NUI Maynooth Eprint Archive
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Modes of climate variability: Synthesis and review of proxy-based reconstructions through the Holocene
No full textModes of climate variability affect global and regional climates on different spatio-temporal scales, and they
have important impacts on human activities and ecosystems. As these modes are a useful tool for simplifying the
understanding of the climate system, it is crucial that we gain improved knowledge of their long-term past
evolution and interactions over time to contextualise their present and future behaviour. We review the literature focused on proxy-based reconstructions of modes of climate variability during the Holocene (i.e., the last
11.7 thousand years) with a special emphasis on i) proxy-based reconstruction methods; ii) available proxybased reconstructions of the main modes of variability, i.e., El Niño Southern Oscillation, Pacific Decadal
Variability, Atlantic Multidecadal Variability, the North Atlantic Oscillation, the Southern Annular Mode and the
Indian Ocean Dipole; iii) major interactions between these modes; and iv) external forcing mechanisms related
to the evolution of these modes. This review shows that modes of variability can be reconstructed using proxy based records from a wide range of natural archives, but these reconstructions are scarce beyond the last millennium, partly due to the lack of robust chronologies with reduced dating uncertainties, technical issues related
to proxy calibration, and difficulty elucidating their stationary impact (or not) on regional climates over time.
While for each mode the available reconstructions tend to agree at multidecadal timescales, they show notable
disagreement on shorter timescales beyond the instrumental period. The reviewed evidence suggests that the
intrinsic variability of modes can be modulated by external forcing, such as orbital, solar, volcanic, and anthropogenic forcing. The review also highlights some modes experience higher variability over the instrumental period, which is partly ascribed to anthropogenic forcing. These features stress the paramount importance of further studying their past variations using long climate-proxy records for the progress of climate
science
Tracing the sources of sediment and associated particulate nitrogen from different land uses in the Johnstone River catchment, Wet Tropics, north-eastern Australia
No full textWhile the ecosystem of the Great Barrier Reef (GBR), north-eastern Australia, is being threatened by the elevated levels of sediments and nutrients discharged from adjacent coastal river systems, the source of these detrimental pollutants are not well understood. Here we used a combined isotopic (δ13C, δ15N) and geochemical (Zn, Pt and S) signatures and stable isotope analysis in R (SIAR) mixing model to estimate the contribution of different land uses to the sediment and associated particulate nitrogen delivered to the Johnstone River. Results showed that rainforest was the largest contributor of suspended and bed sediments in the river estuary (both 33.1%), followed by banana (26.7%, 20.4%), sugarcane (21.5%, 21.4%) and grazing (18.7%, 25.1%). However, bananas and sugarcane land uses had the highest contribution to sediments delivered to the coast per unit of area. This will help land managers to prioritise on-ground activities to improve water quality in the GBR lagoon
UAV-mounted hyperspectral mapping of intertidal macroalgae
No full textIntertidal macroalgal communities mark the boundary of the marine realm and are faced with many direct and indirect anthropogenic pressures. The effective and sustainable management of these resources must be underpinned by accurate, efficient and cost-effective environmental data collection. Traditional field survey methods, whilst accurate, are time-consuming and limited in the area that can be covered. Remote sensing permits large areas to be rapidly surveyed but the effectiveness of satellites and aircraft for mapping fine-scale intertidal macroalgal mapping is limited by their coarse spatial resolution and restricted operational flexibility. The rapid development of unoccupied aerial vehicle (UAV) and sensor technology can address these issues and provide a potential alternative to established remote sensing platforms. Here, a detailed methodology is presented for the assessment of the commercially and ecologically important intertidal brown macroalga Ascophyllum nodosum using a multirotor UAV and pushbroom hyperspectral sensor. Two different classifiers, Maximum Likelihood Classifier (MLC) and Spectral Angle Mapper (SAM), were compared along with two different sources of spectral profiles, one collected in-situ with a spectral radiometer and the other derived from hyperspectral imagery. Of the classifiers