289 research outputs found

    Numerical simulation of combustion instability: flame thickening and boundary conditions

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    Combustion-driven instabilities are a significant barrier for progress for many avenues of immense practical relevance in engineering devices, such as next generation gas turbines geared towards minimising pollutant emissions being susceptible to thermoacoustic instabilities. Numerical simulations of such reactive systems must try to balance a dynamic interplay between cost, complexity, and retention of system physics. As such, new computational tools of relevance to Large Eddy Simulation (LES) of compressible, reactive flows are proposed and evaluated. High order flow solvers are susceptible to spurious noise generation at boundaries which can be very detrimental for combustion simulations. Therefore Navier-Stokes Characteristic Boundary conditions are also reviewed and an extension to axisymmetric configurations proposed. Limitations and lingering open questions in the field are highlighted. A modified Artificially Thickened Flame (ATF) model coupled with a novel dynamic formulation is shown to preserve flame-turbulence interaction across a wide range of canonical configurations. The approach does not require efficiency functions which can be difficult to determine, impact accuracy and have limited regimes of validity. The method is supplemented with novel reverse transforms and scaling laws for relevant post-processing from the thickened to unthickened state. This is implemented into a wider Adaptive Mesh Refinement (AMR) context to deliver a unified LES-AMR-ATF framework. The model is validated in a range of test case showing noticeable improvements over conventional LES alternatives. The proposed modifications allow meaningful inferences about flame structure that conventionally may have been restricted to the domain of Direct Numerical Simulation. This allows studying the changes in small-scale flow and scalar topologies during flame-flame interaction. The approach is applied to a dual flame burner setup, where simulations show inclusion of a neighbouring burner increases compressive flow topologies as compared to a lone flame. This may lead to favouring convex scalar structures that are potentially responsible for the increase in counter-normal flame-flame interactions observed in experiments.Open Acces

    ACARORUM CATALOGUS IX. Acariformes, Acaridida, Schizoglyphoidea (Schizoglyphidae), Histiostomatoidea (Histiostomatidae, Guanolichidae), Canestrinioidea (Canestriniidae, Chetochelacaridae, Lophonotacaridae, Heterocoptidae), Hemisarcoptoidea (Chaetodactylidae, Hyadesiidae, Algophagidae, Hemisarcoptidae, Carpoglyphidae, Winterschmidtiidae)

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    The 9th volume of the series Acarorum Catalogus contains lists of mites of 13 families, 225 genera and 1268 species of the superfamilies Schizoglyphoidea, Histiostomatoidea, Canestrinioidea and Hemisarcoptoidea. Most of these mites live on insects or other animals (as parasites, phoretic or commensals), some inhabit rotten plant material, dung or fungi. Mites of the families Chetochelacaridae and Lophonotacaridae are specialised to live with Myriapods (Diplopoda). The peculiar aquatic or intertidal mites of the families Hyadesidae and Algophagidae are also included.Publishe

    Intelligent robotic disassembly optimisation for sustainability using the bees algorithm

