Dissertations / Theses on the topic 'Network Dynamics Simulation'
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Georgieva, Kristina Boyanova. "Boolean network simulation for exploring the dynamics of industrial networks." Thesis, Lancaster University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.289295.
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Dickson, Scott M. "Stochastic neural network dynamics : synchronisation and control." Thesis, Loughborough University, 2014. https://dspace.lboro.ac.uk/2134/16508.
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Biological brains exhibit many interesting and complex behaviours. Understanding of the mechanisms behind brain behaviours is critical for continuing advancement in fields of research such as artificial intelligence and medicine. In particular, synchronisation of neuronal firing is associated with both improvements to and degeneration of the brain's performance; increased synchronisation can lead to enhanced information-processing or neurological disorders such as epilepsy and Parkinson's disease. As a result, it is desirable to research under which conditions synchronisation arises in neural networks and the possibility of controlling its prevalence. Stochastic ensembles of FitzHugh-Nagumo elements are used to model neural networks for numerical simulations and bifurcation analysis. The FitzHugh-Nagumo model is employed because of its realistic representation of the flow of sodium and potassium ions in addition to its advantageous property of allowing phase plane dynamics to be observed. Network characteristics such as connectivity, configuration and size are explored to determine their influences on global synchronisation generation in their respective systems. Oscillations in the mean-field are used to detect the presence of synchronisation over a range of coupling strength values. To ensure simulation efficiency, coupling strengths between neurons that are identical and fixed with time are investigated initially. Such networks where the interaction strengths are fixed are referred to as homogeneously coupled. The capacity of controlling and altering behaviours produced by homogeneously coupled networks is assessed through the application of weak and strong delayed feedback independently with various time delays. To imitate learning, the coupling strengths later deviate from one another and evolve with time in networks that are referred to as heterogeneously coupled. The intensity of coupling strength fluctuations and the rate at which coupling strengths converge to a desired mean value are studied to determine their impact upon synchronisation performance. The stochastic delay differential equations governing the numerically simulated networks are then converted into a finite set of deterministic cumulant equations by virtue of the Gaussian approximation method. Cumulant equations for maximal and sub-maximal connectivity are used to generate two-parameter bifurcation diagrams on the noise intensity and coupling strength plane, which provides qualitative agreement with numerical simulations. Analysis of artificial brain networks, in respect to biological brain networks, are discussed in light of recent research in sleep theory.
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Corradini, Daniele. "Computational study of resting state network dynamics." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14524/.
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Lo scopo di questa tesi è quello di mostrare, attraverso una simulazione con il software The Virtual Brain, le più importanti proprietà della dinamica cerebrale durante il resting state, ovvero quando non si è coinvolti in nessun compito preciso e non si è sottoposti a nessuno stimolo particolare. Si comincia con lo spiegare cos’è il resting state attraverso una breve revisione storica della sua scoperta, quindi si passano in rassegna alcuni metodi sperimentali utilizzati nell’analisi dell’attività cerebrale, per poi evidenziare la differenza tra connettività strutturale e funzionale. In seguito, si riassumono brevemente i concetti dei sistemi dinamici, teoria indispensabile per capire un sistema complesso come il cervello. Nel capitolo successivo, attraverso un approccio ‘bottom-up’, si illustrano sotto il profilo biologico le principali strutture del sistema nervoso, dal neurone alla corteccia cerebrale. Tutto ciò viene spiegato anche dal punto di vista dei sistemi dinamici, illustrando il pionieristico modello di Hodgkin-Huxley e poi il concetto di dinamica di popolazione. Dopo questa prima parte preliminare si entra nel dettaglio della simulazione. Prima di tutto si danno maggiori informazioni sul software The Virtual Brain, si definisce il modello di network del resting state utilizzato nella simulazione e si descrive il ‘connettoma’ adoperato. Successivamente vengono mostrati i risultati dell’analisi svolta sui dati ricavati, dai quali si mostra come la criticità e il rumore svolgano un ruolo chiave nell'emergenza di questa attività di fondo del cervello. Questi risultati vengono poi confrontati con le più importanti e recenti ricerche in questo ambito, le quali confermano i risultati del nostro lavoro. Infine, si riportano brevemente le conseguenze che porterebbe in campo medico e clinico una piena comprensione del fenomeno del resting state e la possibilità di virtualizzare l’attività cerebrale.
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Quijada, Sergio. "A HYBRID SIMULATION METHODOLOGY TO EVALUATE NETWORK CENTRICDECISION MAKING UNDER EXTREME EVENTS." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2348.
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Currently the network centric operation and network centric warfare have generated a new area of research focused on determining how hierarchical organizations composed by human beings and machines make decisions over collaborative environments. One of the most stressful scenarios for these kinds of organizations is the so-called extreme events. This dissertation provides a hybrid simulation methodology based on classical simulation paradigms combined with social network analysis for evaluating and improving the organizational structures and procedures, mainly the incident command systems and plans for facing those extreme events. According to this, we provide a methodology for generating hypotheses and afterwards testing organizational procedures either in real training systems or simulation models with validated data. As long as the organization changes their dyadic relationships dynamically over time, we propose to capture the longitudinal digraph in time and analyze it by means of its adjacency matrix. Thus, by using an object oriented approach, three domains are proposed for better understanding the performance and the surrounding environment of an emergency management organization. System dynamics is used for modeling the critical infrastructure linked to the warning alerts of a given organization at federal, state and local levels. Discrete simulations based on the defined concept of "community of state" enables us to control the complete model. Discrete event simulation allows us to create entities that represent the data and resource flows within the organization. We propose that cognitive models might well be suited in our methodology. For instance, we show how the team performance decays in time, according to the Yerkes-Dodson curve, affecting the measures of performance of the whole organizational system. Accordingly we suggest that the hybrid model could be applied to other types of organizations, such as military peacekeeping operations and joint task forces. Along with providing insight about organizations, the methodology supports the analysis of the "after action review" (AAR), based on collection of data obtained from the command and control systems or the so-called training scenarios. Furthermore, a rich set of mathematical measures arises from the hybrid models such as triad census, dyad census, eigenvalues, utilization, feedback loops, etc., which provides a strong foundation for studying an emergency management organization. Future research will be necessary for analyzing real data and validating the proposed methodology.Ph.D.
Other
Engineering and Computer Science
Modeling and Simulation
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Rhomberg, Patrick. "On the parallelization of network diffusion models." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5831.
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In this thesis, we investigate methods by which discrete event network diffusion simulators may execute without the restriction of lockstep or near lockstep synchronicity. We develop a discrete event simulator that allows free clock drift between threads, develop a differential equations model to approximate communication cost of such a simulator, and propose an algorithm by which we leverage information gathered in the natural course of simulation to redistribute agents to parallel threads such that the burden of communication is lowered during future replicates.
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Tiefert, Brian E. "Modeling control channel dynamics of the SAAM Architecture using the NS network simulation tool." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1999. http://handle.dtic.mil/100.2/ADA371825.
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Dewan, Leslie. "Molecular dynamics simulation and topological analysis of the network structure of actinide-bearing materials." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/86266.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Nuclear Science and Engineering, 2013.Cataloged from PDF version of thesis.
Includes bibliographical references.
