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CHARACTERIZATION OF CRITICAL NETWORK COMPONENTS OF COUPLED OSCILLATORS
Title
CHARACTERIZATION OF CRITICAL NETWORK COMPONENTS OF COUPLED OSCILLATORS.
Creator
Holifield, Gregory, A. S. Wu, A. Gonzalez,, University of Central Florida
Abstract / Description
This dissertation analyzes the fundamental limits for the determination of the network structure of loosely coupled oscillators based on observing the behavior of the network, specifically, node synchronization. The determination of the requisite characteristics and underlying behaviors necessary for the application of a theoretical mechanism for determining the underlying network topology in a network of loosely coupled natural oscillators are the desired outcome. To that end, this effort...
Show moreThis dissertation analyzes the fundamental limits for the determination of the network structure of loosely coupled oscillators based on observing the behavior of the network, specifically, node synchronization. The determination of the requisite characteristics and underlying behaviors necessary for the application of a theoretical mechanism for determining the underlying network topology in a network of loosely coupled natural oscillators are the desired outcome. To that end, this effort defines an analytical framework where key components of networks of coupled oscillators are isolated in order to determine the relationships between the various components. The relationship between the number of nodes in a network, the number of connections in the network, the number of connections of a given node, the distribution of the phases of the network, and the resolution of measurement of the components of the network, and system noise is investigated.
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Date Issued
2006
Identifier
CFE0001452, ucf:47038
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0001452
FACTORS INFLUENCING EFFECTIVENESS OF INTERORGANIZATIONAL NETWORKS AMONG CRISIS MANAGEMENT ORGANIZATIONS: A COMPARATIVE PERSPECTIVE
Title
FACTORS INFLUENCING EFFECTIVENESS OF INTERORGANIZATIONAL NETWORKS AMONG CRISIS MANAGEMENT ORGANIZATIONS: A COMPARATIVE PERSPECTIVE.
Creator
Sahin, Bahadir, Wan, Thomas, University of Central Florida
Abstract / Description
Crisis management has become one of the most important public policy areas in recent decades with greater numbers of manmade and natural disasters. History showed that well-implemented crisis management policies can save lives and reduce costs in a disaster. Literature offered various suggestions for more effective crisis management policies with different techniques utilizing different theoretical frameworks. Informal relationships among crisis management employees were suggested to have a...
Show moreCrisis management has become one of the most important public policy areas in recent decades with greater numbers of manmade and natural disasters. History showed that well-implemented crisis management policies can save lives and reduce costs in a disaster. Literature offered various suggestions for more effective crisis management policies with different techniques utilizing different theoretical frameworks. Informal relationships among crisis management employees were suggested to have a positive impact on crisis management effectiveness. Yet it was not demonstrated with advanced statistical tools if there is such a relationship. This study considers crisis management effort as a network effort and employs complex adaptive systems theory in order to understand factors influencing effectiveness of crisis management networks. Complex adaptive systems theory presents that more open communication lines in a given network or an organization would increase effectiveness of it since inner processes of the network or organization would obtain more information from the chaotic environment. Quality of informal relationships (casual relationships, social capital etc.) was hypothesized as a tool to open more communication lines within an agency which would eventually increase effectiveness of the network constructed by the organization. Based on the theoretical framework, adaptiveness capacity of the agencies was also tested in order to understand a correlation between adaptation and effectiveness of crisis management networks. Multiple case-study method was employed to identify incidents that can represent crisis management in full perspective. Terrorist attacks carried upon by the same terrorist network hit New York in 2001, Istanbul in 2003, Madrid in 2004, and London in 2005 were selected. First response phase of crisis management and policy changes after and before the attacks were discussed. Public administration processes and other social-economical conditions of countries were examined in terms of crisis management structure. Names of key agencies of selected crisis management systems were suggested by a social network analysis tool-UCINET. Six key agencies per incident were targeted for surveys. Surveys included a nine-item-quality of informal relationships, four-item-adaptiveness capability, and ten-item-perceived effectiveness of crisis management networks-scales. Respondents were asked to fill in online surveys where they could refer to their colleagues in the same incidents. 