NUI Maynooth Eprint Archive
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Effortful & Expert Evaluation in Developing Serious CST
No full textThis paper outlines some challenges involved in developing creativity support tools (CST) aimed at serious creativity. Such domains include: academic research, patent creation, literature-based discovery, creative ideation, etc. By
referencing a common workflow model, we focus on the
role of evaluation within the development cycle and finessing of a CST. Our focus lies in gathering expert evaluations by recognised leaders and critics whose opinions hold
respect within that community. Such evaluations are typically; difficult to acquire, involves experts with very narrow field of expertise and necessitate detailed and complex
evaluations. We outline an approach to evaluation that is
based on pre-selected evaluators for whom personalised
artefacts are created for evaluation
Data Acquisition and Processing for GeoAI Models to Support Sustainable Agricultural Practices
No full textThere are growing opportunities to leverage new technologies and data sources to address global problems related to sustainability, climate change, and biodiversity loss. The emerging discipline of GeoAI resulting from the convergence of AI and Geospatial science (Geo-AI) is enabling the possibility to harness the increasingly available open Earth Observation data collected from different constellations of satellites and sensors with high spatial, spectral and temporal resolutions. However, transforming these raw data into high-quality datasets that could be used for training AI and specifically deep learning models are technically challenging. This paper describes the process and results of synthesizing labelled-datasets that could be used for training AI (specifically Convolutional Neural Networks) models for determining agricultural land use pattern to support decisions for sustainable farming. In our opinion, this work is a significant step forward in addressing the paucity of usable datasets for developing scalable GeoAI models for sustainable agriculture
An Inventory of Buildings in Dublin City for Energy Management
No full textGlobally, about one-third of final energy use and associated carbon dioxide emissions are sourced from buildings, the great majority of which are located in urban areas. Not surprisingly, managing building energy demand is a focus of city-based climate change policies while simultaneously tackling issues of fuel poverty. Assessing the potential for mitigation and evaluating the efficacy of energy policies relies on knowledge of the urban building stock, which varies geographically based on the age of building and retrofits that may have taken place. However, building data at a detailed scale are rarely available for these purposes. In this research, we present a geographic building database for Dublin city centre using a typology approach. The resulting data consists of material and energy attributes of over 25,000 buildings that have been constructed over a 200-year period. These data are used to estimate the energy ratings of households and to evaluate historic and potential retrofitting. In addition to energy studies, they provide a fundamental dataset on buildings that can be used to evaluate official sources and to support a wide range of urban research topics. The methodology used here is sufficiently general in nature that it can be expanded to other cities in Ireland and Europ
Is a View of Green Spaces from Home Associated with a Lower Risk of Anxiety and Depression?
No full textAlthough a large body of research supports the theory that exposure to nature results in mental health benefits, research evidence on the effects of having a view of green space from home is still scarce. The aim of the present study is to assess the impact that access to a green space
view from home has on anxiety and depression. This is a cross-sectional study extracting data from
the “2018 Green Spaces, Daily Habits and Urban Health Survey” conducted in Carmona (Spain).
The study included variables on sociodemographic and lifestyle, view of green spaces from home,
self-perceived health status, and risk of anxiety and depression measured using the Hospital Anxiety
and Depression Scale (HADS). Chi-square tests were used to assess variable’s associations and a
multiple linear regression models used to identify the variables explaining the risk of anxiety and
depression, taking into account sociodemographic characteristics, frequency of visits and view of
green spaces from home. According to our results, adults who enjoy a view of green spaces from
home have a lower risk of anxiety and depression
Multi-hazard dependencies can increase or decrease risk
No full textIn risk analysis, it is recognized that hazards can often combine to worsen their joint impact, but impact data
for a rail network show that hazards can also tend to be mutually exclusive at seasonal timescales. Ignoring this
overestimates worst-case risk, so we therefore champion a broader view of risk from compound hazard
Editors’ Introduction to the Special Issue, “Governing Through Human Rights and Critical Criminology”
No full textAbstract in tex
Citizenship after COVID‐19: thoughts from Poland
No full textWe have to close the borders against coronavirus. It is highly important. This is not a virus that originated in Poland, it is a virus that came from outside. I am extremely sorry to communicate to you a new rule. If you are not a citizen of our country, this is not the time to take a trip, to visit friends. The border is closed
Validation of a CFD-based numerical wave tank model for the power production assessment of the wavestar ocean wave energy converter
No full textCFD-based numerical wave tank (CNWT) models, are a useful tool for the analysis of wave energy converters (WECs). During the development of a CNWT, model validation is vital, to prove the accuracy of the numerical solution. This paper presents an extensive validation study of a CNWT model for the 1:5 scale Wavestar point-absorber device. The previous studies reported by Ransley et al. [1] and Windt et al.
[2] are extended in this paper, by including cases in which the power-take off (PTO) system is included in the model. In this study, the PTO is represented as a linear spring-damper system, providing a good approximation to the full PTO dynamics. The spring stiffness and damping coefficients in the numerical PTO model are determined through a linear least squares fit of the experimental PTO position, velocity
and force data. The numerical results for free surface elevation, PTO data (position, velocity, force),
generated power and pressure on the WEC hull are shown to compare well with the experimental measurement
Analysis and Design of Outphasing Transmitter Using Class-E Power Amplifiers With Shunt Capacitances and Shunt Filters
No full textIn this paper, a novel outphasing power amplifier (PA) based on class-E amplifiers with shunt
capacitances and shunt filters is proposed. The new design provides high drain efficiency for both peak
and back-off power levels. A mathematical model for the class-E power amplifier with shunt capacitance
and shunt filter is presented. The proposed model enables derivation of load circuit parameters that provide
optimum drain efficiency for the peak and back-off power levels using closed form mathematical expressions.
Based on this model, an outphasing power amplifier is designed and subsequently implemented using
microstrip transmission lines and a GaN HEMT devices. The fabricated power amplifier prototype is
optimized for 2.14 GHz and provides drain efficiency of over 60% for back-off power levels up to 8.5 dB.
The amplifier demonstrates a 44.3% drain efficiency for 64QAM OFDM modulated signal with 20 MHz
bandwidth. Adjacent channel leakage ratio (ACLR) of −39.5 dB and error vector magnitude (EVM) of 0.9 %
were achieved after the application of a memory polynomial linearization algorithm
Creating Expert Knowledge by Relying on Language Learners: a Generic Approach for Mass-Producing Language Resources by Combining Implicit Crowdsourcing and Language Learning
No full textWe introduce in this paper a generic approach to combine implicit crowdsourcing and language learning in order to mass-produce
language resources (LRs) for any language for which a crowd of language learners can be involved. We present the approach by
explaining its core paradigm that consists in pairing specific types of LRs with specific exercises, by detailing both its strengths and
challenges, and by discussing how much these challenges have been addressed at present. Accordingly, we also report on on-going
proof-of-concept efforts aiming at developing the first prototypical implementation of the approach in order to correct and extend
an LR called ConceptNet based on the input crowdsourced from language learners. We then present an international network called
the European Network for Combining Language Learning with Crowdsourcing Techniques (enetCollect) that provides the context to
accelerate the implementation of the generic approach. Finally, we exemplify how it can be used in several language learning scenarios
to produce a multitude of NLP resources and how it can therefore alleviate the long-standing NLP issue of the lack of LRs