Actinide waste production and storage is a complex problem, and a whole-cycle approach to actinide management is necessary to minimize the total volume of waste. In this dissertation, I examine three actinide-bearing materials relevant to both the front end and back end of the nuclear fuel cycle: light water reactor (LWR) spent fuel stored in a crystalline ceramic medium (zircon), LWR spent fuel stored in a glassy medium (alkali borosilicate glass), and three molten salt systems (LiF-BeF2, LiF-ThF4 , and LiF-UF4). I model these materials using molecular dynamics (MD) simulations, and then perform a range of material-dependent analyses - including structural evaluation, species segregation, solubility limits, and assessment of transport properties - to examine their suitability as actinide-bearing materials. The initial portion of this work focuses on actinide waste storage media, examining the microstructural changes induced in zircon and alkali borosilicate glass doped with uranium. Alpha-decay of the uranium changes the structure of the host material, inducing amorphousness, recrystallization, and microcracking, among other structural changes. My work on actinide waste storage shows the utility of topological methods for quantifying the intermediate-range structure of amorphous systems. In many cases, the intermediate-range structure correlates with larger-scale properties, such as density and viscosity. I then identify three molten salt systems of interest - LiF-BeF2 , LiF-ThF4, and LiUF4 - as a focus for analysis. LiF-BeF2 is a coolant salt, and LiF-ThF4 and LiF-UF4 are fuel salts used on the front end of the nuclear fuel cycle in molten salt reactors (MSRs). MSRs can, in some configurations, achieve extremely high actinide bum-ups. Some molten salt reactors can also be fueled by the actinides in spent fuel produced by LWRs. While MSRs have many advantages, research into new designs often proceeds slowly because of gaps in available experimental data for the molten fuel and coolant salts. I use MD simulations to evaluate the transport properties and structure of these salts, and show that these simulations can be used reliably to augment the existing body of experimental data describing the salts' material properties. Furthermore, I examine how the structure of the salt correlates with its material properties, in particular its viscosity. I use network topology-based algorithms to describe the amorphous structure quantitatively. Network-based topological methods have never before been applied to molten salts, and many new insights can be gained from the analysis.
by Leslie Dewan.
Ph. D.
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Sorichetti, Valerio. "Nanoparticle dynamics in polymer solutions and gels : a simulation approach." Thesis, Montpellier, 2019. http://www.theses.fr/2019MONTS113.
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Les nanocomposites de polymères, systèmes de polymères contenant des nanoparticules (NP), sont des systèmes fascinants qui ont de nombreuses applications en science des matériaux, en biologie et en médecine, mais qui posent également des défis en physique théorique. L'un des problèmes fondamentaux de la physique des nanocomposites est de comprendre comment la structure et la dynamique du système dépendent de paramètres clés, tels que la taille et la fraction volumique des NP et la taille typique du maillage polymère. Dans cette thèse, nous utilisons des simulations de dynamique moléculaire pour étudier les propriétés structurelles et dynamiques des NP incorporées dans des nanocomposites de polymères liquides et solides. Nous observons que lorsque des NP faiblement attractives et bien dispersées sont ajoutées à une solution de polymères dense, les polymères et les NP subissent un ralentissement dynamique. Nous trouvons que, en accord qualitatif avec les expériences, ce ralentissement dynamique est capturé par un paramètre de confinement sous la forme h/λ, où h est la distance moyenne entre les surfaces des NP voisines (interparticle distance). Nous pouvons montrer que pour les NP lambda peut être interprété comme le rayon hydrodynamique des NP, alors que pour les polymères il se comporte comme une échelle de longueur de coopérativité. En simulant des réseaux de polymères polydispersés désordonnés contenant des NP purement répulsives, nous constatons que les petites NP peuvent diffuser librement à travers le maillage d'enchevêtrement, tandis que les grosses NP sont piégées de façon transitoire et ne peuvent se déplacer que par une séquence de ``sauts'' (mouvement de hopping). Nous constatons que le paramètre contrôlant la localisation des NP est le rapport entre le diamètre des NP et la longueur de localisation des crosslinks. Enfin, nous proposons une nouvelle méthode pour caractériser le maillage géométrique dans les liquides de polymères, une quantité importante pour décrire la diffusion des NP dans un milieu désordonnéPolymer nanocomposites, systems of polymers containing nanoparticles (NPs), are fascinating systems that have many applications in material science, biology and medicine, but also pose challenges to theoretical physics. One of the fundamental problems in the physics of nanocomposites is to understand how the structure and dynamics of the system depends on key parameters, such as NP size and volume fraction and the typical size of the polymeric mesh. In this thesis we use molecular dynamics simulations to study the structural and dynamic properties of NPs embedded in liquid and solid polymer-nanocomposites. We observe that when weakly attractive, well dispersed NPs are added to a dense polymer solution, both the polymers and the NPs experience a dynamical slowing down. We find that, in qualitative agreement with experiments, this dynamical slowing down is captured by a confinement parameter in the form h/λ, where h is the average distance between the surfaces of neighboring NPs (interparticle distance). We are able to show that for the NPs, λ can be interpreted as the hydrodynamic radius of the NP, whereas for the polymers it behaves as a cooperativity length scale. Simulating disordered, polydisperse polymer networks containing purely repulsive NPs, we find that small NPs can freely diffuse through the entanglement mesh, while large NPs are transiently trapped and can only move through a sequence of ``jumps'' (hopping motion). We find that the parameter controlling NP localization is the ratio between the NP diameter and the localization length of the crosslinks. Finally, we propose a new method to characterize the geometrical mesh size in polymer liquids, a quantity that is important to describe the diffusion of NP in a disordered medium
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Ahlstrom, Logan Sommers. "Molecular Dynamics Simulation of the Effect of the Crystal Environment on Protein Conformational Dynamics and Functional Motions." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/255200.
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Proteins are dynamic and interconvert between different conformations to perform their biological functions. Simulation methodology drawing upon principles from classical mechanics - molecular dynamics (MD) simulation - can be used to simulate protein dynamics and reconstruct the conformational ensemble at a level of atomic detail that is inaccessible to experiment. We use the dynamic insight achieved through simulation to enhance our understanding of protein structures solved by X-ray crystallography. Protein X-ray structures provide the most important information for structural biology, yet they depict just a single snapshot of the solution ensemble, which is under the influence of the confined crystal medium. Thus, we ask a fundamental question - how well do static X-ray structures represent the dynamic solution state of a protein? To understand how the crystal environment affects both global and local protein conformational dynamics, we consider two model systems. We first examine the variation in global conformation observed in several solved X-ray structures of the λ Cro dimer by reconstructing the solution ensemble using the replica exchange enhanced sampling method, and show that one X-ray conformation is unstable in solution. Subsequent simulation of Cro in the crystal environment quantitatively assesses the strength of packing interfaces and reveals that mutation in the lattice affects the stability of crystal forms. We also evaluate the Cro models solved by nuclear magnetic resonance spectroscopy and demonstrate that they represent unstable solution states. In addition to our studies of the Cro dimer, we investigate the effect of crystal packing on side-chain conformational dynamics through solution and crystal MD simulation of the HIV microbicide Cyanovirin-N. We find that long, polar surface side-chains can undergo a strong reduction in conformational entropy upon incorporation into crystal contacts, which supports the application of surface engineering to facilitate protein crystallization. Finally, we outline a general framework for using network visualization to aid in the functional interpretation of conformational ensembles generated from MD simulation. Our results will enhance the understanding of X-ray data in establishing protein structure-function-dynamics relationships.
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Rao, Balappa Shrisha. "Fine structure in cortical connectivity : effects on network dynamics and function Dynamics and orientation selectivity in a cortical model of rodent V1 with excess bidirectional connections Theory of orientation selectivity in random binary networks." Thesis, Sorbonne Paris Cité, 2018. https://wo.app.u-paris.fr/cgi-bin/WebObjects/TheseWeb.woa/wa/show?t=2401&f=17357.
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Pas de résuméThe local cortical network connectivity significantly deviates from a random network, giving rise to fine structure at the neuron-to-neuron level. In this study, we have investigated the effects of these fine structures on network dynamics and function. We have investigated two types of fine structure, namely, excess bidirectionality and feature specific connectivity. The study of the effects of excess bidirectionality was conducted in a conductance-based model of layer 2/3 in rodent V1. Through large scale numerical simulations, we showed that excess bidirectional connections in the inhibitory population leads to slower dynamics. Remarkably, we found that bidirectional connections between inhibitory cells are more efficacious in slowing down the dynamics than those between the excitatory cells. Additionally, bidirectional connections between inhibitory cells increases the trial-to-trial variability, while between the excitatory and inhibitory populations it reduces the variability leading to improved coding efficiency. Our results suggest that the strong reciprocal connections between excitatory and PV+ cells that have been experimentally reported can improve coding efficiency by reducing the signal-to-noise ratio. The second part of this work involved an analytical study of a model of layer 2/3 rodent V1 with binary neurons. In our study, we assumed that neurons in layer 4 were selective to stimuli orientation. Our results account for the changes in tuning properties observed during the critical period in mouse V1. Prior to the critical period, the connectivity between pyramidal neurons in the mouse V1 is non-specific. Following previous studies of spiking networks, we analytically demonstrated that with such connectivity, layer 2/3 neurons in our model develop orientation selectivity. A small fraction of strong feature specific connections between pyramidal cells have been reported in the mouse V1 after the critical period. We showed that, in spite of their small number, such connections can substantially impact the tuning of layer 2/3 cells to orientation: excitatory neurons become more selective and through non-specific global changes in their synaptic strengths, the inhibitory cells become more broadly tuned
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Flanery, Trevor H. "Planning Local and Regional Development: Exploring Network Signal, Sites, and Economic Opportunity Dynamics." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/82907.
