Navigating the Information Highway: A Multilayered Approach for First-Year Graduate Students

Taylor University’s Zondervan Library developed a multifaceted approach of engagement with graduate students of the Master of Higher Education and Student Development program, utilizing a variety of venues and courses relating to advanced research and writing. Regular assessments provided feedback for improvement within the embedded program structure. A second component of this model involved an archival project, which facilitated student research with primary documents in the university archives. Overall, graduate student understanding and ownership of the research process increased, and teaching faculty noticed improvement in the quality of research-based assignments as well as the program’s thesis project

Sparse matrix methods research using the CSM testbed software system

Research is described on sparse matrix techniques for the Computational Structural Mechanics (CSM) Testbed. The primary objective was to compare the performance of state-of-the-art techniques for solving sparse systems with those that are currently available in the CSM Testbed. Thus, one of the first tasks was to become familiar with the structure of the testbed, and to install some or all of the SPARSPAK package in the testbed. A suite of subroutines to extract from the data base the relevant structural and numerical information about the matrix equations was written, and all the demonstration problems distributed with the testbed were successfully solved. These codes were documented, and performance studies comparing the SPARSPAK technology to the methods currently in the testbed were completed. In addition, some preliminary studies were done comparing some recently developed out-of-core techniques with the performance of the testbed processor INV

COBE experience with filter QUEST

A gyro based filter variation on the standard QUEST attitude determination algorithm is applied to the Cosmic Background Explorer (COBE). Filter QUEST is found to be three times as fast as the batch estimator and slightly more accurate than regular QUEST. Perhaps more important than its speed or accuracy is the fact that Filter QUEST can provide real time attitude solutions when regular QUEST cannot, due to lack of observability. Filter QUEST is also easy to use and adjust for the proper memory length. Suitable applications for Filter QUEST include coarse and real time attitude determination

Factors predicting physical activity among children with special needs.

IntroductionObesity is especially prevalent among children with special needs. Both lack of physical activity and unhealthful eating are major contributing factors. The objective of our study was to investigate barriers to physical activity among these children.MethodsWe surveyed parents of the 171 children attending Vista Del Mar School in Los Angeles, a nonprofit school serving a socioeconomically diverse group of children with special needs from kindergarten through 12th grade. Parents were asked about their child's and their own physical activity habits, barriers to their child's exercise, and demographics. The response rate was 67%. Multivariate logistic regression was used to examine predictors of children being physically active at least 3 hours per week.ResultsParents reported that 45% of the children were diagnosed with attention deficit hyperactivity disorder, 38% with autism, and 34% with learning disabilities; 47% of children and 56% of parents were physically active less than 3 hours per week. The top barriers to physical activity were reported as child's lack of interest (43%), lack of developmentally appropriate programs (33%), too many behavioral problems (32%), and parents' lack of time (29%). However, child's lack of interest was the only parent-reported barrier independently associated with children's physical activity. Meanwhile, children whose parents were physically active at least 3 hours per week were 4.2 times as likely to be physically active as children whose parents were less physically active (P = .01).ConclusionIn this group of students with special needs, children's physical activity was strongly associated with parental physical activity; parent-reported barriers may have had less direct effect. Further studies should examine the importance of parental physical activity among children with special needs

Structural properties of impact ices accreted on aircraft structures

The structural properties of ice accretions formed on aircraft surfaces are studied. The overall objectives are to measure basic structural properties of impact ices and to develop finite element analytical procedures for use in the design of all deicing systems. The Icing Research Tunnel (IRT) was used to produce simulated natural ice accretion over a wide range of icing conditions. Two different test apparatus were used to measure each of the three basic mechanical properties: tensile, shear, and peeling. Data was obtained on both adhesive shear strength of impact ices and peeling forces for various icing conditions. The influences of various icing parameters such as tunnel air temperature and velocity, icing cloud drop size, material substrate, surface temperature at ice/material interface, and ice thickness were studied. A finite element analysis of the shear test apparatus was developed in order to gain more insight in the evaluation of the test data. A comparison with other investigators was made. The result shows that the adhesive shear strength of impact ice typically varies between 40 and 50 psi, with peak strength reaching 120 psi and is not dependent on the kind of substrate used, the thickness of accreted ice, and tunnel temperature below 4 C

COBE attitude as seen from the FDF

The goal of the Flight Dynamics Facility (FDF) attitude support is twofold: to determine spacecraft attitude and to explain deviations from nominal attitude behavior. Attitude determination often requires resolving contradictions in the sensor observations. This may be accomplished by applying calibration corrections or by revising the observation models. After accounting for all known sources of error, solution accuracy should be limited only by observation and propagation noise. The second half of the goal is to explain why the attitude may not be as originally intended. Reasons for such deviations include sensor or actuator misalignments and control system performance. In these cases, the ability to explain the behavior should, in principle, be limited only by knowledge of the sensor and actuator data and external torques. Documented here are some results obtained to date in support of the Cosmic Background Explorer (COBE). Advantages and shortcomings of the integrated attitude determination/sensor calibration software are discussed. Some preliminary attitude solutions using data from the Diffuse Infrared Background Experiment (DIRBE) instrument are presented and compared to solutions using Sun and Earth sensors. A dynamical model is constructed to illustrate the relative importance of the various sensor imprefections. This model also shows the connection between the high- and low-frequency attitude oscillations

Ultrafast processing of pixel detector data with machine learning frameworks

Modern photon science performed at high repetition rate free-electron laser (FEL) facilities and beyond relies on 2D pixel detectors operating at increasing frequencies (towards 100 kHz at LCLS-II) and producing rapidly increasing amounts of data (towards TB/s). This data must be rapidly stored for offline analysis and summarized in real time. While at LCLS all raw data has been stored, at LCLS-II this would lead to a prohibitive cost; instead, enabling real time processing of pixel detector raw data allows reducing the size and cost of online processing, offline processing and storage by orders of magnitude while preserving full photon information, by taking advantage of the compressibility of sparse data typical for LCLS-II applications. We investigated if recent developments in machine learning are useful in data processing for high speed pixel detectors and found that typical deep learning models and autoencoder architectures failed to yield useful noise reduction while preserving full photon information, presumably because of the very different statistics and feature sets between computer vision and radiation imaging. However, we redesigned in Tensorflow mathematically equivalent versions of the state-of-the-art, "classical" algorithms used at LCLS. The novel Tensorflow models resulted in elegant, compact and hardware agnostic code, gaining 1 to 2 orders of magnitude faster processing on an inexpensive consumer GPU, reducing by 3 orders of magnitude the projected cost of online analysis at LCLS-II. Computer vision a decade ago was dominated by hand-crafted filters; their structure inspired the deep learning revolution resulting in modern deep convolutional networks; similarly, our novel Tensorflow filters provide inspiration for designing future deep learning architectures for ultrafast and efficient processing and classification of pixel detector images at FEL facilities.Comment: 9 pages, 9 figure