Modeling and simulation of a synthetic genetic circuit that implements a multicelled behavior in a growing microcolony of E. coli

Synthetic Biology is a relatively new discipline, born at the beginning of the New Millennium, that brings the typical engineering approach (abstraction, modularity and standardization) to biotechnology. These principles aim to tame the extreme complexity of the various components and aid the construction of artificial biological systems with specific functions, usually by means of synthetic genetic circuits implemented in bacteria or simple eukaryotes like yeast. The cell becomes a programmable machine and its low-level programming language is made of strings of DNA. This work was performed in collaboration with researchers of the Department of Electrical Engineering of the University of Washington in Seattle and also with a student of the Corso di Laurea Magistrale in Ingegneria Biomedica at the University of Bologna: Marilisa Cortesi. During the collaboration I contributed to a Synthetic Biology project already started in the Klavins Laboratory. In particular, I modeled and subsequently simulated a synthetic genetic circuit that was ideated for the implementation of a multicelled behavior in a growing bacterial microcolony. In the first chapter the foundations of molecular biology are introduced: structure of the nucleic acids, transcription, translation and methods to regulate gene expression. An introduction to Synthetic Biology completes the section. In the second chapter is described the synthetic genetic circuit that was conceived to make spontaneously emerge, from an isogenic microcolony of bacteria, two different groups of cells, termed leaders and followers. The circuit exploits the intrinsic stochasticity of gene expression and intercellular communication via small molecules to break the symmetry in the phenotype of the microcolony. The four modules of the circuit (coin flipper, sender, receiver and follower) and their interactions are then illustrated. In the third chapter is derived the mathematical representation of the various components of the circuit and the several simplifying assumptions are made explicit. Transcription and translation are modeled as a single step and gene expression is function of the intracellular concentration of the various transcription factors that act on the different promoters of the circuit. A list of the various parameters and a justification for their value closes the chapter. In the fourth chapter are described the main characteristics of the gro simulation environment, developed by the Self Organizing Systems Laboratory of the University of Washington. Then, a sensitivity analysis performed to pinpoint the desirable characteristics of the various genetic components is detailed. The sensitivity analysis makes use of a cost function that is based on the fraction of cells in each one of the different possible states at the end of the simulation and the wanted outcome. Thanks to a particular kind of scatter plot, the parameters are ranked. Starting from an initial condition in which all the parameters assume their nominal value, the ranking suggest which parameter to tune in order to reach the goal. Obtaining a microcolony in which almost all the cells are in the follower state and only a few in the leader state seems to be the most difficult task. A small number of leader cells struggle to produce enough signal to turn the rest of the microcolony in the follower state. It is possible to obtain a microcolony in which the majority of cells are followers by increasing as much as possible the production of signal. Reaching the goal of a microcolony that is split in half between leaders and followers is comparatively easy. The best strategy seems to be increasing slightly the production of the enzyme. To end up with a majority of leaders, instead, it is advisable to increase the basal expression of the coin flipper module. At the end of the chapter, a possible future application of the leader election circuit, the spontaneous formation of spatial patterns in a microcolony, is modeled with the finite state machine formalism. The gro simulations provide insights into the genetic components that are needed to implement the behavior. In particular, since both the examples of pattern formation rely on a local version of Leader Election, a short-range communication system is essential. Moreover, new synthetic components that allow to reliably downregulate the growth rate in specific cells without side effects need to be developed. In the appendix are listed the gro code utilized to simulate the model of the circuit, a script in the Python programming language that was used to split the simulations on a Linux cluster and the Matlab code developed to analyze the data.

