Space transportation systems, launch systems, and propulsion for the Space Exploration Initiative: Results from Project Outreach

A number of transportation and propulsion options for Mars exploration missions are analyzed. As part of Project Outreach, RAND received and evaluated 350 submissions in the launch vehicle, space transportation, and propulsion areas. After screening submissions, aggregating those that proposed identical or nearly identical concepts, and eliminating from further consideration those that violated known physical princples, we had reduced the total number of viable submissions to 213. In order to avoid comparing such disparate things as launch vehicles and electric propulsion systems, six broad technical areas were selected to categorize the submissions: space transportation systems; earth-to-orbit (ETO) launch systems; chemical propulsion; nuclear propulsion; low-thrust propulsion; and other. To provide an appropriate background for analyzing the submissions, an extensive survey was made of the various technologies relevant to the six broad areas listed above. We discuss these technologies with the intent of providing the reader with an indication of the current state of the art, as well as the advances that might be expected within the next 10 to 20 years

Dense Antihydrogen: Its Production and Storage to Envision Antimatter Propulsion

We discuss the possibility that dense antihydrogen could provide a path towards a mechanism for a deep space propulsion system. We concentrate at first, as an example, on Bose-Einstein Condensate (BEC) antihydrogen. In a Bose-Einstein Condensate, matter (or antimatter) is in a coherent state analogous to photons in a laser beam, and individual atoms lose their independent identity. This allows many atoms to be stored in a small volume. In the context of recent advances in producing and controlling BECs, as well as in making antihydrogen, this could potentially provide a revolutionary path towards the efficient storage of large quantities of antimatter, perhaps eventually as a cluster or solid.Comment: 12 pages, 3 figure

Organic Reference Materials for Hydrogen, Carbon, and Nitrogen Stable Isotope-Ratio Measurements: Caffeines, n-Alkanes, Fatty Acid Methyl Esters, Glycines, L-Valines, Polyethylenes, and Oils

An international project developed, quality-tested, and determined isotope−δ values of 19 new organic reference materials (RMs) for hydrogen, carbon, and nitrogen stable isotope-ratio measurements, in addition to analyzing pre-existing RMs NBS 22 (oil), IAEA-CH-7 (polyethylene foil), and IAEA-600 (caffeine). These new RMs enable users to normalize measurements of samples to isotope−δ scales. The RMs span a range of δ^2H_(VSMOW-SLAP) values from −210.8 to +397.0 mUr or ‰, for δ^(13)C_(VPDB-LSVEC) from −40.81 to +0.49 mUr and for δ^(15)N_(Air) from −5.21 to +61.53 mUr. Many of the new RMs are amenable to gas and liquid chromatography. The RMs include triads of isotopically contrasting caffeines, C_(16) n-alkanes, n-C_(20)-fatty acid methyl esters (FAMEs), glycines, and L-valines, together with polyethylene powder and string, one n-C_(17)-FAME, a vacuum oil (NBS 22a) to replace NBS 22 oil, and a ^2H-enriched vacuum oil. A total of 11 laboratories from 7 countries used multiple analytical approaches and instrumentation for 2-point isotopic normalization against international primary measurement standards. The use of reference waters in silver tubes allowed direct normalization of δ2H values of organic materials against isotopic reference waters following the principle of identical treatment. Bayesian statistical analysis yielded the mean values reported here. New RMs are numbered from USGS61 through USGS78, in addition to NBS 22a. Because of exchangeable hydrogen, amino acid RMs currently are recommended only for carbon- and nitrogen-isotope measurements. Some amino acids contain ^(13)C and carbon-bound organic ^2H-enrichments at different molecular sites to provide RMs for potential site-specific isotopic analysis in future studies

Simulation-Based Training for Patient Safety: 10 Principles That Matter

Simulation-based training can improve patient care when factors influencing its design, delivery, evaluation, and transfer are taken into consideration. In this paper, we provide a number of principles and practical tips that organizations in health care can use to begin implementing effective simulation-based training as a way to enhance patient safety. We commend the health care community for their efforts thus far. We hope that the information provided in this paper will encourage thinking beyond the bells and whistles of the simulation and bring to light full potential of simulation-based training in health care and patient safety

emoji2vec: Learning Emoji Representations from their Description

Many current natural language processing applications for social media rely on representation learning and utilize pre-trained word embeddings. There currently exist several publicly-available, pre-trained sets of word embeddings, but they contain few or no emoji representations even as emoji usage in social media has increased. In this paper we release emoji2vec, pre-trained embeddings for all Unicode emoji which are learned from their description in the Unicode emoji standard. The resulting emoji embeddings can be readily used in downstream social natural language processing applications alongside word2vec. We demonstrate, for the downstream task of sentiment analysis, that emoji embeddings learned from short descriptions outperforms a skip-gram model trained on a large collection of tweets, while avoiding the need for contexts in which emoji need to appear frequently in order to estimate a representation

Basic principles of stable isotope analysis in humanitarian forensic science.

While the identity of a victim of a localized disaster – such as a train or bus crash – may be established quickly through personal effects, fingerprints, dental records, and a comparison of decedent DNA to family reference specimen DNA, a different scenario presents itself in mass disasters, such as the Asian Tsunami of 2004. In the aftermath of the tsunami, visual appearance was initially used to assign “foreign” or “indigenous” classifications to the remains of thousands of victims. However, this visual identification approach was undermined by the speed with which bodies deteriorated under the hot and humid conditions. Time was spent populating ante-mortem DNA databases for different nationalities, which led to problems when creating a post-mortem DNA database because recovery of viable DNA was compromised due to rapid decomposition. As a consequence, only 1.3% of victims were identified by DNA; in contrast, 61% were identified based on dental examination, although this process took several months and a significant number of deceased from the 2004 Asian Tsunami still remain to be identified

Distant supervision from knowledge graphs

In this chapter, we discuss approaches leveraging distant supervision for relation extraction. We start by introducing the key ideas behind distant supervision as well as their main shortcomings. We then discuss approaches that improve over the basic method, including approaches based on the at-least-one-principle along with their extensions for handling false negative labels, and approaches leveraging topic models. We also describe embeddings-based methods including methods leveraging convolutional neural networks. Finally, we discuss how to take advantage of auxiliary information to improve relation extraction