Physician-prescribed Asthma Treatment Regimen does not differ Between Smoking and Non-smoking Patients With Asthma in Seoul and Gyunggi province of Korea

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. ACKNOWLEDGMENTS The authors thank Lauren Weisenfluh and Melissa Stauffer, PhD, in collaboration with SCRIBCO, for medical writing assistance. Funding for this research was provided by Merck & Co., Inc. The authors also wish to thank Eric Maiese and Sharlette Everett for their contributions to the design and implementation of the study and the analytic plan. The authors would also like to thank the study investigators who contributed to patient enrollment and data collection: Drs. Young Il Hwang (Hallym University Sacred Heart Hospital), Young Min Ye (Ajou University Medical Center), Joo Hee Kim (Ajou University Medical Center), Heung Woo Park (Seoul National University Hospital), Tae Wan Kim (Seoul National University Hospital), Jae Jeong Shim (Korea University Guro Hospital), Gyu Young Hur (Korea University Guro Hospital), Soo Taek Uh (SoonChunHyang University Hospital), Sang Ha Kim (Wonju Christian Hospital), Myoung Kyu Lee (Wonju Christian Hospital), Soo Keol Lee (Dong-A Medical Center), Jin Hong Chung (Yeungnam University Medical Center), Kyu Jin Kim (Yeungnam University Medical Center), Young Koo Jee (Dankook University Hospital), Kyung Mook Kim (Dankook University Hospital), Young Il Koh (Chonnam National University Hospital), Cheol Woo Kim (Inha university Hospital), You Sook Cho (Seoul Asan Medical Center), Tae Bum Kim (Seoul Asan Medical Center), Jae Myung Lee (Myeong Internal Medicine), Young Mok Lee (Good Friends Internal Medicine), Bong Chun Lee (Namsan Hospital), So Yoen Park (A&A Clinic).Peer reviewedPublisher PD

Lee

Non-fiction by Ann Flemin

Dedication of Robert Lee Moore Hall

Robert Lee Moore Hall, located in the northeast section of the campus, at the southeast corner of the intersection of 26th Street and Speedway, is named for Professor Emeritus Robert Lee Moore, distinguished University of Texas mathematician. The building, which was first occupied in the Fall Semester of 1972-1973, is constructed of warm tan brick and contains classrooms, laboratories, and general offices for the Departments of Astronomy, Mathematics, and Physics.Friday, October 5th, 1973, 5:40pm. Dedication Program -- Presiding: President Stephen H. Spurr -- Welcome and Recognitions: President Spurr -- Introduction of the Guest Speakers: Dr. Leonard Gillman -- Addresses: Dr. Raymond L. Wilder, Mrs. Gordon T. Whyburn, Dr. R.H. Bing, and Dr. Ralph Krause -- Dedication of Robert Lee Moore Hall: The Honorable Frank C. Erwin, Jr. -- Response: Dr. Robert Lee Moore.AstronomyMathematicsPhysicsUT Librarie

Essays on Applied Macroeconomics

This thesis investigates the U.S. business cycle dynamics considering time-variations and breaks predominantly associated with the Great Recession in the late 2000s. In the first essay, I evaluate the predictive content of financial variables and unconventional monetary policy measures for the U.S. output growth and inflation before, during, and after the Great Recession from 1960–2015. I compare the local forecasting performances of the variables with attention to the Great Recession period when the Federal Reserve System and market participants were not able to use the federal funds rate for a policy instrument and a leading indicator for the economy. This shows that the predictive ability of the credit spread, stock price, and market expectation measures for output growth and inflation change significantly increased during the Great Recession. The result is consistent with the idea that the Great Recession was primarily driven by a financial shock, and that financial condition measures might be useful indicators for the future economy to investors and central bankers. Additionally, it is important that financial market conditions are not exacerbated by a future economic shock to avoid a vicious cycle. In the second essay, I examine how the conditional volatilities of the U.S. macroeconomic variables have changed before and during the Great Recession considering conditional mean changes. I implement multiple structural break tests in a reduced form model to find structural changes in the volatilities and means of the variables using the data from 1960–2015. The test results show that the increase in the volatility in the economy during the Great Recession was temporary, and there was no structural break in the growth rate of GDP during the Great Recession. But, there was a structural break in the growth rates of consumption variables, which are major parts of the economy, and demand-related variables, such as real disposable income and liabilities of consumers. A simulation result ii suggests that a structural break in the growth rate of the economy might have occurred before the Great Recession if the recent sluggish economy continues in the coming years. This evidence suggests that the monetary policy in the period of the Great Moderation might be reconsidered for the sustainable growth of the economy beyond the short-run, and policy for improving the recent sluggish economy, especially consumption, might be necessary to avoid a structural decline in the growth rate of the economy

Frequency based Classification of Activities using Accelerometer Data

This work presents, the classification of user activities such as Rest, Walk and Run, on the basis of frequency component present in the acceleration data in a wireless sensor network environment. As the frequencies of the above mentioned activities differ slightly for different person, so it gives a more accurate result. The algorithm uses just one parameter i.e. the frequency of the body acceleration data of the three axes for classifying the activities in a set of data. The algorithm includes a normalization step and hence there is no need to set a different value of threshold value for magnitude for different test person. The classification is automatic and done on a block by block basis.Comment: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2008. MFI 200