Doctor of Philosophy, The Ohio State University, 2012, EDU Physical Activity and Educational Services

Over 250 sport management degree programs and over 25 academic journals are in existence currently. Despite the dramatic growth of academic programs and publications, as a relatively young academic field, there are ongoing debates on diverse issues such as definition, boundary, and methodology in the field of sport management (Chalip, 2006; Costa, 2005; Pitts, 2001, Quatman & Chelladurai, 2008; Slack, 1998). Reflecting the notions of Kuhn (1970), the field of sociology of science has contributed to provide insights on these ongoing issues in academic fields. Based on the ontological and epistemological foundations of the social construction of knowledge in sociology of science, this study identified critical concepts and paradigms and explored the structural patterns of those concepts in knowledge structure of sport management. For this, the study employed bibliometric analysis and social network analysis on keywords and citations data retrieved from the articles of the Journal of Sport Management between 1997 and 2010. Embracing the advantages of the multilevel design, this study conducted two different levels of analyses with keywords and citations data – keyword analysis (KA) and citation analysis (CA) for individual attributes and keyword co-occurrence network analysis (KCNA) and co-citation network analysis (CCNA) for relational attributes. The findings of the study indicated that there has been a shift on trends and themes in sport management in both levels – analytical and structural levels. The results of KA and CA revealed the shift on the popularity of certain individual keywords and publications whereas the results of KCNA and CCNA disclosed the shift on the popularity of structures of groups of keywords and publications.

PHD, Kent State University, 2019, College of Communication and Information

The study explores the personal knowledge networks of professional librarians to understand how current career stage and individual career motivations may influence characteristics of personal knowledge networks. any observed differences. Various social network attributes including size, direction of contact, and boundary spanning relationships were explored to understand: (1) how social networks differ based on early, mid, and late career stages; (2) how differing career motivations impact the growth and development of social networks, and (3) whether gender plays a significant role in how social networks develop. Data about the social networks that support librarians in performing their work were collected from a sample of 280 librarians employed in different types of libraries within the U.S. Additionally, the short form Career Orientation Inventory (COI) developed by Igbaria and Baroudi (1993) based on the earlier work of Schein and DeLong was employed to measure career motivations. Multiple quantitative tests were run on the data, including non-parametric, ANOVA-equivalent tests and simultaneous linear regressions. Results demonstrate a tentative link between career stage and the network attributes direction of contact, as well as certain boundary spanning relationships. Social network analysis (SNA) shows promise as an untapped methodology for exploring career development within librarianship and knowledge network assessment via SNA was demonstrated to provide valuable insight to practicing librarians.

Master of Science (MS), Ohio University, 2014, Journalism (Communication)

This content analysis of ABC Network Nightly News stories from 1968 through 2012 of the Christmas holiday shopping season documents specific social, cultural, and economic indicators. A critical studies approach to this research allows the examination of the social ecology where journalistic norms, news sources, business imperatives and cultural phenomena converge. Overall, the results show a 300 percent increase in the number of Christmas-related stories that aired during the first year and the final year of the study. This work contributes to the critical taxonomy of television journalism's relationship with America's commercial culture.

MS, University of Cincinnati, 2006, Pharmacy : Pharmaceutical Sciences

Gel network technology in conditioning shampoo represents an advantage over traditional silicone 2-in-1 technology due to its main benefits: dry conditioning, wet feel and lower cost. The purpose of this study was to do a proof of principle investigation and to study the main factors that affected partitioning of PRMs into the gel network system shampoos and determine the effect that perfume incorporation had on the shampoo stability of the different formulations . Gel network premixes (literally a conditioner) were formulated then incorporated into a standard shampoo base. Changes in formulation of the gel network such as chain length of fatty alcohols and fatty alcohol ratios were done and its effect on stability and perfume migration studied. A technical accord with 25 PRMs with a very wide range of physical properties was used as a marker. Other perfume variables studied were hydrophobicity of the perfume, hydrophobically modified accords, and other user practices such as combing/wetting. The formulations were evaluated for stability using microscopy and differential scanning calorimetry. Compositional analysis was done using GC/MS and headspace analysis. Consumer acceptance was evaluated using sensory panel. The compositional analysis partition data was used in a QSAR model to predict future PRMs tendency to partition into the gel network. Three main conclusions were reached: 1) Hydrophobically modified accords partition favorably into the gel network, however, whether that translates into greater consumer benefit will need to be further tested. 2) PRMs that partition favorably into the gel network follow a structure-property relationship of lipophilicity and rigidity. 3) Changes in processing parameters influence the partitioning of PRMs into the gel network and can be stronger levers than formulations parameters for enhancing perfume bloom and longevity.