Modeling and simulations of some anisotropic soft-matter systems: from biaxial to chiral materials

We have modeled various soft-matter systems with molecular dynamics (MD) simulations. The first topic concerns liquid crystal (LC) biaxial nematic (Nb) phases, that can be possibly used in fast displays. We have investigated the phase organization of biaxial Gay-Berne (GB) mesogens, considering the effects of the orientation, strength and position of a molecular dipole. We have observed that for systems with a central dipole, nematic biaxial phases disappear when increasing dipole strength, while for systems characterized by an offset dipole, the Nb phase is stabilized at very low temperatures. In a second project, in view of their increasing importance as nanomaterials in LC phases, we are developing a DNA coarse-grained (CG) model, in which sugar and phosphate groups are represented with Lennard-Jones spheres, while bases with GB ellipsoids. We have obtained shape, position and orientation parameters for each bead, to best reproduce the atomistic structure of a B-DNA helix. Starting from atomistic simulations results, we have completed a first parametrization of the force field terms, accounting for bonded (bonds, angles and dihedrals) and non-bonded interactions (H-bond and stacking). We are currently validating the model, by investigating stability and melting temperature of various sequences. Finally, in a third project, we aim to explain the mechanism of enantiomeric discrimination due to the presence of a chiral helix of poly(gamma-benzyl L-glutamate) (PBLG), in solution of dimethylformamide (DMF), interacting with chiral or pro-chiral molecules (in our case heptyl butyrate, HEP), after tuning properly an atomistic force field (AMBER). We have observed that DMF and HEP molecules solvate uniformly the PBLG helix, but the pro-chiral solute is on average found closer to the helix with respect to the DMF. The solvent presents a faster isotropic diffusion, twice as HEP, also indicating a stronger interaction of the solute with the helix.

Numerical optimization,modeling and system evaluation of a thermophotovoltaic hybrid panel

Photovoltaic (PV) solar panels generally produce electricity in the 6% to 16% efficiency range, the rest being dissipated in thermal losses. To recover this amount, hybrid photovoltaic thermal systems (PVT) have been devised. These are devices that simultaneously convert solar energy into electricity and heat. It is thus interesting to study the PVT system globally from different point of views in order to evaluate advantages and disadvantages of this technology and its possible uses. In particular in Chapter II, the development of the PVT absorber numerical optimization by a genetic algorithm has been carried out analyzing different internal channel profiles in order to find a right compromise between performance and technical and economical feasibility. Therefore in Chapter III ,thanks to a mobile structure built into the university lab, it has been compared experimentally electrical and thermal output power from PVT panels with separated photovoltaic and solar thermal productions. Collecting a lot of experimental data based on different seasonal conditions (ambient temperature,irradiation, wind...),the aim of this mobile structure has been to evaluate average both thermal and electrical increasing and decreasing efficiency values obtained respect to separate productions through the year. In Chapter IV , new PVT and solar thermal equation based models in steady state conditions have been developed by software Dymola that uses Modelica language. This permits ,in a simplified way respect to previous system modelling softwares, to model and evaluate different concepts about PVT panel regarding its structure before prototyping and measuring it. Chapter V concerns instead the definition of PVT boundary conditions into a HVAC system . This was made trough year simulations by software Polysun in order to finally assess the best solar assisted integrated structure thanks to F_save(solar saving energy)factor. Finally, Chapter VI presents the conclusion and the perspectives of this PhD work.

Model reduction of stochastic groundwater flow and transport processes

This work presents a comprehensive methodology for the reduction of analytical or numerical stochastic models characterized by uncertain input parameters or boundary conditions. The technique, based on the Polynomial Chaos Expansion (PCE) theory, represents a versatile solution to solve direct or inverse problems related to propagation of uncertainty. The potentiality of the methodology is assessed investigating different applicative contexts related to groundwater flow and transport scenarios, such as global sensitivity analysis, risk analysis and model calibration. This is achieved by implementing a numerical code, developed in the MATLAB environment, presented here in its main features and tested with literature examples. The procedure has been conceived under flexibility and efficiency criteria in order to ensure its adaptability to different fields of engineering; it has been applied to different case studies related to flow and transport in porous media. Each application is associated with innovative elements such as (i) new analytical formulations describing motion and displacement of non-Newtonian fluids in porous media, (ii) application of global sensitivity analysis to a high-complexity numerical model inspired by a real case of risk of radionuclide migration in the subsurface environment, and (iii) development of a novel sensitivity-based strategy for parameter calibration and experiment design in laboratory scale tracer transport.

