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

Title: Validation of two growth and yield models on red pine plantations in Michigan
Creator: Smith-Mateja, Erin E.
Date: 2003
Collection: Electronic Theses & Dissertations

Title: Diverse platform modeling of dynamical systems
Creator: Mitchell, Robert Alex
Date: 1991
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: On the beneficial effects of deleterious mutations
Creator: Covert, Arthur W.
Date: 2010
Collection: Electronic Theses & Dissertations

Title: Mechanisms of adaptation and speciation : an experimental study using artificial life
Creator: Anderson, Carlos Jesus
Date: 2013
Collection: Electronic Theses & Dissertations
Description: Detailed experimental studies in evolutionary biology are sometimes difficult--even with model organisms. Theoretical models alleviate some of these difficulties and often provide clean results, but they cannot always capture the complexity of dynamic evolutionary processes. Artificial life systems are tools that fall somewhere between model organisms and theoretical models that have been successfully used to study evolutionary biology. These systems simulate simple organisms that replicate,...
Show moreDetailed experimental studies in evolutionary biology are sometimes difficult--even with model organisms. Theoretical models alleviate some of these difficulties and often provide clean results, but they cannot always capture the complexity of dynamic evolutionary processes. Artificial life systems are tools that fall somewhere between model organisms and theoretical models that have been successfully used to study evolutionary biology. These systems simulate simple organisms that replicate, acquire random mutations, and reproduce differentially; as a consequence, they evolve naturally (i.e., evolution itself is not simulated). Here I use the software Avida to study several open questions on the genetic mechanisms of adaptation and speciation.In Chapter 1 (p. 13), I investigated whether beneficial alleles during adaptation came from new mutations or standing genetic variation--alleles already present in the population. I found that most beneficial alleles came from standing genetic variation, but new mutations were necessary for long-term evolution. I also found that adaptation from standing genetic variation was faster than from new mutations. Finally, I found that recombination brought together beneficial combinations of alleles from standing genetic variation.In Chapter 2 (p. 31), I investigated the probability of compensatory adaptation vs. reversion. Compensatory adaptation is the fixation of mutations that ameliorate the effects of deleterious mutations while the original deleterious mutations remain fixed. I found that compensatory adaptation was very common, but the window of opportunity for reversion was increased when the initial fitness of the population was high, the population size was large, and the mutation rate was high. The reason that the window of opportunity for reversion was constrained was that negative epistatic interactions with compensatory mutations prevented the revertant from being beneficial to the population.In Chapter 3 (p. 58), I showed experimentally that compensatory adaptation can lead to reproductive isolation (specifically, postzygotic isolation). In addition, I found that the strength of this isolation was independent of the effect size of the original deleterious mutations. Finally, I found that both deleterious and compensatory mutations contribute equally to reproductive isolation.Reproductive isolation between populations often evolves as a byproduct of independent adaptation to new environments, but the selective pressures of these environments may be divergent (`ecological speciation') or uniform (`mutation-order speciation'). In Chapter 4 (p. 75), I compared directly the strength of postzygotic isolation generated by ecological and mutation-order processes with and without migration. I found that ecological speciation generally formed stronger isolation than mutation-order speciation and that mutation-order speciation was more sensitive to migration than ecological speciation.Under the Dobzhansky-Muller model of speciation, hybrid inviability or sterility results from the evolution of genetic incompatibilities (DMIs) between species-specific alleles. This model predicts that the number of pairwise DMIs between species should increase quadratically through time, but the few tests of this `snowball effect' have had conflicting results. In Chapter 5 (p. 101), I show that pairwise DMIs accumulated quadratically, supporting the snowball effect. I found that more complex genetic interactions involved alleles that rescued pairwise incompatibilities, explaining the discrepancy between the expected accumulations of DMIs and observation.
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Title: The Köhler effect : intergroup competition using software-generated partners
Creator: Moss, Omotayo Micheal
Date: 2015
Collection: Electronic Theses & Dissertations
Description: Past research has examined the Köhler motivation gain effect (i.e., when an inferior team member performs better when paired with a more capable partner, from knowledge of his/her individual performance) in an active video game (AVG) using a superior, software-generated partner (SGP). The present investigation examined how incorporating a superior SGP into an AVG would affect an individual’s motivation when competing against one other human/virtual-partner team in an planking competition....
