The transit industry is changing rapidly due to technology, which in turn changes business models, ridership, travel patterns, and the transit workforce. As transit agencies introduce new technology systems, research is needed on how these systems impact demand for paratransit and on-demand mobility services. This research addresses this topic by studying the impact of technology on demand-responsive transportation and urban mobility. Over the past two decades, this sector has been transformed by cloud computing, machine learning, artificial intelligence, ridesharing, and mobility-on-demand. This dissertation explores the adoption of new technology by transit agencies and service providers, focusing on implementing app-based dynamic technologies for dispatching and scheduling demand-responsive transportation modes such as microtransit services, on-demand transit, and paratransit. Although studies on technological changes in other sectors have been conducted, public transit agencies need a more systematic approach to adopting new technology. Current literature on technology adoption in public transit focuses on the benefits and outcomes of technology adoption, with limited discussions of the challenges faced in adopting and implementing technologies. Comprehensive research on the emerging and evolving transit technological landscape is essential to bridge this gap. This research examines how transit agencies react to internal and external technological changes as their operational, tactical, and strategic operating conditions evolve. The aim is to enhance the current comprehension of the topic by providing a comprehensive overview of the technology adoption methodology and to offer practical planning and policy recommendations where possible. A mixed-methods approach was applied to explore the research questions. Transit practitioners and managers in the Washington DC region were surveyed, and the analysis techniques employed included cross-tabulation and descriptive statistics. This dissertation focuses on gaining insight into adopting real-time dynamic dispatching and scheduling, on-demand transit, and microtransit technologies, including the opinions of transit practitioners and policymakers involved in facilitating technology adoption. Specifically, the study aims to: 1) understand the impact of adopting emerging paratransit technologies; 2) investigate on-demand transit system performance outcomes under ridership, on-time performance, and operating costs, using a survey and expert interviews; and 3) investigate the use of a multicriteria decision-making approach to evaluate accessibility considerations in microtransit adoption planning and design strategies. The results suggest that current technology adoption approaches in transit can significantly enhance decision-making and transit outcomes while addressing the equity and accessibility needs of the community and maintaining coverage and route frequency. The Socio-Technical-Systems (STS) approach was applied to help understand the adoption of new technology in demand response transit. This approach provides insights into technology, accessibility, decision-making, functionality, and interchangeability, enhancing our understanding of social complexity. Additionally, this research introduces a multi-level decision-making framework to measure service performance and provides insights into the impact of transportation technology on planning, policy, and decision-making processes. / Doctor of Philosophy / This research examines how transportation technology advancements affect mobility in the United States. It focuses on how transit agencies adapt to technological changes inside and outside the organization as their operating conditions evolve at operational, tactical, and strategic levels. This study aims to provide a comprehensive understanding of this subject by offering a thorough overview of the technology adoption process and practical planning and policy recommendations where appropriate. The study delves into how real-time information coupled with new business models create more accessible transit options and informed decisions. The research investigates on-demand transit, microtransit, and real-time dynamic dispatching and scheduling, which pose challenges regarding demand and costs. These technologies aim to maximize operational capacity, route frequencies, and reduce vehicle travel time and mileage while considering the uncertainties of funding and travel behaviors that arise with technology adoption. The study examines three key technologies: 1) real-time dynamic dispatching and scheduling in paratransit; 2) performance outcomes of on-demand transit services in the Washington DC region; and 3) a multi-attribute decision-making approach in evaluating microtransit accessibility. The research reviews the technology adoption methods employed by transit agencies. It discusses the potential technology deployment of future projects in three domains: real-time dynamic dispatching and scheduling, on-demand transit, and microtransit accessibility.

