A survey of the existing literature indicates that optimization on the power management logic of hybrid electric vehicle is mostly performed after the design of the powertrain architecture or the power source components are finalized. The goal of this research is to utilize Multidisciplinary Design Optimization (MDO) to automate and optimize the vehicle’s powertrain component sizes, while simultaneously determining the optimal power management logic in developing the most cost-effective system solution. A generic, modular, and flexible vehicle model utilizing a backward-looking architecture is created using scalable powertrain components. The objective of the research work is to study the energy efficiency of the vehicle system, where the dynamics of the vehicle is not of concern; a backward-looking architecture could be used to compute the power consumption and the overall efficiency accurately while minimizing the required computing resource. An optimization software platform utilizing multidisciplinary design optimization approach is implemented containing the generic vehicle model and an optimizer of the user’s choice. The software model is created in the MATLAB/Simulink environment, where the optimization code and the powertrain component properties are implemented using m-files, and the power consumption calculations of the vehicle system are performed in Simulink. Furthermore, a feature-based optimization technique is developed with the motivation of significantly reducing the simulation run-time. To demonstrate the capabilities of the developed approach and contributions of the research, two optimization case studies are undertaken: (i) series hybrid electric vehicles, and (ii) police vehicle anti-idling system. As the first case study, a plug-in battery-only series hybrid electric vehicle with similar power components as the Chevrolet Volt is created, where the battery size and the power management logic are simultaneously optimized. The objective function of the optimizer is defined from the financial cost perspective, where the objective is to minimize the initial cost of batteries, gasoline and electricity consumption over a period of five years, and the carbon tax as a penalty function for fuel emissions. The battery-only series hybrid electric vehicle is subsequently extended to include ultracapacitors, and the optimization process is expanded to the rest of the powertrain components and power management logic. A comparison between the optimization algorithms found that only genetic algorithm (GA) was capable of finding the optimal solution during a full simulation, while simulated annealing and pattern search were not able to converge to any solution after a 24-hour period. A comparison between the full genetic algorithm optimization and the feature-based (FB) method with secondary optimization found that although the final cost function of the FB methodology is higher than that of the full GA optimization, the total simulation run-time is approximately ten times less using the FB method. The behaviour of the solutions found via both methods exhibited almost identical characteristics, further confirming the validity of the feature-based methodology. Finally, a benchmarking comparison found that with more accurate manufacturers’ component data and additional appropriate performance requirements, the proposed software platform will be capable of predicting a solution that is comparable to the Chevrolet Volt. The second case study involves optimizing an anti-idling system for police vehicles using the same optimization algorithm and generic vehicle model. The goal of the optimization study is to select an additional battery and determine the power management logic to reduce the engine idling time of a police vehicle. It is found that depending on the SOC threshold, the duration of time over which the engine is activated varies in a non-linear fashion, where local minima and maxima exist. A design study confirmed that by utilizing the anti-idling system, significant cost reduction can be realized when compared to one without the anti-idling system. A comparison between the various optimization algorithms showed that the feature-based optimization can obtain a relatively accurate solution while reducing simulation time by approximately 90%. This significant reduction in simulation time warrants the feature-based optimization technique a powerful tool for vehicle design. Due to the high cost of the electrical energy storage components, it is currently still more cost-effective to use the fossil fuel as the primary energy source for transportation. However, given the rise of fuel cost and the advancement in the electrical energy storage technology, it is inevitable that the cost of the electrical and chemical energy storage method will reach a balance point. The proposed optimization platform allows the user the capability and flexibility to obtain the optimal vehicle solution with ease at any given time in the future.

An air hybrid vehicle is an alternative to the electric hybrid vehicle that stores the kinetic energy of the vehicle during braking in the form of pressurized air. In this thesis, a novel compression strategy for an air hybrid engine is developed that increases the efficiency of conventional air hybrid engines significantly. The new air hybrid engine utilizes a new compression process in which two air tanks are used to increase the air pressure during the engine compressor mode. To develop the new engine, its mathematical model is derived and validated using GT-Power software. An experimental setup has also been designed to test the performance of the proposed system. The experimental results show the superiority of the new configuration over conventional single-tank system in storing energy. In addition, a new switchable cam-based valvetrain and cylinder head is proposed to eliminate the need for a fully flexible valve system in air hybrid engines. The cam-based valvetrain can be used both for the conventional and the proposed double-tank air hybrid engines. To control the engine braking torque using this valvetrain, the same throttle that controls the traction torque is used. Model-based and model-free control methods are adopted to develop a controller for the engine braking torque. The new throttle-based air hybrid engine torque control is modeled and validated by simulation and experiments. The fuel economy obtained in a drive cycle by a double-tank air hybrid vehicle is evaluated and compared to that of a single-tank air hybrid vehicle.

