Neural Network-Based Prediction Model to Investigate the Influence of Temperature and Moisture on Vibration Characteristics of Skew Laminated Composite Sandwich Plates

The present study deals with the development of a prediction model to investigate the impact of temperature and moisture on the vibration response of a skew laminated composite sandwich (LCS) plate using the artificial neural network (ANN) technique. Firstly, a finite element model is generated to incorporate the hygro-elastic and thermo-elastic characteristics of the LCS plate using first-order shear deformation theory (FSDT). Graphite-epoxy composite laminates are used as the face sheets, and DYAD606 viscoelastic material is used as the core material. Non-linear strain-displacement relations are used to generate the initial stiffness matrix in order to represent the stiffness generated from the uniformly varying temperature and moisture concentrations. The mechanical stiffness matrix is derived using linear strain-displacement associations. Then the results obtained from the numerical model are used to train the ANN. About 11,520 data points were collected from the numerical analysis and were used to train the network using the Levenberg–Marquardt algorithm. The developed ANN model is used to study the influence of various process parameters on the frequency response of the system, and the outcomes are compared with the results obtained from the numerical model. Several numerical examples are presented and conferred to comprehend the influence of temperature and moisture on the LCS plates.

Epidemiology of mental disability using Indian Disability Evaluation Assessment Scale among general population in an urban area of Puducherry, India

BACKGROUND

There is paucity of information on epidemiology of mental disability in India.

OBJECTIVE

The objective of this study was to assess mental disability, and to study the association between sociodemographic and comorbid chronic conditions with mental disability.

MATERIALS AND METHODS

This community-based cross-sectional study was conducted among ≥5 years age group in an urban area attached to a Tertiary Care Medical Institute in Puducherry, India. Mental disability was assessed using Indian Disability Evaluation and Assessment Scale. Chronic morbid conditions and other associated factors were collected using pretested questionnaire.

STATISTICAL ANALYSIS

Univariate and multiple logistic regression analysis.

RESULTS

About 2537 subjects were covered with a response rate of 94.1%. Overall, the prevalence of mental disability was found to be 7.1% (181/2537). Among them, majority had mild mental disability (151, 83.4%), followed by moderate (21, 11.6%), severe (8, 4.4%), and profound (1, 0.6%) mental disability. Univariate analysis showed that age group status, marital status, education level, occupation, family type, religion, hypertension, joint pain, backache, current smoking, current alcohol use, and conflicts were associated with mental disability (P < 0.05). Multiple logistic regression analysis showed that male gender (adjusted odds ratio [AOR] =2.064), widowed status (AOR = 27.022), separated/divorced status (AOR = 16.674), currently married status (AOR = 18.487), being illiterate (AOR = 4.352), having 1st-10th standard education (AOR = 2.531), being in an unskilled (AOR = 0.287) or semiskilled/skilled occupation (AOR = 0.025), belonging to a nuclear family (AOR = 1.816), and absence of family conflicts (AOR = 0.259) were significantly associated with mental disability compared to their counterparts.

CONCLUSION

Mental disability is more common in this area. Males, lesser education level, skilled or unskilled occupation, nuclear family, and conflicts were associated with mental disability after adjusting other variables. Multicentric cross-sectional analytical studies will explore the mental disability burden and its associated factors at regional or country level.