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VOICE TRACK COMPUTER BASED SIMULATION FOR MEDICAL TRAINING
Title
VOICE TRACK COMPUTER BASED SIMULATION FOR MEDICAL TRAINING.
Creator
Makwana, Alpesh, Kincaid, J. Peter, University of Central Florida
Abstract / Description
Varying the delivery rate of audio-based text within web-based training increases the effectiveness of the learning process and improves retention when compared with a fixed audio-based text delivery rate. To answer this question, two groups of 20 participants and one group of 10 participants were tested using the Web-based Anatomy & Physiology course modules developed by Medsn, Inc. The control group received the static speed of 128 words per minute while the experimental group received the...
Show moreVarying the delivery rate of audio-based text within web-based training increases the effectiveness of the learning process and improves retention when compared with a fixed audio-based text delivery rate. To answer this question, two groups of 20 participants and one group of 10 participants were tested using the Web-based Anatomy & Physiology course modules developed by Medsn, Inc. The control group received the static speed of 128 words per minute while the experimental group received the initial speed of 128 words per minute with the option to change the speed of the audio-based text. An additional experimental group received the initial speed of 148 words per minute also having the option to vary the speed of the audio-based text. A three way single variable Analysis of Variance (ANOVA) was utilized to examine speed of voice presentation differences. The results were significant, F (2, 47) = 4.67, p=0.014, ç2 = 0.166. The mean for the control group was (M = 7.2, SD = 1.69) with the experimental groups at, (M = 8.4, SD = 1.31) and with extra groups at (M = 8.6, SD = 1.26).
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Date Issued
2005
Identifier
CFE0000639, ucf:46533
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0000639
Network Partitioning in Distributed Agent-Based Models
Title
Network Partitioning in Distributed Agent-Based Models.
Creator
Petkova, Antoniya, Deo, Narsingh, Hughes, Charles, Bassiouni, Mostafa, Shaykhian, Gholam, University of Central Florida
Abstract / Description
Agent-Based Models (ABMs) are an emerging simulation paradigm for modeling complex systems, comprised of autonomous, possibly heterogeneous, interacting agents. The utility of ABMs lies in their ability to represent such complex systems as self-organizing networks of agents. Modeling and understanding the behavior of complex systems usually occurs at large and representative scales, and often obtaining and visualizing of simulation results in real-time is critical.The real-time requirement...
Show moreAgent-Based Models (ABMs) are an emerging simulation paradigm for modeling complex systems, comprised of autonomous, possibly heterogeneous, interacting agents. The utility of ABMs lies in their ability to represent such complex systems as self-organizing networks of agents. Modeling and understanding the behavior of complex systems usually occurs at large and representative scales, and often obtaining and visualizing of simulation results in real-time is critical.The real-time requirement necessitates the use of in-memory computing, as it is dif?cult and challenging to handle the latency and unpredictability of disk accesses. Combining this observation with the scale requirement emphasizes the need to use parallel and distributed computing platforms, such as MPI-enabled CPU clusters. Consequently, the agent population must be "partitioned" across different CPUs in a cluster. Further, the typically high volume of interactions among agents can quickly become a signi?cant bottleneck for real-time or large-scale simulations. The problem is exacerbated if the underlying ABM network is dynamic and the inter-process communication evolves over the course of the simulation. Therefore, it is critical to develop topology-aware partitioning mechanisms to support such large simulations.In this dissertation, we demonstrate that distributed agent-based model simulations bene?t from the use of graph partitioning algorithms that involve a local, neighborhood-based perspective. Such methods do not rely on global accesses to the network and thus are more scalable. In addition, we propose two partitioning schemes that consider the bottom-up individual-centric nature of agent-based modeling. The ?rst technique utilizes label-propagation community detection to partition the dynamic agent network of an ABM. We propose a latency-hiding, seamless integration of community detection in the dynamics of a distributed ABM. To achieve this integration, we exploit the similarity in the process flow patterns of a label-propagation community-detection algorithm and self-organizing ABMs.In the second partitioning scheme, we apply a combination of the Guided Local Search (GLS) and Fast Local Search (FLS) metaheuristics in the context of graph partitioning. The main driving principle of GLS is the dynamic modi?cation of the objective function to escape local optima. The algorithm augments the objective of a local search, thereby transforming the landscape structure and escaping a local optimum. FLS is a local search heuristic algorithm that is aimed at reducing the search space of the main search algorithm. It breaks down the space into sub-neighborhoods such that inactive sub-neighborhoods are removed from the search process. The combination of GLS and FLS allowed us to design a graph partitioning algorithm that is both scalable and sensitive to the inherent modularity of real-world networks.
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Date Issued
2017
Identifier
CFE0006903, ucf:51706
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006903