Aerospace medicine and biology: A continuing bibliography with indexes (supplement 349)

This bibliography lists 149 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during April, 1991. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

Massive Stars In The W33 Giant Molecular Complex

Rich in H II regions, giant molecular clouds are natural laboratories to study massive stars and sequential star formation. The Galactic star-forming complex W33 is located at = ∼ ◦ l 12.8 and at a distance of 2.4 kpc and has a size of ≈10 pc and a total mass of ≈(0.8−8.0) × 105 M⊙. The integrated radio and IR luminosity of W33—when combined with the direct detection of methanol masers, the protostellar object W33A, and the protocluster embedded within the radio source W33 main—mark the region as a site of vigorous ongoing star formation. In order to assess the long-term star formation history, we performed an infrared spectroscopic search for massive stars, detecting for the first time 14 early-type stars, including one WN6 star and four O4–7 stars. The distribution of spectral types suggests that this population formed during the past ∼2–4 Myr, while the absence of red supergiants precludes extensive star formation at ages 6–30 Myr. This activity appears distributed throughout the region and does not appear to have yielded the dense stellar clusters that characterize other star-forming complexes such as Carina and G305. Instead, we anticipate that W33 will eventually evolve into a loose stellar aggregate, with Cyg OB2 serving as a useful, albeit richer and more massive, comparator. Given recent distance estimates, and despite a remarkably similar stellar population, the rich cluster Cl 1813–178 located on the northwest edge of W33 does not appear to be physically associated with W33

Batch-Incremental Learning for Mining Data Streams

The data stream model for data mining places harsh restrictions on a learning algorithm. First, a model must be induced incrementally. Second, processing time for instances must keep up with their speed of arrival. Third, a model may only use a constant amount of memory, and must be ready for prediction at any point in time. We attempt to overcome these restrictions by presenting a data stream classification algorithm where the data is split into a stream of disjoint batches. Single batches of data can be processed one after the other by any standard non-incremental learning algorithm. Our approach uses ensembles of decision trees. These tree ensembles are iteratively merged into a single interpretable model of constant maximal size. Using benchmark datasets the algorithm is evaluated for accuracy against state-of-the-art algorithms that make use of the entire dataset

Decomposition of Changes in Poverty Measures: Sectoral and Institutional Considerations for the Poverty Reduction Strategy Paper of Pakistan

Two extremely significant empirical questions on the relationship between growth, distribution and poverty have remained the focus of attention for researchers and academicians. First, how does a change in aggregate poverty reflect intrasectoral gains/losses versus intersectoral shifts in population? Second, how much of an observed change in poverty can be attributed to the changes in the distribution of income, as distinct from growth in average incomes? Standard inequality measures like the Gini coefficient can be misleading in this context. At any rate, the change in an inequality measure can be a poor guide to its quantitative impact on poverty. Ravallion and Huppi (1991) proposed decomposition formulae to throw light on the contributions of sectoral gains and population shifts (on the one hand) and economic growth and changes in inequality (on the other) to aggregate changes in poverty. They found that both population shifts and gains to the urban and rural sectors alleviated aggregate poverty in Indonesia over the 1984–87 period. In addition, they obtained estimates of the relative contributions of growth and greater equity to poverty alleviation in Indonesia. Datt and Ravallion (1992) extended the analysis to study poverty in Brazil and India during the 1980s. Kakwani (1993) explored the relation between economic growth and poverty for Cote d’Ivoire from 1980–85. He developed his own methodology to measure separately the impact of changes in average income and income inequality on poverty. Kakwani (2000) applied the same methodology to analyse changes in poverty in Thailand covering the period from 1988–94. Recently, Contreas (2003) examined the evolution of poverty and inequality in Chile between 1990 and 1996. Using the “Datt-Ravallion decomposition”, he computed that economic growth accounted for over 85 percent of the poverty reduction in Chile.

INTERNATIONAL LAND QUALITY INDEXES

Land Economics/Use,