Doctor of Philosophy, The Ohio State University, 2010, ED Physical Activities and Educational Services

The purpose of the study was to examine the relationships among perceived effectiveness of NBTAs, extent of advice networks, and perceived job outcomes in a semiconductor manufacturing company in Korea, using a mixed method. The data for the quantitative study were collected from an online survey questionnaire. The population consisted of all employees (N=15,000) who were working in production facilities of the company or branch offices in Korea. The total number of respondents was 188 out of 375 employees randomly selected, with an overall response rate of 50.13%. The data for the qualitative study were gathered from semi-structured interviews with eight employees who responded to the online survey. Canonical correlation analysis and hierarchical regression analysis were utilized to analyze the survey data. Additionally, content analysis was employed to analyzed and interpret the interview data. The results showed that on-the-job training approaches and training approaches within a business unit were perceived to be more helpful than common training approaches to develop advice relations. Yet, no relationships were found between advice networks and the perceived effectiveness of NBTAs. The results also indicated that no mediation occurred between the perceived effectiveness of NBTAs and perceived job outcomes. Although the study failed to reveal the mediation between the perceived effectiveness of NBTAs and perceived job outcomes, the findings from the quantitative and qualitative studies provided evidences that NBTAs helped individuals develop advice networks, and the development of advice networks through NBTAs had an impact on individual job performance and job satisfaction. In addition, the results of this study identified four processes which create advice networks through training approaches: 1) developing advice networks based on job-relatedness, 2) sharing a common interest among others, 3) spending time doing group activities with others, and 4) spending (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2024, Environment and Natural Resources

Departing from literature on social-ecological fitness and social-ecological network analysis, this dissertation explores the degree to which social-ecological theory reflects underlying social processes of issue engagement and partnership evaluation and identifies pathways for future research to engage practitioners with social-ecological network data. In total, the research presented in this dissertation shows that social-ecological network analysis and theory can both be strengthened by participant engagement and qualitative analyses and can be translated into actionable information that practitioners can use to inform their management decisions. This research – which includes three consecutive empirical studies (chapters 2 through 4) – presents one of the first comprehensive accounts of confirming social-ecological network theory with participant populations. Each of the three chapters seeks to determine how practitioners navigate social-ecological interdependence by assessing whether practitioners' strategies align with social-ecological motifs that are commonly used in empirical network analyses (i.e., small-scale network structures that impart theoretically important processes). Further, all three empirical chapters analyze separate components of a dataset pertaining to climate change adaptation governance in Columbus, Ohio, which is a system comprised of over one hundred unique stakeholder organizations, 19 climate adaptation-related issues, and their interconnections. In the first chapter, I explore how community-engaged network tools can help to overcome fragmentation in environmental governance systems. I helped to develop a network tool that offers personalized partnership recommendations to practitioners that would close “collaborative gaps,” which are instances where two actors who manage the same issue(s) fail to collaborate with one another. Results from focus group conversations with practitioners suggest that engaged network tools can be 1) hampere (open full item for complete abstract)

Doctor of Philosophy, Case Western Reserve University, 2022, Art History

In the early modern period, elite collectors began amassing magnificent collections of both locally produced and imported objects. Few were as innovative as Amalia van Solms-Braunfels (1602-1675), Princess of Orange. Under Amalia and her husband, Stadtholder Frederik Hendrik (1584-1647), Prince of Orange, the United Provinces flourished as a cultural and global power. The strength and wealth of the country, and by association the House of Orange-Nassau, is embedded in Amalia's cabinets or closets, private spaces where she carefully curated assemblages of locally produced and imported decorative and fine artworks. Under the weight of a historiographic tradition that privileges male rulers, much of the scholarship produced on the princely couple's cultural activities marks Frederik Hendrik or Constantijn Huygens as the deciding factor without discussion or justification. While scholarly interest in Amalia's role as an independent patron and collector has grown over the last two decades, the focus to date on individual, extant objects, while informative, does not provide a comprehensive understanding of Amalia's interests and motivations as a patron and collector-- how she acquired and employed objects, both individually and in decorative ensembles, to construct her various identities. My dissertation focuses on Amalia's cabinets found in the Stadtholder's Quarters (Stadhouderlijk Kwartier) and the Oude Hof (‘Old Court') at Noordeinde, and the objects displayed within. Uniting textual and visual evidence in the form of inventories, correspondence, and objects with novel digital tools, it first applies social network analysis to visualize Amalia's social, global network that provided her with access to other impressive collections and artists, as well as assisted her with acquiring objects originating from outside of Europe. It interrogates how, once acquired, objects were employed by Amalia in ensembles within the most intimate spaces of her residences to construct her (open full item for complete abstract)

