Living with impoundment

This research-based thesis uses Miami as a site to investigate how landscape intervention can contribute to adaptation to sea level rise (SLR) and shifts it will bring to this region in the future. The whole thesis is divided into three phases and each phase has specific objectives and builds foundations for research and experiments in the next phase. Phase One: Possible Future of Freshwater Wetlands in Miami Depict a full picture of the impact that sea level rise has had and will have on the city of Miami with particular emphasis on wetlands. The investigations include overlaying sea level rise projections from the National Oceanic and Atmospheric Administration (NOAA) with other information in order to hypothesize future SLR scenarios, and identifying a potential test area for the next phase. Phase Two: Alternatives under SLR: Living with Water Explore SLR footprints based on the selected site in terms of plant communities, surrounding neighborhoods, traffic and potential benefits it may have for people. Study possible approaches that can equip typical fragile region with capacity to live with SLR issue. Phase Three: Spatial Strategies: Protect and Adapt Unveil more details of the site with spatial and dimensional explorations conducted by digital modeling, sketches and models. Develop comprehensive strategies for the site to survive and evolve under future treats. Utilize typological or sectional studies to suggest a future alternative

Self-Calibration of Two-Dimensional Precision Metrology Systems

In modern industry, there usually exist high-precision stages, such as reticle stages and wafer stages in VLSI lithography tools, metrology stages in coordinate measurement machines, motion stages in CNC machine tools, etc. These stages need high-precision measurement/metrology systems for monitoring its XY movement. As the metrology systems are quite accurate, we often cannot find a standard tool with better accuracy to implement a traditional calibration process for systematic measurement error (i.e., stage error) determination and measurement accuracy compensation. Subsequently, self-calibration technology is developed to meet this challenge and to solve the calibration problem. In this chapter, we study the self-calibration of two-dimensional precision metrology systems and present a holistic self-calibration strategy. This strategy utilizes three measurement views of an artifact plate with mark positions not precisely known on the un-calibrated two-dimensional metrology stage and constructs relevant symmetry, transitivity, and redundancy of the stage error of the metrology stage. The misalignment errors of all measurement views, especially including those of the translation view, are totally determined by detailed mathematical manipulations. Then, a redundant equation group is synthesized, and a least-square–based robust estimation law is employed to calculate out the stage error even under the existence of random measurement noise. Furthermore, as the determination of the misalignment error components of the measurement views is rather complicated but important in previous and the proposed methods, this chapter also significantly analyzes the necessity of this costly computation. The proposed approach is investigated by simulation computation, and the simulation results prove that the proposed determination scheme can calculate out the stage error rather exactly without random measurement noise. Furthermore, when there exist various random measurement noises, the calibration accuracy of the proposed strategy is also investigated, and the results illustrate that the standard deviations of the calibration error are consistently with the same level of those of the random measurement noises. All these results verify that the proposed scheme can realize the stage error rather accurately even under the existence of random measurement noise. The practical procedure for performing a standard self-calibration is also introduced for engineers to facilitate actual implementation

Different effects of anesthetic isoflurane on caspase-3 activation and cytosol cytochrome c levels between mice neural progenitor cells and neurons

Commonly used anesthetic isoflurane has been reported to promote Alzheimer’s disease (AD) neuropathogenesis by inducing caspase-3 activation. However, the up-stream mechanisms of isoflurane’s effects remain largely to be determined. Specifically, there is a lack of a good model/system to elucidate the underlying mechanism of the isoflurane-induced caspase-3 activation. We therefore set out to assess and compare the effects of isoflurane on caspase-3 activation in neural progenitor cells (NPCs) and in primary neurons from wild-type (WT) and AD transgenic (Tg) mice. The NPCs and neurons were obtained, cultured and then treated with either 2% isoflurane or under control condition for 6 h. The NPCs or neurons were harvested at the end of the treatment and were subjected to Western blot analysis. Here we showed for the first time that the isoflurane treatment induced caspase-3 activation in neurons, but not in NPCs, from either WT or AD Tg mice. Consistently, the isoflurane treatment increased cytosol levels of cytochrome c, a potential up-stream mechanism of isoflurane-induced caspase-3 activation in the mice neurons, but not NPCs. Finally, the isoflurane treatment induced a greater casapse-3 activation in the neurons, but not the NPCs, from AD Tg mice as compared to the WT mice. These data demonstrated that investigation and comparison of isoflurane’s effects between mice NPCs and neurons would serve as a model/system to determine the underlying mechanism by which isoflurane induces caspase-3 activation. These findings would promote more research to investigate the effects of anesthetics on AD neuropathogenesis and the underlying mechanisms

Learning Predictive Safety Filter via Decomposition of Robust Invariant Set

Ensuring safety of nonlinear systems under model uncertainty and external disturbances is crucial, especially for real-world control tasks. Predictive methods such as robust model predictive control (RMPC) require solving nonconvex optimization problems online, which leads to high computational burden and poor scalability. Reinforcement learning (RL) works well with complex systems, but pays the price of losing rigorous safety guarantee. This paper presents a theoretical framework that bridges the advantages of both RMPC and RL to synthesize safety filters for nonlinear systems with state- and action-dependent uncertainty. We decompose the robust invariant set (RIS) into two parts: a target set that aligns with terminal region design of RMPC, and a reach-avoid set that accounts for the rest of RIS. We propose a policy iteration approach for robust reach-avoid problems and establish its monotone convergence. This method sets the stage for an adversarial actor-critic deep RL algorithm, which simultaneously synthesizes a reach-avoid policy network, a disturbance policy network, and a reach-avoid value network. The learned reach-avoid policy network is utilized to generate nominal trajectories for online verification, which filters potentially unsafe actions that may drive the system into unsafe regions when worst-case disturbances are applied. We formulate a second-order cone programming (SOCP) approach for online verification using system level synthesis, which optimizes for the worst-case reach-avoid value of any possible trajectories. The proposed safety filter requires much lower computational complexity than RMPC and still enjoys persistent robust safety guarantee. The effectiveness of our method is illustrated through a numerical example

Safe Reinforcement Learning with Dual Robustness

Reinforcement learning (RL) agents are vulnerable to adversarial disturbances, which can deteriorate task performance or compromise safety specifications. Existing methods either address safety requirements under the assumption of no adversary (e.g., safe RL) or only focus on robustness against performance adversaries (e.g., robust RL). Learning one policy that is both safe and robust remains a challenging open problem. The difficulty is how to tackle two intertwined aspects in the worst cases: feasibility and optimality. Optimality is only valid inside a feasible region, while identification of maximal feasible region must rely on learning the optimal policy. To address this issue, we propose a systematic framework to unify safe RL and robust RL, including problem formulation, iteration scheme, convergence analysis and practical algorithm design. This unification is built upon constrained two-player zero-sum Markov games. A dual policy iteration scheme is proposed, which simultaneously optimizes a task policy and a safety policy. The convergence of this iteration scheme is proved. Furthermore, we design a deep RL algorithm for practical implementation, called dually robust actor-critic (DRAC). The evaluations with safety-critical benchmarks demonstrate that DRAC achieves high performance and persistent safety under all scenarios (no adversary, safety adversary, performance adversary), outperforming all baselines significantly