An assessment of foster youth and the California High School Exit Exam

The purpose of this study was to identify possible variables that may contribute to a foster child\u27s passing or failing of the California High School Exit Exam

Ariel - Volume 3 Number 4

Editors Richard J. Bonanno Robin A. Edwards Associate Editors Steven Ager Tom Williams Lay-out Editor Eugenia Miller Contributing Editors Paul Bialas Robert Breckenridge Lynne Porter David Jacoby Terry Burt Mark Pearlman Michael Leo Mike LeWitt Editors Emeritus Delvyn C. Case, Jr. Paul M. Fernhof

Ariel - Volume 2 Number 4

Copyright 1969 Arie

Ariel - Volume 1 Number 2

Copyright 1969 Arie

Gap analysis: a geographic approach for assessing national biological diversity

The global concern with reduction in biodiversity has generated responses in the United States, such as the Endangered Species Act (ESA). Although the ESA has had some effect, the species-by-species approach presents a problem because it does not consider the broad ecological principles of biodiversity including the need for balance between different species and their combined influence on a given habitat. There is an implicit assumption that national parks, wildlife sanctuaries, and other protected areas provide for conservation needs. However, these areas have not necessarily been delineated on the basis of animal habitat zones or ecologically significant units. Gap Analysis is an evaluation method providing a systematic approach for assessing the protection afforded biodiversity in a given area. It uses geographic information systems to identify gaps in biodiversity protection that may be filled by the establishment of new preserves or changes in land-use practices. Gap Analysis has three primary layers: (1) distribution of vegetation types delineated from satellite imagery, (2) land ownership, and (3) distribution of vegetation types delineated from satellite imagery, habitat preference models. Vegetation classification procedures using satellite image or aerial photograph analysis are linked to wildlife/ habitat databases. Gap analysis includes seral as well as climax vegetation, and classes must be compatible with those used in neighboring states. The examples of these procedures for the Utah Gap Analysis are given with some reference to Gap Analysis in other states. The overall approach provides a logical base for evaluating and protecting national biological diversity

Ariel - Volume 2 Number 3

Editors Delvyn C. Case, Jr. Paul M. Fernhoff News Editors Richard Bonanno Daniel B. Gould Robin A. Edwards Lay-Out Editor Carol Dolinskas Sports Editor James J. Nocon Contributing Editors Michael J. Blecker Lin Sey Edwards Jack Guralnik W. Cherry Light Features Editor Steven A. Ager Donald A. Bergman Stephen P. Flynn Business Manager Nick Greg

Laminar-Turbulent Transition Behind Discrete Roughness Elements in a High-Speed Boundary Layer

Computations are performed to study the flow past an isolated roughness element in a Mach 3.5, laminar, flat plate boundary layer. To determine the effects of the roughness element on the location of laminar-turbulent transition inside the boundary layer, the instability characteristics of the stationary wake behind the roughness element are investigated over a range of roughness heights. The wake flow adjacent to the spanwise plane of symmetry is characterized by a narrow region of increased boundary layer thickness. Beyond the near wake region, the centerline streak is surrounded by a pair of high-speed streaks with reduced boundary layer thickness and a secondary, outer pair of lower-speed streaks. Similar to the spanwise periodic pattern of streaks behind an array of regularly spaced roughness elements, the above wake structure persists over large distances and can sustain strong enough convective instabilities to cause an earlier onset of transition when the roughness height is sufficiently large. Time accurate computations are performed to clarify additional issues such as the role of the nearfield of the roughness element during the generation of streak instabilities, as well as to reveal selected details of their nonlinear evolution. Effects of roughness element shape on the streak amplitudes and the interactions between multiple roughness elements aligned along the flow direction are also investigated