230 respondents were aimed and 246 survey responses were obtained as a result. Surveys formed a structural equation model representing 23 observed factors and 2 latent constructs. Confirmatory factor analysis was utilized to validate hypothesis-driven conceptual models. Quality of informal relationships was found to have a significant positive impact on perceived crisis management network effectiveness (Standardized regression coefficient = .39). Two of the adaptiveness variables, openness to change and intra-organizational training were also positively correlated with the dependent variable of the study (Standardized regression coefficient = .40 and .26 respectively). Turkish and American groups' differences suggested a social-economical difference in societies. Majority of the respondents were some type of managers which made it possible to generalize the results for all phases of crisis management. Discussions suggested improved informal relationships among crisis management employees to provide a better crisis management during an extreme event. Collaborative social events were offered to improve crisis management effectiveness. An agency's openness to change proposed that a crisis management organization should be flexible in rules and structureto gain more efficacy. The other adaptiveness variable, intra-organizational training efforts were proposed to have certain influence on effectiveness of crisis management network. Factors built latent construct of perceived crisis management effectiveness were also found out to be important on crisis management, which of some are ability to carry out generic crisis management functions, mobilize personnel and resources efficiently, process information adequately, blend emergent and established entities, provide appropriate reports for news media etc. Study contributed to the complex adaptive system theory since the fundamentals of the theory were tested with an advanced quantitative method. Non-linear relationships within a system were tested in order to reveal a correlation as the theory suggested, where the results were convincingly positive. Crisis management networks' effectiveness was demonstrated to be validated by a ten-item-scale successfully. Future research might utilize more disaster cases both natural and manmade, search for impact of different communication tools within a system, and look at the relationships among members of crisis management networks instead looking within an organization.
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Date Issued
2009
Identifier
CFE0002709, ucf:48173
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0002709
THE IMPACT OF ORGANIZATIONAL GOAL CONVERGENCE, INFORMATION-COMMUNICATION TECHNOLOGY UTILIZATION, AND INTER-ORGANIZATIONAL TRUST ON NETWORK FORMATION AND SUSTAINABILITY: THE CASE OF EMERGENCY MANAGEMENT IN THE UNITED STATES
Title
THE IMPACT OF ORGANIZATIONAL GOAL CONVERGENCE, INFORMATION-COMMUNICATION TECHNOLOGY UTILIZATION, AND INTER-ORGANIZATIONAL TRUST ON NETWORK FORMATION AND SUSTAINABILITY: THE CASE OF EMERGENCY MANAGEMENT IN THE UNITED STATES.
Creator
Garayev, Vener, Kapucu, Naim, University of Central Florida
Abstract / Description
With the increase of severity and scope of disasters, collaborative networks have become the main tool to tackle with complex emergencies. Networks, however, are mostly effective to the extent they are maintained over time. This study analyzes whether organizational goal convergence, information-communication technology utilization, and inter-organizational trust impacts network sustainability. The main research questions of the study are: (1) How are organizational goals, technical...
Show moreWith the increase of severity and scope of disasters, collaborative networks have become the main tool to tackle with complex emergencies. Networks, however, are mostly effective to the extent they are maintained over time. This study analyzes whether organizational goal convergence, information-communication technology utilization, and inter-organizational trust impacts network sustainability. The main research questions of the study are: (1) How are organizational goals, technical/technological capacity of organizations, and trust among organizations of a network are related to the sustainability of collaborative network relationships? (2) Which of the above-mentioned factors plays the most significant role in affecting network sustainability? Covering the context of emergency management system in the United States, this study utilized a self-administered survey that was electronically distributed to county emergency managers across the country. The data consisting of 534 complete responses was analyzed in Statistical Package for the Social Sciences (SPSS) Inc. software's PASW (Predictive Analytics SoftWare) Statistics version 18.0 and transferred to Amos 18.0 software for structural equation modeling (SEM) analysis. The findings suggest that organizational goal convergence, information-communication technology utilization, and inter-organizational trust have positive and statistically significant relationships with network sustainability; and, inter-organizational trust is the strongest factor followed by information-communication technology utilization and organizational goal convergence. The study contributes to the literature on network sustainability with specific suggestions for emergency management practitioners.
