PhD, University of Cincinnati, 2008, Engineering : Computer Science and Engineering

Currently, Peer-to-Peer overlays can be classified into two main categories: unstructured and structured ones. Unstructured overlays are simple, robust, and powerful in keyword search. Structured ones can scale to very large systems in terms of node number and geography, and guarantee to locate an object within O(Log N) hops. However, both of them face difficulties in efficiency and security of overlays. For unstructured ones, the efficiency problem presented is poor scalability. For structured ones, it is long routing latency and enormous overhead on handling system churn. Moreover, both of them are vulnerable to malicious attacks. Peer-to-Peer overlays belong to application-level network. To a great extension, overlay network designs ignore physical characteristics. As the result, their structures are far from underlying physical network or the distribution pattern of overlay peers. These inconsistencies induce system common operations costly, such as routing and lookup. On the other hand, most peers are assumed to have uniform resources and similar behaviors. Thus, Peer-to-Peer protocols were designed to be symmetric. However, in the realistic environment, peers' resources and behaviors are highly skewed. Symmetric protocols actually compromise system performance. Frequently joining and leaving of peers generates enormous traffic. The significant fraction of peers with high latency/low bandwidth links increase lookup latency. Moreover, under the environment without mutual trust, Peer-to-Peer systems are very vulnerable for varied attacks because they lack a practical authentication mechanism. From a different perspective, this dissertation proposes to construct a highly efficient and secure Peer-to-Peer overlay based on the physical network structure of the Internet and network locality of overlay peers. By naturally integrating different network-aware techniques into the Peer-to-Peer overlay, a novel SNSA (Scalable Network Structure Aware) technique has been dev (open full item for complete abstract)

PhD, University of Cincinnati, 2008, Engineering : Computer Science and Engineering

Wireless Ad Hoc Networks have emerged as an advanced networking paradigm based on collaborative efforts among multiple self-organized wireless communication devices. Without the requirement of a fixed infrastructure support, wireless ad hoc networks can be quickly deployed anywhere at any time when needed. The decentralized nature, minimal configuration and quick deployment of wireless ad hoc networks make them suitable for various applications, from disaster rescue, target tracking to military conflicts. Wireless ad hoc networks can be further categorized into mobile ad hoc networks (MANETs), wireless sensor networks (WSNs), and wireless mesh networks (WMNs) depending on their applications.Security is a big challenge in wireless ad hoc networks due to the lack of any infrastructure support, dynamic network topology, shared radio medium, and resource-constrained wireless users. Most existing security mechanisms applied for the Internet or traditional wireless networks are neither applicable nor suitable for wireless ad hoc network environments. In MANETs, routing security is an extremely important issue, as the majority of the standard routing protocols assume non-hostile environments. Once deployed in a hostile environment and working in an unattended mode, existing routing protocols are vulnerable to various attacks. To address these concerns, we propose an anonymous secure routing protocol for MANETs in this dissertation, which can be incorporated with existing routing protocols and achieve enhanced routing security with minimum additional overheads. In WSNs, key distribution and management is the core issue of any security approaches. Due to extremely resource-constrained sensor nodes and lack of any infrastructure support, traditional public-key based key distribution and management mechanisms are commonly considered as too expensive to be employed in WSNs. In this dissertation, we propose two efficient pairwise key pre-distribution and management mechanisms f (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2012, Electrical and Computer Engineering

Network-on-Chip (NoC) communication architectures have been recognized as the most scalable and efficient solution for on chip communication challenges in the multi-core era. Diverse demanding applications coupled with the ability to integrate billions of transistors on a single chip are some of the main driving forces behind ever increasing performance requirements towards the level that requires several tens to over a hundred of cores per chip. Small scale multicore processors so far have been a great commercial success and found applicability in many applications. Systems using multi-core processors are now the norm rather than the exception. As the number of cores or components integrated into a single system is keep increasing, the design of on-chip communication architecture is becoming more challenging. The increasing number of components in a system translates into more inter-component communication that must be handled by the on-chip communication infrastructure. Future system-on-chip (SoC) designs require predictable, scalable and reusable on-chip communication architectures to increase reliability and productivity. Current bus-based interconnect architectures are inherently non-scalable, less adaptable for reuse and their reliability decreases with system size. NoC communication guarantees scalability, high-speed, high-bandwidth communication with minimal wiring overhead and routing issues. NoCs are layered, packet-based on-chip communication networks integrated onto a single chip. NoC consists of resources and switches that are directly connected in a way that resources are able to communicate with each other by sending messages. The proficiency of a NoC to meet its design goals and budget requirements for the target application depends on its design. Often, these design goals conflict and trade-off with each other. The multi-dimensional pull of design constraints in addition to technology scaling complicates the process of NoC design in many aspects, as (open full item for complete abstract)

