Matching Items (467)
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ContributorsChan, Robbie (Performer) / McCarrel, Kyla (Performer) / Sadownik, Stephanie (Performer) / ASU Library. Music Library (Contributor)
Created2018-04-18
ContributorsMayo, Joshua (Contributor) / Betts, Sarah (Performer) / Cericola, Marco (Performer) / Gonzalez, Nikolas (Performer) / ASU Library. Music Library (Publisher)
Created2021-04-28
Description
Whenever a text is transmitted, or communicated by any means, variations may occur because editors, copyists, and performers are often not careful enough with the source itself. As a result, a flawed text may come to be accepted in good faith through repetition, and may often be preferred over the authentic version because familiarity with the flawed copy has been established. This is certainly the case with regard to Manuel M. Ponce's guitar editions. An inexact edition of a musical work is detrimental to several key components of its performance: musical interpretation, aesthetics, and the original musical concept of the composer. These phenomena may be seen in the case of Manuel Ponce's Suite in D Major for guitar. The single published edition by Peer International Corporation in 1967 with the revision and fingering of Manuel López Ramos contains many copying mistakes and intentional, but unauthorized, changes to the original composition. For the present project, the present writer was able to obtain a little-known copy of the original manuscript of this work, and to document these discrepancies in order to produce a new performance edition that is more closely based on Ponce's original work.
ContributorsReyes Paz, Ricardo (Author) / Koonce, Frank (Thesis advisor) / Solis, Theodore (Committee member) / Rotaru, Catalin (Committee member) / Arizona State University (Publisher)
Created2013
ContributorsDaval, Charles (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-26
ContributorsMayo, Joshua (Performer) / ASU Library. Music Library (Publisher)
Created2021-04-29
ContributorsDominguez, Ramon (Performer) / ASU Library. Music Library (Publisher)
Created2021-04-15
ContributorsWhite, Bill (Performer) / ASU Library. Music Library (Publisher)
Created2021-04-03
ContributorsSanchez, Armand (Performer) / Nordstrom, Nathan (Performer) / Roubison, Ryan (Performer) / ASU Library. Music Library (Publisher)
Created2018-04-13
Description
In mixture-process variable experiments, it is common that the number of runs is greater than in mixture-only or process-variable experiments. These experiments have to estimate the parameters from the mixture components, process variables, and interactions of both variables. In some of these experiments there are variables that are hard to change or cannot be controlled under normal operating conditions. These situations often prohibit a complete randomization for the experimental runs due to practical and economical considerations. Furthermore, the process variables can be categorized into two types: variables that are controllable and directly affect the response, and variables that are uncontrollable and primarily affect the variability of the response. These uncontrollable variables are called noise factors and assumed controllable in a laboratory environment for the purpose of conducting experiments. The model containing both noise variables and control factors can be used to determine factor settings for the control factor that makes the response "robust" to the variability transmitted from the noise factors. These types of experiments can be analyzed in a model for the mean response and a model for the slope of the response within a split-plot structure. When considering the experimental designs, low prediction variances for the mean and slope model are desirable. The methods for the mixture-process variable designs with noise variables considering a restricted randomization are demonstrated and some mixture-process variable designs that are robust to the coefficients of interaction with noise variables are evaluated using fraction design space plots with the respect to the prediction variance properties. Finally, the G-optimal design that minimizes the maximum prediction variance over the entire design region is created using a genetic algorithm.
ContributorsCho, Tae Yeon (Author) / Montgomery, Douglas C. (Thesis advisor) / Borror, Connie M. (Thesis advisor) / Shunk, Dan L. (Committee member) / Gel, Esma S (Committee member) / Kulahci, Murat (Committee member) / Arizona State University (Publisher)
Created2010