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Date Issued
2011
Identifier
CFE0003920, ucf:48738
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0003920
Applying the Technology Acceptance Model to Predict and Explain Elementary and Secondary Preservice Teachers' Continuance Behavioral Intentions and Pedagogical Usage of Twitter to build Professional Capital: A Structural Equation Modeling Inquiry
Title
Applying the Technology Acceptance Model to Predict and Explain Elementary and Secondary Preservice Teachers' Continuance Behavioral Intentions and Pedagogical Usage of Twitter to build Professional Capital: A Structural Equation Modeling Inquiry.
Creator
Gurjar, Nandita, Sivo, Stephen, Roberts, Sherron, Xu, Lihua, Vie, Stephanie, University of Central Florida
Abstract / Description
The purpose of this research study was to predict and explain elementary and secondary preservice teachers' continuance behavioral intentions and pedagogical usage of Twitter, a web based social networking, microblogging platform, to build professional growth and capital. The objective of the research study was to examine preservice teachers' beliefs associated with the specified constructs that formed the latent variables of the hypothesized research model; these latent variables were then...
Show moreThe purpose of this research study was to predict and explain elementary and secondary preservice teachers' continuance behavioral intentions and pedagogical usage of Twitter, a web based social networking, microblogging platform, to build professional growth and capital. The objective of the research study was to examine preservice teachers' beliefs associated with the specified constructs that formed the latent variables of the hypothesized research model; these latent variables were then measured with their associated indicators or manifest variables, and the relationship between the manifest variables was examined through the Structural Equation Modeling (SEM) process. A non-experimental empirical research study was conducted using the survey methodology; purposive, criterion referenced, sampling of elementary and secondary preservice teachers, N=379, was employed using social media platforms and intern listserv at a large Southeastern university. The final sample of N= 250 participants was determined through the process of regression imputation of elementary and secondary preservice teachers' survey responses. The results demonstrated that constructs of the extended Technology Acceptance Model showed significant goodness-of-fit indices and coefficients of determination after analyzing the data from the survey. Implications of this research contribute significantly toward teacher education and training by providing insights into the factors that impact the pedagogical use of Twitter, a web-based social networking and microblogging platform, for building professional capital in preservice teachers.
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Date Issued
2016
Identifier
CFE0006314, ucf:51551
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006314
Optimal distribution network reconfiguration using meta-heuristic algorithms
Title
Optimal distribution network reconfiguration using meta-heuristic algorithms.
Creator
Asrari, Arash, Wu, Thomas, Lotfifard, Saeed, Haralambous, Michael, Atia, George, Pazour, Jennifer, University of Central Florida
Abstract / Description
Finding optimal configuration of power distribution systems topology is an NP-hard combinatorial optimization problem. It becomes more complex when time varying nature of loads in large-scale distribution systems is taken into account. In the second chapter of this dissertation, a systematic approach is proposed to tackle the computational burden of the procedure. To solve the optimization problem, a novel adaptive fuzzy based parallel genetic algorithm (GA) is proposed that employs the...
