27716 research outputs found
Sort by
Contribution of Electromagnetic Interference to the Safety of Complex Systems: Reasoning on Safety Processes and Assurance
Full text linkElectromagnetic Interference (EMI) can contribute to the emergence of safety hazards in the operation of complex systems throughout various industries. This has led to the specification of Electromagnetic Compatibility (EMC) standards which aim to minimise the impact of EMI on the performance of the systems. Complying with these standards has historically been the only argument for achieving safety in the system. However, due to the rising complexity of the electromagnetic environment and the increasing complexity and autonomy of systems, the need for a risk-based approach has been more felt. A risk-based approach toward EMI, highlights the necessity of a methodology to not only mitigate the impact of EMI on safety but to produce safety assurance arguments as complying with EMC standards may not be adequate to provide safety assurance for a system while employing a risk-based EMI management.
In this thesis, the EMI-related safety arguments produced by the rule-based approach and the state-of-the-art risk-based approach are investigated and the deficiencies of the current safety argument in regard to the contribution of EMI toward safety are identified. The features of an adequate methodology are also identified. Then, an EMI-aware safety assurance methodology is proposed. The methodology is proposed to be followed during the development of the system and to produce essential safety arguments and supporting evidence. Moreover, as part of the methodology, the concept of Electromagnetic Operational Domain Model (EODM) is defined which facilitates the specification of operational conditions in which the system is assured against the contribution of EMI to the safety of the system. Evaluation of the methodology is carried out by peer reviews, case study and analysis. It demonstrated that the methodology can be integrated into system development processes and provides adequate arguments and supporting evidence in regard to the contribution of EMI to the system's safety
Embodying Women as Agents of Revolutionary change within the History of Ideas on Revolution
Full text linkThis thesis is about women in revolutionary struggle. More specifically, it is about women who, locally, nationally, and internationally, are set on building a new and better world. My overall objective is to enrich our understanding of how and why their contributions as historical ‘agents of revolutionary change’ are critical to the history of ideas on revolution.
The existing literature provided fruitful material for this aim, but I found little on such women, other than as dramatic icons or in non-essential roles, so peripheral to the ideas, arguments and theories on revolution. Further research indicated that, as Louise Raw has argued, many such women are not just absent, neglected, or forgotten, within revolutionary history, they are ‘hidden’ both from and by it (Raw, 2011).
This insight informs the preliminary ideas on the adoption of the concept of ‘embodiment’ as the basis for a for a theoretical framework of analysis and the development of a broad definition of revolution applicable to women. This incorporates consideration of the sources, processes and possible consequences of women’s invisibility for the theory and practice of revolution. It, however, moves beyond it to embody women’s revolutionary actions with the aim of re-envisioning them as active subjects within revolutionary history. I have drawn on Raya Dunayevskaya’s (1991) description of such women as both revolutionary “Reason and force” (author’s capitals). This signals that, as well as being a force for change, they have the capacity for developing ideas and theories that are grounded in, but go beyond, their revolutionary experiences.
To draw the strands together, a case study of a strike in 1888 led by women in the match-making industry is included. Its primary function is to illustrate, within a real-life context of women’s collective revolutionary action, how the identified research problem of theoretical ‘blindness’ serves to render women invisible as historical agents of revolutionary change. It is also to reflexively link theory and practice and the role of revolutionary women in deepening our understanding of both
Investigation of mechanical properties and damage behaviour in TFP composites: a multiscale characterisation and modelling framework
No full textThe application of carbon fibre reinforced composites is gradually increasing in product fabrication in the aerospace and automotive industries due to their superior mechanical performance. However, the inherent anisotropic properties of fibre reinforced composites present challenges when these structures are subjected to multi-axial loads. On the other hand, the manual placement of fibre preforms for manufacturing complex structures leads to material wastage, as preforms need to be trimmed to fit the mould. In recent years, automated fibre placement techniques have been developed to enable multi-directional fibre orientations within the same ply, reducing material wastage while maintaining high mechanical performance. Tailored Fibre Placement (TFP) technique, as one of the advanced fibre placement approaches, provides greater manufacturing flexibility and reduced material usage without compromising the quality of composite structures. Nevertheless, there remains a lack of numerical tools capable of predicting the mechanical behaviour of composite laminates produced using the TFP technique. Therefore, this thesis seeks to develop multiscale models to predict the elastic properties and strength of composites laminates, focusing on characterising their microstructure morphologies at the mesoscale level and the development of the unit cell model.
