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Process drama in the classroom: A case study of developing participation for advanced EAL learners in an international school
No full textThis paper reports on a study of the use of process drama in an international primary school in the Netherlands. The research investigated the extent to which using process drama could develop participation for advanced EAL learners. In addition, we sought to understand pupils’ perspectives. Using a qualitative methodology, we undertook a case study approach focusing on six advanced EAL learner pupils (9-10-year-olds). We implemented the process drama approach during a series of nine science lessons. We collated and analysed Video recording of lessons, the class teacher’s written observations, a research journal, two interviews and a focus group with the case study participants using an arts-based framework of participation, previously employed by Pérez-Moreno (2018). We deployed embodied research methods. The findings suggest that using process drama as a teaching methodology increased participation, but not immediately. In addition, pupils who had not previously spoken out in lessons began to volunteer their ideas. All case study pupils reported that they considered that their participation increased
Covid-19, non-Covid-19 and excess mortality rates not comparable across countries
No full textEvidence that more people in some countries and fewer in others are dying because of the pandemic, than is reflected by reported coronavirus disease 2019 (Covid-19) mortality rates, is derived from mortality data. Using publicly available databases, deaths attributed to Covid-19 in 2020 and all deaths for the years 2015–2020 were tabulated for 35 countries together with economic, health, demographic and government response stringency index variables. Residual mortality rates (RMR) in 2020 were calculated as excess mortality minus reported mortality rates due to Covid-19 where excess deaths were observed deaths in 2020 minus the average for 2015–2019. Differences in RMR are differences not attributed to reported Covid-19. For about half the countries, RMR\u27s were negative and for half, positive. The absolute rates in some countries were double those in others. In a regression analysis, population density and proportion of female smokers were positively associated with both Covid-19 and excess mortality while the human development index and proportion of male smokers were negatively associated with both. RMR was not associated with any of the investigated variables. The results show that published data on mortality from Covid-19 cannot be directly comparable across countries. This may be due to differences in Covid-19 death reporting and in addition, the unprecedented public health measures implemented to control the pandemic may have produced either increased or reduced excess deaths due to other diseases. Further data on cause-specific mortality is required to determine the extent to which residual mortality represents non-Covid-19 deaths and to explain differences between countries
Convergence of blockchain, autonomous agents, and knowledge graph to share electronic health records
No full textIn this article, we discuss a data sharing and knowledge integration framework through autonomous agents with blockchain for implementing Electronic Health Records (EHR). This will enable us to augment existing blockchain-based EHR Systems. We discuss how major concerns in the health industry, i.e., trust, security and scalability, can be addressed by transitioning from existing models to convergence of the three technologies blockchain, agent-based modeling, and knowledge graph in a decentralized ecosystem. Each autonomous agent is responsible for instantiating key processes, such as user authentication and authorization, smart contracts, and knowledge graph generation through data integration among the participating stakeholders in the network. We discuss a layered approach for the design of the proposed system leading to an enhanced, safer clinical decision-making system. This can pave the way toward more informed and engaged patients and citizens by delivering personalized healthcare
Unearthing the artist: An autoethnographic investigation
No full textIn this paper I address how autoethnography was utilized to research the role and value of arts practice research in Western classical music professional training and practice, by a classically trained professional violinist. As a researcher, I use the philosophy and method of Dalcroze Eurhythmics as a framework to excavate the multiple layers of my own practice and investigate whether there is wider potential resonance for other professional performers. I utilize a mixed-mode approach, combining artistic practice with a number of documenting strategies, in particular using autoethnography as a tool for
documentation and reflection. I propose key findings concerning the value of arts practice, and how an autoethnographic journey facilitated the emergence of the self as artist, within the Western classical music culture. The processes of excavation, enabled by autoethnography, attempt to unearth the holistic artist within the performing musician
Effect of plane of nutrition during the first 12 weeks of life on growth, metabolic and reproductive hormone concentrations, and testicular relative mRNA abundance in preweaned Holstein Friesian bull calves
No full textThe objective of this study was to examine the effect of nutrition during the first 12 wk of life on aspects of the physiological and
transcriptional regulation of testicular and overall sexual development in the bull calf. Holstein Friesian bull calves with a mean
(SD) age and bodyweight of 17.5 (2.85) d and 48.8 (5.30) kg, respectively, were assigned to either a high (HI; n = 15) or moderate
(MOD; n = 15) plane of nutrition and were individually fed milk replacer and concentrate to achieve overall target growth rates of
at least 1.0 and 0.5 kg/d, respectively. Throughout the trial, animal growth performance, feed intake, and systemic concentrations
of metabolites, metabolic hormones, and reproductive hormones were assessed. Additionally, pulsatility of reproductive
hormones (luteinizing hormone, follicle-stimulating hormone, and testosterone) was recorded at 15-min intervals during a 10-h
period at 10 wk of age. At 87 ± 2.14 d of age, all calves were euthanized, testes were weighed, and testicular tissue was harvested.