Show moreFinding optimal configuration of power distribution systems topology is an NP-hard combinatorial optimization problem. It becomes more complex when time varying nature of loads in large-scale distribution systems is taken into account. In the second chapter of this dissertation, a systematic approach is proposed to tackle the computational burden of the procedure. To solve the optimization problem, a novel adaptive fuzzy based parallel genetic algorithm (GA) is proposed that employs the concept of parallel computing in identifying the optimal configuration of the network. The integration of fuzzy logic into GA enhances the efficiency of the parallel GA by adaptively modifying the migration rates between different processors during the optimization process. A computationally efficient graph encoding method based on Dandelion coding strategy is developed which automatically generates radial topologies and prevents the construction of infeasible radial networks during the optimization process. The main shortcoming of the proposed algorithm in Chapter 2 is that it identifies only one single solution. It means that the system operator will not have any option but relying on the found solution. That is why a novel hybrid optimization algorithm is proposed in the third chapter of this dissertation that determines Pareto frontiers, as candidate solutions, for multi-objective distribution network reconfiguration problem. Implementing this model, the system operator will have more flexibility in choosing the best configuration among the alternative solutions. The proposed hybrid optimization algorithm combines the concept of fuzzy Pareto dominance (FPD) with shuffled frog leaping algorithm (SFLA) to recognize non-dominated suboptimal solutions identified by SFLA. The local search step of SFLA is also customized for power systems applications so that it automatically creates and analyzes only the feasible and radial configurations in its optimization procedure which significantly increases the convergence speed of the algorithm. In the fourth chapter, the problem of optimal network reconfiguration is solved for the case in which the system operator is going to employ an optimization algorithm that is automatically modifying its parameters during the optimization process. Defining three fuzzy functions, the probability of crossover and mutation will be adaptively tuned as the algorithm proceeds and the premature convergence will be avoided while the convergence speed of identifying the optimal configuration will not decrease. This modified genetic algorithm is considered a step towards making the parallel GA, presented in the second chapter of this dissertation, more robust in avoiding from getting stuck in local optimums. In the fifth chapter, the concentration will be on finding a potential smart grid solution to more high-quality suboptimal configurations of distribution networks. This chapter is considered an improvement for the third chapter of this dissertation for two reasons: (1) A fuzzy logic is used in the partitioning step of SFLA to improve the proposed optimization algorithm and to yield more accurate classification of frogs. (2) The problem of system reconfiguration is solved considering the presence of distributed generation (DG) units in the network. In order to study the new paradigm of integrating smart grids into power systems, it will be analyzed how the quality of suboptimal solutions can be affected when DG units are continuously added to the distribution network.The heuristic optimization algorithm which is proposed in Chapter 3 and is improved in Chapter 5 is implemented on a smaller case study in Chapter 6 to demonstrate that the identified solution through the optimization process is the same with the optimal solution found by an exhaustive search.
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Date Issued
2015
Identifier
CFE0005575, ucf:50238
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005575
Automatic Detection of Brain Functional Disorder Using Imaging Data
Title
Automatic Detection of Brain Functional Disorder Using Imaging Data.
Creator
Dey, Soumyabrata, Shah, Mubarak, Jha, Sumit, Hu, Haiyan, Weeks, Arthur, Rao, Ravishankar, University of Central Florida
Abstract / Description
Recently, Attention Deficit Hyperactive Disorder (ADHD) is getting a lot of attention mainly for two reasons. First, it is one of the most commonly found childhood behavioral disorders. Around 5-10% of the children all over the world are diagnosed with ADHD. Second, the root cause of the problem is still unknown and therefore no biological measure exists to diagnose ADHD. Instead, doctors need to diagnose it based on the clinical symptoms, such as inattention, impulsivity and hyperactivity,...