To create highly precise multiscale models of composites, experimental characterisations were conducted at three scales, namely micro, meso, and macro, with particular emphasis on the development of the mesoscopic model. Fibre distribution behaviour was statistically analysed at the microscopic scale using Scanning Electron Microscopy (SEM) alongside several statistical descriptors. A microscopic model was developed using a Python script. The fibre bundle structure was characterised using X-ray imaging, and an idealised mesoscopic model of the TFP composite was designed, identifying the fibre bundle structures. The homogenised properties, simulated from the mesoscopic model with different morphologies, were explored through the macroscopic model. The elastic and strength properties of TFP composite laminates were predicted and validated. The stress-strain response from the TFP composite laminate model was compared to in-situ experimental results obtained from X-ray Computed Tomography (XCT). Modelling results were compared to three-dimensional strain distribution measurements obtained using a novel Digital Volume Correlation (DVC) algorithm developed in this research. The Hashin damage criterion was employed to predict crack initiation and propagation in the TFP composite. Numerical results were compared and validated with experimental results from XCT images.
This work has highlighted the critical role of experimental characterisation in developing the multiscale models for composite laminates. By considering manufacturing parameters, the multiscale model developed in this work for TFP composites shows reasonably good agreement with experimental results. Moreover, it highlights significant potential for predicting and optimising the mechanical performance of TFP composite structures fabricated using varying manufacturing parameters
Demand for electric and hybrid cars - modelling vehicle transactions and type choice
No full textThere has been interest among transport policy makers in initiatives that would increase the uptake of Electric Vehicles (EVs). These initiatives are obtained from output from relevant car demand models. Among the core inputs in these models are car and household variables that result in the variations in car fuel type preferences by creating the trade-offs between market alternatives, and variations on how long they would keep the cars they have chosen. In this study, the car preferences and car holding duration decisions by households have been examined by deploying several model structures. The model structures included discrete choice models for the car preferences, hazard-based duration models for the car holding duration, and a novel copula-based joint discrete choice and hazard-based duration model for the dependent decision of car preferences and car holding duration. To test the effectiveness of these models in capturing the two different decision-making behaviours by households, several primary and secondary data types were input in these models. The data types deployed to these models included retrospective preference and holding duration, stated preference and prospective holding duration data on cars. The prospective holding duration data is a novel data type that was found useful in predicting future car transaction timing decisions. The general findings from testing the different model structures demonstrated that households make decisions on car type dependent on how long they want to keep their cars. The joint model was used to test different retrospective policies that would have increased the uptake of EVs at the time of carrying out data collection.
For the policy options scenario analysis, the joint model with a component of SP added (scaled to the RP component of the joint model) was deployed to carry out retrospective assessment of policy options. The results showed that changes to the Value Added Tax (VAT) on the Battery Electric Vehicles (BEV) purchase and leasing costs result in significant additions of BEVs to the car fleet. Changes to the fuel duty on petrol and diesel cars and VAT on the purchasing and leasing cost of BEVs result in significant removal of CO2e (equivalent carbon dioxide) from the passenger cars.
As far as novelty is concerned, this thesis has contributed to two major innovations. The first novelty addresses the gap on whether households make decisions on type choice dependent on how long they plan to keep their cars. This was achieved by developing a model that combines the decisions on type choice and transaction timing (car holding duration) using copula theory. Secondly the research addresses the gap on the usefulness of data on future decisions on cars such as when to replace, dispose or add another car to their household – a data type that has been termed as ‘prospective.’ This was achieved by deploying the prospective duration data to the different model structures to test its applicability in such decisions that are likely stable due to the long-term planning associated with transaction timing decisions on cars.