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Urban development planning efforts are challenged to enhance coevolving spatial and socioeconomic systems that exist and interact at multiple scales. While network and simulation sciences have created new tools and theories suitable for urban studies, models of development are not yet suitable for local and regional development planning. A case study of the City of Roanoke, Virginia, grounded network development theories of scaling, engagement, and collective perception function, as well as network forms. By advancing urban development network theory, frameworks for urban simulation like agent-based models take more coherent shape. This in turn better positions decision-making and planning practitioners to adapt, transform, or renew local network-oriented development systems, and conceptualize a framework for computational urban development planning for regions and localities.Ph. D.
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Johnson, Tina V. "The Influence of Social Network Graph Structure on Disease Dynamics in a Simulated Environment." Thesis, University of North Texas, 2010. https://digital.library.unt.edu/ark:/67531/metadc33173/.
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The fight against epidemics/pandemics is one of man versus nature. Technological advances have not only improved existing methods for monitoring and controlling disease outbreaks, but have also provided new means for investigation, such as through modeling and simulation. This dissertation explores the relationship between social structure and disease dynamics. Social structures are modeled as graphs, and outbreaks are simulated based on a well-recognized standard, the susceptible-infectious-removed (SIR) paradigm. Two independent, but related, studies are presented. The first involves measuring the severity of outbreaks as social network parameters are altered. The second study investigates the efficacy of various vaccination policies based on social structure. Three disease-related centrality measures are introduced, contact, transmission, and spread centrality, which are related to previously established centrality measures degree, betweenness, and closeness, respectively. The results of experiments presented in this dissertation indicate that reducing the neighborhood size along with outside-of-neighborhood contacts diminishes the severity of disease outbreaks. Vaccination strategies can effectively reduce these parameters. Additionally, vaccination policies that target individuals with high centrality are generally shown to be slightly more effective than a random vaccination policy. These results combined with past and future studies will assist public health officials in their effort to minimize the effects of inevitable disease epidemics/pandemics.
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Schroeder, Florian Alexander Yinkan Nepomuk. "Tensor network states simulations of exciton-phonon quantum dynamics for applications in artifcial light-harvesting." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/275988.
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Light-harvesting in nature is known to work differently than conventional man-made solar cells. Recent studies found electronic excitations, delocalised over several chromophores, and a soft, vibrating structural environment to be key schemes that might protect and direct energy transfer yielding increased harvest efficiencies even under adversary conditions. Unfortunately, testing realistic models of noise assisted transport at the quantum level is challenging due to the intractable size of the environmental wave function. I developed a powerful tree tensor network states (TTNS) method that finds an optimally compressed explicit representation of the combined electronic and vibrational quantum state. With TTNS it is possible to simulate exciton-phonon quantum dynamics from small molecules to larger complexes, modelled as an open quantum system with multiple bosonic environments. After benchmarking the method on the minimal spin-boson model by reproducing ground state properties and dynamics that have been reported using other methods, the vibrational quantum state is harnessed to investigate environmental dynamics and its correlation with the spin system. To enable simulations of realistic non-Born-Oppenheimer molecular quantum dynamics, a clustering algorithm and novel entanglement renormalisation tensors are employed to interface TTNS with ab initio density functional theory (DFT). A thereby generated model of a pentacene dimer containing 252 vibrational normal modes was simulated with TTNS reproducing exciton dynamics in agreement with experimental results. Based on the environmental state, the (potential) energy surfaces, underlying the observed singlet fission dynamics, were calculated yielding unprecedented insight into the super-exchange mediated avoided crossing mechanism that produces ultrafast and high yield singlet fission. This combination of DFT and TTNS is a step towards large scale material exploration that can accurately predict excited states properties and dynamics. Furthermore, application to biomolecular systems, such as photosynthetic complexes, may give valuable insights into novel environmental engineering principles for the design of artificial light-harvesting systems.
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Nagel, Michaela Büschken Joachim. "Flexibilitätsmanagement : ein systemdynamischer Ansatz zur quantitativen Bewertung von Produktionsflexibilität /." Wiesbaden : Dt. Univ.-Verl, 2003. http://www.gbv.de/dms/zbw/366397524.pdf.
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Charlebois, Daniel. "Computational Investigations of Noise-mediated Cell Population Dynamics." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/30339.
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Fluctuations, or "noise", can play a key role in determining the behaviour of living systems. The molecular-level fluctuations that occur in genetic networks are of particular importance. Here, noisy gene expression can result in genetically identical cells displaying significant variation in phenotype, even in identical environments. This variation can act as a basis for natural selection and provide a fitness benefit to cell populations under stress.
This thesis focuses on the development of new conceptual knowledge about how gene expression noise and gene network topology influence drug resistance, as well as new simulation techniques to better understand cell population dynamics. Network topology may at first seem disconnected from expression noise, but genes in a network regulate each other through their expression products. The topology of a genetic network can thus amplify or attenuate noisy inputs from the environment and influence the expression characteristics of genes serving as outputs to the network.
The main body of the thesis consists of five chapters:
1. A published review article on the physical basis of cellular individuality.
2. A published article presenting a novel method for simulating the dynamics of cell populations.
3. A chapter on modeling and simulating replicative aging and competition using an object-oriented framework.
4. A published research article establishing that noise in gene expression can facilitate adaptation and drug resistance independent of mutation.
5. An article submitted for publication demonstrating that gene network topology can affect the development of drug resistance.
These chapters are preceded by a comprehensive introduction that covers essential concepts and theories relevant to the work presented.
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Lu, Xiaonan. "Effects of Transition Metal Oxide and Mixed-Network Formers on Structure and Properties of Borosilicate Glasses." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1404587/.
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First, the effect of transition metal oxide (e.g., V2O5, Co2O3, etc.) on the physical properties (e.g., density, glass transition temperature (Tg), optical properties and mechanical properties) and chemical durability of a simplified borosilicate nuclear waste glass was investigated. Adding V2O5 in borosilicate nuclear waste glasses decreases the Tg, while increasing the fracture toughness and chemical durability, which benefit the future formulation of nuclear waste glasses. Second, structural study of ZrO2/SiO2 substitution in silicate/borosilicate glasses was systematically conducted by molecular dynamics (MD) simulation and the quantitative structure-property relationships (QSPR) analysis to correlate structural features with measured properties. Third, for bioactive glass formulation, mixed-network former effect of B2O3 and SiO2 on the structure, as well as the physical properties and bioactivity were studied by both experiments and MD simulation. B2O3/SiO2 substitution of 45S5 and 55S5 bioactive glasses increases the glass network connectivity, correlating well with the reduction of bioactivity tested in vitro. Lastly, the effect of optical dopants on the optimum analytical performance on atom probe tomography (APT) analysis of borosilicate glasses was explored. It was found that optical doping could be an effective way to improve data quality for APT analysis with a green laser assisted system, while laser spot size is found to be critical for optimum performance. The combined experimental and simulation approach adopted in this dissertation led to a deeper understanding of complex borosilicate glass structures and structural origins of various properties.
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VASCIAVEO, ALESSANDRO. "Computational models and algorithms to solve large-scale problems in Network Biology." Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2667593.
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The acquisition and measurement of biological data have dramatically changed in the recent years. In fact, High-Throughtput Sequencing (HTS) technologies are now able to capture almost all the "blueprints" that encode for life within a organism’s cell, in a parallel and rapid manner. This newly and cheaper data enabled a whole novel ensemble of approaches to unravel the mechanisms that regulate the processes in living cells.
