113 research outputs found
PhD
dissertationRecent investigations clarifying the monoaminergic influences on sympathetic preganglionic neurons in the cat have allowed a direct assessment of the effects of narcotic analgesics and antagonists on central transmission. Intravenous morphine, methadone, meperidine or codeine produced an immediate and sustained depression of discharges evoked from these spinal sympathetic neurons. The potency rations for production of similar degrees of depression by these four agents compared favorably to the potency ratios reported for equianalgesic doses. The depression appeared to be specific in that it was rapidly reversed by very low doses of naloxone or nalorphine and that a non-analgesic stereoisomer related to these drugs produced qualitatively different effects on the sympathetic preganglionic neurons. The depression of these neurons offers an adequate and sufficient mechanism to account for the centrally mediated vasodepressor effect produced by morphine and other narcotic analgesics. The pharmacological characterization of the morphine and naloxone actions strongly suggests that morphine functions as a 5-HT agonist in this system, while naloxone can both block 5-HT receptors and also stimulate these spinal neurons. The significance of these findings relative to other acute and chronic effects of the narcotic analgesics are discussed
Crystal Structure Search with Random Relaxations Using Graph Networks
Materials design enables technologies critical to humanity, including
combating climate change with solar cells and batteries. Many properties of a
material are determined by its atomic crystal structure. However, prediction of
the atomic crystal structure for a given material's chemical formula is a
long-standing grand challenge that remains a barrier in materials design. We
investigate a data-driven approach to accelerating ab initio random structure
search (AIRSS), a state-of-the-art method for crystal structure search. We
build a novel dataset of random structure relaxations of Li-Si battery anode
materials using high-throughput density functional theory calculations. We
train graph neural networks to simulate relaxations of random structures. Our
model is able to find an experimentally verified structure of Li15Si4 it was
not trained on, and has potential for orders of magnitude speedup over AIRSS
when searching large unit cells and searching over multiple chemical
stoichiometries. Surprisingly, we find that data augmentation of adding
Gaussian noise improves both the accuracy and out of domain generalization of
our models.Comment: Removed citations from the abstract, paper content is unchange
OpenContrails: Benchmarking Contrail Detection on GOES-16 ABI
Contrails (condensation trails) are line-shaped ice clouds caused by aircraft
and are likely the largest contributor of aviation-induced climate change.
Contrail avoidance is potentially an inexpensive way to significantly reduce
the climate impact of aviation. An automated contrail detection system is an
essential tool to develop and evaluate contrail avoidance systems. In this
paper, we present a human-labeled dataset named OpenContrails to train and
evaluate contrail detection models based on GOES-16 Advanced Baseline Imager
(ABI) data. We propose and evaluate a contrail detection model that
incorporates temporal context for improved detection accuracy. The human
labeled dataset and the contrail detection outputs are publicly available on
Google Cloud Storage at gs://goes_contrails_dataset
A scalable system to measure contrail formation on a per-flight basis
Persistent contrails make up a large fraction of aviation's contribution to
global warming. We describe a scalable, automated detection and matching (ADM)
system to determine from satellite data whether a flight has made a persistent
contrail. The ADM system compares flight segments to contrails detected by a
computer vision algorithm running on images from the GOES-16 Advanced Baseline
Imager. We develop a 'flight matching' algorithm and use it to label each
flight segment as a 'match' or 'non-match'. We perform this analysis on 1.6
million flight segments. The result is an analysis of which flights make
persistent contrails several orders of magnitude larger than any previous work.
We assess the agreement between our labels and available prediction models
based on weather forecasts. Shifting air traffic to avoid regions of contrail
formation has been proposed as a possible mitigation with the potential for
very low cost/ton-CO2e. Our findings suggest that imperfections in these
prediction models increase this cost/ton by about an order of magnitude.
Contrail avoidance is a cost-effective climate change mitigation even with this
factor taken into account, but our results quantify the need for more accurate
contrail prediction methods and establish a benchmark for future development.Comment: 25 pages, 6 figure
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