MS, University of Cincinnati, 2011, Engineering and Applied Science: Computer Engineering

Multi-cores processor architecture has proven to be the solution of diminishing return in processing power when the frequency of uniprocessors is further increased. Because of the bandwidth limit, the current Common Bus communication architecture becomes inefficient if the number of cores in a processor increases more than a handful.New on chip communication architectures has been explored and packet switch Network on Chip has come out to be the future of multi-core interconnects architectures. The performance of the NoC architecture is sensitive to the topology, queue sizes, cache size and its associativity, arbitration scheme, flow control etc. Hence, to study the tradeoffs between area, timing and performance in NoCs is time consuming. The software platform available to run experiments on NoC architectures has many limitations such as •The time to run benchmarks on the big NoC configurations is huge (in days) making it infeasible to run experiments. •It does not give accurate information about the cost function such as area, timing and power. •It does give the feasibility of hardware implementation for these architectures. The freely available hardware platform tool is very specific for a particular application and does not support re-configurability and detailed simulation. The proposed Generic Synthesizable HDL (Hardware descriptive platform) Platform for NoCs provides a platform for the multi-core NoC architecture experimentation with the following features:- •It provides support for Common bus, NoC, hybrid NoCs and hybrid NoC with core migration architectures with customizable parameters such as data width, address width, queue sizes, cache sizes etc. •Speeds up the simulation if implemented in hardware compared to any software platform. •Other NoC architectures can be implemented by reusing the components used in the design. •Gives the accurate estimation of area, timing and performance. •This can be implemented in any hardware platform such as FPGA, AS (open full item for complete abstract)

Committee: Ranganadha Vemuri PhD (Committee Chair); Wen Ben Jone PhD (Committee Member); Carla Purdy PhD (Committee Member) Subjects: Computer Engineering

Master of Science (MS), Ohio University, 2000, Mechanical Engineering (Engineering)

The Department of Mechanical Engineering and the Avionics Engineering Center at Ohio University are developing an electromechanical system for the calibration of an inertial measurement unit (IMU) using global positioning system (GPS) antennas. The GPS antennas and IMU are mounted to a common platform to be oriented in the angular roll, pitch, and yaw motions. Vertical motion is also included to test the systems in a vibrational manner. A four-DOF system based n the parallel Carpal Wrist has been developed as a test platform for this calibration process. High-accuracy positioning is not required from the platform since the GPS technology provides absolute positioning data for the IMU calibration.

Committee: Robert Williams II (Advisor) Subjects: Engineering, Mechanical

Doctor of Philosophy, The Ohio State University, 2023, Electrical and Computer Engineering

We are currently in an era of "Information Exploration," which extends to modern intelligent platforms such as robotics, vehicles, and unmanned aerial systems. These platforms are typically equipped with multiple sensors, including but not limited to a vision system, radar, Light Detection and Ranging (LiDAR), and inertial measurement units (IMUs). With an abundance of raw data at their disposal, machine platforms must possess the capability to comprehend these data and take action, representing a hallmark of machine intelligence. In technical terms, this process is known as situational awareness, which primarily encompasses three stages: perception, comprehension, and projection. Among these three stages, comprehension ability is of paramount importance, akin to the human brain's function in a machine. It is essential to effectively comprehend the raw data collected by various sensors and extract valid information for subsequent decision-making. The core of enabling platform-agnostic situational awareness is to effectively develop the machine's cognitive capabilities and design the necessary algorithms, irrespective of the specific platform, whether on the ground or in the air. In this dissertation, our research primarily focuses on the development of advanced comprehension functions using modern machine learning models. Given that image data collected through vision systems often contain redundant natural signals and semantic clues, we place our emphasis on the vision system's robustness, as well as some aspects of geometric understanding, particularly in the context of 2D perspective views. To meet the requirements of enabling situational awareness in various domains across different platforms, we employ a range of deep learning models, including multilayer perceptrons, convolutional neural networks, and deep reinforcement learning. In summary, we outline the contributions of this dissertation as follows: 1. Present a unified framework in an end-to-en (open full item for complete abstract)

