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.

Damage Quantification Using Dynamic Response of a Structure in Conjunction With Thermal Loading

In structures or machine components, fatigue failure is very common. It is initiated by a small defect which leads it to a catastrophic failure. The material defects, inclusion, impurities and machine operation can always be vulnerable to crack initiation and hence fatigue cannot be avoided. In metallic structures, the thermal loads can also alter the material properties such as young’s modulus, tangent modulus, yield stress, and ultimate tensile strength, etc. Consequently, in the presence of increasing temperature, it can be inferred that the material might become soft near the vicinity of the crack tip, which can lead to increase the size of the plastic zone under the same mechanical loads. Therefore, it is very complicated to estimate the retardation or acceleration of fatigue crack propagation under thermo-mechanical loads. This research investigates the interdependencies of crack depth and crack location on the dynamic response of a non-prismatic cantilever beam under thermo-mechanical loads. Temperature can influence the stiffness of the structure, thus, the change in stiffness can lead to variation in frequency, damping and amplitude response. These variations are used as key parameters to quantify damage of Aluminum 2024 specimen under thermo-mechanical loads. Experiments are performed on non-prismatic cantilever beams at non-heating (room temperature) and elevated temperature, i.e., 50°C, 100°C, 150°C and 200°C. This study considers a non-prismatic cantilever beam having various initially seeded crack depth (0.5 mm to 2.5 mm) and crack of 0.5 mm with natural propagation under load located at various locations, i.e., 5%, 10% and 15% of the total length from fixed end, respectively. The analytical, numerical and experimental results for all configurations are found in good agreement. Using available experimental data, a novel tool is formulated for in-situ damage assessment in the metallic structures for the first time under thermo-mechanical loads. This tool can quantify and locate damage using the dynamic response and temperature including the diagnosis of subsurface cracking. It fits around 82% of available data for validation within 10% of prediction error against a small change in the response parameter. The obtained results demonstrate the possibility to diagnose the crack growth at any instant within the operational condition under thermo-mechanical loads.