Search results

Title: A multiport approach to modeling and solving large-scale dynamic systems
Creator: Wang, Yanying
Date: 1992
Collection: Electronic Theses & Dissertations

Title: A particle-in-cell method for the simulation of plasmas based on an unconditionally stable wave equation solver
Creator: Wolf, Eric Matthew
Date: 2015
Collection: Electronic Theses & Dissertations
Description: In this dissertation, we present a particle-in-cell method for the simulation of plasmas based on an unconditionally stable solver for the second-order scalar wave equation, that is, a wave equation solver that is not subject to a Courant-Friedrichs-Lewy (CFL) stability restriction, typical of explicit methods, while maintaining a computational cost and code complexity comparable to such explicit solvers. This permits the use of a time step size many times larger than allowed by widely-used...
Show moreIn this dissertation, we present a particle-in-cell method for the simulation of plasmas based on an unconditionally stable solver for the second-order scalar wave equation, that is, a wave equation solver that is not subject to a Courant-Friedrichs-Lewy (CFL) stability restriction, typical of explicit methods, while maintaining a computational cost and code complexity comparable to such explicit solvers. This permits the use of a time step size many times larger than allowed by widely-used explicit methods.
Show less

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.
Show less

Title: Advances in metal ion modeling
Creator: Li, Pengfei (Chemist)
Date: 2016
Collection: Electronic Theses & Dissertations
Description: Metal ions play fundamental roles in geochemistry, biochemistry and materials science.With the tremendous increasing power of the computational resources and largelyinventions of the computational tools, computational chemistry became a more and moreimportant tool to study various chemical processes. Force field modeling strategy, whichis built on physical background, offered a fast way to study chemical systems at atomiclevel. It could offer considerable accuracy when combined with the Monte...
Show moreMetal ions play fundamental roles in geochemistry, biochemistry and materials science.With the tremendous increasing power of the computational resources and largelyinventions of the computational tools, computational chemistry became a more and moreimportant tool to study various chemical processes. Force field modeling strategy, whichis built on physical background, offered a fast way to study chemical systems at atomiclevel. It could offer considerable accuracy when combined with the Monte Carlo orMolecular Dynamics simulation protocol. However, there are various metal ions and it isstill challenging to model them using available force field models. Generally there areseveral models available for modeling metal ions using the force field approach such asthe nonbonded model, the bonded model, the cationic dummy atom model, the combinedmodel, and the polarizable models. Our work concentrated on the nonbonded and bondedmodels, which are widely used nowadays. Firstly, we focused on filling in the blanks ofthis field. We proposed a noble gas curve, which was used to describe the relationshipbetween the van der Waals radius and well depth parameters in the 12-6 Lennard-Jonespotential. By using the noble gas curve and multiple target values (the hydration freeenergy, ion-oxygen distance, coordination number values), we have consistentlyparameterized the 12-6 Lennard-Jones nonbonded model for 63 different ions (including11 monovalent cations, 4 monovalent anions, 24 divalent cations, 18 trivalent cations,and 6 tetravalent cations) combined with three widely used water models (TIP3P, SPC/E, and TIP4PEW). Secondly, we found there is limited accuracy of the 12-6 model, whichmakes it hard to simulate different properties simultaneously for ions with formal chargeequal or larger than +2. By considering the physical origins of the 12-6 model, weproposed a new nonbonded model, named the 12-6-4 LJ-type nonbonded model. Wehave systematically parameterized the 12-6-4 model for 55 different ions (including 11monovalent cations, 4 monovalent anions, 16 divalent cations, 18 trivalent cations, and 6tetravalent cations) in the three water models. It was shown that the 12-6-4 model couldreproduce several properties at the same time, showing remarkable improvement over the12-6 model. Meanwhile, through the usage of a proposed combining rule, the 12-6-4model showed excellent transferability to mixed systems. Thirdly, we have developed theMCPB.py program to facilitate building of the bonded model for metal ion containingsystems, which can largely reduce human efforts. Finally, an application case of ametallochaperone - CusF was shown, and based on the simulations we hypothesized anion transfer mechanism.
Show less

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.
Show less

Title: Calibration of optical see through head mounted displays for augmented reality
Creator: Zhou, Ji
Date: 2007
Collection: Electronic Theses & Dissertations

