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Ongoing Research Projects supported by Research IT

Listing of project codes and abstracts, describing work undertaken which use the resources of the compute clusters hosted by the Research IT team.

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Showing 10 of 419 Results
Project Title NICAP Ireland
Project Code HPC_19_01055
Principal Investigator Prof Jane McGrath
Start Date 2019-03-11
End Date 2020-12-31
Abstract Aberrant structure, function and connectivity of the basal ganglia and limbic system networks have been associated with core features of ADHD as well as with the emotional regulation difficulties commonly associated with the disorder. This study aims to use innovative neuroimaging analytic techniques to interrogate the morphology of these subcortical brain regions and to define the functional and structural connectivity networks between these regions and the rest of the brain. There is a dearth of longitudinal neuroimaging data in ADHD, and this dataset offers a unique opportunity to investigate developmental changes in the structure, function and connectivity of these subcortical regions as well as the correlation between these brain changes, neuropsychological measures and clinical symptomatology.
Project Title Structural Cortical Connectivity Mediating Motor Function
Project Code HPC_19_01054
Principal Investigator Prof Richard Carson
Start Date 2019-03-06
End Date 2019-09-30
Abstract The focus of the research is the relationship that exists between the structural connectivity of the human brain, and the facility with which movement skills are acquired. On the basis of diffusion weighted imaging (DWI) scans, estimates of the integrity of white matter projections between different regions of interest (RoIs) within the brain have been derived for 43 human volunteers. For each person, various behavioural and electrophysiological measures relating to skill acquisition were also obtained. The objective of the phase of the project for which access to the cluster is requested, is to determine the specific white matter projections that, through their variation across participants, are predictive of the individual differences in learning that are observed. This is achieved through application of a machine learning technique known as elastic net (EN) regularisation. As the particular (robust) variant of the EN technique being employed is based on quantile regression (rather than least squares), it is computationally intensive.
Project Title Evidence from Irish Migration
Project Code HPC_19_01053
Principal Investigator Prof Gaia Narciso
Start Date 2018-10-10
End Date 2019-04-01
Abstract What is the long run impact of large negative historical events on migration flows? The main aim of the proposed paper is to provide both a qualitative and a quantitative investigation on whether personal characteristics and radical historical events matter in the decision to migrate. In a previous contribution, Narciso and Severgnini (2016) exploited the information contained in a unique dataset based on Irish historical data during the first two decades of the 20th century. By combining different historical data sources, they identified the personal features and determinants of those who voiced their discontent and actively participated in the movement for the independence of Ireland from Great Britain. Furthermore, they tested the intergenerational transmission of rebellion generated by a large negative radical shock, the Irish Potato Famine (1845-1852) on the probability of joining the movement of independence in Ireland during the Irish revolution over the period 1913-1921. Starting from the abovementioned dataset, we plan to the test whether the Great Famine had also an effect on the individual decision after half century. Our paper will investigate and link two of the most relevant demographic changes of the recent European history in modern time. First, the Great Irish Famine is one of the biggest hungers in history, which led to the death of about 1 million people, about 20 percent of the total population, having a huge impact on the demographic of Ireland (� Gr�da (1989)). Second, between 1890 and 1924 the Irish migration flows to the USA contributed in a significant part on the economic and social conditions both in the sending countries and in America (see, for example, O�Rourke (1994), Hatton and Williamson (1998), Abramitzky, Boustan and Eriksson (2014)). In particular, we are interested in understanding how potential long-run effects of the Famine can impact the decision of migrating. The mechanism we have in mind and we would like to test is that migration networks formed soon after the famine in the US still have this driving force two generations afterwards. More in detail, during the 50-70 year period followed by the Famine, we 2 believe that the Irish counties hurt most had more short-term migration, which created even more migration due to the network effect and there has been an accumulated network effect. We suppose that county level inequality has resulted in bigger network effects for counties hurt most. Furthermore, there might have been a famine-induced increase in county level inequality. This has resulted in more migration from the counties that have been hurt most and "trapped" in a bad economic situation, resulting in more emigration. To test this, we exploit the methodology proposed by Abramitzky, Boustan and Eriksson (2014) for matching two different set of data. The first one is represented by the early waves of the 20th century Irish Census provide extraordinary pieces of information for creating a complete and realistic picture of the Irish societies before World War I. We consider the two Censuses held in 1901 and 1911, respectively. For the entire Irish population, we can have several pieces of information related to the personal characteristics of the individuals. More precisely, we can collect name and family names, gender, birth date and county of birth, the relation to the head of family, the religion beliefs, the level of education, the languages