Master of Science, University of Toledo, 2021, Engineering (Computer Science)

This research examines Echo State Network, a reservoir computer, performance using four different benchmark problems, then proposes heuristics or rules of thumb for configuring the architecture, as well as the selection of parameters and their values, which are applicable to problems within the same domain, to help serve to fill the ‘experience gap' needed by those entering this field of study. The influence of various parameter selections and their value adjustments, as well as architectural changes made to an Echo State Network, a powerful recurrent neural network configured as a reservoir computer, can be difficult to understand without experience in the field, and even some hyperparameter optimization algorithms may have difficulty adjusting parameter values without proper manual selections made first; therefore, it is imperative to understand the effects of parameters and their value selection on echo state network architecture performance for a successful build. Thus, to address the requirement for an extensive background in Echo State Network architecture, as well as examine how Echo State Network performance is affected with respect to variations in architecture, design, and parameter selection and values, a series of benchmark tasks representing different problem domains, including time series prediction, pattern generation, chaotic system prediction, and time series classification, were modeled and experimented on to show the impact on the performance of Echo State Network.

Master of Science, University of Toledo, 2020, Engineering (Computer Science)

In many application settings involving networks, such as friendships or messages among users of an on-line social network and transactions between traders in financial markets, understanding network dynamics has been a long-standing problem with implications in numerous disciplines including computer science, physics, mathematics, biology, social sciences, and economics. Due to high computational complexity of dynamic network analysis, most models assume networks are static which sacrifices expressiveness for scalability. Here we set forth two new concepts which enhance link prediction and recommendation as well as modeling communities and interactions in continuous-time dynamic networks. In particular, we first introduce the notion of personalized degree and find that neighbors with higher personalized degree are more likely to lead to new link formations when they serve as common neighbors with other nodes, both in undirected and directed settings. Next, we propose the Community Hawkes Independent Pairs (CHIP) generative model for continuous-time networks of timestamped relational events. We show that spectral clustering provides consistent community detection, for a growing number of nodes, on networks generated by the CHIP model and develop consistent and computationally efficient estimators for the model parameters. Personalized degree provides a lens into the latent information in the topology of on-line social networks and how this information can be utilized to better understand the evolution of a network over time and to predict future interactions. We find that incorporating personalized degree into common neighbor based link prediction algorithms can improve mean link prediction accuracy by up to 35%, while incorporating directions of edges further improves accuracy by up to 11%. Moreover, the CHIP model is able to capture network dynamics and its underlying community structure with only a few parameters and scale to much larger networks compared t (open full item for complete abstract)

Doctor of Philosophy (PhD), Ohio University, 2020, Industrial and Systems Engineering (Engineering and Technology)

The dissertation develops a methodology framework to support decision-making in effectively predicting and disrupting an interdependent incomplete network. The proposed framework is tested on the Organized Criminal Network (OCNs), i.e. interdependent sex trafficking network and the time-evolving drug trafficking network. Two models are developed for dedicated problems. First, the dissertation develops an iterative Network Interdiction Model (NIM) that can:1) Captures the interdependency of the multi-layer networks; 2) Identifies and disrupts the network with the worst damage effect. The NIM is tested in three federally prosecuted sex trafficking cases in the United States. Numerical experiments show the capability of the NIM to provide valuable direction for interdiction strategies. The study result illustrates the potential benefits of using network modeling and analytical technics in the fight against sex trafficking. Second, the dissertation develops a mathematical model capable of predicting the creation or destruction of connections in dynamic networks. The model uses a convex combination of two mechanisms: 1) link's memory, which explores the impact of historical existence on link dynamic; and 2) network topology, which explores the impact of the network topology on creation and destruction of links. The model uses a constrained maximum likelihood approach for model calibration. The estimated parameters are used to predict links; existence probability in the future. The proposed model is tested on an evolving drug trafficking network where it outperforms other baseline models. The dissertation demonstrates the importance of collecting, documenting, and analyzing the criminal network from a dynamic perspective. The study insights and predictive results can support law enforcement agents in devising a more proactive anticriminal strategy.

