3,031 research outputs found

    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

    Evaluating the sustainability and resiliency of local food systems

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    With an ever-rising global population and looming environmental challenges such as climate change and soil degradation, it is imperative to increase the sustainability of food production. The drastic rise in food insecurity during the COVID-19 pandemic has further shown a pressing need to increase the resiliency of food systems. One strategy to reduce the dependence on complex, vulnerable global supply chains is to strengthen local food systems, such as by producing more food in cities. This thesis uses an interdisciplinary, food systems approach to explore aspects of sustainability and resiliency within local food systems. Lifecycle assessment (LCA) was used to evaluate how farm scale, distance to consumer, and management practices influence environmental impacts for different local agriculture models in two case study locations: Georgia, USA and England, UK. Farms were grouped based on urbanisation level and management practices, including: urban organic, peri-urban organic, rural organic, and rural conventional. A total of 25 farms and 40 crop lifecycles were evaluated, focusing on two crops (kale and tomatoes) and including impacts from seedling production through final distribution to the point of sale. Results were extremely sensitive to the allocation of composting burdens (decomposition emissions), with impact variation between organic farms driven mainly by levels of compost use. When composting burdens were attributed to compost inputs, the rural conventional category in the U.S. and the rural organic category in the UK had the lowest average impacts per kg sellable crop produced, including the lowest global warming potential (GWP). However, when subtracting avoided burdens from the municipal waste stream from compost inputs, trends reversed entirely, with urban or peri-urban farm categories having the lowest impacts (often negative) for GWP and marine eutrophication. Overall, farm management practices were the most important factor driving environmental impacts from local food supply chains. A soil health assessment was then performed on a subset of the UK farms to provide insight to ecosystem services that are not captured within LCA frameworks. Better soil health was observed in organically-farmed and uncultivated soils compared to conventionally farmed soils, suggesting higher ecosystem service provisioning as related to improved soil structure, flood mitigation, erosion control, and carbon storage. However, relatively high heavy metal concentrations were seen on urban and peri-urban farms, as well as those located in areas with previous mining activity. This implies that there are important services and disservices on farms that are not captured by LCAs. Zooming out from a focus on food production, a qualitative methodology was used to explore experiences of food insecurity and related health and social challenges during the COVID-19 pandemic. Fourteen individuals receiving emergency food parcels from a community food project in Sheffield, UK were interviewed. Results showed that maintaining food security in times of crisis requires a diverse set of individual, household, social, and place-based resources, which were largely diminished or strained during the pandemic. Drawing upon social capital and community support was essential to cope with a multiplicity of hardship, highlighting a need to develop community food infrastructure that supports ideals of mutual aid and builds connections throughout the food supply chain. Overall, this thesis shows that a range of context-specific solutions are required to build sustainable and resilient food systems. This can be supported by increasing local control of food systems and designing strategies to meet specific community needs, whilst still acknowledging a shared global responsibility to protect ecosystem, human, and planetary health

    Automated Distinct Bone Segmentation from Computed Tomography Images using Deep Learning

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    Large-scale CT scans are frequently performed for forensic and diagnostic purposes, to plan and direct surgical procedures, and to track the development of bone-related diseases. This often involves radiologists who have to annotate bones manually or in a semi-automatic way, which is a time consuming task. Their annotation workload can be reduced by automated segmentation and detection of individual bones. This automation of distinct bone segmentation not only has the potential to accelerate current workflows but also opens up new possibilities for processing and presenting medical data for planning, navigation, and education. In this thesis, we explored the use of deep learning for automating the segmentation of all individual bones within an upper-body CT scan. To do so, we had to find a network architec- ture that provides a good trade-off between the problem’s high computational demands and the results’ accuracy. After finding a baseline method and having enlarged the dataset, we set out to eliminate the most prevalent types of error. To do so, we introduced an novel method called binary-prediction-enhanced multi-class (BEM) inference, separating the task into two: Distin- guishing bone from non-bone is conducted separately from identifying the individual bones. Both predictions are then merged, which leads to superior results. Another type of error is tack- led by our developed architecture, the Sneaky-Net, which receives additional inputs with larger fields of view but at a smaller resolution. We can thus sneak more extensive areas of the input into the network while keeping the growth of additional pixels in check. Overall, we present a deep-learning-based method that reliably segments most of the over one hundred distinct bones present in upper-body CT scans in an end-to-end trained matter quickly enough to be used in interactive software. Our algorithm has been included in our groups virtual reality medical image visualisation software SpectoVR with the plan to be used as one of the puzzle piece in surgical planning and navigation, as well as in the education of future doctors

