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Housing plays a crucial role in the transition towards sustainability. Zero-energy or low-carbon housing solutions are key, but their success depends on their acceptance by the users. We propose a conceptual and an operational framework for understanding h ...
Flexible ring net barriers have become increasingly popular in practical mitigation of debris flow worldwide. Systematic assessments of their response and performance subjected to the impact of realistic debris flows remain challenging. This study presents ...
Metal‐organic frameworks (MOFs) are highly versatile materials owing to their vast structural and chemical tunability. These hybrid inorganic–organic crystalline materials offer an ideal platform to incorporate light‐harvesting and catalytic centers and th ...
The fabrication of particle-based photoelectrodes by coating or dipping procedures-similar to the scaled fabrication of battery electrodes-can be a route to overcome the efficiency-cost tradeoff of photoelectrochemical (PEC) water splitting devices. Additi ...
This thesis aims to further investigate rare natural disasters and studies adaptation decisions under uncertainty by solving several computational economic models. The modeling of rare natural disasters depends on the treatment of catastrophic outcomes wit ...
The ever-increasing requirement for reliability and quality of supply suggests to enable the self-healing features of modern distribution networks employing the intelligent measurement, communication, and control facilities of smart grids. In this paper, t ...
We consider multiagent decision making where each agent optimizes its convex cost function subject to individual and coupling constraints. The constraint sets are compact convex subsets of a Euclidean space. To learn Nash equilibria, we propose a novel dis ...
This paper develops a distributed variance-reduced strategy for a collection of interacting agents that are connected by a graph topology. The resulting diffusion-AVRG (where AVRG stands for "amortized variance-reduced gradient") algorithm is shown to have ...
This paper studies the problem of learning under both large datasets and large-dimensional feature space scenarios. The feature information is assumed to be spread across agents in a network, where each agent observes some of the features. Through local co ...