A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item CONNECTEDNESS EFFICIENCY ANALYSIS OF WEIGHTED U.S. FREIGHT RAILROAD NETWORKS(2022) Hamed, Majed; Ayyub, Bilal M.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Freight rail networks serve an essential role in transporting goods to accommodate marketdynamic demands and public needs, and subsequently network analyses of such systems become of importance for providing insights into enhancing transportation efficiency and resilience. This thesis develops and investigates a topological analysis model termed as connectedness efficiency, which is associated with the connectedness of a network’s nodes by its links and corresponding attributes. Analysis outcomes from such a model can be utilized for providing economical insights on the network’s performance. This model can be used to analyze network topologies without assigning weights to their nodes and links, or with weight assignments to nodes and links based on different attributes, such as volumes of goods handled at nodes, physical-length of links, commodity volume transported through links, and travel fuel cost through links. Such an analysis can be utilized for: (1) defining distinctions that may be employed for the assignment of node and link weights, (2) gaining understanding of node and link criticality, and (3) providing methods for objectively maintaining and enhancing network performance. This analysis informs decisions to be considered by rail managers and executives in financial management, planning expansions, route changes, or preparations for potential node or link failures. A case study using an aggregate U.S. freight railroad network along with other example topologies is presented to examine different network attributes as well as their influence on connectedness efficiency and loss impacts of nodes and links.Item Spatial and temporal modeling of large-scale brain networks(2017) Najafi, Mahshid; Pessoa, Luiz; Simon, Jonathan Z.; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The human brain is the most fascinating and complex organ. It directs all our actions and thoughts. Despite the large body of brain studies, little is known about the neural basis of its large-scale structure. In this dissertation, I take advantage of several network-based and statistical techniques to investigate the spatial and temporal aspects of large-scale functional networks of the human brain during "rest" and "task" conditions using functional MRI data. Large-scale analysis of human brain function has revealed that brain regions can be grouped into networks or communities. Most studies adopt a framework in which brain regions belong to only one community. Yet studies in general fields of knowledge suggest that in most cases complex networks consist of interwoven sets of overlapping communities. A mixed-membership framework can better characterize the complex networks. In this dissertation, I employed a mixed-membership Bayesian model to characterize overlapping community structure of the brain at both "rest" and "task" conditions. The approach allowed us to quantify how task performance reconfigures brain communities at rest, and determine the relationship between functional diversity (how diverse is a region's functional activation repertoire) and membership diversity (how diverse is a region's affiliation to communities). Furthermore, I could study the distribution of key regions, named "bridges", in transferring information across the brain communities. Our findings revealed that the overlapping framework described the brain in ways that were not captured by disjoint clustering, and thus provided a richer landscape of large-scale brain networks. Overall, I suggest that overlapping networks are better suited to capture the flexible and task-dependent mapping between brain regions and their functions. Finally, I developed a dynamic intersubject network analysis technique to study the temporal changes of the emotional brain at the level of large-scale brain networks by formulating a manipulation in which threat levels varied continuously during the experiment. Our results illustrate that cohesion within and between networks changed dynamically with threat level. Together, our findings reveal that characterizing emotional processing should be done at the level of distributed networks, and not simply at the level of evoked responses in specific brain regions.