Show moreRecently, Attention Deficit Hyperactive Disorder (ADHD) is getting a lot of attention mainly for two reasons. First, it is one of the most commonly found childhood behavioral disorders. Around 5-10% of the children all over the world are diagnosed with ADHD. Second, the root cause of the problem is still unknown and therefore no biological measure exists to diagnose ADHD. Instead, doctors need to diagnose it based on the clinical symptoms, such as inattention, impulsivity and hyperactivity, which are all subjective.Functional Magnetic Resonance Imaging (fMRI) data has become a popular tool to understand the functioning of the brain such as identifying the brain regions responsible for different cognitive tasks or analyzing the statistical differences of the brain functioning between the diseased and control subjects. ADHD is also being studied using the fMRI data. In this dissertation we aim to solve the problem of automatic diagnosis of the ADHD subjects using their resting state fMRI (rs-fMRI) data.As a core step of our approach, we model the functions of a brain as a connectivity network, which is expected to capture the information about how synchronous different brain regions are in terms of their functional activities. The network is constructed by representing different brain regions as the nodes where any two nodes of the network are connected by an edge if the correlation of the activity patterns of the two nodes is higher than some threshold. The brain regions, represented as the nodes of the network, can be selected at different granularities e.g. single voxels or cluster of functionally homogeneous voxels. The topological differences of the constructed networks of the ADHD and control group of subjects are then exploited in the classification approach.We have developed a simple method employing the Bag-of-Words (BoW) framework for the classification of the ADHD subjects. We represent each node in the network by a 4-D feature vector: node degree and 3-D location. The 4-D vectors of all the network nodes of the training data are then grouped in a number of clusters using K-means; where each such cluster is termed as a word. Finally, each subject is represented by a histogram (bag) of such words. The Support Vector Machine (SVM) classifier is used for the detection of the ADHD subjects using their histogram representation. The method is able to achieve 64% classification accuracy.The above simple approach has several shortcomings. First, there is a loss of spatial information while constructing the histogram because it only counts the occurrences of words ignoring the spatial positions. Second, features from the whole brain are used for classification, but some of the brain regions may not contain any useful information and may only increase the feature dimensions and noise of the system. Third, in our study we used only one network feature, the degree of a node which measures the connectivity of the node, while other complex network features may be useful for solving the proposed problem.In order to address the above shortcomings, we hypothesize that only a subset of the nodes of the network possesses important information for the classification of the ADHD subjects. To identify the important nodes of the network we have developed a novel algorithm. The algorithm generates different random subset of nodes each time extracting the features from a subset to compute the feature vector and perform classification. The subsets are then ranked based on the classification accuracy and the occurrences of each node in the top ranked subsets are measured. Our algorithm selects the highly occurring nodes for the final classification. Furthermore, along with the node degree, we employ three more node features: network cycles, the varying distance degree and the edge weight sum. We concatenate the features of the selected nodes in a fixed order to preserve the relative spatial information. Experimental validation suggests that the use of the features from the nodes selected using our algorithm indeed help to improve the classification accuracy. Also, our finding is in concordance with the existing literature as the brain regions identified by our algorithms are independently found by many other studies on the ADHD. We achieved a classification accuracy of 69.59% using this approach. However, since this method represents each voxel as a node of the network which makes the number of nodes of the network several thousands. As a result, the network construction step becomes computationally very expensive. Another limitation of the approach is that the network features, which are computed for each node of the network, captures only the local structures while ignore the global structure of the network.Next, in order to capture the global structure of the networks, we use the Multi-Dimensional Scaling (MDS) technique to project all the subjects from an unknown network-space to a low dimensional space based on their inter-network distance measures. For the purpose of computing distance between two networks, we represent each node by a set of attributes such as the node degree, the average power, the physical location, the neighbor node degrees, and the average powers of the neighbor nodes. The nodes of the two networks are then mapped in such a way that for all pair of nodes, the sum of the attribute distances, which is the inter-network distance, is minimized. To reduce the network computation cost, we enforce that the maximum relevant information is preserved with minimum redundancy. To achieve this, the nodes of the network are constructed with clusters of highly active voxels while the activity levels of the voxels are measured based on the average power of their corresponding fMRI time-series. Our method shows promise as we achieve impressive classification accuracies (73.55%) on the ADHD-200 data set. Our results also reveal that the detection rates are higher when classification is performed separately on the male and female groups of subjects.So far, we have only used the fMRI data for solving the ADHD diagnosis problem. Finally, we investigated the answers of the following questions. Do the structural brain images contain useful information related to the ADHD diagnosis problem? Can the classification accuracy of the automatic diagnosis system be improved combining the information of the structural and functional brain data? Towards that end, we developed a new method to combine the information of structural and functional brain images in a late fusion framework. For structural data we input the gray matter (GM) brain images to a Convolutional Neural Network (CNN). The output of the CNN is a feature vector per subject which is used to train the SVM classifier. For the functional data we compute the average power of each voxel based on its fMRI time series. The average power of the fMRI time series of a voxel measures the activity level of the voxel. We found significant differences in the voxel power distribution patterns of the ADHD and control groups of subjects. The Local binary pattern (LBP) texture feature is used on the voxel power map to capture these differences. We achieved 74.23% accuracy using GM features, 77.30% using LBP features and 79.14% using combined information.In summary this dissertation demonstrated that the structural and functional brain imaging data are useful for the automatic detection of the ADHD subjects as we achieve impressive classification accuracies on the ADHD-200 data set. Our study also helps to identify the brain regions which are useful for ADHD subject classification. These findings can help in understanding the pathophysiology of the problem. Finally, we expect that our approaches will contribute towards the development of a biological measure for the diagnosis of the ADHD subjects.