Show morePast research has examined the Köhler motivation gain effect (i.e., when an inferior team member performs better when paired with a more capable partner, from knowledge of his/her individual performance) in an active video game (AVG) using a superior, software-generated partner (SGP). The present investigation examined how incorporating a superior SGP into an AVG would affect an individual’s motivation when competing against one other human/virtual-partner team in an planking competition. Participants (N = 90 college-aged students) were randomly assigned to one of three conditions: individual control, conjunctive partner no competition (PNC), or conjunctive partner with opposing-team competition (PWT) in a 3 (conditions) x 2 (gender) factorial design. Participants performed the first series of five exercises alone, and after a rest period those in the partner conditions were told that they would do the remaining trials with a same-sex SGP whom they could observe during their performance. The partner’s performance was always superior to the participant’s. Participants were also told that they would work with their SGP as a team, and that the team’s score would be defined as the score of the person who stops holding the exercise first. Those in the opposing-team competition condition were also told that they and their virtual partner would be competing against one other human-virtual partner team. A significant motivation gain was observed in all partnered conditions compared to the control, F(2,89) = 15.63, p < .001, but the PNC and PWT groups were not significantly different from each other (p = 0.35). These findings suggest that competing against an opposing team does not ultimately boost the Köhler effect in AVGs.
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Title: Sensitivities of simulated fire-induced flows to fire shape and background wind profile using a cloud-resolving model
Creator: Stageberg, Marshall S.
Date: 2018
Collection: Electronic Theses & Dissertations
Description: Wildland fire behavior can be very difficult to predict because of inherent non-linearities and multi-scale processes associated with fire-atmosphere interactions. Circulations and complex flows in the vicinity of a fire are driven by heat release from the fire. Since extreme conditions in the fire environment make collecting meteorological observations difficult, we employ a high-resolution numerical model to simulate the atmospheric responses to a fire. Specifically, we have chosen Cloud...
Show moreWildland fire behavior can be very difficult to predict because of inherent non-linearities and multi-scale processes associated with fire-atmosphere interactions. Circulations and complex flows in the vicinity of a fire are driven by heat release from the fire. Since extreme conditions in the fire environment make collecting meteorological observations difficult, we employ a high-resolution numerical model to simulate the atmospheric responses to a fire. Specifically, we have chosen Cloud Model 1 (CM1) because it is designed to simulate high resolution, cloud scale processes that are comparable in scale to fire-induced flows. A surface sensible heat flux is added to CM1 to simulate the effect of a fire and the resultant fire-induced circulations and complex flows are examined. Using CM1 allows us to produce simulations with fine spatial and temporal resolution with a detailed representation of the evolution of the fire-atmosphere system. For the purpose of this study, we perform a series of simulations to examine the sensitivity of fire-induced flows to the shape of the simulated fire and to background wind profile. We show how fire shape and the background wind profile affect the intensity and extent of fire-induced perturbations to the lower atmosphere. The results from these numerical simulations, when combined with field observations, help improve our understanding of fire-atmosphere interactions. The results from this study can potentially help fire managers with decision-making when fighting wildland fires.
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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.
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Title: The evolutionary potential of populations on complex fitness landscapes
Creator: Bryson, David Michael
Date: 2012
Collection: Electronic Theses & Dissertations
Description: Evolution is a highly contingent process, where the quality of the solutions produced is affected by many factors. I explore and describe the contributions of three such aspects that influence overall evolutionary potential: the prior history of a population, the type and frequency of mutations that the organisms are subject to, and the composition of the underlying genetic hardware. I have systematically tested changes to a digital evolution system, Avida, measuring evolutionary potential in...
Show moreEvolution is a highly contingent process, where the quality of the solutions produced is affected by many factors. I explore and describe the contributions of three such aspects that influence overall evolutionary potential: the prior history of a population, the type and frequency of mutations that the organisms are subject to, and the composition of the underlying genetic hardware. I have systematically tested changes to a digital evolution system, Avida, measuring evolutionary potential in seven different computational environments ranging in complexity of the underlying fitness landscapes. I have examined trends and general principles that these measurements demonstrate and used my results to optimize the evolutionary potential of the system, broadly enhancing performance. The results of this work show that history and mutation rate play significant roles in evolutionary potential, but the final fitness levels of populations are remarkably stable to substantial changes in the genetic hardware and a broad range of mutation types.
