Space Radiation Analysis for the Mark III Spacesuit

NASA has continued the development of space systems by applying and integrating improved technologies that include safety issues, lightweight materials, and electronics. One such area is extravehicular (EVA) spacesuit development with the most recent Mark III spacesuit. In this paper the Mark III spacesuit is discussed in detail that includes the various components that comprise the spacesuit, materials and their chemical composition that make up the spacesuit, and a discussion of the 3-D CAD model of the Mark III spacesuit. In addition, the male (CAM) and female (CAF) computerized anatomical models are also discussed in detail. We combined the spacesuit and the human models, that is, we developed a method of incorporating the human models in the Mark III spacesuit and performed a ray-tracing technique to determine the space radiation shielding distributions for all of the critical body organs. These body organ shielding distributions include the BFO (Blood-Forming Organs), skin, eye, lungs, stomach, and colon, to name a few, for both the male and female. Using models of the trapped (Van Allen) proton and electron environments, radiation exposures were computed for a typical low earth orbit (LEO) EVA mission scenario including the geostationary (GEO) high electron environment. A radiation exposure assessment of these mission scenarios is made to determine whether or not the crew radiation exposure limits are satisfied, and if not, the additional shielding material that would be required to satisfy the crew limits

Space Radiation Risk Assessment for Future Lunar Missions

For lunar exploration mission design, radiation risk assessments require the understanding of future space radiation environments in support of resource management decisions, operational planning, and a go/no-go decision. The future GCR flux was estimated as a function of interplanetary deceleration potential, which was coupled with the estimated neutron monitor rate from the Climax monitor using a statistical model. A probability distribution function for solar particle event (SPE) occurrence was formed from proton fluence measurements of SPEs occurred during the past 5 solar cycles (19-23). Large proton SPEs identified from impulsive nitrate enhancements in polar ice for which the fluences are greater than 2 10(exp 9) protons/sq cm for energies greater than 30 MeV, were also combined to extend the probability calculation for high level of proton fluences. The probability with which any given proton fluence level of a SPE will be exceeded during a space mission of defined duration was then calculated. Analytic energy spectra of SPEs at different ranks of the integral fluences were constructed over broad energy ranges extending out to GeV, and representative exposure levels were analyzed at those fluences. For the development of an integrated strategy for radiation protection on lunar exploration missions, effective doses at various points inside a spacecraft were calculated with detailed geometry models representing proposed transfer vehicle and habitat concepts. Preliminary radiation risk assessments from SPE and GCR were compared for various configuration concepts of radiation shelter in exploratory-class spacecrafts

A standard tag set expounding traditional morphological features for Arabic language part-of-speech tagging

The SALMA Morphological Features Tag Set (SALMA, Sawalha Atwell Leeds Morphological Analysis tag set for Arabic) captures long-established traditional morphological features of grammar and Arabic, in a compact yet transparent notation. First, we introduce Part-of-Speech tagging and tag set standards for English and other European languages, and then survey Arabic Part-of-Speech taggers and corpora, and long-established Arabic traditions in analysis of morphology. A range of existing Arabic Part-of-Speech tag sets are illustrated and compared; and we review generic design criteria for corpus tag sets. For a morphologically-rich language like Arabic, the Part-of-Speech tag set should be defined in terms of morphological features characterizing word structure. We describe the SALMA Tag Set in detail, explaining and illustrating each feature and possible values. In our analysis, a tag consists of 22 characters; each position represents a feature and the letter at that location represents a value or attribute of the morphological feature; the dash ‘-’ represents a feature not relevant to a given word. The first character shows the main Parts of Speech, from: noun, verb, particle, punctuation, and Other (residual); these last two are an extension to the traditional three classes to handle modern texts. ‘Noun’ in Arabic subsumes what are traditionally referred to in English as ‘noun’ and ‘adjective’. The characters 2, 3, and 4 are used to represent subcategories; traditional Arabic grammar recognizes 34 subclasses of noun (letter 2), 3 subclasses of verb (letter 3), 21 subclasses of particle (letter 4). Others (residuals) and punctuation marks are represented in letters 5 and 6 respectively. The next letters represent traditional morphological features: gender (7), number (8), person (9), inflectional morphology (10) case or mood (11), case and mood marks (12), definiteness (13), voice (14), emphasized and non-emphasized (15), transitivity (16), rational (17), declension and conjugation (18). Finally there are four characters representing morphological information which is useful in Arabic text analysis, although not all linguists would count these as traditional features: unaugmented and augmented (19), number of root letters (20), verb root (21), types of nouns according to their final letters (22). The SALMA Tag Set is not tied to a specific tagging algorithm or theory, and other tag sets could be mapped onto this standard, to simplify and promote comparisons between and reuse of Arabic taggers and tagged corpora

