How Cooperation and Competition Arise in Regional Climate Policies: RICE as a Dynamic Game

One of the most widely used models for studying the geographical economics of climate change is the Regional Integrated model of Climate and the Economy (RICE). In this paper, we investigate how cooperation and competition arise in regional climate policies under the RICE framework from the standpoints of game theory and optimal control. First, we show that the RICE model is inherently a dynamic game. Second, we study both cooperative and non-cooperative solutions to this RICE dynamic game. In cooperative settings, we investigate the global social welfare equilibrium that maximizes the weighted and cumulative social welfare across regions. We next divide the regions into two clusters: developed and developing, and look at the social welfare frontier under the notion of Pareto optimality. We also present a receding horizon approach to approximate the global social welfare equilibrium for robustness and computational efficiency. For non-cooperative settings, we study best-response dynamics and open-loop Nash equilibrium of the RICE game. A Recursive Best-response Algorithm for Dynamic Games (RBA-DG) is proposed to describe the sequences of best-response decisions for dynamic games, which indicates convergence to open-loop Nash equilibrium when applied to the RICE game by numerical studies. We also study online receding horizon feedback decisions of the RICE game. A Receding Horizon Feedback Algorithm for Dynamic Games (RHFA-DG) is proposed. All these proposed solution concepts are implemented and open sourced using the latest updated parameters and data. The results reveal how game theory may be used to facilitate international negotiations towards consensus on regional climate-change mitigation policies, as well as how cooperative and competitive regional relations shape climate change for our future

Synthesis of Epoxidatied Castor Oil and Its Effect on the Properties of Waterborne Polyurethane

AbstractIn this study, a new method for synthesis poxidatied castor oil (ECO) is engaged. A series of waterborne polyurethane dispersions (WPUs) were synthesized using polytetramethylene ether glycol (PTMEG), toluene diisocyanate (TDI-80), and ECO. These WPUs can be crosslinked spontaneously upon drying, without extra additives or processing steps. Moreover, the particle size, and morphology of WPUs were examined with light scattering ultrafine particle analyzer, and transmission electron microscopy. The anti-water, thermal and mechanical properties were also studied. Results reveal that the particle size of WPUs mainly depends on the concentrations of ECO. The particle size decreases when the ECO is used. Furthermore, increased amount of ECO results in an improvement of the anti-water, thermal and mechanical properties of WPU films

Analysis of optimum Lamb wave tuning

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2002.Includes bibliographical references (p. 243-251).Guided waves are of enormous interest in the nondestructive evaluation of thin-walled structures and layered media. Due to their dispersive and multi-modal nature, it is desirable to tune the waves by discriminating one mode from the others. The objectives of this thesis are (1) to develop schemes and procedures for Lamb wave tuning, (2) to develop tools for understanding and analyzing the mechanism of various tuning techniques, and (3) to provide suggestions and guidelines for selecting optimum tuning parameters. In order to remedy the inherent problems of traditional tuning techniques using angle wedge and comb transducers (such as the inability to tune the modes with low phase velocities, and the inability to control the propagation direction of tuned waves), a novel dynamic phase tuning concept using phased arrays is proposed. In this approach, the constructive interference of desired modes is achieved by properly adjusting the time delays. As an extension to this concept, the synthetic phase tuning (SPT) scheme is introduced, in which the tuning effect is achieved by constructing virtual waves. The effectiveness of SPT against other techniques is experimentally demonstrated, which shows its feasibility. To understand the mechanism of tuning, an analytical model is developed to study transient waves, based on the Fourier integral transform method. The excitation conditions for both angle wedge and array transducers are taken into account. The surface displacements of individual modes and their temporal and spatial Fourier spectrum are derived and used to study the tuning behavior. The analytical results are compared with the experimental results as well as the numerical results obtained from the finite element simulation studies.(cont.) In dealing with broadband signals, laser generated Lamb waves are investigated. Both line and circular source loading models are developed to study the behavior in the ablation regime. The predicted waveforms and dispersion curves are in good agreement with the experimental results. Based on the same SPT scheme, virtually-tuned waves are constructed by processing a set of broadband signals. Finally, Lamb waves in a transversely isotropic composite plate are investigated. Although the analysis is limited only to the waves propagating in the principal directions, it could serve as the basis for future work on tuning of Lamb waves in composites. It is concluded from this thesis that the SPT method enjoys advantages over other methods including its low operation cost, ability to tune the. modes of low phase velocities, and capability to control the propagation direction of tuned waves. The analysis of transient waves allows us to examine various tuning scenarios. The investigation of the tuning effectiveness enables us to select optimum modes for the given conditions.by Yijun Shi.Ph.D

Planning Emergency Shelters for Urban Disasters: A Multi-Level Location–Allocation Modeling Approach

In recent years, cities are threatened by various natural hazards. Planning emergency shelters in advance is an effective approach to reducing the damage caused by disasters and ensuring the safety of residents. Thus, providing the optimal layout of urban emergency shelters is an important stage of disaster management and an act of humanitarian logistics. In order to study the optimal layout of emergency shelters in small mountain cities, this paper constructs multi-level location models for different grades of emergency shelters so as to minimize the travel and construction costs and maximize the coverage rate. Specifically, the actual service of emergency shelters is determined using Geographic Information System (GIS) software and Weighted Voronoi Diagram (WVD) models under the limitation of site capacity, and the space layout is adjusted through combining the actual urban land with the construction position. In this paper, the Jianchuan county seat at Yunnan Province, China, was considered as a case study to illustrate the models of emergency shelters in which the feasibility of the presented models is verified. The proposed research methods and models have provided theoretical basis and a benchmark for the optimal layout of emergency shelters in other small mountain cities

