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WHO declared coronavirus 2(SARS-CoV-2) as a pandemic on March 11, 2020. COVID-19 vaccines have been rapidly developed and distributed to help fight the pandemic. By January 2021, use of the Covishield vaccine and Covaxin vaccine use was approved. India began its vaccination program on 16 January 2021. The objective of the study is to assess the severity of COVID 19 infection among those vaccinated and unvaccinated as this will help us to assess the efficacy of the vaccines.By Convenient sampling technique the data of all those patients who were admitted in tertiary care centers in Tumkur district during second wave (April-July 2021) of COVID 19 infection were assessed from their medical records. The severity of the COVID 19 infection was assessed by evaluating the CT scores, lymphocyte/ neutrophil ratio, platelet distribution width, medical treatment provided and outcome which was later compared to vaccine status of the patients. Out of 220 patients in the study, 188(85.5%) were not vaccinated, 183(83.2%) had a positive RT PCR report, with most having CT of moderate changes (45.9%). Only 42(19.1%) patients underwent intubation and 28(12.7%) patients died. Using Chi Square test it was seen that those who were not vaccinated had moderate to severe CT scores (P = 0.026).Those who were not vaccinated had a higher chance of developing moderate to severe CT changes, though the death rate was less. Vaccine is found to be efficacious in fighting against COVID-19.

The dwindling competition for shelf space in retail stores added with high advertising costs gave FMCG sectors an opportunity for brand extensions and maximizing their profits. In such a scenario, though the value of brand could not be undermined from a consumer perspective, the complexities of its management determining the various contributing factors for new extension success, and the attitude of retailers and consumers towards new products or extensions based on various variables need to be pragmatically considered. The data were collected from 360 retailers and 360 consumers from the four major cities of South India. Review of the literature on management of brands pointed to several important variables. In this context, discriminant analysis was applied to assess the responsiveness of retailers and consumers towards the various factors affecting the success of brand extensions. Nevertheless, the present study may throw light on the effect of hypothetical brand extensions on their core brands of FMCGs in India.

Multiple regression approach has been used to forecast the crop production widely. This study has been undertaken to evaluate the performance of stepwise and Lasso (Least absolute shrinkage and selection operator) regression technique in variable selection and development of wheat forecast model for crop yield using weather data and wheat yield for the period of 1984-2015, collected from IARI, New Delhi. Statistical parameters viz. R2, RMSE, and MAPE were 0.81, 195.90 and 4.54 per cent respectively with stepwise regression and 0.95, 99.27, 2.7 percentage, respectively with Lasso regression. Forecast models were validated during 2013-14 and 2014-15. Prediction errors were -8.5 and 10.14 per cent with stepwise and 1.89 and 1.64 percent with the Lasso. This shows that performance of Lasso regression is better than stepwise regression to some extent.

A physics based hydrologic model (either lumped or distributed) is generally characterized by a multitude of parameters. The calibration of such models is always a complex task. Though, manual calibration is by far the most widely used approach for parameter estimation of such models, it is highly time consuming, and uncertainty of obtaining the best fit exists. Attempts to develop automatic calibration procedures that use optimization algorithms for distributed watershed models have resulted in long computational time and effort. This was mainly because the model has to perform simulations a number of times before it can evaluate the objective function of the algorithm during the parameter search. This study presents a novel framework for calibration of watershed models under simulation-optimization environment. The framework identifies the parameters of the model through an optimization algorithm that uses the objective function from a pseudo simulator, which reduces the computational time significantly. The pseudo simulator will map the objective function in terms of the parameters thus avoiding complex simulation every time. The methodology is demonstrated by calibrating SWAT model for Illinois watershed in Arkansas, USA. The calibrated model is found to simulate the flows reasonably good. The model performance indices when computed on a daily basis, the R2 value between the simulated and measured flows ranged from 0.36 to 0.60 during calibration period and are 0.77 for the validation period. The model resulted in an efficiency of 0.75 during the validation period. Overall, the study reports an auto-calibration procedure for watershed models that can be used for calibration of models irrespective of their complexity.