Unfortunately, these different sources of information can easily reach Terabytes of data for one single study. Consequently, in order to integrate and analyze systemically all the different data sources, novel methodologies, algorithms and software are needed to uncover the real benefits of this biological Big Data.
Therefore, in my Ph.D studies I investigated those mathematical and Information Theory models that are well suited for the representation of biological phenomena using network concepts applied to large datasets. Moreover, the main idea that has driven my studies has been about making models understandable to elucidate the mechanisms of cell regulation, rather than using the most recent and very powerful deep learning approaches that may solve the problem but give you back a black box hard to dissect - i.e. making better inferences rather than robust predictions. Thus, whenever it was possible I chose simple models over complex ones.
Ultimately, new models and software tools have been the results of these studies, and either research advances about new conceptual frameworks or their implementation have been published on several international conferences and journals. A full publication list is in the conclusions.
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Rahimi, Mostafa. "Modeling and simulation of vehicle emissions for the reduction of road traffic pollution." Doctoral thesis, Università degli studi di Trento, 2023. https://hdl.handle.net/11572/365449.
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The transportation sector is responsible for the majority of airborne particles and global energy consumption in urban areas. Its role in generating air pollution in urban areas is even more critical, as many visitors, commuters and citizens travel there daily for various reasons. Emissions released by transport fleets have an exhaust (tailpipe) and a non-exhaust (brake wears ) origin. Both exhaust and non-exhaust airborne particles can have destructive effects on the human cardiovascular and respiratory systems and even lead to premature deaths. This dissertation aims to estimate the amount of exhaust and brake emissions in a real case study by proposing an innovative methodology. For this purpose, different levels of study have been introduced, including the subsystem level, the system level, the environmental level and the suprasystem level. To address these levels, two approaches were proposed along with a data collection process. First, a comprehensive field survey was conducted in the area of Buonconsiglio Castle and data was collected on traffic and non-traffic during peak hours. Then, in the first approach, a state-of-the-art simulation-based method was presented to estimate the amount of exhaust emissions generated and the rate of fuel consumption in the case study using the VISSIM traffic microsimulation software and Enviver emission modeler at the suprasystem level. In order to calculate the results under different mobility conditions, a total of 18 scenarios were defined based on changes in vehicle speeds and the share of heavy vehicles (HV%) in the modal split. Subsequently, the scenarios were accurately modelled in the simulation software VISSIM and repeated 30 times with a simulation runtime of three hours. The results of the first approach confirmed the simultaneous effects of considering vehicle speed and HV % on fuel consumption and the amount of exhaust emissions generated. Furthermore, the sensitivity of exhaust emissions and fuel consumption to variations in vehicle speed was found to be much higher than HV %. In other words, the production of NOx and VOC emissions can be increased by up to 20 % by increasing the maximum speed of vehicles by 10 km/h. Conversely, increasing the HV percentage at the same speed does not seem to produce a significant change. Furthermore, increasing the speed from 30 km/h to 50 km/h increased CO emissions and fuel consumption by up to 33%. Similarly, a reduction in speed of 20 or 10 km/h with a 100% increase in HV resulted in a 40% and 27% decrease in exhaust emissions per seat, respectively. In the second approach, a novel methodology was proposed to estimate the number of brake particles in the case study. To achieve this goal, a downstream approach was proposed starting from the suprasystem level (microscopic traffic simulation models in VISSIM) and using a developed mathematical vehicle dynamics model at the system level to calculate the braking torques and angular velocities of the front and rear wheels, and proposes an artificial neural network (ANN) as a brake emission model, which has been appropriately trained and validated using emission data collected through more than 1000 experimental tribological tests on a reduced-scale dynamometer at the subsystem level (braking system). Consideration of this multi-level approach, from tribological to traffic-related aspects, is necessary for a realistic estimation of brake emissions. The proposed method was implemented on a targeted vehicle, a dominant SUV family car in the case study, considering real driving conditions. The relevant dynamic quantities of the targeted vehicle (braking torques and angular velocities of the wheels) were calculated based on the vehicle trajectory data such as speed and deceleration obtained from the traffic microsimulation models and converted into braking emissions via the artificial neural network. The total number of brake emissions emitted by the targeted vehicles was predicted for 10 iterations route by route and for the entire traffic network. The results showed that a large number of brake particles (in the order of billions of particles) are released by the targeted vehicles, which significantly affect the air quality in the case study. The results of this dissertation provide important information for policy makers to gain better insight into the rate of exhaust and brake emissions and fuel consumption in metropolitan areas and to understand their acute negative impacts on the health of citizens and commuters.
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Garrihy, G. "Neural network simulation of dynamic speech perception." Thesis, University of Essex, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.317930.
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Grygier, Paul Arthur. "Distributed Dynamic System Simulation Using a Computer Network." The Ohio State University, 1988. http://rave.ohiolink.edu/etdc/view?acc_num=osu1391676035.
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Ebert, Anthony C. "Dynamic queueing networks: Simulation, estimation and prediction." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/180771/1/Anthony_Ebert_Thesis.pdf.
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Inspired by the problem of managing passenger flow in airport terminals, novel statistical approaches to simulation, estimation and prediction of these systems were developed. A simulation algorithm was developed with computational speed-ups of more than one hundred-fold. The computational improvement was leveraged to infer parameters governing a dynamic queueing system for the first time. Motivated by the original application, contributions to both functional data analysis as well as combined parameter and state inference were made.
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ABDEL-MOMEN, SHERIF SAMIR. "DYNAMIC RESOURCE BALANCING BETWEEN TWO COUPLED SIMULATIONS." University of Cincinnati / OhioLINK, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1060893659.
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Baker, Joseph Lee. "Steered Molecular Dynamics Simulations of Biological Molecules." Diss., The University of Arizona, 2011. http://hdl.handle.net/10150/205416.
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Molecular dynamics (MD) simulation, which employs an empirical potential energy function to describe the interactions between the atoms in a system, is used to investigate atomistic motions of proteins. However, the timescale of many biological processes exceeds the reach of standard MD due to computational limitations. To circumvent these limitations, steered molecular dynamics (SMD), which applies external forces to the simulated system, can be used.Dynamical properties of the gonococcal type IV pilus (GC-T4P) from the bacteria Neisseria gonorrhoeae are first considered. T4 pili are long, filamentous proteins constructed from a subunit (pilin) found to emanate from the surface of pathogenic bacteria. They can withstand large forces (~100 pN), and are implicated in infection. SMD simulations are performed to study the response of the filament to an applied force. Our simulations reveal that stability of the pilus likely results from hydrophobic contacts between pilin domains buried within the filament core. Along the filament surface, gaps are formed between pilin globular head domains. These gaps reveal an amino acid sequence that was also observed to become exposed in the experimentally stretched filament. We propose two other regions initially hidden in the native filament that might become exposed upon stretching.The multidrug resistance transporter EmrD, found in the inner membrane of Escherichia coli is also the target of our studies. EmrD removes harmful drugs from the bacterial cell. We use MD to explore equilibrium dynamics of the protein, and MD/SMD to study drug interactions and transport along its central cavity. Motions supporting a previously proposed lateral diffusion pathway for substrate from the cytoplasmic membrane leaflet into the central cavity were observed. Additionally, interactions of a few specific residues with CCCP have been identified.Finally, we describe network analysis as an approach for analyzing conformational sampling by MD simulations. We demonstrate for several model systems that networks can be used to visualize both the dominant conformational substates of a trajectory and the connectivity between them. Specifically, we compare the results of various clustering algorithms to the network layouts and show how information from both methods can be combined.
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Mariotte, Guilhem. "Dynamic Modeling of Large-Scale Urban Transportation Systems." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSET010/document.
