Disability in children gives rise to various needs in parents, which may vary according to the nature of disability and parental characteristics. Cross-cultural findings will help in understanding the process of meeting those needs, which ultimately help in designing appropriate interventions. The main objectives of the study were to compare the perceived needs of fathers and mothers having a child with intellectual disability, and to understand their cumulative needs with reference to the age, sex, and severity of functional disability of the child. Thirty couples, each having a child with intellectual disability, were assessed with the NIMH Family Needs Schedule. The needs expressed by fathers and mothers differed significantly. Needs of the parents varied according to the age and sex of the child. Severity of intellectual disability had less impact on the nature of parental needs. The needs of mothers and fathers can be different. Some needs of the parents may subside as their intellectually disabled child grows, but they are duly replaced by others. Needs of the parents grossly vary according to the sex but not the severity of intellectual disability of the child. Wherever applicable, family intervention should focus on the needs of the mothers and fathers separately with due consideration to the sex and age of the child.

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Glass fibre reinforced epoxy polymers (GFRP) composites have gathered enormous attraction because of their exceptional engineering properties such as superior proportion in strength-to-weight and enhanced durability. However, to develop a machined component is a difficult task due to nonhomogeneity and anisotropic behavior of GFRP. In this study a hybrid module, Grey relational analysis (GRA) embedded in artificial neural network (ANN) based on Taguchi approach for multicriteria optimization in Turning of GFRP materials has been carried out. The desired machining characteristics are minimum cutting force, minimum surface roughness & maximum rate of material removal & these have been used to calculate the Grey relational coefficient for individual response and then converted into the single response function i.e. Grey relational grade (GRG) value which was used for converting multiobjective problem into single objective problem and then well-trained ANN based on the Levenberg-Marquardt Back Propagation (LMBP) algorithm has been used to predict the most favourable process parameters setting i.e. spindle speed (880 rpm), feed rate (0.05 mm/rev.), depth of cut (0.4 mm). The predicted GRG of process parameters via L 16 OA of Taguchi-based GRA with ANN has been improved by 8.5%.This setting has been selected based on highest GRG predicted by well-trained ANN which finally has been checked by the confirmatory test which produces satisfactory results.

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