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Title: Using computer simulations to study relativistic heavy ion collisions
Creator: Murray, Joelle
Date: 1998
Collection: Electronic Theses & Dissertations

Title: The quest for active media models : a self-consistent framework for simulating wave propagation in nonlinear systems
Creator: Glosser, Connor Adrian
Date: 2018
Collection: Electronic Theses & Dissertations
Description: This work presents new approaches to simulations of active media at the level of individual particles. Active systems contain internal, nonlinear, processes beyond those of simple scattering systems; thus these new models afford high degrees of fidelity in exploring the underlying physics without recourse to continuum or spatially-averaged approximations.First, I examine the dynamics of microspheres set into motion by ambient acoustic radiation in a fluid described by potential flow in the...
Show moreThis work presents new approaches to simulations of active media at the level of individual particles. Active systems contain internal, nonlinear, processes beyond those of simple scattering systems; thus these new models afford high degrees of fidelity in exploring the underlying physics without recourse to continuum or spatially-averaged approximations.First, I examine the dynamics of microspheres set into motion by ambient acoustic radiation in a fluid described by potential flow in the long-wavelength limit. Variations in the local surface pressure caused by scattering from each microsphere set each microsphere into motion following Newton’s second law. By expanding this pressure in terms of spherical harmonics—natural eigenfunctions of the unretarded radiation kernel—I recover an analytic description of the force on individual microspheres due to an incident waveform. High-order numerical integrations then relate the surface potential on one microsphere to the surface pressure on the others, thereby coupling the microspheres’ trajectories. These simulations predict a dominant translational effect along the direction of propagation of the incident waveform, though they also reveal significant dipolar interactions between microspheres that produce secondary expansions and contractions of the collective microsphere system.Extending my approach from acoustic to electromagnetic systems, I apply it to a collection of quantum dots: “artificial” two-level atoms with a size-dependent energy structure. The optical Maxwell-Bloch equations give the evolution of quantum dots under the influence of electromagnetic fields; this evolution then produces secondary radiation that couples a collection of quantum dots together. In my computational model, I castmy secondary electromagnetic fields in terms of a point-to-point integral operator that accurately recovers both near- and far-field effects. These fields, then, drive a set of implicitly coupled Bloch equations (solved with an exponentially-fitted predictor/corrector scheme) to give the dynamics of the system as a whole. In ensembles of up to 10 000 quantum dots, my model predicts synchronized multiplets of particles that exchange energy, quantum dots that dynamically couple to screen the effect of incident external radiation, localization of the polarization due to randomness and interactions, as well as wavelength-scale regionsof enhanced and suppressed polarization.The remainder of the work uses the same physical quantum dot system while moving towards efficient computer-aided device design. I detail an improved propagation algorithm to reduce the time and space complexity of the simulation dramatically, thereby facilitating rapid analysis of promising device structures. The algorithm makes use of physical and numerical approximations to effect large-scale calculations in reasonable CPU time. A rotating-frame approximation removes high-frequency components in the evolution of the system while simultaneously preserving accurate interference phenomena in space,thereby affording far larger simulation timesteps. Additionally, projecting the source current distribution onto a regular spatial grid makes use of a low-rank approximation to the field propagator to communicate radiation information between distant groups of particles via fast Fourier transforms in a manner reminiscent of fast multipole methods.
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Title: Balancing exploration and exploitation in bottom-up organizational learning contexts
Creator: Walker, Ross Ian
Date: 2018
Collection: Electronic Theses & Dissertations
Description: In order to keep pace with a rapidly changing environment, organizations must navigate a fundamental tension between exploration and exploitation. Over time, organizations often drift toward exploitation of known strengths and established resources, but this tendency can be harmful in a dynamic and competitive landscape. A classic simulation by James March (1991) demonstrated the importance of maintaining some degree of belief heterogeneity in an organization for the sake of long-term...
Show moreIn order to keep pace with a rapidly changing environment, organizations must navigate a fundamental tension between exploration and exploitation. Over time, organizations often drift toward exploitation of known strengths and established resources, but this tendency can be harmful in a dynamic and competitive landscape. A classic simulation by James March (1991) demonstrated the importance of maintaining some degree of belief heterogeneity in an organization for the sake of long-term learning. In March’s lineage, this thesis examines the effects of various exploratory strategies (i.e., individual experimentation, codification frequency, structural modularity, and employee turnover) on organizational learning in a bottom-up, networked, interpersonal learning context. Results demonstrate the complex interdependency of these variables in the exploration/exploitation tradeoff. Exploratory analyses suggest that a small degree of random individual experimentation has a favorable reward-to-risk ratio and that it is preferable to turnover as an exploratory strategy.