Doctor of Philosophy, University of Toledo, 2021, Spatially Integrated Social Science

The purpose of this research is to explore the interactions of social, environmental, and economic aspects of green infrastructure for inclusion into an ex-ante benefit based conceptual framework that can guide the planning and valuation of multi-use trails in urban areas. GIS was used to analyze factors of the built environment including bicycle network connectivity, population density, and the density of destinations. Landscape metrics, including the division, contagion, and clumpiness indices were calculated using FRAGSTAT to determine the potential for native greenspace development surrounding multi-use trails in urban areas. Additionally, a meta-analysis of hedonic studies, focused on multi-use trails in urban areas, revealed that home values can increase anywhere from 0-27% after a trail is built. Data from the meta-analysis was used to set parameters for a Monte Carlo simulation that provided an estimate of the percent change in home values that can occur after trail implementation. Once created, the conceptual framework was applied to the Chessie Circle Trail (CCT) in Toledo, Ohio. The analysis revealed that opportunity exists for the CCT to enhance bicycle network connectivity and active transit as population and destination density are highest at the north section of the CCT. Green space analysis revealed that high and interspersed patches of developed land limit the potential to reduce habitat fragmentation. Data from the Monte Carlo simulation provides a sensitivity analysis that reveals potential changes in home values after the CCT is built. Application of the conceptual framework to the CCT illustrated that the conceptual framework can aid the planning and valuation of multi-use trails in urban areas. Moreover, ex-ante benefit-based valuation frameworks can be applied as tools that help agencies consider the competing aspects of social, environmental, and economic aspects of green infrastructure.

Doctor of Philosophy, The Ohio State University, 2021, Biomedical Sciences

Metabolism is a process that touches all aspects of life, from homeostasis to disease, such that the study of metabolites yields valuable insights into the inner workings of biological systems. Translating the findings of metabolomic and lipidomic experiments into biological insight, biomarkers, or actionable targets associated with disease requires functional interpretation of the data, which is challenging. One common strategy for interpreting metabolomic data is pathway enrichment analysis. Pathway analysis is useful because pathway-level perturbation can be more reproducible across samples than individual metabolite shifts, which are hindered by inconsistent experimental coverage of metabolites and functional redundancy of metabolites. However, pathway analysis of metabolites faces many barriers for success. Issues with metabolite pathway analysis include lack of metabolite pathway annotations, highly overlapping pathway definitions, and (again) lack of reproducibility in metabolite detection between experiments. Here, I present two complementary software resources, RaMP and MetaboSPAN, which I helped to develop in order to address these issues. RaMP is a metabolite annotations database that consolidates pathway, reaction, chemical structure, and other information from multiple publicly available data sources. RaMP's associated R package allows users to query information on metabolites of interest as well as perform pathway enrichment analysis using the Fisher's exact test. MetaboSPAN is an advanced pathway enrichment analysis strategy that infers activity in undetected portions of the metabolome using the vast extent of knowledge in RaMP to expand pathway-level findings and improve reproducibility between experiments. I demonstrate the utility of these tools on a metabolite data set generated in patient-derived cell lines of dedifferentiated liposarcoma with varying amplification of the MDM2 oncogene.

Master of Sciences, Case Western Reserve University, 2021, EMC - Mechanical Engineering

Flow boiling has become a reliable mode of adapting to larger power densities and greater functions because it is able to utilize both the latent and sensible heat contained within a specified coolant. There are currently few available tools proven reliable when predicting heat transfer coefficients during flow boiling in microchannels. The most popular methods rely on semi-empirical correlations derived from experimental data but can only be applied to a narrow subset of testing conditions. This study will use multiple data science methods to accurately predict the heat transfer coefficient during flow boiling in micro-channels on a database consisting of 16,953 observations collected across 50 experiments using 12 working fluids. The support vector machine model performed best, with a Mean Absolute Percentage Error (MAPE) of 11.3%. The heat flux, vapor-only Froude number, and quality proved to be especially significant variables across 90% of over 110 different models.