Improvement of Risk Assessment from Space Radiation Exposure for Future Space Exploration Missions

Protecting astronauts from space radiation exposure is an important challenge for mission design and operations for future exploration-class and long-duration missions. Crew members are exposed to sporadic solar particle events (SPEs) as well as to the continuous galactic cosmic radiation (GCR). If sufficient protection is not provided the radiation risk to crew members from SPEs could be significant. To improve exposure risk estimates and radiation protection from SPEs, detailed variations of radiation shielding properties are required. A model using a modern CAD tool ProE (TM), which is the leading engineering design platform at NASA, has been developed for this purpose. For the calculation of radiation exposure at a specific site, the cosine distribution was implemented to replicate the omnidirectional characteristic of the 4 pi particle flux on a surface. Previously, estimates of doses to the blood forming organs (BFO) from SPEs have been made using an average body-shielding distribution for the bone marrow based on the computerized anatomical man model (CAM). The development of an 82-point body-shielding distribution at BFOs made it possible to estimate the mean and variance of SPE doses in the major active marrow regions. Using the detailed distribution of bone marrow sites and implementation of cosine distribution of particle flux is shown to provide improved estimates of acute and cancer risks from SPEs

Analytical Representations for Characterizing the Global Aviation Radiation Environment Based on Model and Measurement Databases

The Nowcast of Atmospheric Ionizing Radiation for Aviation Safety climatological model and the Automated Radiation Measurements for Aerospace Safety (ARMAS) statistical database are presented as polynomial fit equations. Using equations based on altitude, L shell, and geomagnetic conditions an effective dose rate for any location from a galactic cosmic ray (GCR) environment can be calculated. A subset of the ARMAS database is represented by a second polynomial fit equation for the GCR plus probable relativistic energetic particle (REP; Van Allen belt REP) effective dose rates within a narrow band of L shells with altitudinal and geomagnetic dependency. Solar energetic particle events are not considered in this study since our databases do not contain these events. This work supports a suggestion that there may be a REP contribution having an effect at aviation altitudes. The ARMAS database is rich in Western Hemisphere observations for L shells between 1.5 and 5; there have been many cases of enhanced radiation events possibly related to effects from radiation belt particles. Our work identifies that the combined effects of an enhanced radiation environment in this L shell range are typically 15% higher than the GCR background. We also identify applications for the equations representing the Nowcast of Atmospheric Ionizing Radiation for Aviation Safety and ARMAS databases. They include (i) effective dose rate climatology in comparison with measured weather variability and (ii) climatological and statistical weather nowcasting and forecasting. These databases may especially help predict the radiation environment for regional air traffic management, for airport overflight operations, and for air carrier route operations of individual aircraft