In sum, this study found that four policy variables contributed to the variations on car fuel type choices. These variables are purchase cost, lease cost, annual running cost and fuelling cost. Household income plays a significant role in the addition of other cars to the existing car fleet of consumers. The decisions by car consumers on choosing a particular transaction (to replace, acquire or dispose a car) are dependent on the fuel type of the car. A 10% reduction of the purchase or lease cost of BEV cars would add about 10% of new, used or lease BEV cars. Furthermore, a similar percentage reduction of the purchase or lease cost of BEV cars would remove 0.1MtCO2e on average from private car use. That said, these findings can point policy makers into tailoring appropriate policies bearing in mind the context of this study
Adhiron reagents as novel genetic and cell biology tools to modulate the protein-protein interactions of Aurora kinase A
No full textAbstract
Aurora Kinase A (AurA) is a mitotic kinase and established therapeutic target that plays essential roles in cell division and is frequently overexpressed in cancer. Most clinical inhibitors of AurA target the conserved ATP-binding pocket, but this strategy often leads to limited selectivity and off-target toxicities due to high structural conservation across the kinome. Allosteric inhibition, by contrast, offers a promising but underexploited approach to selectively modulate kinase function via less conserved, conformationally dependent sites. In this project, Adhirons, small synthetic binding proteins, were identified and characterised as allosteric inhibitors of AurA. A ‘phage display screen against phosphorylated AurA yielded a panel of Adhirons that inhibit kinase activity through engagement of a previously uncharacterised cryptic site on the αG-helix of the C-lobe. This site, which we term the ‘T-pocket’, was revealed by X-ray crystallography to induce conformational changes in the T-loop (activation loop), a key structural element required for kinase activation. Adhiron binding stabilises a DFG-in conformation of AurA, distinct from classical ATP-competitive inhibitors, and is compatible with regulatory protein interactions such as TPX2. In vitro kinase assays confirmed direct inhibition of AurA catalytic activity, while in cellulo studies demonstrated that Adhiron expression results in impaired AurA function and prolonged mitotic progression, consistent with targeted inhibition. These findings establish the T-pocket as a novel allosteric regulatory site and demonstrate the utility of Adhirons as high-affinity, conformation-selective reagents for kinase modulation. More broadly, this work highlights the potential of using engineered binders to uncover cryptic regulatory pockets in challenging targets and guide the development of structure-informed, allosteric therapeutic strategies
Sustainable research software engineering practices for fluctuating finite element analysis
Full text linkResearch software is often developed with a focus on the output over the software's quality, and made by researchers untrained in formal software development techniques. Conditions such as these lead to an unsustainable environment of rapid software decay, and the wasteful re-invention of software for similar research purposes. The field of research software engineering (RSE) was coined to focus on the development and maintenance of sustainable research software.
Fluctuating Finite Element Analysis (FFEA) is a numerical method and software suite that uses continuum mechanics to simulate mesoscopic biomolecules. As research software, FFEA is subject to the same conditions which threaten software decay. This research applies sustainable RSE principles to FFEA to analyse and mitigate for this threat.
FFEA is evaluated to determine the major bottlenecks in the workflow impairing the software's sustainability. Results show these issues stem from the difficulty and time cost of setting up FFEA experiments, especially due to the current meshing algorithm producing unstable outputs that disrupt setup and frequently terminate FFEA simulations due to physics errors.
Two solutions are developed to attempt to solve these problems. The first extends the features of a new meshing algorithm started by Gravett (2022) which divides voxel representations of molecules into tetrahedra to create a consistent, stable geometry. This research adds new features to the meshing algorithm, and compares it to the previous meshing algorithm to show that it produces meshes more reliably with fewer steps in the process.
The second solution works towards automatic generation of FFEA KOBRA models for rod-shaped biomolecules through using machine learning algorithms to predict the presence of rod structures. Machine learning algorithms were able to categorise isosurface meshes of biomolecules through analysing the curvatures across the surface. This proved the concept and highlights necessary steps required to achieve full automation
Speeding up Graph Programs
Full text linkThis thesis presents an improvement in rule-based graph programming which enables the design of graph programs that achieve the time complexity of imperative linear-time algorithms. Achieving such complexity in conventional languages using graph transformation rules is challenging due to the high cost of graph matching. Previous work demonstrated that with rooted rules, certain algorithms can be executed in linear time using the graph programming language GP 2. However, for non-destructive algorithms that preserve the structure of input graphs, achieving linear runtime required input graphs to be both connected and of bounded node degree. In this thesis, we overcome these limitations by enhancing the graph data structure generated by the GP 2 compiler and exploiting this new structure within the programs. We present four case studies: a program that checks graph’s connectedness, a program that two-colours a graph, a cycle detection program, and a program that computes the shortest weighted distances from a single source to all other nodes in a weighted graph. The first three programs run in linear time on both connected and disconnected input graphs with arbitrary node degrees. The fourth program achieves a time complexity comparable to that of conventional implementations in imperative programming languages. For each program, we provide a formal analysis of its correctness and complexity, supplemented by empirical benchmarking
Predicting the tactile properties of fabrics from vision and touch
Full text linkFabric is one of the biggest markets in the world, as nearly everyone is a consumer of fabrics. Tactile properties of fabrics convey vital information and influence consumer decision and satisfaction. With the rapid development of online shopping, consumers face new challenges, including predicting the tactile properties based on fabric images presented on displays and bridging the gap between visual perception and actual tactile perception. Moreover, the understanding of the tactile properties remains uncomplete due to the multiple tactile properties, the various conditions under which they are assessed, various influencing factors, and the limited efforts made on prediction.