PhD, University of Cincinnati, 2007, Engineering : Computer Science and Engineering

Recent years have witnessed an extensive proliferation of wireless technology in every domain of day-to-day life. Examples include mobile phones, broadband communication, wireless LAN, wireless enabled PDAs, cordless phones, garage-door openers and the list continues. Advancements in radio technology, antenna technology, low power computational digital signal processing (DSP) and micro-electro-mechanical systems (MEMS) are instrumental in reducing the size and cost of wireless devices. A wireless network consists of wireless devices forming an infrastructure-based or a peer-to-peer network. A network can be a single-hop or multihop network. Single-hop networks are already in existence and have been substantially investigated. This dissertation thus focuses on multihop wrireless networks, where the intermediate wireless devices also act as routers. Depending on their functionality, multihop wireless networks can be categorized into ad hoc, mesh and sensor networks. A mobile ad hoc network (MANET) aims at provding a mobile network with connectivity similar to a wired network without the need for any infrastructure support. A wireless mesh network (WMN) typically extends the infrastructure based single hop wireless network and has become a new paradigm for providing last mile broadband access. A wireless sensor network (WSN) is similar to an ad hoc network, providing a cheap alternative to monitoring applications. Each of these multihop wireless networks has their own set of challenges with respect to operation and implementation. The first part of this dissertation focuses on developing on-demand medium access control (MAC) protocols for multiple beam smart antennas (MBSAs) in ad hoc and mesh environments. MBSA has the unique capability of simultaneously initiating packet transmissions or receptions in multiple beams. Thus, compared to traditional omnidirectional antennas, MBSA can better utilize the spatial bandwidth, thereby increasing the capacity of wireless netwo (open full item for complete abstract)

Doctor of Philosophy in Engineering, University of Toledo, 2012, College of Engineering

This dissertation research proposes embedding artificial neural networks into wireless sensor networks in parallel and distributed processing framework to implant intelligence for in-network processing, wireless protocol or application support, and infusion of adaptation capabilities. The goal is to develop in-network "intelligent computation" and "adaptation" capability for wireless sensor networks to improve their functionality, utility and survival aspects. The characteristics of wireless sensor networks bring many challenges, such as the ultra large number of sensor nodes, complex dynamics of network operation, changing topology structure, and the most importantly, the limited resources including power, computation, storage, and communication capability. All these require the applications and protocols running on wireless sensor network to be not only energy-efficient, scalable and robust, but also "adapt" to changing environment or context, and application scope and focus among others, and demonstrate intelligent behavior. The expectation from the research endeavor is to introduce computational intelligence capability for the wireless sensor networks to become adaptive to changes within a variety of operational contexts and to exhibit intelligent behavior. The proposed novel approach entails embedding a wireless sensor network with an artificial neural network algorithm while preserving the parallelism and distributed nature of computations associated with the neural network algorithm. The procedure of embedding an artificial neural network, which may be configured for a problem either at wireless protocol or application levels, into the wireless sensor network hardware platform, which is a parallel and distributed processing system that is composed of a network of motes, is defined. This procedure is demonstrated for a case study with a Hopfield neural network and a minimum weakly connected dominating set problem as the model of wireless sensor network backbon (open full item for complete abstract)

Master of Science in Computer Engineering, University of Dayton, 2021, Electrical and Computer Engineering

The growing demands for communication and complex network infrastructure relay on overcoming the network measurement and management challenges. Lately, artificial intelligence (AI) algorithms have considered to improve the network system, e.g., AI-based network traffic classification, traffic prediction, intrusion detection system, etc. Most of the development of networking AI models require abundant traffic data samples to have a proper measuring or managing. However, such databases are rare to be found publicly. To counter this issue, we develop a real-time network traffic generator to be used by network AI models. This network traffic generator has a data enabler that reads data from real applications and establishes packet payload database and a traffic pattern database. The packet payload database has the data packets of real application, where network traffic generator locates the payload in the capture file (PCAP). The other database is traffic pattern database that contains the traffic patterns of a real application. This database depends on the timestamp in each packet and the number of packets in the traffic sample to form a traffic database. The network traffic generator has a built-in network simulator that allows to mimic the real application network traffic flows using these databases to simulate the real-traffic application. The simulator provides a configurable network environment as an interface. To assess our work, we tested the network traffic generator on two network AI models based on simulated traffic, i.e., AI classification model, and AI traffic prediction. The simulation performance and the evaluation result showed improvement in networking AI models using the proposed network traffic generator, which reduce time consuming and data efficiency challenges.