Continual Task Allocation in Meta-Policy Network via Sparse Prompting

How to train a generalizable meta-policy by continually learning a sequence of tasks? It is a natural human skill yet challenging to achieve by current reinforcement learning: the agent is expected to quickly adapt to new tasks (plasticity) meanwhile retaining the common knowledge from previous tasks (stability). We address it by "Continual Task Allocation via Sparse Prompting (CoTASP)", which learns over-complete dictionaries to produce sparse masks as prompts extracting a sub-network for each task from a meta-policy network. By optimizing the sub-network and prompts alternatively, CoTASP updates the meta-policy via training a task-specific policy. The dictionary is then updated to align the optimized prompts with tasks' embedding, thereby capturing their semantic correlations. Hence, relevant tasks share more neurons in the meta-policy network via similar prompts while cross-task interference causing forgetting is effectively restrained. Given a trained meta-policy with updated dictionaries, new task adaptation reduces to highly efficient sparse prompting and sub-network finetuning. In experiments, CoTASP achieves a promising plasticity-stability trade-off without storing or replaying any past tasks' experiences and outperforms existing continual and multi-task RL methods on all seen tasks, forgetting reduction, and generalization to unseen tasks.Comment: Accepted by ICML 202

A Nonlinear Negative Imaginary Systems Framework with Actuator Saturation for Control of Electrical Power Systems

In the transition to net zero, it has been suggested that a massive expansion of the electric power grid will be required to support emerging renewable energy zones. In this paper, we propose the use of battery-based feedback control and nonlinear negative imaginary systems theory to reduce the need for such an expansion by enabling the more complete utilization of existing grid infrastructure. By constructing a novel Lur'e-Postnikov-like Lyapunov function, a stability result is developed for the feedback interconnection of a nonlinear negative imaginary system and a nonlinear negative imaginary controller. Additionally, a new class of nonlinear negative imaginary controllers is proposed to deal with actuator saturation. We show that in this control framework, the controller eventually leaves the saturation boundary, and the feedback system is locally stable in the sense of Lyapunov. This provides theoretical support for the application of battery-based control in electrical power systems. Validation through simulation results for single-machine-infinite-bus power systems supports our results. Our approach has the potential to enable a transmission line to operate at its maximum power capacity, as stability robustness is ensured by the use of a feedback controller.Comment: 8 pages, 5 figures, European Control Conferenc

Transactive Multi-Agent Systems over Flow Networks

This paper presented insights into the implementation of transactive multi-agent systems over flow networks where local resources are decentralized. Agents have local resource demand and supply, and are interconnected through a flow network to support the sharing of local resources while respecting restricted sharing/flow capacity. We first establish a competitive market with a pricing mechanism that internalizes flow capacity constraints into agents' private decisions. We then demonstrate through duality theory that competitive equilibrium and social welfare equilibrium exist and agree under convexity assumptions, indicating the efficiency of the pricing mechanism. Additionally, a new social acceptance sharing problem is defined to investigate homogeneous pricing when the optimal sharing prices at all agents under competitive equilibrium are always equal for social acceptance. A conceptual computation method is proposed, prescribing a class of socially admissible utility functions to solve the social acceptance problem. A special case of linear-quadratic multi-agent systems over undirected star graphs is provided as a pedagogical example of how to explicitly prescribe socially admissible utility functions. Finally, extensive experiments are provided to validate the results

High-performance cVEP-BCI under minimal calibration

The ultimate goal of brain-computer interfaces (BCIs) based on visual modulation paradigms is to achieve high-speed performance without the burden of extensive calibration. Code-modulated visual evoked potential-based BCIs (cVEP-BCIs) modulated by broadband white noise (WN) offer various advantages, including increased communication speed, expanded encoding target capabilities, and enhanced coding flexibility. However, the complexity of the spatial-temporal patterns under broadband stimuli necessitates extensive calibration for effective target identification in cVEP-BCIs. Consequently, the information transfer rate (ITR) of cVEP-BCI under limited calibration usually stays around 100 bits per minute (bpm), significantly lagging behind state-of-the-art steady-state visual evoked potential-based BCIs (SSVEP-BCIs), which achieve rates above 200 bpm. To enhance the performance of cVEP-BCIs with minimal calibration, we devised an efficient calibration stage involving a brief single-target flickering, lasting less than a minute, to extract generalizable spatial-temporal patterns. Leveraging the calibration data, we developed two complementary methods to construct cVEP temporal patterns: the linear modeling method based on the stimulus sequence and the transfer learning techniques using cross-subject data. As a result, we achieved the highest ITR of 250 bpm under a minute of calibration, which has been shown to be comparable to the state-of-the-art SSVEP paradigms. In summary, our work significantly improved the cVEP performance under few-shot learning, which is expected to expand the practicality and usability of cVEP-BCIs.Comment: 35 pages, 5 figure