112 research outputs found
Austin Papers: Series IV, 1830
No full textCopy of transcript for a letter from John Raney to Stephen F. Austin, in which Raney informs Austin that he has had to postpone his travels to San Felipe to sign his colonization contract because he has been severely ill since July and can barely walk. He appeals to Austin to continue to hold the allotment of land until he recovers, arguing that he has already paid the necessary fees to have the area surveyed, etc
Jimmy Raney: blurring the barlines
No full textDespite the institutionalization of jazz music, and the large output of academic activity surrounding the music’s history, one is hard pressed to discover any information on the late jazz guitarist Jimmy Raney or the legacy Jimmy Raney left on the instrument. Guitar, often times, in the history of jazz has been regulated to the role of the rhythm section, if the guitar is involved at all. While the scope of the guitar throughout the history of jazz is not the subject matter of this thesis, the aim is to present, or bring to light Jimmy Raney, a jazz guitarist who I believe, while not the first, may have been among the first to pioneer and challenge these conventions. I have researched Jimmy Raney’s background, and interviewed two people who knew Jimmy Raney: his son, Jon Raney, and record producer Don Schlitten. These two individuals provide a beneficial contrast as one knew Jimmy Raney quite personally, and the other knew Jimmy Raney from a business perspective, creating a greater frame of reference when attempting to piece together Jimmy Raney. In addition, I have taken a look at two arrangements and solos, from what has often been referred to as Jimmy Raney’s seminal years. The results of which showed a great deal of versatility from Jimmy Raney, as the two have little in common. In conclusion, Jimmy Raney helped define a new role for the jazz guitar, with abilities to create spontaneous lines from one song to the next with as much originality as any instrument or musician in the jazz idiom might.M.A.Includes bibliographical referencesby Zachary Streete
Regional Ecological Resource Assessment of the Rio Grande Riparian Corridor - Data
No full textGIS data for the report: Regional Ecological Resource Assessment of the
Rio Grande Riparian Corridor: A Multidisciplinary Approach to Understanding Anthropogenic Effects on Riparian Communities in Semiarid Environments
Jay Raney, Principal Investigator, William White, and Thomas Tremblay
Bureau of Economic Geology, Jackson School of Geosciences,
The University of Texas at Austin
Melba Crawford, Co-Principal Investigator, Tatiana Encheva, and Amy Neuenschwander
Center for Space Research, The University of Texas at Austin
Frank Judd, Co-Principal Investigator, and Robert Lonard
The University of Texas-Pan American
Gene Paull, Co-Principal Investigator, Andrea Lopez, and Danny Govea
The University of Texas at Brownsvill
A nanostructural study of Raney-type nickel catalysts
No full textRaney-type nickel catalysts have been applied in commercial hydrogenation reactions for decades. They are relatively cheap and have proven to be very efficient in hydrogenation. The preparation process is relatively simple, but it appears that many parameters have an influence on the performance of the final catalyst. Consequently, the manufacturing of Raney-type nickel catalyst with an absolute control of the preparation process is hard to accomplish, leading to unpredictable variation in the catalyst selectivity and activity. A lot of information can be found in literature about characterization of Raney-type nickel catalysts, but many discrepancies exist so that general conclusions are hard to draw. It was the goal of this project to predict the catalytic properties from structure - catalytic performance relationships. Understanding the mechanism of transformation of starting alloys into Raney-type nickel catalysts was then required. To achieve this goal, a detailed characterization at different stages of the leaching process, coupled with the determination of the catalytic properties was performed (chapters 3 and 4). The usual starting alloy used in the preparation of Raney-type nickel catalysts is a 50-50 wt % Ni-Al alloy. As the addition of a second component to metal catalysts is widely used in order to enhance activity and/or selectivity, the effect of Mo, and a combination of Cr and Fe promoters on the performance of Raney-type nickel catalysts has also been investigated (chapter 5). Two extra different approaches in studying Raney-type nickel catalysts have been considered: a rapid solidification technique (melt spinning) to prepare the starting alloy (chapter 6) and the use of cryo-ultramicrotomy to prepare specimens For TEM/HREM characterization (chapter 7).Applied Science
Comparative study on catalytic hydrodehalogenation of halogenated aromatic compounds over Pd/C and Raney Ni catalysts
