Democratic Attitude of Youth Evidence from Pakistan

Since the Arab spring, youth have been politically active in Asia through various modes. Regime revolution or failed takeover on 15 July 2016, in Turkey is its vibrant example. However, it’s unfortunate that the political perception, intention or attitude of youth towards democracy have not been included in the process of policy making. As a result, youth activism in politics remained unrecognized. Therefore, present work is an effort to explore democratic attitude of youth in Pakistan. The state of Pakistan has been selected because 64 % of her population comprises youth. Current study examines democratic attitude through four major independent variables: economic growth, democratic values, public participation and political stability. Data was collected from the respondents who were in between 18-35 years of age by distributing a survey questionnaire which was adopted from the literature. Data was analyzed through SPSS version 24.0. Result of the study suggests that all the developed hypotheses were accepted. Additionally, study finds that despite massive corruption, social injustice, favoritism, nepotism, political instability, economic crisis and many other problems, Pakistani youth overwhelmingly considers democracy, as the potential form of government and dislike any alternative of it. Current study may be the first stone in the stagnant water not only in measuring democratic attitude of young generation but it will also help them to understand what sort of democracy is needed in Pakistan.

Application of Evolutionary Pplications Computing Techniques to Non-Linear Ing Systems

This dissertation presents an application of heuristic computational intelligence for the solution of non-linear systems in engineering. The design scheme is comprised of mathematical model based on feed-forward artificial neural network (ANN). The linear combination of these networks defines the unsupervised error for the system. The most suitable weights to minimize the error are obtained by training the networks employing stochastic solvers. These techniques are based on nature inspired heuristics including Pattern Search (PS), Simulated Annealing (SA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms. Rapid local convergent algorithms such as Interior Point (IP) and Active Set (AS) methods are hybridized with these global search techniques. To validate the scheme, a number of linear and non-linear initial and boundary value problems have been solved. The design methodology is also applied to a number of problems having special applications in engineering including, singular systems based on non-linear Lane Emden Fowler equation, non-linear van der Pol oscillator with stiff and non-stiff conditions and systems with high nonlinearity governed by Painlevé transcendent I. In addition to that, the scheme also provides an alternate solution for biomedical application like model of heart for low, high and normal blood pressure. It is found that the proposed results are in good agreement with available exact solution and numerical solvers like Adomian decomposition method, Homotopy Perturbation method, Homotopy analysis method and Optimal Homotopy asymptotic method, ODE15i and Runge Kutta method. The comparative studies of stochastic solvers are carried out under a stringent criterion of accuracy, effectiveness, reliability and robustness of the results based on Monte Carlo simulation and its analysis. The solvers based on SA, PS, GA, PSO, GA and PSO hybrid with IP or AS algorithms are used for optimization of neural network. It is found that the GA-IP, GA-AS, PSO-IP and PSO-AS algorithms are the best stochastic optimizers. The other perk up of the scheme have in its simplicity of the concept, ease in use, efficiency and unlike other numerical techniques, it provides the solution on continuous inputs with finite interval instead of predefine discrete grid of inputs.