Programming GUI and multimedia in functional languages has been a long-term challenge, and no solution convinces the community at large. Purely functional GUI and multimedia toolkits enable abstract thinking, but have enormous maintenance costs. General solutions like Functional Reactive Programming present a number of limitations. FRP has traditionally resisted efficient implementation, and existing libraries sacrifice determinism and abstraction in the name of performance. FRP also enforces structural constraints that facilitate reasoning, but at the cost of modularity and separation of concerns. This work addresses those limitations with the introduction of Monadic Stream Functions, an extension to FRP parameterised over a monad. I demonstrate that, in spite of being simpler than other FRP proposals, Monadic Stream Functions subsume and exceed other FRP implementations. Unlike other proposals, Monadic Stream Functions maintain purity at the type level, which is crucial for testing and debugging. I demonstrate this advantage by introducing FRP testing facilities based on temporal logics, together with debugging tools specific for FRP. I present two uses cases for Monadic Stream Functions: First, I show how the new constructs improved the design of game features and non-trivial games. Second, I present Reactive Values and Relations, an abstraction for model-view coordination in GUI programs based on a relational language, built on top of Monadic Stream Functions. Comprehensive examples are used to illustrate the benefits of this proposal in terms of clarity, modularity, feature coverage, and its low maintenance costs. The testing facilities mentioned before are used to encode and statically check desired interaction properties.

Diminishing fossil fuel reserves and the drawbacks of conventional crop-based biofuels has catalysed recent research into the microbial conversion of lignocellulosic biomass into liquid biofuel. Fatty acids represent the most abundant form of reduced carbon chain in nature, and represent the basic building blocks for the creation of a wide-range of advanced biofuels; such as alkanes, fatty alcohols, and fatty acid methyl- and ethyl-esters. It is hoped that the use of a thermophilic platform strain, that is capable of producing fatty acid-derived biofuels at elevated temperatures, will circumvent some of the challenges faced by established mesophilic organisms such as Escherichia coli or Saccharomyces cerevisiae. Here we describe the heterologous expression of an alkane biosynthesis pathway from the thermophilic cyanobacteria Thermosynechococcus elongatus BP-1 in both E. coli and the thermophilic production organism Geobacillus thermoglucosidasius. Alkane biosynthesis in T. elongatus BP-1 is facilitated by two enzymes: fatty acyl-ACP reductase (AAR) and aldehyde deformylating oxygenase (ADO): both of which were found to demonstrate a level of activity in vivo at mesophilic and thermophilic temperatures (30 - 52°C). Expression of an alkane biosynthesis operon in G. thermoglucosidasius NCIMB 11955 resulted in the production of ~100 mg OD-1 L-1 fatty alcohols, and an inconsistent formation of minute amounts of heptadecane. Improved titres of alkane may be achievable through the identification and elimination of competing pathways, and a better understanding of n-alkane biodegradation in G. thermoglucosidasius. However, we recommend the continued pursuit of fatty alcohol production using G. thermoglucosidasius as a host. Elimination of several fatty acid degradation (fad) genes in G. thermoglucosidasius was undertaken with the hope of showing an ability to manipulate the cellular pool of fatty acyl-ACP substrates available to the alkane biosynthesis pathway. The combined elimination of two long-chain-fatty-acid—CoA ligase genes (fadD1 and fadD2) resulted in increased levels of pentadecanoic- and heptadecanoic acid. The heterologous expression of a fatty acyl-ACP thioesterase (FAT) from Clostridium thermocellum and from the Aminicenantes candidate phylum (OP-8) was also undertaken in an attempt to manipulate levels of cellular FFAs, although we postulate that observation of a differential phenotype requires the development of a strain completely defunct of long-chain-fatty-acid—CoA ligase activity. Fatty acid metabolism in G. thermoglucosidasius represents a complex myriad of multiple genes that are subject to strong homeostasis. Nevertheless, we present evidence that genetic manipulations of G. thermoglucosidasius are sufficient to bring about changes in the fatty acid profile of cells, and encourage the further genetic characterization of fatty acid metabolism in the organism through targeted gene deletions, with the hope of producing an improved platform strain for fatty alcohol and alkane biosynthesis at thermophilic temperatures.

The Gram-positive, anaerobic, acetogenic bacterium Clostridium autoethanogenum is regarded as an important biocatalyst in the current advancement of industrial gas fermentation. The ever-widening diversity of industrially-relevant acetogenic bacteria has inspired a rational approach into utilising industrial waste gases as a viable feedstock, with goals of mitigating greenhouse gas emissions, and supporting an alternative means of fuel and high-value chemical production. Combined with this effort, is the expanding repertoire of gene editing tools that have allowed for the improvement of gas fermentation processes and increased spectrum of fermentative products. Despite these advances, there remain many pertinent questions, which addressed, can further the understanding of metabolism and physiology in acetogenic bacteria. This includes cell-to-cell communication and signalling, Quorum Sensing. In this project, these questions are addressed through the study of the Agr QS system in C. autoethanogenum. Signalling peptide genes, agrD1 and agrD2 were disabled separately and more importantly, in tandem, which effectively abolished Agr signalling. Phenotypic characterisation of the double agrD mutants revealed a significant increase in ethanol at the expense of acetate output. Further observations exhibited a complete utilisation of the fructose carbon source, and the inability to fully re-assimilate CO2. These findings markedly contrasted with the wild type, and both single knock-out agrD mutants. Proteomics and enzyme activity analysis of the double AgrD mutant revealed a marked down-regulation of Wood-Ljungdahl pathway genes that included the CO2-assimilating, carbon monoxide dehydrogenase / acetyl-CoA synthase complex subunits and hydrogenases. An up-regulation of alcohol dehydrogenases was observed explaining ethanol increases, alongside an unexpected upregulation of bacterial micro-compartment clusters. These findings led to the hypothesis that the C. autoethanogenum Agr system influences the ancient Wood-Ljungdahl pathway, primarily as a means of survival by managing carbon-source utilisation and regulation.

Access to renewable energy and reduction of carbon emissions represent two major issues facing humankind in the twenty first century and beyond. The underlying driving forces behind both are multi-faceted and often intrinsically connected, ranging from environmental concerns over climate change to improving economic security through self-sustaining energy production. Possible solutions to reliance on non-renewable, carbon-emitting fossil fuels have been explored over recent decades, with significant interest placed on biofuels. Due to ease of integration into liquid-based petrochemical fuel infrastructure, these renewable alternatives have been a consistent topic of both industrial and academic interest. Despite offering renewable energy, conventional crop-based biofuel production has faced criticism due to consumption of land, water and other resources associated with agriculture. Acetogens provide a solution to conventional biofuel production due to their utilisation of carbon monoxide and carbon dioxide gas as carbon and energy sources, rather than plant matter. This allows generation of a range of chemical products from a broad range of sources, including industrial waste gases and gasified solid waste. Acetogens offer the double benefit of both renewable energy production, and carbon emission sequestation. This study outlines the development of genetic tools to provide a foundation for using synthetic biology approaches to improve performance of acetogens as industrial chassis. Specifically, development of tools and techniques for the acetogen Clostridium autoethanogenum are described, with further applications of such technology to other Clostridia.

Phosphotransferase systems (PTS) represent an important method of sugar uptake in bacteria and have been well described in the past. However, research into PTS within the genus Clostridium has been mainly restricted to the non-pathogens. Analysis of the genome of Clostridium difficile 630 revealed over 40 intact PTS; this is over three times as many as in other pathogenic Clostridia, such as Clostridium perfringens and Clostridium botulinum. Previously, carbon catabolite repression has been shown to affect toxin production in C. difficile. Being capable of utilising different carbohydrates efficiently could be important for C. difficile to adapt to, grow, and survive in the human gut. So far, little work has been done to corroborate the role of individual PTS in carbohydrate uptake, sensing of environmental stimuli and regulation of virulence, i.e. toxin expression. A deeper understanding of the PTS in C. difficile, and their importance in virulence, could lead to the development of new drug targets. The aim of this study is to characterise the main PTS of C. difficile, determine their role in carbohydrate uptake, and their effect on regulation of virulence. To date, we have chosen the main candidates thought to be involved in mannitol and sorbitol uptake, and have inactivated these PTS using the ClosTron and in-frame deletion methods. Phenotypic characterisation of these mutants was undertaken to prove their role in uptake of the relevant sugar and to determine their role in virulence regulation. This study has demonstrated, by growth assays and HPLC, that the operons at CD630_0762-8 and CD630_2331-4 respectively encode PTS specific for sorbitol and mannitol uptake. In the case of the mannitol operon, it has been proved (through the use of cytotoxicity assays, which showed reduced bacterial toxicity in the presence of the sugar) that the suppression of toxin synthesis in the presence of mannitol is dependent upon uptake of the substrate via this operon. With sorbitol, toxin levels are, seemingly, not directly reliant upon uptake of the sugar, resulting in, mainly, an increase of toxin in sorbitol. Presently, it is not possible to say whether these systems have a distinct role or not in the motility of the organism.

This thesis presents a PhD research project using a mathematical programming approach to solve a home healthcare problem (HHC) as well as general workforce scheduling and routing problems (WSRPs). In general, the workforce scheduling and routing problem consists of producing a schedule for mobile workers to make visits at different locations in order to perform some tasks. In some cases, visits may have time-wise dependencies in which a visit must be made within a time period depending on the other visit. A home healthcare problem is a variant of workforce scheduling and routing problems, which consists in producing a daily schedule for nurses or care workers to visit patients at their home. The scheduler must select qualified workers to make visits and route them throughout the time horizon. We implement a mixed integer programming model to solve the HHC. The model is an adaptation of the WSRP from the literature. However, the MIP solver cannot solve a large-scale real-world problem defined in this model form because the problem requires large amounts of memory and computational time. To tackle the problem, we propose heuristic decomposition approaches which split a main problem into sub-problems heuristically and each sub-problem is solved to optimality by the MIP solver. The first decomposition approach is a geographical decomposition with conflict avoidance (GDCA). The algorithm avoids conflicting assignments by solving sub-problems in a sequence in which worker's availabilities are updated after a sub-problem is solved. The approach can find a feasible solution for every HHC problem instance tackled in this thesis. The second approach is a decomposition with conflict repair and we propose two variants: geographical decomposition with conflict repair (GDCR) and repeated decomposition and conflict repair (RDCR). The GDCR works in the same way as GDCA but instead of solving sub-problems in a given sequence, they are solved with no specific order and conflicting assignments are allowed. Later on, the conflicting assignments are resolved by a conflicting assignments repair process. The remaining unassigned visits are allocated by a heuristic assignment algorithm. The second variant, RDCR, tackles the unassigned visits by repeating the decomposition and conflict repair until no further improvement has been found. We also conduct an experiment to use different decomposition rules for RDCR. Based on computational experiments conducted in this thesis, the RDCR is found to be the best of the heuristic decomposition approaches. Therefore, the RDCR is extended to solve a WSRP with time-dependent activities constraints. The approach requires modification to accommodate the time-dependent activities constraints which means that two visits may have time-wise requirements such as synchronisation, time overlapped, etc. In addition, we propose a reformulated MIP model to solve the HHC problem. The new model is considered to be a compact model because it has significantly fewer constraints. The aim of the reformulation is to reduce the solver requirements for memory and computational time. The MIP solver can solve all the HHC instances formulated in a compact model. Most of solutions obtained with this approach are the best known solutions so far except for those the instances for which the optimal solution can be found using the full MIP model. Typically, this approach requires computational time below one hour per instance. This problem reformulation is so far the best approach to solve the HHC instances considered in this thesis. The heuristic decomposition and model reformulation proposed in this thesis can find solutions to the real-world home healthcare problem. The main achievement is the reduction of computational memory and computational time which are required by the optimisation solver. Our studies show the best way to control the use of solver memory is the heuristic decomposition approach, particularly the RDCR method. The RDCR method can find a solution for every instance used throughout this thesis and keep the memory usage within personal computer memory ranges. Also, the computational time required to solve an instance being less than 8 minutes, for which the solution gap to the optimal solution is on average 12%. In contrast, the strong point of the model reformulation approach over the heuristic decomposition is that the model reformulation provides higher quality solutions. The relative gaps of solutions between the solution for solving the reformulated model and the solution from solving the full model is less than 1% whilst its the computational time could be up to one hour and its computational memory could require up to 100 GB. Therefore, the heuristic decomposition approach is a method for finding a solution using restricted resources while the model reformulation is an approach for when a high solution quality is required. Hence, two mathematical programming based heuristic approaches are each more suitable in different circumstances in which both find high quality solutions within an acceptable time limit.

Staff scheduling is a complex combinatorial optimisation problem concerning allocation of staff to duty rosters in a wide range of industries and settings. This thesis presents a novel approach to solving staff scheduling problems, and in particular nurse scheduling, by simplifying the problem space through information granulation. The complexity of the problem is due to a large solution space and the many constraints that need to be satisfied. Published research indicates that methods based on random searches of the solution space did not produce good-quality results consistently. In this study, we have avoided random searching and proposed a systematic hierarchical method of granulation of the problem domain through pre-processing of constraints. The approach is general and can be applied to a wide range of staff scheduling problems. The novel approach proposed here involves a simplification of the original problem by a judicious grouping of shift types and a grouping of individual shifts into weekly sequences. The schedule construction is done systematically, while assuring its feasibility and minimising the cost of the solution in the reduced problem space of weekly sequences. Subsequently, the schedules from the reduced problem space are translated into the original problem space by taking into account the constraints that could not be represented in the reduced space. This two-stage approach to solving the scheduling problem is referred to here as a domain-transformation approach. The thesis reports computational results on both standard benchmark problems and a specific scheduling problem from Kajang Hospital in Malaysia. The results confirm that the proposed method delivers high-quality results consistently and is computationally efficient.

Artificial Neural Networks (ANN) are biologically inspired algorithms, and it is natural that it continues to inspire research in artificial neural networks. From the recent breakthrough of deep learning to the wake-sleep training routine, all have a common source of drawing inspiration: biology. The transfer functions of artificial neural networks play the important role of forming decision boundaries necessary for learning. However, there has been relatively little research on transfer function optimization compared to other aspects of neural network optimization. In this work, neuronal diversity - a property found in biological neural networks- is explored as a potentially promising method of transfer function optimization. This work shows how neural diversity can improve generalization in the context of literature from the bias-variance decomposition and meta-learning. It then demonstrates that neural diversity - represented in the form of transfer function diversity- can exhibit diverse and accurate computational strategies that can be used as ensembles with competitive results without supplementing it with other diversity maintenance schemes that tend to be computationally expensive. This work also presents neural network meta-features described as problem signatures sampled from models with diverse transfer functions for problem characterization. This was shown to meet the criteria of basic properties desired for any meta-feature, i.e. consistency for a problem and discriminatory for different problems. Furthermore, these meta-features were also used to study the underlying computational strategies adopted by the neural network models, which lead to the discovery of the strong discriminatory property of the evolved transfer function. The culmination of this study is the co-evolution of neurally diverse neurons with their weights and topology for efficient learning. It is shown to achieve significant generalization ability as demonstrated by its average MSE of 0.30 on 22 different benchmarks with minimal resources (i.e. two hidden units). Interestingly, these are the properties associated with neural diversity. Thus, showing the properties of efficiency and increased computational capacity could be replicated with transfer function diversity in artificial neural networks.

This research dealt with the problem of neuronal membrane detection, in which the core challenge is distinguishing membranes from organelles. A simple and efficient optimisation framework is proposed based on several basic processing steps, including local contrast enhancement, denoising, thresholding, hole-filling, watershed segmentation, and morphological operations. The two main algorithms proposed Image Processing Chain Optimisation (IPCO) and Multiple IPCO (MIPCO)combine elements of Genetic Algorithms, Differential Evolution, and Rank-based uniform crossover. 91.67% is the highest recorded individual IPCO score with a speed of 280 s, and 92.11% is the highest recorded ensembles IPCO score whereas 91.80% is the highest recorded individual MIPCO score with a speed of 540 s for typically less than 500 optimisation generations and 92.63% is the highest recorded ensembles MIPCO score. Further, IPCO chains and MIPCO networks do not require specialised hardware and they are easy to use and deploy. This is the first application of this approach in the context of the Drosophila first instar larva ventral nerve cord. Both algorithms use existing image processing functions, but optimise the way in which they are configured and combined. The approach differs from related work in terms of the set of functions used, the parameterisations allowed, the optimisation methods adopted, the combination framework, and the testing and analyses conducted. Both IPCO and MIPCO are efficient and interpretable, and facilitate the generation of new insights. Systematic analyses of the statistics of optimised chains were conducted using 30 microscopy slices with corresponding ground truth. This process revealed several interesting and unconventional insights pertaining to preprocessing, classification, post-processing, and speed, and the appearance of functions in unorthodox positions in image processing chains, suggesting new sets of pipelines for image processing. One such insight revealed that, at least in the context of our membrane detection data, it is typically better to enhance, and even classify, data before denoising them.

In this work, we develop a Data Mining framework to explore the multifaceted nature of consumer indebtedness. Data Mining with its numerous techniques and methods poses as a powerful toolbox to handle the sensitivity of these data and explore the psychological aspects of this social phenomenon. Thus, we begin with a series of transformations that deal with any inconsistencies the data may contain but more importantly they capture the essential psychological information hidden in the data and represent it in a new feature space as behavioural data. Then, we propose a novel consensus clustering framework to uncover patterns of consumer behaviour which draws upon the ability of cluster ensembles to reveal robust clusters from diffcult datasets. Our Homals Consensus, models successfully the relationships between different clusterings in the cluster ensemble and manages to uncover representative clusters that are more suitable for explaining the complex patterns of a socio-economic dataset. Finally under a supervised learning approach the behavioural aspects of consumer indebtedness are assessed. In more detail, we take advantage of the exibility Neural Networks provide in determining their architecture in order to propose a novel Neural Network solution, named TopDNN, that can handle non-linearities in the data and takes into account the extracted behavioural knowledge by incorporating it in the model. All the above sketch an elaborate framework that can reveal the potential of the behavioural data to support Knowledge Discovery in Consumer Debt Analysis on one hand and the ability of Data Mining to supplement existing models and theories of complex and sensitive nature on the other.