Kinetic modeling and experiments on gas uptake and chemical transformation of organic aerosol in the atmosphere

Aerosolpartikel beeinflussen das Klima durch Streuung und Absorption von Strahlung sowie als Nukleations-Kerne für Wolkentröpfchen und Eiskristalle. Darüber hinaus haben Aerosole einen starken Einfluss auf die Luftverschmutzung und die öffentliche Gesundheit. Gas-Partikel-Wechselwirkunge sind wichtige Prozesse, weil sie die physikalischen und chemischen Eigenschaften von Aerosolen wie Toxizität, Reaktivität, Hygroskopizität und optische Eigenschaften beeinflussen. Durch einen Mangel an experimentellen Daten und universellen Modellformalismen sind jedoch die Mechanismen und die Kinetik der Gasaufnahme und der chemischen Transformation organischer Aerosolpartikel unzureichend erfasst. Sowohl die chemische Transformation als auch die negativen gesundheitlichen Auswirkungen von toxischen und allergenen Aerosolpartikeln, wie Ruß, polyzyklische aromatische Kohlenwasserstoffe (PAK) und Proteine, sind bislang nicht gut verstanden.rn Kinetische Fluss-Modelle für Aerosoloberflächen- und Partikelbulk-Chemie wurden auf Basis des Pöschl-Rudich-Ammann-Formalismus für Gas-Partikel-Wechselwirkungen entwickelt. Zunächst wurde das kinetische Doppelschicht-Oberflächenmodell K2-SURF entwickelt, welches den Abbau von PAK auf Aerosolpartikeln in Gegenwart von Ozon, Stickstoffdioxid, Wasserdampf, Hydroxyl- und Nitrat-Radikalen beschreibt. Kompetitive Adsorption und chemische Transformation der Oberfläche führen zu einer stark nicht-linearen Abhängigkeit der Ozon-Aufnahme bezüglich Gaszusammensetzung. Unter atmosphärischen Bedingungen reicht die chemische Lebensdauer von PAK von wenigen Minuten auf Ruß, über mehrere Stunden auf organischen und anorganischen Feststoffen bis hin zu Tagen auf flüssigen Partikeln. rn Anschließend wurde das kinetische Mehrschichtenmodell KM-SUB entwickelt um die chemische Transformation organischer Aerosolpartikel zu beschreiben. KM-SUB ist in der Lage, Transportprozesse und chemische Reaktionen an der Oberfläche und im Bulk von Aerosol-partikeln explizit aufzulösen. Es erforder im Gegensatz zu früheren Modellen keine vereinfachenden Annahmen über stationäre Zustände und radiale Durchmischung. In Kombination mit Literaturdaten und neuen experimentellen Ergebnissen wurde KM-SUB eingesetzt, um die Effekte von Grenzflächen- und Bulk-Transportprozessen auf die Ozonolyse und Nitrierung von Protein-Makromolekülen, Ölsäure, und verwandten organischen Ver¬bin-dungen aufzuklären. Die in dieser Studie entwickelten kinetischen Modelle sollen als Basis für die Entwicklung eines detaillierten Mechanismus für Aerosolchemie dienen sowie für das Herleiten von vereinfachten, jedoch realistischen Parametrisierungen für großskalige globale Atmosphären- und Klima-Modelle. rn Die in dieser Studie durchgeführten Experimente und Modellrechnungen liefern Beweise für die Bildung langlebiger reaktiver Sauerstoff-Intermediate (ROI) in der heterogenen Reaktion von Ozon mit Aerosolpartikeln. Die chemische Lebensdauer dieser Zwischenformen beträgt mehr als 100 s, deutlich länger als die Oberflächen-Verweilzeit von molekularem O3 (~10-9 s). Die ROIs erklären scheinbare Diskrepanzen zwischen früheren quantenmechanischen Berechnungen und kinetischen Experimenten. Sie spielen eine Schlüsselrolle in der chemischen Transformation sowie in den negativen Gesundheitseffekten von toxischen und allergenen Feinstaubkomponenten, wie Ruß, PAK und Proteine. ROIs sind vermutlich auch an der Zersetzung von Ozon auf mineralischem Staub und an der Bildung sowie am Wachstum von sekundären organischen Aerosolen beteiligt. Darüber hinaus bilden ROIs eine Verbindung zwischen atmosphärischen und biosphärischen Mehrphasenprozessen (chemische und biologische Alterung).rn Organische Verbindungen können als amorpher Feststoff oder in einem halbfesten Zustand vorliegen, der die Geschwindigkeit von heterogenen Reaktionenen und Mehrphasenprozessen in Aerosolen beeinflusst. Strömungsrohr-Experimente zeigen, dass die Ozonaufnahme und die oxidative Alterung von amorphen Proteinen durch Bulk-Diffusion kinetisch limitiert sind. Die reaktive Gasaufnahme zeigt eine deutliche Zunahme mit zunehmender Luftfeuchte, was durch eine Verringerung der Viskosität zu erklären ist, bedingt durch einen Phasenübergang der amorphen organischen Matrix von einem glasartigen zu einem halbfesten Zustand (feuchtigkeitsinduzierter Phasenübergang). Die chemische Lebensdauer reaktiver Verbindungen in organischen Partikeln kann von Sekunden bis zu Tagen ansteigen, da die Diffusionsrate in der halbfesten Phase bei niedriger Temperatur oder geringer Luftfeuchte um Größenordnungen absinken kann. Die Ergebnisse dieser Studie zeigen wie halbfeste Phasen die Auswirkung organischeer Aerosole auf Luftqualität, Gesundheit und Klima beeinflussen können. rn