Doctor of Philosophy, The Ohio State University, 2021, Electrical and Computer Engineering

In applications such as networked monitoring and control systems, wireless sensor networks, and autonomous vehicles, it is crucial for the destination node to receive timely status updates so that it can make accurate decisions. For example, a moving car with a speed of 65 mph will traverse almost 29 meters in 1 second, and hence, stale information (regarding the location of surrounding vehicles, velocities, etc.) has a dramatic serious impact on this situation. Age of information (AoI), or simply age, has been used to measure the freshness of status updates. More specifically, AoI is the age of the freshest update at the destination, i.e., it is the time elapsed since the freshest received update was generated. It should be noted that optimizing traditional network performance metrics, such as throughput or delay, does not attain the goal of timely updating. For instance, it is well known that AoI could become very large when the offered load is high or low. In other words, AoI captures the information lag at the destination, and is hence more apt for achieving the goal of timely updates. In this thesis, we leverage rigorous theory to develop low-complexity scheduling algorithms that are apt for a wide range of information update systems. In particular, we consider the following systems: -Information update systems with stochastic packet arrivals: We consider single and multihop networks with stochastic arrivals, where our goal is to answer the following fundamental questions: (i) Which queueing discipline can minimize the age? And (ii) under what conditions is the minimum age achievable? Towards this goal, we design low-complexity scheduling policies to achieve (near) age-optimality in single and multihop networks with single source. The achieved results that we present here hold under quite general conditions, including (i) arbitrary packet generation and arrival processes, (ii) for minimizing both the age processes in stochastic ordering and any non-d (open full item for complete abstract)

Doctor of Philosophy (PhD), Ohio University, 2020, Industrial and Systems Engineering (Engineering and Technology)

A supply chain is a network of suppliers, production, or manufacturing facilities, retailers, and transportation channels which are structured to acquire supplies, produce new products, and distribute the finished products to retailers and customers. Closed Loop Supply Chain (CLSC) networks incorporate the flow of the returned, used, or recycled products from the customers through the retailers to the manufacturing, recycling, or refurbishing facilities to support managing the full lifecycle of the products. Social Network Analysis (SNA) has been developed to identify and analyze the patterns in social networks. SNA is used as a theoretical framework for better understanding of social networks by characterizing the structure of a network in terms of nodes and links. SNA is applied to various types of networks including telecommunication networks, protein interaction networks, animal disease epidemics, and customer interaction and analysis. Although SNA is a powerful method to study networks in many areas, it has not been comprehensively applied to supply chain networks. Likewise, there is no application and interpretation of SNA metrics in CLSCs. In this study, SNA metrics are introduced and interpreted for components in CLSC networks and forward and reverse logistic activities. Correspondingly, a decision making tool is developed based on selected SNA metrics for comparing alternative network designs in terms of network reliability and balance of the flows.

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

In many picture handling methods, the capacity to segment items of concern automatically is a helpful pre-processing phase. There are several algorithms segmenting pictures depending on one or more parameters or previous understanding of the necessary category of items. While these algorithms generate visually attractive segmentations, their complexities are making many of them computationally costly. One of the approaches which has received a lot of traction recently is semantic segmentation. It's a process of associating each pixel of an image with a class label, (such as flower, person, road, sky, ocean, or car). Semantic segmentation of a picture has recently been addressed by deep end-to-end neural networks. One of the main problems among all architectures is to consider the global visual context of the input to improve this segmentation forecasting. State of the art drawings use architectures that try to link different components of the image in order to understand the interactions between the items. To solve this problem, the concept of KeyPoint recognition is introduced within semantic segmentation by researchers. A KeyPoint recognition can be described as a procedure to find the most repeatable object or key points for the specific target object during the training phase. Thus, a general semantic segmentation algorithm gets enhanced to a global level which ensures understanding of a scene at the top level. This approach moves much of the computational burden to the training phase, without sacrificing recognition efficiency. Therefore, the resulting algorithm is robust, precise and fast enough for frame rate efficiency. General global semantic segmentation algorithms generate super pixels based solely on color. However, limits of objects do not necessarily overlap with contours of color, allowing segmentation in different situations to fail. In such instances, information beyond color channels is needed to achieve significant and accurate segmentations. (open full item for complete abstract)

