Wind enhanced planetary escape: Collisional modifications

The problem of thermal escape is considered in which both the effects of thermospheric winds at the exobase and collisions below the exobase are included in a Monte Carlo calculation. The collisions are included by means of a collisional relaxation layer of a background gas which models the transition region between the exosphere and the thermosphere. The wind effects are considered in the limiting cases of vertical and horizontal flows. Two species are considered: terrestrial hydrogen and terrestrial helium. In the cases of terrestrial hydrogen the escape fluxes were found to be strongly filtered or throttled by collisions at high exospheric temperatures. The model is applied to molecular hydrogen diffusing through a methane relaxation layer under conditions possible on Titan. The results are similar to the case of terrestrial hydrogen with wind enhanced escape being strongly suppressed by collisions. It is concluded that wind enhanced escape is not an important process on Titan

Interferometric tracking system for the tracking and data relay satellite

This report documents construction and testing of the Interferometric Tracking System project developed under the NASA SBIR contract NAS5-30313. Manuals describing the software and hardware, respectively entitled: 'Field Station Guide to Operations' and 'Field Station Hardware Manual' are included as part of this final report. The objective of this contract was to design, build, and operate a system of three ground stations using Very Long Baseline Interferometry techniques to measure the TDRS orbit. The ground stations receive signals from normal satellite traffic, store these signals in co-located computers, and transmit the information via phone lines to a central processing site which correlates the signals to determine relative time delays. Measurements from another satellite besides TDRS are used to determine clock offsets. A series of such measurements will ultimately be employed to derive the orbital parameters, yielding positions accurate to within 50 meters or possibly better

Polarization observables in the longitudinal basis for pseudo-scalar meson photoproduction using a density matrix approach

The complete expression for the intensity in pseudo-scalar meson photoproduction with a polarized beam, target, and recoil baryon is derived using a density matrix approach that offers great economy of notation. A Cartesian basis with spins for all particles quantized along a single direction, the longitudinal beam direction, is used for consistency and clarity in interpretation. A single spin-quantization axis for all particles enables the amplitudes to be written in a manifestly covariant fashion with simple relations to those of the well-known CGLN formalism. Possible sign discrepancies between theoretical amplitude-level expressions and experimentally measurable intensity profiles are dealt with carefully. Our motivation is to provide a coherent framework for coupled-channel partial-wave analysis of several meson photoproduction reactions, incorporating recently published and forthcoming polarization data from Jefferson Lab.Comment: 11 pages, 2 figure

Art Neural Networks for Remote Sensing: Vegetation Classification from Landsat TM and Terrain Data

A new methodology for automatic mapping from Landsat Thematic Mapper (TM) and terrain data, based on the fuzzy ARTMAP neural network, is developed. System capabilities are tested on a challenging remote sensing classification problem, using spectral and terrain features for vegetation classification in the Cleveland National Forest. After training at the pixel level, system performance is tested at the stand level, using sites not seen during training. Results are compared to those of maximum likelihood classifiers, as well as back propagation neural networks and K Nearest Neighbor algorithms. ARTMAP dynamics are fast, stable, and scalable, overcoming common limitations of back propagation, which did not give satisfactory performance. Best results are obtained using a hybrid system based on a convex combination of fuzzy ARTMAP and maximum likelihood predictions. A prototype remote sensing example introduces each aspect of data processing and fuzzy ARTMAP classification. The example shows how the network automatically constructs a minimal number of recognition categories to meet accuracy criteria. A voting strategy improves prediction and assigns confidence estimates by training the system several times on different orderings of an input set.National Science Foundation (IRI 94-01659, SBR 93-00633); Office of Naval Research (N00014-95-l-0409, N00014-95-0657

Problem areas in the use of the firefly luciferase assay for bacterial detection

By purifying the firefly luciferase extract and adding all necessary chemicals but ATP in excess, an assay for ATP was performed by measuring the amount of light produced when a sample containing soluble ATP is added to the luciferase reaction mixture. Instrumentation, applications, and basic characteristics of the luciferase assay are presented. Effect of the growth medium and length of time grown in this medium on ATP per viable E. coli values is shown in graphic form, along with an ATP concentration curve showing relative light units versus ATP injected. Reagent functions and concentration methods are explored. Efforts to develop a fast automatable system to detect the presence of bacteria in biological fluids, especially urine, resulted in the optimization of procedures for use with different types of samples

