Assessment of bactericidal role of epidermal mucus of Heteropneustes fossilis and Clarias batrachus (Asian cat fishes) against pathogenic microbial strains

This research was directed to understand the bactericidal effect of epidermal mucus of two Asian cat fish species viz.Clarias batrachus and Heteropneustes fossilis.Epidermal mucus extracts(raw and diluted)of both cat fish species were tested against several Gram negative(Pseudomonas aeruginosa,Escherichia coli,Klebsiella pneumonia,A.hydrophila)and Gram positive bacterial strains(Bacillius cereus,Staphylococcus aureus,S.epidermidis)and antibacterial results were also compared with two standard antibiotics viz.amikacin and chloramphenicol used as positive control.An A.hydrophila challenge experiment was also performed on all selected test fish species to examine the change in the amount of mucus production and its bactericidal impact..Both epidermal mucus extracts(raw and diluted)of all selected normal and bacterial challenged test objects showed potent bactericidal effect against all pathogenic bacterial strains taken under study.However,former was more effective than later.Also raw epidermal mucus extracts of both normal and bacterial challenged cat fish species exhibited significantly higher ZOI values against all selected microbial strains than diluted mucus extracts and antibiotic chloramphenicol.Hence,these outcomes have clearly revealed that this cost effective natural product acquired from fishes is the key component of their defensive system.Therefore,it could be utilized as a novel‘antimicrobial’in human as well as veterinary sector for combating against several bacterial diseases.

A PLS-SEM Based Approach: Analyzing Generation Z Purchase Intention Through Facebook’s Big Data

The objective of this paper is to provide a better rendition of Generation Z purchase intentions of retail products through Facebook.The study gyrated around the favorable attitude formation of Generation Z translating into intentions to purchase retail products through Facebook.The role of antecedents of attitude,namely enjoyment,credibility,and peer communication was also explored.The main purpose was to analyze the F-commerce pervasiveness(retail purchases through Facebook)among Generation Z in India and how could it be materialized effectively.A conceptual fac¸ade was proposed after trotting out germane and urbane literature.The study focused exclusively on Generation Z population.The data were statistically analyzed using partial least squares structural equation modelling.The study found the proposed conceptual model had a high prediction power of Generation Z intentions to purchase retail products through Facebook verifying the materialization of F-commerce.Enjoyment,credibility,and peer communication were proved to be good predictors of attitude(R^(2)=0.589)and furthermore attitude was found to be a stellar antecedent to purchase intentions(R^(2)=0.540).

AI-Based Hybrid Models for Predicting Loan Risk in the Banking Sector

Every real-world scenario is now digitally replicated in order to reduce paperwork and human labor costs.Machine Learning(ML)models are also being used to make predictions in these applications.Accurate forecasting requires knowledge of these machine learning models and their distinguishing features.The datasets we use as input for each of these different types of ML models,yielding different results.The choice of an ML model for a dataset is critical.A loan risk model is used to show how ML models for a dataset can be linked together.The purpose of this study is to look into how we could use machine learning to quantify or forecast mortgage credit risk.This phrase refers to the process of evaluating massive amounts of data in order to derive useful information for making decisions in a variety of fields.If credit risk is considered,a method based on an examination of what caused and how mortgage credit risk affected credit defaults during the still-current economic crisis of 2021 will be tried.Various approaches to credit risk calculation will be examined,ranging from the most basic to the most complex.In addition,we will conduct a case study on a sample of mortgage loans and compare the results of three different analytical approaches,logistic regression,decision tree,and gradient boost to see which one produced the most commercially useful insights.