spoken, the types of occupation and eventually physical and mental handicaps in the family. Furthermore, the Census contains very precise information on the location and the quality of the houses. The 1901 and 1911 Census contains 4,429,866 and 4,384,519 observations, respectively and it has recently been digitized by the Irish national archive. The Ellis Island Administrative Records on Irish migrants represent the second type of data. This source contains characteristics of about 800,000 individuals derived from passengers� documents required to enter in the USA. Finally, information on the effect of the Irish Famine on different Irish county can be obtained by statistical work from Bourke (1959), which contains information on arable areas and the intensity of the potato cultivations for the 32 different counties in Ireland. After having matched the two datasets, we will analyse the main drivers of migration using the same methodology introduced by Krueger (2007 and 2009). We will start considering a simple linear 3 regressions analysis in order to understand the different level of correlations and the structure of our dataset: the dependent variable will be a binary variable, which takes the value of 1 if the individual in the dataset has migrated and 0 otherwise. We will add as explanatory variables our measure of the impact of famine and both individual and family characteristic. We can also construct more aggregate variables such as age structure, share of literate populations and share of different religious groups. We will use geographical information system (GIS) data and additional aggregate statistics, and include additional variables for controlling geographical and institutional variations at local level. Possible covariates can be urban densities, distance to water resources, agricultural productivity and different proxies for production. Preliminary results, also supported by different instrumental variable regressions based on the exogenous movement of the potato blight, suggest that the Famine was an important and significant driver of individuals� migration. We believe that our findings can shed some lights on the individual characteristics of migrants and the relationship between historical shocks and the long-run effects of mobility during the Age of Mass Migration.
Project Title The role of rare genetic variation in neurodevelopmental disorders
Project Code HPC_19_01052
Principal Investigator Prof Louise Gallagher
Start Date 2019-02-18
End Date 2020-02-17
Abstract Neurodevelopmental disorders affect the development of the brain which can result in cognitive, behavioural, language and motor deficits and lead to lifelong impairments. A number of rare genetic variants have been associated with neurodevelopmental disorders. This project aims to elucidate how previously identified rare genetics variants may contribute to behavioural and cognitive difficulties characterised in neurodevelopmental disorders such as autism spectrum disorder. Specifically, this study will use anatomical magnetic resonance imaging and diffusion tensor imaging to investigate brain structure and how it relates to behavioural and cognitive measures in individuals with rare genetic variants. We will assess surface area, volume and cortical thickness in individuals with rare genetic variants and compare to controls. We will also examine group differences in white matter structural architecture. This study will further our knowledge of the interactions between genes, brain structure and behaviour in neurodevelopmental disorders.
Project Title AV1 Video Encoding
Project Code HPC_19_01051
Principal Investigator Prof Aljosa Smolic
Start Date 2019-02-14
End Date 2019-03-03
Abstract I am working on a project about the development of full-reference quality metrics. For that, I need to test some metrics on videos encoded with the AV1 compression approach. In oder to encode the videos I need the computer cluster, since on a normal PC it would take too much time.
Project Title IoCT
Project Code HPC_19_01050
Principal Investigator Dr Nicola Marchetti
Start Date 2019-02-12
End Date 2020-07-01
Abstract The Internet of Things (IoT) is composed by a vast amount of devices, heterogeneous in function and connectivity, applications range from telemetry to smart city, traffic, health actuation and disruptive smart grid applications. Ensuring that the currently designed communication infrastructure will be capable to collect and transport the massive amount of data generated by the distributed devices and, at the same time, ensure a reliable and robust feedback to the actuators is no small task. Current networks are not meant to inter-operate and have been designed to be part of separate communications systems, rather than allowing seamless and heterogeneous data transfer; the effects of multiple communication technologies working at the same time, in the same space for dense and distributed networks are poorly understood. Network management Solutions, based on the interactions between all the layers of a communication network, need to consider the interactions between all the communication agents, in time and space, to determine causal relationships in a heterogeneous network and allow dynamic and distributed management. The first focus of this project is then to develop a functional model of hierarchical communication systems for the Internet of Things. The basis of this work is rooted in a new rediscovery of complexity theory as a powerful framework for the understanding of the interactions within large, heterogeneous systems in which a central controller is either impossible or undesirable. Secondly, the novel taxonomy developed will be used in cross-layer multi-agent simulators developed for the purpose to identify causality links within the communication networks. Techniques borrowed from climate science, econometrics and machine learning will be employed to determine how information spreads over a network, from node to node and across the layers, and which elements are relevant for a dynamic and proactive management of complex, large and dense IoT networks.
