In India, the first coronavirus disease (COVID) case was confirmed on 30/01/2020. During the first wave, the number of new cases increase and become minimum of 9102 on 25/1/2021. After fluctuations for 4 days, COVID cases start to increase for the second wave from 13044 on 30/01/2021. In this manuscript, mathematical models based on probability density function was proposed to predict new cases infected with COVID during the second wave in India and the models were validated using various error functions. Gaussian, fuzzy logic generalized membership, hyperbolic secant, Witch of Agnesi, logistic and random algebraic functions were used for probability modelling. Average of error (AE), sum of square of error (SSE), average of relative error (ARE), correlation coefficient (R), determination coefficient (R²), adjusted determination coefficient (Adj. R²), coefficient of variance (CV), Chi-square (X²), bias, Akaike information criterion (AIC), Bayesian information criterion (BIC)andAmemiya’s prediction criterion (APC) were used for validating the models. From the results, not a single probability model could fit the new COVID cases data in India during the second wave.

Many researchers have applied Interpretive Structural Modeling (ISM) technique in knowing the variables of a situation and there by developing a model to prioritize the actions to address the situation. In this study, ISM is applied in understanding the Discouraging Factors (DF) of Small and Medium Scale Enterprises (SMEs) to implement SCM in their organization. Industry experts’ opinion and literature is used as raw data for identifying the Discouraging Factors (DF) for adopting Supply Chain Management. Identified elements are ranked by using ISM techniques. Further, these identified variables are interrelated by using Ray Diagram & in the end an ISM model is developed. The ISM model prepared here will help the SMEs to implement SCM and prioritize their actions in implementing it. Literature review of deployment of ISM by researchers is presented in this paper. This literature review helps the researchers in using ISM in their study. Further, preparation of a ray diagram for each and every variable is demonstrated. This will help the researchers to apply transitivity tests by using ray diagram in their study.

In this study the anaerobic treatability of streptomycin was investigated in a sequential anaerobic baffled reactor (ABR)/ completely stirred tank reactor (CSTR) system. The ABR reactor was operated continuously through 83 days using glucose as primary substrate with constant streptomycin concentration of 200 mg/L. 200mg/L streptomycin gives an additional COD concentration to total COD thought continuous operation. 200 mg/l of streptomycin gave approximately a COD of 131.38 mg/L. The effects of decreasing hydraulic retention times (HRT) (38.4-19.2-12.8-9.60-7.68 days) on COD, antibiotic removal efficiencies and gas productions in anaerobic baffled (ABR) reactor were investigated at constant streptomycin concentration of 200mg/L. Moreover, the effects of decreasing HRT on the change of, volatile fatty acid (VFA) accumulation were investigated in the effluent and in the compartments of ABR reactor.