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Probabilistic Inference for Model Based Control

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    Robotic systems are essential for enhancing productivity, automation, and performing hazardous tasks. Addressing the unpredictability of physical systems, this thesis advances robotic planning and control under uncertainty, introducing learning-based methods for managing uncertain parameters and adapting to changing environments in real-time. Our first contribution is a framework using Bayesian statistics for likelihood-free inference of model parameters. This allows employing complex simulators for designing efficient, robust controllers. The method, integrating the unscented transform with a variant of information theoretical model predictive control, shows better performance in trajectory evaluation compared to Monte Carlo sampling, easing the computational load in various control and robotics tasks. Next, we reframe robotic planning and control as a Bayesian inference problem, focusing on the posterior distribution of actions and model parameters. An implicit variational inference algorithm, performing Stein Variational Gradient Descent, estimates distributions over model parameters and control inputs in real-time. This Bayesian approach effectively handles complex multi-modal posterior distributions, vital for dynamic and realistic robot navigation. Finally, we tackle diversity in high-dimensional spaces. Our approach mitigates underestimation of uncertainty in posterior distributions, which leads to locally optimal solutions. Using the theory of rough paths, we develop an algorithm for parallel trajectory optimisation, enhancing solution diversity and avoiding mode collapse. This method extends our variational inference approach for trajectory estimation, employing diversity-enhancing kernels and leveraging path signature representation of trajectories. Empirical tests, ranging from 2-D navigation to robotic manipulators in cluttered environments, affirm our method's efficiency, outperforming existing alternatives

    ETHICAL EVALUATION IN WILDLIFE CONSERVATION: ART, ANIMAL-VISITOR INTERACTIONS AND EMERGENCIES IN WILDLIFE MANAGEMENT AND CONSERVATION