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    Robotic disassembly plays a pivotal role in achieving efficient and sustainable product lifecycle management, with a focus on resource conservation and waste reduction. This thesis discusses robotic disassembly sequence planning (RDSP) and robotic disassembly line balancing (RDLB), with a specific emphasis on optimising sustainability models. The overarching goal was to enhance the efficiency and effectiveness of disassembly processes through intelligent robotic disassembly optimisation techniques. At the heart of this research lies the application of the Bees Algorithm (BA), a metaheuristic optimisation algorithm inspired by the foraging behaviour of honeybees. By harnessing the power of the BA, this research aims to address the challenges associated with RDSP and RDLB, ultimately facilitating sustainable disassembly practices. The thesis gives an extensive literature review of RDSP and RDLB to gain deeper insight into the current research landscape. The challenges of the RDSP problem were addressed in this work by introducing a sustainability model and various scenarios to enhance disassembly processes. The sustainability model considers three objectives: profit, energy savings, and environmental impact reduction. The four explored scenarios were recovery (REC), remanufacture (REM), reuse (REU), and an automatic recovery scenario (ARS). Two novel tools were developed for assessing algorithm performance: the statistical performance metric (SPM) and the performance evaluation index (PEI). To validate the proposed approach, a case study involving the disassembly of gear pumps was used. To optimise the RDSP, single-objective (SO), multiobjective (MO) aggregate, and multiobjective nondominated (MO-ND) approaches were adopted. Three optimisation algorithms were employed — Multiobjective Nondominated Bees Algorithm (MOBA), Nondominated Sorting Genetic Algorithm - II (NSGA-II), and Pareto Envelope-based Selection Algorithm - II (PESA-II), and their results were compared using SPM and PEI. The findings indicate that MO-ND is more suitable for this problem, highlighting the importance of considering conflicting objectives in RDSP. It was shown that recycling should be considered the last-resort recovery option, advocating for the exploration of alternative recovery strategies prior to recycling. Moreover, MOBA outperformed other algorithms, demonstrating its effectiveness in achieving a more efficient and sustainable RDSP. The problem of sequence-dependent robotic disassembly line balancing (RDLBSD) was next investigated by considering the interconnection between disassembly sequence planning and line balancing. Both aspects were optimised simultaneously, leading to a balanced and optimal disassembly process considering profitability, energy savings, environmental impact, and line balance using the MO-ND approach. The findings further support the notion that recycling should be considered the last option for recovery. Again, MOBA outperformed other algorithms, showcasing its capability to handle more complex problems. The final part of the thesis explains the mechanism of a new enhanced BA, named the Fibonacci Bees Algorithm (BAF). BAF draws inspiration from the Fibonacci sequence observed in the drone ancestry. This adoption of the Fibonacci-sequence-based pattern reduces the number of algorithm parameters to four, streamlining parameter setting and simplifying the algorithm’s steps. The study conducted on the RDSP problem demonstrates BAF’s performance over the basic BA, particularly in handling more complex problems. The thesis concludes by summarising the key contributions of the work, including the enhancements made to the BA and the introduction of novel evaluation tools, and the implications of the research, especially the importance of exploring alternative recovery strategies for end-of-life (EoL) products to align with Circular Economy principles

    Ancestral Sequence Reconstructions of Stator Proteins of the Bacterial Flagellar Motor

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    The bacterial flagellar motor (BFM) is a bidirectional nanomachine that confers motility to several bacteria. The BFM is powered by ion transfer across the cell membrane through its stator. The stator consists of two membrane proteins: MotA and MotB in proton (H+)-powered motors or PomA and PomB in sodium (Na+)-powered motors. Over the years, several parts of the BFM have been resolved using numerous mutagenesis studies and different microscopic techniques. However, the entire structure of the BFM, its ion selection mechanism, the functional roles of each structural residue, and how its complexity evolves and adapts over time are not completely known. In this thesis, we used ancestral sequence reconstruction (ASR) to study the evolutionary history and roles of the key structural residues of the stator complex of the BFM. First, we reconstructed and synthesised thirteen combined transmembrane (TM) and plug domains of ancestral MotBs (MotB-ASRs) to test previously hypothesised critical motifs for the ion-selectivity of BFM. The results showed that all resurrected MotB-ASRs were functional and restored motility with the contemporary E. coli MotA in a stator-deleted strain. In addition, all MotB-ASRs exhibited Na+-independent motility in different ionic conditions, suggesting that the synthesised MotB-ASRs were more likely to be proton-powered. Secondly, we reconstructed and synthesised ten complete ancient MotAs (MotA-ASRs) to study the role of the key structural residues of MotA in BFM function. We identified that four of the ten MotA-ASRs were functional and restored motility in combination with contemporary E. coli MotB and several previously synthesised MotB-ASRs. The functional MotA-ASRs also showed Na+-independent motility in different ionic conditions, like our MotB-ASRs. Additionally, the resurrected MotA-ASRs provided evidence of several variable regions of MotA and revealed 30 conserved residues that were essential for flagellar function. Lastly, we screened two novel motility inhibitors, HM2-16F and BB2-50F, and characterised their anti-motility activity on multiple strains and stator types. We also optimised and developed new high-resolution assays for the phenotypic study of stator function to verify the targets of the motility inhibitors. Our results confirmed that these compounds inhibited bacterial swimming but did not target the stator. In summary, this thesis shows the use of ASR as a tool to study the stator proteins of the BFM

