Frequency and Psychosocial Determinants of Gender Discrimination Regarding Food Distribution among Families

Due to male dominance in society as well as in households, the rights of females are ignored. Hence, there exists gender discrimination while giving food to family members which in turn results in poor health status for females. Therefore, it is important to explore the causes of this unequal distribution of food among family members Objective: To determine psychosocial factors causing gender discrimination regarding food distribution among families Methods: Data collected from fifty females aged 15-80 years, selected from the urban community using non-probability consecutive sampling, were used for analysis. Females with malnutrition, psychological disorders, with laparotomy and major surgery were excluded. Gender discrimination was assessed as males or male children were preferred for better and more food items like fresh food, meat, fruits, milk, dairy products and multivariate logistic regression analysis was done to see the impact selected factors on gender discrimination Results: The large family size (> 6 members) showed significantly higher odds of discrimination (OR=3.89; 95% CI= 1.03-15.26) than smaller families. The odds of food discrimination were 4 times more for the families, with males being earning hand (OR=4.57; 95% CI= 1.19-18.31). Similarly, there exist higher odds of gender discrimination in low-income families (OR=5.10; 95% CI= 1.18-23.87). While maternal education reduces the chances of food discrimination (OR=0.10; 95% CI= 0.02-0.42)  Conclusions: Psychosocial factors such as large family size, low monthly income, males being earning hand and maternal education were found to be associated with gender discrimination regarding food distribution among family members.

Quality of Service Based Cloud Computing Framework for Resource Management

Cloud Computing is an evolving information technology development, deployment and delivery model consisting of a collection of interconnected and virtualized computers enabling real time delivery of services and solutions over the Internet. One of the critical concerns in this environment is the provisioning of optimal software and hardware resources to ensure a better quality of service (QoS). The classic cloud computing model where services are provided by a single vendor introduces numerous challenges. Cloud services may be interrupted due to unavailability, natural disaster or abrupt increase of the load and hence the system may not be able to provide services to thousands of customers who solely rely and pay for resources. One of the recently emerging areas in cloud computing is deployment of virtual machines across multiple clouds based on providers’ ranking. This involves benchmarking of different cloud providers, development of different techniques for selection of candidate providers, frameworks for ranking cloud providers and monitoring service level agreement (SLA) violations. Most of the existing literature is focused on employing centralized approaches for overall system ranking and monitoring, however, these approaches are not efficient for an environment where job migration and auto-scaling of virtual machines take place across cloud boundaries. The main objective of this research work is the development and evaluation of a QoS based ranking framework for IaaS computing resources across multiple clouds for resource negotiation, provisioning of physical resources, monitoring and ranking, based on job execution experience. We propose a broker enabled QoS ranking, negotiation and monitoring framework based on user level QoS requirements that determine users’ needs and utility for choosing a best-fit cloud provider among a list of candidate cloud providers. Simulation and real test-bed experimentation results suggest that our proposed framework not only gained higher profit margin but also attained more user satisfaction in terms of lower job rejection and failure rate.