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La congestion en milieu urbain est un enjeu majeur que ce soit d’un point vue économique, social ou environnemental. À court et moyen terme, l’utilisation de la simulation dynamique du trafic routier peut permettre d’analyser et de guider des politiques d’optimisation des infrastructures existantes. Aujourd’hui, du fait de la complexité des systèmes de transport, les outils de modélisation classiques sont limités à des échelles géographiques peu étendues (de l’ordre du quartier). À grande échelle, le temps de calcul devient rapidement un facteur limitant tout comme le calibrage et la scénarisation. Néanmoins les dernières décennies ont vu l’apparition d’une nouvelle génération de modèles bien adaptés aux métropoles urbaines. Ceux-ci sont basés sur une relation phénoménologique entre la production de déplacements et le nombre de véhicules dans une zone spatiale d’un réseau routier, appelée Diagramme Fondamental de Zone (Macroscopic Fundamental Diagram, MFD). Cette relation, validée empiriquement sur de nombreuses villes, a permis d’étudier différentes méthodes de contrôle du trafic pour une ville entière, mais a été peu utilisée à des fins de prévision de la congestion. L’objectif de cette thèse est de proposer un premier outil opérationnel de simulation et d’analyse des grands réseaux de métropoles, en utilisant et développant les modèles de trafic basés sur la relation MFD. Cet outil doit posséder un cadre théorique cohérent qui puisse convenir à des applications telles que la prévision d’états de trafic, le développement de nouvelles politiques de contrôle, l’estimation de pollutions liées au trafic, etc. Les contributions de la thèse portent sur deux aspects. Le premier est l’analyse des propriétés mathématiques et physiques des modèles existants, en incluant une formalisation complète de la gestion de plusieurs longueurs de parcours au sein d’une même zone urbaine. En particulier, cette formalisation traite de la distinction des trajets internes à la zone et des problèmes de flux convergents et divergents pour les trajets traversant la zone lorsque la congestion se propage d’une zone à l’autre. Le deuxième aspect est la proposition d’un nouveau modèle basé sur la distance individuelle parcourue à l’intérieur d’une zone urbaine (trip-based). Cette approche permet d’individualiser les usagers (auparavant représentés sous forme de flux continus) et donc de définir plus finement leurs caractéristiques, en vue de coupler leurs déplacements à des modèles d’affectations sur différentes routes. Enfin, des exemples d’application illustrant diverses collaborations sont donnés en dernière partie de la thèse. La simulation du trafic sur l’aire urbaine du Grand Lyon (France) y est présentée, ainsi que de nouveaux modules de modélisation de la recherche de parking ou de contrôle périphérique. Cette thèse est partie intégrante d’un projet européen ERC intitulé MAGnUM : Approche multi-échelle et multimodale de la modélisation du trafic pour une gestion durable de la mobilité urbaineCongestion in urban areas has become a major issue in terms of economic, social or environmental impact. For short or mid term, using dynamic road traffic simulation can help analyzing and providing guidelines to optimization policies of existing infrastructures. Today, because of the complexity of transport systems, classical modeling tools are limited to small geographical areas (of a district size). Computational time, together with simulation calibration, are notably very constraining at large scales. However, a new generation of models designed for metropolitan areas has arisen over the past decades. These models are based on a phenomenological relationship between travel production and the number of vehicles in a given spatial area of a road network, known as the Macroscopic Fundamental Diagram (MFD). This relationship, supported by empirical evidences from several cities around the world, has allowed the study of different traffic control schemes at a whole city scale, but was rarely used for traffic state forecasting. The aim of this PhD is to propose an efficient modeling tool, based upon the concept of MFD, to simulate and analyze traffic states in large metropolitan areas. The theoretical framework of this tool must be consistent and applicable for traffic state forecasting, development of new control policies, traffic emission estimation, etc. There are two major contributions in this PhD. The first one is analyzing the mathematical and physical properties of existing models, and formalizing the dynamics of several trip lengths inside the same urban zone. In particular, this formalization distinguishes between internal trips and trips crossing the zone. Flow merging and diverging issues are also addressed when congestion propagates from one zone to another. The second contribution is proposing a new trip-based model based on individual traveled distance. This approach allows to treat users independently (previously represented with continuous flows), and thus to define their characteristics more precisely to couple their trips with assignment models on different paths. Finally, examples of application from various collaborations are given in the last part of this thesis. It includes a simulation study of the Grand Lyon urban area (France), as well as new modules to simulate search-for-parking or perimeter control. This PhD is part of a European ERC project entitled MAGnUM: Multiscale and Multimodal Traffic Modeling Approach for Sustainable Management of Urban Mobility
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Cairns, David Edward. "Synchronization in dynamic neural networks." Thesis, University of Stirling, 1993. http://hdl.handle.net/1893/21611.
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This thesis is concerned with the function and implementation of synchronization in networks of oscillators. Evidence for the existence of synchronization in cortex is reviewed and a suitable architecture for exhibiting synchronization is defined. A number of factors which affect the performance of synchronization in networks of laterally coupled oscillators are investigated. It is shown that altering the strength of the lateral connections between nodes and altering the connective scope of a network can be used to improve synchronization performance. It is also shown that complete connective scope is not required for global synchrony to occur. The effects of noise on synchronization performance are also investigated and it is shown that where an oscillator network is able to synchronize effectively, it will also be robust to a moderate level of noise in the lateral connections. Where a particular oscillator model shows poor synchronization performance, it is shown that noise in the lateral connections is capable of improving synchronization performance. A number of applications of synchronizing oscillator networks are investigated. The use of synchronized oscillations to encode global binding information is investigated and the relationship between the form of grouping obtained and connective scope is discussed. The potential for using learning in synchronizing oscillator networks is illustrated and an investigation is made into the possibility of maintaining multiple phases in a network of synchronizing oscillators. It is concluded from these investigations that it is difficult to maintain multiple phases in the network architecture used throughout this thesis and a modified architecture capable of producing the required behaviour is demonstrated.
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Pham, Khoa Ngoc. "Conformational Dynamics and Stability Associated with Magnesium or Calcium Binding to DREAM in the Regulation of Interactions between DREAM and DNA or Presenilins." FIU Digital Commons, 2016. http://digitalcommons.fiu.edu/etd/2589.
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Downstream regulatory element antagonist modulator (DREAM) is involved in various interactions with targets both inside and outside of the nucleus. In the cytoplasm, DREAM interacts with the C-terminal fragments of presenilins to facilitate the production of β-amyloid plaques in Alzheimer’s disease. In the nucleus, Ca2+ free DREAM directly binds to specific downstream regulatory elements of prodynorphin/c-fos gene to repress the gene transcription in pain modulation. These interactions are regulated by Ca2+ and/or Mg2+ association at the EF-hands in DREAM. Therefore, understanding the conformational dynamics and stability associated with Ca2+ and/or Mg2+ binding to DREAM is crucial for elucidating the mechanisms of interactions of DREAM with DNA or presenilins. The critical barrier for envisioning the mechanisms of these interactions lies in the lack of NMR/crystal structures of Apo and Mg2+DREAM.
Using a combination of fluorescence spectroscopy, circular dichroism, isothermal titration calorimetry, photothermal spectroscopy, and computational approaches, I showed that Mg2+ association at the EF-hand 2 structurally stabilizes the N-terminal alpha-helices 1, 2, and 5, facilitating the interaction with DNA. Binding of Ca2+ at the EF-hand 3 induces significant structural changes in DREAM, mediated by several hydrophobic residues in both the N- and C-domains. These findings illustrate the critical role of EF-hand 3 for Ca2+ signal transduction from the C- to N-terminus in DREAM. The Ca2+ association at the EF-hand 4 stabilizes the C-terminus by forming a cluster consisting of several hydrophobic residues in C-terminal domain. I also demonstrated that association of presenilin-1 carboxyl peptide with DREAM is Ca2+ dependent and the complex is stabilized by aromatic residues F462 and F465 from presenilin-1 and F252 from DREAM. Stabilization is also provided by residues R200 and R207 in the loop connecting a7 and a8 in DREAM and the residues D450 and D458 in presenilin-1.
These findings provide a structural basis for the development of new drugs for chronic pain and Alzheimer’s disease treatments.
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Lamers, Eugen. "Contributions to Simulation Speed-up : Rare Event Simulation and Short-term Dynamic Simulation for Mobile Network Planning /." Wiesbaden : Vieweg + Teubner in GWV Fachverlage, 2008. http://d-nb.info/988382016/04.
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Lamers, Eugen. "Contributions to simulation speed-up rare event simulation and short-term dynamic simulation for mobile network planning." Wiesbaden Vieweg + Teubner, 2007. http://d-nb.info/988382016/04.
