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Title: Adaptation to visual perturbations while learning a novel virtual reaching task
Creator: Narayanan, Sachin Devnathan
Date: 2019
Collection: Electronic Theses & Dissertations
Description: "Introduction and Purpose: The movements we do to perform our day-to-day activities have always been riddled with perturbations, to which we adapt and learn. The studies looking at this aspect of motor learning should consider, the biomechanical differences that exist between individuals and create a novel task that can test every individual without any bias. This was achieved in our study by using a virtual environment to perform a novel motor skill in order to investigate how people learn...
Show more"Introduction and Purpose: The movements we do to perform our day-to-day activities have always been riddled with perturbations, to which we adapt and learn. The studies looking at this aspect of motor learning should consider, the biomechanical differences that exist between individuals and create a novel task that can test every individual without any bias. This was achieved in our study by using a virtual environment to perform a novel motor skill in order to investigate how people learn to adapt to perturbations. Methods: 13 college-age participants (females = 7, Mean = 21.74 +/- 2.55) performed upper body movements to control a computer cursor. Visual rotation of the cursor position was introduced as a perturbation for one-half of the practice trials. Movement time and normalized path length were calculated. One way repeated measures ANOVA was performed to analyze significance between the performance at different times of the task. Results: Significant learning seen while learning the initial baseline task (p<0.0001) and a significant drop in performance upon immediate exposure to the perturbation (p =0.005). No significant adaptation over practice with the perturbation (p = 0.103) or significant after-effects on removal of the perturbation (p = 0.383). Conclusions: Results suggests differences in adaptation when the task is novel when compared to other adaptation studies and such novel tasks trigger a different type of learning mechanism when compared to adaptation."--Page ii.
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Title: Design and simulation of single-crystal diamond diodes for high voltage, high power and high temperature applications
Creator: Suwanmonkha, Nutthamon
Date: 2016
Collection: Electronic Theses & Dissertations
Description: ABSTRACTDESIGN AND SIMULATION OF SINGLE-CRYSTAL DIAMOND DIODES FOR HIGH VOLTAGE, HIGH POWER AND HIGH TEMPERATURE APPLICATIONSByNutthamon SuwanmonkhaDiamond has exceptional properties and great potentials for making high-power semiconducting electronic devices that surpass the capabilities of other common semiconductors including silicon. The superior properties of diamond include wide bandgap, high thermal conductivity, large electric breakdown field and fast carrier mobilities. All of these...
Show moreABSTRACTDESIGN AND SIMULATION OF SINGLE-CRYSTAL DIAMOND DIODES FOR HIGH VOLTAGE, HIGH POWER AND HIGH TEMPERATURE APPLICATIONSByNutthamon SuwanmonkhaDiamond has exceptional properties and great potentials for making high-power semiconducting electronic devices that surpass the capabilities of other common semiconductors including silicon. The superior properties of diamond include wide bandgap, high thermal conductivity, large electric breakdown field and fast carrier mobilities. All of these properties are crucial for a semiconductor that is used to make electronic devices that can operate at high power levels, high voltage and high temperature.Two-dimensional semiconductor device simulation software such as Medici assists engineers to design device structures that allow the performance requirements of device applications to be met. Most physical material parameters of the well-known semiconductors are already compiled and embedded in Medici. However, diamond is not one of them. Material parameters of diamond, which include the models for incomplete ionization, temperature-and-impurity-dependent mobility, and impact ionization, are not readily available in software such as Medici. Models and data for diamond semiconductor material have been developed for Medici in the work based on results measured in the research literature and in the experimental work at Michigan State University. After equipping Medici with diamond material parameters, simulations of various diamond diodes including Schottky, PN-junction and merged Schottky/PN-junction diode structures are reported. Diodes are simulated versus changes in doping concentration, drift layer thickness and operating temperature. In particular, the diode performance metrics studied include the breakdown voltage, turn-on voltage, and specific on-resistance. The goal is to find the designs which yield low power loss and provide high voltage blocking capability. Simulation results are presented that provide insight for the design of diamond diodes using the various diode structures. Results are also reported on the use of field plate structures in the simulations to control the electric field and increase the breakdown voltage.
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Title: Reliability improvement of DFIG-based wind energy conversion systems by real time control
Creator: Elhmoud, Lina Adnan Abdullah
Date: 2015
Collection: Electronic Theses & Dissertations
Description: Reliability is the probability that a system or component will satisfactorily perform its intended function under given operating conditions. The average time of satisfactory operation of a system is called the mean time between failures (MTBF) and. the higher value of MTBF indicates higher reliability and vice versa. Nowadays, reliability is of greater concern than in the past especially for offshore wind turbines since the access to these installations in case of failures is both costly and...
