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This survey focuses on two families of nonlinear vector time series models, the family of vector threshold regression (VTR) models and that of vector smooth transition regression (VSTR) models. These two model classes contain incomplete models in the sense that strongly exogeneous variables are allowed in the equations. The emphasis is on stationary models, but the considerations also include nonstationary VTR and VSTR models with cointegrated variables. Model specification, estimation and evaluation is considered, and the use of the models illustrated by macroeconomic examples from the literature.

This chapter presents a novel human arm gesture tracking and recognition technique based on fuzzy logic and nonlinear Kalman filtering with applications in crane guidance. A Kinect visual sensor and a Myo armband sensor are jointly utilised to perform data fusion to provide more accurate and reliable information on Euler angles, angular velocity, linear acceleration and electromyography data in real time. Dynamic equations for arm gesture movement are formulated with Newton–Euler equations based on Denavit–Hartenberg parameters. Nonlinear Kalman filtering techniques, including the extended Kalman filter and the unscented Kalman filter, are applied in order to perform reliable sensor fusion, and their tracking accuracies are compared. A Sugeno-type fuzzy inference system is proposed for arm gesture recognition. Hardware experiments have shown the efficacy of the proposed method for crane guidance applications.

Current motivation theory and research face serious theoretical, methodological, and practical challenges. One central challenge is the fact that research has focused mainly on motivation for traditional achievement tasks such as graded assignments and normative educational trajectories. Arguably, current motivation theory and research may be inadequate to characterize adaptive motivation in the uncertain, changing, and unpredictable environments of the twenty-first century. How might motivation researchers conceptualize students’ motivation in such dynamic and complex contexts? How can motivational research inform educators, administrators, and policymakers in designing curricula, pedagogy, and evaluation and accountability systems to prepare students for such a world? In the current chapter, we address these challenges with a perspective on motivation as a complex dynamic system (CDS) that is based in the person’s identity. We begin with a brief review of the challenges to the current prevalent approach to motivation research, highlighting the need for a new paradigm. We then review assumptions of the CDSs approach that render it useful for understanding motivation in continuously changing and unpredictable environments. We then present a CDS conceptual model of identity and motivation that incorporates constructs and processes from a variety of identity and motivational theories – the Dynamic Systems Model of Role Identity (DSMRI). We follow with a conceptualization of the characteristics of the identity-motivation system most adaptive for growth in changing and unpredictable environments. We end by considering the implications of this perspective for motivational theory and research and for educational practice and policy.