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    Nell’attuale crisi globale della biodiversità è sempre più cruciale valutare le questioni eticamente rilevanti e considerare la natura pluralistica della conservazione della biodiversità. L'etica della conservazione fornisce strumenti per eseguire tali valutazioni e assistere nei processi decisionali. La tesi di questo dottorato di ricerca presenta studi in cui vengono utilizzati strumenti per eseguire valutazioni etiche e multidisciplinari per valutare progetti di conservazione e gestione della fauna selvatica. Pertanto, questo lavoro di dottorato mostra tre diverse aree di applicazione dell'etica della conservazione: Conservation ART, le interazioni animale-visitatore e le sfide nella gestione della fauna selvatica durante l'emergenza COVID-19. Nella prima sezione, la valutazione etica è stata applicata nel contesto del progetto BioRescue, in cui le tecnologie di riproduzione assistita (ART) sono utilizzate nello sforzo di salvare il rinoceronte bianco settentrionale (Ceratotherium simum cottoni) dall’estinzione. Le tecnologie di riproduzione assistita possono fare la differenza nella conservazione della biodiversità, ma la loro applicazione può sollevare questioni eticamente rilevanti che necessitano di essere affrontate. Pertanto, in primo luogo, è stata utilizzata la Matrice Etica (EM) per presentare un quadro per l'analisi etica dell'applicazione delle procedure ART nella conservazione. L'EM, anche se specificamente costruita attorno alle procedure di prelievo di ovociti (OPU) effettuate su rinoceronti bianchi, ha permesso di raggruppare i fattori eticamente rilevanti, identificare e valutare complessi scenari morali in cui diversi bisogni, interessi e preoccupazioni etiche possono entrare in conflitto e fornire infine un modello per la valutazione delle procedure ART in progetti che coinvolgono altre specie in via di estinzione. In seguito, viene presentato un nuovo strumento di valutazione etica (ETHAS) specificamente sviluppato per valutare l’applicazione delle procedure ART in conservazione, e vengono illustrati i risultati delle prime applicazioni. ETHAS, con le sue due liste checklist che lo compongono, permette di effettuare un'autovalutazione integrata, multilivello e standardizzata della procedura in esame, generando una classifica di accettabilità etica e consentendo l'attuazione di misure per affrontare o gestire eventuali problemi in anticipo. ETHAS, specificatamente customizzato per l'OPU e le procedure di fecondazione in vitro eseguite sul rinoceronte bianco settentrionale, hanno permesso di garantire un elevato standard delle procedure, migliorare alcuni aspetti della comunicazione tra i partner del progetto e migliorare lo strumento stesso al fine di essere applicato nel prossimo futuro ad altri contesti in cui le ART vengono utilizzate per la conservazione di altre specie di mammiferi. Nell'ultimo studio presentato nella prima sezione, la matrice etica, l'albero decisionale e il cubo di Bateson sono stati adattati per assistere nell'analisi etica di un complesso scenario relativo alla decisione se continuare o meno la raccolta di biomateriale sul più anziano dei due rimanenti rinoceronti bianchi settentrionali, Najin. Strutturando questi strumenti per implementare le diverse dimensioni di valore (ambientale, sociale e benessere animale) coinvolte nell'etica della conservazione, è stato possibile raccogliere pro e contro, confrontare le diverse opzioni e stabilire una soglia di accettabilità etica. L'applicazione degli strumenti è stata fondamentale per strutturare il processo decisionale e aiutare a raggiungere la decisione condivisa, ragionata e trasparente di sospendere Najin da qualsiasi ulteriore procedura di prelievo di ovociti. L'etica della conservazione può anche aiutare ad esplorare le questioni etiche riguardanti la gestione della fauna selvatica durante le interazioni animale-visitatore (AVI) che si svolgono nelle strutture zoologiche. A questo proposito, la Sezione 2In the global biodiversity crisis, it is increasingly crucial to evaluate ethically relevant issues and consider the pluralistic nature of biodiversity conservation. Conservation ethics provides tools to perform such evaluation and assist in the decision-making processes. This Ph.D. thesis presents studies in which ethical tools are used to perform ethical evaluation and multidisciplinary assessments to approach conservation projects and wildlife management. Three different areas of application of conservation ethics are discussed: Conservation ART, animal-visitor interactions, and challenges in wildlife management during the COVID-19 emergency. In the first area, ethical evaluation has been applied in the context of the BioRescue project, an international project in which assisted reproductive technologies (ARTs) are used in the effort to save the endangered northern white rhinoceros (Ceratotherium simum cottoni). Assisted reproductive technologies can make a difference in biodiversity conservation, but their application can raise ethical issues that need to be addressed. Therefore, firstly, an Ethical Matrix (EM) has been used to present a framework for the ethical analysis of the application of ART procedures in conservation. The EM, specifically built around the ovum pick-up (OPU) procedures carried out on white rhinoceros, allowed to collect ethically relevant factors to identify issues and value conflicts, evaluates complex moral scenarios where different needs, interests, and ethical concerns may conflict, and provides a template for the assessment of ART procedures in projects involving endangered species. Therefore, a new ethical evaluation tool (ETHAS) specifically developed to assess ART procedures in conservation is presented, and the first application results are reported. ETHAS, with its two checklists, provides an integrated, multilevel, and standardized self-assessment of the procedure under scrutiny, generating an ethical acceptability ranking and allowing for implementing measures to address or manage issues beforehand. ETHAS customized for OPU and in vitro fertilization procedures performed on the northern white rhinoceros allowed for ensuring a high standard of procedures, improving some aspects of the communication among the projects’ partners, and improving the tool itself, in order to be applied in the near future to other contexts in which ARTs are applied for the conservation of other mammal species. Finally, in the last study presented in the first section, the ethical matrix, decision tree, and Bateson’s cube have been adapted to assist in the ethical analysis of a complex conservation scenarios relative to the decision regarding whether or not to continue collecting biomaterial on the oldest of the two remaining northern white rhinoceroses. By structuring these tools to implement the different value dimensions (environmental, social, and animal welfare) involved in conservation ethics, it has been possible to gather ethical pros and cons, compare the different options at stake, and establish a threshold of ethical acceptability. The application of the tools was pivotal in structuring the decision-making process and helping reach the shared, reasoned, and the transparent decision to discontinue Najin from any further oocyte collection procedures. Conservation ethics can also assist in exploring the ethical issues concerning wildlife management during animal-visitor interactions (AVI). In this regard, Section 2 of this thesis presents studies concerning AVIs. Firstly, a participatory process has been followed with an Ethical Matrix to explore welfare and management issues related to AVIs. The inclusion of the stakeholders' perspectives allowed to record all the value demands concerning AVI and provide a map of the ethically relevant aspects involved. This map shows how the ethical acceptability of AVIs is linked to different relevant issues like animal welfare, education, and biodiversit