Show moreReliability is the probability that a system or component will satisfactorily perform its intended function under given operating conditions. The average time of satisfactory operation of a system is called the mean time between failures (MTBF) and. the higher value of MTBF indicates higher reliability and vice versa. Nowadays, reliability is of greater concern than in the past especially for offshore wind turbines since the access to these installations in case of failures is both costly and difficult. Power semiconductor devices are often ranked as the most vulnerable components from reliability perspective in a power conversion system. The lifetime prediction of power modules based on mission profile is an important issue. Furthermore, lifetime modeling of future large wind turbines is needed in order to make reliability predictions in the early design phase. By conducting reliability prediction in the design phase a manufacture can ensure that the new wind turbines will operate within designed reliability metrics such as lifetime.This work presents reliability analysis of power electronic converters for wind energy conversion systems (WECS) based on semiconductor power losses. A real time control scheme is proposed to maximize the system's lifetime and the accumulated energy produced over the lifetime. It has been verified through the reliability model that a low-pass-filter-based control can effectively increase the MTBF and lifetime of the power modules. The fundamental cause to achieve higher MTBF lies in the reduction of the number of thermal cycles.The key element in a power conversion system is the power semiconductor device, which operates as a power switch. The improvement in power semiconductor devices is the critical driving force behind the improved performance, efficiency, reduced size and weight of power conversion systems. As the power density and switching frequency increase, thermal analysis of power electronic system becomes imperative. The analysis provides information on semiconductor device rating, reliability, and lifetime calculation. The power throughput of the state-of-the-art WECS that is equipped with maximum power point control algorithms is subjected to wind speed fluctuations, which may cause significant thermal cycling of the IGBT in power converter and in turn lead to reduction in lifetime. To address this reliability issue, a real-time control scheme based on the reliability model of the system is proposed. In this work a doubly fed induction generator is utilized as a demonstration system to prove the effectiveness of the proposed method. Average model of three-phase converter has been adopted for thermal modeling and lifetime estimation. A low-pass-filter based control law is utilized to modify the power command from conventional WECS control output. The resultant reliability performance of the system has been significantly improved as evidenced by the simulation results.
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Title: A global modeling framework for plasma kinetics : development and applications
Creator: Parsey, Guy Morland
Date: 2017
Collection: Electronic Theses & Dissertations
Description: The modern study of plasmas, and applications thereof, has developed synchronously with com-puter capabilities since the mid-1950s. Complexities inherent to these charged-particle, many-body, systems have resulted in the development of multiple simulation methods (particle-in-cell,fluid, global modeling, etc.) in order to both explain observed phenomena and predict outcomesof plasma applications. Recognizing that different algorithms are chosen to best address specifictopics of interest, this...
Show moreThe modern study of plasmas, and applications thereof, has developed synchronously with com-puter capabilities since the mid-1950s. Complexities inherent to these charged-particle, many-body, systems have resulted in the development of multiple simulation methods (particle-in-cell,fluid, global modeling, etc.) in order to both explain observed phenomena and predict outcomesof plasma applications. Recognizing that different algorithms are chosen to best address specifictopics of interest, this thesis centers around the development of an open-source global model frame-work for the focused study of non-equilibrium plasma kinetics. After verification and validationof the framework, it was used to study two physical phenomena: plasma-assisted combustion andthe recently proposed optically-pumped rare gas metastable laser.Global models permeate chemistry and plasma science, relying on spatial averaging to focusattention on the dynamics of reaction networks. Defined by a set of species continuity and energyconservation equations, the required data and constructed systems are conceptually similar acrossmost applications, providing a light platform for exploratory and result-search parameter scan-ning. Unfortunately, it is common practice for custom code to be developed for each application-an enormous duplication of effort which negatively affects the quality of the software produced.Presented herein, the Python-based Kinetic Global Modeling framework (KGMf) was designed tosupport all modeling phases: collection and analysis of reaction data, construction of an exportablesystem of model ODEs, and a platform for interactive evaluation and post-processing analysis. Asymbolic ODE system is constructed for interactive manipulation and generation of a Jacobian,both of which are compiled as operation-optimized C-code.Plasma-assisted combustion and ignition (PAC/PAI) embody the modernization of burning fuelby opening up new avenues of control and optimization. With applications ranging from engineefficiency and pollution control to stabilized operation of scramjet technology in hypersonic flows,developing an understanding of the underlying plasma chemistry is of the utmost importance.While the use of equilibrium (thermal) plasmas in the combustion process extends back to the ad-vent of the spark-ignition engine, works from the last few decades have demonstrated fundamentaldifferences between PAC and classical combustion theory. The KGMf is applied to nanosecond-discharge systems in order to analyze the effects of electron energy distribution assumptions onreaction kinetics and highlight the usefulness of 0D modeling in systems defined by coupled andcomplex physics.With fundamentally different principles involved, the concept of optically-pumped rare gasmetastable lasing (RGL) presents a novel opportunity for scalable high-powered lasers by takingadvantage of similarities in the electronic structure of elements while traversing the periodic ta-ble. Building from the proven concept of diode-pumped alkali vapor lasers (DPAL), RGL systemsdemonstrate remarkably similar spectral characteristics without problems associated with heatedcaustic vapors. First introduced in 2012, numerical studies on the latent kinetics remain immature.This work couples an analytic model developed for DPAL with KGMf plasma chemistry to bet-ter understand the interaction of a non-equilibrium plasma with the induced laser processes anddetermine if optical pumping could be avoided through careful discharge selection.
