The flagellar motor of Escherichia coli is a bidirectional rotary nano-motor, powered by a transmembrane influx of protons. The maximum speed of rotation is about 300 Hz. The motor rotates either counterclockwise (CCW) or clockwise (CW) and the rotation direction is controlled by a chemotactic protein, CheY-P. Despite extensive structural and functional studies, precise mechanisms regarding the torque generation and the directional switching processes remain unclear. In this work, a bottom-up strategy is proposed and followed to investigate this motor. This strategy, named as a multiscale modeling approach, integrates various publicly available experimental data and ‘state-of-the-art’ molecular modeling methods to build structural models for the two most important parts of the motor, the C ring and the stator. Starting from the primary sequences of the composition proteins of these two substructures, tertiary structures are predicted by means of comparative modeling or de novo prediction when the comparative modeling is not available. Quaternary structures of these proteins’s complexes are then predicted by data-driven protein-protein docking or multiscale molecular dynamics (MD) simulations. Finally, structural models of the C ring at CCW and CW rotational states are constructed by cryo-EM aided assembling methods (constraint search that is based on the multiscale modeling tools and under the constraint of the EM images). In the case of the stator, its composition proteins, MotA and MotB, are assembled by coarse-grained MD simulations. This is the first molecular model based atomistic details for the stator. A new molecular mechanism for rotational switching is proposed based on the structural models of the C ring and the stator. The two states of the C ring display significant differences in the interfaces among the self-assembled FliMs and the orientations of FliG C-terminal domain. Based on protein docking results, a binding site of CheY-P is identified on FliM which is close to the interfaces of FliMs for self-assembling. Thereby, I propose that the CheY-P binding interferes with the interactions between neighboring FliM proteins, and thus, induces ~65° rotation of the FliGc domain with respect to FliM. Subsequently, the interactions between the stator and FliGc domains are altered and the rotation direction is changed. Furthermore, a mechanism accounting for the directed rotation of the flagellar motor is proposed based on systematic MD studies on the dynamics of FliGc. It is found that the C-terminal subdomain of FliGc is capable of rotating by ~180° with respect to the N-terminal subdomain of FliGc. If this dynamics is considered in the framework of the whole C ring, the rotation of the C-terminal subdomain of FliGc exhibits an asymmetric feature. As a result, the C ring is decorated with asymmetric teeth on the outer periphery and hence similar with Feynman’s ratchet. The preference of the motor in CCW rotation or in CW rotation is then explained based on the Feynman’s ratchet model. / published_or_final_version / Chemistry / Doctoral / Doctor of Philosophy

Organic electronic materials, possessing conjugated π-systems, are extensively used as the active layers in organic electronic devices, where they are responsible for charge transport. In this dissertation, we employ a combination of quantum-mechanical and molecular- mechanics methods to provide insight into how molecular structure, orientation, packing, and local molecular environment influence the energetic landscape experienced by an excess charge in these organic electronic materials. We begin with an overview of charge transport in organic electronic materials with a focus on electronic polarization while discussing recent models, followed by a review of the computational methods employed throughout our investigations. We provide a bottom-up approach to the problem of describing electronic polarization by first laying the framework of our model and comparing calculated properties of bulk materials to available experimental data and previously proposed models. We then explore the effects of changing the electronic structure of our systems though perfluorination, and investigate the effects of modifying the crystalline packing through the addition of bulky functional groups while investigating how the non-bonded interactions between molecular neighbors change in different packing motifs. As interfaces are common in organic electronics and important processes such as charge transport and charge separation occur at these interfaces, we model organic-vacuum and organic-organic interfaces to determine the effect changing the environment from bulk to interface has on the electronic polarization. We first investigate the effects of removing polarizable medium adjacent to the charge carrier and then, by modeling a realistic organic- organic interface in a model solar cell, probe the environment of each molecular site at the interface to gain a more complete understanding of the complex energetic landscape. Finally, we conclude with a study of the non-bonded interactions in linear oligoacene dimers, model π-conjugated materials, to assess the impact of dimer configuration and acene length on the intermolecular interaction energy, and highlight the importance of dispersion and charge penetration to these systems.

No description available.

Recent short pulse (femtosecond) laser experiments have shown the existence of a so called superelastic precursor for short time periods after shock wave formation. The superelastic precursor is characterised as having amplitude far greater than the Hugoniot Elastic limit. This work reviews the current orthotropic thermoelastic plastic-damage model developed at Cranfield University, which includes the ability to model high velocity, shock wave forming impacts. The current model is unable to reproduce the superelastic precursor. Recent methods of looking at plasticity are reviewed and model improvements are suggested to enable the Cranfield model to reproduce superelastic precursor waves. The methods investigated are both dislocation based as it is determined that it is necessary to model deformation on the microscale in order to achieve reproduction of phenomena on the timescales of the early stages of shock wave formation and propagation. The methods investigated are the so-called self-organisation of dislocations and a mobile and immobile dislocation method proposed by Mayer. The plasticity part of the model proposed by Mayer is suggested for further investigation, including implementation into the DYNA 3D hydrocode which contains the current Cranfield model, to numerically asses the models capabilities. Similar, the self-organisation model is put forward for further numerical analysis. Further, calculation of the continuum Cauchy stress using purely atomistic variables is investigated in the form of the virial stress. It is determined that the virial stress calculation is unsuitable for modelling shock waves, however an alternative atomistic stress calculation which is more suited to shock waves is discussed. It is proposed that this stress calculation could be used to investigate the stresses contained within the thin shock front.

