Plasmid DNA purification and formulation for vaccine applications

Plasmid DMA offers the promise of a new generation of pharmaceuticals that will address the often overlooked issue of vaccine production by offering a simple and reproducible method for producing a vaccine. Through reverse engineering, production could be reduced from up to 9 months to as little as 1 month. Simplified development and faster turn-around times means that DMA offers a solution to the vaccine crisis and will help to contain future viral outbreaks by enabling the production of a vaccine against new viral strains in the shortest possible time. Work currently being completed in the area of plasmid DMA production, purification and encapsulation will be presented.

Fabrication of Nb2O5 nanosheets for high-rate lithium ion storage applications

Nb2O5 nanosheets are successfully synthesized through a facile hydrothermal reaction and followed heating treatment in air. The structural characterization reveals that the thickness of these sheets is around 50 nm and the length of sheets is 500~800 nm. Such a unique two dimensional structure enables the nanosheet electrode with superior performance during the charge-discharge process, such as high specific capacity (~184 mAh.g-1) and rate capability. Even at a current density of 1 A.g-1, the nanosheet electrode still exhibits a specific capacity of ~90 mAh.g-1. These results suggest the Nb2O5 nanosheet is a promising candidate for high-rate lithium ion storage applications.

Enhancements to dielectric response measurements of oil-paper insulation for online and offline applications

The research introduces a promising technique for monitoring the degradation status of oil-paper insulation systems of large power transformers in an online mode and innovative enhancements are also made on the existing offline measurements, which afford more direct understanding of the insulation degradation process. Further, these techniques benefit from a quick measurement owing to the chirp waveform signal application. The techniques are improved and developed on the basis of measuring the impedance response of insulation systems. The feasibility and validity of the techniques was supported by the extensive simulation works as well as experimental investigations.

Coil enhancements for high efficiency wireless power transfer applications

Achieving high efficiency with improved power transfer range and misalignment tolerance is the major design challenge in realizing Wireless Power Transfer (WPT) systems for industrial applications. Resonant coils must be carefully designed to achieve highest possible system performance by fully utilizing the available space. High quality factor and enhanced electromagnetic coupling are key indices which determine the system performance. In this paper, design parameter extraction and quality factor optimization of multi layered helical coils are presented using finite element analysis (FEA) simulations. In addition, a novel Toroidal Shaped Spiral (TSS) coil is proposed to increase power transfer range and misalignment tolerance. The proposed shapes and recommendations can be used to design high efficiency WPT resonator in a limited space.

Characterisation of the cascode gate drive of power MOSFETs in clamped inductive switching applications

This thesis proposes a novel gate drive circuit to improve the switching performance of MOSFET power switches in power electronic converters. The proposed topology exploits the cascode configuration, allowing the minimisation of switching losses in the presence of practical circuit constraints, which enables efficiency and power density improvements. Switching characteristics of the new topology are investigated and key mechanisms that control the switching process are identified. Unique analysis tools and techniques are also developed to demonstrate the application of the cascode gate drive circuit for switching performance optimisation.

A statistical model for combustion resonance from a DI diesel engine with applications

Introduced in this paper is a Bayesian model for isolating the resonant frequency from combustion chamber resonance. The model shown in this paper focused on characterising the initial rise in the resonant frequency to investigate the rise of in-cylinder bulk temperature associated with combustion. By resolving the model parameters, it is possible to determine: the start of pre-mixed combustion, the start of diffusion combustion, the initial resonant frequency, the resonant frequency as a function of crank angle, the in-cylinder bulk temperature as a function of crank angle and the trapped mass as a function of crank angle. The Bayesian method allows for individual cycles to be examined without cycle-averaging|allowing inter-cycle variability studies. Results are shown for a turbo-charged, common-rail compression ignition engine run at 2000 rpm and full load.

Swing load stabilization for mining and construction applications

This paper discusses the issue of sensing and control for stabilizing a swinging load. Our work has focused in particular on the dragline as used for overburden stripping in open-pit coal mining, but many of the principles would also be applicable to construction cranes. Results obtained from experimental work on a full-scale production dragline are presented.

Achievement unlocked: Investigating the design of effective gamification experiences for mobile applications and devices

This thesis investigates the design of motivating and engaging software experiences. In particular it examines the use of video game elements in non-game contexts, known as gamification, and how to effectively design gamification experiences for smartphone applications. The original contribution of this thesis is a novel framework for designing gamification, derived from an iterative process of evaluating gamified prototypes. The outcomes of this research can help us to better understand the impact of gamification in today's society and how it can be used to design more effective software.

Evolutionary optimization and game strategies for advanced multi-disciplinary design : applications to aeronautics and UAV design

Many complex aeronautical design problems can be formulated with efficient multi-objective evolutionary optimization methods and game strategies. This book describes the role of advanced innovative evolution tools in the solution, or the set of solutions of single or multi disciplinary optimization. These tools use the concept of multi-population, asynchronous parallelization and hierarchical topology which allows different models including precise, intermediate and approximate models with each node belonging to the different hierarchical layer handled by a different Evolutionary Algorithm. The efficiency of evolutionary algorithms for both single and multi-objective optimization problems are significantly improved by the coupling of EAs with games and in particular by a new dynamic methodology named “Hybridized Nash-Pareto games”. Multi objective Optimization techniques and robust design problems taking into account uncertainties are introduced and explained in detail. Several applications dealing with civil aircraft and UAV, UCAV systems are implemented numerically and discussed. Applications of increasing optimization complexity are presented as well as two hands-on test cases problems. These examples focus on aeronautical applications and will be useful to the practitioner in the laboratory or in industrial design environments. The evolutionary methods coupled with games presented in this volume can be applied to other areas including surface and marine transport, structures, biomedical engineering, renewable energy and environmental problems.

Fractional models in space for diffusive processes in heterogeneous media with applications in cell motility and electrical signal propagation

This work addresses fundamental issues in the mathematical modelling of the diffusive motion of particles in biological and physiological settings. New mathematical results are proved and implemented in computer models for the colonisation of the embryonic gut by neural cells and the propagation of electrical waves in the heart, offering new insights into the relationships between structure and function. In particular, the thesis focuses on the use of non-local differential operators of non-integer order to capture the main features of diffusion processes occurring in complex spatial structures characterised by high levels of heterogeneity.

Towards robust automatic affective classification of images using facial expressions for practical applications

Affect is an important feature of multimedia content and conveys valuable information for multimedia indexing and retrieval. Most existing studies for affective content analysis are limited to low-level features or mid-level representations, and are generally criticized for their incapacity to address the gap between low-level features and high-level human affective perception. The facial expressions of subjects in images carry important semantic information that can substantially influence human affective perception, but have been seldom investigated for affective classification of facial images towards practical applications. This paper presents an automatic image emotion detector (IED) for affective classification of practical (or non-laboratory) data using facial expressions, where a lot of “real-world” challenges are present, including pose, illumination, and size variations etc. The proposed method is novel, with its framework designed specifically to overcome these challenges using multi-view versions of face and fiducial point detectors, and a combination of point-based texture and geometry. Performance comparisons of several key parameters of relevant algorithms are conducted to explore the optimum parameters for high accuracy and fast computation speed. A comprehensive set of experiments with existing and new datasets, shows that the method is effective despite pose variations, fast, and appropriate for large-scale data, and as accurate as the method with state-of-the-art performance on laboratory-based data. The proposed method was also applied to affective classification of images from the British Broadcast Corporation (BBC) in a task typical for a practical application providing some valuable insights.

Remaining useful life prediction of rotating equipment using covariate-based hazard models : industry applications

The ability to estimate the expected Remaining Useful Life (RUL) is critical to reduce maintenance costs, operational downtime and safety hazards. In most industries, reliability analysis is based on the Reliability Centred Maintenance (RCM) and lifetime distribution models. In these models, the lifetime of an asset is estimated using failure time data; however, statistically sufficient failure time data are often difficult to attain in practice due to the fixed time-based replacement and the small population of identical assets. When condition indicator data are available in addition to failure time data, one of the alternate approaches to the traditional reliability models is the Condition-Based Maintenance (CBM). The covariate-based hazard modelling is one of CBM approaches. There are a number of covariate-based hazard models; however, little study has been conducted to evaluate the performance of these models in asset life prediction using various condition indicators and data availability. This paper reviews two covariate-based hazard models, Proportional Hazard Model (PHM) and Proportional Covariate Model (PCM). To assess these models’ performance, the expected RUL is compared to the actual RUL. Outcomes demonstrate that both models achieve convincingly good results in RUL prediction; however, PCM has smaller absolute prediction error. In addition, PHM shows over-smoothing tendency compared to PCM in sudden changes of condition data. Moreover, the case studies show PCM is not being biased in the case of small sample size.

Parallel streaming signature EM-tree: A clustering algorithm for web scale applications

The proliferation of the web presents an unsolved problem of automatically analyzing billions of pages of natural language. We introduce a scalable algorithm that clusters hundreds of millions of web pages into hundreds of thousands of clusters. It does this on a single mid-range machine using efficient algorithms and compressed document representations. It is applied to two web-scale crawls covering tens of terabytes. ClueWeb09 and ClueWeb12 contain 500 and 733 million web pages and were clustered into 500,000 to 700,000 clusters. To the best of our knowledge, such fine grained clustering has not been previously demonstrated. Previous approaches clustered a sample that limits the maximum number of discoverable clusters. The proposed EM-tree algorithm uses the entire collection in clustering and produces several orders of magnitude more clusters than the existing algorithms. Fine grained clustering is necessary for meaningful clustering in massive collections where the number of distinct topics grows linearly with collection size. These fine-grained clusters show an improved cluster quality when assessed with two novel evaluations using ad hoc search relevance judgments and spam classifications for external validation. These evaluations solve the problem of assessing the quality of clusters where categorical labeling is unavailable and unfeasible.

The development of virtual leaf surface models for interactive agrichemical spray applications

This project constructed virtual plant leaf surfaces from digitised data sets for use in droplet spray models. Digitisation techniques for obtaining data sets for cotton, chenopodium and wheat leaves are discussed and novel algorithms for the reconstruction of the leaves from these three plant species are developed. The reconstructed leaf surfaces are included into agricultural droplet spray models to investigate the effect of the nozzle and spray formulation combination on the proportion of spray retained by the plant. A numerical study of the post-impaction motion of large droplets that have formed on the leaf surface is also considered.