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Title: Unconstrained 3D face reconstruction from photo collections
Creator: Roth, Joseph (Software engineer)
Date: 2016
Collection: Electronic Theses & Dissertations
Description: This thesis presents a novel approach for 3D face reconstruction from unconstrained photo collections. An unconstrained photo collection is a set of face images captured under an unknown and diverse variation of poses, expressions, and illuminations. The output of the proposed algorithm is a true 3D face surface model represented as a watertight triangulated surface with albedo data colloquially referred to as texture information. Reconstructing a 3D understanding of a face based on 2D input...
Show moreThis thesis presents a novel approach for 3D face reconstruction from unconstrained photo collections. An unconstrained photo collection is a set of face images captured under an unknown and diverse variation of poses, expressions, and illuminations. The output of the proposed algorithm is a true 3D face surface model represented as a watertight triangulated surface with albedo data colloquially referred to as texture information. Reconstructing a 3D understanding of a face based on 2D input is a long-standing computer vision problem. Traditional photometric stereo-based reconstruction techniques work on aligned 2D images and produce a 2.5D depth map reconstruction. We extend face reconstruction to work with a true 3D model, allowing us to enjoy the benefits of using images from all poses, up to and including profiles. To use a 3D model, we propose a novel normal field-based Laplace editing technique which allows us to deform a triangulated mesh to match the observed surface normals. Unlike prior work that require large photo collections, we formulate an approach to adapt to photo collections with few images of potentially poor quality. We achieve this through incorporating prior knowledge about face shape by fitting a 3D Morphable Model to form a personalized template before using a novel analysis-by-synthesis photometric stereo formulation to complete the fine face details. A structural similarity-based quality measure allows evaluation in the absence of ground truth 3D scans. Superior large-scale experimental results are reported on Internet, synthetic, and personal photo collections.
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Title: Energy utilization modeling of animal draft power (EUMDAP) for Kenyan small-holder semi-arid agriculture
Creator: Mungai, George S. N.
Date: 1998
Collection: Electronic Theses & Dissertations

Title: Atomic simulation on chemical-mechanical coupled deformations in complex nano structures
Creator: Liu, Jialin (Graduate of Michigan State University)
Date: 2019
Collection: Electronic Theses & Dissertations
Description: "Nano-structured materials often exhibit very different mechanical properties comparing with their bulk counterpart and are more sensitive and active to chemical interactions with the environments due to the large surface-to-volume ratio. In this thesis, predictive modeling techniques including density functional theory (DFT) and reactive molecular dynamics method (MD) are designed and applied to understand the deformation mechanisms of complex nano-structured material and describe chemical...
Show more"Nano-structured materials often exhibit very different mechanical properties comparing with their bulk counterpart and are more sensitive and active to chemical interactions with the environments due to the large surface-to-volume ratio. In this thesis, predictive modeling techniques including density functional theory (DFT) and reactive molecular dynamics method (MD) are designed and applied to understand the deformation mechanisms of complex nano-structured material and describe chemical-mechanical coupled interactions. Three technologically important materials are investigated, to understanding the high strain rate toughening mechanism in nacre, predicting the formation and fracture of aluminum oxide bifilms in aluminum castings, and revealing the lithium growth morphology as a function of oxygen partial pressure. For nacre, its hierarchical structure and toughening mechanisms have inspired many materials developments. Recently, a new toughening mechanism, deformation twins was observed in nacre after dynamic loading (103 s--1). The deformation twinning tendency and the competition between fracture and deformation twinning were revealed by DFT calculations. We discovered that the ratio of the unstable and the stable stacking fault energy in aragonite is hitherto the highest in a broad range of metallic and oxide materials and the bonding nature for this high ratio is explained. Both aluminum and lithium have high oxygen affinity. Their interaction with the oxygen environment affects the mechanical properties and vice versa. During casting of aluminum, it has long been proposed that the entrapped alumina "bifilms" are detrimental to the fatigue properties of the cast product. However, its properties have never been measured due to experimental limitations. Therefore, a ReaxFF based MD protocol was designed to simulate aging, folding, and fracture of oxide bifilms. The predicted fracture energy, fracture location, and differences between old and young oxides are explained a series of experimental observations. To illustrate the Li-growth mechanism in a solid-state-battery testing platform, we modeled the morphology of Li nano-structure growth in oxygen environment via ReaxFF-based MD. The simulation revealed that the competition of the Li growth rate and oxidation rate leads to the sphere-nanowire-sphere morphology transition with increasing oxygen partial pressure. Understanding the impact of chemical reaction on Li dendrite growth mechanisms and morphology evolution provided insights on the formation of the solid electrolyte interface (SEI) layer in a Li-ion battery. Finally, a shortcoming of the current charge transfer scheme (qEq) used in the ReaxFF MD simulation is discussed. It is demonstrated that qEq method will lead to overductile ionic materials in the MD simulation. A new Force field method and new parameters are proposed to mitigate this problem."--Pages ii-iii.
