Dissertations / Theses on the topic 'Novel hybrid approach'

Large Eddy Simulation is a method of obtaining high accuracy computational results for modelling fluid flow. Unfortunately it is computationally expensive limiting it to users of large parallel machines. However, it may be that the use of LES leads to an over-resolution of the problem because the bulk of the computational domain could be adequately modelled using the Reynolds averaged approach. A study has been undertaken to assess the feasibility, both in accuracy and computational efficiency of using a parallel computer to solve both LES and RANS type turbulence models on the same domain for the problem flow over a circular cylinder at Reynolds number 3 900 To do this the domain has been created and then divided into two sub-domains, one for the LES model and one for the kappa - epsilon turbulence model. The hybrid model has been developed specifically for a parallel computing environment and the user is able to allocate modelling techniques to processors in a way which enables expansion of the model to any number of processors. Computational experimentation has shown that the combination of the Smagorinsky model can be used to capture the vortex shedding from the cylinder and the information successfully passed to the kappa - epsilon model for the dissipation of the vortices further downstream. The results have been compared to high accuracy LES results and with both kappa - epsilon and Smagorinsky LES computations on the same domain. The hybrid models developed compare well with the Smagorinsky model capturing the vortex shedding with the correct periodicity. Suggestions for future work have been made to develop this idea further, and to investigate the possibility of using the technology for the modelling of mixing and fast chemical reactions based on the more accurate prediction of the turbulence levels in the LES sub-domain.

Among the various approaches to 3-D imaging, structured light illumination (SLI) is widely spread. SLI employs a pair of digital projector and digital camera such that the correspondences can be found based upon the projecting and capturing of a group of designed light patterns. As an active sensing method, SLI is known for its robustness and high accuracy. In this dissertation, I study the phase shifting method (PSM), which is one of the most employed strategy in SLI. And, three novel approaches in PSM have been proposed in this dissertation. First, by regarding the design of patterns as placing points in an N-dimensional space, I take the phase measuring profilometry (PMP) as an example and propose the edge-pattern strategy which achieves maximum signal to noise ratio (SNR) for the projected patterns. Second, I develop a novel period information embedded pattern strategy for fast, reliable 3-D data acquisition and reconstruction. The proposed period coded phase shifting strategy removes the depth ambiguity associated with traditional phase shifting patterns without reducing phase accuracy or increasing the number of projected patterns. Thus, it can be employed for high accuracy realtime 3-D system. Then, I propose a hybrid approach for high quality 3-D reconstructions with only a small number of illumination patterns by maximizing the use of correspondence information from the phase, texture, and modulation data derived from multi-view, PMP-based, SLI images, without rigorously synchronizing the cameras and projectors and calibrating the device gammas. Experimental results demonstrate the advantages of the proposed novel strategies for 3-D SLI systems.

This work investigates new approaches to the container loading problem which address the issue of how to load three-dimensional, rectangular items (e.g. boxes) into the container in such a way that maximum utilisation is made of the container space. This problem occurs in several industry sectors where the loading approach places cargo effectively into aeroplanes, ships, trailers or trucks in order to save considerable cost. In carrying out this work, the investigation starts by developing a new heuristic approach to the two-dimensional bin packing problem, which has lower complexity than container loading in the aspects of constraints and geometry. A novel approach, including the heuristic strategies and handling method for remaining areas, is developed that can produce good results when testing with benchmark and real world data. Based on the research for two-dimensional bin packing, a novel heuristic approach is developed to deal with the container loading problem with some practical constraints. The heuristic approach to container loading also includes heuristic strategies and the handling of remaining spaces. The heuristic strategies construct effective loading arrangements where combinations of identical or different box types are loaded in blocks. The handling method for remaining spaces further improves the loading arrangements through the representation, partitioning and merging of remaining spaces. The heuristic approach obtains better volume utilisation and the highest stability compared with other published heuristic approaches. However, it does not achieve as high a volume utilisation as metaheuristic approaches, e.g. genetic algorithms and tabu search.To improve volume utilisation, a new hybrid heuristic approach to the container loading problem is further developed based on the tabu search technique which covers the encoding, evaluation criterion and configuration of neighbourhood and candidate solutions. The heuristic strategies as well as the handling method for remaining spaces developed in the heuristic approach are used in this new hybrid tabu search approach. It is shown that the hybrid approach has better volume utilisation than the published approaches under the condition that all loaded boxes with one hundred per cent support from below. In addition, the experimental results show that both the heuristic and hybrid tabu search approaches can also be applied to the multiple container loading problem.