No full textCatalytic hydrodehalogenation (HDH) has proved to be an efficient approach to dispose halogenated aromatic compounds (HACs). Liquid-phase HDH of single and mixed halobenzenes/4-halophenols with H-2 over 5% Pd/C and Raney Ni catalyst are investigated and compared. For liquid-phase HDH of single HACs, hydrogenolytic scission reactivity of C-X bonds decreases in order of C-Br > C-Cl > C-I > C-F over Pd/C catalyst, and in order of C-I > C-Br > C-Cl > C-F over Raney Ni catalyst. To clarify the reason why hydrogenolytic scission reactivity of C-X bonds over Pd/C and Raney Ni catalysts exhibits different trends, liquid-phase HDH of mixed HACs over Pd/C and Raney Ni catalysts were studied, and catalysts are characterized by SEM, EDX, and XRD techniques. It was found that the high adsorption of iodoarenes on Pd/C catalyst caused the HDH reactivity of iodoarenes to be lower than that of chloroarenes and bromoarenes in the HDH of single HACs. Moreover, the adsorption of in situ produced iodine ion (I-) to catalyst surface would result in the decline of catalytic activity, which might be the main reason why the HDH reactivity of HACs in the presence of NaI is rather low
Raney a novel
No full text"Clyde Edgerton's Raney is the comic love story of a marriage between Raney, a small-town Southern Baptist, and Charles, a librarian with liberal leanings from Atlanta, united by their shared enthusiasm for country music. The novel both interrogates and honors the faiths and foibles of its subjects as the relationship is tested through trials and revelations. Despite the couple's differences, their marriage slowly evolves into a relationship of equals in which both are willing to compromise for the good of the other and the marriage. Told though Raney's naive and mesmerizing perspective as a southern storyteller, serious and sometimes heartbreaking moments give way to a humorous and joyful tale that pokes fun at and holds respect for just about everyone who passes through these pages. Raney, Edgerton's first novel, was originally published in 1985. It represents some of Edgerton's most comic, candid, and ambitious writing. This Southern Revivals edition includes a new introduction by the author and a preface from series editor Robert H. Brinkmeyer Jr., director of the University of South Carolina Institute for Southern Studies."..
Recommended from our members
Geologic Review of Propsed Amarillo Area Site for the Superconducting Super Collider (SSC)
Get PDFIn June 1987, the Texas National Research Laboratory Commission commissioned the Bureau of Economic Geology at The University of Texas at Austin to conduct a review and brief report on the geology of the proposed site for the Superconducting Super Collider (SSC) in the Amarillo area. They also requested a surface geologic map of the site. An informal task force was assembled for this purpose, including Jay A. Raney (Coordinator), Thomas C. Gustavson, and S. Christopher Caran from the Bureau of Economic Geology. This report is accompanied by the geologic map (Plate 1) of the proposed Amarillo area site in the Texas Panhandle.Bureau of Economic Geolog
Water: the most effective solvent for liquid-phase hydrodechlorination of chlorophenols over Raney Ni catalyst
No full textCatalytic hydrodechlorination (HDC) has proved to be an efficient approach to dispose chlorinated organic compounds (COCs). The influence of solvent on the HOC of chlorophenols with H-2 over Raney Ni catalyst was investigated. We found that protic solvents, especially water, significantly accelerated liquid-phase HOC that was sluggish in aprotic solvents. Among the selected solvents, water was the most effective solvent for liquid-phase HDC over Raney Ni catalyst. To study the mechanism for the rate acceleration in the HOC that was promoted by protic solvents, catalysts were characterized by SEM, EDX, and XRD techniques. It was found that solvent could affect surface composition and surface micro-topography of Raney Ni catalyst. On the basis of these studies, an effective and practical reaction system was developed to dispose chlorophenols over Raney Ni catalyst; a broad range of chlorophenols were efficiently hydrodechlorinated in aqueous solutions under mild conditions, and the catalyst could be reused at least five times without any loss of catalytic activity. (C) 2014 Elsevier B.V. All rights reserved
Metal fluoride promoted catalytic hydrogenation of aromatic nitro compounds over RANEY (R) Ni
No full textThe catalytic hydrogenation reactivity of aromatic nitro compounds over RANEY (R) Ni was substantially improved when a moderate amount of metal fluoride (NaF, KF, MgF2, and CaF2) was added into the reaction system.The catalytic hydrogenation reactivity of aromatic nitro compounds over RANEY (R) Ni was substantially improved when a moderate amount of metal fluoride (NaF, KF, MgF2, and CaF2) was added into the reaction system
A Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDET-GEE)
No full textA Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDET)
This is a Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDET) developed by the Surface Dynamics and Modeling Lab at the University of Alabama that calculates flood depth using a flood extent layer and a digital elevation model. This research is made possible by the CyberSeed Program at the University of Alabama. Project name: WaterServ: A Cyberinfrastructure for Analysis, Visualization and Sharing of Hydrological Data.
