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The article features Commander Rick Husband of Texas and how he became a member of the American Astronaut Corps. Also cited are the tragic accident involving the Space Shuttle Columbia that led to the deaths of Husband, Willie McCool, David Brown, Laurel Clark, Ilan Ramon, Mike Anderson, and Kalpana Chawla, and how members of the astronaut corps are selected.

Two-mirror telescopes possess an agreeable tradeoff between performance and complexity that suits the needs of many scientific tasks, and recent developments in manufacturing technology have enabled unobscured two-mirror designs with freeform surfaces. We illustrate how structural aberration coefficients reveal the fundamental aberration characteristics of such systems and provide a summary table of general solutions that prescribe freeform surface shapes and tilts. Two unobscured design forms with remarkable aberration cancellation, inspired by well-known on-axis anastigmatic solutions, are presented. [ABSTRACT FROM AUTHOR]

Abstract Growing concerns over the environmental impact of greenhouse gas (GHG) emissions, uncertainty of future fossil fuel prices, geopolitical instability, and consumer preferences are a few factors driving the development and use of biomass as a source of renewable energy. Government mandates and policy frameworks have further built consensus for effective utilization of sustainable resources for energy. As a result, biomass derived densified solid biofuels have experienced a phenomenal growth in the last decade and are expected to grow by 56% during the three decades from 2010 to 2040. Solid biomass represents the largest share of biomass used for heat and electricity generation. Biomass sources range from agricultural, municipal, and commercial wastes, to forest-litter, and energy crops. Because of the varied nature of these sources, harvesting practices and the cost of transportation, several methods have been developed to densify the biomass. The long-term viability and growth of biobased energy products hinges on the technical innovation and wider societal acceptance of these products. This paper reviews multiple biomass feedstock, processing characteristics, product properties, transportation, and storage options. The paper also discusses the economics of densifying biomass, the impacts to regions where biomass utilization is implemented, outstanding challenges, and national and global trends in biomass utilization. Highlights • Biomass feedstocks are major source for densified solid biofuels. • Environmental concerns, GHG gases emission and government mandates driving biomass utilization. • Woody and non-woody biomass represent largest share of biomass for heat and electricity generation. • Biomass feedstock type, preprocessing, densification, and transportation are critical factors for adoption of biomass for energy applications. • Technical innovation and wider societal acceptance important for growth of solid biofuels. [ABSTRACT FROM AUTHOR]

Freeform imaging systems are a subject of current interest. To aid in the design of such systems, we introduce structural aberration coefficients for plane symmetric imaging with XY-polynomial freeform surfaces that may also be tilted. Further, we extend the coefficients to systems with an arbitrary number of components and specifically provide analytical equations for two-mirror systems. Distinguishing between system configuration and structural parameters enhances clarity, which we highlight by deriving new conditions for minimum root-mean-square (RMS) spot size and minimum RMS wavefront error in aplanatic two-mirror telescopes. [ABSTRACT FROM AUTHOR]

In Urama, there are two clause-final particles, ka and ra, that encode a variety of both semantic and pragmatic meanings. While previous approaches have treated these particles as clause-type markers or evidential morphemes, this paper argues that one of these particles, ka, has another previously undocumented function in conversation: to mark speaker-knowledge and what the speaker assumes the addressee to know. We term these interactional uses of ka and ra. Functionally, the interactional use of ka follows from its clause-typing and speech act properties. Theoretically, Urama represents a language that has a grammatical strategy for tracking information in the Common Ground, which is close in spirit to evidentiality and clause-typing, but qualitatively different. [ABSTRACT FROM AUTHOR]

Despite sensitivity to the contextual demands of modification, children struggle with the production of complex NPs. What syntactic or semantic properties of NP embedding specifically introduce complexity? We compare production of definite descriptions with two modifiers that contrast in the attachment of the second modifier: sequential vs. recursive modification. Children (n = 71) produced overall fewer targets than adults (n = 13), but both groups found double nonrecursive modification (e.g. the plate with oranges under the table) much easier than recursive modification (e.g. the bird on the alligator in the water). We conclude that each embedding step introduces complexity beyond the elements and operations employed in the semantic composition of the structure, or the cyclic syntax that generates it. [ABSTRACT FROM AUTHOR]

Oral cancer is one of the most prevalent cancers, especially in middle- and low-income countries such as India. Automatic segmentation of oral cancer images can improve the diagnostic workflow, which is a significant task in oral cancer image analysis. Despite the remarkable success of deep-learning networks in medical segmentation, they rarely provide uncertainty quantification for their output. We aim to estimate uncertainty in a deep-learning approach to semantic segmentation of oral cancer images and to improve the accuracy and reliability of predictions. This work introduced a UNet-based Bayesian deep-learning (BDL) model to segment potentially malignant and malignant lesion areas in the oral cavity. The model can quantify uncertainty in predictions. We also developed an efficient model that increased the inference speed, which is almost six times smaller and two times faster (inference speed) than the original UNet. The dataset in this study was collected using our customized screening platform and was annotated by oral oncology specialists. The proposed approach achieved good segmentation performance as well as good uncertainty estimation performance. In the experiments, we observed an improvement in pixel accuracy and mean intersection over union by removing uncertain pixels. This result reflects that the model provided less accurate predictions in uncertain areas that may need more attention and further inspection. The experiments also showed that with some performance compromises, the efficient model reduced computation time and model size, which expands the potential for implementation on portable devices used in resource-limited settings. Our study demonstrates the UNet-based BDL model not only can perform potentially malignant and malignant oral lesion segmentation, but also can provide informative pixel-level uncertainty estimation. With this extra uncertainty information, the accuracy and reliability of the model's prediction can be improved. [ABSTRACT FROM AUTHOR]

Significance: Early detection of oral cancer is vital for high-risk patients, and machine learning-based automatic classification is ideal for disease screening. However, current datasets collected from high-risk populations are unbalanced and often have detrimental effects on the performance of classification. Aim: To reduce the class bias caused by data imbalance. Approach: We collected 3851 polarized white light cheek mucosa images using our customized oral cancer screening device. We use weight balancing, data augmentation, undersampling, focal loss, and ensemble methods to improve the neural network performance of oral cancer image classification with the imbalanced multi-class datasets captured from high-risk populations during oral cancer screening in low-resource settings. Results: By applying both data-level and algorithm-level approaches to the deep learning training process, the performance of the minority classes, which were difficult to distinguish at the beginning, has been improved. The accuracy of "premalignancy" class is also increased, which is ideal for screening applications. Conclusions: Experimental results show that the class bias induced by imbalanced oral cancer image datasets could be reduced using both data- and algorithm-level methods. Our study may provide an important basis for helping understand the influence of unbalanced datasets on oral cancer deep learning classifiers and how to mitigate. [ABSTRACT FROM AUTHOR]