compared, both trained using image-derived spectra, MLC more accurately classified A. nodosum, and other common intertidal species and substratum (Overall Accuracy (OA) 94.7%) than SAM (OA 81.1%). In addition, SAM, trained using in-situ spectra, was the least accurate of the three classifier workflows used (OA 71.4%). The low accuracy of the spectral radiometer approach was likely due to high levels of noise present in the hyperspectral data, a result of the relative instability of the UAV platform causing vibration. The accurate mapping of non-target species also highlights the applicability of this methodology for a broader range of intertidal macroalgal species and communities. This research clearly demonstrates the potential of UAV-mounted hyperspectral remote sensing for mapping the spatially and spectral complex macroalgal habitats found within the intertidal zone
Boundary element and integral methods in potential flow theory: a review with a focus on wave energy applications
No full textThis paper presents a comprehensive review of boundary element methods for hydrodynamic modelling of wave energy
systems. To design and optimise a wave energy converter (WEC), it is estimated that several million hours of WEC operation
must be simulated. Linear boundary element methods are sufficiently fast to provide this volume of simulation and high speed
of execution is one of the reasons why linear boundary element methods continue to underpin many, if not most, applied
wave energy development efforts; however, the fidelity of the physics included is inadequate for some of the required design
calculations. Judicious use of non-linear boundary element methods provides a route to increase the fidelity of the modeling
while maintaining speed and other advantages over more computationally demanding alternatives such as Reynolds averaged
Navier–Stokes (RANS) or smooth particle hydrodynamics (SPH). The paper presents some background to each aspect of the
boundary methods reviewed, building up a relatively complete theoretical framework. Both linear and nonlinear methods are
covered, and consideration is given to the computational complexity of the methods reviewed. The paper aims to provide a
review that is useful in selection of the most appropriate techniques for the next generation of WEC design tools
Bonseyes AI Pipeline—Bringing AI to You
No full textNext generation of embedded Information and Communication Technology (ICT) systems are interconnected and collaborative systems able to perform autonomous tasks. The remarkable expansion of the embedded ICT market, together with the rise and breakthroughs of Artificial Intelligence (AI), have put the focus on the Edge as it stands as one of the keys for the next technological revolution: the seamless integration of AI in our daily life. However, training and deployment of custom AI solutions on embedded devices require a fine-grained integration of data, algorithms, and tools to achieve high accuracy and overcome functional and non-functional requirements. Such integration requires a high level of expertise that becomes a real bottleneck for small and medium enterprises wanting to deploy AI solutions on the Edge, which, ultimately, slows down the adoption of AI on applications in our daily life.
In this work, we present a modular AI pipeline as an integrating framework to bring data, algorithms, and deployment tools together. By removing the integration barriers and lowering the required expertise, we can interconnect the different stages of particular tools and provide a modular end-to-end development of AI products for embedded devices. Our AI pipeline consists of four modular main steps: (i) data ingestion, (ii) model training, (iii) deployment optimization, and (iv) the IoT hub integration. To show the effectiveness of our pipeline, we provide examples of different AI applications during each of the steps. Besides, we integrate our deployment framework, Low-Power Deep Neural Network (LPDNN), into the AI pipeline and present its lightweight architecture and deployment capabilities for embedded devices. Finally, we demonstrate the results of the AI pipeline by showing the deployment of several AI applications such as keyword spotting, image classification, and object detection on a set of well-known embedded platforms, where LPDNN consistently outperforms all other popular deployment frameworks
'A tale of two lost lunulae – one with its ogham story to tell'
No full textTwo unlocalised Early Bronze Age gold lunulae originally in the collection of the earls of Dunraven are each notable
for distinctive modern features, including a stitched repair and an ogham inscription. This paper considers some aspects
of the history of the Dunraven collection, in particular the prehistoric and more recent biographies of the lunulae.