Project Title Structure of concentrated ionic solutions and their response to magnetic fields
Project Code HPC_18_01047
Principal Investigator Prof John Coey
Start Date 2018-09-03
End Date 2022-01-03
Abstract Under the influence of a magnetic field, paramagnetic ionic solutions exhibit behaviour that indicate correlation. By analysing the electronic structure and dynamics of the molecule in solution, it is possible to gain valuable insight into solute-solvent interaction with which the observed phenomena could be explained.
Project Title iHEAR
Project Code HPC_18_01046
Principal Investigator Prof Mary Cannon
Start Date 2017-06-01
End Date 2022-05-31
Abstract Up to one fifth of young people have had the experience of psychotic symptoms, such as hearing voices or sounds when there is no-one around or seeing visions. We now know that young people who experience these symptoms are at increased risk of developing psychotic disorders in adulthood. We also know that these young people are at higher risk of a range of co-morbid disorders, such as depression and anxiety, and suicidal behaviours. On the other hand, many of these young people will remain well and, for them, the psychotic experiences were merely a transitory phenomenon. Childhood trauma is known to be associated with increased risk for psychotic symptoms and is a promising target for intervention. However, we do not yet know enough about what types or timing of stressors are involved in the pathogenesis of psychotic symptoms, nor the mechanism by which early life stress may lead to changes in brain structure and function resulting in symptoms such as hallucinations. We do not know which young people who report psychotic experiences are most at risk of adverse outcomes and will benefit from intervention. This ground-breaking, multi-disciplinary project sets out to address these issues by drawing together epidemiology, social science, anthropology and neuroscience, to devise a comprehensive programme of work examining the relationship between early life stress and psychotic symptoms among young people. Designed as three inter-related work packages, the iHEAR programme will exploit a large population-based cohort and will capitalise on my existing unique cohort of young people who were known to have experienced psychotic symptoms in childhood as they enter young adulthood. This iHEAR programme will result in new information which will allow the development of innovative interventions for young people to prevent or pre-empt severe mental illness in later life.
Project Title FERROVOLT
Project Code HPC_18_01045
Principal Investigator Prof Stefano Sanvito
Start Date 2018-11-28
End Date 2019-08-31
Abstract The goal of this project is to help find the rules for a domain-wall engineering that optimizes photovoltaic efficiency of potential future-generation ferroelectric solar cells. The material to be studied is BiFeO3 as the most promising photovoltaic ferroelectric material known. Does the photovoltaic effect in BiFeO3 occur at the domain walls or in the bulk? What does it take a domain-wall to conduct electrons? The project aims at establishing the necessary conditions for electric fields and electrical conductivity at ferroelectric domain walls. Since experimental evidence is inconclusive, state-of-the-art ab initio methods will be applied. Electric fields have a long spatial range, so we will go beyond the standard supercell approach to obtain the spatial gradient of the band structure at the domain wall, needed to obtain charge-carrier distributions and electric fields. The Green's-function method for electronic quantum transport will be used for this purpose because it is suitable for extended, non-periodic systems. We will obtain the electrical conductivity as a function of the domain-wall type, structure, and purity. Conclusions for the role of the domain walls in BiFeO3 will be generalized as far as possible in order to apply them to other ferroelectric semiconductors as well. The project is positioned where fundamental condensed-matter physics meets applied solar-cell research. It is expected to advance the frontier of knowledge in basic research and to lay the ground for further research on ferroelectric photovoltaics. It is a contribution to the efforts of the European Union to develop innovative solutions for a sustainable energy supply that help achieve independence of fossil energy. In addition, high-throughput screening and computational characterization for promising photovoltaic absorber materials is planned, including, but maybe not limited to, perovskites and related compounds.
Project Title Aviation noise reduction using acoustic metamaterials
Project Code HPC_18_01044
Principal Investigator Associate Professor Gareth Bennett
Start Date 2018-11-08
End Date 2020-01-01
Abstract This project aims to develop novel sound absorbing materials which are capable of high absorption in the sub-wavelength scale, specifically targetting the range of 50 - 1000 Hz. The designs are based on acoustically coupled resonant platelets with the shape and damping optimised to maximise the absorption spectrum in the targetted range. The project combines experimental, analytical and numerical work to optimise and validate the absorber designs. The numerical simulations are performed using Comsol multiphysics which allows the necessary coupling between solid mechanics, acoustics and thermoviscous acoustics.