This thesis project is a first-grade curriculum that is tailored for schools with school gardens. The curriculum contains worksheets and activities for the students, making it easier for teachers to take care of the school garden while also playing a part in fighting food injustice. The curriculum has 4 subjects: Math, Language Arts, Science, and Nutrition Education.

The purpose of this thesis is to determine whether Tai Chi Qigong or Health Information podcasts are more effective for improving mental health and sleep outcomes for midlife women with mobility impairments. No other studies have been done to investigate whether Tai Chi can be more effective for sleep, depressive symptoms, and anxiety for midlife women with mobility impairments specifically. Overall, it was found that midlife women with mobility impairments experienced better sleep when they focused on health information podcasts in comparison to Tai Chi. Change in anxiety and depressive symptoms were negligible.

The purpose of this thesis is to determine whether Tai Chi Qigong or Health Information podcasts are more effective for improving mental health and sleep outcomes for midlife women with mobility impairments. No other studies have been done to investigate whether Tai Chi can be more effective for sleep, depressive symptoms, and anxiety for midlife women with mobility impairments specifically. Overall, it was found that midlife women with mobility impairments experienced better sleep when they focused on health information podcasts in comparison to Tai Chi. Change in anxiety and depressive symptoms were negligible.

In this paper, we present a Bayesian analysis for the Weibull proportional hazard (PH) model used in step-stress accelerated life testings. The key mathematical and graphical difference between the Weibull cumulative exposure (CE) model and the PH model is illustrated. Compared with the CE model, the PH model provides more flexibility in fitting step-stress testing data and has the attractive mathematical properties of being desirable in the Bayesian framework. A Markov chain Monte Carlo algorithm with adaptive rejection sampling technique is used for posterior inference. We demonstrate the performance of this method on both simulated and real datasets.

We describe mechanical metamaterials created by folding flat sheets in the tradition of origami, the art of paper folding, and study them in terms of their basic geometric and stiffness properties, as well as load bearing capability. A periodic Miura-ori pattern and a non-periodic Ron Resch pattern were studied. Unexceptional coexistence of positive and negative Poisson's ratio was reported for Miura-ori pattern, which are consistent with the interesting shear behavior and infinity bulk modulus of the same pattern. Unusually strong load bearing capability of the Ron Resch pattern was found and attributed to the unique way of folding. This work paves the way to the study of intriguing properties of origami structures as mechanical metamaterials.

Extreme events, a type of collective behavior in complex networked dynamical systems, often can have catastrophic consequences. To develop effective strategies to control extreme events is of fundamental importance and practical interest. Utilizing transportation dynamics on complex networks as a prototypical setting, we find that making the network “mobile” can effectively suppress extreme events. A striking, resonance-like phenomenon is uncovered, where an optimal degree of mobility exists for which the probability of extreme events is minimized. We derive an analytic theory to understand the mechanism of control at a detailed and quantitative level, and validate the theory numerically. Implications of our finding to current areas such as cybersecurity are discussed.

We develop a completely data-driven approach to reconstructing coupled neuronal networks that contain a small subset of chaotic neurons. Such chaotic elements can be the result of parameter shift in their individual dynamical systems and may lead to abnormal functions of the network. To accurately identify the chaotic neurons may thus be necessary and important, for example, applying appropriate controls to bring the network to a normal state. However, due to couplings among the nodes, the measured time series, even from non-chaotic neurons, would appear random, rendering inapplicable traditional nonlinear time-series analysis, such as the delay-coordinate embedding method, which yields information about the global dynamics of the entire network. Our method is based on compressive sensing. In particular, we demonstrate that identifying chaotic elements can be formulated as a general problem of reconstructing the nodal dynamical systems, network connections and all coupling functions, as well as their weights. The working and efficiency of the method are illustrated by using networks of non-identical FitzHugh–Nagumo neurons with randomly-distributed coupling weights.