12,895 research outputs found

    Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients

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    In this article, we build on previous work to present an optimization algorithm for nonlinearly constrained multi-objective optimization problems. The algorithm combines a surrogate-assisted derivative-free trust-region approach with the filter method known from single-objective optimization. Instead of the true objective and constraint functions, so-called fully linear models are employed, and we show how to deal with the gradient inexactness in the composite step setting, adapted from single-objective optimization as well. Under standard assumptions, we prove convergence of a subset of iterates to a quasi-stationary point and if constraint qualifications hold, then the limit point is also a KKT-point of the multi-objective problem

    Exploring the Training Factors that Influence the Role of Teaching Assistants to Teach to Students With SEND in a Mainstream Classroom in England

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    With the implementation of inclusive education having become increasingly valued over the years, the training of Teaching Assistants (TAs) is now more important than ever, given that they work alongside pupils with special educational needs and disabilities (hereinafter SEND) in mainstream education classrooms. The current study explored the training factors that influence the role of TAs when it comes to teaching SEND students in mainstream classrooms in England during their one-year training period. This work aimed to increase understanding of how the training of TAs is seen to influence the development of their personal knowledge and professional skills. The study has significance for our comprehension of the connection between the TAsā€™ training and the quality of education in the classroom. In addition, this work investigated whether there existed a correlation between the teaching experience of TAs and their background information, such as their gender, age, grade level taught, years of teaching experience, and qualification level. A critical realist theoretical approach was adopted for this two-phased study, which involved the mixing of adaptive and grounded theories respectively. The multi-method project featured 13 case studies, each of which involved a trainee TA, his/her college tutor, and the classroom teacher who was supervising the trainee TA. The analysis was based on using semi-structured interviews, various questionnaires, and non-participant observation methods for each of these case studies during the TAā€™s one-year training period. The primary analysis of the research was completed by comparing the various kinds of data collected from the participants in the first and second data collection stages of each case. Further analysis involved cross-case analysis using a grounded theory approach, which made it possible to draw conclusions and put forth several core propositions. Compared with previous research, the findings of the current study reveal many implications for the training and deployment conditions of TAs, while they also challenge the prevailing approaches in many aspects, in addition to offering more diversified, enriched, and comprehensive explanations of the critical pedagogical issues

    Multidimensional adaptive order GP-WENO via kernel-based reconstruction

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    This paper presents a fully multidimensional kernel-based reconstruction scheme for finite volume methods applied to systems of hyperbolic conservation laws, with a particular emphasis on the compressible Euler equations. Non-oscillatory reconstruction is achieved through an adaptive order weighted essentially non-oscillatory (WENO-AO) method cast into a form suited to multidimensional stencils and reconstruction. A kernel-based approach inspired by Gaussian process (GP) modeling is presented here. This approach allows the creation of a scheme of arbitrary order with simply defined multidimensional stencils and substencils. Furthermore, the fully multidimensional nature of the reconstruction allows a more straightforward extension to higher spatial dimensions and removes the need for complicated boundary conditions on intermediate quantities in modified dimension-by-dimension methods. In addition, a new simple-yet-effective set of reconstruction variables is introduced, as well as an easy-to-implement effective limiter for positivity preservation, both of which could be useful in existing schemes with little modification. The proposed scheme is applied to a suite of stringent and informative benchmark problems to demonstrate its efficacy and utility.Comment: Submitted to Journal of Computational Physics April 202

    A Decision Support System for Economic Viability and Environmental Impact Assessment of Vertical Farms

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    Vertical farming (VF) is the practice of growing crops or animals using the vertical dimension via multi-tier racks or vertically inclined surfaces. In this thesis, I focus on the emerging industry of plant-specific VF. Vertical plant farming (VPF) is a promising and relatively novel practice that can be conducted in buildings with environmental control and artificial lighting. However, the nascent sector has experienced challenges in economic viability, standardisation, and environmental sustainability. Practitioners and academics call for a comprehensive financial analysis of VPF, but efforts are stifled by a lack of valid and available data. A review of economic estimation and horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. This thesis proposes an open-source DSS framework to evaluate business sustainability through financial risk and environmental impact assessments. Data from the literature, alongside lessons learned from industry practitioners, would be centralised in the proposed DSS using imprecise data techniques. These techniques have been applied in engineering but are seldom used in financial forecasting. This could benefit complex sectors which only have scarce data to predict business viability. To begin the execution of the DSS framework, VPF practitioners were interviewed using a mixed-methods approach. Learnings from over 19 shuttered and operational VPF projects provide insights into the barriers inhibiting scalability and identifying risks to form a risk taxonomy. Labour was the most commonly reported top challenge. Therefore, research was conducted to explore lean principles to improve productivity. A probabilistic model representing a spectrum of variables and their associated uncertainty was built according to the DSS framework to evaluate the financial risk for VF projects. This enabled flexible computation without precise production or financial data to improve economic estimation accuracy. The model assessed two VPF cases (one in the UK and another in Japan), demonstrating the first risk and uncertainty quantification of VPF business models in the literature. The results highlighted measures to improve economic viability and the viability of the UK and Japan case. The environmental impact assessment model was developed, allowing VPF operators to evaluate their carbon footprint compared to traditional agriculture using life-cycle assessment. I explore strategies for net-zero carbon production through sensitivity analysis. Renewable energies, especially solar, geothermal, and tidal power, show promise for reducing the carbon emissions of indoor VPF. Results show that renewably-powered VPF can reduce carbon emissions compared to field-based agriculture when considering the land-use change. The drivers for DSS adoption have been researched, showing a pathway of compliance and design thinking to overcome the ā€˜problem of implementationā€™ and enable commercialisation. Further work is suggested to standardise VF equipment, collect benchmarking data, and characterise risks. This work will reduce risk and uncertainty and accelerate the sectorā€™s emergence