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Title: Regional climate response to land use and land cover change in contiguous United States
Creator: Nikolić, Jovanka
Date: 2020
Collection: Electronic Theses & Dissertations
Description: Future land use and land cover (LULC) pattern in the Contiguous United States (CONUS) is expected to be significantly different from that of the present, and as an important surface forcing for earth's climate system, the potential changes in LULC will contribute to climate change at all scales (local, regional to global). While numerous studies have examined how the earth's climate will respond to the anthropogenic increase of greenhouse gas concentrations in the earth's atmosphere, this...
Show moreFuture land use and land cover (LULC) pattern in the Contiguous United States (CONUS) is expected to be significantly different from that of the present, and as an important surface forcing for earth's climate system, the potential changes in LULC will contribute to climate change at all scales (local, regional to global). While numerous studies have examined how the earth's climate will respond to the anthropogenic increase of greenhouse gas concentrations in the earth's atmosphere, this research aims to quantify the response of several climate variables to the expected LULC change in the CONUS using simulations from a regional climate model. The research is composed of three individual studies. The first study assesses the sensitivity of simulated low-level jet (LLJ) characteristics on changes in LULC pattern. As a prominent weather and climate process responsible for transport of moisture from the Gulf of Mexico northward into central CONUS, LLJ plays an important role in the hydrological cycle and wind energy generation over the Great Plains. Therefore, it is important to quantify the potential changes in jet characteristics, such as jet speed, height and frequency, under the influence of LULC change. The second study investigates the impact of LULC change on frost indices - the dates of last spring frost and first fall frost and the length of frost free seasons. Frost is one of the major factors affecting the growth and development of plants and crop production. Future changes in LULC could make some regions more beneficial, while others more harmful to agricultural practice. Finally, the third study examines the potential impact of the changes in LULC pattern on future wind energy resources. As a zero carbon energy resource, wind energy helps limit greenhouse gasses emissions and mitigate climate change. Knowledge gained on where in the CONUS wind power class would likely to change from unsuitable or marginal to suitable, and vice versa, as a result of LULC change can be useful for future wind farm sitting and for making better informed energy policies.
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Title: Agronomic management of corn using seasonal climate predictions, remote sensing and crop simulation models
Creator: Jha, Prakash Kumar
Date: 2019
Collection: Electronic Theses & Dissertations
Description: Management decisions in corn (Zea mays mays L) production are usually based on specific growth stages. However, because of climate and weather variability, phenological stages vary from season to season across geographic locations. This variability in growth and phenology entails risks and quantifying it will help in managing climate related risks. Crop simulation models can play a significant role in minimizing these risks through designing management strategies; however, they are not always...