Eagle Wallet

The COVID-19 pandemic has pushed the world towards contactless technology. With the increase of these tech innovations the advantages have become clear. Using a smart phone to pay for items is convenient and efficient. The “Eagle Wallet” Android application utilizes Radio Frequency ID (RFID)/Near Field Communications (NFC) technology so students, faculty, and staff can use their smart phone to pay for meals. The application allows users to take advantage of the University’s “Dining Dollars” discount of ten percent off posted prices as well as view progress of any meal plans purchased. The user’s login credentials are stored safely in a remote server, and bank information uses bank grade security. The future implementations for the application would be to have the application work with on campus scanners at vending machines, campus merchants, bookstore, and buildings. The less contact between people and public items the less the virus is spread on campus

Space Environmental Effects on the Optical Properties of Selected Transparent Polymers

Transparent polymer films are currently considered for use as solar concentrating lenses for spacecraft power and propulsion systems. These polymer films concentrate solar energy onto energy conversion devices such as solar cells and thermal energy systems. Conversion efficiency is directly related to the polymer transmission. Space environmental effects will decrease the transmission and thus reduce the conversion efficiency. This investigation focuses on the effects of ultraviolet and charged particle radiation on the transmission of selected transparent polymers. Multiple candidate polymer samples were exposed to near ultraviolet (NUV) radiation to screen the materials and select optimum materials for further study. All materials experienced transmission degradation of varying degree. A method was developed to normalize the transmission loss and thus rank the materials according to their tolerance of NUV. Teflon(Tm) FEP and Teflon(Tm) PFA were selected for further study. These materials were subjected to a combined charged particle dose equivalent to 5 years in a typical geosynchronous Earth orbit (GEO). Results from these NUV screening tests and the 5 year GEO equivalent dose are presented

Machine learning for predicting soil classes in three semi-arid landscapes

Mapping the spatial distribution of soil taxonomic classes is important for informing soil use and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial distribution of soil taxonomic classes. Key components of DSM are the method and the set of environmental covariates used to predict soil classes. Machine learning is a general term for a broad set of statistical modeling techniques. Many different machine learning models have been applied in the literature and there are different approaches for selecting covariates for DSM. However, there is little guidance as to which, if any, machine learning model and covariate set might be optimal for predicting soil classes across different landscapes. Our objective was to compare multiple machine learning models and covariate sets for predicting soil taxonomic classes at three geographically distinct areas in the semi-arid western United States of America (southern New Mexico, southwestern Utah, and northeastern Wyoming). All three areas were the focus of digital soil mapping studies. Sampling sites at each study area were selected using conditioned Latin hypercube sampling (cLHS). We compared models that had been used in other DSM studies, including clustering algorithms, discriminant analysis, multinomial logistic regression, neural networks, tree based methods, and support vector machine classifiers. Tested machine learning models were divided into three groups based on model complexity: simple, moderate, and complex. We also compared environmental covariates derived from digital elevation models and Landsat imagery that were divided into three different sets: 1) covariates selected a priori by soil scientists familiar with each area and used as input into cLHS, 2) the covariates in set 1 plus 113 additional covariates, and 3) covariates selected using recursive feature elimination. Overall, complex models were consistently more accurate than simple or moderately complex models.Random forests (RF) using covariates selected via recursive feature elimination was consistently most accurate, or was among the most accurate, classifiers sets within each study area. We recommend that for soil taxonomic class prediction, complex models and covariates selected by recursive feature elimination be used. Overall classification accuracy in each study area was largely dependent upon the number of soil taxonomic classes and the frequency distribution of pedon observations between taxonomic classes. 43 Individual subgroup class accuracy was generally dependent upon the number of soil pedon 44 observations in each taxonomic class. The number of soil classes is related to the inherent variability of a given area. The imbalance of soil pedon observations between classes is likely related to cLHS. Imbalanced frequency distributions of soil pedon observations between classes must be addressed to improve model accuracy. Solutions include increasing the number of soil pedon observations in classes with few observations or decreasing the number of classes. Spatial predictions using the most accurate models generally agree with expected soil-landscape relationships. Spatial prediction uncertainty was lowest in areas of relatively low relief for each study area