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Purpose: Due to the developmental nature of autism, which is often diagnosed in preschool or elementary school-aged children, non-autistic parents of autistic children typically play a prominent role in autism advocacy. However, as autistic children become adults and adult diagnoses of autism continue to rise, autistic adults have played a more prominent role in advocacy. The purpose of this chapter is to explore the histories of adult and non-autistic parent advocacy in the United States and to examine the points of divergence and convergence.

Approach: Because of their different perspectives and experiences, advocacy by autistic adults and non-autistic parents can have distinctive goals and conflicting priorities. Therefore, the approach we take in the current chapter is a collaboration between an autistic adult and a non-autistic parent, both of whom are research scholars.

Findings: The authors explore the divergence of goals and discourse between autistic self-advocates and non-autistic parent advocates and offer three principles for building future alliances to bridge the divide between autistic adults and non-autistic parents.

Implications: The chapter ends with optimism that US national priorities can bridge previous gulfs, creating space for autistic adult and non-autistic parent advocates to work together in establishing policies and practices that improve life for autistic people and their families and communities.

There has been a rapid expansion in the literature on the measurement of multidimensional poverty in recent years. This paper focuses on the longitudinal aspects of multidimensional poverty and its link to dynamic income poverty measurement. Using panel household survey data in Vietnam from 2007, 2008, and 2010, the paper analyses the prevalence and dynamics of both multidimensional and monetary poverty from the same dataset. The results show that the monetary poor (or non-poor) are not always multidimensionally poor (or non-poor) – indeed the overlap between the two measures is much less than 50 percent. Additionally, monetary poverty shows faster progress as well as a higher level of fluctuation than multidimensional poverty. We suggest that rapid economic growth as experienced by Vietnam has had a larger and more immediate impact on monetary than on multidimensional poverty.

The following is an introductory profile of the fastest growing firms over the three-year period of the study listed by corporate reputation ranking order. The business activities in which the firms are engaged are outlined to provide background information for the reader.

It is possible to see effective use of Artificial Intelligence-based systems in many fields because it easily outperforms traditional solutions or provides solutions for the problems not previously solved. Prediction applications are a widely used mechanism in research because they allow for forecasting of future states. Logical inference mechanisms in the field of Artificial Intelligence allow for faster and more accurate and powerful computation. Machine Learning, which is a sub-field of Artificial Intelligence, has been used as a tool for creating effective solutions for prediction problems.

In this chapter the authors will focus on employing Machine Learning techniques for predicting data for future states of economic using techniques which include Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, Dynamic Boltzmann Machine, Support Vector Machine, Hidden Markov Model, Bayesian Learning on Gaussian process model, Autoregressive Integrated Moving Average, Autoregressive Model (Poggi, Muselli, Notton, Cristofari, & Louche, 2003), and K-Nearest Neighbor Algorithm. Findings revealed positive results in terms of predicting economic data.