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The aim of this thesis is to investigate the ability of cardiovascular biomarkers calculated from peripheral pulse waveforms to estimate central properties of the cardiovascular system (e.g. aortic stiffness) using nonlinear one-dimensional (1-D) modelling of pulse wave propagation in the arterial network. To test these biomarkers, I have produced novel 1-D models of pulse wave propagation under normal and pathological conditions. In the first part of my thesis, I extended the modelling capabilities of the existing 1-D/0-D code to represent arterial blood flow under diabetes, hypertension, and combined diabetes and hypertension. Cardiac and vascular parameters of the 1-D model were tailored to best match data available in the literature to produce generalised hypertensive, diabetic, and combined diabetic and hypertensive population models. Using these models, I have shown that the pulse waveform at the finger is strongly affected by the aortic flow wave and the muscular artery stiffness and diameter. Furthermore the peak to peak time measured from the pulse waveform at the finger can identify hypertensive from diabetic patients. In the second part, I developed a new methodology for optimising the number of arterial segments in 1-D modelling required to simulate precisely the blood pressure and flow waveforms at an arbitrary arterial location. This is achieved by systematically lumping peripheral 1-D model branches into 0-D models that preserve the net resistance and total compliance of the original model. The methodology is important to simplify the computational domain while maintaining the precision of the numerical predictions — an important step to translate 1-D modelling to the clinic. This thesis provides novel computational tools of blood flow modelling and waveform analysis for the design, development and testing of pulse wave biomarkers. These tools may help bridge the gap between clinical and computational approaches.

Trabalho apresentado ao currículo da Disciplina de Programação de Computadores 2

This is the LaTeX template for Eastern Mediterranean University (EMU), Cyprus PhD Thesis submissions created by Ali Övgün Please chech: http://grad.emu.edu.tr/

Haroon Ahmed's CV

Beamer Presentation LaTeX Template Version 2.0 (10/06/16) Attention: The self-defined font is used, because 'Calibri' is not supported in the latex font packages. 'LuaLatex' should be used. This template has been generated according to the Power Point template of LUMC in 2016. This is generated purely with images as the background. The bullet point color was used purely for personal preference. Any more adding to the template are welcome. In order to use the navigation bar, the title for each section should not be to long. Adding animation is possible. I prefer to add another pdf file with: \animategraphics[parameters]{1}{fname}{startnum}{endnum} This is my first template, the files might be not well organized, sorry for that. Author: Shengnan Liu (sliu729@gmail.com) Division Medical imaging processing, Leiden University Medical Center Modification Log: Generated by Shengnan Liu on 21-01-2016 Cleaned up for further usage on 10-06-2016

A quick explanation of why the Alexander polynomial is an invariant for knots.

Template criado por Alexandre do Nascimento Silva (asilva5@area1.edu.br)a partir da classe ABNT para uso das monografias de conclusão de curso da Faculdade ÁREA1 e modificado pelo professor Lázaro Silva (lazaro.silva@ifba.edu.br) para uso na internet sem a necessidade de instalação.

This article proposes to obtain a statistical model of the daily peak electricity load of a household located in Austin-TX,USA. The Box-Jenkins methodology was followed to obtain the best fit for the time-series. Four models provided a good fit: ARIMA(0,1,2), ARIMA(1,1,2), SARIMA(0,1,2)(0,1,1) and SARIMA(1,1,2)(0,1,1). The model with the highest Akaike Information Criteria was the ARIMA(1,2,2). However, the model with the highest forecast accuracy was the SARIMA(1,1,2)(0,1,1), which obtained an RMSE of 0.296 and a MAPE Of 15.00.

Accurate prognosis and prediction of a patient's current disease state is critical in an ICU. The use of vast amounts of digital medical information can help in predicting the best course of action for the diagnosis and treatment of patients. The proposed technique investigates the strength of using a combination of latent variable models (latent dirichlet allocation) and structured data to transform the information streams into potentially actionable knowledge. In this project, I use Apache Spark to predict mortality among ICU patients so that it can be used as an acuity surrogate to help physicians identify the patients in need of immediate care.