
Cardiff University
Cardiff University
Funder (3)
2,524 Projects, page 1 of 505
assignment_turned_in Project2012 - 2015 Cardiff UniversityFunder: UKRI Project Code: MR/J013366/1Funder Contribution: 852,230 GBPOur aim in the Cardiff Child Development Study (CCDS) has been to identify children who are highly aggressive and have serious difficulties dealing with other children, while taking into account any natural rise and fall in aggressiveness over the first years of life. It is extremely important to distinguish clinically significant behavioural problems from more ordinary forms of misbehaviour and the self-assertion that is characteristic of two-and three-year-olds. Over 300 families joined the study when the mother was pregnant with her first child. During the pregnancy, we interviewed both mother and father about their backgrounds, their health, and their hopes and plans for their babies. We then observed the infants four times between 6 months and the third birthday, during home visits (at 6 and 21 months) and birthday parties at the School of Psychology, where infants met other infants from other families who were participating in the study. Our observations of the birthday parties revealed that, contrary to many parents' beliefs, infants were more likely to share toys with each other than to try to grab toys, and were highly unlikely to strike out at other babies. However, even in the first year of life it was clear that some infants seemed more angry and aggressive than others; infants were more likely to show aggression if their families had experienced hardship, if their parents had a history of behavioural problems, and if their mothers had smoked cigarettes or been depressed during the pregnancy. Their early signs of aggressiveness were reported by mothers, fathers, and other family members who knew the infants well. What the family members reported was echoed by our observations during the birthday parties; for example, infants whose parents had behavioural problems were more likely to strike out at other infants. Infants' tendencies to show anger or be aggressive were still apparent a year later, as reported by parents and other family members. We therefore believe it is possible that we have already identified those infants will have the most difficulties settling into school and who may develop serious behavioural and emoitonal problems. To test this hypothesis, we propose to complete the CCDS by seeing the children again when they are 6 years old. We will visit the families at home and observe the children with their parents and, in many cases, younger siblings. We plan to use a variety of games and virtual reality tasks to measure the children's abilities to solve problems, control their impulses, and choose non-aggressive solutions to the dilemmas created by other people's challenging behaviour. We will also use well-established methods for determining whether any of the children are showing clinically significant emotional or behavioural problems, and we will get parents' permission to ask teachers to report on the children's behaviour at school. We will also measure the children's activity and heart rate to see if biological factors underlie their behavioural and emotional problems. If our hypothesis is confirmed--if we have indeed identified those infants who are already on a pathway to violent behaviour--it may be possible to identify the factors at home and in school that help children control their anger and find constructive ways to deal with everyday challenges. Thus the CCDS provides a unique body of evidence that can inform prevention and intervention strategies to help troubled children.
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For further information contact us at helpdesk@openaire.euassignment_turned_in Project2014 - 2018 Cardiff UniversityFunder: UKRI Project Code: EP/K041169/1Funder Contribution: 343,364 GBPThe problem of hydraulic resistance in wall-bounded flows remains among the hottest research topics in theoretical and applied fluid mechanics in spite of also being one of the most long-standing hydraulic problems. Researchers continue exploring a wide variety of empirical and conceptual approaches to resolve this problem, particularly focusing on the parameterisation of the bed friction that controls water levels, flood inundation extent, flow rates, depths, and water velocities. The approach currently used for quantifying bed friction is mostly empirical and thus should be considered the weakest component of otherwise quite sophisticated design and modelling methodologies. Despite world-wide efforts to advance capabilities for prediction and control of water levels in free surface flows, especially during flood events, hydraulic engineers still use empirical or semi-empirical relationships for 'roughness' or 'friction' factors. These resistance coefficients subsume the combined effects of complex hydrodynamic processes in simple forms making them convenient for practical applications. There is a general agreement that these resistance coefficients depend on parameters of the flow, bed material, bed and channel forms, and in-stream and bank vegetation. Although the quantitative form of this dependence has been targeted by several generations of hydraulicians, available relationships linking the resistance coefficients to flow and roughness parameters are still largely empirical rather than theoretically justified. As a result, the level of uncertainties of hydraulic models of overland flows, canals, waterways, rivers, and estuaries remains high, often exceeding 20-40%. The central goal of the project is therefore to develop advanced predictive capabilities for quantification of hydraulic resistance in rough-bed open-channel flows and propose a methodology for incorporation of the theoretical and physical insights from this study into applied hydraulic models that are most relevant to the end-users. To achieve this goal, the project team will build a rigorous theoretical framework to explicitly reveal contributions to the total bed friction from viscous, turbulent, and form-induced stresses, secondary currents, non-uniformity, and unsteadiness, and link these contributions to the physics of the flow. This theoretical analysis will underpin sophisticated laboratory experiments in Aberdeen and Large Eddy Simulation numerical studies in Cardiff to clarify the nature of bed friction in open-channel flows, refine the definitions of the roughness regimes, and identify and quantify the contributions to the overall friction from the dominant friction-generated mechanisms. The combination of the theoretical analysis with laboratory and numerical studies will lead to the generalised relationships for the friction coefficients suitable for applied hydraulic models. The examples of benefits that the proposed research will bring include significantly reduced uncertainties in predictions of water levels and flood inundation extent; better urban planning and new design philosophies based on friction control/reduction aptitudes that this research intends to develop (e.g., 'friction-reduced' urban planning as part of 'green cities' concept and more efficient drainage systems); and improved stream restoration design and implementation, among many others. The theoretical and methodological developments of the project will be also applicable, in addition to water engineering, to other areas such as aerospace and mechanical engineering, where drag control studies are particularly important and continue to grow. The interdisciplinary fields of overland flow and soil erosion, biomimetics, and ecosystems (both terrestrial and aquatic), represent other examples where the outcomes of this project can be directly employed.