Committee: Alper Yilmaz (Advisor); Jay Myung (Committee Member); Charles Toth (Committee Member); Rongjun Qin (Committee Member) Subjects: Computer Engineering; Electrical Engineering

PhD, University of Cincinnati, 2023, Arts and Sciences: Geology

Integration of event-stratigraphy, biostratigraphy, chemostratigraphy, and allostratigraphic methodology yields high-resolution correlations and helps to elucidate processes in the ancient past. This study presents an integrated stratigraphic approach applied to a Late Ordovician (Sandbian - Katian) mixed carbonate platform, cratonic basin system in Kentucky and Ohio. Identification of four widespread carbon isotope excursions (Logana, Macedonia, Brannon, and Bromley) enhanced regional chronostratigraphic correlations. Further, event beds (faunal epiboles, ash beds, deformed horizons) differentiated non-unique signatures in the stratigraphy and corroborated correlations. Numerous stratigraphic sections of the Lexington Platform (outcrops and core throughout) were studied to improve the existing stratigraphic framework and revisions to the regional correlation and nomenclature are documented. Key basin and basin margin reference sections (Cadiz Core, Middletown Core and others) were analyzed for stratigraphic patterns and whole rock carbon isotopes (d13Ccarb), percent total organic carbon, and elemental abundance (pXRF) in addition to detailed (bed scale) lithostratigraphic study. The Point Pleasant Basin (east-central Ohio) underwent differential subsidence relative to the Lexington Platform (central Kentucky) in three distinctive phases. Despite the progressive differentiation of the two depositional environments, integrative stratigraphy (especially faunal epiboles and d13Ccarb excursions) permits direct comparison. Widespread soft-sediment deformation (seismites) and increased occurrence of ash beds (k-bentonites) are coincident with the onset of increased differentiation of the Lexington Platform from the Point Pleasant Basin supporting a tectonic driver. However, small-scale sequences are recognizable across significant facies changes, supporting an overall eustatic origin of cyclicity. Carbon isotopic trends across much of the Laurentian Craton, plat (open full item for complete abstract)

Committee: Carlton Brett Ph.D. (Committee Chair); Thomas Algeo Ph.D. (Committee Member); Daniel Sturmer (Committee Member); Patrick McLaughlin Ph.D. (Committee Member); Peter Holterhoff Ph.D. (Committee Member) Subjects: Geology

Master of Science (MS), Wright State University, 2022, Computer Science

Education about implicit bias in clinical settings is essential for improving the quality of healthcare for underrepresented groups. Such a learning experience can be delivered in the form of a serious game simulation. WrightLIFE (Lifelike Immersion for Equity) is a project that combines two serious game simulations, with each addressing the group that faces implicit bias. These groups are individuals that identify as LGBTQIA+ and people with autism spectrum disorder (ASD). The project presents healthcare providers with a training tool that puts them in the roles of the patient and a medical specialist and immerses them in social and clinical settings. WrightLIFE games are distributed on both mobile and desktop devices and go through the entire cycle of providing healthcare professionals with experiential learning, which starts with defining the goals of the simulation and ends with collecting feedback. In this thesis work, cross-platform software frameworks like the Unity Engine have been used to develop survey scenes to comprehensively document users' pre- and post-simulation experience and attitudes towards implicit bias. Life course scenes were designed to convey an enhanced user experience that bridges the socio-technical gap between the real and virtual worlds. By applying existing user-experience design methodologies to design the survey scenes and life course scenes, it was possible to create an immersive experiential-learning assessment tool that has the potential to deliver data-driven and targeted learning.

