governance process. Originality/value: This paper can supplement and act as the support for decision-making in conflicting situations on different flexible scenarios. Moreover, such work can synergize conflicting ideas of decision makers, academics and various other stakeholders of the Indian IT sector

Clinical Utility of Vitamin D3 As Potent Biomarker in Cardiovascular and Liver Disorders

Vitamin D is lipophilic substance needed for calcium and phosphate balance and the regulation of the osteo-metabolic system.The purpose of the study was to look at the possible significance of Vitamin D3 as a biomarker for liver enzymes and cholesterol. 249 cases were analysed, with different combinations of liver enzymes, vitamin D3, and cholesterol.75 clinical samples were processed over the course of the 3year study.46(61.3%) were females and 29 (38.6%) were males.Males were more affected than females.In respect to Vitamin D3, the age group of 41-60 years had a large range of cholesterol levels and liver enzyme values. 11% of cases had high SGOT levels, while 13% had aberrant cholesterol values.Above the age of 60, there was a linear connection between cholesterol and liver enzymes.There was seasonal variations in serum 25-OHD levels.Winter (November-March) indicated a Vitamin D3 deficiency in the blood serum, accounting for 74 cases(66%).Autumn and summer had the best range, with only 0 and 16 cases (14.2%), respectively.Despite wide variability in serum vitamin D levels, the differences were not statistically significant.Vitamin D3 can be an important biomarker in clinical practice since it can aid in the early detection of potential hazards linked with cardiovascular disease and liver dysfunction

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

An Indian study on probability attributed to COVID-19 coronavirus infection using machine learning algorithm

As a result of the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) outbreak, the present COVID-19 public health crisis has resulted in the loss of human life as well as significant economic disruptions. Using a machine-learning algorithm, we can determine if a patient is more likely to survive than die, or the other way around, for a particular case. We use previous data, such as medical records, demographics, and information specific to the COVID-19, to fine-tune this algorithm. Data on confirmed and probable COVID-19 infections in India, which the Indian Government has collated and made publicly accessible, is used in this report. We show that the suggested technique can predict high-risk patients with high accuracy in each of the four indicated clinical phases, hence enhancing hospital capacity planning and prompt treatment. Our approach may be expanded to produce optimum estimations for hypothesis-testing procedures that are extensively used in biological and medical statistics. With the present epidemic, we hope that the information we've gathered may help doctors make better, more timely decisions about patient treatment

228 OPTIMIZATION OF FAILURE IN MACHINE PARTS BY HYBRID METHOD 377-381 1086

Abstract During the operation of these machine tools, different kinds of failures are faced by the industry. CNC machines into groups helps maintenance department to focus their attention to the machine that has high tendency to produce defect. A field failure analysis of computerized numerical control (CNC) lathes is described. Field failure data was collected over a period of one year on approximately ten CNC lathes. A coding system to code failure data was devised and a failure analysis data bank of CNC machine was established. The failure position and subsystem, failure mode and cause were analyzed to indicate the weak subsystem of a CNC lathe. If the machine producing the defect is inspected late, the damage caused might be large. Moreover, to remove this problem Hybrid method is use to identify machine failure

Formulations of sustained release matrix tablets of Furosemide using natural and synthetic polymers

The primary benefit of a sustained release dosage form compared to a conventional dosage form, is the consistent drug plasma concentration and consequently uniform therapeutic effect. Matrix system are preferential because of their ease, patient compliance etc, than  traditional drug delivery which have several drawbacks like reiterated administration, variation in blood concentration level etc. The aim of the present research study was to develop and evaluate sustained release matrix tablets of furosemide using direct compression method using  natural  gummy  and  waxy  materials (Xanthan  gum, bees  wax)  and synthetic  polymers  (HPMC K4M). The matrix tablet formulations were prepared by using different drug: polymer ratios (1:1, 1:2 and 1:3). All formulations were assessed using micromeritics studies of powder blend and diverse physicochemical tests. All the physicochemical characters of the formulated tablets were within acceptable limits. The release pattern of the drug was viewed over a period of 12 hours and determined the amount of drug by the UV-Visible spectroscopic method. Dissolution data demonstrated that the formulated tablets with Xanthan gum and hydroxyl propyl methylcellulose (HPMC) provided sustained release of the drug up to 12 hrs. Therefore inexpensively it may be appropriate for the pharmaceutical industries to employ this kind of simple technologies for the expansion of advanced formulations. Hence, we conclude that the purpose of this study was to formulate a sustained release matrix tablet of furosemide using diverse polymers and their dissimilar proportions have been attained. Keywords: Furosemide, Direct compression, Natural, Synthetic polymers, Sustained release tablets

Preparation and characterization of amphotericin B mannosylated liposomes for effective management of visceral leishmaniasis

Visceral leishmaniasis (VL) is a chronic debilitating disease prevalent in tropical and subtropical regions, caused by protozoan parasites of the genus Leishmania. Annually, it is approximated the occurrence of 0.2 to 0.4 million novel cases of the disease worldwide. The cast film method was used to prepared cationic and mannosylated liposomes. The surface of the Amphotericin B (Amp B)-bearing cationic multilamellar liposomes was covalently coupled with p-aminophenyl-α-D-mannoside using glutaraldehyde as a coupling agent, which was proved by agglutination of the vesicles with concanavalin A. The prepared liposomes were characterized for shape, size, % drug entrapment, vesicle count, zeta potential and in vitro drug release. Vesicle sizes of cationic and mannosylated liposomes were establish to be 2.71±0.12and 1.62±0.08μm, respectively. Zeta potential of cationic liposomes was higher (28.38 ± 0.3 mV), as compared to mannosylated liposomes (15.7 ± 0.8 mV). % drug release from cationic and mannose-coupled liposomes was established to be 45.7% and 41.9%, respectively, after 24 hrs. In the present work, cationic and mannosylated liposomes of Amp B were prepared, optimized and characterized for effectual organization of VL. Keywords: Mannosylated liposomes, Amphotericin B, Leishmaniasis, % drug release