The transit industry is changing rapidly due to technology, which in turn changes business models, ridership, travel patterns, and the transit workforce. As transit agencies introduce new technology systems, research is needed on how these systems impact demand for paratransit and on-demand mobility services. This research addresses this topic by studying the impact of technology on demand-responsive transportation and urban mobility. Over the past two decades, this sector has been transformed by cloud computing, machine learning, artificial intelligence, ridesharing, and mobility-on-demand. This dissertation explores the adoption of new technology by transit agencies and service providers, focusing on implementing app-based dynamic technologies for dispatching and scheduling demand-responsive transportation modes such as microtransit services, on-demand transit, and paratransit. Although studies on technological changes in other sectors have been conducted, public transit agencies need a more systematic approach to adopting new technology. Current literature on technology adoption in public transit focuses on the benefits and outcomes of technology adoption, with limited discussions of the challenges faced in adopting and implementing technologies. Comprehensive research on the emerging and evolving transit technological landscape is essential to bridge this gap. This research examines how transit agencies react to internal and external technological changes as their operational, tactical, and strategic operating conditions evolve. The aim is to enhance the current comprehension of the topic by providing a comprehensive overview of the technology adoption methodology and to offer practical planning and policy recommendations where possible. A mixed-methods approach was applied to explore the research questions. Transit practitioners and managers in the Washington DC region were surveyed, and the analysis techniques employed included cross-tabulation and descriptive statistics. This dissertation focuses on gaining insight into adopting real-time dynamic dispatching and scheduling, on-demand transit, and microtransit technologies, including the opinions of transit practitioners and policymakers involved in facilitating technology adoption. Specifically, the study aims to: 1) understand the impact of adopting emerging paratransit technologies; 2) investigate on-demand transit system performance outcomes under ridership, on-time performance, and operating costs, using a survey and expert interviews; and 3) investigate the use of a multicriteria decision-making approach to evaluate accessibility considerations in microtransit adoption planning and design strategies. The results suggest that current technology adoption approaches in transit can significantly enhance decision-making and transit outcomes while addressing the equity and accessibility needs of the community and maintaining coverage and route frequency. The Socio-Technical-Systems (STS) approach was applied to help understand the adoption of new technology in demand response transit. This approach provides insights into technology, accessibility, decision-making, functionality, and interchangeability, enhancing our understanding of social complexity. Additionally, this research introduces a multi-level decision-making framework to measure service performance and provides insights into the impact of transportation technology on planning, policy, and decision-making processes. / Doctor of Philosophy / This research examines how transportation technology advancements affect mobility in the United States. It focuses on how transit agencies adapt to technological changes inside and outside the organization as their operating conditions evolve at operational, tactical, and strategic levels. This study aims to provide a comprehensive understanding of this subject by offering a thorough overview of the technology adoption process and practical planning and policy recommendations where appropriate. The study delves into how real-time information coupled with new business models create more accessible transit options and informed decisions. The research investigates on-demand transit, microtransit, and real-time dynamic dispatching and scheduling, which pose challenges regarding demand and costs. These technologies aim to maximize operational capacity, route frequencies, and reduce vehicle travel time and mileage while considering the uncertainties of funding and travel behaviors that arise with technology adoption. The study examines three key technologies: 1) real-time dynamic dispatching and scheduling in paratransit; 2) performance outcomes of on-demand transit services in the Washington DC region; and 3) a multi-attribute decision-making approach in evaluating microtransit accessibility. The research reviews the technology adoption methods employed by transit agencies. It discusses the potential technology deployment of future projects in three domains: real-time dynamic dispatching and scheduling, on-demand transit, and microtransit accessibility.

This work presents a solution for a real-time fire suppression control system. It also serves as a support tool that allows creation of virtual ship models and testing them against a range of representative fire scenarios. Model testing includes generating predictions faster than real time, using the simulation network model developed by Hughes Associates, Inc., their visualization, as well as interactive modification of the model settings through the user interface. In the example, the ship geometry represents ex-USS Shadwell, test area 688, imitating a submarine. Applying the designed visualization techniques to the example model revealed the ability of the system to process, store and render data much faster than the real time (in average, 40 times faster).

Distributed real-time applications require support from the underlying middleware to meet the strict requirements for jitter, latency, and bandwidth. While most existing middleware standards such as MPI do not support Quality of Service (QoS), the MPI/RT standard supports QoS in addition to striving for high performance. This thesis presents HARE, the first known implementation of a subset of the MPI/RT 1.1 standard with time-driven QoS support. This thesis proves the following hypothesis: It is possible to achieve zero-sided communication (a model of communication characterized by the absence of any explicit per-message transfer calls by any of the participating sides) in a real-time environment using a QoS contract between an application and message-passing middleware. Furthermore, it is shown that the performance and predictability of a time-driven task using zero-sided communication is better than that of a best-effort task. The hypothesis is validated through compact MPI/RT application programs that achieve zero-sided communication.