Master of Sciences, Case Western Reserve University, 0, EECS - Computer and Information Sciences

Brain functional connectivity measures are used to study interactions between brain regions in various neurological disorders such as Alzheimer's Disease and epilepsy. In particular, high-resolution electrophysiological signal data recorded from intracranial electrodes, such as stereotactic electroencephalography (SEEG) signal data, is often used to characterize the properties of brain connectivity in neurological disorders. For example, SEEG data is used to lateralize the epileptogenic zone and characterize seizure networks in epilepsy. However, there are several computational challenges associated with efficient and scalable analysis of signal data in neurological disorders due to the large volume and complexity of signal data. In order to address the challenges associated with processing and analyzing signal datasets, we have developed an integrated platform called Neuro-Integrative Connectivity (NIC) platform that integrates and streamlines multiple data processing and analysis steps into a single tool. In particular, in this thesis we have developed a suite of new approaches covering signal data format, indexing structure, and Apache Spark libraries to support efficient and scalable signal data management for applications in neurological disorders such as epilepsy. Our evaluations demonstrate the utility of Apache Spark in supporting neuroscience Big Data application; however, our results also demonstrate that Apache Spark is not well suited for all types of computational tasks associated with signal data management. Therefore, the overall objective of this thesis is to identify specific computational tasks that benefit from the use of main memory-based Apache Spark methods in neuroscience Big Data applications. The new NIC platform developed in this thesis is a significant resource for the brain connectivity research community as it has applications in real world settings for advancing research in neurological disorders using signal data.

Doctor of Philosophy, The Ohio State University, 2020, Computer Science and Engineering

The growing need to understand and process data has driven innovation in many disparate areas of data science. The computational biology, graphics, and machine learning communities, among others, are striving to develop robust and efficient methods for such analysis. In this work, we demonstrate the utility of topological data analysis (TDA), a new and powerful tool to understand the shape and structure of data, to these diverse areas. First, we develop a new way to use persistent homology, a core tool in topological data analysis, to extract machine learning features for image classification. Our work focuses on improving modern image classification techniques by considering topological features. We show that incorporating this information to supervised learning models allows our models to improve classification, thus providing evidence that topological signatures can be leveraged for enhancing some of the pioneering applications in computer vision. Next, we propose a topology based, fast, scalable, and parameter-free technique to explore a related problem in protein analysis and classification. On an initial simplicial complex built using constituent protein atoms and bonds, simplicial collapse is used to construct a filtration which we use to compute persistent homology. This is ultimately our signature for the protein-molecules. Our method, besides being scalable, shows sizable time and memory improvements compared to similar topology-based approaches. We use the signature to train a protein domain classifier and compare state-of-the-art structure-based protein signatures to achieve a substantial improvement in accuracy. Besides considering the intervals of persistent homology like our first two applications, some applications need to find representative cycles for them. These cycles, especially the minimal ones, are useful geometric features functioning as augmentations for the intervals in a purely topological barcode. We address the problem of computing th (open full item for complete abstract)

Master of Computer Science, Miami University, 2020, Computer Science and Software Engineering

Network analysis techniques help investigate the significance of nodes/actors that play central roles where the nodes represent people, and the links represent the communication between them. This thesis analyzes how collaboration helps students' learning process and proposes a tool that could be integrated with Canvas to analyze student discussion data. To begin, we analyzed data collected from online student discussions on Canvas, in a Level-1 Programming course. These discussion topics were classified into classroom experiences/learning, question/answers, opinions, and comments. Modeling of the patterns of discussion board interactions as networks and applying various node-based network measures helped to unravel the similarities of student interaction patterns, and gain insights into their progress in the course. The experimental analyses include finding the most challenging/debated topics in the course, analyzing the leadership and team-based qualities, and analyzing trends in student participation. The results of the study reveal that participation in online discussion forums has a positive impact on the students' grades. In summary, the inferences drawn from this research can help instructors understand the student learning behaviors/patterns and guide the development of better pedagogical approaches that benefit students to overcome the common misconceptions that they confront in the course concepts.