Modeling and simulation of fibrinogen and its adsorption behavior

In this thesis different approaches for the modeling and simulation of the blood protein fibrinogen are presented. The approaches are meant to systematically connect the multiple time and length scales involved in the dynamics of fibrinogen in solution and at inorganic surfaces. The first part of the thesis will cover simulations of fibrinogen on an all atom level. Simulations of the fibrinogen protomer and dimer are performed in explicit solvent to characterize the dynamics of fibrinogen in solution. These simulations reveal an unexpectedly large and fast bending motion that is facilitated by molecular hinges located in the coiled-coil region of fibrinogen. This behavior is characterized by a bending and a dihedral angle and the distribution of these angles is measured. As a consequence of the atomistic detail of the simulations it is possible to illuminate small scale behavior in the binding pockets of fibrinogen that hints at a previously unknown allosteric effect. In a second step atomistic simulations of the fibrinogen protomer are performed at graphite and mica surfaces to investigate initial adsorption stages. These simulations highlight the different adsorption mechanisms at the hydrophobic graphite surface and the charged, hydrophilic mica surface. It is found that the initial adsorption happens in a preferred orientation on mica. Many effects of practical interest involve aggregates of many fibrinogen molecules. To investigate such systems, time and length scales need to be simulated that are not attainable in atomistic simulations. It is therefore necessary to develop lower resolution models of fibrinogen. This is done in the second part of the thesis. First a systematically coarse grained model is derived and parametrized based on the atomistic simulations of the first part. In this model the fibrinogen molecule is represented by 45 beads instead of nearly 31,000 atoms. The intra-molecular interactions of the beads are modeled as a heterogeneous elastic network while inter-molecular interactions are assumed to be a combination of electrostatic and van der Waals interaction. A method is presented that determines the charges assigned to beads by matching the electrostatic potential in the atomistic simulation. Lastly a phenomenological model is developed that represents fibrinogen by five beads connected by rigid rods with two hinges. This model only captures the large scale dynamics in the atomistic simulations but can shed light on experimental observations of fibrinogen conformations at inorganic surfaces.

Corpus-Based Speech Enhancement With Uncertainty Modeling and Cepstral Smoothing

We present a new approach for corpus-based speech enhancement that significantly improves over a method published by Xiao and Nickel in 2010. Corpus-based enhancement systems do not merely filter an incoming noisy signal, but resynthesize its speech content via an inventory of pre-recorded clean signals. The goal of the procedure is to perceptually improve the sound of speech signals in background noise. The proposed new method modifies Xiao's method in four significant ways. Firstly, it employs a Gaussian mixture model (GMM) instead of a vector quantizer in the phoneme recognition front-end. Secondly, the state decoding of the recognition stage is supported with an uncertainty modeling technique. With the GMM and the uncertainty modeling it is possible to eliminate the need for noise dependent system training. Thirdly, the post-processing of the original method via sinusoidal modeling is replaced with a powerful cepstral smoothing operation. And lastly, due to the improvements of these modifications, it is possible to extend the operational bandwidth of the procedure from 4 kHz to 8 kHz. The performance of the proposed method was evaluated across different noise types and different signal-to-noise ratios. The new method was able to significantly outperform traditional methods, including the one by Xiao and Nickel, in terms of PESQ scores and other objective quality measures. Results of subjective CMOS tests over a smaller set of test samples support our claims.