Rhetoric of Academe

This piece explores the ways in which legal education moved from clerkships in law offices to receiving law degrees from academic institutions. As such, this piece also tracks the formation of legal precedent over time, which too, has shifted from rigid reliance on case law, to more reliance on academic rhetoric propounded by legal scholars

What Is Our Research For? Responsibility, Humility and the Production of Knowledge about Burundi

Political space in Burundi underwent a remarkable opening during the Burundian peace process and its immediate aftermath, which led to a rise in social science scholarship in Burundi. This space has increasingly narrowed, particularly since the crisis in 2015, presenting important challenges for social science scholars of Burundi. This changing political environment has consequences for the production of knowledge on Burundi. It is therefore timely to ask what purposes does research on Burundi serve. This article reflects upon different motivations and goals for social science research in Burundi and how these affect the types of research questions that are asked and the formats for knowledge dissemination. It argues that both the opening and closing of the Burundian political landscape bring into sharp relief the need for greater scholarly reflexivity. The article argues that in contexts of structural inequality and increased political control such as Burundi, we need to be particularly attentive to the need for scholarly responsibility and humility, as well as an awareness of the dynamics that have led to calls for the decolonisation of knowledge within the social sciences.Der Friedensprozess in Burundi hat eine bemerkenswerte politische Öffnung bewirkt, die zu einem Anstieg sozialwissenschaftlicher Forschung über Burundi führte. Diese Spielräume nehmen jedoch vor allem seit der Krise 2015 wieder ab, was eine große Herausforderung für die sozialwissenschaftliche Forschung über Burundi darstellt. Die sich wandelnde politische Landschaft beeinflusst die Wissensproduktion über Burundi, sodass sich die Frage aufdrängt, welchem Zweck diese Forschung dient. Dieser Artikel reflektiert verschiedene Motivationen und Ziele sozialwissenschaftlicher Forschung über Burundi und zeigt auf, wie sie die Fragestellungen und Formen des Wissenstransfers beeinflussen. Es wird argumentiert, dass sowohl Zeiten politischer Öffnung als auch die Einschränkung politischer Freiheiten die Notwendigkeit stärkerer wissenschaftlicher Reflexivität deutlich machen. In Kontexten wie Burundi, die von strukturellen Ungleichheiten und zunehmender politischer Kontrolle geprägt sind, spielen wissenschaftliche Verantwortung, Bescheidenheit sowie das Bewusstsein für Dynamiken, die Forderungen nach eine Dekolonialisierung des Wissens in den Sozialwissenschaften befördert haben, eine besondere Rolle

Microfabricated high-finesse optical cavity with open access and small volume

We present a microfabricated optical cavity, which combines a very small mode volume with high finesse. In contrast to other micro-resonators, such as microspheres, the structure we have built gives atoms and molecules direct access to the high-intensity part of the field mode, enabling them to interact strongly with photons in the cavity for the purposes of detection and quantum-coherent manipulation. Light couples directly in and out of the resonator through an optical fiber, avoiding the need for sensitive coupling optics. This renders the cavity particularly attractive as a component of a lab-on-a-chip, and as a node in a quantum network

ART and ARTMAP Neural Networks for Applications: Self-Organizing Learning, Recognition, and Prediction

ART and ARTMAP neural networks for adaptive recognition and prediction have been applied to a variety of problems. Applications include parts design retrieval at the Boeing Company, automatic mapping from remote sensing satellite measurements, medical database prediction, and robot vision. This chapter features a self-contained introduction to ART and ARTMAP dynamics and a complete algorithm for applications. Computational properties of these networks are illustrated by means of remote sensing and medical database examples. The basic ART and ARTMAP networks feature winner-take-all (WTA) competitive coding, which groups inputs into discrete recognition categories. WTA coding in these networks enables fast learning, that allows the network to encode important rare cases but that may lead to inefficient category proliferation with noisy training inputs. This problem is partially solved by ART-EMAP, which use WTA coding for learning but distributed category representations for test-set prediction. In medical database prediction problems, which often feature inconsistent training input predictions, the ARTMAP-IC network further improves ARTMAP performance with distributed prediction, category instance counting, and a new search algorithm. A recently developed family of ART models (dART and dARTMAP) retains stable coding, recognition, and prediction, but allows arbitrarily distributed category representation during learning as well as performance.National Science Foundation (IRI 94-01659, SBR 93-00633); Office of Naval Research (N00014-95-1-0409, N00014-95-0657