    Computational Approaches to Drug Profiling and Drug-Protein Interactions

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    Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a long period of stagnation in drug approvals. Due to the extreme costs associated with introducing a drug to the market, locating and understanding the reasons for clinical failure is key to future productivity. As part of this PhD, three main contributions were made in this respect. First, the web platform, LigNFam enables users to interactively explore similarity relationships between ‘drug like’ molecules and the proteins they bind. Secondly, two deep-learning-based binding site comparison tools were developed, competing with the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold relationships and has already been used in multiple projects, including integration into a virtual screening pipeline to increase the tractability of ultra-large screening experiments. Together, and with existing tools, the contributions made will aid in the understanding of drug-protein relationships, particularly in the fields of off-target prediction and drug repurposing, helping to design better drugs faster

    Technology for Low Resolution Space Based RSO Detection and Characterisation

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    Space Situational Awareness (SSA) refers to all activities to detect, identify and track objects in Earth orbit. SSA is critical to all current and future space activities and protect space assets by providing access control, conjunction warnings, and monitoring status of active satellites. Currently SSA methods and infrastructure are not sufficient to account for the proliferations of space debris. In response to the need for better SSA there has been many different areas of research looking to improve SSA most of the requiring dedicated ground or space-based infrastructure. In this thesis, a novel approach for the characterisation of RSO’s (Resident Space Objects) from passive low-resolution space-based sensors is presented with all the background work performed to enable this novel method. Low resolution space-based sensors are common on current satellites, with many of these sensors being in space using them passively to detect RSO’s can greatly augment SSA with out expensive infrastructure or long lead times. One of the largest hurtles to overcome with research in the area has to do with the lack of publicly available labelled data to test and confirm results with. To overcome this hurtle a simulation software, ORBITALS, was created. To verify and validate the ORBITALS simulator it was compared with the Fast Auroral Imager images, which is one of the only publicly available low-resolution space-based images found with auxiliary data. During the development of the ORBITALS simulator it was found that the generation of these simulated images are computationally intensive when propagating the entire space catalog. To overcome this an upgrade of the currently used propagation method, Specialised General Perturbation Method 4th order (SGP4), was performed to allow the algorithm to run in parallel reducing the computational time required to propagate entire catalogs of RSO’s. From the results it was found that the standard facet model with a particle swarm optimisation performed the best estimating an RSO’s attitude with a 0.66 degree RMSE accuracy across a sequence, and ~1% MAPE accuracy for the optical properties. This accomplished this thesis goal of demonstrating the feasibility of low-resolution passive RSO characterisation from space-based platforms in a simulated environment
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