Community Led Disaster Planning: Lessons Learned in Red Hook, Brooklyn Post Superstorm Sandy

New York City has long operated under the perceived low risk of severe hurricanes impacting the major city. In late October of 2012, Superstorm Sandy struck with ferocious intensity and exposed many weaknesses on multiple levels, from city to the federal government. As far back as 2007, New York City has been publishing groundbreaking and forward thinking long-term sustainability reports to deal with the threat of climate change on the city, and the impact it will have on various stakeholders. This thesis will examine the key points of three of the major reports, and identify to what extent areas in which vulnerable community stakeholders were involved. PlaNYC, A Stronger, More Resilient New York, and the Hazard Mitigation Plan all have attempted to plan for the long term across numerous hazards and risks that the city faces. The destruction that Sandy caused in the Brooklyn neighborhood of Red Hook epitomized the failures on multiple levels of city's response. At the same time, it became a case study for community led disaster response in the face of great neglect for some of New York's most geographically and socially vulnerable population

Effect of Base on the Facile Hydrothermal Preparation of Highly Active IrO_x Oxygen Evolution Catalysts

The efficient electrochemical splitting of water is limited by the anodic oxygen evolution reaction (OER). IrO2 is a potential catalyst with sufficient activity and stability in acidic conditions to be applied in water electrolyzers. The redox properties and structural flexibility of amorphous iridium oxo-hydroxide compared to crystalline rutile-IrO2 are associated with higher catalytic activity for the OER. We prepared IrOx OER catalysts by a simple hydrothermal method varying the alkali metal base (Li2CO3, LiOH, Na2CO3, NaOH, K2CO3, KOH) employed during the synthesis. This work reveals that the surface area, particle morphology, and the concentration of surface hydroxyl groups can be controlled by the base used and greatly influence the catalyst activity and stability for OER. It was found that materials prepared with bases containing lithium cations can lead to amorphous IrOx materials with a significantly lower overpotential (100 mV @ 1.5 mA·cm–2) and increased stability compared to materials prepared with other bases and rutile IrO2. This facile method leads to the synthesis of highly active and stable catalysts which can potentially be applied to larger scale catalyst preparations

Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems

Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems.Comment: Presented at NeurIPS 2019 Workshop "Machine Learning and the Physical Sciences

Direct and oxidative dehydrogenation of propane: From catalyst design to industrial application

The direct formation of propene from propane is a well-established commercial process, which based on energy consumption, is environmentally preferred to the current large-scale sources of propene from steam cracking and fluid catalytic cracking. Current sources of propane are mostly non-renewable, but the development of technologies to produce renewable “green” propane are gaining traction, which coupled with new catalytic processes will provide the platform to produce green propene. We evaluate the technological and environmental merits of dehydrogenation catalysts. Currently, non-oxidative direct dehydrogenation (DDH) is the only commercialised process, and this is reflected in the high space-time yield commonly reported over the most active Pt or Cr catalysts. However, the formation of coke necessitates multi-reactor cycling to facilitate regeneration. Oxidative dehydrogenation using O2 (ODH-O2) does not suffer from coke formation, but can lead to overoxidation, limiting the yield of propene. While no commercial processes have yet been developed, a promising new class of active and selective ODH-O2 catalysts has emerged which use boron as the active component. The use of CO2 as a soft oxidant (ODH-CO2) has also gained interest due to the environmental advantages of utilising CO2. Although this is an attractive prospect, the propene yields with these catalysts are considerably less active then DDH and ODH-O2 catalysts. Despite significant advances in the past decade, current ODH-CO2 catalysts remain far from displaying the activity levels necessary to be considered for commercial application. The specific requirements of catalyst design for each sub-reaction are discussed and we identify that the balance of acid and base sites on the catalyst surface is of paramount importance. Future catalyst design in DDH and ODH-O2 should focus on improving selectivity to propene, while ODH-CO2 catalysts are limited by their low intrinsic activity. The scarcity of some common catalytic elements is also discussed, with recommendations focusing on more abundant chemical elements. Future research should focus on the low temperature activation of CO2 as a priority. With further research and development of lower energy routes to propene based on the dehydrogenation of sustainably-sourced propane, it should be possible to transform the manufacturing landscape of this key chemical intermediate

Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers

This paper presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). GNNs are still a relatively novel technique, and have shown great promise for similar reconstruction tasks in the LHC. In this paper, a multihead attention message passing network is used to classify the relationship between detector hits by labelling graph edges, determining whether hits were produced by the same underlying particle, and if so, the particle type. The trained model is 84% accurate overall, and performs best on the EM shower and muon track classes. The model's strengths and weaknesses are discussed, and plans for developing this technique further are summarised.Comment: 7 pages, 3 figures, submitted to the 25th International Conference on Computing in High-Energy and Nuclear Physic

Track Seeding and Labelling with Embedded-space Graph Neural Networks

To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. The most promising of these solutions, graph neural networks (GNN), process the event as a graph that connects track measurements (detector hits corresponding to nodes) with candidate line segments between the hits (corresponding to edges). Detector information can be associated with nodes and edges, enabling a GNN to propagate the embedded parameters around the graph and predict node-, edge- and graph-level observables. Previously, message-passing GNNs have shown success in predicting doublet likelihood, and we here report updates on the state-of-the-art architectures for this task. In addition, the Exa.TrkX project has investigated innovations in both graph construction, and embedded representations, in an effort to achieve fully learned end-to-end track finding. Hence, we present a suite of extensions to the original model, with encouraging results for hitgraph classification. In addition, we explore increased performance by constructing graphs from learned representations which contain non-linear metric structure, allowing for efficient clustering and neighborhood queries of data points. We demonstrate how this framework fits in with both traditional clustering pipelines, and GNN approaches. The embedded graphs feed into high-accuracy doublet and triplet classifiers, or can be used as an end-to-end track classifier by clustering in an embedded space. A set of post-processing methods improve performance with knowledge of the detector physics. Finally, we present numerical results on the TrackML particle tracking challenge dataset, where our framework shows favorable results in both seeding and track finding.Comment: Proceedings submission in Connecting the Dots Workshop 2020, 10 page

Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems