A Multi-Moded RF Delay Line Distribution System for the Next Linear Collider

The Delay Line Distribution System (DLDS) is an alternative to conventional pulse compression, which enhances the peak power of rf sources while matching the long pulse of those sources to the shorter filling time of accelerator structures. We present an implementation of this scheme that combines pairs of parallel delay lines of the system into single lines. The power of several sources is combined into a single waveguide delay line using a multi-mode launcher. The output mode of the launcher is determined by the phase coding of the input signals. The combined power is extracted from the delay line using mode-selective extractors, each of which extracts a single mode. Hence, the phase coding of the sources controls the output port of the combined power. The power is then fed to the local accelerator structures. We present a detailed design of such a system, including several implementation methods for the launchers, extractors, and ancillary high power rf components. The system is designed so that it can handle the 600 MW peak power required by the NLC design while maintaining high efficiency.Comment: 25 pages, 11 figure

UC-277 Cosmic Cookoff

A cooking rogue-like game where you cook the weapons you fight with

Uncertainty Analysis for the Miniaturized Laser Heterodyne Radiometer (mini-LHR)

Presented here is a sensitivity analysis for the miniaturized laser heterodyne radiometer (mini-LHR). This passive, ground-based instrument measures carbon dioxide (CO2) in the atmospheric column and has been under development at NASA/GSFC since 2009. The goal of this development is to produce a low-cost, easily-deployable instrument that can extend current ground measurement networks in order to (1) validate column satellite observations, (2) provide coverage in regions of limited satellite observations, (3) target regions of interest such as thawing permafrost, and (4) support the continuity of a long-term climate record. In this paper an uncertainty analysis of the instrument performance is presented and compared with results from three sets of field measurements. The signal-to-noise ratio (SNR) and corresponding uncertainty for a single scan are calculated to be 329.4+/-1.3 by deploying error propagation through the equation governing the SNR. Reported is an absorbance noise of 0.0024 for 6 averaged scans of field data, for an instrument precision of approximately 0.2 ppmv for CO2

Conserved Charges in the Principal Chiral Model on a Supergroup

The classical principal chiral model in 1+1 dimensions with target space a compact Lie supergroup is investigated. It is shown how to construct a local conserved charge given an invariant tensor of the Lie superalgebra. We calculate the super-Poisson brackets of these currents and argue that they are finitely generated. We show how to derive an infinite number of local charges in involution. We demonstrate that these charges Poisson commute with the non-local charges of the model

Disentangling jet and disc emission from the 2005 outburst of XTE J1118+480

The black hole X-ray transient, XTE J1118+480, has now twice been observed in outburst - 2000 and 2005 - and on both occasions remained in the low/hard X-ray spectral state. Here we present radio, infrared, optical, soft X-ray and hard X-ray observations of the more recent outburst. We find that the lightcurves have very different morphologies compared with the 2000 event and the optical decay is delayed relative to the X-ray/radio. We attribute this lesser degree of correlation to contributions of emission from multiple components, in particular the jet and accretion disc. Whereas the jet seemed to dominate the broadband spectrum in 2000, in 2005 the accretion disc seems to be more prominent and we use an analysis of the lightcurves and spectra to distinguish between the jet and disc emission. There also appears to be an optically thin component to the radio emission in the 2005 data, possibly associated with multiple ejection events and decaying as the outburst proceeds. These results add to the discussion that the term "low/hard state'" covers a wider range of properties than previously thought, if it is to account for XTE J1118+480 during these two outbursts.Comment: Accepted for publication in MNRA

A new approach to generating research-quality data through citizen science: The USA National Phenology Monitoring System

Phenology is one of the most sensitive biological responses to climate change, and recent changes in phenology have the potential to shake up ecosystems. In some cases, it appears they already are. Thus, for ecological reasons it is critical that we improve our understanding of species’ phenologies and how these phenologies are responding to recent, rapid climate change. Phenological events like flowering and bird migrations are easy to observe, culturally important, and, at a fundamental level, naturally inspire human curiosity— thus providing an excellent opportunity to engage citizen scientists. The USA National Phenology Network has recently initiated a national effort to encourage people at different levels of expertise—from backyard naturalists to professional scientists—to observe phenological events and contribute to a national database that will be used to greatly improve our understanding of spatio-temporal variation in phenology and associated phenological responses to climate change.

Traditional phenological observation protocols identify specific dates at which individual phenological events are observed. The scientific usefulness of long-term phenological observations could be improved with a more carefully structured protocol. At the USA-NPN we have developed a new approach that directs observers to record each day that they observe an individual plant, and to assess and report the state of specific life stages (or phenophases) as occurring or not occurring on that plant for each observation date. Evaluation is phrased in terms of simple, easy-to-understand, questions (e.g. “Do you see open flowers?”), which makes it very appropriate for a citizen science audience. From this method, a rich dataset of phenological metrics can be extracted, including the duration of a phenophase (e.g. open flowers), the beginning and end points of a phenophase (e.g. traditional phenological events such as first flower and last flower), multiple distinct occurrences of phenophases within a single growing season (e.g multiple flowering events, common in drought-prone regions), as well as quantification of sampling frequency and observational uncertainties. These features greatly enhance the utility of the resulting data for statistical analyses addressing questions such as how phenological events vary in time and space, and in response to global change. This new protocol is an important step forward, and its widespread adoption will increase the scientific value of data collected by citizen scientists.
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Machine learning algorithms for the prediction of EUROP classification grade and carcass weight, using 3-dimensional measurements of beef carcasses

Introduction: Mechanical grading can be used to objectively classify beef carcasses. Despite its many benefits, it is scarcely used within the beef industry, often due to infrastructure and equipment costs. As technology progresses, systems become more physically compact, and data storage and processing methods are becoming more advanced. Purpose-built imaging systems can calculate 3-dimensional measurements of beef carcasses, which can be used for objective grading.Methods: This study explored the use of machine learning techniques (random forests and artificial neural networks) and their ability to predict carcass conformation class, fat class and cold carcass weight, using both 3-dimensional measurements (widths, lengths, and volumes) of beef carcasses, extracted using imaging technology, and fixed effects (kill date, breed type and sex). Cold carcass weight was also included as a fixed effect for prediction of conformation and fat classes.Results: Including the dimensional measurements improved prediction accuracies across traits and techniques compared to that of results from models built excluding the 3D measurements. Model validation of random forests resulted in moderate-high accuracies for cold carcass weight (R2 = 0.72), conformation class (71% correctly classified), and fat class (55% correctly classified). Similar accuracies were seen for the validation of the artificial neural networks, which resulted in high accuracies for cold carcass weight (R2 = 0.68) and conformation class (71%), and moderate for fat class (57%).Discussion: This study demonstrates the potential for 3D imaging technology requiring limited infrastructure, along with machine learning techniques, to predict key carcass traits in the beef industry