Rapid changes in global climate are pushing nations to reduce CO2 emissions and adopt clean energy technologies for renewable energy generation and storage. As wind and solar are implemented worldwide, a commensurate response in energy storage will need to be installed to meet fluctuations in peak energy demands and generation...
This thesis focuses on the application of neural networks to three types of classification tasks, each work with its own chapter. The objective of the first work is to take advantage of deep neural networks in order to make next day crime count predictions in a fine-grain city partition. We...
Harnessing the metabolic potential of methanotrophic bacteria is a compelling strategy for the bioremediation of environmentally harmful methane gas. Methanotrophs can activate a 105 kcal/mol C-H bond in methane at ambient conditions using metalloenzymes called methane monooxygenases (MMOs). Particulate methane monooxygenase (pMMO) is a copper-dependent, membrane-bound enzyme that is the...
Selective attention enables people to focus on a small number of objects, features, or events with good resolution. Sometimes attention may also be less selective and distributed across numerous items, which allows more information to be processed at a lower resolution. The degree to which attention is more or less...
Abstract This dissertation aims to understand how the domestic high courts in Latin America rely on the jurisprudence of the Inter-American Court of Human Rights. The “relationship between courts” is a phenomenon that happens in domestic and international politics when domestic high courts start to resist or follow international jurisprudence....
The spatial autoregressive model has been widely applied in science, in areas such as economics, public finance, political science, agricultural economics, environmental studies and transportation analyses. The classical spatial autoregressive model is a linear model for describing spatial correlation. In this work, we expand the classical model to include time...