Summary of Dissertation Recitals Three Programs of Violin Music

Three violin recitals were given in lieu of a written dissertation. The repertoire for three dissertation recitals was chosen to demonstrate different genres of violin music. The first recital was comprised of works from 1798-1887 in German musical tradition. The second recital contains music from the violin repertoire with French influences. The final recital explored composer’s nationalistic musical traits and style in their composition. First Dissertation recital: Monday, February 16, 2015 at Stamps Auditorium, University of Michigan. The program includes; Romance in F Major, op.50 and String Quartet no.11 in F minor, op 95 by Ludwig van Beethoven and sonata for violin and piano in E flat Major, op 18 by Richard Strauss. Pianist, Trevor Chartrand and Evita String Quartet. Second Dissertation recital: Wednesday, December 2, 2015 at Stamps auditorium, University of Michigan. The program includes; violin Sonata in G minor, L140 by Claude Debussy, Poeme, op.25 by Ernest Chausson, and violin Sonata no.2 in D Major, op.94 by Sergei Prokofiev. Pianists Cesar Canon and Trevor Chartrand. Third Dissertation recital: Monday, February 22, 2016 at Britton Recital Hall. The program includes; Mediation for Violin and Piano, op.42 by Piotr Ilyich Tchaikovsky, Sonata for violin and Piano by Aaron Copland, and violin sonata no.3, op.45 by Edvard Grieg. Pianists, Trevor Chartrand and Tuomas Juutilainen.AMUMusic: PerformanceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147492/1/hyorimh_1.pd

Estimating δ15N and δ13C in Barley and Pea Mixtures Using Near-Infrared Spectroscopy with Genetic Algorithm Based Partial Least Squares Regression

Stable isotope measurements have been increasingly used as a method to obtain information on relationships between plants and their environment (Dawson et al., 2002). Stable isotopes are seen as a powerful tool for advancing our knowledge on stock cycling and, nitrogen and carbon isotopic compositions have provided key insights into biogeochemical interactions between plants, soils and the atmosphere (Robinson, 2001). For the stable isotope measurements, the δ13C isotopic signature has been used successfully to disentangle physiological, ecological and biogeochemical processes and, δ15N studies have significantly improved our knowledge on nitrogen cycling pathways and nitrogen acquisition by plants (Vallano and Sparks, 2008). For the stable isotope measurements, traditional laboratory methods using isotope analysis are accurate and reliable, but usually time-consuming and expensive. Near-infrared spectroscopy (NIRS) analysis provides rapid, accurate and less expensive estimation. NIRS have been made to estimate herbage parameters using statistical methods such as multiple linear regression and partial least square regression (PLSR). PLSR uses all available wavebands in multivariate calibration for quantitative analysis of the spectral data. However, previous studies indicated that PLSR with waveband selection might improve their predictive accuracy in multivariate calibration at laboratory (Leardi, 2000) and the selection of appropriate wavelengths can refine the predictive accuracy of the PLS model by optimizing important spectral wavebands both in laboratory NIRS (Jiang et al., 2002). To optimize important spectral wavebands by wavelength selection, genetic algorithms (GA) is widely used, because GA has the ability to simulate the natural evolution of an individual and GA is well suited for solving variable subset selection problems (Ding et al., 1998). Barley and pea mixture is one of the most important forage species for livestock farming in Korea. To investigate nitrogen fixation and transfer in barley and pea mixture, stable isotope measurements was widely used. However, there was no research to estimate stable isotope in barley and pea mixture using NIRS in Korea. The aim of this study was to investigate performance of NIRS with PLSR using genetic algorithms based wavelength selection (GA-PLSR) and compare with PLSR without wavelength selection (FS-PLSR) for the estimation of δ15N and δ13C in barley and pea mixture

An Analysis of IS Programs Accredited by ABET

As of April 2008 there were 30 Information Systems (IS) programs at 23 universities accredited by the Accreditation Board for Engineering and Technology (ABET). This paper compares these ABET accredited IS programs to a sample of 20 IS programs randomly chosen from 251 IS programs in the United States listed by ISWorld. The data was collected by visiting the web pages of the selected IS programs. This paper includes a study of the required IS courses, elective IS courses, required programming courses, required business courses, the title of the IS program (IS/MIS/CIS), the department and college where the IS program was located and the year of ABET accreditation. It is interesting to notice that only two of the ABET accredited IS degrees are BBA degrees. This study points out several significant differences between the ABET accredited IS programs and the typical BBA IS programs at the sample universities

Antecedents of Generalized Computer Self-Efficacy Judgements

Computer self-efficacy is frequently used as an explanatory variable in software training and technology acceptance investigations and it has been frequently used to predict training and learning outcomes. While self-efficacy models identify prior experience with computers as an important determinant of generalized self-efficacy judgments, relatively few studies have systematically examined the types of experience that drive such judgments. Gender and frequency of computer use have also been identified as other predictors of generalized computer self-efficacy. In this investigation, self-reported knowledge/skill attainments levels with each of nineteen computer use/knowledge dimensions are used to measure prior experience/knowledge of computers. These were collected from 340 university students at the same time that they completed a generalized computer self-efficacy scale. This data is used to test two predictions: 1) that greater prior computer knowledge/experience is directly related to higher computer self-efficacy scores and 2) for comparable levels of prior experience/knowledge, males will have higher self-efficacy scores than females. Our results provide support for the first prediction but not the second. Our findings suggest that experience/knowledge of less common computer applications may be more important in shaping self-efficacy judgments than are greater levels of experience/knowledge with common computer applications

Satisfaction with E-Learning--Courses are Good for Some of the People, Some of the Time?

This paper reports the results of a study of college students in the US that examined specific learner characteristics affecting satisfaction with e-learning courses. It finds that satisfaction is largely governed by the degree to which one is confident in one’s ability to regulate the factors that influence course work and one’s goals in taking them. These goals can be both in terms of grades and results or a perception that the course has added value to their education experience. The findings suggest that not all people are suited to e-learning and institutions need to find ways to identify and encourage efficacious characteristics in the students. It also has some implications for those offering IS courses online

A Survey of Online Purchasing Decision Factors and Shopping and Purchasing Behaviors of University Students

Shopping and purchasing on retailers’ websites continues to expand. According to ACNielsen’s 2005 report, more than one tenth of the world population (627 million) have shopped online, and more than half of them have shopped (over 325 million) online within a month of the report’s release (ACNielsen, 2005). Compared to the third quarter 2004, the third quarter of 2005 showed a 26.7% increase in online retail sales in the U.S., generating $22.3 billion for online merchants (U.S. Census Bureau, 2005). Marketers and researchers have verified that university students are one of the most “wired” demographic groups of online users and continue to investigate the characteristics of shopping/purchasing behaviors among these students. The aim of this investigation is to further examine factors that drive university students’ online shopping and purchasing decisions, and the types of items they shop for and purchase online