Chapter 1 is an introduction to meta-regression analysis. The authors demonstrate that meta-regression analysis is a methodological framework that allows modeling and correcting problems of publication bias while explaining the variability of results usually found in social sciences literature. In Chapter 2, a new model order reduction technique for the simplification of the complex large-scale stable linear dynamic systems is presented. Chapter 3 discusses the importance of identifying variables, scales, and methods for the sensory analysis of novel food products. Chapter 4 focuses on structural stability and dynamic quantum models. Chapter 5 is an introduction to linear difference equations. Chapter 6 includes a survey of various measures used in cohort analysis with alternative multifaceted indices used in theory and often by practitioners. The chapter is intended to offer a concise description of these measures and elaborate where and how they may be used in different cohort analysis. In Chapter 7, two mixed initial-boundary value problems describing motions of incompressible Maxwell fluids with power-law dependence of viscosity on the pressure are analytically investigated. In Chapter 8, new permanent solutions for Stokes’ second problem of incompressible burgers fluids and their applications is explored. Next, for the first time, the authors propose the definitions of the fractional sum and fractional difference on non-uniform lattices in two different ways. Chapter 10 focuses on the Carleman linearization method for boundary value problems. In the last chapter, Chapter 11, the authors review some of the recent work on pattern packing, superpatterns, and pattern avoidance when colored or circular patterns/permutations are considered.