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For further information contact us at helpdesk@openaire.euassignment_turned_in Project2017 - 2018 Cardiff UniversityFunder: UKRI Project Code: 1938544This project will look at improving the radiation transport in the "Arepo" moving-mesh code, and then use these new techniques to explore cluster formation in 'zoom-ins' of full galaxy-scale simulations. The first task will be to include a rough prescription to treat the heating of the gas and dust from newly-formed stars within our cluster formation simulations. This will involve modifying the existing TreeCol algorithm to obtain column densities to the nearest 'sink particles'. Andrew will then look at improving the existing the TreeCol algorithm, by modifying it to use a look-up table. This should provide a factor of roughly 3 in speed up from our current implementation. The science in the PhD will then be focused on the affect that radiative feedback has on the assembly of cluster-forming clumps and how this in turn affects the evolution of the parent cloud. The cluster formation will be studied in the full galactic context, exploiting the full adaptability of Arepo's moving mesh scheme. Andrew will then be able to study how the galactic environment affects the formation of clusters.
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For further information contact us at helpdesk@openaire.euassignment_turned_in Project2019 - 2023 Cardiff UniversityFunder: UKRI Project Code: 2271823Recent Welsh Government policies have sought to develop Welsh educational leadership, most especially through the establishment of the National Academy for Educational Leadership in Wales, which aims to 'secure, nurture and inspire' educational leaders. The successful applicant will work with head teachers throughout Wales to assess their professional learning and development needs to inform the work of the newly formed Academy. The project has two foci: the first is to assess head teachers' use of professional leadership standards for their own and their staff's professional development; the second is to identify professional development needs of Welsh head teachers that could be embedded within the Academy. The design of the project is mixed methods: the first phase will involve a survey of Welsh head teachers; the second phase will be a qualitative exploration of head teachers' professional needs.
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For further information contact us at helpdesk@openaire.euassignment_turned_in Project2019 - 2023 Cardiff UniversityFunder: UKRI Project Code: 2717630Supernovae are catastrophic stellar explosions shaping the visible Universe. They play an important role in the synthesis and distribution of almost all elements and especially heavy elements such as iron, enriching the Universe since the first supernova explosion when the cosmos was metal-free. We are now in the golden era of supernova astronomy - and in general of transients - since astronomical surveys are discovering roughly 11000 transients per year. Unfortunately, the majority of them remain unrecognised, severely diminishing the scientific return. Future astronomical experiments (for example the Large Synoptic Survey Telescope - LSST) will make it more challenging boosting the number of yearly discoveries by a factor of 100, making almost impossible to recognise the different types of supernovae in the first days of their evolution, when important information about the nature of the object can be obtained. Nevertheless, it is usually easier and possible to gather information at a later time (e.g. more than 50 days after the explosion of the star). However, we are still missing the tools to connect the early time (less than 30 days from the explosion) information of a supernova, such as luminosity evolution and electromagnetic spectra signatures, with the later ones. To connect late to early time data/observations several approaches are possible. Among them, the application of supervised and unsupervised learning algorithms to large datasets, supplied by surveys such as the Public ESO Spectroscopic Survey of Transient Objects (PESSTO) of which the supervisor is a member, provides an innovative view of the issue. This methodology will give the possibility to recognise the transient but also to retrieve the information carried by the early electromagnetic behaviour (even when not available) and shed lights on the supernova physics and its connection to the environment in which they explode. In this project, the PhD student will gather knowledge of supernova explosions linked to the life and death of massive stars as well as programming skills in python and experience in observational astronomy, data reduction and data analysis.
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