<p>When simulating multiscale stochastic differential equations (SDEs) in high-dimensions, separation of timescales and high-dimensionality can make simulations expensive. The computational cost is dictated by microscale properties and interactions of many variables, while interesting behavior often occurs on the macroscale with few important degrees of freedom. For many problems bridging the gap between the microscale and macroscale by direct simulation is computationally infeasible, and one would like to learn a fast macroscale simulator. In this paper we present an unsupervised learning algorithm that uses short parallelizable microscale simulations to learn provably accurate macroscale SDE models. The learning algorithm takes as input: the microscale simulator, a local distance function, and a homogenization scale. The learned macroscale model can then be used for fast computation and storage of long simulations. I will discuss various examples, both low- and high-dimensional, as well as results about the accuracy of the fast simulators we construct, and its dependency on the number of short paths requested from the microscale simulator.</p> / Dissertation

Image processing applications such as image denoising, image segmentation, object detection, object recognition and texture synthesis often require a multi-scale analysis of images. This is useful because different features in the image become prominent at different scales. Traditional imaging models, which have been used for multi-scale analysis of images, have several limitations such as high sensitivity to noise and structural degradation observed at higher scales. Parametric models make certain assumptions about the image structure which may or may not be valid in several situations. Non-parametric methods, on the other hand, are very flexible and adapt to the underlying image structure more easily. It is highly desirable to have effi cient non-parametric models for image analysis, which can be used to build robust image processing algorithms with little or no prior knowledge of the underlying image content. In this thesis, we propose a non-parametric pixel neighbourhood based framework for multi-scale image analysis and apply the model to build image denoising and saliency detection algorithms for the purpose of illustration. It has been shown that the algorithms based on this framework give competitive results without using any prior information about the image statistics.

Quite often, the data we observe can be effectively represented using graphs. The underlying structure of the resulting graph, however, might contain noise and does not always hold constant across scales. With the right tools, we could possibly address these two problems. This thesis focuses on developing the right tools and provides insights in looking at them. Specifically, I study several problems that incorporate network data within the multi-scale framework, aiming at identifying common patterns and differences, of signals over networks across different scales. Additional topics in network denoising and network bootstrapping will also be discussed. The first problem we consider is the connectivity changes in dynamic networks constructed from multiple time series data. Multivariate time series data is often non-stationary. Furthermore, it is not uncommon to expect changes in a system across multiple time scales. Motivated by these observations, we in-corporate the traditional Granger-causal type of modeling within the multi-scale framework and propose a new method to detect the connectivity changes and recover the dynamic network structure. The second problem we consider is how to denoise and approximate signals over a network adjacency matrix. We propose an adaptive unbalanced Haar wavelet based transformation of the network data, and show that it is efficient in approximation and denoising of the graph signals over a network adjacency matrix. We focus on the exact decompositions of the network, the corresponding approximation theory, and denoising signals over graphs, particularly from the perspective of compression of the networks. We also provide a real data application on denoising EEG signals over a DTI network. The third problem we consider is in network denoising and network inference. Network representation is popular in characterizing complex systems. However, errors observed in the original measurements will propagate to network statistics and hence induce uncertainties to the summaries of the networks. We propose a spectral-denoising based resampling method to produce confidence intervals that propagate the inferential errors for a number of Lipschitz continuous net- work statistics. We illustrate the effectiveness of the method through a series of simulation studies.

Agricultural grasslands have replaced native Midwestern prairies in the form of pasture, idle cropland and conservation fields. The condition of these cover types directly and indirectly influences the distribution, variety and productivity of avian populations within these landscapes. CP33 habitat buffers are an incentive-based conservation practice specifically designed to increase upland bird habitat and productivity. Landowners are encouraged to remove row crops from production and return them to early successional grassland habitat along the margin of agricultural fields. However, buffers exhibit a high perimeter-to-area ratio, which may increase negative edge effects, thereby creating sink populations. During the 2013 and 2014 breeding seasons, I assessed grassland bird response to CP33 habitat buffers in southern Illinois. Focal species included the northern bobwhite (Colinus virginianus), dickcissel (Spiza americana), eastern meadowlark (Sturnella magna), field sparrow (Spizella pusilla), indigo bunting (Passerina cyanea), and red-winged blackbird (Agelaius phoeniceus). I used a hierarchical multiscale framework to examine the influence of habitat variables at multiple scales on avian abundance, species richness, and occupancy. I also used this same framework, and logistic exposure modeling, to examine daily survival rates of nests found within CP33 habitat buffers. Multiscale occupancy and logistic exposure models consistently performed better than single-scale models for focal bird species; however, relative importance of local variables and landscape variables differed considerably among focal species. Nest survival rate was not strongly affected by edge effects or edge type. Microhabitat variables were much more influential in predicting nest survival. In my study area, CP33 habitat buffers are unlikely to support source populations for most of the focal grassland bird species I studied. To increase nest survival rates within established CP33 habitat buffers, managers should focus on microhabitat vegetation characteristics. To increase bird occupancy of CP33 habitat buffers in southern Illinois, managers should increase the size of CP33 habitat buffers within a landscape having adequate grassland cover. However, managers should not consider CP33 habitat buffers a panacea for most grassland avian species.

