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Uncertainty is an inherent component of the modern-day workplace. Professionals need to learn to navigate their work lives through these uncertainties. Thus, learning to manage continuous change and uncertainty is key to professionals carrying out their practice successfully. Although past research has sought to unpack the intricacies of uncertainty perception and management, it has been predominantly at an organisational level. Two factors remain largely unexplored in the current literature: 1) Uncertainty perception and learning at an individual level and 2) Role of technology in supporting individual learning during uncertainty. The research questions posed in this thesis, stem from an inclination to address this gap and evaluate whether empirical evidence exists for an association between perception of uncertainty and how individuals learn during periods of uncertainty, and the role of technology in mediating this association. This thesis aims to address the following research questions through a mixed methods research design, including semi-structured interviews, questionnaires, and co-design approach with members from the Chartered Institute for Securities and Investments (CISI). What is the nature of environmental uncertainties within the finance sector and the perception of finance professionals towards these uncertainties? How do finance professionals self-regulate their learning in times of uncertainty? How do finance professionals perceive the role of technology in supporting their self-regulated learning during uncertainty? How do finance professionals perceive the usefulness of the LiU framework in recommending SRL strategies based on the type of perceived uncertainty? Three studies were planned to answer these research questions. Study 1 focused on delineating the nature and sources of environmental uncertainties in the finance sector. Results from the thematic analysis of interviews from Study 1 (n = 9) provided the categorisation of sources of objective uncertainty within the finance sector. It also provided insights into how finance professionals perceived the antecedents and consequences of uncertainty and what type of self-regulated learning strategies they used when faced with uncertain times. Whether their self-regulation strategies differed based on the type of uncertainty they perceived was investigated in Study 2. Study 2 investigated the relationship between self-regulated learning and perceived environmental uncertainty using the quantitative survey method and secondary analysis of interview data. Statistical analysis of survey data (n = 39) revealed that certain self-regulated learning strategies were significantly different across the types of perceived uncertainties. Among the self-regulation phases, statistically significant differences were seen in forethought and self-reflection phases; however, no significant differences were found in the performance phase based on the type of perceived uncertainty. This means that the way professionals planned their learning activities, set goals to achieve the learning objective and the extent to which they valued the learning activity depended on the type of uncertainty they perceived. Similarly, their self-reflective learning processes also differed based on the type of perceived uncertainty. However, the wide range of strategies they undertook to achieve the learning goals, their critical thinking, and help-seeking abilities were the same throughout, without a differential effect of perceived uncertainty. These results were in alignment with the thematic analysis of secondary interview data (n = 26), where the SRL strategies were associated with the type of perceived uncertainty. Study 3 investigated the role of technology in supporting the self-regulated learning behaviour of professionals using a co-design approach with two iterative cycles (Iteration 1: n=10, Iteration 2: n=10). Results from Study 3 showed that of the three phases of learning in uncertainty (identify- introspect-implement), any technology support was most effective in the introspection phase. It also revealed the importance of reflection as a dominant theme when learning in uncertainty. Altogether, the original contribution of this thesis was an examination of the relationship between perceived environmental uncertainty and self-regulated learning and the role of technology in supporting it. In doing so, it highlighted the importance of guiding the professionals in the regulation of their learning and the positive implications it had in terms of motivational and metacognitive aspects. It also highlighted the role of a technological scaffold in helping the professionals become more reflective in their learning and strategic in managing their uncertainty.

This thesis presents published work advancing Neurorehabilitation science. At the outset of the work (2010), gaps in knowledge impeded the investigation of neurosensorimotor recovery from neurotrauma. To begin to fill these gaps, I investigated engagement of the human system on two fronts: dance (Papers 1,2) and biomechanics (Papers 3-5). In Aim 1 of this thesis, I investigate application of arts-based concepts to performance of activity-based training, presenting 2 papers that established dance technology as augmentative for hospital-based Neurorehabilitation treatment. In Aim 2, I investigate biomechanics within the context of activity-based training, presenting 3 papers that characterize the dynamic solution space - or manifold - of healthy gait and posture to establish quantitative, physics-based parameters of clinical practices relevant to locomotor and balance Neurorehabilitation.

This thesis describes the nano-engineering of self-assembly processes, to accomplish nanoscale metal-organic framework (MOF) systems potentially useful for optoelectronic applications. The well-known fact underlying the large crystal size formation of common MOF compounds has been re-investigated here, not only to minimise the prolonged synthesis time but also to yield facile deposition in uniform thin film formats. This thesis presents the innovative concept of high concentration reaction (HCR) and its utility for making nano MOFs. Surprisingly, this method resulted in formation of a new kind of gel-like soft materials, for which I coined the term - supraMOFs - meaning supramolecular MOF hybrid materials. A detailed study of supraMOFs focussing on their constituent nano MOF elements, stimuli-responsive behaviours, and sol-gel conversion phenomena have been systematically performed. Akin to low molecular weight gels (LMWG), mechanical properties of supraMOFs displaying storage modulus (G') > loss modulus (G") have been confirmed by rheological experiments. The use of sol-gel MOF system to attain a uniform MOF film, has been demonstrated through an example of HKUST-1 thin coating of ~10 nm roughness and ~1 μm thickness. The HCR concept was further extended to develop advanced functional nano MOF systems, where one-step rapid synthesis of fluorescent MOF nanosheets has been accomplished. The old but effective concept of functionalisation of MOFs using porous coordination space and external guest species was implemented here, but with a twist. Particularly, the challenge of caging larger sized guest species into smaller pore apertures of MOFs, in parallel to controlling material growth in the nanoscale regime were solved by adopting the HCR approach. Furthermore, this thesis has demonstrated new interesting possibilities employing fluorescent nano MOF system to engineer smart sensors for detecting volatile organic compounds (VOCs), and mechanical stresses via a mechanochromic luminescent MOF nanoplate system.

Stringent emission regulations imposed by governments and depleting fossil fuel reserves have promoted the development of the automotive industry towards novel technologies. Various types of hybrid power plants for transport and stationary applications have emerged. The methodology of design and development of such power plants varies according to power producing components used in the systems. The practical feasibility of such power plants is a pre-requisite to any further development. This work presents thermodynamic analysis and modelling of such a novel power plant, assesses its feasibility and further discusses the development of a suitable control system. The proposed system consists of a hybrid configuration of a solid oxide fuel cell and IC engine as the main power producing components. A reformer supplies fuel gas to the fuel cell while the IC engine is supplied with a liquid fuel. The excess fuel from the fuel cell anode and the oxygen-depleted air from cathode of the fuel cell are also supplied to the engine. This gas mixture is aspirated into the engine with the balance of energy provided by the liquid fuel. The fuel cell exhaust streams are used to condition the fuel in the engine to ensure minimum pollutants and improved engine performance. Both, fuel cell and engine share the load on the system. The fuel cell operates on a base load while the engine handles majority of the transient load. This system is particularly suitable for a delivery truck or a bus cycle. Models of the system components reformer, solid oxide fuel cell, IC engine and turbocharger were developed to understand their steady state and dynamic behaviour. These models were validated against sources of literature and used to predict the effect of different operating conditions for each component. The main control parameters for each component were derived from these models. A first law analysis of the system at steady state was conducted to identify optimum operating region, verify feasibility and efficiency improvement of the system. The results suggested reduced engine fuel consumption and a 10 % improvement in system efficiency over the conventional diesel engines. Further, a second law analysis was conducted to determine the key areas of exergy losses and the rational efficiency of the system at full load operating conditions. The results indicate a rational efficiency of 25.4 % for the system. Sensitivity to changes in internal exergy losses on the system work potential was also determined. The exergy analysis indicates a potential for process optimisation as well as design improvements. This analysis provides a basis for the development of a novel control strategy based on exergy analysis and finite-time thermodynamics. A dynamic simulation of the control oriented system model identified the transient response and control parameters for the system. Based on these results, control systems were developed based on feedback control and model predictive control theories. These controllers mainly focus on air and fuel path management within the system and show an improved transient response for the system. In a hierarchical control structure for the system, the feedback controllers or the model predictive controller can perform local optimisation for the system, while a supervisory controller can perform global optimisation. The objective of the supervisory controller is to determining the load distribution between the fuel cell and the engine. A development strategy for such a top-level supervisory controller for the system is proposed. The hybrid power plant proposed in this thesis shows potential for application for transport and stationary power production with reduced emissions and fuel consumption. The first and second law of thermodynamics can both contribute to the development of a comprehensive control system. This work integrates research areas of powertrain design, thermodynamic analysis and control design. The development and design strategy followed for such a novel hybrid power plant can be useful to assess the potential of other hybrid systems as well.

In recent years, the paradigm of personal urban mobility has radically evolved as an increasing number of Mobility-on-Demand (MoD) systems continue to revolutionize urban transportation. Hailed as the future of sustainable transportation, with significant implications on urban planning, these systems typically utilize a fleet of shared vehicles such as bikes, electric scooters, cars, etc., and provide a centralized matching platform to deliver point-to-point mobility to passengers. In this dissertation, we study MoD systems along three operational directions – (1) modeling: developing analytical models that capture the rich stochasticity of passenger demand and its impact on the fleet distribution, (2) economics: devising strategies to maximize revenue, and (3) control: developing coordination mechanisms aimed at optimizing platform throughput. First, we focus on the metropolitan bike-sharing systems where platforms typically do not have access to real-time location data to ascertain the exact spatial distribution of their fleet. We formulate the problem of accurately predicting the fleet distribution as a Markov Chain monitoring problem on a graph representation of a city. Specifically, each monitor provides information on the exact number of bikes transitioning to a specific node or traversing a specific edge at a particular time. Under budget constraints on the number of such monitors, we design efficient algorithms to determine appropriate monitoring operations and demonstrate their efficacy over synthetic and real datasets. Second, we focus on the revenue maximization strategies for individual strategic driving partners on ride-hailing platforms. Under the key assumption that large-scale platform dynamics are agnostic to the actions of an individual strategic driver, we propose a series of dynamic programming-based algorithms to devise contingency plans that maximize the expected earnings of a driver. Using robust optimization techniques, we rigorously reason about and analyze the sensitivity of such strategies to perturbations in passenger demand distributions. Finally, we address the problem of large-scale fleet management. Recent approaches for the fleet management problem have leveraged model-free deep reinforcement learning (RL) based algorithms to tackle complex decision-making problems. However, such methods suffer from a lack of explainability and often fail to generalize well. We consider an explicit need-based coordination mechanism to propose a non-deep RL-based algorithm that augments tabular Q-learning with a combinatorial optimization problem. Empirically, a case study on the New York City taxi demand enables a rigorous assessment of the value, robustness, and generalizability of the proposed approaches.

Transit-oriented development is a planning instrument where land use and developments are driven by the presence of transit around them. This planning tool is particularly crucial in urban cities facing rapid growth, where local and regional governments encourage sustainable developments via land use planning, zoning laws, and changes to building codes, among other things (Transit-Oriented Development, 2015). Many metropolitan cities may have Council-adopted districts to promote transit-oriented developments with an aim to balance density, mix use, affordability, sustainability, alternate modes of transportation, using land use policies and public transit investments. The objectives of any given city for their transit-oriented developments will be unique, subject to context and priorities. Currently, the City of Austin regulates and promotes such developments for three transit-oriented developments and two special regulating districts. It is important to evaluate the performance of Austin’s transit-oriented development districts against the city’s larger strategic goals. The City of Austin adopts many strategic plans with different scopes; some are city-wide, while some as program-based or area-specific. These comply with each other in order to address various city priorities. A crucial component of Austin’s strategic planning process is performance management. While there are many ways to approach performance evaluation in planning, currently there is no globally accepted gold standard for assessing transit-oriented developments. The City also revises and updates these plans/ ordinances when required, to reflect the current conditions and challenges. Large public investments, capital improvements and partnerships are driven by these strategic plans. Hence it becomes important to evaluate the results of implementation. By identifying and informing the gaps and opportunities from periodic performance evaluation, the city stands to optimize the benefits of transit-oriented developments. This report identifies ideal metrics for TODs in Austin, derived from the city’s strategic planning goals and evaluates these metrics using empirical data. This progress report will focus on a quantitative approach and serve as an important tool for making informed revisions in policy and providing transparency and accountability in decision-making. This assessment has potential importance to the city’s officials to keep track of progress, or lack thereof, of their implemented policies. Such studies also help for future relevant planning decisions.

This thesis characterizes the training process of deep neural networks. We are driven by two apparent paradoxes. First, optimizing a non-convex function such as the loss function of a deep network should be extremely hard, yet rudimentary algorithms like stochastic gradient descent are phenomenally successful at this. Second, over-parametrized models are expected to perform poorly on new data, yet large deep networks with millions of parameters achieve spectacular generalization performance.We build upon tools from two main areas to make progress on these questions: statistical physics and a continuous-time point-of-view of optimization. The former has been popular in the study of machine learning in the past and has been rejuvenated in recent years due to the strong correlation of empirical properties of modern deep networks with existing, older analytical results. The latter, i.e., modeling stochastic first-order algorithms as continuous-time stochastic processes, gives access to powerful tools from the theory of partial differential equations, optimal transportation and non-equilibrium thermodynamics.The confluence of these ideas leads to fundamental theoretical insights that explain observed phenomena in deep learning as well as the development of state-of-the-art algorithms for training deep networks.

Due to ever increasing usage of wireless devices and data hungry applications, it has become necessary to improve the spectral efficiency of existing wireless networks. One way of improving spectral efficiency is to share the spectrum amongst different coexisting networks and serve multiple devices simultaneously. Spectrum sharing mechanisms for coexistence of a licensed network, such as LTE, with an unlicensed network, such as Wi-Fi, are being considered in the recent literature and standardizations. In order to enable the coexistence between licensed and unlicensed users, it is necessary to include interference mitigation techniques to protect the licensed primary users (PUs) from harmful interference. Typical interference mitigation mechanisms are based on spectrum sensing and cognitive radio (CR), wherein unlicensed secondary users (SUs) observe the spectrum and utilize it when licensed PUs are inactive. Thus, the SUs utilize empty time-slots in the shared spectrum to avoid the interference. The spectral efficiency can be further improved if the SUs are allowed to transmit concurrently with PUs by exploiting the spatial dimension provided by multiple antenna techniques.The underlay CR paradigm allows such coexistence where SUs transmit its signal in the same time-slots as PUs by exploiting the spatial and frequency resources in the network. In order to exploit the spatial dimension, SUs can utilize the location coordinates of PUs to steer its signal away from PUs to mitigate the interference. The SU transmitter can also employ multiple antenna techniques to serve a large number of devices. Further, the SUs can utilize frequency bands occupied by PUs by dynamically selecting the frequency band that provides the highest rate. In this work, we develop techniques for PU location estimation, spatial resource allocation and frequency band selection for SUs in underlay CR networks.We begin by considering the problem of estimation of PU location coordinates in a network of SUs in the presence of spectrally overlapped interference. A localization algorithm based on cyclostationary properties of the PU signal is proposed in order to mitigate the impact of the interference. The proposed scheme identifies and eliminates the SUs in the vicinity of the interferer thereby improving the localization accuracy.Next, we propose a low-complexity algorithm to solve a resource allocation and interference control problem in a network where secondary BS (SBS) is equipped with a large antenna array. The proposed algorithm selects maximum number of SUs and allocates power for downlink transmission from the SBS, while keeping the interference to PUs below a specified limit. It has been shown that the proposed low-complexity algorithm provides optimum solution if the number of antennas at the SBS is order of magnitude larger than the number of SUs and PUs in the network.Finally, we analyze power control and frequency band selection policies for a SU transmitter-receiver pair that can select one out of multiple available frequency band in each time-slot to maximize its achievable rate. We derive an expression for transmit power in a frequency band as a function of interference constraints, PU traffic statistics in the frequency band, and temporal correlation of channels. We show that instead of hopping to a different frequency band in each time-slot, the SU can stay on one frequency band in order to maximize its own rate while keeping the interference toward PUs to a predetermined level.

Insulin-like growth factor (IGF) signaling plays an important role in cell growth and proliferation and is implicated in regulation of cancer, metabolism, and aging. Here we report that IGF-1 level in blood and IGF-1 signaling demonstrates circadian rhythms. Circadian control occurs through cryptochromes (CRYs)—transcriptional repressors and components of the circadian clock. IGF-1 rhythms are disrupted in Cry-deficient mice, and IGF-1 level is reduced by 80% in these mice, which leads to reduced IGF signaling. In agreement, Cry-deficient mice have reduced body (~30% reduction) and organ size. Down-regulation of IGF-1 upon Cry deficiency correlates with reduced Igf-1 mRNA expression in the liver and skeletal muscles. Igf-1 transcription is regulated through growth hormone–induced, JAK2 kinase–mediated phosphorylation of transcriptional factor STAT5B. The phosphorylation of STAT5B on the JAK2-dependent Y699 site is significantly reduced in the liver and skeletal muscles of Cry-deficient mice. At the same time, phosphorylation of JAK2 kinase was not reduced upon Cry deficiency, which places CRY activity downstream from JAK2. Thus CRY's link the circadian clock and JAK-STAT signaling through control of STAT5B phosphorylation, which provides the mechanism for circadian rhythms in IGF signaling in vivo.

