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Background: We estimated and compared the differences in frailty, disability, and functional limitation among men and women, and among urban and rural dwellers. Further, this study also provides the analysis of key factors influencing frailty, functional limitation and disability among older persons in India. Study design: Two cross-sectional surveys. Methods: WHO-SAGE (2007-10) and BKPAI-2011 (Building Knowledgebase for Population Ageing in India) (2007-10) were used. Oaxaca decomposition method was used to decompose the gender and place of resident differentials. Statistical software RStudio (Version 1.2.1335) was used to perform these analyses Results: The decomposition model was able to explain 46.5%, 41.6% and 46.4% of the difference between frailty, functional limitation and disability among older persons respectively. The key factors, which significantly ( P <0.05) explained the gap for both frailty and functional limitation, were Education (0.009 &1.24), working status (0.018 & 1.93), physical activity (0.001 & 0.15) and migration (0.018 & 1.98). Higher educational attainment (0.008 & 1.10) and wealth quintile (0.009 & 1.18) in urban areas might be a factors resulting in the lowering of frailty and functional limitations. Conclusion: The poorer functional health among older women can largely be explained by gender differentials in socioeconomic status and consequent empowerment (such as less control of their mobility and financial independence). This implies that efforts to improve gender disadvantages in earlier life stages might get reflected in better health for females in older age.

With the association of software security assurance in the development of code based systems; software developers are relying on the Vulnerability discovery models to mitigate the breaches by estimating the total number of vulnerabilities, before they’re exploited by the intruders. Vulnerability Discovery Models (VDMs) provide the quantitative classification of the flaws that exists in a software that will be discovered after a software is released. In this paper, we develop a vulnerability discovery model that accumulate the vulnerabilities due to the influence of previously discovered vulnerabilities. We further evaluate the proportion of previously discovered vulnerabilities along with the fraction additional vulnerabilities detected. The quantification methodology presented in this article has been accompanied with an empirical illustration on popular operating systems’ vulnerability data.

The number of security failure discovered and disclosed publicly are increasing at a pace like never before. Wherein, a small fraction of vulnerabilities encountered in the operational phase are exploited in the wild. It is difficult to find vulnerabilities during the early stages of software development cycle, as security aspects are often not known adequately. To counter these security implications, firms usually provide patches such that these security flaws are not exploited. It is a daunting task for a security manager to prioritize patches for vulnerabilities that are likely to be exploitable. This paper fills this gap by applying different machine learning techniques to classify the vulnerabilities based on previous exploit‐history. Our work indicates that various vulnerability characteristics such as severity, type of vulnerabilities, different software configurations, and vulnerability scoring parameters are important features to be considered in judging an exploit. Using such methods, it is possible to predict exploit‐prone vulnerabilities with an accuracy >85%. Finally, with this experiment, we conclude that supervised machine learning approach can be a useful technique in predicting exploit‐prone vulnerabilities.

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