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Date Issued
2014
Identifier
CFE0005786, ucf:50060
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005786
Development of Traffic Safety Zones and Integrating Macroscopic and Microscopic Safety Data Analytics for Novel Hot Zone Identification
Title
Development of Traffic Safety Zones and Integrating Macroscopic and Microscopic Safety Data Analytics for Novel Hot Zone Identification.
Creator
Lee, JaeYoung, Abdel-Aty, Mohamed, Radwan, Ahmed, Nam, Boo Hyun, Kuo, Pei-Fen, Choi, Keechoo, University of Central Florida
Abstract / Description
Traffic safety has been considered one of the most important issues in the transportation field. With consistent efforts of transportation engineers, Federal, State and local government officials, both fatalities and fatality rates from road traffic crashes in the United States have steadily declined from 2006 to 2011.Nevertheless, fatalities from traffic crashes slightly increased in 2012 (NHTSA, 2013). We lost 33,561 lives from road traffic crashes in the year 2012, and the road traffic...
Show moreTraffic safety has been considered one of the most important issues in the transportation field. With consistent efforts of transportation engineers, Federal, State and local government officials, both fatalities and fatality rates from road traffic crashes in the United States have steadily declined from 2006 to 2011.Nevertheless, fatalities from traffic crashes slightly increased in 2012 (NHTSA, 2013). We lost 33,561 lives from road traffic crashes in the year 2012, and the road traffic crashes are still one of the leading causes of deaths, according to the Centers for Disease Control and Prevention (CDC). In recent years, efforts to incorporate traffic safety into transportation planning has been made, which is termed as transportation safety planning (TSP). The Safe, Affordable, Flexible Efficient, Transportation Equity Act (-) A Legacy for Users (SAFETEA-LU), which is compliant with the United States Code, compels the United States Department of Transportation to consider traffic safety in the long-term transportation planning process. Although considerable macro-level studies have been conducted to facilitate the implementation of TSP, still there are critical limitations in macroscopic safety studies are required to be investigated and remedied. First, TAZ (Traffic Analysis Zone), which is most widely used in travel demand forecasting, has crucial shortcomings for macro-level safety modeling. Moreover, macro-level safety models have accuracy problem. The low prediction power of the model may be caused by crashes that occur near the boundaries of zones, high-level aggregation, and neglecting spatial autocorrelation.In this dissertation, several methodologies are proposed to alleviate these limitations in the macro-level safety research. TSAZ (Traffic Safety Analysis Zone) is developed as a new zonal system for the macroscopic safety analysis and nested structured modeling method is suggested to improve the model performance. Also, a multivariate statistical modeling method for multiple crash types is proposed in this dissertation. Besides, a novel screening methodology for integrating two levels is suggested. The integrated screening method is suggested to overcome shortcomings of zonal-level screening, since the zonal-level screening cannot take specific sites with high risks into consideration. It is expected that the integrated screening approach can provide a comprehensive perspective by balancing two aspects: macroscopic and microscopic approaches.
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Date Issued
2014
Identifier
CFE0005195, ucf:50653
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005195