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Pan, YaDung. "Fuzzy adaptive recurrent counterpropagation neural networks: A neural network architecture for qualitative modeling and real-time simulation of dynamic processes." Diss., The University of Arizona, 1995. http://hdl.handle.net/10150/187101.
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In this dissertation, a new artificial neural network (ANN) architecture called fuzzy adaptive recurrent counterpropagation neural network (FARCNN) is presented. FARCNNs can be directly synthesized from a set of training data, making system behavioral learning extremely fast. FARCNNs can be applied directly and effectively to model both static and dynamic system behavior based on observed input/output behavioral patterns alone without need of knowing anything about the internal structure of the system under study. The FARCNN architecture is derived from the methodology of fuzzy inductive reasoning and a basic form of counterpropagation neural networks (CNNs) for efficient implementation of finite state machines. Analog signals are converted to fuzzy signals by use of a new type of fuzzy A/D converter, thereby keeping the size of the Kohonen layer of the CNN manageably small. Fuzzy inferencing is accomplished by an application-independent feedforward network trained by means of backpropagation. Global feedback is used to represent full system dynamics. The FARCNN architecture combines the advantages of the quantitative approach (neural network) with that of the qualitative approach (fuzzy logic) as an efficient autonomous system modeling methodology. It also makes the simulation of mixed quantitative and qualitative models more feasible. In simulation experiments, we shall show that FARCNNs can be applied directly and easily to different types of systems, including static continuous nonlinear systems, discrete sequential systems, and as part of large dynamic continuous nonlinear control systems, embedding the FARCNN into much larger industry-sized quantitative models, even permitting a feedback structure to be placed around the FARCNN.
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Rebholz, Matthew John. "Dynamic Spectrum Access Network Simulation and Classification of Secondary User Properties." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/23244.
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This thesis explores the use of the Naïve Bayesian classifier as a method of determining high-level information about secondary users in a Dynamic Spectrum Access (DSA) network using a low complexity channel sensing method. With a growing number of users generating an increased demand for broadband access, determining an efficient method for utilizing the limited available broadband is a developing current and future issue. One possible solution is DSA, which we simulate using the Universal DSA Network Simulator (UDNS), created by our team at Virginia Tech.However, DSA requires user devices to monitor large amounts of bandwidth, and the user devices are often limited in their acceptable size, weight, and power. This greatly limits the usable bandwidth when using complex channel sensing methods. Therefore, this thesis focuses on energy detection for channel sensing.
Constraining computing requirements by operating with limited spectrum sensing equipment allows for efficient use of limited broadband by user devices. The research on using the Naïve Bayesian classifier coupled with energy detection and the UDNS serves as a strong starting point for supplementary work in the area of radio classification.
Master of Science
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Stattner, Erick. "Contributions à l'étude des réseaux sociaux : propagation, fouille, collecte de données." Phd thesis, Université des Antilles-Guyane, 2012. http://tel.archives-ouvertes.fr/tel-00830882.
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Le concept de réseau offre un modèle de représentation pour une grande variété d'objets et de systèmes, aussi bien naturels que sociaux, dans lesquels un ensemble d'entités homogènes ou hétérogènes interagissent entre elles. Il est aujourd'hui employé couramment pour désigner divers types de structures relationnelles. Pourtant, si chacun a une idée plus ou moins précise de ce qu'est un réseau, nous ignorons encore souvent les implications qu'ont ces structures dans de nombreux phénomènes du monde qui nous entoure. C'est par exemple le cas de processus tels que la diffusion d'une rumeur, la transmission d'une maladie, ou même l'émergence de sujets d'intérêt commun à un groupe d'individus, dans lesquels les relations que maintiennent les individus entre eux et leur nature s'avèrent souvent être les principaux facteurs déterminants l'évolution du phénomène. C'est ainsi que l'étude des réseaux est devenue l'un des domaines émergents du 21e siècle appelé la "Science des réseaux". Dans ce mémoire, nous abordons trois problèmes de la science des réseaux: le problème de la diffusion dans les réseaux sociaux, où nous nous sommes intéressés plus particulièrement à l'impact de la dynamique du réseau sur le processus de diffusion, le problème de l'analyse des réseaux sociaux, dans lequel nous avons proposé une solution pour tirer parti de l'ensemble des informations disponibles en combinant les informations sur la structure du réseau et les attributs des noeuds et le problème central de la collecte de données sociales, où nous nous sommes intéressés au cas particulier de la collecte de données en milieux sauvages.
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Gantt, James Dale. "Resource PERT (R-PERT) : a model for the dynamic simulation of project implementation with resource constraints." Diss., Georgia Institute of Technology, 1989. http://hdl.handle.net/1853/32789.
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Barbosa, Pedro. "Scalable wireless sensor networks for dynamic communication environments : simulation and modelling." Thesis, University of Southampton, 2011. https://eprints.soton.ac.uk/177575/.
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This thesis explores the deployment of Wireless Sensor Networks (WSNs) on localised maritime events. In particular, it will focus on the deployment of a WSN at sea and estimating what challenges derive from the environment and how they affect communication. This research addresses these challenges through simulation and modelling of communication and environment, evaluating the implications of hardware selection and custom algorithm development. The first part of this thesis consists of the analysis of aspects related to the Medium Access Control layer of the network stack in large-scale networks. These details are commonly hidden from upper layers, thus resulting in misconceptions of real deployment characteristics. Results show that simple solutions have greater advantages when the number of nodes within a cluster increases. The second part considers routing techniques, with focus on energy management and packet delivery. It is shown that, under certain conditions, relaying data can increase energy savings, while at the same time allows a more even distribution of its usage between nodes. The third part describes the development of a custom-made network simulator. It starts by considering realistic radio, channel and interference models to allow a trustworthy simulation of the deployment environment. The MAC and Routing techniques developed thus far are adapted to the simulator in a cross-layer manner. The fourth part consists of adapting the WSN behaviour to the variable weather and topology found in the chosen application scenario. By analysing the algorithms presented in this work, it is possible to find and use the best alternative under any set of environmental conditions. This mechanism, the environment-aware engine, uses both network and sensing data to optimise performance through a set of rules that involve message delivery and distance between origin and cluster head
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Kettemann, Anita Stefanie. "Mathematical modelling and numerical simulation of chemosensitive cell dynamics in multidimensional tissue networks." Aachen Shaker, 2009. http://d-nb.info/1001048598/04.
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Kos, Cristoffer, and Kristoffer Hermansson. "BUILDING AND SIMULATING DYNAMIC MODELS OF DISTRICT HEATING NETWORKS WITH MODELICA : Using Matlab to process data and automate modelling and simulation." Thesis, Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-36107.
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District heating systems are common in Nordic countries today and accounts for a great portion of the heat demand. In Sweden, total district heating end use in the last years has been around 50 TWh and district heating accounts for roughly 50 % of the total heat demand. Suppliers of district heating must balance demand and supply, often in large and complex networks. Heat propagation can be in the range of hours and it is not known in detail how the heat will propagate during transient conditions. A dynamic model has been developed in OpenModelica and a method for modeling, handling data, simulating and visualizing the results of a district heating network was developed using Matlab as core. Data from Mälarenergi AB, a district heating producer and grid operator, was used for validation of the model. Validation shows that the model works well in predicting heat propagation and temperature distribution in the network and that the model can be scaled up to a large number of heat exchangers and pipes. The model is robust and can handle bi-directional and reversing flows in complex ring structures. It was concluded that OpenModelica together with Matlab is a good combination for creating models of district heating networks, as a high degree of standardization and automation can be achieved. This, together with visualization of the heat propagation, makes it useful for the understanding of the district heating network during transient conditions.Smarta Flöden
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Oko, Eni. "Study of power plant, carbon capture and transport network through dynamic modelling and simulation." Thesis, University of Hull, 2015. http://hydra.hull.ac.uk/resources/hull:11315.
