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Optimization,Modeling, and Control: ...

Lankford, George Bernard.

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Optimization,Modeling, and Control: Applications to Klystron Designing and Hepatitis C Virus Dynamics.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Optimization,Modeling, and Control: Applications to Klystron Designing and Hepatitis C Virus Dynamics./
Author:	Lankford, George Bernard.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2016,
Description:	119 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
Contained By:	Dissertation Abstracts International78-08B(E).
Subject:	Applied mathematics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10583450
ISBN:	9781369621297

Optimization,Modeling, and Control: Applications to Klystron Designing and Hepatitis C Virus Dynamics.
Lankford, George Bernard.

Optimization,Modeling, and Control: Applications to Klystron Designing and Hepatitis C Virus Dynamics. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 119 p.

Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.

Thesis (Ph.D.)--North Carolina State University, 2016.

In this dissertation, we address applying mathematical and numerical techniques in the fields of high energy physics and biomedical sciences. The first portion of this thesis presents a method for optimizing the design of klystron circuits. A klystron is an electron beam tube lined with cavities that emit resonant frequencies to velocity modulate electrons that pass through the tube. Radio frequencies (RF) inserted in the klystron are amplified due to the velocity modulation of the electrons. The routine described in this work automates the selection of cavity positions, resonant frequencies, quality factors, and other circuit parameters to maximize the efficiency with required gain. The method is based on deterministic sampling methods. We will describe the procedure and give several examples for both narrow and wide band klystrons, using the klystron codes AJDISK (Java) and TESLA (Python).

ISBN: 9781369621297Subjects--Topical Terms:

2122814
Applied mathematics.

Optimization,Modeling, and Control: Applications to Klystron Designing and Hepatitis C Virus Dynamics.
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245 1 0 $a Optimization,Modeling, and Control: Applications to Klystron Designing and Hepatitis C Virus Dynamics.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2016
300 $a 119 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
500 $a Adviser: Hien Tran.
502 $a Thesis (Ph.D.)--North Carolina State University, 2016.
520 $a In this dissertation, we address applying mathematical and numerical techniques in the fields of high energy physics and biomedical sciences. The first portion of this thesis presents a method for optimizing the design of klystron circuits. A klystron is an electron beam tube lined with cavities that emit resonant frequencies to velocity modulate electrons that pass through the tube. Radio frequencies (RF) inserted in the klystron are amplified due to the velocity modulation of the electrons. The routine described in this work automates the selection of cavity positions, resonant frequencies, quality factors, and other circuit parameters to maximize the efficiency with required gain. The method is based on deterministic sampling methods. We will describe the procedure and give several examples for both narrow and wide band klystrons, using the klystron codes AJDISK (Java) and TESLA (Python).
520 $a The rest of the dissertation is dedicated to developing, calibrating and using a mathematical model for hepatitis C dynamics with triple drug combination therapy. Groundbreaking new drugs, called direct acting antivirals, have been introduced recently to fight off chronic hepatitis C virus infection. The model we introduce is for hepatitis C dynamics treated with the direct acting antiviral drug, telaprevir, along with traditional interferon and ribavirin treatments to understand how this therapy affects the viral load of patients exhibiting different types of response. We use sensitivity and identifiability techniques to determine which parameters can be best estimated from viral load data. We use these estimations to give patient-specific fits of the model to partial viral response, end-of-treatment response, and breakthrough patients. We will then revise the model to incorporate an immune response dynamic to more accurately describe the dynamics. Finally, we will implement a suboptimal control to acquire a drug treatment regimen that will alleviate the systemic cost associated with constant drug treatment.
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