Title: Computational chemistry : investigations of protein-protein interactions and post-translational modifications to peptides
Creator: Jones, Michael R. (Graduate of Michigan State University)
Date: 2017
Collection: Electronic Theses & Dissertations
Description: Computational chemistry plays a vital role in understanding chemical and physical processes and has been useful in advancing the understanding of reactions in biology. Improper signaling of the nuclear factor-κB (NF-κB) pathway plays a critical role in many inflammatory disease states, including cancer, stroke, and viral infections. Aberrant regulation of this pathway happens upon the signal-induced degradation of the inhibitor of κB (IκB) proteins. The activation of IκB kinase (IKK) subunit...
Show moreComputational chemistry plays a vital role in understanding chemical and physical processes and has been useful in advancing the understanding of reactions in biology. Improper signaling of the nuclear factor-κB (NF-κB) pathway plays a critical role in many inflammatory disease states, including cancer, stroke, and viral infections. Aberrant regulation of this pathway happens upon the signal-induced degradation of the inhibitor of κB (IκB) proteins. The activation of IκB kinase (IKK) subunit β (IKKβ) or NF-κB Inducing Kinase (NIK), initiates this cascade of events. Understanding the structure-property relationships associated with IKKβ and NIK is essential for the development of prevention strategies. Although the signaling pathways are known, how the molecular mechanisms respond to changes in the intracellular microenvironment (i.e., pH, ionic strength, temperature) remains elusive. In this dissertation, computer simulation and modeling techniques were used investigate two protein kinases complexed with either small molecule activators or inhibitors in the active, inactive, and mutant states to correlate structure-property and structure-function relationships as a function of intracellular ionic strength. Additionally, radical-induced protein fragmentation pathways, as a result of reactions with reactive oxygen species, were investigated to yield insight into the thermodynamic preference of the fragmentation mechanisms. Analyses of the relationship between structure-activity and conformational-activity indicate that the protein-protein interactions and the binding of small molecules are sensitive to changes in the ionic strength and that there are several factors that influence the selectivity of peptide backbone cleavage. As there are many computational approaches for predicting physical and chemical properties, several methods were considered for the predictions of protein-protein dissociation, protein backbone fragmentation, and partition coefficients of drug-like molecules.
Show less

Title: Computational modeling of cardiac mechanics : microstructual modeling & pulmonary arterial hypertension
Creator: Xi, Ce
Date: 2019
Collection: Electronic Theses & Dissertations
Description: Heart diseases, which approximately account for 31% of all human mortality every year, are the leading cause of death worldwide. Computational cardiac models have gained increasing popularity and become an indispensable and powerful tool in elucidating the pathological process of different heart diseases. They can be used to estimate important physiological and clinically relevant quantities that are difficult to directly measure in experiments. The broad goals of this thesis were to develop...
Show moreHeart diseases, which approximately account for 31% of all human mortality every year, are the leading cause of death worldwide. Computational cardiac models have gained increasing popularity and become an indispensable and powerful tool in elucidating the pathological process of different heart diseases. They can be used to estimate important physiological and clinically relevant quantities that are difficult to directly measure in experiments. The broad goals of this thesis were to develop 1) a microstructure-based constitutive model of the heart and 2) patient-specific computational models that would ultimately help medical scientists to diagnose and treat heart diseases.Heart diseases such as heart failure with preserved ejection fraction (HFpEF) are characterized by abnormalities of ventricular function that can be attributed to, changes in geometry, impaired myocyte (LV) relaxation, cardiac fibrosis and myocyte passive stiffening. Understanding how LV filling is affected by each of the many microstructural pathological features in heart diseases is very important and may help in the development of appropriate treatments. To address this need, we have developed and validated a microstructure-based computational model of the myocardium to investigate the role of tissue constituents and their ultrastructure in affecting the heart function. The model predicted that the LV filling function is sensitive to the collagen ultrastructure and the load taken up by the tissue constituents varies depending on the LV transmural location. This finding may have implications in the development of new pharmaceutical treatments targeting individual cardiac tissue constituents to normalize LV filling function in HFpEF.Pulmonary arterial hypertension (PAH) is a life-threatening disease characterized by elevated pulmonary artery pressure (PAP) and pulmonary artery vascular resistance, with limited survival rate and can affect patients of all ages. The increased pressure or afterload in the right ventricle (RV) can result in pathological changes in RV mechanics, which are currently not well-understood. To FB01ll this void, we have developed patient-specific computational models to investigate effects of PAH on ventricular mechanics. SpeciFB01cally, we have quantified regional ventricular myoFB01ber stress, myoFB01ber strain, contractility, and passive tissue stiffness in PAH patients, and compare them to those found in age- and gender-matched normal controls. Our results showed that RV longitudinal, circumferential and radial strain were depressed in PAH patients compared with controls; RV passive stiffness increased progressively with the degree of remodeling as indexed by the RV and LV end-diastolic volume ratio (RVEDV/LVEDV); Peak contractility of the RV was found to be strongly correlated, and had an inverse relationship with RVEDV/LVEDV. These results provide the mechanical basis of using RVEDV/LVEDV as a clinical index for delineating disease severity and estimating RVFW contractility in PAH patients.
Show less