Show moreManagement decisions in corn (Zea mays mays L) production are usually based on specific growth stages. However, because of climate and weather variability, phenological stages vary from season to season across geographic locations. This variability in growth and phenology entails risks and quantifying it will help in managing climate related risks. Crop simulation models can play a significant role in minimizing these risks through designing management strategies; however, they are not always accurate. Remote sensing observations and climate predictions can improve the accuracy in managing time bound climate-sensitive decisions at larger spatiotemporal scale. However, there is also a disconnect between climate forecasts and crop models. The unavailability of downscaling tool that can downscale rainfall and temperature forecasts simultaneously make this task more challenging. To address these knowledge gaps, this dissertation consists of three studies focused on interdisciplinary approaches to agronomic management of corn.In the first study, we calibrated and validated genetic coefficients of CERES-Maize using field data from the Michigan corn performance trials. Multiple methods of estimating genetic coefficients GENCALC (Genotype Coefficient Calculator), GLUE (Generalized Likelihood Uncertainty Estimate), and NMCGA (Noisy Monte Carlo Genetic Algorithm) were evaluated and ensembled to estimate more reliable genetic coefficients. The calibrations were done under irrigated conditions and validation under rainfed conditions. The results suggested that ensembled genetic coefficients performed best among all, with d-index of 0.94 and 0.96 in calibration and validation for anthesis and maturity dates, and yield.In the second study, simulated growth stages from the calibrated crop model were used to develop site-specific crop coefficients (kc) using ensembled ET and reference ET from the nearest weather station. ET from multiple models were ensembled and validated with the measured ET from eddy-covariance flux towers for 2010 - 2017. Results suggest that the ensembled ET performed best among all ET models used, with highest d-index of 0.94. Likewise, the performance of the newly derived kc-curve was compared with FAO-kc curve using a soil water balance model. Then, the derived region-specific Kc-curve was used to design irrigation scheduling and results suggest that it performed better than FAO Kc-curve in minimizing the amount irrigation while maintaining a prescribed allowable water stress.The third study used the calibrated crop model to simulate anthesis using downscaled seasonal climate forecasts. The predicted anthesis and downscaled seasonal climate forecasts were used to develop risk analysis model for ear rot disease management in corn. In this study an innovative downscaling tool, called FResamplerPT, was introduced to downscale rainfall and temperature simultaneously. The results suggest that temperature and relative humidity are better predictors (combined) as compared to temperature and rainfall (combined). With this risk analysis model, growers can evaluate and assess the future climatic conditions in the season before planting the crops. The seasonal climate information with the lead-time of 3 months can help growers to prepare integrated management strategies for ear rot disease management in maize.
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Title: Experiments and model development of a dual mode, turbulent jet ignition engine
Creator: Tolou, Sedigheh
Date: 2019
Collection: Electronic Theses & Dissertations
Description: "The number of vehicles powered by a source of energy other than traditional petroleum fuels will increase as time passes. However, based on current predictions, vehicles run on liquid fuels will be the major source of transportation for decades to come. Advanced combustion technologies can improve fuel economy of internal combustion (IC) engines and reduce exhaust emissions. The Dual Mode, Turbulent Jet Ignition (DM-TJI) system is an advanced, distributed combustion technology which can...
Show more"The number of vehicles powered by a source of energy other than traditional petroleum fuels will increase as time passes. However, based on current predictions, vehicles run on liquid fuels will be the major source of transportation for decades to come. Advanced combustion technologies can improve fuel economy of internal combustion (IC) engines and reduce exhaust emissions. The Dual Mode, Turbulent Jet Ignition (DM-TJI) system is an advanced, distributed combustion technology which can achieve high diesel-like thermal efficiencies at medium to high loads and potentially exceed diesel efficiencies at low-load operating conditions. The DM-TJI strategy extends the mixture flammability limits by igniting lean and/or highly dilute mixtures, leading to low-temperature combustion (LTC) modes in spark ignition (SI) engines. A novel, reduced order, and physics-based model was developed to predict the behavior of a DM-TJI engine with a pre-chamber air valve assembly. The engine model developed was calibrated based on experimental data from a Prototype II DM-TJI engine. This engine was designed, built, and tested at the MSU Energy and Automotive Research Laboratory (EARL). A predictive, generalized model was introduced to obtain a complete engine fuel map for the DM-TJI engine. The engine fuel map was generated in a four-cylinder boosted configuration under highly dilute conditions, up to 40% external exhaust gas recirculation (EGR). A vehicle simulation was then performed to further explore fuel economy gains using the fuel map generated for the DM-TJI engine. The DM-TJI engine was embodied in an industry-based vehicle to examine the behavior of the engine over the U.S. Environmental Protection Agency (EPA) driving schedules. The results obtained from the drive cycle analysis of the DM-TJI engine in an industry-based vehicle were compared to the results of the same vehicle with its original engine. The vehicle equipped with the DM-TJI system was observed to benefit from 103033% improvement in fuel economy and 103031% reduction in CO2 emission over the EPA combined city/high driving schedules. Potential improvements were discussed, as these results of the drive cycle analysis are the first-ever reported results for a DM-TJI engine embodied in an industry-based vehicle. The resulting fuel economy and CO2 emission were used to conduct a cost-benefit analysis of a DM-TJI engine. The cost-benefit analysis followed the economic and key inputs used by the U.S. EPA in a Proposed Determination prepared by that agency. The outcomes of the cost-benefit analysis for the vehicle equipped with the DM-TJI system were reported in comparison with the same vehicle with its base engine. The extra costs of a DM-TJI engine were observed to be compensated over the first three years of the vehicle's life time. The results projected maximum savings of approximately 2400 in 2019 dollars. This includes the lifetime-discounted present value of the net benefits of the DM-TJI technology, compared to the base engine examined. In this dollar saving estimate, the societal effects of CO2 emission were calculated based on values by the interagency working group (IWG) at 3% discount rate."--Pages ii-iii.
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Title: Monte-Carlo simulations of the (d,²He) reaction in inverse kinematics
Creator: Carls, Alexander B.
