Managing Climate Risk

Stabilization of atmospheric greenhouse gas (GHG) concentrations at a safe level is a paradigm that the scientific and policy communities have widely adopted for addressing the problem of climate change. However, aiming to stabilize concentrations at a single target level might not be a robust strategy, given that the environment is extremely uncertain. The static stabilization paradigm is based primarily on two assumptions: (i), that a safe level of GHG concentrations exists and can be sustained, and (ii) that such a level can be determined ex ante. The United Nations Framework Convention on Climate Change (UNFCCC) calls for stabilization of GHGs at a safe level, and it also prescribes precautionary measures to anticipate, prevent, or minimize the causes of climate change and mitigate their adverse effects...

Model Predictive Control Based Trajectory Generation for Autonomous Vehicles - An Architectural Approach

Research in the field of automated driving has created promising results in the last years. Some research groups have shown perception systems which are able to capture even complicated urban scenarios in great detail. Yet, what is often missing are general-purpose path- or trajectory planners which are not designed for a specific purpose. In this paper we look at path- and trajectory planning from an architectural point of view and show how model predictive frameworks can contribute to generalized path- and trajectory generation approaches for generating safe trajectories even in cases of system failures.Comment: Presented at IEEE Intelligent Vehicles Symposium 2017, Los Angeles, CA, US

Use of harm reduction strategies in an occupational therapy life skills intervention

Thesis (M.S.)--Boston UniversityObjectives of Study: Harm reduction intervention strategies have the potential to support positive health outcomes. However, no studies have explored how these strategies can be implemented in an occupational therapy intervention. This study addresses this knowledge gap by examining harm reduction strategies that were discussed during group and individual sessions of an occupational therapist-led life skills intervention for people who have a mental illness and are or were homeless. Methods: This study is a secondary analysis of a larger study that used a longitudinal repeated measures design to implement a life skills intervention. This secondary analysis uses a mixed methods design. Qualitative methods were used for data collection and initial analysis. Quantitative methods were then used to analyze differences between settings. Results: Three major themes emerged from the data: Financial, Physical, and Psychosocial Hann Reduction. The most prevalent theme was Financial Harm Reduction. All three themes were present throughout all of the different life skills intervention modules. There was no significant difference in the themes used between settings. Limitations and Recommendations for Further Research: This study was limited to what was documented in the therapy notes. Although the notes may not include every discussion that occurred, these results suggest that harm-reduction strategies can be utilized in an occupational therapy intervention. Additional research is needed to investigate how harm reduction can be implemented in other areas of occupational therapy practice

Actor-Critic Reinforcement Learning for Control with Stability Guarantee

Reinforcement Learning (RL) and its integration with deep learning have achieved impressive performance in various robotic control tasks, ranging from motion planning and navigation to end-to-end visual manipulation. However, stability is not guaranteed in model-free RL by solely using data. From a control-theoretic perspective, stability is the most important property for any control system, since it is closely related to safety, robustness, and reliability of robotic systems. In this paper, we propose an actor-critic RL framework for control which can guarantee closed-loop stability by employing the classic Lyapunov's method in control theory. First of all, a data-based stability theorem is proposed for stochastic nonlinear systems modeled by Markov decision process. Then we show that the stability condition could be exploited as the critic in the actor-critic RL to learn a controller/policy. At last, the effectiveness of our approach is evaluated on several well-known 3-dimensional robot control tasks and a synthetic biology gene network tracking task in three different popular physics simulation platforms. As an empirical evaluation on the advantage of stability, we show that the learned policies can enable the systems to recover to the equilibrium or way-points when interfered by uncertainties such as system parametric variations and external disturbances to a certain extent.Comment: IEEE RA-L + IROS 202

Control Barrier Function Based Quadratic Programs for Safety Critical Systems

Safety critical systems involve the tight coupling between potentially conflicting control objectives and safety constraints. As a means of creating a formal framework for controlling systems of this form, and with a view toward automotive applications, this paper develops a methodology that allows safety conditions -- expressed as control barrier functions -- to be unified with performance objectives -- expressed as control Lyapunov functions -- in the context of real-time optimization-based controllers. Safety conditions are specified in terms of forward invariance of a set, and are verified via two novel generalizations of barrier functions; in each case, the existence of a barrier function satisfying Lyapunov-like conditions implies forward invariance of the set, and the relationship between these two classes of barrier functions is characterized. In addition, each of these formulations yields a notion of control barrier function (CBF), providing inequality constraints in the control input that, when satisfied, again imply forward invariance of the set. Through these constructions, CBFs can naturally be unified with control Lyapunov functions (CLFs) in the context of a quadratic program (QP); this allows for the achievement of control objectives (represented by CLFs) subject to conditions on the admissible states of the system (represented by CBFs). The mediation of safety and performance through a QP is demonstrated on adaptive cruise control and lane keeping, two automotive control problems that present both safety and performance considerations coupled with actuator bounds

Municipal wastewater treatment with pond technology : historical review and future outlook

Facing an unprecedented population growth, it is difficult to overstress the assets for wastewater treatment of waste stabilization ponds (WSPs), i.e. high removal efficiency, simplicity, and low cost, which have been recognized by numerous scientists and operators. However, stricter discharge standards, changes in wastewater compounds, high emissions of greenhouse gases, and elevated land prices have led to their replacements in many places. This review aims at delivering a comprehensive overview of the historical development and current state of WSPs, and providing further insights to deal with their limitations in the future. The 21st century is witnessing changes in the way of approaching conventional problems in pond technology, in which WSPs should no longer be considered as a low treatment technology. Advanced models and technologies have been integrated for better design, control, and management. The roles of algae, which have been crucial as solar-powered aeration, will continue being a key solution. Yet, the separation of suspended algae to avoid deterioration of the effluent remains a major challenge in WSPs while in the case of high algal rate pond, further research is needed to maximize algal growth yield, select proper strains, and optimize harvesting methods to put algal biomass production in practice. Significant gaps need to be filled in understanding mechanisms of greenhouse gas emission, climate change mitigation, pond ecosystem services, and the fate and toxicity of emerging contaminants. From these insights, adaptation strategies are developed to deal with new opportunities and future challenges