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The unfavourable role of CO₂ in stimulating climate change has generated concerns as CO₂ levels in the atmosphere continue to increase. As a result, it has been recommended that coal-fired power plants which are major CO₂ emitters should be operated with a carbon capture and storage (CCS) system to reduce CO₂ emission levels from the plant. Studies on CCS chain have been limited except a few high profile projects. Majority of previous studies focused on individual components of the CCS chain which are insufficient to understand how the components of the CCS chain interact dynamically during operation. In this thesis, model-based study of the CCS chain including coal-fired subcritical power plant, post-combustion CO₂ capture (PCC) and pipeline transport components is presented. The component models of the CCS chain are dynamic and were derived from first principles. A separate model involving only the drum-boiler of a typical coal-fired subcritical power plant was also developed using neural networks. The power plant model was validated at steady state conditions for different load levels (70-100%). Analysis with the power plant model show that load change by ramping cause less disturbance than step changes. Rate-based PCC model obtained from Lawal et al. (2010) was used in this thesis. The PCC model was subsequently simplified to reduce the CPU time requirement. The CPU time was reduced by about 60% after simplification and the predictions compared to the detailed model had less than 5% relative difference. The results show that the numerous non-linear algebraic equations and external property calls in the detailed model are the reason for the high CPU time requirement of the detailed PCC model. The pipeline model is distributed and includes elevation profile and heat transfer with the environment. The pipeline model was used to assess the planned Yorkshire and Humber CO₂ pipeline network. Analysis with the CCS chain model indicates that actual changes in CO₂ flowrate entering the pipeline transport system in response to small load changes (about 10%) is very small (<5%). It is therefore concluded that small changes in load will have minimal impact on the transport component of the CCS chain when the capture plant is PCC.
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Paothong, Arnut. "Dynamic Processes in Network Goods: Modeling, Analysis and Applications." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4558.
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The network externality function plays a very important role in the study of economic network industries. Moreover, the consumer group dynamic interactions coupled with network externality concept is going to play a dominant role in the network goods in the 21st century. The existing literature is stemmed on a choice of externality function with certain quantitative properties. The utility function coupled with the network externality function is used to investigate static properties of rational equilibrium. The aim of this work is to systematically initiate a development of quantitative effects of the concept of network externality and its influence on the characteristics of network market equilibrium.
We introduce several basic concepts, notably, network externality process and network goods. Formulating a principle of network externality, we developed a mathematical dynamic model (1) for the network externality process. A closed form solution of the mathematical model was determined and analyzed (2). The presented qualitative and quantitative analysis provides a systematic and unified way of constructing the existing network externality function. The solution process is called "Generalized Network Externality Function (GNEF)". Moreover, our study of quantitative description, parametric representation of attributes and sensitivity analysis of network externality process provides a tool for planning, policy and performance for network goods (3).
In the absence of desired data set, we presented an illustration to exhibit the significance of GNEF. We used two types of data sets on the US banking asset and deposit. Employing nonlinear regression methods and data sets, we developed statistical models for the US banking asset and deposit, and constructed two normalized the US banking deposit models (4). Finally, using the concept of theory of relative growth and GNEF (4), we developed two dynamic models for the network externality for the US banking asset with respect to the US banking deposit as a financial market share (5).
Incorporating the GNEF (2) in the consumer utility function, a concept of market share adjustment function is introduced and utilized to develop dynamic models for existing rational and static expectation processes (6). In fact, the role and scope of dynamic models of market share adjustment process are extended to the well-known adaptive expectation and its extension process (7). Using a fixed point theorem and the method of upper and lower solutions of discrete time processes, the existence of equilibrium states of developed dynamic models of market share adjustment processes are established in a systematic way (8). Furthermore, the qualitative properties (stability and oscillatory) of equilibrium states are investigated in terms of model and speed of adjustment parameters. Moreover, the system parameter space is decomposed according to qualitative properties (stability, instability and oscillatory) and the type of expectation processes.
Very recently, the idea of local network externality is utilized to characterize the rational equilibrium (under fulfilled expectation assumptions). From the study on two-scale network dynamic model of human mobility process an eco-socio-culture interactions, we note that heterogeneity in the network goods consumer community generates a local network externality. Furthermore, dynamic models of adaptive expectation processes (6,7) for network goods provide tool to extend the characterization of rational equilibrium study to static, current and lagged adaptive types equilibriums. Here, we treat the consumer decision to be a dynamic process. We formulate a dynamic structural representation of a consumer network structure, structure of utility function and decision rule under the influence of local network externality concept (9). For the consumer network structure, we generalize the one-dimensional Hotelling location line model to multi-dimensional location (10). This formulation generates a mathematical model for a consumer decision dynamic process (11). The byproduct of the dynamic model leads to an agent-based simulation model (12). The simulation model is employed to investigate different types of consumer decision dynamic market equilibriums. Moreover, prototype illustrations are given to exhibit the association between network attributes and its market equilibriums.
We extend the work of two firms (duopoly) into multi-firms (oligopoly and monopolistic competition). This work shed light on the policies for manager to meet performance goal of their firm in network goods industry.
In future, we propose to extend this work to incorporate random fluctuations, to remove restrictions and the local and global economic framework in the 21st century.
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Smejkal, Jakub. "Dynamický stav modelu OMNeT++ pomocí SNMP." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236595.
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This work outlines ways of getting dynamic state into OMNeT++ model. SNMP as major technology was selected for reaching the goals. Protocols CDP and LLDP are participating in topology detection and in theoretical layer Breadth- first search is used. The output of this tool is file syntactically specied by NED language which is describing computer network.
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Pant, Gaurav. "Hybrid Dynamic Modelling of Engine Emissions on Multi-Physics Simulation Platform. A Framework Combining Dynamic and Statistical Modelling to Develop Surrogate Models of System of Internal Combustion Engine for Emission Modelling." Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/17223.
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McConky, Katie Theresa. "Design and analysis of information fusion, dynamic sensor management rules for cyber security systems using simulation /." Online version of thesis, 2007. http://hdl.handle.net/1850/4895.
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Pant, Gaurav, I. Felician Campean, Aleksandrs Korsunovs, Daniel Neagu, and Oscar Garcia-Afonso. "Hybrid Dynamic Modelling of Engine Emissions on Multi-Physics Simulation Platform." SAE, 2020. http://hdl.handle.net/10454/18376.
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YesThis paper introduces a hybrid dynamic modelling approach for the prediction of NOx emissions for a Diesel engine, based on a multi-physics simulation platform coupling a 1-D air path model (GT-Suite) with in-cylinder combustion model (CMCL Stochastic Reactor Model Engine Suite). The key motivation for this research was the requirement to establish a real time stochastic simulation capability for emissions predictions early in engine development, which required the replacement of the slow combustion chemistry solver (SRM) with an appropriate surrogate model. The novelty of the approach in this research is the introduction of a hybrid approach to metamodeling that combines dynamic experiments for the gas path model with a zonal optimal space-filling design of experiments (DoEs) for the combustion model. The dynamic experiments run on the virtual Diesel engine model (GT- Suite) was used to fit a dynamic model for the parameters required as input to the SRM. Optimal Latin Hypercubes (OLH) DoE run on the SRM model was used to fit a response surface model for the NOx emissions. This surrogate NOx model was then used to replace the computationally expensive SRM simulation, enabling real time simulations of transient drive cycles to be executed. The performance of the proposed approach was validated on a simulated NEDC drive cycle against experimental data collected for the engine case study, which proved the capability of methodology to capture the transient trends for the NOx emissions. The significance of this work is that it provided an efficient approach to the development of a global model with real time transient modelling capability based on the integration of dynamic and local DoE metamodeling experiments.
The full-text of this article will be released for public view at the end of the publisher embargo on 12 Aug 2021.
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Lingasubramanian, Karthikeyan. "Estimation of switching activity in sequential circuits using dynamic Bayesian Networks." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000411.
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Lee, Jung-Hoon. "The modelling and simulation of dynamic supply chain networks : a multi-agent systems approach." Thesis, University of Cambridge, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.411233.
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Chen, Xiaoting. "Optimal Control of Non-Conventional Queueing Networks: A Simulation-Based Approximate Dynamic Programming Approach." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1427799942.
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GARAU, MICHELE. "DEVELOPMENT OF CO-SIMULATION PLATFORMS FOR STATIC AND DYNAMIC ANALYSIS OF SMART DISTRIBUTION NETWORKS." Doctoral thesis, Università degli Studi di Cagliari, 2018. http://hdl.handle.net/11584/255955.