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Title: Simulation of batch drying rice
Creator: Chan, Nguyen Kim
Date: 1976
Collection: Electronic Theses & Dissertations

Title: Surface matching and chemical scoring to detect unrelated proteins binding similar small molecules
Creator: Van Voorst, Jeffrey Ryan
Date: 2011
Collection: Electronic Theses & Dissertations
Description: SURFACE MATCHING AND CHEMICAL SCORING TO DETECT UNRELATED PROTEINS BINDING SIMILAR SMALL MOLECULESByJeffrey Ryan Van VoorstHow can one deduce if two clefts or pockets in different protein structures bind the same small molecule if there is no significant sequence or structural similarity between the proteins? Human pattern recognition, based on extensive structural biology or ligand design experience, is the best choice when the number of sites is small. However, to be able to scale to the...
Show moreSURFACE MATCHING AND CHEMICAL SCORING TO DETECT UNRELATED PROTEINS BINDING SIMILAR SMALL MOLECULESByJeffrey Ryan Van VoorstHow can one deduce if two clefts or pockets in different protein structures bind the same small molecule if there is no significant sequence or structural similarity between the proteins? Human pattern recognition, based on extensive structural biology or ligand design experience, is the best choice when the number of sites is small. However, to be able to scale to the thousands of structures in structural databases requires implementing that experience as computational method. The primary advantage of such a computational tool is to be able to focus human expertise on a much smaller set of enriched binding sites.Although a number of tools have been developed for this purpose by many groups [61, 51, 86, 88, 91], to our knowledge, a basic hypothesis remains untested: two proteins that bind the same small molecule have binding sites with similar chemical and shape features, even when the proteins do not share significant sequence or structural similarity. A computational method to compare protein small molecule binding sites based on surface and chemical complementarity is proposed and implemented as a software package named SimSite3D. This method is protein structure based, does not rely on explicit protein sequence or main chain similarities, and does not require the alignment of atomic centers. It has been engineered to provide a detailed search of one fragment site versus a dataset of about 13,000 full ligand sites in 2&ndash4 hours (on one processor core).Several contributions are presented in this dissertation. First, several examples are presented where SimSite3D is able to find significant matches between binding sites that have similar ligand fragments bound but are unrelated in sequence or structure. Second, including the complementarity of binding site molecular surfaces helps to distinguish between sites that share a similar chemical motif, but do not necessarily bind the same molecule. Third, a number of clear examples are provided to illustrate the challenges in comparing binding sites which should be addressed in order for a binding site comparison method to gain widespread acceptance similar to that enjoyed by BLAST[3, 4]. Finally, an optimization method for addressing protein (and small molecule) flexibility in the context of binding site comparisons is presented, prototyped, and tested.Throughout the work, computational models were chosen to strike a delicate balance between achieving sufficient accuracy of alignments, discriminating between accurate and poor alignments, and discriminating between similar and dissimilar sites. Each of these criteria is important. Due to the nature of the binding site comparison problem, each criterion presents a separate challenge and may require compromises to balance performance to achieve acceptable performance in all three categories.At the present, the problem of addressing flexibility when comparing binding site surfaces has not been presented or published by any other research group. In fact, the problem of modeling flexibility to determine correspondences between binding sites is an untouched problem of great importance. Therefore, the final goal of this dissertation is to prototype and evaluate a method that uses inverse kinematics and gradient based optimization to optimize a given objective function subject to allowed protein motions encoded as stereochemical constraints. In particular, we seek to simultaneously maximize the surface and chemical complementarity of two closely aligned sites subject to directed changes in side chain dihedral angles.
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Title: Predictive control of a hybrid powertrain
Creator: Yang, Jie
Date: 2015
Collection: Electronic Theses & Dissertations
Description: Powertrain supervisory control strategy plays an important role in the overall performance of hybrid electric vehicles (HEVs), especially for fuel economy improvement. The supervisory control includes power distribution, driver demand fulfillment, battery boundary management, fuel economy optimization, emission reduction, etc. Developing an optimal control strategy is quite a challenge due to the high degrees of freedom introduced by multiple power sources in the hybrid powertrain. This...