DNA post-replication repair (PRR) is a cellular tolerance mechanism by which eukaryotic cells survive lethal lesions during or after DNA synthesis. In the yeast Saccharomyces cerevisiae, modification of proliferating cell nuclear antigen (PCNA) by ubiquitin and by small ubiquitin-like modifier (SUMO) plays an important role in PRR. PCNA ubiquitination is dependent on Rad6, a ubiquitin-conjugating enzyme (E2) and Rad18, a ubiquitin ligase (E3). Rad6 and Rad18 form a stable complex. PCNA sumoylation is dependent on Ubc9, an E2 specific to SUMO modification. <p>PRR in mammalian cells is less well understood. However, human Rad18 (hRad18) has been found to interact with human Rad6 (HHR6A/B). In this study, we detected physical interaction between hRad18 and human Ubc9 (hUbc9) through yeast two-hybrid assays. In order to define the domain(s) of hRad18 involved in the formation of a complex with HHR6B or hUbc9, a series of yeast two-hybrid constructs containing various hRAD18 gene deletions and mutations were made. A C-terminal region of hRad18, containing the putative HHR6A/B binding domain (amino acids 340 to 395), interacts with HHR6A/B while the N-terminus (amino acids 1-93) does not. Yeast Rad18 has a homologous fragment of the HHR6A/B binding domain and this fragment is sufficient to interact with yeast Rad6 in yeast two-hybrid assays, so we infer that hRad18 interacts with HHR6B through the same domain. Surprisingly, both the N-terminal and C-terminal fragments of hRad18 can interact with hUbc9, suggesting the existence of two separate domains in hRad18 interacting with hUbc9. The N-terminal fragment of hRad18 contains only a RING finger domain (amino acids 25-64), which is probably responsible for binding to hUbc9. The C-terminal fragment of hRad18 with HHR6A/B binding domain deletion can still interact with hUbc9, suggesting that the HHR6A/B binding domain is not involved in hUbc9 interaction. A key cysteine mutation (C28F) in the RING finger domain abolished the interactions of hRad18 with both HHR6A/B and hUbc9. This amino acid substitution is likely to alter the three-dimensional structure of the protein, thus making the protein unstable. Taken together, results obtained from this study suggest that hRad18 may regulate the modification status of PCNA by interacting with two different E2s, HHR6A/B and hUbc9, through distinct domains.

The goal of this research is to understand how three different parameters affect single chain variable fragment (scFv) binding capacity. The parameters that were varied include the number of variable complementarity determining regions (CDRs), the number amino acids used to diversify CDRs, and configuration of the structure. How the parameters affect the binding capacity will be tested using the yeast two hybrid assay against five different protein domains. Eight scFv libraries were generated; the genes expressing the scFvs were constructed and the CDRs were randomized using PCR amplification. Genes expressing scFvs were cloned, using the homologous gap repair mechanism in <i>Saccharomyces cerevisiae</I>. Representative members of scFv libraries were sequenced to confirm correct construction.<p> Library diversity was calculated from the library transformation efficiency. Transformation efficiency refers to the number of cells that grew at the time of transformation of the scFv gene into yeast cells. There were significant differences in the diversity of the scFv libraries, which created difficulty in comparing the library binding capacities. Sequencing the scFv libraries revealed that on average 50% of each library contained correct scFv sequences. The percent of correct sequences within each library was then used to calculate the functional diversity.<p> The yeast two-hybrid assay was used to screen the scFv libraries for interactions and to test binding capacity. The binding capacity of the scFv libraries was tested and compared in five different yeast two-hybrid assays using five protein domains as the targets for each screen. The screening results showed that in all cases cyclic scFv libraries had a statistically significant higher binding capacity than linear scFv libraries despite a diversity bias against the cyclic libraries. There was no clear trend in binding capacity with the other two parameters; however, the four amino acid three CDR libraries dominated over the other libraries in almost every screen.<p> Some of the scFvs isolated from the screens were expressed in <i>E. coli</i> and <i>S. cerevisiae</i>to analyze for proper expression and correct size. All the scFvs that were isolated and analyzed were the correct size and could be purified using a poly histidine tag.<p> Due to its bioaffinity and specificity, scFvs were constructed to profile disease patterns, and to identify potential drug targets. In addition to its original application to health-related studies, scFvs could also be extended to locate potential metabolic bottlenecks, to alter metabolic flux to enhance productivity, and regulate metabolic bionetworks. Industrial microorganisms are generally carrying more than two sets of chromosomes, making it difficult to be genetically engineered when conventional approaches are employed. With the availability of scFvs as reported in this thesis, we are able to design specific scFvs that selectively bind to target proteins, resulting in re-routing of metabolic flux within the microorganism, toward a high productivity of desired product. ScFvs can be applied to industrial microorganisms directly, leading to the development of new fermentation processes.