碩士
中原大學
化學系
88
Organic-Inorganic hybrid materials are the materials that combining the characteristics from multiplicated speciality of the organic and inorganic moieties. One of the way to produce these series of materials is so called “Sol-Gel” reaction. The major content of this essay is to enhance the life time of electrochromic character of aniline oligomer on ITO glass by incorporating the inorganic sol-gel component. After doping by CSA, the organic-inorganic hybrid electrochromic window was found to effectively increase the adhesion of hybrid materials on ITO glass based on the CV and Scotch Tape test results. The electrochemical polymerization of ortho-substituted polyanilines are also investigated.

Over the past several years, there has been an explosion in the amount of medical data generated and subsequently collected in medical domain. Data mining techniques have been used extensively in mining the medical data. Obtaining high quality data mining results is very challenging because of the inconsistency of the results of different data mining algorithms and noise in the medical data.

This thesis presents a novel hybrid data mining approach for knowledge extraction and classification in medical databases. The proposed approach is formulated to cluster extracted features from medical databases into soft clusters using unsupervised learning strategies and fuse the decisions using serial and parallel data fusion techniques. The idea is to observe associations in the features and fuse the decisions made by learning algorithms to find the strong clusters which can make impact on overall classification accuracy. The novel techniques such as serial cascaded data fusion, parallel majority-voting based neural data fusion and parallel neural network based data fusion are proposed that allow integration of various clustering algorithms for hybrid data mining approach.

The proposed approach has been implemented and evaluated on the benchmark databases such as Digital Database for Screening Mammograms, Wisconsin Breast Cancer, Pima Indian Diabetics and ECG Heart Arrhythmia.

A comparative performance analysis of the proposed hybrid data mining approach with other existing approaches for knowledge extraction and classification is presented. The experimental results demonstrate the effectiveness of the proposed approach in terms of improved classification accuracy on benchmark medical databases.

abstract: Cigarette smoking remains a major global public health issue. This is partially due to the chronic and relapsing nature of tobacco use, which contributes to the approximately 90% quit attempt failure rate. The recent rise in mobile technologies has led to an increased ability to frequently measure smoking behaviors and related constructs over time, i.e., obtain intensive longitudinal data (ILD). Dynamical systems modeling and system identification methods from engineering offer a means to leverage ILD in order to better model dynamic smoking behaviors. In this dissertation, two sets of dynamical systems models are estimated using ILD from a smoking cessation clinical trial: one set describes cessation as a craving-mediated process; a second set was reverse-engineered and describes a psychological self-regulation process in which smoking activity regulates craving levels. The estimated expressions suggest that self-regulation more accurately describes cessation behavior change, and that the psychological self-regulator resembles a proportional-with-filter controller. In contrast to current clinical practice, adaptive smoking cessation interventions seek to personalize cessation treatment over time. An intervention of this nature generally reflects a control system with feedback and feedforward components, suggesting its design could benefit from a control systems engineering perspective. An adaptive intervention is designed in this dissertation in the form of a Hybrid Model Predictive Control (HMPC) decision algorithm. This algorithm assigns counseling, bupropion, and nicotine lozenges each day to promote tracking of target smoking and craving levels. Demonstrated through a diverse series of simulations, this HMPC-based intervention can aid a successful cessation attempt. Objective function weights and three-degree-of-freedom tuning parameters can be sensibly selected to achieve intervention performance goals despite strict clinical and operational constraints. Such tuning largely affects the rate at which peak bupropion and lozenge dosages are assigned; total post-quit smoking levels, craving offset, and other performance metrics are consequently affected. Overall, the interconnected nature of the smoking and craving controlled variables facilitate the controller's robust decision-making capabilities, even despite the presence of noise or plant-model mismatch. Altogether, this dissertation lays the conceptual and computational groundwork for future efforts to utilize engineering concepts to further study smoking behaviors and to optimize smoking cessation interventions.
Dissertation/Thesis
Doctoral Dissertation Bioengineering 2014