Date: 2019
Collection: Electronic Theses & Dissertations
Description: Charge-exchange reactions offer an indirect method for the testing of theoretical models for Gamow-Teller strengths that are used to calculate electron-capture rates on medium-heavy nuclei, which play important roles in astrophysical phenomena. Many of the relevant nuclei are unstable. However, a good general probe for performing charge-exchange reactions in inverse kinematics in the (n,p) reaction has not yet been established. The (d,2He) reaction in inverse kinematics is being developed as...
Show moreCharge-exchange reactions offer an indirect method for the testing of theoretical models for Gamow-Teller strengths that are used to calculate electron-capture rates on medium-heavy nuclei, which play important roles in astrophysical phenomena. Many of the relevant nuclei are unstable. However, a good general probe for performing charge-exchange reactions in inverse kinematics in the (n,p) reaction has not yet been established. The (d,2He) reaction in inverse kinematics is being developed as a potential candidate for this probe. This method uses the Active-Target Time Projection Chamber (AT-TPC) to detect the two protons from the unbound 2He system, and the S800 spectrograph to detect the heavy recoil. The feasibility of this method is demonstrated through Monte-Carlo simulations. The ATTPCROOTv2 code is the framework which allows for simulation of reactions within the AT-TPC as well as digitization of the results in the pad planes for realistic simulated data. The analysis performed on this data using the ATTPCROOTv2 code shows the techniques that can be done in experiment to track the scattered protons through the detector using Random Sampling Consensus (RANSAC) algorithms.
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Title: Improving the representation of irrigation and groundwater in global land surface models to advance the understanding of hydrology-human-climate interactions
Creator: Felfelani, Farshid
Date: 2019
Collection: Electronic Theses & Dissertations
Description: Hydrological models and satellite observations have been widely used to study the variations in the Earth's hydrology and climate over multitude of scales, especially in relation to natural and human-induced changes in the terrestrial water cycle. Yet, both satellite products and model results suffer from inherent uncertainties, calling for the need to improve the representation of critical processes in the models and to make a combined use of satellite data and models to examine the...
Show moreHydrological models and satellite observations have been widely used to study the variations in the Earth's hydrology and climate over multitude of scales, especially in relation to natural and human-induced changes in the terrestrial water cycle. Yet, both satellite products and model results suffer from inherent uncertainties, calling for the need to improve the representation of critical processes in the models and to make a combined use of satellite data and models to examine the variations in the terrestrial hydrology. The representation of irrigation and groundwater-two major hydrologic processes with complex reciprocal interplay-in large-scale hydrological models is rather poorly parameterized and heavily simplified, hindering our ability to realistically simulate groundwater-human-climate interactions. This dissertation advances the physical basis for irrigation and groundwater parameterizations in global land surface models, leveraging the potential of emerging satellite data (i.e., data from GRACE and SMAP satellite missions) toward a more realistic quantification of the impacts of human activities on the hydrological cycle. A comprehensive global analysis is developed to examine the historical spatial patterns and long-term temporal response, i.e., the terrestrial water storage (TWS), of two models to natural and human-induced drivers. Human-induced changes in TWS are then quantified in the highly managed global regions to identify the uncertainties arising from a simplistic representation of irrigation and groundwater. The potential of improving irrigation representation in the Community Land Model version 4.5 (CLM4.5) is then investigated by assimilating the soil moisture data from SMAP satellite mission using 1-D Kalman Filter assimilation approach. The new irrigation scheme is then tested over the heavily irrigated central U.S. Next, the existing groundwater module of CLM5 is broadly evaluated over conterminous U.S. and a new prognostic groundwater module is implemented in CLM5 to account for lateral groundwater flow, pumping, and conjunctive water use for irrigation. In particular, an explicit parameterization for the steady-state well equation is introduced for the first time in large-scale hydrological modeling. Finally, the impacts of climate change on global TWS variabilities and the implications on sea level change are examined for the entire 21st century using multi-model hydrological simulations. The key findings and conclusions from the aforementioned multi-scale analysis and model developments are: (1) in terms of TWS, notable differences exist not only between simulations of hydrological models and GRACE but also among different GRACE products, therefore, TWS variations from a single model cannot be reliably used for global analyses; (2) these differences significantly increase in projections of TWS under climate change, however, models agree in sign of change for most global areas; (3) TWS is expected to decline in many regions in southern hemisphere, but increase in northern high latitudes, projected to accelerate sea level rise by the mid- and late-21st century; (4) constraining the target soil moisture in CLM4.5 using SMAP data assimilation with 1-D Kalman Filter reduces the bias in the simulated irrigation water by up to 60% on average, improving irrigation and soil moisture simulations in CLM4.5; (5) the new groundwater model significantly improves the simulation of groundwater level change and promisingly captures most of the hotspots of groundwater depletion across the U.S. overexploited aquifers; and (6) the simulation with the lateral groundwater flow substantially enhances the TWS trends relative to the default CLM5. These results and findings could provide a basis for improved large-scale irrigation and groundwater modeling and improve our understanding of hydrology-human-climate interactions.
