Triticale (Triticosecale) a hybrid of wheat (Triticum) and rye (Secale) is an important cereal crop and has more production as compared to wheat. It has gained importance in fulfilling the increased food demands of the world. For a breeding programme genetic variability and the genetic relationship among genotypes is important. To study this, 70 lines of triticale introduced from CIMMYT were planted in complete block design. Data was recorded for plant height, spike length, number of spikelets per spike, number of grains per spike, number of tillers per meter, grain yields per meter and 1000 grain weight. The data showing significant differences was subjected correlation and cluster analysis. Correlation analysis gave the results that plant height had negative and non-significant correlation with grain yield per meter. It was found that spike length had positive and significant correlation with grain yield per meter. Number of spikelets per spike and grain yield per meter was reported to have positive and significant correlation whereas number of grains per spike had positive and highly significant correlation.

Starting inrush current and pulsations in the inducedtorque affect the performance of an induction motor.Artificial neural networks (ANNs) and adaptive neurofuzzy inference system (ANFIS) can enhance theperformance of the motor by making a control systemwhich would provide smooth starting to inductionmotor. Dynamic model of induction machine indifferent frames of reference was implemented usingMatlab Simulink. Feed forward back propagation basedand radial basis neural networks were trained, withdata obtained using simulations, to estimatedifferent parameters required by ANFIS to adjustfiring angle of back-to-back connected pairs ofthyristors in AC voltage controller. Inrush currentand pulsations in torque were reduced significantly.Radial basis and feed forward neural networks werecompared for off-line and on-line training, trainingtime, memory required for implementations, number ofneurons, computational procedures and algorithms, reliability of the system and most important cost ofimplementation. Artificial neural networks andAdaptive neuro fuzzy inference system were developedusing tool boxes in Matlab Simulink

Numerical simulations of sheet metal forming process based on finite element method (FEM) is widely applied for its powerful capability in forming process prediction. Since there are parameters which could affect the result of forming process, it becomes important to approach a set of parameters to improve the formability. In the present work, first, a comprehensive literature review was made for different optimization methodologies in sheet metal forming process. Then we proposed an optimization methodology using Response Surface methodology (RSM) and Genetic Algorithm (GA) for the optimization of sheet metal forming process, and the theory of RSM and GA are illustrated. The presented method was first applied to an example from literature, the results verified the feasibility of the proposed methodology. Then the methodology was applied to variable binder force optimization of the NUMISHEET'93 2D draw bending problem. The work indicated that the proposed optimization methodology is efficient and universal, which means it can also be applied in other applications of aerospace industry.