Geopolymer is a novel cementitious material which can be a potential alternative to ordinary Portland cement (OPC) for all practical applications. However, until now research on this revolutionary material is limited mainly to experimental studies, which have the limitations in considering the details of the atomic- and meso-scale structure and atomic scale mechanisms that govern the properties at the macro-scale. Most experimental studies on geopolymer have been conducted focusing only on the macroscopic properties and considering it as a single-phase material. However, research has shown that geopolymer is a composite material consisting of geopolymer binder (GB), unreacted source material, and, in the presence of Ca in the source material, calcium silicate hydrate (CSH). Therefore, in this research, a multiscale/multiphysics modeling approach has been taken to understand geopolymer structure and mechanical properties under varying conditions and at different length scales. First, GB was prepared at the atomic scale using molecular dynamics (MD) simulations with varying Si/Al ratios and water contents within the nano voids. The MD simulated geopolymer structure was validated based on comparison with experiments using X-ray pair distribution function (PDF), infra-red (IR) spectra, coordination of atoms, and density. The results indicate that the highest strength occurs at a Si/Al ratio of 2-3 and the presence of molecular water negatively affects the mechanical properties of GB. The loss of strength for GB with increased water content is linked to the diffusion of Na atoms and subsequent weakening of Al tetrahedra. The GB was also subjected to nanoindentation using MD and the effect of indenter size and loading rate was investigated at an atomic scale. A clear correlation between the indenter size and observed hardness of GB was observed which proves indentation size effects (ISE). Realizing the composite nature of geopolymer, the presence of unreacted and secondary phases such as quartz and CSH in geopolymer was also investigated. To do that, the mechanical properties of GB, the secondary phases and their interfaces was first determined from MD simulations. Using the MD generated properties, a meso-scale model of geopolymer composite was prepared in Peridynamics (PD) framework which considered large particles of GB and secondary phases of nanometers in size which cannot be easily modeled in MD. The meso-scale model provides a larger platform to study geopolymer in the presence of large nano-voids and multiple phases. Results from the PD simulations were directly comparable to experimentally observed mechanical properties. Findings of this study can be directly used in future to construct more advanced and sophisticated models of geopolymer and will be instrumental in designing the synthesis condition for geopolymer with superior mechanical properties.

La représentation de phénomènes de rupture localisée (fissure, décohésion…) rend nécessaire l'utilisation de modèle de plus en plus fin et couteux en calcul. Pour pallier à ce problème de nombreux modèles multi-échelles ont permis de faire cohabiter deux échelles de modèles différents dont l'un plus grossier permet des gains de calculs substantiels. Cependant, coupler deux modèles différents peut entrainer un certain nombre d'incompatibilités qui peuvent générer notamment des réflexions parasites au niveau des interfaces. La première partie de mes travaux traite de l'incompatibilité issue du passage d'un comportement non-local à un comportement local dans un milieu discret. De nouveaux schémas de construction à l'interface sont proposés afin de réduire in fine les phénomènes de réflexions. La deuxième partie aborde une seconde source d'incompatibilité due au changement d'échelle de discrétisation dans un milieu continu local. Une nouvelle méthode de dissipation sélective permet de réduire les phénomènes de réflexions en absorbant les phénomènes microscopiques. Finalement, l'ensemble de ces démarches sera mis en place sur un modèle de béton fibré pour observer l'évolution de phénomène localisé en dynamique explicite. / Representation of localized phenomena like failures or cracks requires models increasingly precise and cpu consuming. To overcome this problem many multiscale models allowed to coexist two scales of different models including a coarser one that enables to gain substantial time calculations. However, coupling two different models may cause a number of incompatibilities that can generate difficulties such as spurious reflections at the interfaces. The first part of my work deals with the incompatibility after the passage of a non-local behavior at a local behavior in a discreet environment. New construction schemes at the interface are proposed to reduce the reflection phenomena. The second part deals with a second source of incompatibility due to the scaling of discretization in a local continuum. A new method for selective dissipation reduces reflection phenomena absorbing microscopic phenomena. Finally, all of these steps will be implemented on a model of fiber-reinforced concrete to observe the evolution of localized phenomenon in explicit dynamics.