Committee: Ashutosh Shivakumar Ph.D. (Committee Chair); Yong Pei Ph.D. (Committee Co-Chair); Paul J. Hershberger Ph.D. (Committee Member); Thomas Wischgoll Ph.D. (Committee Member) Subjects: Computer Engineering; Computer Science

MS, University of Cincinnati, 2021, Education, Criminal Justice, and Human Services: Information Technology

Diabetes is one of the highest causes of death in the world. Diabetes is caused when the blood glucose level is too high in the body. Gradually, high blood glucose leads to heart disease, stroke, eye, and foot problems. To prevent the dreadful effects among people, early detection is required that would lead to proper medical treatment and change in lifestyle. Therefore, with the rise of machine learning we can predict if a patient has diabetes or not. Furthermore, we will integrate the trained model to a web application that will connect the model to generate predictions in real-time considering factors responsible for diabetes like body mass index (BMI), age, insulin, etc. In this paper, we are using the Pima Indian dataset that is originally from the National Institute of Diabetes, Digestive and Kidney Diseases for diabetes prediction model design using machine learning. The proposed system in this paper is the Soft Voting ensemble classifier. The algorithm with the best accurate result was used in making predictions. This model was deployed to the web using flask (a python framework), it takes inputs from the user to make predictions. This model is implemented using python programming language and flask (a web base framework) hosted in GCP. Soft Voting ensemble classifiers even perform better than other classifiers with an accuracy of 91.55% which is quite promising considering the other classification models in the literature for this problem.

Committee: Nelly Elsayed Ph.D. (Committee Chair); Bilal Gonen Ph.D. (Committee Member); M. Murat Ozer Ph.D. (Committee Member) Subjects: Computer Science

PhD, University of Cincinnati, 2020, Engineering and Applied Science: Computer Science and Engineering

Nowadays, it is becoming very easy to have a huge collection of healthcare data, especially because of relatively cheap wearable devices. Subsequently, we can mine clinical data and acquire meaningful information. It helps in making better decisions and improve the healthcare sector by minimizing the costs. Healthcare datasets that are available in public domain have lots of features and it is manually impossible to identify the factors that contribute to the disease [1]. Therefore, it is necessary to use Machine Learning (ML) algorithms to identify the most important features that will help in finding out the occurrence of diseases from huge number of features. Thus, we could predict the disease more accurately with the model trained by only the top features of the dataset. Considering the fact that the healthcare data is coming from different sources with different sizes, there is a need for cloud-based platform. The first aim of this dissertation is to focus on the important field where big data is used for health care to diagnose diseases before they occur or to avoid them. Breast Cancer (BC) is the second most common cancer in women after skin cancer and has become a major health issue. As a result, it is very important to diagnose BC correctly and categorizing the tumors into malignant or benign groups. We know that ML techniques that have unique advantages and are widely used to analyze complex BC dataset and predict the disease. Wisconsin Diagnostic Breast Cancer (WDBC) dataset has been used to develop predictive models for BC by researchers in this field. In this dissertation, we propose a method for analyzing and predicting BC on the same dataset using Apache Spark. The experiments are executed on Hadoop cluster, a cloud platform provided by the Electrical Engineering and Computer Science (EECS) department at the University of Cincinnati. Our results show that selecting the right features significantly improves the accuracy in predicting BC. The s (open full item for complete abstract)

Committee: Marc Cahay Ph.D. (Committee Chair); Dharma Agrawal D.Sc. (Committee Member); Rui Dai Ph.D. (Committee Member); Wen-Ben Jone Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member); Carla Purdy Ph.D. (Committee Member) Subjects: Computer Science

MS, University of Cincinnati, 2020, Engineering and Applied Science: Computer Science

Today, most successful business models widely use software programs to bridge the gap between data and business requirements. Changes in business strategies also require software programs to adapt with it. As a result, the available software products are continuously evolving, and are rapidly changing with new technologies and user requirements. Earlier in 2017, the Ohio Department of Transportation (ODOT) and University of Cincinnati started developing a web application, called the Common Operating Platform (COP), to remotely process the drone-captured images into 3D models using commercial (Pix4D) and open-source (OpenDroneMap). The idea is to engage shared hardware and software resources to perform such complex tasks. The platform immediately gained popularity and actively used by the personnel at ODOT. Preliminary study shows that the Common Operating Platform has a lot of room to incorporate more features. Hence, this thesis introduces the Common Operating Platform v11.0 that comes more complex 3D modeling and visualization workflows. The purpose of this work is to enhance functionality, reliability, efficiency, and usability of the Common Operating Platform. Initially, this document enlists shortcomings of the existing system and proposes new solutions to eliminate these shortcomings. Secondly, the proposed system architecture is compared against the existing architecture. In the final stage, the proposed enhancements are implemented by leveraging commercial (Pix4D) and open-source (MeshLabJS) software tools. Other miscellaneous features to improve system performance, efficiency and reliability are also discussed.