Situations like war, terrorist attacks, fire accidents, floods, storms, etc., which threaten human life and property, demand immediate action to decrease the damage caused by them. A system is needed that predicts the future events based on what has happened and notifies the concerned personnel. The situation could be better understood in less time if the data is represented as colored, shaded and moving images rather than as numbers. Such a system requires a real-time Web-based visualization system with easy and secure access to grid resources, presenting easy-to-read graphics through a simple interface provided by a Web browser, and responding to user actions immediately. The Web and grid environments impose severe performance constraints such as communication time, latency of the network, etc., making it highly difficult to have a highly responsive real-time visualization. This work aims in finding an appropriate design that satisfies the above requirements. It also aims in understanding the limitations of a distributed environment for real-time applications and finding ways to overcome those limitations. A three-tier architecture is proposed, implemented, and tested to find the bottlenecks of the distributed environment. Relevant design principles are applied to a case study eliminating or minimizing the bottlenecks until the case study system satisfies all the requirements. The case study is the Fire-Smoke system, simulating the propagation of fire in the ex-USS Shadwell test area emulating a submarine. This system is re-implemented from a stand-alone system in OpenGL to a real-time Web-based visualization system using Java3D and J2EE technologies.

Monocytes are professional antigen presenting cells (APC). They serve as precursors of macrophages and dendritic cells (DC). We have used cytopathic (cp) and non-cytopathic (ncp) Bovine Viral Diarrhea Viruses (BVDV) to determine the genes and proteins expression levels in bovine monocytes. Four specific aims were accomplished in this study. The first aim was to assess the baseline expression of the proteins involved in professional antigen presentation in bovine monocytes. The results showed that the differential detergent fractionation (DDF) approach can provide interpretable and meaningful functional information in bovine monocytes. The second aim was to evaluate the role of in vitro cp and ncp BVDV infection in the expression of the selected bovine genes involved in professional antigen presentation. The results showed that both BVDV could escape innate immune responses by modulating toll-like receptor (TLR) gene expression, followed by pro-inflammatory, type I interferon (IFN), Th1/Th2 type cytokine genes expression, and decreasing the expression levels of CD80/CD86 in professional APC. The third objective was to determine how the two biotypes affect selective antigen uptake, receptor-mediated endocytosis and non-selective uptake, macropinocytosis in bovine monocytes. The results indicated that bovine monocytes use macropinocytosis for a bulklow uptake of soluble antigens. The final aim was to characterize protein profiles in peripheral blood monocytes infected with cp BVDV isolate in vitro. Comparative profiling of the membrane and cytosolic proteins related to professional antigen presentation were assessed. The results showed that 47 bovine proteins, involved in immune function of professional APC have been significantly altered after cp BVDV infection. Overall, we hypothesize that by modulating expression levels of multiple proteins and genes related to immune responses BVDV could significantly compromise immune defense mechanisms resulting in uncontrolled immune activation or suppression.

Proliferative gill disease (PGD) caused by the myxozoan parasite Henneguya ictaluri is one of the most devastating parasitic infections in channel catfish aquaculture. Currently, there is no effective treatment for H. ictaluri and the unpredictable outbreaks can result in 100% mortality. Management strategies have been developed to prevent losses in newly stocked fingerlings by evaluating the PGD status of a pond prior to stocking, which is difficult since resident fish may not show clinical signs even when actinospore levels are lethal to naive fish. Current diagnostic methods are limited to the identification of an active infection and methods of predicting potential outbreaks have several limitations. The PGD status of a pond to be stocked can be determined using sentinel fish exposures which are labor intensive and require a source of parasite free fish. These limitations necessitated the development of more rapid and efficient means of determining actinospore concentrations to determine the risk of losing fish prior to stocking. The development of a quantitative real-time polymerase chain reaction (QPCR) assay provided a more rapid, sensitive and quantitative method of diagnosing active infections and also provides a means to predict potential PD outbreaks and determine the PGD status of a pond prior to stocking. Another approach in the control of this parasite is the identification of a less susceptible culturable species or to identify traits that could be targeted in a selective breeding program. Challenge studies have shown that the closely related blue catfish (Ictalurus furcatus) does not exhibit as severe an inflammatory response to H. ictaluri and mortalities are significantly lower than in channel catfish. Comparisons of PGD severity and H. ictaluri infection in channel catfish, blue catfish and channel x blue catfish backcross hybrids by gross examination, histopathology and the newly developed H. ictaluri real-time PCR (QPCR) assay supported previous research suggesting the life cycle of the parasite can not be completed as efficiently through the blue catfish host. This dissertation describes the development and validation of a QPCR assay to detect H. ictaluri in both fish tissues and environmental samples and the application of this assay in both research and production settings.

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