    The determinants of value addition: a crtitical analysis of global software engineering industry in Sri Lanka

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    It was evident through the literature that the perceived value delivery of the global software engineering industry is low due to various facts. Therefore, this research concerns global software product companies in Sri Lanka to explore the software engineering methods and practices in increasing the value addition. The overall aim of the study is to identify the key determinants for value addition in the global software engineering industry and critically evaluate the impact of them for the software product companies to help maximise the value addition to ultimately assure the sustainability of the industry. An exploratory research approach was used initially since findings would emerge while the study unfolds. Mixed method was employed as the literature itself was inadequate to investigate the problem effectively to formulate the research framework. Twenty-three face-to-face online interviews were conducted with the subject matter experts covering all the disciplines from the targeted organisations which was combined with the literature findings as well as the outcomes of the market research outcomes conducted by both government and nongovernment institutes. Data from the interviews were analysed using NVivo 12. The findings of the existing literature were verified through the exploratory study and the outcomes were used to formulate the questionnaire for the public survey. 371 responses were considered after cleansing the total responses received for the data analysis through SPSS 21 with alpha level 0.05. Internal consistency test was done before the descriptive analysis. After assuring the reliability of the dataset, the correlation test, multiple regression test and analysis of variance (ANOVA) test were carried out to fulfil the requirements of meeting the research objectives. Five determinants for value addition were identified along with the key themes for each area. They are staffing, delivery process, use of tools, governance, and technology infrastructure. The cross-functional and self-organised teams built around the value streams, employing a properly interconnected software delivery process with the right governance in the delivery pipelines, selection of tools and providing the right infrastructure increases the value delivery. Moreover, the constraints for value addition are poor interconnection in the internal processes, rigid functional hierarchies, inaccurate selections and uses of tools, inflexible team arrangements and inadequate focus for the technology infrastructure. The findings add to the existing body of knowledge on increasing the value addition by employing effective processes, practices and tools and the impacts of inaccurate applications the same in the global software engineering industry

    Omics measures of ageing and disease susceptibility

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    While genomics has been a major field of study for decades due to relatively inexpensive genotyping arrays, the recent advancement of technology has also allowed the measure and study of various ā€œomicsā€. There are now numerous methods and platforms available that allow high throughput and high dimensional quantification of many types of biological molecules. Traditional genomics and transcriptomics are now joined by proteomics, metabolomics, glycomics, lipidomics and epigenomics. I was lucky to have access to a unique resource in the Orkney Complex Disease Study (ORCADES), a cohort of individuals from the Orkney Islands that are extremely deeply annotated. Approximately 1000 individuals in ORCADES have genomics, proteomics, lipidomics, glycomics, metabolomics, epigenomics, clinical risk factors and disease phenotypes, as well as body composition measurements from whole body scans. In addition to these cross-sectional omics and health related measures, these individuals also have linked electronic health records (EHR) available, allowing the assessment of the effect of these omics measures on incident disease over a ~10-year follow up period. In this thesis I use this phenotype rich resource to investigate the relationship between multiple types of omics measures and both ageing and health outcomes. First, I used the ORCADES data to construct measures of biological age (BA). The idea that there is an underlying rate at which the body deteriorates with age that varies between individuals of the same chronological age, this biological age, would be more indicative of health status, functional capacity and risk of age-related diseases than chronological age. Previous models estimating BA (ageing clocks) have predominantly been built using a single type of omics assay and comparison between different omics ageing clocks has been limited. I performed the most exhaustive comparison of different omics ageing clocks yet, with eleven clocks spanning nine different omics assays. I show that different omics clocks overlap in the information they provide about age, that some omics clocks track more generalised ageing while others track specific disease risk factors and that omics ageing clocks are prognostic of incident disease over and above chronological age. Second, I assessed whether individually or in multivariable models, omics measures are associated with health-related risk factors or prognostic of incident disease over 10 years post-assessment. I show that 2,686 single omics biomarkers are associated with 10 risk factors and 44 subsequent incident diseases. I also show that models built using multiple biomarkers from whole body scans, metabolomics, proteomics and clinical risk factors are prognostic of subsequent diabetes mellitus and that clinical risk factors are prognostic of incident hypertensive disorders, obesity, ischaemic heart disease and Framingham risk score. Third, I investigated the genetic architecture of a subset of the proteomics measures available in ORCADES, specifically 184 cardiovascular-related proteins. Combining genome-wide association (GWAS) summary statistics from ORCADES and 17 other cohorts from the SCALLOP Consortium, giving a maximum sample size of 26,494 individuals, I performed 184 genome-wide association meta-analyses (GWAMAs) on the levels of these proteins circulating in plasma. I discovered 592 independent significant loci associated with the levels of at least one protein. I found that between 8-37% of these significant loci colocalise with known expression quantitative trait loci (eQTL). I also find evidence of causal associations between 11 plasma protein levels and disease susceptibility using Mendelian randomisation, highlighting potential candidate drug targets