Sense of belonging is a key factor in college student development and learning. For multiracial and multiethnic college students, limited information exists on sense of belonging as an independent construct with full attention to its social, cognitive, and affective elements. As such, it is difficult to clearly depict how these students experience institutional or contextual sense of belonging in college and what factors contribute to it. Further research is warranted to better support the growing multiracial and multiethnic college student population for the reasons that they have unique racial and ethnic needs and experiences that may bear on belongingness. This qualitative research study aimed to understand the factors that enhanced or hindered multiracial and multiethnic undergraduate students’ sense of belonging in college and explored how race- and ethnicity-related factors influenced their institutional and contextual belonging in college. A constructivist paradigm using a phenomenological perspective and grounded theory via constant comparative analysis was adopted. Eleven self-identifying multiracial and multiethnic college students attending a large, predominantly white public university participated in the study. Participants represented all class years and a range of majors, with the majority identifying as female (N=9). Strayhorn’s (2012) core elements of sense of belonging was the conceptual framework informing this study. Data were collected through single one-on-one in-depth semi-structured interviews. Results revealed, among the multiple factors identified as influencing sense of belonging, the perpetuation of monoracial norms, fluidity of students’ racial and ethnic identity, and experiences of multiracial microaggressions were uniquely reflected factors that impacted mixed race and multiethnic students’ institutional and contextual belonging. Two behavioral strategies were used by students to manage their reduced sense of belonging: (a) accommodate to monoracial norms through situational identity and (b) resist conforming to monoracial norms through disengagement and disruption of multiracial microaggressions. Lastly, friendships were critical contexts where students’ belongingness was enhanced through validation. These findings are important because they support and expand understandings of how mixed race and multiethnic students’ unique racial and ethnic needs and experiences bear on their sense of belonging.

One motivation underlying the adoption of digital technologies is that people are looking for ways to preserve their memories, document their lives and store their life’s story. We often feel the need to capture moments in our lives, retain them and revisit some earlier ones. Materiality also plays a role in our memorialization processes in the form of artifacts that we associate with our memories.In this research, I study how tangible technology could be used to enhance our memorialization processes by combining physical and digital memories. I employed two projects – Penseive Box and Phylactery - to exemplify this concept. Penseive Box aims to serve as an embodiment of a person who has passed away. Phylactery provides a way to explicitly secure memories to physical objects.In order to understand people’s perspectives on the systems, I conducted two ethnographic studies. Here, I present my motivations behind the two projects, their conception and the findings from the two studies. I conclude that tangible technology could be a valuable means to forge a connection between physical objects and our memories.

The goal of this research was the synthesis of graphene and graphene nanocomposite for use as sensor materials. This dissertation describes the optimization of a novel approach to the synthesis of few layer graphene films on SiC, the modification of the graphene surface by wet chemical methods, the nucleation of nanoparticles to form graphene-nanoparticle composites, the fabrication of chemoresistive sensor structures from these materials, and the characterization of these surfaces and films.;In this work, the basic graphene synthesis method which uses halogen based plasma etching and ultra-high vacuum annealing (UHVA), has been optimized to reliably produce one, two, and three layer graphene on SiC films. The process has also been extended by replacing the UHVA step with rapid thermal annealing (RTA) in atmospheric pressure argon. Graphene films produced by both methods have been characterized using x-ray photoelectron spectroscopy (XPS), Raman microscopy, and atomic force microscopy (AFM). The UHVA process produces films with halogen-based and possibly some oxygen-based defects, whereas the RTA processes produces exclusively oxygen-based defects which include epoxide, hydroxyl, and carbonyl groups similar to, but at much lower levels, than that observed for graphene oxide (GO). As in the case for GO, the defect density was further reduced by wet chemical surface modification.;Nanoparticles (Ag, Au, Pt, Ir) were attached to these surfaces using solution based methods. The particle diameter and height distributions along with surface coverage were characterized using AFM methods. Key parameters in these studies included solution composition and incubation time. For electrical characterization and sensor testing, two structures were then fabricated using lithography free methods and electron beam evaporation. The first of these structures, referred to as the transmission line method (TLM) structure, was used in the present work for electrical characterization. Using the TLM structure, the electrical properties were characterized using two and four point probe methods. The films exhibited semiconducting behavior which is believed to be due to the opening of a band gap by the halogen- and oxygen-based defects. Using the two and four pint methods, the Schottky barrier height, the carrier density, electrical resistivity, and the carrier mobility were determined. The electrical resistivity was found to have an inverse relationship with number of graphene layers for one, two, and three layer films. The second device structure was a simple interdigitated sensor structure which was passed on to other researchers for sensor studies. Overall, reliable and reproducible synthesis and fabrication methods for graphene and graphene-nanoparticle composites have been developed for the next stage of testing and sensor development.