Committee: Arthur Helmicki Ph.D. (Committee Chair); Victor Hunt Ph.D. (Committee Member); Nan Niu Ph.D. (Committee Member) Subjects: Computer Science

Master of Science, The Ohio State University, 0, Computer Science and Engineering

Customer Relationship Management (CRM) platforms connect business units with potential and active customers. CRM platforms affect every facet of a business from sales referrals to touch points to targeted advertising and deal making. However, recent cloud computing services, e.g., Force.com, have revolutionized CRM design and implementation, deprecating CRM curriculum in computer science curriculum. This thesis contends that a new curriculum for CRM that is grounded in modern design concepts would interest students and advance learning outcomes in computer and data science courses. Specifically, the research underlying this thesis (1) outlines a curriculum for enterprise CRM, (2) provides technical knowledge on the matter and (3) combines and leverages data management computer science fundamentals to provide a real world, practical application of an in-demand skill set not normally taught in academia--CRM Engineering. First, the curriculum introduces the basic skills and concepts used in CRM plat- forms in today's cloud computing landscape. Second, an overview of Salesforce and its proprietary programming languages and technologies are presented to further reinforce real world applications of CRM programming. Finally, detailed analyses of programming best practices are examined specifically analyzing bulkification when dealing with large data management scenarios in the cloud. The curriculum was created as 3 slideshow presentations, recorded videos and a knowledge-check quiz. We presented the curriculum to a section of Data Management in the Cloud, a course taught in the data analytics sequence at The Ohio State University. After going through the curriculum, the students were presented a quiz to assess (1) their learning outcomes related to CRM and (2) their appreciation of the material in the class. Our results show that the material was effective in establishing foundational CRM platform knowledge and programming best practices with (open full item for complete abstract)

Committee: Christopher Stewart (Advisor); Srinivasan Parthasarathy (Committee Member) Subjects: Computer Science

Master of Science (MS), Ohio University, 2020, Mechanical Engineering (Engineering and Technology)

Stewart Platforms have been used for simulation because of their strength and position accuracy. Simulators have remained large structures that contained internal replicas of airplane cockpits and/or driving seats. These large structures come at high prices, it would be beneficial for the training and gaming industry or markets to make smaller more affordable platforms that can perform at or near the same motion performance of large expensive platforms. An added benefit to include for the training community would be to add portability so the simulator can travel to a classroom setting. In this thesis, tools such as MATLAB and Mechanical Analysis are used to design a smaller, more affordable platform that is capable of similar performance capabilities to professional motion platforms.

Committee: Robert Williams II (Advisor); Jesus Pagan (Committee Member); Todd Young (Committee Member); Timothy Cyders (Committee Member) Subjects: Mechanical Engineering; Robotics

MDES, University of Cincinnati, 2020, Design, Architecture, Art and Planning: Design

"Retail community" has been defined as curated shopping experiences that build relationships with "an engaged customer base" (Creating Community, n.d.) beyond mere product purchases and transactions. To cultivate local communities by facilitating dialogue amongst shopkeepers and shoppers, and by networking retail community assets, this thesis project explores design opportunities for formulating a communication platform in a retailing district of small scale brick-and-mortar stores. The design intervention taps into the shopper's journey, examines the needs and desires of small-scale retailers, and proposes a digitally-enhanced retail community (Chung, 2000) that engages, interacts with, and brings together individual shopping experiences into a network of collective communications, narratives, and information to promote social interactions and physical engagement in the district.