    Socio-endocrinology revisited: New tools to tackle old questions

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    Animalsā€™ social environments impact their health and survival, but the proximate links between sociality and fitness are still not fully understood. In this thesis, I develop and apply new approaches to address an outstanding question within this sociality-fitness link: does grooming (a widely studied, positive social interaction) directly affect glucocorticoid concentrations (GCs; a group of steroid hormones indicating physiological stress) in a wild primate? To date, negative, long-term correlations between grooming and GCs have been found, but the logistical difficulties of studying proximate mechanisms in the wild leave knowledge gaps regarding the short-term, causal mechanisms that underpin this relationship. New technologies, such as collar-mounted tri-axial accelerometers, can provide the continuous behavioural data required to match grooming to non-invasive GC measures (Chapter 1). Using Chacma baboons (Papio ursinus) living on the Cape Peninsula, South Africa as a model system, I identify giving and receiving grooming using tri-axial accelerometers and supervised machine learning methods, with high overall accuracy (~80%) (Chapter 2). I then test what socio-ecological variables predict variation in faecal and urinary GCs (fGCs and uGCs) (Chapter 3). Shorter and rainy days are associated with higher fGCs and uGCs, respectively, suggesting that environmental conditions may impose stressors in the form of temporal bottlenecks. Indeed, I find that short days and days with more rain-hours are associated with reduced giving grooming (Chapter 4), and that this reduction is characterised by fewer and shorter grooming bouts. Finally, I test whether grooming predicts GCs, and find that while there is a long-term negative correlation between grooming and GCs, grooming in the short-term, in particular giving grooming, is associated with higher fGCs and uGCs (Chapter 5). I end with a discussion on how the new tools I applied have enabled me to advance our understanding of sociality and stress in primate social systems (Chapter 6)

    Antibody Targeting of HIV-1 Env: A Structural Perspective

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    A key component of contemporary efforts toward a human immunodeficiency virus 1 (HIV-1) vaccine is the use of structural biology to understand the structural characteristics of antibodies elicited both from human patients and animals immunized with engineered 'immunogens,' or early vaccine candidates. This thesis will report on projects characterizing both types of antibodies against HIV-1. Chapter 1 will introduce relevant topics, including the reasons HIV-1 is particularly capable of evading the immune system in natural infection and after vaccination, the 20+ year history of unsuccessful HIV-1 vaccine large-scale efficacy trials, an introduction to broadly neutralizing antibodies (bNAbs), and a review of common strategies utilized in HIV-1 immunogen design today. Chapter 2 describes the isolation, high-resolution structural characterization, and in vitro resistance profile of a new bNAb, 1-18, that is both very broad and potent, as well as able to restrict HIV-1 escape in vivo. Chapter 3 reports the results of an epitope-focusing immunogen design and immunization experiment carried out in wild type mice, rabbits, and non-human primates where it was shown that B cells targeting the desired epitope were expanded after a single prime immunization with immunogen RC1 or a variant, RC1-4fill. Chapter 4 describes Ab1245, an off-target non-neutralizing monoclonal antibody isolated in a macaque that had been immunized with a series of sequential immunogens after the prime immunization reported in Chapter 3. The antibody structure describes a specific type of distracting response as it binds in a way that causes a large structural change in Env, resulting in the destruction of the neutralizing fusion peptide epitope. Chapter 5 is adapted from a review about how antibodies differentially recognize the viruses HIV-1, SARS-CoV-2, and Zika virus. This review serves as an introduction to the virus SARS-CoV-2, which is the topic of the final chapter, Chapter 6. In this chapter, structures of many neutralizing antibodies isolated from SARS-CoV-2 patients were used to define potentially therapeutic classes of neutralizing receptor-binding domain (RBD) antibodies based on their epitopes and binding profiles
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