Doctor of Philosophy, Miami University, 2019, Chemistry and Biochemistry

Using dynamic chemistry to develop functional polymers is an emerging area in material science. This class of polymers possesses intrinsic reversibility owing to the covalent or noncovalent bonds within, therefore respond to external stimuli. In addition, combining dynamic interactions with polymers offers exciting dynamic features such as environmental adaptivity, malleability, self-healing, and shape memorizing properties. Noncovalent interactions, e.g., hydrogen bonds, metal-ligand coordination, host-guest interactions, ionomers or π-stacking, have been successfully built into polymers over the last decades. Researchers have also relied on dynamic covalent bonds, e.g., Diels-Alder adducts, disulfide exchange, imine bonds, or boronic ester bonds. However, the underlying kinetics of some covalent interactions have not been demonstrated explicitly. Besides, the dynamic nature of the crosslinkers introduces the potential for the material not only the weak toughness but also to creep or deform over time under load. Recently, a combination of dynamic and static crosslinkers on either the main polymer chains or side chains with different structures has been used to overcome these limitations and enhance the mechanical properties. Other than that, materials containing orthogonal dynamic chemistries enable the synthesis of intricate macromolecules which can respond to multiple stimuli to achieve the desired response. Our work mainly focuses on a deep understanding of the mechanism of the covalent interactions in terms of small molecule models to better manipulate them in the bulk polymers, making new dynamic materials, and exploring the impact of the macromolecular architectures on their properties. A mechanistic study of the thermally activated dynamic covalent chemistry of thiol-Michael adducts is the focus of Chapter two, using a model system of thiophenol/mercaptoethanol dynamic equilibrium with phenylvinylketone based Michael acceptors. Chapter three works on f (open full item for complete abstract)

Master of Science, University of Toledo, 2018, Engineering (Computer Science)

Cognitive Radio Network (CRN) technology has offered the opportunistic solution for the spectrum scarcity problem in wireless communication. The Dynamic Spectrum Access (DSA) solution enables radio system to sense and learn the spectrum and reconfigure the parameters to apply cognitive decisions. With these properties, the technology is threatened by attackers and malicious users trying to exploit the network operation and its learning capabilities. Along with traditional threats, a few specific threats have been inadvertently \textit{created} by this technology due to its characteristic behavior and operation. This thesis provides a brief discussion on the threats and attacks with recent contributions on the security of CRN and proposes a security algorithm that uses the Artificial Neural Network (ANN) based machine learning methods to verify incumbent signals in a CRN. The proposed model is trained using Levenberg-Marquardt (LM) algorithm and Scaled Conjugate Gradient (SCG) algorithms to implement signal identification in two sub-categories, namely, known and unknown signals. Signal datasets were collected from the popular NASA Space Communications and Navigation (SCaN) testbed located at international space station (ISS) and also generated from a small in-lab Software Defined Radio (SDR) device to train and test the proposed model. The performances of the two algorithms on multiple datasets were compared using confusion matrices and mean squared error (MSE). Our study concluded that the best performing model exhibits MSE as low as 0.018 and the confusion matrix shows promising results of more than 98\% as the percentage of accurate prediction. The proposed model can be used in a CRN to monitor the signal activity of the users in the network and verify them for genuineness. The model can also alert the system when an unknown user is operating in the network for further security evaluations.

Master of Sciences, Case Western Reserve University, 2018, Biology

Flexible and robust neural pathways are ubiquitous in animals. Previous work has demonstrated that variability in feeding behavior in the marine mollusk Aplysia californica can be useful to the animal – in general, motor components relevant to feeding show higher variability within animals, even as they vary less across different animals. (Cullins et al. Current Biology 2015). This variability, though, makes interpreting neural recordings challenging, especially in an automated context. In this research, we explore the ability for a combination of artificial neural network architectures (Long Short-Term Memory [LSTM] and Dense Fully Connected) to not only classify behaviors but to distinguish behaviors prior to any observable cue. The examined four channel recordings came from the key protractor muscle (I2) and three motor nerves that control the feeding apparatus of Aplysia californica during feeding behaviors. Each channel of the recordings had an LSTM dedicated to learning how to discern bites from swallows from white noise. The output from these four LSTMs were then passed to a dense, fully connected layer for a final classification using context from all channels. Surprisingly, the overall architecture appears able to discriminate bites from swallows (at an accuracy between 97 and 99%) at least half a second before the classic marker (I2 firing frequency exceeding 10hz) occurs. These results suggest that previously disregarded sub-threshold activity may contain high (or at least sufficient) levels of contextual information for behavioral classification which raises exciting questions about possible implications for closed circuit controllers and medical technology. TensorFlow was used with a Python interface to implement the networks.