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Title: Calibration of optical see through head mounted displays for augmented reality
Creator: Zhou, Ji
Date: 2007
Collection: Electronic Theses & Dissertations

Title: Parallel discrete event simulation and its application on logic simulation
Creator: Xu, Jinsheng
Date: 2002
Collection: Electronic Theses & Dissertations

Title: On the evolution of mutation bias in digital organisms
Creator: Rupp, Matthew
Date: 2011
Collection: Electronic Theses & Dissertations
Description: Mutation is one of the primary drivers of genetic change. In this work I study mutation biases, which are sets of different genetic-state inflow probabilities. Mutation biases have the potential to change the composition of genomes over time, leading to divergent short- and long-term evolutionary outcomes. I use digital organisms, self-replicating computer programs, to explore whether or not mutation biases are capable of altering the long-term adaptive behavior of populations; whether...
Show moreMutation is one of the primary drivers of genetic change. In this work I study mutation biases, which are sets of different genetic-state inflow probabilities. Mutation biases have the potential to change the composition of genomes over time, leading to divergent short- and long-term evolutionary outcomes. I use digital organisms, self-replicating computer programs, to explore whether or not mutation biases are capable of altering the long-term adaptive behavior of populations; whether mutation biases can be competitive traits; and whether mutation biases can evolve. I find that mutation biases can alter the long-term adaptive behavior of mutation bias-obligate populations in terms of both mean fitness and complex trait evolution. I also find that mutation biases can compete against one another under a variety of conditions, meaning mutation bias can selectable over relatively-short periods of time. The competitive success of a mutation bias does not always depend upon the presence of beneficial mutations, implicating an increase in the probability of neutral mutations as a sufficient mechanism for bias selection. Finally, I demonstrate that by giving organisms a mutable mutation bias allele, populations preferentially evolve to possess specific biases over others. Overall, this work shows that mutation bias can act as a selectable trait, influencing the evolution of populations with regard to both their internal-genetic and external environments.
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Title: Evaluation of calibration for optical see-through augmented reality systems
Creator: McGarrity, Erin Scott
Date: 2001
Collection: Electronic Theses & Dissertations

Title: Integration of topological data analysis and machine learning for small molecule property predictions
Creator: Wu, Kedi
Date: 2018
Collection: Electronic Theses & Dissertations
Description: Accurate prediction of small molecule properties is of paramount importance to drug design and discovery. A variety of quantitative properties of small molecules has been studied in this thesis. These properties include solvation free energy, partition coefficient, aqueous solubility, and toxicity endpoints. The highlight of this thesis is to introduce an algebraic topology based method, called element specific persistent homology (ESPH), to predict small molecule properties. Essentially ESPH...
Show moreAccurate prediction of small molecule properties is of paramount importance to drug design and discovery. A variety of quantitative properties of small molecules has been studied in this thesis. These properties include solvation free energy, partition coefficient, aqueous solubility, and toxicity endpoints. The highlight of this thesis is to introduce an algebraic topology based method, called element specific persistent homology (ESPH), to predict small molecule properties. Essentially ESPH describes molecular properties in terms of multiscale and multicomponent topological invariants and is different from conventional chemical and physical representations. Based on ESPH and its modified version, element-specific topological descriptors (ESTDs) are constructed. The advantage of ESTDs is that they are systematical, comprehensive, and scalable with respect to molecular size and composition variations, and are readily suitable for machine learning methods, rendering topological learning algorithms. Due to the inherent correlation between different small molecule properties, multi-task frameworks are further employed to simultaneously predict related properties. Deep neural networks, along with ensemble methods such as random forest and gradient boosting trees, are used to develop quantitative predictive models. Physical based molecular descriptors and auxiliary descriptors are also used in addition to ESTDs. As a result, we obtain state-of-the-art results for various benchmark data sets of small molecule properties. We have also developed two online servers for predicting properties of small molecules, TopP-S and TopTox. TopP-S is a software for topological learning predictions of partition coefficient and aqueous solubility, and TopTox is a software for computing element-specific tological descriptors (ESTDs) for toxicity endpoint predictions. They are available at http://weilab.math.msu.edu/TopP-S/ and http://weilab.math.msu.edu/TopTox/, respectively.