Many researchers and engineers designing laser drivers for data rates at or above 10 gigabits per second (Gbps) implemented their designs using integrated circuit technologies that provide high bandwidth and good quality passive components such as GaAs, silicon bipolar, and InP. However, in low-cost and high volume short-haul applications at data rates of around 10 Gbps (such as LAN, MAN, and board-to-board interconnection), there has been an increasing interest in commercial CMOS technology for implementing the laser driver. This is because CMOS technology has unique advantages such as low power and low cost of fabrication that are the result of high yield and a high degree of integration. Therefore, the objective of this research in this dissertation is to investigate the possibility of implementing a high-speed CMOS laser driver for these cost sensitive applications. The high-speed CMOS laser drivers designed in this research are of two types. The first type is a low power laser driver for driving a vertical cavity surface emitting laser (VCSEL). The other driver type is a high current laser driver for driving edge-emitting lasers such as double-heterojunction (DH), multiquantum well (MQW), or Febry-Perrot (FP) lasers. The parasitic effects of the layout geometry are crucial in the design of the high-speed laser drivers. Thus, in this research, all simulations contain a complete set of parasitic elements extracted from the layout of the laser driver. To test laser drivers, chip-on-board (COB) technology is employed, and printed circuit boards (PCBs) to test the laser drivers are designed at the same time as the laser drivers themselves and manufactured specifically for these tests. This research makes two significant new contributions to the technology that are reported and described here. One is the first 10 Gbps performance of a differential CMOS laser driver with better than 10-14 bit-error-rate (BER). The second is the first demonstration of a heterogeneous integration method to integrate independently grown and customized thin film lasers onto CMOS laser driver circuits to form an optical transmitter.

Disulfide bonds are special covalent cross links between two cysteines in a protein. This kind of bonding state plays an important role in protein folding and stabilization. For connectivity pattern prediction, it is a very difficult problem because of the fast growth of possible patterns with respect to the number of cysteines. In this thesis, we propose a new approach to address this problem. The method is based on hybrid models with SVM. Via this strategy, we can improve the prediction accuracies by selecting appropriate models. In order to evaluate the performance of our method, we apply the method by 4-fold cross-validation on SP39 dataset, which contains 446 proteins. We achieve accuracies with 70.8% and 65.9% for pair-wise and pattern-wise prediction respectively, which is better than the previous works.