    Towards a New Ontology of Polling Inaccuracy: The Benefits of Conceiving of Elections as Heterogenous Phenomena for the Study of Pre-election Polling Error

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    A puzzle exists at the heart of pre-election polling. Despite continual methodological improvement and repeated attempts to identify and correct issues laid bare by misprediction, average polling accuracy has not notably improved since the conclusion of the Second World War. In this thesis, I contend that this is the result of a poll-level focus within the study of polling error that is both incommensurate with its evolution over time and the nature of the elections that polls seek to predict. I hold that differences between elections stand as a plausible source of polling error and situate them within a novel four-level model of sources of polling error. By establishing the heterogenous nature of elections as phenomena and its expected impact on polling error, I propose a new election-level ontology through which the inaccuracy of polls can be understood. I test the empirical validity of this new ontology by using a novel multi-level model to analyse error across the most expansive polling dataset assembled to date, encompassing 11,832 in-campaign polls conducted in 497 elections across 83 countries, finding that membership within different elections meaningfully impacts polling error variation. With the empirical validity of my proposed ontology established, I engage in an exploratory analysis of its benefits, finding electoral characteristics to be useful in the prediction of polling error. Ultimately, I conclude that the adoption of a new, multi-level ontology of polling error centred on the importance of electoral heterogeneity not only offers a more comprehensive theoretical account of its sources than current understandings, but is also more specifically tailored to the reality of pre-election polling than existing alternatives. I also contend that it offers pronounced practical benefits, illuminating those circumstances in which polling error is likely to vary

    Operational research:methods and applications

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    Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order