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Title: Shelf life estimation of USP 10mg Prednisone calibrator tablets in relation to dissolution & new windows-based shelf life computer program
Creator: Yoon, Seungyil
Date: 2000
Collection: Electronic Theses & Dissertations

Title: Network-wide traffic state analysis : estimation, characterization, and evaluation
Creator: Saedi Germi, Ramin
Date: 2020
Collection: Electronic Theses & Dissertations
Description: The Network Fundamental Diagram (NFD) represents dynamics of traffic flow at the network level. It is exploited to design various network-wide traffic control and pricing strategies to improve mobility and mitigate congestion. This study presents a framework to estimate NFD and incorporates it for three specific applications in large-scale urban networks. Primarily, a resource allocation problem is formulated to find the optimal location of fixed measurement points and optimal sampling of...
Show moreThe Network Fundamental Diagram (NFD) represents dynamics of traffic flow at the network level. It is exploited to design various network-wide traffic control and pricing strategies to improve mobility and mitigate congestion. This study presents a framework to estimate NFD and incorporates it for three specific applications in large-scale urban networks. Primarily, a resource allocation problem is formulated to find the optimal location of fixed measurement points and optimal sampling of probe trajectories to estimate NFD accounting for limited resources for data collection, network traffic heterogeneity and asymmetry in OD demand in a real-world network. Using a calibrated simulation-based dynamic traffic assignment model of Chicago downtown network, a successful application of the proposed model and solution algorithm to estimate NFD is presented. The proposed model, then, is extended to take into account the stochasticity of day-to-day fluctuations of OD demand in NFD estimation.Three main applications of NFD are also shown in this research: network-wide travel time reliability estimation, network-wide emission estimation, and real-time traffic state estimation for heterogenous networks experiencing inclement weather impact. The main objective of the travel time reliability estimation application is to improve estimation of this network-wide measure of effectiveness using network partitioning. To this end, a heterogeneous large-scale network is partitioned into homogeneous regions (clusters) with well-defined NFDs using directional and non-directional partitioning approaches. To estimate the network travel time reliability, a linear relationship is estimated that relates the mean travel time with the standard deviation of travel time per unit of distance at the network level. Partitioning and travel time reliability estimation are conducted for both morning and afternoon peak periods to demonstrate the impacts of travel demand pattern variations.This study also proposes a network-level emission modeling framework via integrating NFD properties with an existing microscopic emission model. The NFDs and microscopic emission models are estimated using microscopic and mesoscopic traffic simulation tools at different scales for various traffic compositions. The major contribution is to consider heterogenous vehicle types with different emission generation rates in the network-level model. Non-linear and support vector regression models are developed using simulated trajectory data of thirteen simulated scenarios. The results show a satisfactory calibration and successful validation with acceptable deviations from underlying microscopic emission model, regardless of the simulation tool that is used to calibrate the network-level emission model.Finally, the NFD application for real-time traffic state estimation in a network experiencing inclement weather conditions is explored. To this end, the impacts of weather conditions on the NFD and travel time reliability relation are illustrated through a scenario-based analysis using traffic simulation. Then, the real-time traffic state prediction framework in the literature is adjusted to capture weather conditions as a key parameter. The extended Kalman filter algorithm is employed as an estimation engine to predict the real-time traffic state. The results highlight the importance of considering weather conditions in the traffic state prediction model.
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Title: Still learning : introducing the learning transfer model, a formal model of transfer
Creator: Olenick, Jeffrey David
Date: 2020
Collection: Electronic Theses & Dissertations
Description: Although training has been a key topic of study in organizational psychology for over a century, a century which has seen great progress in our understanding of what a quality training program entails, a substantial gap persists between what is trained and what is transferred to the job. Reduction of the training-transfer gap has driven research on transfer-focused interventions which have proven effective. However, although we know a lot regarding how individuals learn new material, and...
Show moreAlthough training has been a key topic of study in organizational psychology for over a century, a century which has seen great progress in our understanding of what a quality training program entails, a substantial gap persists between what is trained and what is transferred to the job. Reduction of the training-transfer gap has driven research on transfer-focused interventions which have proven effective. However, although we know a lot regarding how individuals learn new material, and correlates of whether they transfer that material back to their work environment, we know very little about how individuals go about choosing whether to apply their new knowledge to, typically, previously-encountered situations in their work environment and how those decisions unfold over time. Improving our knowledge regarding how individuals transfer learned material will lead to new insights on how to support the transfer of organizationally directed training, or any learning event, back to the work environment. Thus, the present paper introduces a formal model of the transfer process, the Learning Transfer Model (LTM), which proposes a process for how transfer unfolds over time and gives rise to many of the findings we have accumulated in the transfer literature. This is accomplished by reconceptualizing transfer as its own learning process which is affected by the dual nature of human cognitive systems, the learner's social group, and their self-regulatory processes. The LTM was then instantiated in a series of computational models for virtual experimentation. Findings and implications for research and practice are discussed throughout.
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