The unprecedented pandemic COVID-19 has impacted businesses across the globe. A significant jump in the credit default risk is expected. Credit default is an indicator of financial distress experienced by the business. Credit default often leads to bankruptcy filing against the defaulting company. In India, the Insolvency and Bankruptcy Code (IBC) is the law that governs insolvency and bankruptcy. As reported by the Insolvency and Bankruptcy Board of India (IBBI), the number of companies filing for bankruptcy under IBC is on a rise, and the industrial sector has witnessed the maximum number of bankruptcy filings. The present article attempts to develop a credit default prediction model for the Indian industrial sector based on a sample of 164 companies comprising an equal number of defaulting and nondefaulting companies. A total of 120 companies are used as training samples and 44 companies as the testing samples. Binary logistic regression analysis is employed to develop the model. The diagnostic ability of the model is tested using receiver operating characteristic curve, area under the curve and annual accuracy. According to the study, return on assets, current ratio, debt to total assets ratio, sales to working capital ratio and cash flow to total assets ratio is statistically significant in predicting default. The findings of the study have significant implications in lending and investment decisions.

Background: Understanding of young women's attitudes towards BF should be an early step in the design and implementation of BF interventions. There is a need for breastfeeding promotion strategies among female students to encourage them, in the future, to breastfeed and also champion the cause of breastfeeding as a child survival strategy. This study was conducted to assess knowledge of breastfeeding among female college students and to study factors associated with knowledge of breastfeeding. Methods: This was an institution based cross-sectional study conducted in August to October 2015. A validated structured questionnaire was used to assess knowledge of breastfeeding among female degree college students. Sample size derived was 580. Complete enumeration was done of all the students from the selected colleges. P-value <0.05 was considered statistically significant. Binary logistic regression analysis (enter method) was performed to find out the effect of co-variates on breastfeeding knowledge. Results: Total 630 students participated in the study. Approximately 66% of the students had adequate knowledge regarding breastfeeding. As per logistic regression analysis mother being a housewife and living in a nuclear family were predictors for higher breastfeeding knowledge among participants. Conclusion: There is a great scope for improving adolescents' knowledge of breastfeeding in colleges.