Show morePowertrain supervisory control strategy plays an important role in the overall performance of hybrid electric vehicles (HEVs), especially for fuel economy improvement. The supervisory control includes power distribution, driver demand fulfillment, battery boundary management, fuel economy optimization, emission reduction, etc. Developing an optimal control strategy is quite a challenge due to the high degrees of freedom introduced by multiple power sources in the hybrid powertrain. This dissertation focuses on driving torque prediction, battery boundary management, and fuel economy optimization.For a hybrid powertrain, when the desired torque (driver torque demand) is outside of battery operational limits, the internal combustion (IC) engine needs to be turned on to deliver additional power (torque) to the powertrain. But the slow response of the IC engine, compared with electric motors (EMs), prevents it from providing power (torque) immediately. As a result, before the engine power is ready, the battery has to be over-discharged to provide the desired powertrain power (torque). This dissertation presents an adaptive recursive prediction algorithm to predict the future desired torque based on past and current vehicle pedal positions. The recursive nature of the prediction algorithm reduces the computational load significantly and makes it feasible for real-time implementation. Two weighting coefficients are introduced to make it possible to rely more on the data newly sampled and avoid numerical singularity. This improves the prediction accuracy greatly, and also the prediction algorithm is able to adapt to different driver behaviors and driving conditions.Based on the online-predicted desired torque and its error variance, a stochastic predictive boundary management strategy is proposed in this dissertation. The smallest upper bound of future desired torque for a given confidence level is obtained based on the predicted desired torque and prediction error variance and it is used to determine if the engine needs to be proactively turned on. That is, the engine can be ready to provide power for the “future” when the actual power (torque) demand exceeds the battery output limits. Correspondingly, the battery over-discharging duration can be reduced greatly, leading to extended battery life and improved HEV performance.To optimize powertrain fuel economy, a model predictive control (MPC) strategy is developed based on the linear quadratic tracking (LQT) approach. The finite horizon LQT control is based on the discrete-time system model obtained by linearizing the nonlinear HEV and only the first step of the solution is applied for current control. This process is repeated for each control step. The effectiveness of the supervisory control strategy is studied and validated in simulations under typical driving cycles based on a forward power split HEV model. The developed MPC-LQT control scheme tracks the predicted desired torque trajectory over the prediction horizon, minimizes the powertrain fuel consumption, maintains the battery state of charge at the desired level, and operates the battery within its designed boundary.
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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.
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Title: Modeling and control of pre-chamber initiated turbulent jet ignition combustion systems
Creator: Song, Ruitao
Date: 2018
Collection: Electronic Theses & Dissertations
Description: "Turbulent jet ignition (TJI) combustion is a promising concept for achieving high thermal efficiency and low NOx (nitrogen oxides) emissions. A control-oriented TJI combustion model with satisfactory accuracy and low computational effort is usually a necessity for optimizing the TJI combustion system and developing the associated model-based TJI control strategies. A control-oriented TJI combustion model was first developed for a rapid compression machine (RCM) configured for TJI combustion....
Show more"Turbulent jet ignition (TJI) combustion is a promising concept for achieving high thermal efficiency and low NOx (nitrogen oxides) emissions. A control-oriented TJI combustion model with satisfactory accuracy and low computational effort is usually a necessity for optimizing the TJI combustion system and developing the associated model-based TJI control strategies. A control-oriented TJI combustion model was first developed for a rapid compression machine (RCM) configured for TJI combustion. A one-zone gas exchange model is developed to simulate the gas exchange process in both pre- and main-combustion chambers. The combustion process is modeled by a two-zone combustion model, where the ratio of the burned and unburned gases flowing between the two combustion chambers is variable. To simulate the influence of the turbulent jets to the rate of combustion in the main-combustion chamber, a new parameter-varying Wiebe function is proposed and used for mass fraction burned (MFB) calculation in the main-combustion chamber. The developed model is calibrated using the Least-Squares fitting and optimization procedure. The RCM model was then extended to a TJI engine model. The combustion process is modeled by a similar two-zone combustion model based on the newly proposed parameter-varying Wiebe function. The gas exchange process is simulated by one-zone model considering piston movement and intake and exhaust processes. Since the engine uses liquid fuel, a pre-chamber air-fuel mixing and vaporization model is developed. And correspondingly, the pre-chamber uses a chemical kinetics based model for combustion rate calculation. The model was validated using the experimental data from a single cylinder TJI engine under different operational conditions, and the simulation results show a good agreement with the experimental data. For control design, a nonlinear state-space engine model with cycle-to-cycle dynamics is developed based on the previous crank-angle-resolved (CAR) TJI engine model. The state-space model successfully linked the combustion processes in the two chambers using the parameter-varying Wiebe function. The validated CAR model is used to calibrate and validate the state-space engine model. The simulation results of the two engine models show a good agreement with each other. Thereafter, a linear-quadratic tracking controller is developed for combustion phasing control. Simulation results are presented and a baseline controller has been implemented on the research engine. Combustion phasing control is very important for internal combustion engines to achieve high thermal efficiency with low engine-out emissions. Traditional open-loop map-based control becomes less favorable in terms of calibration effort, robustness to engine aging, and especially control accuracy for TJI engines due to the increased number of control variables over conventional spark-ignition engines. In this research, a model-based feedforward controller is developed for the TJI engine, and a feedback controller is also designed based on the linear quadratic tracking control with output covariance constraint. Since the TJI main-chamber combustion is influenced by the pre-chamber one, the proposed controller optimizes the control variables in both combustion chambers. The proposed feedforward and feedback controllers show significant performance improvement over a group of baseline controllers through a series of dynamometer engine tests."--Pages ii-iii.