Doctor of Philosophy, Case Western Reserve University, 2014, EECS - Computer and Information Sciences

Network measurement is crucial for ensuring Internet's effective operation, security, and continued development. However, collecting representative measurements in a complex infrastructure like the Internet is extremely challenging. To address this challenge, we propose a novel approach to provide focused, on-demand Internet measurements called DipZoom (for Deep Internet Performance Zoom). Unlike prior approaches that face a difficulty in building a measurement platform with sufficiently diverse measurements and measuring hosts, DipZoom implements a matchmaking service, which uses P2P concepts to bring together experimenters in need of measurements and external measurement providers. Further, to demonstrate the utility of DipZoom as a tool for real-world research, we use it to answer some of the challenging questions regarding Internet operation. Specifically, we use DipZoom to conduct an extensive study of content delivery networks (CDN ), which are among the key components of today Internet infrastructure. in performance, security, and improvement aspects. First, we conduct a large-scale performance study of the CDN platform operated by the leading DNS service provider. The study's result shows that the number of worldwide data centers in CDN platform could be significantly reduced without affecting the content delivery performance. Therefore, system designers can decide on the number of data centers to meet their other objectives without having to worry about performance degradation. Second, we used some measuring techniques developed for the above performance study to uncover a significant security vulnerability in CDNs. We showed that several CDNs, including commercial CDNs, not only left their customers vulnerable to the application-level denial of service attack, but CDNs themselves are also susceptible to be recruited to amplify the attack. Finally, based on insights gained in our CDN studies, we propose an approach to improve the cont (open full item for complete abstract)

PhD, University of Cincinnati, 2013, Engineering and Applied Science: Computer Science and Engineering

Multi-hop network has been envisioned to be a key technology in the next generation wireless networks. It offers the most flexible characteristic of the networks, requires no centralized or very small control, and very little configuration, thereby enjoying the quick deployment property. Yet, it has been named as the "next generation" for so many times that its usefulness is being questioned. The difficulty mainly comes from the inferiority in the performance being poor enough to make such networks overshadowed by other type of networks, unless quick deployment is a requirement, or performance is not a major concern. In this dissertation, we investigate the performance issues in multi-hop wireless networks. Some new architectures of multi-hop wireless networks with network coding are thoroughly explored. We first revisit network coding briefly. Being the latest revolution in the wireless world, network coding has evolved into a more practical shape, and has matured to a level so as to be adopted. A general introduction of network coding (network information theory) is covered for better understanding of how this technology would change the computer networks, and why we pick this topic for our research. The first work this dissertation covers is Murco, an opportunistic framework that brings the benefits of both multi-radio multi-channel technology and network coding to the multi-hop wireless networks. This combination, though it seems natural, faces many challenges. We address them with a loose collaboration between network coding and multi-radio technology coordinated by our framework. Our framework requires few changes or compromises on either side, and the simulation results demonstrate enhanced throughput. Following this work, we get into a more complex problem. Coding-aware routing in multi-hop wireless networks is vital for network coding's possible boom. We address this problem with a heuristic routing metric ETOX and a hybrid routing protocol HyCare. (open full item for complete abstract)

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, 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.

Doctor of Philosophy, The Ohio State University, 2008, Computer Science and Engineering

The Internet age has exponentially increased the volume of digital media that is being shared and distributed. Broadband Internet has made technologies such as high quality streaming video on demand possible. Large scale supercomputers also consume and create huge quantities of data. This media and data must be stored, cataloged and retrieved with high-performance. Researching high-performance storage subsystems to meet the I/O demands of applications in modern scenarios is crucial. Advances in microprocessor technology have given rise to relatively cheap off-the-shelf hardware that may be put together as personal computers as well as servers. The servers may be connected together by networking technology to create farms or clusters of workstations (COW). The evolution of COWs has significantly reduced the cost of ownership of high-performance clusters and has allowed users to build fairly large scale machines based on commodity server hardware. As COWs have evolved, networking technologies like InfiniBand and 10 Gigabit Ethernet have also evolved. These networking technologies not only give lower end-to-end latencies, but also allow for better messaging throughput between the nodes. This allows us to connect the clusters with high-performance interconnects at a relatively lower cost. With the deployment of low-cost, high-performance hardware and networking technology, it is increasingly becoming important to design a storage system that can be shared across all the nodes in the cluster. Traditionally, the different components of the file system have been stringed together using network connections. The protocol generally used over the network is TCP/IP. The TCP/IP protocol stack in general has been shown to have poor performance especially for high-performance networks. In this dissertation, we research the problem of designing high-performance communication subsystems for network attached storage (NAS) systems. Specifically, we delve i (open full item for complete abstract)