Synthetic chemistry has played a pivotal role in the evolution of modern life. More recently, the emerging field of synthetic biology holds the promise to bring about a paradigm shift with designer microbes to renewably synthesize complex molecules in a fraction of the time and cost. Still, given synthetic chemistry’s superior parsing powers to access a greater number of unnatural end products and nature’s virtuosity at stitching a staggering palette of carbon frameworks with ease, a hybrid approach that leverages the respective strengths of the two fields could prove advantageous for the efficient production of valuable natural molecules and their analogs. In a first demonstration of the hybrid approach where the biosynthesized intermediate is not part of the target molecule’s biosynthetic pathway, we engineered E. coli to produce Z,E-farnesol, which we subsequently transformed into a library of novel analogs of the commercially important amber fragrance Ambrox®, including the first synthetic patchouli scent. In a second demonstration of the hybrid approach, we produced the valuable tocotrienols (vitamin E) from yeast-produced geranylgeraniol in a single step C–C coupling with concomitant regioselective cycloetherification of the most proximal vinyl of the polyene, the first such process of its kind. The novel acid catalyst system that allowed for this unique regioselective cyclization holds promise as an asymmetric proton transfer tool and could open the door to facile asymmetric synthesis of vitamin E and other molecules.

The complexity and aggressiveness of cancer, as well as its increasing incidence and mortality worldwide, prompts the search for novel and alternative therapeutic strategies with improved effectiveness and safety. In this sense, the identification of therapeutic targets, the discovery of new molecules with antitumor potential, and the design of drug delivery systems create opportunities for a successful cancer management. Among compounds with promising anticancer activity are metal-based complexes and hybrid molecules, with some already approved for clinical use and others undergoing clinical trials or in preclinical research. In the present work, two compounds with promising anticancer potential were studied, the Cu2+ complex Cuphen [Cu(phen)Cl2] and a dual acting hybrid molecule, HM, containing two moieties – a DNA alkylating triazene and a ʟ-tyrosine analogue, 4-S-CAP, with high specificity for tyrosinase. Cuphen may act through the modulation of aquaporins (AQPs), inhibiting AQP3-mediated glycerol transport and affecting cell migration. In turn, the dual acting HM demonstrated a superior antiproliferative activity compared to the clinically approved temozolomide. Also, HM significantly inhibited tyrosinase activity and arrested cell cycle in G0/G1 phase. Following these promising in vitro results, the next goal was to maximize the in vivo therapeutic efficacy of these compounds by exploring the versatility of the most successful lipid-based nanosystem, liposomes. Long circulating liposomal formulations, with suitable physicochemical properties for each molecule, were designed and evaluated in preclinical studies. In the case of Cuphen, liposomes with pH-sensitive properties were designed to promote a locally-triggered release at the slightly acidic tumor microenvironment. In vivo, liposomal Cuphen significantly reduced melanoma and colon cancer progression, compared to free form. Furthermore, Cuphen liposomes displaying magnetic properties were successfully developed for further increase their accumulation at tumor sites upon application of an external magnetic field. For HM, an efficient incorporation in long circulating liposomes was obtained. In a subcutaneous murine melanoma model, liposomal HM remarkably reduced tumor progression, compared to free HM. Moreover, in a syngeneic metastatic melanoma model, a reduction on the number of lung metastases was observed for liposomal HM compared to all groups, including the positive control temozolomide. Remarkably, in the subcutaneous melanoma model, biodistribution studies of LIP HM showed that, 48 h post-administration, 4% of the injected dose per gram of tumor was attained, correlating with the obtained therapeutic activity. Importantly, all developed nanoformulations, for both anticancer compounds, demonstrated to be safe for parenteral administration, in healthy animals, not eliciting hepatic toxic side effects neither hemolytic activity. Furthermore, the long-term stability of liposomes in lyophilized form was achieved using an appropriate cryoprotectant. In conclusion, these encouraging results demonstrate the advantages of exploring novel therapeutic targets and compounds, particularly when associated to liposomes as a delivery system, to potentiate their safety and therapeutic effectiveness for cancer management.

The research presented in this dissertation focuses on the design and synthesis of novel hybrid conjugated polymer materials using two different approaches: (1) inorganic/organic hybrid semiconductors through the incorporation of carboranes into the polymer structure and (2) the modification of surfaces with conjugated polymers via grafting approaches. Hybrid conjugated polymeric materials, which are materials or systems in which conjugated polymers are chemically integrated with non-traditional structures or surfaces, have the potential to harness useful properties from both components of the material to help overcome hurdles in their practical realization in polymer-based devices. This work is centered around the synthetic challenges of creating new hybrid conjugated systems and their potential for advancing the field of polymer-based electronics through both greater understanding of the behavior of hybrid systems, and access to improved performance and new applications. Chapter 1 highlights the potential applications and advantages for these hybrid systems, and provides some historical perspective, along with relevant background materials, to illustrate the rationale behind this work. Chapter 2 explores the synthesis of poly(fluorene)s with pendant carborane cages. The Ni(0) dehalogenative polymerization of a dibromofluorene with pendant carborane cages tethered to the bridging 9-position produced hybrid polymers produced polymers which combined the useful emissive characteristics of poly(fluorene) with the thermal and chemical stability of carborane cages. The materials were found to display increased glass transition temperatures and showed improved emission color stability after annealing at high temperatures relative to the non-hybrid polymer. The design and synthesis of a poly(fluorene)-based hybrid material with carborane cages in the backbone, rather than as pendant groups, begins in chapter 3. Poly(fluorene) with p-carborane in the backbone is synthesized and characterized, and the material is found to be a high MW, soluble blue emitter which shows a higher glass transition temperature and greater stability than a non-hybrid polymer. UV absorbance and fluorescence spectroscopy indicated some electronic interaction between the conjugated polymer and the cages, but they did not appear to be fully conjugated in the traditional sense. Chapter 4 describes the design, synthesis, and characterization of poly(fluorene) with o-carborane in the backbone. Profound changes in the behavior of the polymer, from its polymerization behavior to its emission characteristics, were observed and their origins are discussed. Experiments to explore the nature of the cage/polymer interactions were performed and possible applications which take advantage of the unique nature of the o-carborane hybrid polymer are explored and discussed. Hybrid conjugated polymer materials via grafting approaches to surfaces and surface modification are discussed starting in chapter 5. The synthesis of a dibromofluorene-based silane coupling agent for the surface functionalization of oxide surfaces is presented, and the surface directed Ni(0) dehalogenative polymerization of poly(dihexylfluorene) is explored. Chapter 6 focuses on the exploration of conjugated polymer/cellulose hybrid materials. Surface medication of cellulose materials with monomer-like anchor points is discussed. Grafting of the modified cellulose with conjugated polymers was explored and the grafting of three different repeat structures based on fluorene-, fluorenevinylene-, and fluoreneethynylene motifs were optimized to provide a general route to cellulose/conjugated polymer hybrid materials. Characterization and possible applications of such hybrid materials are discussed. Finally, chapter 7 is devoted to the simultaneous surface patterning and functionalization of poly(2-hydroxyethylmethacrylate) thin films using a silane infusion-based wrinkling technique. While not a conjugated polymer system, the spontaneous patterning and functionalization methods explored in this chapter produce hybrid organic/inorganic polymer thin films which have applications that range from optics, to adhesion, to polymer-based electronics, and the research compliments the other chapters. The spontaneous generation of complex patterns, of a small scale approaching 100nm feature size, over a large area with simultaneous control over surface chemistry is explored. Examples of complex, hierarchically patterned films which integrate lithographic processes such as nanoimprint lithography and electron beam lithography with spontaneous patterning via wrinkling are presented.

The controlled release of a drug from a carrier into a medium over a defined period of time is referred to as Controlled Drug Release (CDR). A major challenge for a sustainable and reproducible CDR is the unintentional initial burst, which occurs in the first hours/days of immersion and during which a large amount of drug is released. Also it can have deleterious effects on the host. Burst release happens with both small drug molecules and large proteins and for both drug-loaded PLGA micro- and nanoparticles. Particle design can, in principal, be used to control the amount of burst but no systematic methods are to date available and the design process is governed by trial and error. One reason might be that the available models for burst release do not explicitly account for the particle design parameters. This thesis proposes novel methodologies that allow for rational design of drug-loaded PLGA micro- and nanoparticles. It is divided in three main parts. Firstly, a quantitative analysis of the physicochemical factors that impact on the amount of burst release and the burst release rate using partial least squares and decision tree methods is performed. The factors with the greatest impact are selected for the subsequent modelling activities. Next, a bootstrap aggregated hybrid model (HM) is developed, which can successfully predict the cumulative drug release of an independent set of CDR experiments. Lastly, a new rational design method is presented for the optimization of the formulation characteristics of protein-loaded PLGA nanoparticles. The method is successfully applied to design the carrier of a mock-protein, α- chymotrypsin, yielding a close to desired release profile. The method can also help to judge upon the similarity of the mock protein with a target protein in terms of their similarities in burst release behavior. This thesis proposes the first rational PLGA particle design method requiring only the specification of the drug and the desired burst release profile. The application of the method can be expected to significantly reduce the time for PLGA particle development. With the increasing availability of CDR data the predictive power of the method can be further improved towards a systematic and reliable tool. The engine of the method is the hybrid model which links the release profile to the design parameters and is the first of its kind in drug release modeling.