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Title: Multiscale modeling of composite laminates with free edge effects
Creator: Cater, Christopher R.
Date: 2015
Collection: Electronic Theses & Dissertations
Description: ABSTRACTMULTISCALE MODELING OF COMPOSITE LAMINATES WITH FREE EDGE EFFECTSByChristopher R. CaterComposite materials are complex structures comprised of several length scales. In composite laminates, the mechanical and thermal property mismatch between plies of varying orientations results in stress gradients at the free edges of the composites. These free edge stresses can cause initial micro-cracking during manufacture, and are a significant driver of delamination failure. While the...
Show moreABSTRACTMULTISCALE MODELING OF COMPOSITE LAMINATES WITH FREE EDGE EFFECTSByChristopher R. CaterComposite materials are complex structures comprised of several length scales. In composite laminates, the mechanical and thermal property mismatch between plies of varying orientations results in stress gradients at the free edges of the composites. These free edge stresses can cause initial micro-cracking during manufacture, and are a significant driver of delamination failure. While the phenomenon of free edge stresses have been studied extensively at the lamina level, less attention has been focused on the influence of the microstructure on initial cracking and development of progressive damage as a consequence of free edge stresses. This work aimed at revisiting the laminate free edge problem by developing a multiscale approach to investigate the effect of the interlaminar microstructure on free edge cracking. First, a semi-concurrent multiscale modelling approach was developed within the commercial finite element software ABAQUS. An energetically consistent method for implementing free edge boundary conditions within a Computational Homogenization scheme was proposed to allow for micro-scale free edge analysis. The multiscale approach was demonstrated in 2D tests cases for randomly spaced representative volume elements of unidirectional lamina under tensile loading. Second, a 3D multiscale analysis of a [25N/-25N/90N]S composite laminate, known for its vulnerability to free edge cracking, was performed using a two-scale approach: the meso-scale model captured the lamina stacking sequence and laminate loading conditions (mechanical and thermal) and the micro-scale model predicted the local matrix level stresses at the free edge. A one-way coupling between the meso- and micro-scales was enforced through a strain based localization rule, mapping meso-scale strains into displacement boundary conditions onto the micro-scale finite element model. The multiscale analysis procedure was used to investigate the local interlaminar microstructure. The results found that a matrix rich interlaminar interface exhibited the highest free edge stresses in the matrix constituent during thermal cooldown. The results from these investigations assisted in understanding the tendency for pre-cracks during manufacture to occur at ply boundaries at the free edge and the preferential orientation to the ply interfaces. Additionally, analysis of various 90/90 ply interfaces in the thicker N=3 laminate found that the free edge stresses were far more sensitive to the local interlaminar microstructure than the meso-scale stress/strain free edge gradients. The multiscale analysis helped explain the relative insensitivity of free edge pre-cracks to progressive damage during extensional loading observed in experiments. Lastly, the multiscale analysis was extended to the interface between the -25 and 90 degree plies in the [25N/-25N/90N]S laminate. A micro-model representing the dissimilar ply interface was developed, and the homogenized properties through linear perturbation steps were used to update the meso-scale analysis to model the interlaminar region as a unique material. The analysis of micro-scale free edge stresses found that significant matrix stresses only occurred at the fiber/matrix boundary at the 90 degree fibers. The highest stresses were located near the matrix rich interface for both thermal and mechanical loading conditions. The highest matrix stresses in the case of extensional loading of the laminate, however, were found at the interior of the micro-model dissimilar ply micro-model within the -25 degree fibers.
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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.
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