Please see the associated publications:
1. Peter, B.G., Cohen, S., Lucey, R., Munasinghe, D., Raney, A. and Brakenridge, G.R., 2020. Google Earth Engine Implementation of the Floodwater Depth Estimation Tool (FwDET-GEE) for rapid and large scale flood analysis. IEEE Geoscience and Remote Sensing Letters, 19, pp.1-5.
https://ieeexplore.ieee.org/abstract/document/9242297
2. Cohen, S., Peter, B.G., Haag, A., Munasinghe, D., Moragoda, N., Narayanan, A. and May, S., 2022. Sensitivity of remote sensing floodwater depth calculation to boundary filtering and digital elevation model selections. Remote Sensing, 14(21), p.5313.
https://www.mdpi.com/2072-4292/14/21/5313>https://www.mdpi.com/2072-4292/14/21/5313
GitHub Repository (ArcMap and QGIS implementations): https://github.com/csdms-contrib/fwdet
3. Cohen, S., A. Raney, D. Munasinghe, J.D. Loftis J, A. Molthan, J. Bell, L. Rogers, J. Galantowicz, G.R. Brakenridge7, A.J. Kettner, Y. Huang, Y. Tsang, (2019). The Floodwater Depth Estimation Tool (FwDET v2.0) for Improved Remote Sensing Analysis of Coastal Flooding. Natural Hazards and Earth System Sciences, 19, 2053–2065. https://doi.org/10.5194/nhess-19-2053-2019
4. Cohen, S., G. R. Brakenridge, A. Kettner, B. Bates, J. Nelson, R. McDonald, Y. Huang, D. Munasinghe, and J. Zhang (2018), Estimating Floodwater Depths from Flood Inundation Maps and Topography, Journal of the American Water Resources Association, 54 (4), 847–858. https://doi.org/10.1111/1752-1688.12609
Sample products and data availability:
https://sdml.ua.edu/models/fwdet/
https://sdml.ua.edu/michigan-flood-may-2020/
https://cartoscience.users.earthengine.app/view/fwdet-gee-mi
https://alabama.app.box.com/s/31p8pdh6ngwqnbcgzlhyk2gkbsd2elq0
GEE implementation output: fwdet_gee_brazos.tif
ArcMap implementation output (see Cohen et al. 2019): fwdet_v2_brazos.tif
iRIC validation layer (see Nelson et al. 2010): iric_brazos_hydraulic_model_validation.tif
Brazos River inundation polygon access in GEE: var brazos = ee.FeatureCollection('users/cartoscience/FwDET-GEE-Public/Brazos_River_Inundation_2016')
Nelson, J.M., Shimizu, Y., Takebayashi, H. and McDonald, R.R., 2010. The international river interface cooperative: public domain software for river modeling. In 2nd Joint Federal Interagency Conference, Las Vegas, June (Vol. 27).