Differential expression of messenger ribonucleic acid (mRNA) candidate genes involved in testicular development was examined
using quantitative polymerase chain reaction assays. All data were analyzed using the MIXED procedure in Statistical Analysis
Software using terms for treatment as well as time for repeated measures. Blood metabolites and metabolic hormones generally
reflected the improved metabolic status of the calves on the HI plane of nutrition though the concentrations of reproductive
hormones were not affected by diet. Calves on the HI diet had greater mean (SED) slaughter weight (112.4 vs. 87.70 [2.98] kg;
P < 0.0001) and testicular tissue weight (29.2 vs. 20.1 [2.21] g; P = 0.0003) than those on the MOD diet. Relative mRNA abundance
data indicated advanced testicular development through upregulation of genes involved in cellular metabolism (SIRT1;
P = 0.0282), cholesterol biosynthesis (EBP; P = 0.007), testicular function (INSL3; P = 0.0077), and Sertoli cell development (CLDN11;
P = 0.0054) in HI compared with MOD calves. In conclusion, results demonstrate that offering dairy-bred male calves a high plane
of nutrition during the first 3 mo of life not only improves growth performance and metabolic status but also advances testicular
development consistent with more precocious sexual maturation
Analysis of N2O emissions and isotopomers to understand nitrogen cycling associated with multispecies grassland swards at a lysimeter scale
No full textThe EGU General Assembly, Online, 4-8 May 2020Nitrous oxide (N2O) is a potent greenhouse gas associated with nitrogen fertiliser inputs to agricultural production systems. Minimising N2O emissions is important to improving the efficiency and sustainability of grassland agriculture. Multispecies grassland swards composed of plants from different functional groups (grasses, legumes, herbs) have been considered as a management strategy to achieve this goal.TeagascUniversity College Dubli
Concentric Annular Liquid-Liquid Phase Separation for Flow Chemistry and Continuous Processing
No full textA low-cost, modular, robust, and easily customisable continuous liquid-liquid phase separator has been developed that uses a tubular membrane and annular channels to allow high fluidic throughputs while maintaining rapid, surface wetting dominated, phase separation. The system is constructed from standard fluidic tube fittings and allows leak tight connections to be made without the need for adhesives, or O-rings. The units tested in this work have been shown to operate at flow rates of 0.1 – 300 mL/min, with equivalent residence times from 80 to 4 seconds, demonstrating the simplicity of scale-up with these units. Further scale-up to litre per minute scales of operation for single units and tens of litres/minute through limited numbering up should allow these low cost concentric annular tubular membrane separators to be used at continuous production scales for pharmaceutical applications for many solvent systems. In principle this approach may be sufficiently scalable to be utilized in-line, in batch pharmaceutical manufacturing also, through further scale-up and numbering up of units. Several solvent systems with varying interfacial tensions have been investigated, and the critical process parameters affecting successful separation have been identified. An additively manufactured diaphragm based back pressure regulator was also developed and printed in PEEK, allowing highly accurate, adjustable, and chemically compatible pressure control to be accessed at low cost.Enterprise IrelandEuropean Commission - European Regional Development FundScience Foundation Irelan
Applications in Image Aesthetics Using Deep Learning: Attribute Prediction, Image Captioning and Score Regression
No full textImage Aesthetics refers to the branch of computer vision which is about the study of aesthetic properties of photographs i.e. the factors which make an image look pleasing or dull. Such factors extend beyond the physical properties of an image such as object category or location to subtler and more nuanced ambiguous concepts such as "candid expression", "harsh lighting", "bad placement" etc. Nevertheless, the problems in Image Aesthetics have traditionally been modelled as classical computer vision tasks such as classification, regression etc. And, as with most other problems in computer vision, deep learning based strategies have proved more effective in this area as well, outperforming the classical approaches by a wide margin. Nowadays, automated systems for Image Aesthetics Analysis have widespread applications from professional multimedia content development to casual creatives in social media and advertising.
