Alien Registration- Daigle, Pierre H. (Madawaska, Aroostook County)

https://digitalmaine.com/alien_docs/35381/thumbnail.jp

Alien Registration- Daigle, Joseph H. (Allagash, Aroostook County)

https://digitalmaine.com/alien_docs/32786/thumbnail.jp

Applying Model-based Diagnosis to a Rapid Propellant Loading System

The overall objective of the US Air Force Research Laboratory (AFRL) Rapid Propellant Loading (RPL) Program is to develop a launch vehicle, payload and ground support equipment that can support a rapid propellant load and launch within one hour. NASA Kennedy Space Center (KSC) has been funded by AFRL to develop hardware and software to demonstrate this capability. The key features of the software would be the ability to recognize and adapt to failures in the physical hardware components, advise operators of equipment faults and workarounds, and put the system in a safe configuration if unable to fly. In December 2008 NASA KSC and NASA Ames Research Center (ARC) demonstrated model based simulation and diagnosis capabilities for a scaled-down configuration of the RPL hardware. In this paper we present a description of the model-based technologies that were included as part of this demonstration and the results that were achieved. In continuation of this work we are currently testing the technologies on a simulation of the complete RPL system. Later in the year, when the RPL hardware is ready, we will be integrating these technologies with the real-time operation of the system to provide live state estimates. In future years we will be developing the capability to recover from faulty conditions via redundancy and reconfiguration

On the relevance of the Tremaine-Weinberg method applied to H-alpha velocity field.Pattern speeds determination in M100 (NGC 4321)

The relevance of the Tremaine-Weinberg (TW) method is tested to measure the bar, spiral and inner structure pattern speeds using a gaseous velocity field. The TW method is applied to various simulated barred galaxies in order to demonstrate its validity in seven different configurations, including star formation or/and dark matter halo. The reliability of the different physical processes involved and of the various observational parameters are also tested. The simulations show that the TW method could be applied to the gaseous velocity fields to get a good estimate of the bar pattern speed, under the condition that regions of shocks are avoided and measurements are confined to regions where the gaseous bar is well formed. We successfully apply the TW method to the \ha velocity field of the Virgo Cluster Galaxy M100 (NGC 4321) and derive pattern speeds of 55+/-5 km/s/kpc for the nuclear structure, 30+/-2 km/s/kpc for the bar and 20+/-1 km/s/kpc for the spiral pattern, in full agreement with published determinations using the same method or alternative ones.Comment: 29 pages, 8 figures, accepted for publication in ApJ. To obtain a higher resolution version, visit to http://www.astro.umontreal.ca/fantomm/bhabar

Drosophila CENP-A Mutations Cause a BubR1- Dependent Early Mitotic Delay without Normal Localization of Kinetochore Components

The centromere/kinetochore complex plays an essential role in cell and organismal viability by ensuring chromosome movements during mitosis and meiosis. The kinetochore also mediates the spindle attachment checkpoint (SAC), which delays anaphase initiation until all chromosomes have achieved bipolar attachment of kinetochores to the mitotic spindle. CENP-A proteins are centromere-specific chromatin components that provide both a structural and a functional foundation for kinetochore formation. Here we show that cells in Drosophila embryos homozygous for null mutations in CENP-A (CID) display an early mitotic delay. This mitotic delay is not suppressed by inactivation of the DNA damage checkpoint and is unlikely to be the result of DNA damage. Surprisingly, mutation of the SAC component BUBR1 partially suppresses this mitotic delay. Furthermore, cid mutants retain an intact SAC response to spindle disruption despite the inability of many kinetochore proteins, including SAC components, to target to kinetochores. We propose that SAC components are able to monitor spindle assembly and inhibit cell cycle progression in the absence of sustained kinetochore localization

Affine T-varieties of complexity one and locally nilpotent derivations

Let X=spec A be a normal affine variety over an algebraically closed field k of characteristic 0 endowed with an effective action of a torus T of dimension n. Let also D be a homogeneous locally nilpotent derivation on the normal affine Z^n-graded domain A, so that D generates a k_+-action on X that is normalized by the T-action. We provide a complete classification of pairs (X,D) in two cases: for toric varieties (n=\dim X) and in the case where n=\dim X-1. This generalizes previously known results for surfaces due to Flenner and Zaidenberg. As an application we compute the homogeneous Makar-Limanov invariant of such varieties. In particular we exhibit a family of non-rational varieties with trivial Makar-Limanov invariant.Comment: 31 pages. Minor changes in the structure. Fixed some typo

Soil Compaction Thresholds for the M1A1 Abrams Tank: Field Study at Camp Minden, La. (Bulletin #891)

The purpose of this study was to establish critical soil compaction thresholds for M1A1 Abrams battle tank traffic in an effort to minimize soil physical properties that adversely affect vegetation regeneration.https://digitalcommons.lsu.edu/agcenter_bulletins/1004/thumbnail.jp

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur