5,487 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Integrating expert-based objectivist and nonexpert-based subjectivist paradigms in landscape assessment

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    This thesis explores the integration of objective and subjective measures of landscape aesthetics, particularly focusing on crowdsourced geo-information. It addresses the increasing importance of considering public perceptions in national landscape governance, in line with the European Landscape Convention's emphasis on public involvement. Despite this, national landscape assessments often remain expert-centric and top-down, facing challenges in resource constraints and limited public engagement. The thesis leverages Web 2.0 technologies and crowdsourced geographic information, examining correlations between expert-based metrics of landscape quality and public perceptions. The Scenic-Or-Not initiative for Great Britain, GIS-based Wildness spatial layers, and LANDMAP dataset for Wales serve as key datasets for analysis. The research investigates the relationships between objective measures of landscape wildness quality and subjective measures of aesthetics. Multiscale geographically weighted regression (MGWR) reveals significant correlations, with different wildness components exhibiting varying degrees of association. The study suggests the feasibility of incorporating wildness and scenicness measures into formal landscape aesthetic assessments. Comparing expert and public perceptions, the research identifies preferences for water-related landforms and variations in upland and lowland typologies. The study emphasizes the agreement between experts and non-experts on extreme scenic perceptions but notes discrepancies in mid-spectrum landscapes. To overcome limitations in systematic landscape evaluations, an integrative approach is proposed. Utilizing XGBoost models, the research predicts spatial patterns of landscape aesthetics across Great Britain, based on the Scenic-Or-Not initiatives, Wildness spatial layers, and LANDMAP data. The models achieve comparable accuracy to traditional statistical models, offering insights for Landscape Character Assessment practices and policy decisions. While acknowledging data limitations and biases in crowdsourcing, the thesis discusses the necessity of an aggregation strategy to manage computational challenges. Methodological considerations include addressing the modifiable areal unit problem (MAUP) associated with aggregating point-based observations. The thesis comprises three studies published or submitted for publication, each contributing to the understanding of the relationship between objective and subjective measures of landscape aesthetics. The concluding chapter discusses the limitations of data and methods, providing a comprehensive overview of the research

    Exploring missing heritability in neurodevelopmental disorders:Learning from regulatory elements

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    In this thesis, I aimed to solve part of the missing heritability in neurodevelopmental disorders, using computational approaches. Next to the investigations of a novel epilepsy syndrome and investigations aiming to elucidate the regulation of the gene involved, I investigated and prioritized genomic sequences that have implications in gene regulation during the developmental stages of human brain, with the goal to create an atlas of high confidence non-coding regulatory elements that future studies can assess for genetic variants in genetically unexplained individuals suffering from neurodevelopmental disorders that are of suspected genetic origin

    Graduate Catalog of Studies, 2023-2024

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    Strategy Tripod Perspective on the Determinants of Airline Efficiency in A Global Context: An Application of DEA and Tobit Analysis

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    The airline industry is vital to contemporary civilization since it is a key player in the globalization process: linking regions, fostering global commerce, promoting tourism and aiding economic and social progress. However, there has been little study on the link between the operational environment and airline efficiency. Investigating the amalgamation of institutions, organisations and strategic decisions is critical to understanding how airlines operate efficiently. This research aims to employ the strategy tripod perspective to investigate the efficiency of a global airline sample using a non-parametric linear programming method (data envelopment analysis [DEA]). Using a Tobit regression, the bootstrapped DEA efficiency change scores are further regressed to determine the drivers of efficiency. The strategy tripod is employed to assess the impact of institutions, industry and resources on airline efficiency. Institutions are measured by global indices of destination attractiveness; industry, including competition, jet fuel and business model; and finally, resources, such as the number of full-time employees, alliances, ownership and connectivity. The first part of the study uses panel data from 35 major airlines, collected from their annual reports for the period 2011 to 2018, and country attractiveness indices from global indicators. The second part of the research involves a qualitative data collection approach and semi-structured interviews with experts in the field to evaluate the impact of COVID-19 on the first part’s significant findings. The main findings reveal that airlines operate at a highly competitive level regardless of their competition intensity or origin. Furthermore, the unpredictability of the environment complicates airline operations. The efficiency drivers of an airline are partially determined by its type of business model, its degree of cooperation and how fuel cost is managed. Trade openness has a negative influence on airline efficiency. COVID-19 has toppled the airline industry, forcing airlines to reconsider their business model and continuously increase cooperation. Human resources, sustainability and alternative fuel sources are critical to airline survival. Finally, this study provides some evidence for the practicality of the strategy tripod and hints at the need for a broader approach in the study of international strategies

    Graduate Catalog of Studies, 2023-2024

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    Evolutionary ecology of obligate fungal and microsporidian invertebrate pathogens

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    The interactions between hosts and their parasites and pathogens are omnipresent in the natural world. These symbioses are not only key players in ecosystem functioning, but also drive genetic diversity through co-evolutionary adaptations. Within the speciose invertebrates, a plethora of interactions with obligate fungal and microsporidian pathogens exist, however the known interactions is likely only a fraction of the true diversity. Obligate invertebrate fungal and microsporidian pathogen require a host to continue their life cycle, some of which have specialised in certain host species and require host death to transmit to new hosts. Due to their requirement to kill a host to spread to a new one, obligate fungal and microsporidian pathogens regulate invertebrate host populations. Pathogen specialisation to a single or very few hosts has led to some fungi evolving the ability to manipulate their host’s behaviour to maximise transmission. The entomopathogenic fungus, Entomophthora muscae, infects houseflies (Musca domestica) over a week-long proliferation cycle, resulting in flies climbing to elevated positions, gluing their mouthparts to the substrate surface, and raising their wings to allow for a clear exit from fungal conidia through the host abdomen. These sequential behaviours are all timed to occur within a few hours of sunset. The E. muscae mechanisms used in controlling the mind of the fly remain relatively unknown, and whether other fitness costs ensue from an infection are understudied.European Commissio

    Smart Gas Sensors: Materials, Technologies, Practical ‎Applications, and Use of Machine Learning – A Review

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    The electronic nose, popularly known as the E-nose, that combines gas sensor arrays (GSAs) with machine learning has gained a strong foothold in gas sensing technology. The E-nose designed to mimic the human olfactory system, is used for the detection and identification of various volatile compounds. The GSAs develop a unique signal fingerprint for each volatile compound to enable pattern recognition using machine learning algorithms. The inexpensive, portable and non-invasive characteristics of the E-nose system have rendered it indispensable within the gas-sensing arena. As a result, E-noses have been widely employed in several applications in the areas of the food industry, health management, disease diagnosis, water and air quality control, and toxic gas leakage detection. This paper reviews the various sensor fabrication technologies of GSAs and highlights the main operational framework of the E-nose system. The paper details vital signal pre-processing techniques of feature extraction, feature selection, in addition to machine learning algorithms such as SVM, kNN, ANN, and Random Forests for determining the type of gas and estimating its concentration in a competitive environment. The paper further explores the potential applications of E-noses for diagnosing diseases, monitoring air quality, assessing the quality of food samples and estimating concentrations of volatile organic compounds (VOCs) in air and in food samples. The review concludes with some challenges faced by E-nose, alternative ways to tackle them and proposes some recommendations as potential future work for further development and design enhancement of E-noses

    Spike timing reshapes robustness against attacks in spiking neural networks

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    The success of deep learning in the past decade is partially shrouded in the shadow of adversarial attacks. In contrast, the brain is far more robust at complex cognitive tasks. Utilizing the advantage that neurons in the brain communicate via spikes, spiking neural networks (SNNs) are emerging as a new type of neural network model, boosting the frontier of theoretical investigation and empirical application of artificial neural networks and deep learning. Neuroscience research proposes that the precise timing of neural spikes plays an important role in the information coding and sensory processing of the biological brain. However, the role of spike timing in SNNs is less considered and far from understood. Here we systematically explored the timing mechanism of spike coding in SNNs, focusing on the robustness of the system against various types of attacks. We found that SNNs can achieve higher robustness improvement using the coding principle of precise spike timing in neural encoding and decoding, facilitated by different learning rules. Our results suggest that the utility of spike timing coding in SNNs could improve the robustness against attacks, providing a new approach to reliable coding principles for developing next-generation brain-inspired deep learning

    AI-based design methodologies for hot form quench (HFQ®)

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    This thesis aims to develop advanced design methodologies that fully exploit the capabilities of the Hot Form Quench (HFQ®) stamping process in stamping complex geometric features in high-strength aluminium alloy structural components. While previous research has focused on material models for FE simulations, these simulations are not suitable for early-phase design due to their high computational cost and expertise requirements. This project has two main objectives: first, to develop design guidelines for the early-stage design phase; and second, to create a machine learning-based platform that can optimise 3D geometries under hot stamping constraints, for both early and late-stage design. With these methodologies, the aim is to facilitate the incorporation of HFQ capabilities into component geometry design, enabling the full realisation of its benefits. To achieve the objectives of this project, two main efforts were undertaken. Firstly, the analysis of aluminium alloys for stamping deep corners was simplified by identifying the effects of corner geometry and material characteristics on post-form thinning distribution. New equation sets were proposed to model trends and design maps were created to guide component design at early stages. Secondly, a platform was developed to optimise 3D geometries for stamping, using deep learning technologies to incorporate manufacturing capabilities. This platform combined two neural networks: a geometry generator based on Signed Distance Functions (SDFs), and an image-based manufacturability surrogate model. The platform used gradient-based techniques to update the inputs to the geometry generator based on the surrogate model's manufacturability information. The effectiveness of the platform was demonstrated on two geometry classes, Corners and Bulkheads, with five case studies conducted to optimise under post-stamped thinning constraints. Results showed that the platform allowed for free morphing of complex geometries, leading to significant improvements in component quality. The research outcomes represent a significant contribution to the field of technologically advanced manufacturing methods and offer promising avenues for future research. The developed methodologies provide practical solutions for designers to identify optimal component geometries, ensuring manufacturing feasibility and reducing design development time and costs. The potential applications of these methodologies extend to real-world industrial settings and can significantly contribute to the continued advancement of the manufacturing sector.Open Acces
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