    Computational analysis of human genomic variants and lncRNAs from sequence data

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    The high-throughput sequencing technologies have been developed and applied to the human genome studies for nearly 20 years. These technologies have provided numerous research applications and have significantly expanded our knowledge about the human genome. In this thesis, computational methods that utilize sequence data to study human genomic variants and transcripts were evaluated and developed. Indel represents insertion and deletion, which are two types of common genomic variants that are widespread in the human genome. Detecting indels from human genomes is the crucial step for diagnosing indel related genomic disorders and may potentially identify novel indel makers for studying certain diseases. Compared with previous techniques, the high-throughput sequencing technologies, especially the next- generation sequencing (NGS) technology, enable to detect indels accurately and efficiently in wide ranges of genome. In the first part of the thesis, tools with indel calling abilities are evaluated with an assortment of indels and different NGS settings. The results show that the selection of tools and NGS settings impact on indel detection significantly, which provide suggestions for tool selection and future developments. In bioinformatics analysis, an indel’s position can be marked inconsistently on the reference genome, which may result in an indel having different but equivalent representations and cause troubles for downstream. This problem is related to the complex sequence context of the indels, for example, short tandem repeats (STRs), where the same short stretch of nucleotides is amplified. In the second part of the thesis, a novel computational tool VarSCAT was described, which has various functions for annotating the sequence context of variants, including ambiguous positions, STRs, and other sequence context features. Analysis of several high- confidence human variant sets with VarSCAT reveals that a large number of genomic variants, especially indels, have sequence features associated with STRs. In the human genome, not all genes and their transcripts are translated into proteins. Long non-coding ribonucleic acid (lncRNA) is a typical example. Sequence recognition built with machine learning models have improved significantly in recent years. In the last part of the thesis, several machine learning-based lncRNA prediction tools were evaluated on their predictions for coding potentiality of transcripts. The results suggest that tools based on deep learning identify lncRNAs best. Ihmisen genomivarianttien ja lncRNA:iden laskennallinen analyysi sekvenssiaineistosta Korkean suorituskyvyn sekvensointiteknologioita on kehitetty ja sovellettu ihmisen genomitutkimuksiin lähes 20 vuoden ajan. Nämä teknologiat ovat mahdollistaneet ihmisen genomin laaja-alaisen tutkimisen ja lisänneet merkittävästi tietoamme siitä. Tässä väitöstyössä arvioitiin ja kehitettiin sekvenssiaineistoa hyödyntäviä laskennallisia menetelmiä ihmisen genomivarianttien sekä transkriptien tutkimiseen. Indeli on yhteisnimitys lisäys- eli insertio-varianteille ja häviämä- eli deleetio-varianteille, joita esiintyy koko genomin alueella. Indelien tunnistaminen on ratkaisevaa geneettisten poikkeavuuksien diagnosoinnissa ja eri sairauksiin liittyvien uusien indeli-markkereiden löytämisessä. Aiempiin teknologioihin verrattuna korkean suorituskyvyn sekvensointiteknologiat, erityisesti seuraavan sukupolven sekvensointi (NGS) mahdollistavat indelien havaitsemisen tarkemmin ja tehokkaammin laajemmilta genomialueilta. Väitöstyön ensimmäisessä osassa indelien kutsumiseen tarkoitettuja laskentatyökaluja arvioitiin käyttäen laajaa valikoimaa indeleitä ja erilaisia NGS-asetuksia. Tulokset osoittivat, että työkalujen valinta ja NGS-asetukset vaikuttivat indelien tunnistukseen merkittävästi ja siten ne voivat ohjata työkalujen valinnassa ja kehitystyössä. Bioinformatiivisessa analyysissä saman indelin sijainti voidaan merkitä eri kohtiin referenssigenomia, joka voi aiheuttaa ongelmia loppupään analyysiin, kuten indeli-kutsujen arviointiin. Tämä ongelma liittyy sekvenssikontekstiin, koska variantit voivat sijoittua lyhyille perättäisille tandem-toistojaksoille (STR), jossa sama lyhyt nukleotidijakso on monistunut. Väitöstyön toisessa osassa kehitettiin laskentatyökalu VarSCAT, jossa on eri toimintoja, mm. monitulkintaisten sijaintitietojen, vierekkäisten alueiden ja STR-alueiden tarkasteluun. Luotettaviksi arvioitujen ihmisen varianttiaineistojen analyysi VarSCAT-työkalulla paljasti, että monien geneettisten varianttien ja erityisesti indelien ominaisuudet liittyvät STR-alueisiin. Kaikkia ihmisen geenejä ja niiden geenituotteita, kuten esimerkiksi ei-koodaavia RNA:ta (lncRNA) ei käännetä proteiiniksi. Koneoppimismenetelmissä ja sekvenssitunnistuksessa on tapahtunut huomattavaa parannusta viime vuosina. Väitöstyön viimeisessä osassa arvioitiin useiden koneoppimiseen perustuvien lncRNA-ennustustyökalujen ennusteita. Tulokset viittaavat siihen, että syväoppimiseen perustuvat työkalut tunnistavat lncRNA:t parhaiten

    A geo-informatics approach to sustainability assessments of floatovoltaic technology in South African agricultural applications

