Discussant\u27s response to Using and evaluating audit decision aids

https://egrove.olemiss.edu/dl_proceedings/1034/thumbnail.jp

Acme Financial Statement Insurance Company Inc.: A case study

https://egrove.olemiss.edu/dl_proceedings/1056/thumbnail.jp

Assertion based approach to auditing

https://egrove.olemiss.edu/dl_proceedings/1174/thumbnail.jp

Boundary terms and their Hamiltonian dynamics

It is described how the standard Poisson bracket formulas should be modified in order to incorporate integrals of divergences into the Hamiltonian formalism and why this is necessary. Examples from Einstein gravity and Yang-Mills gauge field theory are given.Comment: Talk at 29th Ahrenshoop Symposium in Buckow 1995, 6 pages, espcrc2.sty, twoside.sty, fleqn.sty, amssymb.sty, no figure

Variational Principles for Natural Divergence-free Tensors in Metric Field Theories

Let $T^{ab}=T^{ba}=0$ be a system of differential equations for the components of a metric tensor on $R^m$. Suppose that $T^{ab}$ transforms tensorially under the action of the diffeomorphism group on metrics and that the covariant divergence of $T^{ab}$ vanishes. We then prove that $T^{ab}$ is the Euler-Lagrange expression some Lagrangian density provided that $T^{ab}$ is of third order. Our result extends the classical works of Cartan, Weyl, Vermeil, Lovelock, and Takens on identifying field equations for the metric tensor with the symmetries and conservation laws of the Einstein equations

Machine Learning based Extraction of Boundary Conditions from Doppler Echo Images for Patient Specific Coarctation of the Aorta: Computational Fluid Dynamics Study

Purpose- Coarctation of the Aorta (CoA) patient-specific computational fluid dynamics (CFD) studies in resource constrained settings are limited by the available imaging modalities for geometry and velocity data acquisition. Doppler echocardiography has been seen as a suitable velocity acquisition modality due to its higher availability and safety. This study aimed to investigate the application of classical machine learning (ML) methods to create an adequate and robust approach for obtaining boundary conditions (BCs) from Doppler Echocardiography images, for haemodynamic modeling using CFD. Methods- Our proposed approach combines ML and CFD to model haemodynamic flow within the region of interest. With the key feature of the approach being the use of ML models to calibrate the inlet and outlet boundary conditions (BCs) of the CFD model. The key input variable for the ML model was the patients heart rate as this was the parameter that varied in time across the measured vessels within the study. ANSYS Fluent was used for the CFD component of the study whilst the scikit-learn python library was used for the ML component. Results- We validated our approach against a real clinical case of severe CoA before intervention. The maximum coarctation velocity of our simulations were compared to the measured maximum coarctation velocity obtained from the patient whose geometry is used within the study. Of the 5 ML models used to obtain BCs the top model was within 5\% of the measured maximum coarctation velocity. Conclusion- The framework demonstrated that it was capable of taking variations of the patients heart rate between measurements into account. Thus, enabling the calculation of BCs that were physiologically realistic when the heart rate was scaled across each vessel whilst providing a reasonably accurate solution.Comment: Article to be submitted to Springer Nature Cardiovascular Engineering and Technology Journa