Geochemical Modeling and Analysis of the Frac Water Used in the Hydraulic Fracturing of the Marcellus Formation, Pennsylvania

The hydraulic fracturing of the Marcellus Formation creates a byproduct known as frac water. Five frac water samples were collected in Bradford County, PA. Inorganic chemical analysis, field parameters analysis, alkalinity titrations, total dissolved solids(TDS), total suspended solids (TSS), biological oxygen demand (BOD), and chemical oxygen demand (COD) were conducted on each sample to characterize frac water. A database of frac water chemistry results from across the state of Pennsylvania from multiple sources was compiled in order to provide the public and research communitywith an accurate characterization of frac water. Four geochemical models were created to model the reactions between frac water and the Marcellus Formation, Purcell Limestone, and the oil field brines presumed present in the formations. The average concentrations of chloride and TDS in the five frac water samples were 1.1 �± 0.5 x 105 mg/L (5.5X average seawater) and 140,000 mg/L (4X average seawater). BOD values for frac water immediately upon flow back were over 10X greater than the BOD of typical wastewater, but decreased into the range of typical wastewater after a short period of time. The COD of frac water decreases dramatically with an increase in elapsed time from flow back, but remain considerably higher than typicalwastewater. Different alkalinity calculation methods produced a range of alkalinity values for frac water: this result is most likely due to high concentrations of aliphatic acid anions present in the samples. Laboratory analyses indicate that the frac watercomposition is quite variable depending on the companies from which the water was collected, the geology of the local area, and number of fracturing jobs in which the frac water was used, but will require more treatment than typical wastewater regardless of theprecise composition of each sample. The geochemical models created suggest that the presence of organic complexes in an oil field brine and Marcellus Formation aid in the dissolution of ions such as bariumand strontium into the solution. Although equilibration reactions between the Marcellus Formation and the slickwater account for some of the final frac water composition, the predominant control of frac water composition appears to be the ratio of the mixture between the oil field brine and slickwater. The high concentration of barium in the frac water is likely due to the abundance of barite nodules in the Purcell Limestone, and the lack of sulfate in the frac water samples is due to the reducing, anoxic conditions in the earth's subsurface that allow for the degassing of H2S(g).

Modeling And Predicting The Performance Of Infiltrated Solid Oxide Fuel Cell Anodes

Fuel cells are a topic of high interest in the scientific community right now because of their ability to efficiently convert chemical energy into electrical energy. This thesis is focused on solid oxide fuel cells (SOFCs) because of their fuel flexibility, and is specifically concerned with the anode properties of SOFCs. The anodes are composed of a ceramic material (yttrium stabilized zirconia, or YSZ), and conducting material. Recent research has shown that an infiltrated anode may offer better performance at a lower cost. This thesis focuses on the creation of a model of an infiltrated anode that mimics the underlying physics of the production process. Using the model, several key parameters for anode performance are considered. These are the initial volume fraction of YSZ in the slurry before sintering, the final porosity of the composite anode after sintering, and the size of the YSZ and conducting particles in the composite. The performance measures of the anode, namely percolation threshold and effective conductivity, are analyzed as a function of these important input parameters. Simple two and three-dimensional percolation models are used to determine the conditions at which the full infiltrated anode would be investigated. These more simple models showed that the aspect ratio of the anode has no effect on the threshold or effective conductivity, and that cell sizes of 303 are needed to obtain accurate conductivity values. The full model of the infiltrated anode is able to predict the performance of the SOFC anodes and it can be seen that increasing the size of the YSZ decreases the percolation threshold and increases the effective conductivity at low conductor loadings. Similar trends are seen for a decrease in final porosity and a decrease in the initial volume fraction of YSZ.