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    South African project engineers recently pioneered the first agricultural floating solar photovoltaic tech nology systems in the Western Cape wine region. This effort prepared our country for an imminent large scale diffusion of this exciting new climate solver technology. However, hydro-embedded photovoltaic sys tems interact with environmentally sensitive underlying aquatic ecosystems, causing multiple project as sessment uncertainties (energy, land, air, water) compared to ground-mounted photovoltaics. The dissimi lar behaviour of floatovoltaic technologies delivers a broader and more diversified range of technical advan tages, environmental offset benefits, and economic co-benefits, causing analytical modelling imperfections and tooling mismatches in conventional analytical project assessment techniques. As a universal interna tional real-world problem of significance, the literature review identified critical knowledge and methodology gaps as the primary causes of modelling deficiencies and assessment uncertainties. By following a design thinking methodology, the thesis views the sustainability assessment and modelling problem through a geo graphical information systems lens, thus seeing an academic research opportunity to fill critical knowledge gaps through new theory formulation and geographical knowledge creation. To this end, this philosophi cal investigation proposes a novel object-oriented systems-thinking and climate modelling methodology to study the real-world geospatial behaviour of functioning floatovoltaic systems from a dynamical system thinking perspective. As an empirical feedback-driven object-process methodology, it inspired the thesis to create new knowledge by postulating a new multi-disciplinary sustainability theory to holistically characterise agricultural floatovoltaic projects through ecosystems-based quantitative sustainability profiling criteria. The study breaks new ground at the frontiers of energy geo-informatics by conceptualising a holistic theoretical framework designed for the theoretical characterisation of floatovoltaic technology ecosystem operations in terms of the technical energy, environmental and economic (3E) domain responses. It campaigns for a fully coupled model in ensemble analysis that advances the state-of-the-art by appropriating the 3E theo retical framework as underpinning computer program logic blueprint to synthesise the posited theory in a digital twin simulation. Driven by real-world geo-sensor data, this geospatial digital twin can mimic the geo dynamical behaviour of floatovoltaics through discrete-time computer simulations in real-time and lifetime digital project enactment exercises. The results show that the theoretical 3E framing enables project due diligence and environmental impact assessment reporting as it uniquely incorporates balanced scorecard performance metrics, such as the water-energy-land-food resource impacts, environmental offset benefits and financial feasibility of floatovoltaics. Embedded in a geoinformatics decision-support platform, the 3E theory, framework and model enable numerical project decision-supporting through an analytical hierarchy process. The experimental results obtained with the digital twin model and decision support system show that the desktop-based parametric floatovoltaic synthesis toolset can uniquely characterise the broad and diverse spectrum of performance benefits of floatovoltaics in a 3E sustainability profile. The model uniquely predicts important impact aspects of the technology’s land, air and water preservation qualities, quantifying these impacts in terms of the water, energy, land and food nexus parameters. The proposed GIS model can quantitatively predict most FPV technology unknowns, thus solving a contemporary real-world prob lem that currently jeopardises floating PV project licensing and approvals. Overall, the posited theoretical framework, methodology model, and reported results provide an improved understanding of floating PV renewable energy systems and their real-world behaviour. Amidst a rapidly growing international interest in floatovoltaic solutions, the research advances fresh philosophical ideas with novel theoretical principles that may have far-reaching implications for developing electronic, photovoltaic performance models worldwide.GeographyPh. D. (Geography

    Identification of Crohn’s disease immunopathotypes

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    Crohn’s disease (CD) describes an inflammatory condition characterised by chronic gastrointestinal (GI) inflammation. Currently, >1 in 150 people are estimated to be affected in the UK, with the incidence rising. The pathology of CD is linked to an aberrant immune response against the gut microbiota. CD is a heterogenous disease with different disease phenotypes (stricturing and fistulating disease) and locations; it can affect any part of the GI tract. A range of drugs is approved for CD treatment. However, response rates to these specific therapies are low, and 46% of patients still require bowel resective surgery. The high rates of surgical interventions and unpredictable disease relapse pose challenges for the clinical management of CD. Little is known about the molecular mechanisms underlying the different treatment responses, and cost-effective and feasible approaches to predict these responses are still a long way off. Disease relapse has been linked with decreased T cell immunoreceptor with Ig and ITIM (TIGIT) on CD38+ CD4+ T cells in the peripheral blood of paediatric patients. Other clinical markers of inflammation have been found to lack specificity in predicting disease progression. Several biomarkers predicting non-response to frontline therapeutics blocking the pro-inflammatory cytokine tumour necrosis factor (TNF) have been proposed, including the levels of luminal membrane-bound TNF and gene expression of the pro-inflammatory cytokines oncostatin-M (OSM) and TNF itself. None of these markers are currently used clinically, and there is still a need for reliable ways to predict disease progression and treatment responses. Based on these findings I hypothesised that distinct immunopathotypes in CD drive distinct disease phenotypes and underlie distinct treatment responses to biologic therapies, such as anti-TNF. My project aimed to elucidate distinct immunopathotypes in the blood and intestines of CD patients and link them to disease phenotype, state of inflammation and treatment response. Two distinct immunopathotypes were identified. One showed decreased circulating T cells and increased intestinal CD8+ and cytotoxic immune responses. The second immunopathotype was defined by IL-1β-driven inflammation and granuloma formation in the intestines of people and was linked to anti-TNF refractory disease
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