Doctor of Philosophy, Case Western Reserve University, 2020, EECS - Computer and Information Sciences

Complex diseases, like cancer or Type II Diabetes, result from the interplay between multiple genetic factors at different cellular levels as well as environmental factors. Deciphering the etiology of complex diseases mandates characterizing the function of many underlying factors and the relationships between these factors. The availability of a plethora of omic data at a genomic scale and the availability of disease-associated data from a broad range of populations present a valuable resource. When such wealth of data is utilized by integrative and efficient computational methods and robust statistical frameworks, it could help in elucidating the etiology of complex diseases and the realization of precision medicine. Due to the complexity of biological systems; the intricacy of inter-genomic interactions, the obscuring effect of many confounding factors, the high dimensionality and the high degree of noise in the data, effective use of omic data for accurate disease risk prediction faces important challenges. This problem is especially clear when dealing with complex diseases like cancer. In this thesis, we utilize multiple types of omic data as well as population-specific data and develop integrative computational methods to characterize the interplay between various factors that underlie complex diseases. We perform computational analyses at multiple levels, capture functional commonalities of disease-associated variants across different populations and model the interplay between disease-associated genes at the cellular level. We model and spot distortion in omic data by discovering new features that mitigate its negative impact on the predictive ability of biomarkers, hence improving the accuracy of disease risk prediction.

MS, University of Cincinnati, 2019, Engineering and Applied Science: Computer Science

Community detection in Social Networks has been a major research interest in recent years. In graphical community detection, the principal consideration is the connection between users in the network data. On the other hand, document clustering is a paradigm where text documents are clustered together based on their textual properties. In this thesis, we have used document clustering techniques on data collected from the social networking site, Twitter to cluster the users associated with them. We then compared the user clusters formed by the document clustering techniques and compared them with the communities detected in the graphical representation to investigate the possibilities of any correlation between these two methods. We utilized tools such as NodeXL and Gephi for collecting and visualizing the network data respectively. For user clustering based on their tweets, we used four different feature representation techniques and two clustering algorithms.

Doctor of Philosophy, The Ohio State University, 2018, Environmental Science

The West Nile Virus (WNV) was introduced into the U.S. in the summer of 1999 and caused the outbreaks of West Nile encephalitis. The virus is responsible for more than 45,000 cases in the U.S., including 2,017 fatalities. This virus is passed back and forth between infected birds and mosquitoes, but humans can be infected by the bite of an infected data mosquito, and no vaccine is currently available for humans. The WNV is difficult to eradicate because of its complex transmission behaviors and the transmission dynamics can also be altered by a variety of factors. Key factors include host (birds) and vector (mosquitoes) abundances, the numbers of susceptible individuals of humans, weather patterns, land use, and land cover. Furthermore, small water bodies are the source for breeding mosquitoes and provide promising components for the mosquito management. The ultimate goal of this dissertation was to better understand how WNV occurs in the continental U.S. by linking hydrological frameworks and other environmental and social-economic factors for disease transmission. In the first study, statistical models were designed to identify the factors leading to human infection at a local area in North Dakota. This study addressed how variability in meteorological data and river management can affect the disease transmission through its association with mosquitoes. The developed models also allowed the prediction of the onset of virus infections, which can contribute to mosquito control or lead to a preemptive warning for protection. In addition, the findings and conceptual framework of my statistical approach could potentially be applied to prediction analysis of other mosquito-borne diseases. In the next study, a network analysis was applied to clarify how multiple factors affect WNV incidence rates of humans in the continental U.S. The study identified which factors are associated with the conditions that are susceptible to the virus, and when the surge of disease (open full item for complete abstract)

Doctor of Philosophy (Ph.D.), Bowling Green State University, 2017, Media and Communication

This study investigated the intermedia agenda-setting dynamics among various media Twitter accounts during the last seven weeks before the 2016 U.S. presidential election. Media Twitter accounts included in analysis were those of print media, television networks, news magazines, online partisan media, online non-partisan media, and political commentators. This study applied the intermedia agenda-setting theory as the theoretical framework, and network analysis and computer-assisted content analysis enabling hyperlink and sentiment analysis as the methods. A total of 5,595,373 relationships built via Tweets among media Twitter accounts was collected. After removal of irrelevant data, a total of 16,794 relationships were used for analysis. The results showed that traditional media Twitter accounts, such as print media and television networks, play roles in the Tweeting network by bridging isolated media Twitter accounts, and are located in the center of networks, so that information reaches them quickly; further, they are connected to other important accounts. Together with the changes in the volume of Tweeting that signaled media interest, the set of popular URLs and keywords/word pairs in Tweets also served as sensors that detected media Twitter accounts' interest about that time. The results also supported the previous research findings that, as political events, the debates affect the production and dissemination patterns of news. Not only did the volume of Tweeting produced spiked immediately after each debate, but various types of hyperlinks and sentiment words used in Tweets increased as well. The number of negative sentiment words observed in the Tweeting network surpassed the number of positive sentiment words observed in the Tweeting network across different time points, and the gap between them decreased as the election approached. The use of positive and negative sentiment words differed across different media Twitter account categories. Online non- (open full item for complete abstract)