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The development of the future energy system in accordance with the smart grid paradigm requires a radical change in the management of the electricity distribution network, which needs to become increasingly intelligent and adaptive. Smart distribution networks (SDN) will have systems in place to control a combination of distributed energy resources, and distribution system operators will be able to manage the electricity flows using a flexible network topology. The transition towards SDN involves software, automation and control systems, to ensure that the power distribution network not only remains within its operating limits (e.g. node voltages and branch currents within the acceptable thresholds), but also that it is operated in an optimal way. The SDN operation requires the extensive use of information and communication technology (ICT) and innovative control systems in order to enable the active management of DERs and expected growing number of electric vehicles.
In SDN context, therefore, the ICT is not a simple add-on of the electrical system, but its availability and efficiency are essential to the operation of the entire power distribution system. In fact, the electric system will be managed and controlled through the communication network, which must guarantee a bidirectional exchange of large amount of data creating a keen interdependence between electric system and ICT system.
The theme of the joint simulation of the electrical system and the communication infrastructure is becoming increasingly important in the scientific research on power systems. For planning the evolution towards the implementation of SDN, appropriate computing tools (not yet available among commercial software) should be developed to take into account the reciprocal interdependencies among the two systems. The co-simulation is the most common method for studying SDNs as a cyber-physical system (CPS), since it permits simulating each subsystem (electrical and ICT) simultaneously, and has represented the focus of this Ph. D. work.
In this thesis different simulators specifically developed for co-simulation of smart distribution network analysis have been developed. They permit analyzing the slow dynamic operation of smart distribution networks (e.g. optimal power and reactive flow management, voltage regulation) as well the fast dynamic operation (e.g. fault management and reconfiguration). Different case studies have been investigated in order to show the effectiveness of the co-simulation platform features in analyzing the behavior of the smart distribution network in terms of capabilities, performances and reliability.
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Jeynes, Smith Cailan. "Mathematical modelling of ultrasensitivity in enzyme-mediated reactions." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/174502/1/Cailan_Jeynes%20Smith_Thesis.pdf.
Full textAbstract:
This thesis presents a thorough mathematical investigation of the important phenomenon of Ultrasensitivity in enzyme-mediated chemical reaction networks. The study considers several novel chemical reaction networks, and develops a robust new nomenclature that allows Ultrasensitivity to be analysed in precise quantitative terms. This investigation encompasses both stochastic and deterministic simulations, as well as analytical methods, which together provide a comprehensive understanding of ultrasensitivity-promoting mechanisms in simple chemical reaction networks.
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Kettemann, Anita S. [Verfasser]. "Mathematical modelling and numerical simulation of chemosensitive cell dynamics in multidimensional tissue networks / Anita S Kettemann." Aachen : Shaker, 2010. http://d-nb.info/1124364048/34.
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Zone, Douglas Davidson. "Simulation and optimization of dynamic resource allocation strategies in a single server queu[e]ing network." Thesis, Massachusetts Institute of Technology, 1990. http://hdl.handle.net/1721.1/14114.
Full textAbstract:
Thesis (M.S.)--Massachusetts Institute of Technology, Sloan School of Management, 1990.Title as it appears in the M.I.T. Graduate List, June 1990: Performance analysis and optimization queing [sic] networks in a dynamic environment.
Includes bibliographical references (leaf 162).
by Douglas Davidson Zone.
M.S.
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Bataineh, Mohammad Hindi. "New neural network for real-time human dynamic motion prediction." Thesis, The University of Iowa, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3711174.
Full textAbstract:
Artificial neural networks (ANNs) have been used successfully in various practical problems. Though extensive improvements on different types of ANNs have been made to improve their performance, each ANN design still experiences its own limitations. The existing digital human models are mature enough to provide accurate and useful results for different tasks and scenarios under various conditions. There is, however, a critical need for these models to run in real time, especially those with large-scale problems like motion prediction which can be computationally demanding. For even small changes to the task conditions, the motion simulation needs to run for a relatively long time (minutes to tens of minutes). Thus, there can be a limited number of training cases due to the computational time and cost associated with collecting training data. In addition, the motion problem is relatively large with respect to the number of outputs, where there are hundreds of outputs (between 500-700 outputs) to predict for a single problem. Therefore, the aforementioned necessities in motion problems lead to the use of tools like the ANN in this work.
This work introduces new algorithms for the design of the radial-basis network (RBN) for problems with minimal available training data. The new RBN design incorporates new training stages with approaches to facilitate proper setting of necessary network parameters. The use of training algorithms with minimal heuristics allows the new RBN design to produce results with quality that none of the competing methods have achieved. The new RBN design, called Opt_RBN, is tested on experimental and practical problems, and the results outperform those produced from standard regression and ANN models. In general, the Opt_RBN shows stable and robust performance for a given set of training cases.
When the Opt_RBN is applied on the large-scale motion prediction application, the network experiences a CPU memory issue when performing the optimization step in the training process. Therefore, new algorithms are introduced to modify some steps of the new Opt_RBN training process to address the memory issue. The modified steps should only be used for large-scale applications similar to the motion problem. The new RBN design proposes an ANN that is capable of improved learning without needing more training data. Although the new design is driven by its use with motion prediction problems, the consequent ANN design can be used with a broad range of large-scale problems in various engineering and industrial fields that experience delay issues when running computational tools that require a massive number of procedures and a great deal of CPU memory.
The results of evaluating the modified Opt_RBN design on two motion problems are promising, with relatively small errors obtained when predicting approximately 500-700 outputs. In addition, new methods for constraint implementation within the new RBN design are introduced. Moreover, the new RBN design and its associated parameters are used as a tool for simulated task analysis. This work initiates the idea that output weights (W) can be used to determine the most critical basis functions that cause the greatest reduction in the network test error. Then, the critical basis functions can specify the most significant training cases that are responsible for the proper performance achieved by the network. The inputs with the most change in value can be extracted from the basis function centers (U) in order to determine the dominant inputs. The outputs with the most change in value and their corresponding key body degrees-of-freedom for a motion task can also be specified using the training cases that are used to create the network's basis functions.
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Hertweck, Bryan M. "Examining Electronic Markets in Which Intelligent Agents Are Used for Comparison Shopping and Dynamic Pricing." Diss., Virginia Tech, 2005. http://hdl.handle.net/10919/29037.
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Electronic commerce markets are becoming increasingly popular forums for commerce. As those markets mature, buyers and sellers will both vigorously seek techniques to improve their performance. The Internet lends itself to the use of agents to work on behalf of buyers and sellers. Through simulation, this research examines different implementations of buyers' agents (shopbots) and sellers' agents (pricebots) so that buyers, sellers, and agent builders can capitalize on the evolution of e-commerce technologies.
Internet markets bring price visibility to a level beyond what is observed in traditional brick-and-mortar markets. Additionally, an online seller is able to update prices quickly and cheaply. Due to these facts, there are many pricing strategies that sellers can implement via pricebot to react to their environments. The best strategy for a particular seller is dependent on characteristics of its marketplace. This research shows that the extent to which buyers are using shopbots is a critical driver of the success of pricing strategies. When measuring profitability, the interaction between shopbot usage and seller strategy is very strong - what works well at low shopbot usage levels may perform poorly at high levels. If a seller is evaluating strategies based on sales volume, the choice may change. Additionally, as markets evolve and competitors change strategies, the choice of most effective counterstrategies may evolve as well. Sellers need to clearly define their goals and thoroughly understand their marketplace before choosing a pricing strategy.
Just as sellers have choices to make in implementing pricebots, buyers have decisions to make with shopbots. In addition to the factors described above, the types of shopbots in use can actually affect the relative performance of pricing strategies. This research also shows that varying shopbot implementations (specifically involving the use of a price memory component) can affect the prices that buyers ultimately pay - an especially important consideration for high-volume buyers.
Modern technology permits software agents to employ artificial intelligence. This work demonstrates the potential of neural networks as a tool for pricebots. As discussed above, a seller's best strategy option can change as the behavior of the competition changes. Simulation can be used to evaluate a multitude of scenarios and determine what strategies work best under what conditions. This research shows that a neural network can be effectively implemented to classify the behavior of competitors and point to the best counterstrategy.Ph. D.