EFFECTIVENESS OF COMMUNITY SCHOOL NETWORKING FOR CHILDREN WITH AUTISM: PERSPECTIVES OF TEACHERS AND SCHOOL ADMINISTRATORS

Objective: In order to provide equal educational opportunities, community school networking is an emerging trend to facilitate inclusion of children with mild-moderate Autism. This quantitative research aims to investigate the effectiveness of community networking for children with Autism from Pakistani lower socio-economic stratum of society. Study Design: Qualitative Research Design Study Settings and Participants: Six mild-moderate autistic children were enrolled in three mainstreams schools and a liaison between these schools, and a rehabilitation center located in the same area was created to facilitate inclusion. The researchers interviewed six teachers from mainstream schools, three school administrators and one administrator of rehabilitation regarding the effectiveness of community school networking for children with Autism after eight months of this collaboration. Data Collection Tool: Data were obtained through semi-structured interviews Results: All participating administrators and teachers underscored the changes in social and behavioral patterns of autistic children which included an imitation of positive behaviors from peers, acceptance, and awareness as strengths of community school networking model. However, major challenges faced were unacceptability from parents of normal children, learning differences, curriculum modifications, time constraints and dependency on the resource teacher. The administrators and teachers recommended that creating awareness programs for parents of normal children, curriculum modifications and in-house psychologists can further facilitate inclusion of children with disabilities. Conclusion: It was concluded that community school networking model can assist inclusive education and encourage engagement for all children, including those who are autistic.

Development of Hybrid Metaheuristic for Global Optimization.

Metaheuristics is a research area that delivers general purpose high quality optimization algorithms, proved effectual in dealing with complex global optimization problems. Success of metaheuristics greatly depends on their aptitude to establish equilibrium between their essential characters: exploration and exploitation. But the advent of No Free Lunch theorems by Wolpert and Macready established a general opinion that all algorithms perform equally when averaged over the whole function space and hence none of them can be claimed to be the best over the entire function space. For this reason, the basic algorithms require essential refinements and enhancements. The main goal of this thesis is twofold: to develop new effective hybrid metaheuristic strategies for solving selected global optimization problems and to analyze the performances of developed hybrid metaheuristics on mathematical benchmark functions and complex real world problems that can be modeled as global optimization problems. Generally, hybridization is carried out by integrating powerful components of different algorithms. The first hybrid metaheuristic proposed in this work is Controlled Showering Optimization (CSO) algorithm which is a combination of Artificial Showering Algorithm and frame based search mechanism. The second proposed hybrid algorithm is Cooperative Multi-Simplex algorithm (CMSA) that is based on collaborative search of multiple simplexes working under the iterations of a Non- Stagnated Nelder-Mead Simplex algorithm (NS-NMSA). The evolvement of the provably convergent variant NS-NMSA is also carried out in this work by identifying and coping the failures and stagnations of standard Nelder-Mead simplex algorithm. Multi-Simplex Imperialist Competitive Algorithm (MS-ICA) is the third hybrid metaheuristic which is designed by embedding NS-NMSA iterations in Imperialist Competitive Algorithm. The fourth hybrid metaheuristic designed in this continuation is obtained by integrating CMSA and Differential Evolution (DE) algorithm. In a specifically constructed computational framework, this hybrid algorithm in collaboration with Padé approximation is named as hybrid Evolutionary Padé Approximation (EPA) scheme. The efficiencies of developed hybrid metaheuristics are validated empirically along with some theoretical results. Statistical analysis of simulation results of CSO applications to diversified small as well as large scale benchmark functions is conducted for evaluating its computational efficiency and consistency. The posterior non-parametric statistical analyses of the results indicate significantly better performance of CSO algorithm. Theoretical convergence results of NS-NMSA are also accompanied by numerical simulations on reported counter examples and a test suite of 24 benchmark functions. The two proposed hybrid algorithms, CMSA and MS-ICA, are applied to solve physical nonlinear systems of equations and excellent results are observed. Finally the proposed EPA framework is implemented for numerical treatments of the nonlinear model of virus propagation in computer networks and the model of Dengue fever with incubation period of virus. Numerical simulations and residuals based error analysis confirm the abilities of the proposed hybrid EPA scheme to preserve the essential characteristics of the epidemiological models.