A MANET (Mobile Ad hoc NETwork) is a network with the features of infrastructure-less, multi-hop, self-configuring, and distributed-routing, which are quite different from a traditional wired network. Since nodes in a MANET are free to move, causing the topology of the MANET to change frequently, a routing protocol able to accommodate the rapidly changed topology is required. The MANET routing protocols can be classified into three categories based on routing information update mechanism: (1) proactive/table-driven protocol (2) reactive/on-demand protocol (3) hybrid protocol. Every category has its own advantages and disadvantages. Among these, the hybrid protocol tries to combine the advantages of proactive and reactive ones. This work presents a novel hybrid routing protocol - CG2R (Core Gateway Relay Protocol). The CG2R partitions a network into several regions called zones. The proactive mechanism is used within the zone, while the reactive one is applied outside the zone. Each zone contains one core gateway; the core gateway constitutes the backbone of the routing path. Unlike conventional cluster-based routing protocols which require the algorithm of electing cluster head to get some value such as ID number or Weight to elect cluster heads, the node in CG2R can decide itself a core gateway or not by using the algorithm that we present. The core gateway covers more cells and manages more nodes than the others to reduce the cases of a node moving out the zone. Based on this feature, the backbone of the network can be formed by the gateway nodes. The simulation results reveal that CG2R is more scalable and efficient than CGSR and AODV protocols.

This research is to synthesize polyaniline (PANI) thin film on the Poly(3,4-ethylenedioxythiophene):poly(styrene sulfonic acid) (PEDOT:PSS) by using potentiostatic deposition of electrochemical method. The hybrid film composed of PEDOT:PSS and PANI was fabricated to replace the ITO layer for polymer solar cells as an anode. In the future, the hybrid film can develop the flexible polymer solar cells. In this study, we fixed the total thickness of the hybrid film, and we investigated optical transmittance, conductivity, Highest Occupied Molecular Orbital (HOMO), surface roughness, and surface morphology of hybrid films by changing the ratio of PEDOT:PSS and PANI, and to discuss the factors on device efficiency. Then, we compared the device structures with anode made by PEDOT: PSS. We found the hybrid films fabricated with different ratio of PEDOT:PSS and PANI, and the HOMO results were similar. In addition, we found optical transmittance, conductivity, surface roughness, and surface morphology of hybrid films that varies with different ratio of PEDOT:PSS and PANI. The power conversion efficiencies of the device mainly were affected by the surface roughness and morphology of the hybrid film surface. Comparing to other parameters, the hybrid film fabricated by PEDOOT:PSS(280nm) and PANI(30nm) owns the most appropriate surface roughness and surface morphology. The power conversion efficiency(PCE) was up to 0.68%, and then via post-annealing of 90¢J 10 minutes the PCE was increase to 1.06% under AM 1.5G illumination based on PEDOT:PSS (280 nm) / PANI (30 nm) / P3HT: PCBM (100 nm) / Al (200 nm), and the device area of 0.16 cm2.

Linearized equations of motion are important in engineering applications, especially with respect to stability analysis and control design. Traditionally, the full, nonlinear equations are formed and then linearized about the desired equilibrium configuration using methods such as Taylor series expansions. However, it has been shown that the quadratic form of the Lagrangian function can be used to directly linearize the equations of motion for discrete dynamical systems. Here, this development is extended to directly generate linearized equations of motion for both continuous and hybrid dynamical systems, where a hybrid system is described with both discrete and continuous generalized coordinates. The results presented require only velocity level kinematics to form the Lagrangian and find equilibrium configuration(s) for the system. A set of partial derivatives of the Lagrangian are then computed and used to directly construct the linearized equations of motion about the equilibrium configuration of interest. This study shows that the entire nonlinear equations of motion do not have to be generated in order to construct the linearized equations of motion. Several examples are presented to illustrate application of these results to both continuous and hybrid system problems.

Executing sequential code in parallel on a multithreaded machine has been an elusive goal of the academic and industrial research communities for many years. It has recently become more important due to the widespread introduction of multicores in PCs. Automatic multithreading has not been achieved because classic, static compiler analysis was not powerful enough and program behavior was found to be, in many cases, input dependent. Speculative thread level parallelization was a welcome avenue for advancing parallelization coverage but its performance was not always optimal due to the sometimes unnecessary overhead of checking every dynamic memory reference. In this dissertation we introduce a novel analysis technique, Hybrid Analysis, which unifies static and dynamic memory reference techniques into a seamless compiler framework which extracts almost maximum available parallelism from scientific codes and incurs close to the minimum necessary run time overhead. We present how to extract maximum information from the quantities that could not be sufficiently analyzed through static compiler methods, and how to generate sufficient conditions which, when evaluated dynamically, can validate optimizations. Our techniques have been fully implemented in the Polaris compiler and resulted in whole program speedups on a large number of industry standard benchmark applications.