Committee: Matthew Wizinsky M.F.A. (Committee Chair); Heekyoung Jung Ph.D. (Committee Member) Subjects: Design

Master of Sciences, Case Western Reserve University, 2020, Biology

The “mycobiome" refers to the composition of both bacterial and fungal communities in the human gut microbiome and has been the focus of disease-state correlations investigated by researchers and pursued with commercial interests by biotech startups. A microbiome startup currently sells direct-to-consumer at-home microbiome sequencing kits and probiotics that aim to balance the gut biofilm that contributes to the dysbiosis-associated conditions. This company has expressed an interest in developing new business strategies to leverage their intellectual and technical strengths. This thesis is composed of two parts: The first section is a scientific and technical investigation of the micro- and myco-biome, sequencing techniques and strategies (16S, ITS, WGS, and Shotgun Metagenomic Sequencing) that play a role in the characterization and identification of fungal and bacterial colonies in the gut. These strategies aim to overcome challenges in characterizing and quantifying microbiota composition. Next, this sequencing data can form a robust database of patient data that plays a role in disease identification, and this thesis identifies some of the bioinformatic analyses to achieve this goal. The section concludes with how insights derived from patient data can be used in the optimization of cohort design in clinical trials for various diseases. The second section investigates three different business models that a microbiome startup has expressed interest in exploring for future development: (1) medical foods; (2) a therapeutic pipeline; and (3) a data-licensing and discovery platform for drug development. A detailed analysis of the market dynamics, competitive landscape, regulatory issues, and other nascent concerns was performed for each potential vertical as a foundation to develop future business strategy of a microbiome-related startup. The thesis is concluded on a holistic analysis of the scientific and technical assets and business opportunities and str (open full item for complete abstract)

Committee: Christopher Cullis (Committee Member); Emmitt Jolly (Committee Member); Neema Mayhugh (Committee Member) Subjects: Biology; Entrepreneurship

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

Antennas mounted on aircraft, UAVs, and other platforms are used in a number of critical applications, such as navigation, communication, and situational awareness. Since the platform can heavily affect the antenna pattern, one should carry out in situ characterization of the antenna to evaluate the performance of the RF systems. It is often too expensive or impractical to measure the antenna on the intended platform, so instead, the antenna under test (AUT) is measured on a simple ground plane. The measurements are then imported into computational electromagnetics (CEM) codes to simulate platform scattering from the platform of interest. However, current approaches struggle to isolate the antenna radiation from the measurement ground plane interactions, leading to inaccuracies in the AUT representation. Furthermore, many approaches rely on near-field measurements for accuracy and use many current elements to represent the AUT leading to long simulation run-times. This dissertation presents a novel approach for in situ manifold estimation which represents measured data via a weighted sum of simple basis element far-fields. The approach, the Sparse-Constrained Equivalent Element Distribution Method (SC-EEDM), provides a more accurate representation of the AUT compared to existing techniques. The SC-EEDM accurately represents the AUT using measured far-field data only, and represents the AUT using a small number of current elements. In addition, the SC-EEDM isolates antenna radiation from antenna-ground plane interactions, leading to more accurate in situ manifold estimations. Using high-fidelity simulations, the method is shown to accurately estimate antenna far-fields on complex platforms from antenna measurements on simple structures.

Committee: Inder Gupta (Advisor); Robert Burkholder (Committee Member); Teixeira Fernando (Committee Member) Subjects: Electrical Engineering; Electromagnetics; Electromagnetism

Master of Science (M.S.), University of Dayton, 2019, Electrical Engineering

Internet of Things is one of the rapidly developing technologies pervading all walks of life. Most IoT devices are wirelessly connected using different wireless technologies such as Wi-Fi, Bluetooth and Zigbee. As each of these wireless technologies has varied transmit power and supports different data rates, IoT devices with different in-built technologies must be optimized to energy efficient communication. In this work we propose a Seamless IoT platform that promotes periodic switching between two different technologies such as Wi-Fi, Zigbee based on the current data rate requirements and transmit power. A network model with heterogeneous IoT devices is formulated after taking into consideration of interference, received power, arbitrary locations and distance between the devices. Adapting the concept of the Game Scheme the optimal transmit power which ensures energy efficiency was calculated using MATLAB followed by simulations of convergence of Nash Equilibrium, Utility and transmit power, sum data rate.