Computer systems, even when correctly designed, can suffer from temporary errors due to radiation particles striking the circuit or changes in the operating conditions such as the temperature or the voltage. Such transient errors can cause systems to malfunction or even crash. Fault injection is a technique used for simulating the effect of such errors on the system. Fault injection tools inject errors in either the software running on the processors or in the underlying computer hardware to simulate the effect of a fault and observe the system behavior. These tools can be used to determine the different responses of the system to such errors and estimate the probability of occurrence of errors in the computations performed by the system. They can also be used to test the fault tolerance capabilities of the system under test or any proposed technique for providing fault tolerance in circuits or software. As a part of this thesis, I have developed a software implemented fault injection tool, Fiesta++, for evaluating the fault tolerance and fault response of software applications. Software implemented fault injection tools inject faults into the software state of the application as it runs on a processor. Since such fault injection tools are used to conduct experiments on applications executing natively on a processor, the experiments can be carried out at almost the same speed as the application execution and can be run on the same hardware as used by the software application in the field. Fiesta++ offers two modes of operation: whitebox and blackbox. The whitebox mode assumes that users have some degree of knowledge of the structure of the software under test and allows them to specify fault injection targets in terms of the application variables and fault injection time in terms of code locations and events at run time. It can be used for precise fault injection to get reproducible outcomes from the fault injection experiments. The blackbox mode is targeted for the case where the user has very little or no knowledge of the application code structure. In this mode, Fiesta++ provides the user with a view of the active process memory and an array of associated information which a user can use to inject faults.

In association with Department of Energy.s National Energy Technology Laboratory (NETL), a software platform entitled Carbonaceous Chemistry for Computational Modeling (C3M) that can access a variety of kinetic processes and reaction mechanisms typically found in coal gasification, gas clean-up, and carbon capture processes, has been developed to overcome the limitations in terms of applicable operating conditions and fuel types. It interfaces with CFD software such as Multiphase Flow with Interphase Exchanges (MFIX) developed at NETL, ANSYS-FLUENT by ANSYS Inc., and BARRACUDA by CPFD Software and provides relevant parameters to simulate chemical kinetics and/or to replicate laboratory data. The reaction kinetics data in C3M are provided by one or more detailed reaction models such as PC Coal Lab (PCCL), Chemical Percolation Model for Coal Devolatilization (CPD), Solomon.s Functional-Group, Depolymerization, Vaporization, Cross-linking (FGDVC) model, or through experimental data generated at NETL. Algorithms were written to create this interface and to extract the kinetic information from all models. This functionality provides the CFD user with a framework to conduct virtual kinetic experiments to evaluate kinetic predictions as a function of fuel and sorbent type and/or operating conditions. The effort on the user.s part to search, analyze and to check the accuracy of the kinetics of interest is drastically reduced. Validity and compatibility of C3M kinetics were tested by implementing them in a (2-D) transport gasifier and in an industrial GE Texaco gasifier model (1-D). The predicted exit gas composition and trends of gas species matched very closely with the experimental and industrial data. To improve the kinetic database, a detailed coal/biomass derived soot literature review was completed. It was found that there is a gap in coal derived soot formation and gasification kinetics for high temperature and pressure operating conditions. In addition to the kinetic studies, uncertainty quantification (UQ) techniques were employed in the CFD models to study the variations of chemical reaction kinetics in a coal gasifier. The uncertainty in exit gas composition based on the variations in input parameters such as temperature, pressure, heating rate and coal feed composition were implemented. Changes in devolatilization product yields (such as mass fractions of CO, CO2, H2, tar, H2O, and CH4 along with total volatile yield) were used as response variables and were recorded and correlated based on distributions of input parameters such as temperature, pressure and heating rates. The correlations among the response variables and input parameters were investigated by computing a correlation matrix. The uncertainties in output responses were in close agreement with data reported in literature. This study strongly suggested the importance of considering uncertainties in chemical reaction kinetics in CFD modeling.

Nickel based superalloys have superior high temperature mechanical strength, corrosion and creep resistance in harsh environments and found applications in the hot sections as turbine blades and turbine discs in jet engines and gas generator turbines in the aerospace and energy industries. The efficiency of these turbine engines depends on the turbine inlet temperature, which is determined by the high temperature strength and behavior of these superalloys. The microstructure of nickel based superalloys usually contains coherently precipitated gamma prime (?) Ni3Al phase within the random solid solution of the gamma () matrix, with the ? phase being the strengthening phase of the superalloys. How the alloying elements partition into the and ? phases and especially in the site occupancy behaviors in the strengthening ? phases play a critical role in their high temperature mechanical behaviors. The goal of this dissertation is to study the site substitution behavior of the major alloying elements including Cr, Co and Ti through first principles based calculations. Site substitution energies have been calculated using the anti-site formation, the standard defect formation formalism, and the vacancy formation based formalism. Elements such as Cr and Ti were found to show strong preference for Al sublattice, whereas Co was found to have a compositionally dependent site preference. In addition, the interaction energies between Cr-Cr, Co-Co, Ti-Ti and Cr-Co atoms have also been determined. Along with the charge transfer, chemical bonding and alloy chemistry associated with the substitutions has been investigated by examining the charge density distributions and electronic density of states to explain the chemical nature of the site substitution. Results show that Cr and Co atoms prefer to be close by on either Al sublattice or on a Ni-Al mixed lattice, suggesting a potential tendency of Cr and Co segregation in the ? phase.