Effects of eight neuropsychiatric copy number variants on human brain structure

Many copy number variants (CNVs) confer risk for the same range of neurodevelopmental symptoms and psychiatric conditions including autism and schizophrenia. Yet, to date neuroimaging studies have typically been carried out one mutation at a time, showing that CNVs have large effects on brain anatomy. Here, we aimed to characterize and quantify the distinct brain morphometry effects and latent dimensions across 8 neuropsychiatric CNVs. We analyzed T1-weighted MRI data from clinically and non-clinically ascertained CNV carriers (deletion/duplication) at the 1q21.1 (n = 39/28), 16p11.2 (n = 87/78), 22q11.2 (n = 75/30), and 15q11.2 (n = 72/76) loci as well as 1296 non-carriers (controls). Case-control contrasts of all examined genomic loci demonstrated effects on brain anatomy, with deletions and duplications showing mirror effects at the global and regional levels. Although CNVs mainly showed distinct brain patterns, principal component analysis (PCA) loaded subsets of CNVs on two latent brain dimensions, which explained 32 and 29% of the variance of the 8 Cohen’s d maps. The cingulate gyrus, insula, supplementary motor cortex, and cerebellum were identified by PCA and multi-view pattern learning as top regions contributing to latent dimension shared across subsets of CNVs. The large proportion of distinct CNV effects on brain morphology may explain the small neuroimaging effect sizes reported in polygenic psychiatric conditions. Nevertheless, latent gene brain morphology dimensions will help subgroup the rapidly expanding landscape of neuropsychiatric variants and dissect the heterogeneity of idiopathic conditions

Visualisation of long distance grammatical collocation patterns in language

Research in generic unsupervised learning of language structure applied to the Search for Extra-Terrestrial Intelligence (SETI) and decipherment of unknown languages has sought to build up a generic picture of lexical and structural patterns, characteristic of natural language. As part of this toolkit, a generic system is required to facilitate the analysis of behavioural trends amongst selected pairs of terminals and non-terminals alike, regardless of which target natural language was selected. Such a tool may be useful in other areas, such as lexico-grammatical analysis or tagging of corpora. Data-oriented approaches to corpus annotation use statistical n-grams and/or constraint-based models; n-grams or constraints with wider windows can improve error rates by examining the topology of the annotation-combination space. We present a visualisation tool to help linguists find “useful” PoS-tag combinations, and cohesion between linguistic annotations at other levels, and suggest some possible applications

Increasing our ignorance of language:identifying language structure in an unknown 'signal'

This paper describes algorithms and software developed to characterise and detect generic intelligent language-like features in an input signal, using natural language learning techniques: looking for characteristic statistical "language-signatures" in test corpora. As a first step towards such species-independent language-detection, we present a suite of programs to analyse digital representations of a range of data, and use the results to extrapolate whether or not there are language-like structures which distinguish this data from other sources, such as music, images, and white noise. Outside our own immediate NLP sphere, generic communication techniques are of particular interest in the astronautical community, where two sessions are dedicated to SETI at their annual International conference with topics ranging from detecting ET technology to the ethics and logistics of message construction (Elliott and Atwell, 1999; Ollongren, 2000; Vakoch, 2000)

Language identification in unknown signals

This paper describes algorithms and software developed to characterise and detect generic intelligent language-like features in an input signal, using Natural Language Learning techniques: looking for characteristic statistical "language-signatures" in test corpora. As a first step towards such species-independent language-detection, we present a suite of programs to analyse digital representations of a range of data, and use the results to extrapolate whether or not there are language-like structures which distinguish this data from other sources, such as music, images, and white noise. We assume that generic species-independent communication can be detected by concentrating on localised patterns and rhythms, identifying segments at the level of characters, words and phrases, without necessarily having to "understand" the content.We assume that a language-like signal will be encoded symbolically, i.e. some kind of character-stream. Our language-detection algorithm for symbolic input uses a number of statistical clues: data compression ratio, "chunking" to find character bit-length and boundaries, and matching against a Zipfian type-token distribution for "letters" and "words". We do not claim extensive (let alone exhaustive) empirical evidence that our language-detection clues are "correct"; the only real test will come when the Search for Extra-Terrestrial Intelligence finds true alien signals. If and when true SETI signals are found, the first step to interpretation is to identify the language-like features, using techniques like the above. Our current research goal is to apply Natural Language Learning techniques to the identification of "higher-level" grammatical and semantic structure in a linguistic signal