Car ownership modeling and forecasts for China

A great increase of private car ownership took place in China from 1980 to 2009 with the development of the economy. To explain the relationship between car ownership and economic and social changes, an ordinary least squares linear regression model is developed using car ownership per capita as the dependent variable with GDP, savings deposits and highway mileages per capita as the independent variables. The model is tested and corrected for econometric problems such as spurious correlation and cointegration. Finally, the regression model is used to project oil consumption by the Chinese transportation sector through 2015. The result shows that about 2.0 million barrels of oil will be consumed by private cars in conservative scenario, and about 2.6 million barrels of oil per day in high case scenario in 2015. Both of them are much higher than the consumption level of 2009, which is 1.9 million barrels per day. It also shows that the annual growth rate of oil demand by transportation is 2.7% - 3.1% per year in the conservative scenario, and 6.9% - 7.3% per year in the high case forecast scenario from 2010 to 2015. As a result, actions like increasing oil efficiency need to be taken to deal with challenges of the increasing demand for oil.

Modeling and classifying variable width riparian zones utilizing digital elevation models, flood height data, digital soil data and national wetlands inventory : a new approach for riparian zone delineation

Riparian zones are dynamic, transitional ecosystems between aquatic and terrestrial ecosystems with well defined vegetation and soil characteristics. Development of an all-encompassing definition for riparian ecotones, because of their high variability, is challenging. However, there are two primary factors that all riparian ecotones are dependent on: the watercourse and its associated floodplain. Previous approaches to riparian boundary delineation have utilized fixed width buffers, but this methodology has proven to be inadequate as it only takes the watercourse into consideration and ignores critical geomorphology, associated vegetation and soil characteristics. Our approach offers advantages over other previously used methods by utilizing: the geospatial modeling capabilities of ArcMap GIS; a better sampling technique along the water course that can distinguish the 50-year flood plain, which is the optimal hydrologic descriptor of riparian ecotones; the Soil Survey Database (SSURGO) and National Wetland Inventory (NWI) databases to distinguish contiguous areas beyond the 50-year plain; and land use/cover characteristics associated with the delineated riparian zones. The model utilizes spatial data readily available from Federal and State agencies and geospatial clearinghouses. An accuracy assessment was performed to assess the impact of varying the 50-year flood height, changing the DEM spatial resolution (1, 3, 5 and 10m), and positional inaccuracies with the National Hydrography Dataset (NHD) streams layer on the boundary placement of the delineated variable width riparian ecotones area. The result of this study is a robust and automated GIS based model attached to ESRI ArcMap software to delineate and classify variable-width riparian ecotones.

Proteins in silico-modeling and sampling

Proteins are linear chain molecules made out of amino acids. Only when they fold to their native states, they become functional. This dissertation aims to model the solvent (environment) effect and to develop & implement enhanced sampling methods that enable a reliable study of the protein folding problem in silico. We have developed an enhanced solvation model based on the solution to the Poisson-Boltzmann equation in order to describe the solvent effect. Following the quantum mechanical Polarizable Continuum Model (PCM), we decomposed net solvation free energy into three physical terms– Polarization, Dispersion and Cavitation. All the terms were implemented, analyzed and parametrized individually to obtain a high level of accuracy. In order to describe the thermodynamics of proteins, their conformational space needs to be sampled thoroughly. Simulations of proteins are hampered by slow relaxation due to their rugged free-energy landscape, with the barriers between minima being higher than the thermal energy at physiological temperatures. In order to overcome this problem a number of approaches have been proposed of which replica exchange method (REM) is the most popular. In this dissertation we describe a new variant of canonical replica exchange method in the context of molecular dynamic simulation. The advantage of this new method is the easily tunable high acceptance rate for the replica exchange. We call our method Microcanonical Replica Exchange Molecular Dynamic (MREMD). We have described the theoretical frame work, comment on its actual implementation, and its application to Trp-cage mini-protein in implicit solvent. We have been able to correctly predict the folding thermodynamics of this protein using our approach.