Detailed kinetic expressions for the gasification of a wide variety of coals in existing Computational Fluid Dynamics (CFD) codes such as Multiphase Flow with Interphase Exchanges (MFIX) developed by NETL. These expressions describe the fundamental steps taking place in the gasification of coal, namely, coal devolatilization, tar-gas chemistry, soot formation and the heterogeneous and homogeneous gasification reaction along with combustion reactions. For this purpose, the data generated by PC Coal Lab (PCCL) is used to simulate the gasification of various coals in CFD codes. The CFD code of most interest is MFIX, which is used to describe the hydrodynamics, heat transfer and chemical reactions in the reacting fluid-solids systems comprising the contents of a gasifier. The implementation of gasification kinetics in MFIX is currently done through a set of subroutines making up the Carbonaceous Chemistry for Continuum Modeling (C3M) code. C3M has default gasification kinetics for only a few coals. While on the other hand, PC Coal lab can predict the gasification kinetics for over 2000 coal species. This project focuses on the development of a seamless connection between PC Coal Lab and the C 3M. The interface is designed to allow MFIX to transfer information through C3M to PC Coal Lab, run PC Coal Lab with the input data from MFIX, send kinetic information back to C3M in a form that allows C3M to update and continue the MFIX simulation using updated parameters from PC Coal Lab. Current work is focused on developing similar expressions for biomass devolatilization. In this research PCCL predictions for biomass devolatilization were compared with experimental results and they show a good agreement with experimental results. The algorithm developed is being used to develop a Graphical User Interphase (GUI) at NETL site Morgantown by Phil Nicolletti.

Manufacturing supply chains are invariably dynamic and complicated in nature. Hence, steady state models are not sufficient for analyzing and designing supply chains. Models of supply chains must accurately capture their dynamic behavior, which is determined by the structure of the organization, and the policies adopted by management. System dynamics modeling provides a powerful framework for this purpose. The use of system dynamics models in supply chain management has thus far been limited to explaining phenomenon like the bullwhip effect, and for policy development. We provide a structured approach for policy design, which doesn't rely on any simulation experiments. Further, we study the impact that information network design has on the response of supply chains. We use a combinatorial approach to develop guidelines for information network design. Further, we examine the possibility of utilizing a PID information feedback structure to enhance the responsiveness of the supply chain. Lastly, we propose a combined feedback feed-forward information structure to enable a supply chain to rapidly respond to disturbances whose effects are known. The goal of this dissertation is to provide a structured approach for the design of information network structure, and operating policy.

Tremendous advances in wearable computing and storage technologies enable us to record not just snapshots of an event but the whole human experience for a long period of time. Such a \life-logandamp;quot; system captures important events as they happen, rather than an after-thought. Such a system has applications in many areas such as law enforcement, personal archives, police questioning, and medicine. Much of the existing eandamp;reg;orts focus on the pattern recognition and information retrieval aspects of the system. On the other hand, the privacy issues raised by such an intrusive system have not received much attention from the research community. The objectives of this research project are two-fold: andamp;macr;rst, to construct a wearable life-log video system, and second, to provide a solution for protecting the identity of the subjects in the video while keeping the video useful. In this thesis work, we designed a portable wearable life-log system that implements audio distortion and face blocking in a real time to protect the privacy of the subjects who are being recorded in life-log video. For audio, our system automatically isolates the subject's speech and distorts it using a pitch- shifting algorithm to conceal the identity. For video, our system uses a real-time face detection, tracking and blocking algorithm to obfuscate the faces of the subjects. Extensive experiments have been conducted on interview videos to demonstrate the ability of our system in protecting the identity of the subject while maintaining the usability of the life-log video.