Google Earth Engine Code
/* ----------------------------------------------------------------------------------------------------------------------
# FwDET-GEE calculates floodwater depth from a floodwater extent layer and a DEM
Authors: Brad G. Peter, Sagy Cohen, Ronan Lucey, Dinuke Munasinghe, Austin Raney
Emails: [email protected], [email protected], [email protected], [email protected], [email protected]
Organizations: BP, SC, DM, AR - University of Alabama; RL - University of Alabama in Huntsville
Last Modified: 10/08/2020
To cite this code use:
Peter, Brad; Cohen, Sagy; Lucey, Ronan; Munasinghe, Dinuke; Raney, Austin, 2020, "A Google Earth Engine implementation
of the Floodwater Depth Estimation Tool (FwDET-GEE)", https://doi.org/10.7910/DVN/JQ4BCN, Harvard Dataverse, V2
-------------------------------------------------------------------------------------------------------------------------
This is a Google Earth Engine implementation of the Floodwater Depth Estimation Tool (FwDETv2.0) [1] developed by the
Surface Dynamics and Modeling Lab at the University of Alabama that calculates flood depth using a flood extent layer
and a digital elevation model. This research is made possible by the CyberSeed Program at the University of Alabama.
Project name: WaterServ: A Cyberinfrastructure for Analysis, Visualization and Sharing of Hydrological Data.
GitHub Repository (ArcMap and QGIS implementations): https://github.com/csdms-contrib/fwdet
-------------------------------------------------------------------------------------------------------------------------
How to run this code with your flood extent GEE asset:
User of this script will need to update path to flood extent (line 32 or 33) and select from the processing options.
Available DEM options (1) are USGS/NED (U.S.) and USGS/SRTMGL1_003 (global). Other options include (2) running the elevation
outlier filtering algorithm, (3) adding water body data to the inundation extent, (4) add a water body data layer uploaded by
the user rather than using the JRC global surface water data, (5) masking out regular water body data, (6) masking out 0 m
depths, (7) choosing whether or not to export, (8) exporting additional data layers, and (9) setting an export file name.
The simpleVis option (10) bypasses the time consuming processes and is meant for visualization only; set this option to false
to complete the entire process and enable exporting.
-------------------------------------------------------------------------------------------------------------------------
••••••••••••••••••••••••••••••••••••••••••• USER OPTIONS ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••
Load flood extent layer | Flood extent layer must be uploaded to GEE first as an asset. If the flood extent is a
shapefile, upload as a FeatureCollection; otherwise, if the flood extent layer is a raster, upload it as an image.
A raster layer may be required if the flood extent is a highly complex geometry -------------------------------------- */
var flood = ee.FeatureCollection('users/username/folder/flood_extent') // comment out this line if using an Image
// var flood = ee.Image('users/username/folder/flood_extent') // comment out this line if using a FeatureCollection
var waterExtent = ee.FeatureCollection('users/username/folder/water_extent') // *OPTIONAL* comment out this line if using an Image
// var waterExtent = ee.Image('users/username/folder/water_extent') // *OPTIONAL* comment out this line if using a FeatureCollection
// Processing options - refer to the directions above
/*1*/ var demSource = 'USGS/NED' // 'USGS/NED' or 'USGS/SRTMGL1_003'
/*2*/ var outlierTest = 'TRUE' // 'TRUE' (default) or 'FALSE'
/*3*/ var addWater = 'TRUE' // 'TRUE' (default) or 'FALSE'
/*4*/ var userWater = 'FALSE' // 'TRUE' or 'FALSE' (default)
/*5*/ var maskWater = 'FALSE' // 'TRUE' or 'FALSE' (default)
