Spatial and Temporal Analysis of the Morphological and Land use Characteristics of the Buffalo River Watershed

The Buffalo River was established by Congress iQ. 1972 as the first National River in the United States. The Buffalo River, which originates in the higher elevations of the Boston Mountains in Newton County, is one of the few remaining free-flowing streams in Arkansas. It is considered to be one of Arkansas\u27 greatest natural treasures, and thus, there is strong interest in protecting it from anthropogenic influences. An initial characterization of the soil taxonomic units, watershed boundaries, topography and physiographic units in the Buffalo River Watershed was presented by Scott and Smith (1994). The spatial distribution of the geologic units in the watershed was presented by Hofer et al. (1995)

Spatial Distribution of the Surface Geology and 1992 Land Use of the Buffalo River Watershed

The Buffalo River was established by Congress in 1972 as the first National River in the United States and is one of the few remaining free-flowing streams in Arkansas . The Buffalo River flows through the three major physiographic provinces of northern Arkansas, originating in the higher elevations of the Boston Mountains, and flowing generally northeastward to cut through the Springfield and Salem Plateaus. It drops from approximately 2000 feet in the headwaters to around 500 feet above sea level at its confluence with the White River in Marion County. The Buffalo River is considered to be one of Arkansas\u27 greatest natural treasures; thus there is strong interest in protecting it from undue anthropogenic influences. A general description of the area within the Buffalo River Watershed was given by Smith (1967)

Landuse and Landcover Classification for Independence, Union, Bradley and Cleveland Counties of Arkansas

Landuse/landcover (LULC} is a function of natural factors such as soils, climate and water and of anthropogenic factors such as predominant use. In Arkansas, the primary use of the land varies depending on the physiographic region. For example, in the Mississippi Delta region of eastern Arkansas row crop agriculture predominates, whereas in the Gulf Coastal Plains forestry dominates. In both regions LULC is spatially and temporally . variable. Knowledge of LULC can be used in estimating the potential for agricultural production, locations of critical ecological areas, siting of homes, small businesses, industries, roads and landfills as well as source areas of pollution

Spatial and Temporal Analyses of the Morphological and Land Use Characteristics of the Buffalo River Watershed

The Buffalo River was established by Congress it11972 as the first National River in the United States. The Buffalo River, which originates in the higher elevations of the Boston Mountains in Newton County, is one of the few remaining free-flowing streams in Arkansas. It is considered to be one of Arkansas\u27 greatest natural treasures, and thus, there is strong interest in protecting it from anthropogenic influences. An initial characterization of the soil taxonomic units, watershed boundaries, topography and physiographic units in the Buffalo River Watershed was presented by Scott and Smith (1994). The spatial distribution of the geologic units in the watershed was presented by Hofer et al. (1995)

The unity and diversity of executive functions: A systematic review and re-analysis of latent variable studies.

Confirmatory factor analysis (CFA) has been frequently applied to executive function measurement since first used to identify a three-factor model of inhibition, updating, and shifting; however, subsequent CFAs have supported inconsistent models across the life span, ranging from unidimensional to nested-factor models (i.e., bifactor without inhibition). This systematic review summarized CFAs on performance-based tests of executive functions and reanalyzed summary data to identify best-fitting models. Eligible CFAs involved 46 samples (N = 9,756). The most frequently accepted models varied by age (i.e., preschool = one/two-factor; school-age = three-factor; adolescent/adult = three/nested-factor; older adult = two/three-factor), and most often included updating/working memory, inhibition, and shifting factors. A bootstrap reanalysis simulated 5,000 samples from 21 correlation matrices (11 child/adolescent; 10 adult) from studies including the three most common factors, fitting seven competing models. Model results were summarized as the mean percent accepted (i.e., average rate at which models converged and met fit thresholds: CFI ≥ .90/RMSEA ≤ .08) and mean percent selected (i.e., average rate at which a model showed superior fit to other models: ΔCFI ≥ .005/.010/ΔRMSEA ≤ -.010/-.015). No model consistently converged and met fit criteria in all samples. Among adult samples, the nested-factor was accepted (41-42%) and selected (8-30%) most often. Among child/adolescent samples, the unidimensional model was accepted (32-36%) and selected (21-53%) most often, with some support for two-factor models without a differentiated shifting factor. Results show some evidence for greater unidimensionality of executive function among child/adolescent samples and both unity and diversity among adult samples. However, low rates of model acceptance/selection suggest possible bias toward the publication of well-fitting but potentially nonreplicable models with underpowered samples. (PsycINFO Database Record (c) 2018 APA, all rights reserved)

Automated Scenario Generation Environment

Report describes IST\u27s investigation into the feasibility of automating the process of planning and scenario generation for large scale (joint level) simulation exercises and development of an architecture for that purpose

Expanded Thruster Mass Model Incorporating Nested Hall Thrusters

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143089/1/6.2017-4729.pd

Population Inference with Mortality and Attrition in Longitudinal Studies on Aging: A Two-Stage Multiple Imputation Method

First paragraph: Although there are numerous challenges for the investigation of aging-related changes in older adults, statistical analysis with incomplete data and the conceptualization of population processes related to mortality is one of the most difficult. Selective attrition and mortality selection within longitudinal studies on aging are intrinsically related to many aging-related changes and must be carefully considered in the analysis and interpretation of results (e.g., Baltes, 1968; Hofer & Sliwinski, 2006; Schaie, Labouvie, & Barrett, 1973). A key distinction is made between attrition (i.e., selective dropout) and mortality selection (i.e., selective survival) in that attrition affects characteristics of the particular sample under investigation, whereas mortality selection affects both the definition of the population as well as the sample under study (Baltes, 1968). Including time-to-death as a predictor in models for estimating change in outcomes of interest permits conditional inferences to defined populations based on age and survival (and their interaction) and is easily performed when complete data are available for both chronological age and age of death. In most studies, however, complete data for all individuals are not currently available and may not be available for a substantial period of time. The purpose of the current work is to present a two-stage multiple-imputation approach for treating mortality and attrition as distinct processes leading to incomplete data and which permit the use of time-to-death in the predictive models when follow-up is incomplete