Committee: Feng Ye PhD (Advisor); Bradley Ratliff PhD (Committee Member); Dong Cao PhD (Committee Member) Subjects: Electrical Engineering; Engineering

Doctor of Philosophy, University of Toledo, 2019, Curriculum and Instruction

Constructivism, a modern learning philosophy that focuses on a student's experiences within the learning environment rather than on an instructor's influence, can attribute its roots, in part, to the time of John Dewey: the early 1900s. While many educators espouse a belief and commitment to constructivist instructional design, few of them actively engage in its classroom implementation. A number of studies have taken place attempting to determine why teachers are not implementing constructivist-designed lessons in the classroom, and why teachers are not implementing computer technology tools at a “high level.” The literature investigates several factors that can potentially lead to both better integration of educational technology, and better instruction. The purpose of this study was to investigate a potential relationship between the experiences that high school teachers have with their district-assigned computers, and what effect, if any, those experiences had on their willingness to integrate computer technology into classroom instruction, as well as their beliefs surrounding the effectiveness of computer technology to enhance instruction and improve learning. Social constructivism was selected as the research theory for this study. The results show that there is no significant difference in faculty experiences with their computers based on the computing platform (operating system), and no difference regarding faculty's willingness to integrate technology, or their beliefs about computer technology integration's effectiveness to improve instruction. However, the Rasch analysis used in this study was able to determine what computing tasks faculty find easy to respond positively toward, and what tasks are difficult to respond to with positive affirmation. This information provides data that can measure computing task difficulty, and enable the creation of a strata of computing tasks that can be used to: assist with future studies regarding faculty (open full item for complete abstract)

Committee: Berhane Teclehaimanot (Committee Chair); Leigh Chiarlotte (Committee Member); Mark Templin (Committee Member); Peter You (Committee Member) Subjects: Educational Technology; Educational Theory

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

Given the rapid advances in development of unmanned aerial vehicles (UAV), employment of drones in various business functions becomes reasonable and affordable. Usage of drones as data collection tools will give us access to a new set of geo-referenced images and videos that were not easily accessible in the past. The ultimate objective of this web based platform for data processing workflows, from here on referred to as the common operating platform, is to enable users to archive, process and visualize aerial data without a need for advanced hardware and software locally. This work details the development of the the common operating platform which consist of a web-based frontend and a backend. The frontend is a web app developed based on Django, Twitter Bootstrap and Javascript where the user authenticates, uploads data, submit processing tasks and visualizes the results. The back end, developed using Python 3, is where data is being stored and various processing tasks are being done based on commercial(Pix4D), open source(OpenDroneMap) and custom(Traffic Monitoring) processing engines. First, the intricacies of the data processing workflow is discussed and this includes diving into detail the various steps related to processing workflows using proprietary, open software and custom software. Second, procedures for integrating commercial processing engines as well as development of the in house traffic parameter extraction system will be shown and the result of running various case studies and their processing performance will be discussed. And finally, the system architecture design and implementation will be detailed given the scalability, modularity,extensibility and reliablity requirements will be discussed. The idea is to have a secure system, which is accessible to a broad audience, that can receive and service all of their processing requirements. In doing so, it dismisses the need for an uninitiated audience to install highly specialized software on their pe (open full item for complete abstract)

Committee: Arthur Helmicki Ph.D. (Committee Chair); Victor Hunt Ph.D. (Committee Member); Nan Niu Ph.D. (Committee Member) Subjects: Computer Science

Master of Arts, The Ohio State University, 2019, Communication

Social media platforms are characterized by increasingly diverse features and functions over time. This thesis examines how users define their central qualities – or platform essence – and how those qualities depend on the surrounding media environment. A pilot study and online survey study were conducted via MTurk to validate original measures of platform essence and investigate how the perceived socialness of contemporary platforms shapes key social outcomes tied to popular platforms. Overall, results provide evidence that platform essence – and socialness, in particular – is associated with perceptions of social resources and affordances, bolstering the notion of perceived socialness as a self-fulfilling prophecy. Together, this work makes significant contributions to the existing literature by exploring how individuals navigate their social media ecologies, as well as how lay theories shape the experiences and effects of social media use.