/*6*/ var maskZero = 'FALSE' // 'TRUE' or 'FALSE' (default)
/*7*/ var exportLayer = 'TRUE' // 'TRUE' (default) or 'FALSE'
/*8*/ var exportAll = 'FALSE' // 'TRUE' or 'FALSE' (default)
/*9*/ var outputName = 'FwDET_GEE' // text string for naming export file
/*10*/ var simpleVis = 'FALSE' // 'TRUE' or 'FALSE' (default)
// ••••••••••••••••••••••••••••••••• NO USER INPUT BEYOND THIS POINT ••••••••••••••••••••••••••••••••••••••••••••••••••••
// Create buffer around flood area to use for clipping other layers
var area = flood.geometry().bounds().buffer(1000).bounds()
// Load DEM and grab projection info
var dem = ee.Image(demSource).select('elevation').clip(area) // [2,3]
var projection = dem.projection()
var resolution = projection.nominalScale().getInfo()
// Load global surface water layer
var jrc = ee.Image('JRC/GSW1_1/GlobalSurfaceWater').select('occurrence').clip(area) // [4]
var water_image = jrc
// User uploaded flood extent layer
// Identify if a raster or vector layer is being used and proceed with appropriate process
if ( flood.name() == 'FeatureCollection' ) {
var addProperty = function(feature) {
return feature.set('val',0);
};
var flood_image = flood.map(addProperty).reduceToImage(['val'],ee.Reducer.first())
.rename('flood')
} else {
var flood_image = flood.multiply(0)
}
// Optional user uploaded water extent layer
if ( userWater == 'TRUE' ) {
// Identify if a raster or vector layer is being used and proceed with appropriate process
if ( waterExtent.name() == 'FeatureCollection' ) {
var addProperty = function(feature) {
return feature.set('val',0);
};
var water_image = waterExtent.map(addProperty).reduceToImage(['val'],ee.Reducer.first())
.rename('flood')
} else {
var water_image = waterExtent.multiply(0)
}
}
// Add water bodies to flood extent if 'TRUE' is selected
if ( addWater == 'TRUE' ) {
var w = water_image.reproject(projection)
var waterFill = flood_image.mask().where(w.gt(0),1)
flood_image = waterFill.updateMask(waterFill.eq(1)).multiply(0)
}
// Change processing options if 'TRUE' is selected
if ( simpleVis == 'FALSE' ) {
flood_image = flood_image.reproject(projection)
} else {
outlierTest = 'FALSE'
exportLayer = 'FALSE'
}
// Run the outlier filtering process if 'TRUE' is selected
if ( outlierTest == 'TRUE' ) {
// Outlier detection and filling on complete DEM using the modified z-score and a median filter [5]
var kernel = ee.Kernel.fixed(3,3,[[1,1,1],[1,1,1],[1,1,1]])
var kernel_weighted = ee.Kernel.fixed(3,3,[[1,1,1],[1,0,1],[1,1,1]])
var median = dem.focal_median({kernel:kernel}).reproject(projection)
var median_weighted = dem.focal_median({kernel:kernel_weighted}).reproject(projection)
var diff = dem.subtract(median)
var mzscore = diff.multiply(0.6745).divide(diff.abs().focal_median({kernel:kernel}).reproject(projection))
var fillDEM = dem.where(mzscore.gt(3.5),median_weighted)
// Outlier detection and filling on the flood extent border pixels
var expand = flood_image.focal_max({kernel: ee.Kernel.square({
radius: projection.nominalScale(),
units: 'meters'
})}).reproject(projection)
var demMask = fillDEM.updateMask(flood_image.mask().eq(0))
var boundary = demMask.add(expand)
var medianBoundary = boundary.focal_median({kernel:kernel}).reproject(projection)
var medianWeightedBoundary = boundary.focal_median({kernel:kernel_weighted}).reproject(projection)
var diffBoundary = boundary.subtract(medianBoundary)
var mzscoreBoundary = diffBoundary.multiply(0.6745).divide(diffBoundary.abs().focal_median({kernel:kernel}).reproject(projection))
var fill = fillDEM.where(mzscoreBoundary.gt(3.5),medianWeightedBoundary)
} else {
var fill = dem
}
// cumulativeCost floodwater surface elevation model (adaptation of the cost allocation method from FwDETv2.0)
var mod = fill.updateMask(flood_image.mask().eq(0))
var source = mod.mask()
var val = 10000
var push = 5000
var cost0 = ee.Image(val).where(source,0).cumulativeCost(source,push)