STUDY OF HYBRID ELECTRIC VEHICLE BATTERY MODELING AND CONTROL USING AUTONOMIE

This report presents the research results of battery modeling and control for hybrid electric vehicles (HEV). The simulation study is conducted using plug-and-play powertrain and vehicle development software, Autonomie. The base vehicle model used for testing the performance of battery model and battery control strategy is the Prius MY04, a power-split hybrid electric vehicle model in Autonomie. To evaluate the battery performance for HEV applications, the Prius MY04 model and its powertrain energy flow in various vehicle operating modes are analyzed. The power outputs of the major powertrain components under different driving cycles are discussed with a focus on battery performance. The simulation results show that the vehicle fuel economy calculated by the Autonomie Prius MY04 model does not match very well with the official data provided by the department of energy (DOE). It is also found that the original battery model does not consider the impact of environmental temperature on battery cell capacities. To improve battery model, this study includes battery current loss on coulomb coefficient and the impact of environmental temperature on battery cell capacity in the model. In addition, voltage losses on both double layer effect and diffusion effect are included in the new battery model. The simulation results with new battery model show the reduced fuel economy error to the DOE data comparing with the original Autonomie Prius MY04 model.

ELECTRICITY GENERATION MODELING AND PHOTOVOLTAIC FORECASTS IN CHINA

With the economic development of China, the demand for electricity generation is rapidly increasing. To explain electricity generation, we use gross GDP, the ratio of urban population to rural population, the average per capita income of urban residents, the electricity price for industry in Beijing, and the policy shift that took place in China. Ordinary least squares (OLS) is used to develop a model for the 1979-2009 period. During the process of designing the model, econometric methods are used to test and develop the model. The final model is used to forecast total electricity generation and assess the possible role of photovoltaic generation. Due to the high demand for resources and serious environmental problems, China is pushing to develop the photovoltaic industry. The system price of PV is falling; therefore, photovoltaics may be competitive in the future.

MODELING AND PROTOCOL DESIGN FOR EMERGING WIRELESS SYSTEMS

Mobile Mesh Network based In-Transit Visibility (MMN-ITV) system facilitates global real-time tracking capability for the logistics system. In-transit containers form a multi-hop mesh network to forward the tracking information to the nearby sinks, which further deliver the information to the remote control center via satellite. The fundamental challenge to the MMN-ITV system is the energy constraint of the battery-operated containers. Coupled with the unique mobility pattern, cross-MMN behavior, and the large-spanned area, it is necessary to investigate the energy-efficient communication of the MMN-ITV system thoroughly. First of all, this dissertation models the energy-efficient routing under the unique pattern of the cross-MMN behavior. A new modeling approach, pseudo-dynamic modeling approach, is proposed to measure the energy-efficiency of the routing methods in the presence of the cross-MMN behavior. With this approach, it could be identified that the shortest-path routing and the load-balanced routing is energy-efficient in mobile networks and static networks respectively. For the MMN-ITV system with both mobile and static MMNs, an energy-efficient routing method, energy-threshold routing, is proposed to achieve the best tradeoff between them. Secondly, due to the cross-MMN behavior, neighbor discovery is executed frequently to help the new containers join the MMN, hence, consumes similar amount of energy as that of the data communication. By exploiting the unique pattern of the cross-MMN behavior, this dissertation proposes energy-efficient neighbor discovery wakeup schedules to save up to 60% of the energy for neighbor discovery. Vehicular Ad Hoc Networks (VANETs)-based inter-vehicle communications is by now growingly believed to enhance traffic safety and transportation management with low cost. The end-to-end delay is critical for the time-sensitive safety applications in VANETs, and can be a decisive performance metric for VANETs. This dissertation presents a complete analytical model to evaluate the end-to-end delay against the transmission range and the packet arrival rate. This model illustrates a significant end-to-end delay increase from non-saturated networks to saturated networks. It hence suggests that the distributed power control and admission control protocols for VANETs should aim at improving the real-time capacity (the maximum packet generation rate without causing saturation), instead of the delay itself. Based on the above model, it could be determined that adopting uniform transmission range for every vehicle may hinder the delay performance improvement, since it does not allow the coexistence of the short path length and the low interference. Clusters are proposed to configure non-uniform transmission range for the vehicles. Analysis and simulation confirm that such configuration can enhance the real-time capacity. In addition, it provides an improved trade off between the end-to-end delay and the network capacity. A distributed clustering protocol with minimum message overhead is proposed, which achieves low convergence time.