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.
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.
EPR paradox as a result of non-force interaction nonlocal quantum objects
This is a LaTeX template (version from 2016 Feb. 17)
for preparing documents for All-Russian Scientific Conference
of the Mathematical Modeling and Boundary Value Problems
[Matem. Mod. Kraev. Zadachi, Samara, Russian Federation].
It was submitted by an author writing for
the 10th All-Russian Scientific Conference with
international participation (MMiKZ’16).