var cost1 = ee.Image(val).where(source,1).cumulativeCost(source,push)
var cost2 = mod.unmask(val).cumulativeCost(source,push)
var costFill = cost2.subtract(cost0).divide(cost1.subtract(cost0))
var costSurface = mod.unmask(0).add(costFill)
// Interpolation method courtesy of Matt Hancher (Earth Engine Co-Founder) posted to the GEE developer forums
// https://groups.google.com/forum/#!forum/google-earth-engine-developers
// Kernel size for low-pass filter
var boxcar = ee.Kernel.square({
radius: 3, units: 'pixels', normalize: true
});
// Floodwater depth calculation and smoothing using a low-pass filter
var costDepth = costSurface.subtract(fill)
.rename('FwDET_GEE')
.convolve(boxcar)
.reproject(projection)
.updateMask(flood_image.eq(0))
var costDepthFilter = costDepth.where(costDepth.lt(0),0)
// Mask out regular water bodies if 'TRUE' is selected
if ( maskWater === 'TRUE' ) {
var w = jrc.reproject(projection)
costDepthFilter = costDepthFilter.updateMask(w.mask().eq(0))
}
// Mask out zero values if 'TRUE' is selected
if ( maskZero === 'TRUE' ) {
costDepthFilter = costDepthFilter.updateMask(costDepthFilter.neq(0))
}
// Add flood depth to console
Map.clear()
Map.setOptions('HYBRID')
Map.centerObject(flood)
// Add message to user
var message = ui.Panel({
layout: ui.Panel.Layout.flow('vertical'),
style: {position: 'bottom-left', border: '1px solid gray', padding: '2px'}
})
message.widgets().set(0,ui.Label('Check tasks tab for export'));
Map.add(message);
// Change options for visualization sample
if ( simpleVis == 'TRUE' ) {
var histOptions = {
title: 'Depth (m)',
fontSize: 11,
legend: {position: 'none'},
series: {0: {color: '7100AA'}}
};
var histogram = ui.Chart.image.histogram({
image: costDepthFilter.updateMask(costDepthFilter.neq(0)),
region: area,
scale: resolution*10
}).setOptions(histOptions)
Map.addLayer(costDepthFilter,{},'FwDET GEE',true)
message.widgets().set(0,ui.Label("set simpleVis to 'FALSE' to export layers"))
message.widgets().set(1,histogram)
} else {
Map.addLayer(flood_image,{},'flood extent',true)
Map.addLayer(costDepthFilter,{},'FwDET GEE',false)
}
// Export function
var exportFunc = function(i,n) {
return Export.image.toDrive({
image: i,
description: outputName+n,
fileNamePrefix: outputName+n,
maxPixels: 1e13,
scale: resolution,
region: area
})
}
// Export output if 'TRUE' is selected
if ( exportLayer === 'TRUE' ) {
exportFunc(costDepthFilter, '_FwDET')
// Export all layers if 'TRUE' is selected
if ( exportAll == 'TRUE' ) {
exportFunc(costSurface, '_costSurface')
exportFunc(dem, '_dem')
exportFunc(fill, '_demFill')
}
}
/* ----------------------------------------------------------------------------------------------------------------------
// Citations
// [1] Cohen, S., A. Raney, D. Munasinghe, J.D. Loftis J, A. Molthan, J. Bell, L. Rogers, J. Galantowicz,
// G.R. Brakenridge7, A.J. Kettner, Y. Huang, Y. Tsang, (2019). The Floodwater Depth Estimation Tool (FwDET v2.0)
// for Improved Remote Sensing Analysis of Coastal Flooding. Natural Hazards and Earth System Sciences, 19, 2053–2065.
// https://doi.org/10.5194/nhess-19-2053-2019
// [2] Gesch, D., Oimoen, M., Greenlee, S., Nelson, C., Steuck, M. and Tyler, D., 2002. The national elevation dataset.
// Photogrammetric engineering and remote sensing, 68(1), pp.5-32.
// [3] Farr, T.G., Rosen, P.A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E.,
// Roth, L. and Seal, D., 2007. The shuttle radar topography mission. Reviews of geophysics, 45(2).
// [4] Pekel, J.F., Cottam, A., Gorelick, N. and Belward, A.S., 2016. High-resolution mapping of global surface
// water and its long-term changes. Nature, 540(7633), pp.418-422.
// [5] Iglewicz, B. and Hoaglin, D.C., 1993. How to detect and handle outliers (Vol. 16). Asq Press.
---------------------------------------------------------------------------------------------------------------------- */
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