PhD, University of Cincinnati, 2016, Medicine: Systems Biology and Physiology

The response to perturbations in cellular systems is governed by a large number of molecular circuits that coalesce into a complex network. In complex diseases, the breakdown of cellular components is brought about by multiple molecular and environmental perturbations. While individual signatures of cellular components might vary significantly among clinical patients, commonality in signs and symptoms of disease progression is a compelling indicator that key cellular sub-processes follow similar trajectories? -. Our approach aims for an enhanced understanding of the effect of disease perturbations on the cell by developing an automated platform that assigns more significance to changes that occur at the sub-network level – focusing on genes that are “wired” together and change together. The platform that we have developed is motivated by the study of concomitant expression changes in sub-networks. The analysis by our platform produces a small subset of signaling and regulatory genes that are wired together and change together beyond random chance. In order to evaluate the effectiveness of our platform in producing subsets that can distinguish diseases and disease-subtypes, we used publicly available RNA-Seq and microarray breast cancer expression datasets. Each dataset was analyzed independently using our platform and the disease related sub-network perturbations among breast cancer subtypes were identified. The resulting subset was subjected to standard multi-way classification and predictions based on our approach were compared with PAM50 predictions. Biomarkers identified from the microarray and RNA-Seq dataset reproduced the PAM50 classification with 100% and 80% agreement respectively despite having only 10% of genes common with the PAM50. This proof-of-concept analysis using breast cancer datasets is indicative of the platform's stable cross-validation results. This platform can potentially be used for automated and unbiased computational discovery of disease (open full item for complete abstract)

Master of Science (MS), Ohio University, 2015, Computer Science (Engineering and Technology)

This study focuses on understanding how to classify out-of-order network traffic sent using the Transport Control Protocol(TCP). Packets that arrive out of order are the result of network reordering or loss recovery. TCP initiates loss recovery in response to the perceived loss of data, decreasing the congestion window and throughput of the connection. When TCP reacts poorly to loss, throughput may drop, latency may increase, and congestion collapse may occur. This thesis analyzes TCP traffic from an arbitrary observation point in a network, rather than at the TCP endpoint. Observing traffic at a TCP endpoint inhibits the inference of loss and detection of network reordering in one direction of the connection. Alternatively, observing traffic at an arbitrary point between two TCP endpoints allows inference of loss and detection of network reordering in both directions. Positioning the observation point at an arbitrary point can increase the diversity of observed connections, increasing the likelihood of detecting rare forms of aberrant behavior. In this paper, several algorithms and heuristics for classification of out-of-order TCP traffic are analyzed and implemented in a new TCP traffic analyzer called tcprs. An in-depth analysis of each algorithm and heuristic is given and compared with the results from tcptrace and tcpcsm. It was found that tcprs achieves an improvement in classification accuracy as compared with tcptrace and tcpcsm.

Master of Information and Telecommunication Systems (MITS), Ohio University, 2015, Information and Telecommunication Systems (Communication)

This thesis addresses certain technical and financial challenges associated with the deployment and operation of relay spacecraft using the Internet Protocol as the primary routing protocol. Though IP in space has been a hot topic for nearly a decade, few studies address the capabilities of management protocols being used to operate a geostationary fleet. Likewise, few have addressed the real-world cost structure of replacing a traditional bent-pipe fleet with an IP-enabled fleet. Within our research, we investigate whether SNMP, TFTP, and SCP are capable of meeting the Tracking, Telemetry, and Command requirements set by a real-world geostationary relay service provider. We also investigate the driving forces of relay deployment and operational costs, identify Rough Order of Magnitude costs for a geostationary IP-enabled relay, and define a financial profile categorizing the costs of replacing a bent-pipe fleet with an IP-enabled fleet.