Asthma is a chronic inflammatory disease characterized by airway hyperresponsiveness (AHR), airway obstruction, wheezing, and leads to airway remodeling if these symptoms persist. Airway remodeling is characterized by goblet hyperplasia, airway smooth muscle hypertrophy and hyperplasia, subepithelial fibrosis and denudation of the epithelium. Due to weakened protection of the airway epithelium, the body is more susceptible to infections and exacerbation of asthma symptoms.|Airway epithelial cells are damaged and shed during airway remodeling, a characteristic of chronic asthma. During airway remodeling inflammatory cells including eosinophils and neutrophils penetrate the epithelium leading destruction of the tissue. Eosinophils, primary inflammatory cells, release TGF-ßl, a potent mediator of epithelial damage. Expression of TGF-ßl is higher in asthmatics. TGF-ßl can induce apoptosis of airway epithelial cells, however, the underlying mechanism of apoptosis is not well defined. A new member of the TNF superfamily, TRAIL, is predominantly involved in apoptosis. Various studies in hepatoma cells have linked the pathways of TRAIL and TGF-ßl.|Given this information, we hypothesized that TGF-ßl induced apoptosis of airway epithelial cells occurs through the actions of TRAIL. We investigated the role of TGF-ßl in the proliferation and apoptosis of human airway epithelial cells. Also we examined the effect of TGF-ßl on the mRNA transcript and protein expression of TRAIL. To investigate whether TGF-ßl induced apoptosis via TRAIL, we performed a study in which the receptor of TRAIL was blocked to observe whether caspase-dependent apoptosis still occurs. TGF-ßl induced the release of TRAIL by airway epithelial cells. Upon TGF-ßl treatment, proliferation decreased while apoptosis increased. The mRNA transcript and protein expression was increased upon treatment with TGF-ßl. The expression of activated caspase-3 was increased with TGF-ßl treatment as well as the increased expression of Bax. However, there was a decreased expression of Bcl-2. When TRAIL-R2 was blocked, there is no apoptosis induced by TGF-ßl through the actions of caspases.|These data suggest that TGF-ßl induced apoptosis occurs through the actions of TRAIL and its receptors and may have potential in therapy for treating asthma.

Energy is an extremely valuable resource which should be used carefully. After realizing this in the 1990's DOE started various energy efficiency measures in order to reduce the energy consumed by US industry. Motors use almost 50 percent of the electricity in the United States. If motor efficiency could be increased then energy consumption could be reduced. There have been efforts to increase motor efficiency over past decade which lead to development of premium efficiency motors. Designed motor efficiency is normally above 90 percent so motors do not waste energy. However, motor efficiency decreases when load factor is less than 50 percent. Therefore measures need to be taken to make the motor sizes properly fit the application load profile so as to improve operational efficiency. Based on plant assessment experience plant personnel have very limited data on motor loading. The decisions concerning motor sizing are usually based on judgment or the slip method. Accurate load determination methods such as input power measurement involve working around high voltages hence have safety issues. These methods are time consuming and expensive. The purpose of this research was to develop a method of sizing electric motors which is simple to use, economical, safe and accurate. This research collected data during fifteen energy assessments conducted at variety of facilities using the motor load monitoring method as well as by using a stroboscope method. A statistical analysis of the data was conducted to determine if a relationship exists between slip method and input power measurement load factors, so that prediction of accurate load factor can be made based on the load factor obtained by slip method. The model was built and validated based on mathematical and electrical engineering principles.

This thesis investigates the usefulness of Boundary Element Method (BEM) and a software tool called UCWaves developed at University of Cincinnati. The Boundary Element Method for structural analysis has emerged as a very promising alternative for the Finite Element Method (FEM). Although BEM is more complicated than FEM, it is faster and it does reduce a lot of storage space over FEM for solving large models. The advantage of BEM becomes apparent for the infinite domain as well as fracture mechanics problems. There are very few CAD tools, which use BEM for analysis. This thesis is a successful attempt to design and develop an Internet based analysis tool using the BEM. The existing standalone BEM software has also been enhanced. This study will lead to more research activities on the Internet-based software development for the BEM.

This research study addresses a problem of water pollution caused by heavy and toxic metals Cd, Cr, Cu and Pb. The thesis proposes the use of the technique of phytoremediation using Phragmites australis (PA) plants that have the capacity to absorb and to accumulate such metals in their roots and leaves. The metal uptake and their location of accumulation in the PA plants were determined using flame atomic absorption spectroscopy (FAAS) and transmission electron microscopy (TEM) respectively. Leachates from contaminated plant biomass were mixed with silver nitrate to assess the manufacture of metal nanoparticles as an added value step in the process from remediation to biomass disposal. Silver nanoparticles were readily manufactured by the leachates without, with the exception of copper, any incorporation of the pollutant metal. The presence of copper in the manufactured silver nanoparticles may be of some commercial use. The results obtained show that PA plants will accumulate toxic metals when in hydroponic culture and that the majority of the accumulated metals are sequestered in the roots and do not enter the aerial parts of the plants in significant amounts. Silver nanoparticles were manufactured from the biomass using a low energy route with no additional chemicals, apart from silver nitrate thus reducing the environmental load that would otherwise be present if a chemical means of nanoparticle production was used.