Dedicated to the memory of Valerie Dowling, the National Museum of Ireland’s Senior Photographer, whose work
illustrates this paper. Ar dheis Dé go raibh a h-anam
Exploring scientific publications by firms: what are the roles of academic and corporate partners for publications in high reputation or high impact journals?
No full textRecent research suggests that firms, particularly in science-based industries, may publish
scientific articles in order to achieve strategic goals. This paper explores whether the reputation seen as publications in journals with high impact factors and the impact seen as
citations of such scientific publications originating in firms benefit from R&D alliances
with different types of partners. Our empirical analysis is based on a unique dataset in
pharmaceutical cancer research. We analyze publications originating in biotechnology and
pharmaceutical firms, with a comparison of the results to publications that do not involve
a firm-based author. Our results indicate that the returns to the number of partners are
decreasing and are negative after a turning point. More surprisingly, our results suggest
that biotechnology and pharmaceutical firms should focus on establishing R&D alliances
with pharmaceutical firms in order to increase the probability of publishing in journals
with a high reputation. However, in terms of scientific impact, i.e., forward citations, publications originating in firms do not benefit from having access to different types of alliance
partner
Location-scale mixed models and goodness-of-fit assessment applied to insect ecology
No full textSurvival models have been extensively used to analyse time-untilevent data. There is a range of extended models that incorporate different aspects, such as over dispersion/frailty, mixtures, and flexible
response functions through semi-parametric models. In this work, we show how a useful tool to assess goodness-of-fit, the half-normal plot of residuals with a simulated envelope, implemented in the hnp package in R, can be used on a location-scale modelling context. We fitted a range of survival models to time-until-event data, where the
event was an insect predator attacking a larva in a biological control experiment. We started with the Weibull model and then fitted the exponentiated-Weibull location-scale model with regressors both for the location and scale parameters. We performed variable selection
for each model and, by producing half-normal plots with simulated envelopes for the deviance residuals of the model fits, we found that the exponentiated-Weibull fitted the data better. We then included a random effect in the exponentiated-Weibull model to accommodate correlated observations. Finally, we discuss possible implications of
the results found in the case stud
Irish Universities Association Guidance for Universities. How to Respond to Alleged Staff or Student or University Related Sexual Misconduct
No full textIrish Universities Association Guidance for Universities on how to respond to alleged staff or student or university-related sexual misconduc
Transitions from avoidance: Reinforcing competing behaviours reduces generalised avoidance in new contexts
No full textGeneralised avoidance behaviours are a common diagnostic feature of anxiety-related disorders and a barrier to affecting changes in anxiety during therapy. However, strategies to mitigate generalised avoidance are under-investigated. Even less attention is given to reducing the category-based generalisation of avoidance. We therefore investigated the potential of an operant-based approach. Specifically, it was examined whether reinforcing competing (non-avoidance) behaviours to threat-predictive cues would interfere with the expression of generalised avoidance. Using a matching-to-sample task, artificial stimulus categories were established using physically dissimilar nonsense shapes. A member of one category (conditioned stimulus; CS1) was then associated with an aversive outcome in an Acquisition context, unless an avoidance response was made. Next, competing behaviours were reinforced in response to the CS1 in new contexts. Finally, we tested for the generalisation of avoidance to another member of the stimulus category (generalisation stimulus; GS1) in both a Novel context and the Acquisition context. The selective generalisation of avoidance to GS1 was observed, but only in the Acquisition context. In the Novel context, the generalisation of avoidance to GSs was significantly reduced. A comparison group (Experiment 2), which did not learn any competing behaviours, avoided GS1 in both contexts. These findings suggest that reinforcing competing behavioural responses to threat-predictive cues can lead to reductions in generalised avoidance. This study is among the first study to demonstrate sustained reductions in generalised avoidance resulting from operant-based protocols, and the clinical and research implications are discussed