Assessing the Accuracy of Task Time Prediction of an Emerging Human Performance Modeling Software - CogTool

There is a need for a human performance modeling tool which not only has the ability to accurately estimate skilled user task time for any interface design, but can be used by modelers with little or no programming knowledge and at a minimal cost. To fulfill this need, this research investigated the accuracy of task time prediction of a modeling tool – CogTool - on two versions of an interface design used extensively in the petrochemical industry – DeltaV. CogTool uses the KeyStroke Level Model (KLM) to calculate and generate time predictions based on specified operators. The data collected from a previous study (Koffskey, Ikuma, & Harvey, 2013) that investigated how human participants (24 students and 4 operators) performed on these interfaces (in terms of mean speed in seconds) were compared to CogTool’s numeric time estimate. Three tasks (pump I, pump II and cascade system failures) on each interface for both participant groups were tested on both interfaces (improved and poor), on the general hypothesis that CogTool will make task time predictions for each of the modeled tasks, within a certain range of what actual human participants had demonstrated. The 95% confidence interval (CI) tests of the means were used to determine if the predictions fall within the intervals. The estimated task time from CogTool did not fall within the 95% CI in 9 of 12 cases. Of the 3 that were contained in the acceptable interval, two belonged to the experienced operator group for tasks performed on the improved interface, implying that CogTool was better in predicting the operators’ performance than the students’. A control room monitoring task, by its nature, places great demand on an operator’s mental capacity. This also includes the fact that operators work on multiple screens and/or consoles, sometimes requiring them to commit information to memory that they have to revisit a screen to check on some vital information. In this regard, it is suggested that the one user mental operator for “think time” (estimated as 1.2sec), should be revised in CogTool to accommodate the demand on the operator. For this reason, the present CogTool prediction did not meet expectations in estimating control room operator task time, but it however succeeded in showing where the poor interface could be improved by comparing the detailed steps to the improved interface

Modelling Fluid Structure Interaction problems using Boundary Element Method

This dissertation investigates the application of Boundary Element Methods (BEM) to Fluid Structure Interaction (FSI) problems under three main different perspectives. This work is divided in three main parts: i) the derivation of BEM for the Laplace equation and its application to analyze ship-wave interaction problems, ii) the imple- mentation of efficient and parallel BEM solvers addressing the newest challenges of High Performance Computing, iii) the developing of a BEM for the Stokes system and its application to study micro-swimmers.First we develop a BEM for the Laplace equation and we apply it to predict ship-wave interactions making use of an innovative coupling with Finite Element Method stabilization techniques. As well known, the wave pattern around a body depends on the Froude number associated to the flow. Thus, we throughly investigate the robustness and accuracy of the developed methodology assessing the solution dependence on such parameter. To improve the performance and tackle problems with higher number of unknowns, the BEM developed for the Laplace equation is parallelized using OpenSOURCE tech- nique in a hybrid distributed-shared memory environment. We perform several tests to demonstrate both the accuracy and the performance of the parallel BEM developed. In addition, we explore two different possibilities to reduce the overall computational cost from O(N2) to O(N). Firstly we couple the library with a Fast Multiple Method that allows us to reach for higher order of complexity and efficiency. Then we perform a preliminary study on the implementation of a parallel Non Uniform Fast Fourier Transform to be coupled with the newly developed algorithm Sparse Cardinal Sine De- composition (SCSD).Finally we consider the application of the BEM framework to a different kind of FSI problem represented by the Stokes flow of a liquid medium surrounding swimming micro-organisms. We maintain the parallel structure derived for the Laplace equation even in the Stokes setting. Our implementation is able to simulate both prokaryotic and eukaryotic organisms, matching literature and experimental benchmarks. We finally present a deep analysis of the importance of hydrodynamic interactions between the different parts of micro-swimmers in the prevision of optimal swimming conditions, focusing our attention on the study of flagellated \u201crobotic\u201d composite swimmers

Modelling Determinants of Cryptocurrency Prices: A Bayesian Network Approach

The growth of market capitalisation and the number of altcoins (cryptocurrencies other than Bitcoin) provide investment opportunities and complicate the prediction of their price movements. A significant challenge in this volatile and relatively immature market is the problem of predicting cryptocurrency prices which needs to identify the factors influencing these prices. The focus of this study is to investigate the factors influencing altcoin prices, and these factors have been investigated from a causal analysis perspective using Bayesian networks. In particular, studying the nature of interactions between five leading altcoins, traditional financial assets including gold, oil, and S\&P 500, and social media is the research question. To provide an answer to the question, we create causal networks which are built from the historic price data of five traditional financial assets, social media data, and price data of altcoins. The ensuing networks are used for causal reasoning and diagnosis, and the results indicate that social media (in particular Twitter data in this study) is the most significant influencing factor of the prices of altcoins. Furthermore, it is not possible to generalise the coins' reactions against the changes in the factors. Consequently, the coins need to be studied separately for a particular price movement investigation