Title: Computer simulations of high-energy heavy ion collisions
Creator: Kortemeyer, Gerd
Date: 1997
Collection: Electronic Theses & Dissertations

Title: Data-driven and task-specific scoring functions for predicting ligand binding poses and affinity and for screening enrichment
Creator: Ashtawy, Hossam Mohamed Farg
Date: 2017
Collection: Electronic Theses & Dissertations
Description: Molecular modeling has become an essential tool to assist in early stages of drug discovery and development. Molecular docking, scoring, and virtual screening are three such modeling tasks of particular importance in computer-aided drug discovery. They are used to computationally simulate the interaction between small drug-like molecules, known as ligands, and a target protein whose activity is to be altered. Scoring functions (SF) are typically employed to predict the binding conformation ...
Show moreMolecular modeling has become an essential tool to assist in early stages of drug discovery and development. Molecular docking, scoring, and virtual screening are three such modeling tasks of particular importance in computer-aided drug discovery. They are used to computationally simulate the interaction between small drug-like molecules, known as ligands, and a target protein whose activity is to be altered. Scoring functions (SF) are typically employed to predict the binding conformation (docking task), binary activity label (screening task), and binding affinity (scoring task) of ligands against a critical protein in the disease's pathway. In most molecular docking software packages available today, a generic binding affinity-based (BA-based) SF is invoked for the three tasks to solve three different, but related, prediction problems. The vast majority of these predictive models are knowledge-based, empirical, or force-field scoring functions. The fourth family of SFs that has gained popularity recently and showed potential of improved accuracy is based on machine-learning (ML) approaches. Despite intense efforts in developing conventional and current ML SFs, their limited predictive accuracies in these three tasks have been a major roadblock toward cost-effective drug discovery. Therefore, in this work we present (i) novel task- specific and multi-task SFs employing large ensembles of deep neural networks (NN) and other state-of-the-art ML algorithms in conjunction with (ii) data-driven multi-perspective descriptors (features) for accurate characterization of protein-ligand complexes (PLCs) extracted using our Descriptor Data Bank (DDB) platform.We assess the docking, screening, scoring, and ranking accuracies of the proposed task-specific SFs with DDB descriptors as well as several conventional approaches in the context of the 2007 and 2014 PDBbind benchmark that encompasses a diverse set of high-quality PLCs. Our approaches substantially outperform conventional SFs based on BA and single-perspective descriptors in all tests. In terms of scoring accuracy, we find that the ensemble NN SFs, BsN-Score and BgN-Score, have more than 34% better correlation (0.844 and 0.840 vs. 0.627) between predicted and measured BAs compared to that achieved by X-Score, a top performing conventional SF. We further find that ensemble NN models surpass SFs based on other state-of-the-art ML algorithms. Similar results have been obtained for the ranking task. Within clusters of PLCs with different ligands bound to the same target protein, we find that the best ensemble NN SF is able to rank the ligands correctly 64.6% of the time compared to 57.8% obtained by X-Score. A substantial improvement in the docking task has also been achieved by our proposed docking-specific SFs. We find that the docking NN SF, BsN-Dock, has a success rate of 95% in identifying poses that are within 2 Å RMSD from the native poses of 65 different protein families. This is in comparison to a success rate of only 82% achieved by the best conventional SF, ChemPLP, employed in the commercial docking software GOLD. As for the ability to distinguish active molecules from inactives, our screening-specific SFs showed excellent improvements over the conventional approaches. The proposed SF BsN-Screen achieved a screening enrichment factor of 33.90 as opposed to 19.54 obtained from the best conventional SF, GlideScore, employed in the docking software Glide. For all tasks, we observed that the proposed task-specific SFs benefit more than their conventional counterparts from increases in the number of descriptors and training PLCs. They also perform better on novel proteins that they were never trained on before. In addition to the three task-specific SFs, we propose a novel multi-task deep neural network (MT-Net) that is trained on data from three tasks to simultaneously predict binding poses, affinities, and activity labels. MT-Net is composed of shared hidden layers for the three tasks to learn common features, task-specific hidden layers for higher feature representation, and three outputs for the three tasks. We show that the performance of MT-Net is superior to conventional SFs and competitive with other ML approaches. Based on current results and potential improvements, we believe our proposed ideas will have a transformative impact on the accuracy and outcomes of molecular docking and virtual screening.
Show less