The aim of the present study is to provide a consumer-friendly investigation of the perception of fabric tactile properties. A Leeds Fabric Tactile Database was developed and used in a series of psychophysical experiments to achieve the aim. The database consisted of two parts: Part I included colour-rendered fabric images (flat and draped) along with the corresponding real fabrics, and Part II included real fabric images (flat and draped), fabric rotation videos, and the corresponding real fabrics.
Experiment Phase I was carried out using Part I, evaluating flexible-stiff, smooth-rough, soft-firm, spongy-crisp, and warm-cool under the conditions of flat fabric images, draped fabric images, and touching the fabrics without seeing them (touch-only). The effect of individual fabrics, colour, and experiment conditions was analysed, together with the correlations among experiment conditions and among tactile properties.
Following the experiment Phase I, the experiment Phase II was conducted using database Part II. The experiment conditions were expanded to cover all real-life scenarios of human-fabric interaction, with newly added conditions including fabric rotation videos, viewing the fabrics but not touching them (vision-only using real fabrics), and viewing the fabrics while simultaneously touching them (vision+touch). In addition to the analyses of factors and correlations, predictors were extracted from fabric images and videos to model the perception of fabric tactile properties.
All the psychophysical experiments in the present study applied the method of categorical judgement. The analyses were carried out in alignment with the categorical judgement framework, where each score carried a specific perceptual meaning. Taken together, the present study demonstrated a comprehensive investigation of the perception of fabric tactile properties. By using the fabric images and videos, good prediction can be achieved for the perception of fabric tactile properties
Causal Surrogate Models: Adding "What If" to Cyber-Physical System Testing
Full text linkCyber-physical systems (CPS) are becoming more prevalent, especially in human-interacting environments. Identifying incorrect behaviour through software testing is, therefore, paramount to their use. Surrogate-assisted testing approaches aim to effectively test such systems by searching for scenarios that may result in system violations by using surrogate models to search for potential violations and evaluating those scenarios on a high-fidelity simulator. However, the development of surrogate models requires curated datasets to accurately represent system behaviour. Such datasets are typically unavailable for CPSs due to the limitations and expense of physical execution, especially for human-interacting systems. Pre-existing datasets may be used instead, which may contain spurious associations or may not cover all behaviours (a lack of controllability). Unmeasurable environmental factors may also affect system behaviour, making system outputs appear inconsistent for a given input (a lack of observability). In this thesis, we use a motivating example of an artificial pancreas system (APS) to investigate the limitations of testing such a system.
To account for the lack of observability and controllability of CPSs, we define a causal surrogate model to enable more effective testing of their behaviour. This surrogate model integrates with existing surrogate-assisted testing techniques. Our surrogate model uses causal inference, which can account for bias in pre-existing datasets and assess the expected causal relationships between variables. As a result, we enable the testing of systems for which curated data may not be available or that exhibit behaviours affected by external factors.
We perform two evaluations, first replicating an existing study of surrogate-assisted CPS testing on an automated driving system (ADS). In this evaluation, our results demonstrate how our approach found system violations with less computational expense than the state-of-the-art. We then test a more complex, safety-critical APS. However, to test the APS, we first develop and validate a digital twin of a person using an APS to act as a high-fidelity simulator. The APS can, therefore, be disconnected from the human-in-the-loop, allowing for the testing of potentially dangerous scenarios without clinical trials. Our causal surrogate model demonstrates the ability to uncover over double the number of violations using real-world clinical APS data, compared to the state-of-the-art surrogate-assisted testing approach. We show how causal surrogate models can alleviate the requirement of curated data for systems testing and present a novel way of navigating and finding system violations for inconsistent system behaviou
Data sources:
Access Repository Dashboard
Do you manage Open Research Online? Become a CORE Member to access insider analytics, issue reports and manage access to outputs from your repository in the CORE Repository Dashboard! 👇