In this thesis, we study three different applications in Image Aesthetics using deep learning: attribute classification, captioning and score prediction. First, we study the capacity of deep neural networks in capturing the geometric attributes i.e. those which depend on the arrangement of objects within the image. Based on this, we propose a system that predicts the dominant aesthetic attributes in a photograph such as The Rule of Thirds, leading lines etc. Second, we develop an aesthetic image captioning framework by exploiting "in the wild" user feedback from the web. Given an image, our framework generates critical feedback such as "nice composition but the foreground is out of focus". Third, we investigate the limitations of traditional convolutional neural networks with respect to global relational reasoning and handling photographs of arbitrary aspect ratio and resolution. We present a visual attention based graph neural network that addresses these limitations and advances the state-of-the-art in aesthetic score prediction
The emerging role of long non-coding RNAs and MicroRNAs in neurodegenerative diseases: a perspective of machine learning
No full textNeurodegenerative diseases (NDs) are characterized by progressive neuronal dysfunction
and death of brain cells population. As the early manifestations of NDs are similar, their symptoms
are difficult to distinguish, making the timely detection and discrimination of each neurodegenerative
disorder a priority. Several investigations have revealed the importance of microRNAs and long
non-coding RNAs in neurodevelopment, brain function, maturation, and neuronal activity, as well
as its dysregulation involved in many types of neurological diseases. Therefore, the expression
pattern of these molecules in the different NDs have gained significant attention to improve the
diagnostic and treatment at earlier stages. In this sense, we gather the different microRNAs and
long non-coding RNAs that have been reported as dysregulated in each disorder. Since there are a
vast number of non-coding RNAs altered in NDs, some sort of synthesis, filtering and organization
method should be applied to extract the most relevant information. Hence, machine learning is
considered as an important tool for this purpose since it can classify expression profiles of non-coding
RNAs between healthy and sick people. Therefore, we deepen in this branch of computer science, its
different methods, and its meaningful application in the diagnosis of NDs from the dysregulated
non-coding RNAs. In addition, we demonstrate the relevance of machine learning in NDs from the
description of different investigations that showed an accuracy between 85% to 95% in the detection
of the disease with this tool. All of these denote that artificial intelligence could be an excellent
alternative to help the clinical diagnosis and facilitate the identification diseases in early stages based
on non-coding RNAs
Two large-scale forest scenario modelling approaches for reporting CO2 removal: a comparison for the Romanian forests
No full textBackground: Forest carbon models are recognized as suitable tools for the reporting and verification of forest carbon stock and stock change, as well as for evaluating the forest management options to enhance the carbon sink pro‑
vided by sustainable forestry. However, given their increased complexity and data availability, different models may
simulate diferent estimates. Here, we compare carbon estimates for Romanian forests as simulated by two models
(CBM and EFISCEN) that are often used for evaluating the mitigation options given the forest-management choices.
Results: The models, calibrated and parameterized with identical or harmonized data, derived from two successive
national forest inventories, produced similar estimates of carbon accumulation in tree biomass. According to CBM
simulations of carbon stocks in Romanian forests, by 2060, the merchantable standing stock volume will reach an
average of 377 m3
ha−1
, while the carbon stock in tree biomass will reach 76.5 tC ha−1
. The EFISCEN simulations produced estimates that are about 5% and 10%, respectively, lower. In addition, 10% stronger biomass sink was simulated
by CBM, whereby the difference reduced over time, amounting to only 3% toward 2060.
Conclusions: This model comparison provided valuable insights on both the conceptual and modelling algorithms,
as well as how the quality of the input data may affect calibration and projections of the stock and stock change
in the living biomass pool. In our judgement, both models performed well, providing internally consistent results.
Therefore, we underline the importance of the input data quality and the need for further data sampling and model
improvements, while the preference for one model or the other should be based on the availability and suitability of
the required data, on preferred output variables and ease of use