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.
Show less

Title: Designing a package for pharmaceutical tablets in relation to moisture and dissolution
Creator: Yoon, Seungyil
Date: 2003
Collection: Electronic Theses & Dissertations

Title: Diverse platform modeling of dynamical systems
Creator: Mitchell, Robert Alex
Date: 1991
Collection: Electronic Theses & Dissertations

Title: Dynamic simulation of the electrorheological effect in a uniformly distributed electric field
Creator: Cristescu, Nicolae
Date: 2000
Collection: Electronic Theses & Dissertations

Title: Elucidating the evolutionary origins of collective animal behavior
Creator: Olson, Randal S.
Date: 2015
Collection: Electronic Theses & Dissertations
Description: Despite over a century of research, the evolutionary origins of collective animal behavior remain unclear. Dozens of hypotheses explaining the evolution of collective behavior have risen and fallen in the past century, but until recently it has been difficult to perform controlled behavioral evolution experiments to isolate these various hypotheses and test their individual effects. In this dissertation, I outline a relatively new method using digital models of evolution to perform controlled...
Show moreDespite over a century of research, the evolutionary origins of collective animal behavior remain unclear. Dozens of hypotheses explaining the evolution of collective behavior have risen and fallen in the past century, but until recently it has been difficult to perform controlled behavioral evolution experiments to isolate these various hypotheses and test their individual effects. In this dissertation, I outline a relatively new method using digital models of evolution to perform controlled behavioral evolution experiments. In particular, I use these models to directly explore the evolutionary consequence of the selfish herd, predator confusion, and the many eyes hypotheses, and demonstrate how the models can lend key insights useful to behavioral biologists, computer scientists, and robotics researchers. This dissertation lays the groundwork for the experimental study of the hypotheses surrounding the evolution of collective animal behavior, and establishes a path for future experiments to explore and disentangle how the various hypothesized benefits of collective behavior interact over evolutionary time.
Show less

Title: Empirical analysis of the effects of decision type and control over data access and model access on user preference for modeling environments
Creator: Dawson, Margaret (Margaret Leigh)
Date: 1988
Collection: Electronic Theses & Dissertations

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: Evaluation of calibration for optical see-through augmented reality systems
Creator: McGarrity, Erin Scott
Date: 2001
Collection: Electronic Theses & Dissertations

Title: Evaluation of the impacts of a simulated irrigation withdrawal on the habitat and populations of brook trout and benthic macroinvertebrates in Hunt Creek, Michigan
Creator: Baker, Edward Allen
Date: 1995
Collection: Electronic Theses & Dissertations

Title: Evolutionary dynamics of digitized organizational routines
Creator: Liu, Peng
Date: 2013
Collection: Electronic Theses & Dissertations
Description: This dissertation explores the effects of increased digitization on the evolutionary dynamics of organizational routines. Do routines become more flexible, or more rigid, as the mix of digital technologies and human actors changes? What are the mechanisms that govern the evolution of routines? The dissertation theorizes about the effects of increased digitization on path dependence and interdependence mechanisms, and therefore extends current theory on the evolutionary dynamics of...
Show moreThis dissertation explores the effects of increased digitization on the evolutionary dynamics of organizational routines. Do routines become more flexible, or more rigid, as the mix of digital technologies and human actors changes? What are the mechanisms that govern the evolution of routines? The dissertation theorizes about the effects of increased digitization on path dependence and interdependence mechanisms, and therefore extends current theory on the evolutionary dynamics of organizational routines by taking into account the effects of three basic phenomena: digitization, path dependence and interdependence.In this dissertation, I use computer-based simulation, grounded with data collected in field interviews, to model the evolution of routines. More specifically, this dissertation models routines as networks of action that are subject to an evolutionary process of random variation and selective retention. To assess the evolution of routine, I introduce the idea of evolutionary trajectory, which is defined as the product of the magnitude of change and the direction of change in the networks of action.The dissertation also addresses a foundational issue in the literature on organizational routines. Routines are generally believed to remain stable due to path dependence. An alternative explanation is that routines may be stable due to interdependence among actions, which tends to constrain the sequence in which actions can occur. I have developed a simulation that allows meto test the relative importance of these factors, a question that has never been addressed. By addressing this fundamental issue, I provide a deeper, theory driven explanation of the effects of digitization.
Show less

MSU Libraries

Digital Repository

Enabled Filters

Pages

Pages

MSU Libraries

Digital Repository

You are here

Search results

Pages

Pages