Committee: Joseph Bayer (Advisor); Roselyn Lee-Won (Committee Member) Subjects: Communication

MDES, University of Cincinnati, 2019, Design, Architecture, Art and Planning: Design

Ranging from a few seconds to a few minutes, short videos have become a popular form of learning and sharing creative skills such as drawing, photography, and crafting. Short videos in social media platforms are reshaping the experience of learning creative skills by providing visually rich instructional materials and communication features to question and comment on those materials. These functions and features of a video platform can impact a user's learning experience, and this aspect has been under-investigated. This study is motivated to investigate user behaviors in short video sharing platforms and identify any gap between user expectations and behaviors afforded by those platforms for creative practice. This study focused on analyzing TikTok (i.e., a short video platform) and Bilibili (i.e., a video sharing platform), specifically 1) their information architecture and user interfaces, 2) viewers' comments on selected drawing skill sharing videos in both platforms (which resulted in four themes of viewer activities and three types of viewer attitudes in practicing and learning creative skills), and 3) selected TikTok users' online activities and expectations for creative practice based on profiles and in-depth interview. The multi-dimensional data about user behaviors and expectations are synthesized into five different personas, leading to the discussion of design recommendations to support creative practice in short video sharing platforms.

Committee: Heekyoung Jung Ph.D. (Committee Chair); Matthew Wizinsky M.F.A. (Committee Member) Subjects: Design

Doctor of Philosophy (PhD), Ohio University, 2018, Mass Communication (Communication)

Transmedia is the practice of spreading content over multiple delivery channels to create a more immersive experience for the audience. In a transmedia project, the sum of its parts is always bigger than each individual part, but each individual part, while creating its own unique contribution to the overall project, also has to contain the key premises of the main story. This study explains what transmedia is and how transmedia content creators translate their concept and/or idea for a story into a storyworld for multiple platforms. This exploratory case study research focuses on transmedia content creation in independent production as it expands narrative structures from film to other media, guided by the assessment of audience engagement, as a type of approach to storytelling (narrative or documentary). The study examines organic transmedia story content and audience engagement in four projects: Zenith, Body/Mind/Change, Question Bridge, and The Deeper They Bury Me. Formal in-depth interviews were used as a data-gathering tool for case study evidence, on the premise that interviews are key to uncovering a participant's motives and techniques for executing them. The point was to learn about story development from the practical strategies of the four selected transmedia creators. The data were divided into two groups: 1) story structure as determined by creators and 2) the likelihood of audience engagement. Textual Analysis was used to look at the story content of the selected projects and to analyze audience engagement. All the projects demonstrate that using multiple platforms expands the dimensions of the story. Each project was different, had a different approach, and required different details in the incubation and production phases.

Committee: Joseph Slade (Committee Chair); Roger Cooper (Committee Member) Subjects: Communication; Film Studies; Mass Media; Multimedia Communications

MDES, University of Cincinnati, 2017, Design, Architecture, Art and Planning: Design

This thesis proposes to deal with the problem small businesses have of following the retail omnichannel trend by providing them with a digital platform. Omnichannel retailing means creating a seamless shopping experience by integrating online and offline retail channels. The type of small business in this thesis particularly refers to clothing and clothing accessories stores that have one physical location and a store website. I conducted primary research to find out which omnichannel features would be effective to small businesses. Based on the results, I selected the top five omnichannel features for my digital platform. They are “Buy online, pickup in store,” “Online live chat button to contact sales assistant,” “Buy in store, ship to home/other locations,” “In store access to product information online” and “Back in stock email.” Then based on multichannel service provided by an existing commerce service named Shopify, I developed my omnichannel platform for small businesses.

Committee: Craig Vogel M.I.D. (Committee Chair); Steven Doehler M.A. (Committee Member); Dianne Hardin M.S. M.Des. (Committee Member); Gerald Michaud M.A. (Committee Member) Subjects: Design