2,290 research outputs found
Oral history interview with Bob McAdam
No full textBob McAdam discusses cattle roundups in South Dakota in the 1890's, and attending Theodore Roosevelt's presidential inauguration in a group of cowboys
Paul McAdam PhD Dataset
No full textData produced during the thesis of P McAdam which include the following projects: “Adaptive evolution of S. aureus during chronic endobronchial infection of a cystic fibrosis patient”, “Evolution and epidemiology of a pandemic lineage of S. aureus” and “Molecular epidemiology of an outbreak of Legionnaires’ disease in Edinburgh
“Health and welfare of lumpfish in hatchery production and deployed in Scottish salmon cages”
No full textThis is the raw data on the condition and morphometrics of the lumpfish and salmon used and analysed for the manuscript Rey S, Treasurer J, Pattillo C & McAdam BJ (2021) Using model selection to choose a size-based condition index that is consistent with operational welfare indicators. Journal of Fish Biology. https://doi.org/10.1111/jfb.147, as well as the supplementary figures and Rscripts mentioned in the paper.open data for condition,open data for weights and supplementary data (Supplementary Figures 1 and 2 and a supplementary appendix of the R code to recreate all results and figures)
Mcadam, H R, NX6002
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/402455Surname: MCADAM. Given Name(s) or Initials: H R. Military Service Number or Last Known Location: NX6002. Missing, Wounded and Prisoner of War Enquiry Card Index Number: 2163.222101
Item: [2016.0049.34748] "Mcadam, H R, NX6002
Mcadam, C J, VX23147
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/402456Surname: MCADAM. Given Name(s) or Initials: C J. Military Service Number or Last Known Location: VX23147. Missing, Wounded and Prisoner of War Enquiry Card Index Number: 40697.222102
Item: [2016.0049.34749] "Mcadam, C J, VX23147
Mcadam, J T Mcd, NX18339
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/402454Surname: MCADAM. Given Name(s) or Initials: J T MCD. Military Service Number or Last Known Location: NX18339. Missing, Wounded and Prisoner of War Enquiry Card Index Number: 36663.222100
Item: [2016.0049.34747] "Mcadam, J T Mcd, NX18339
Negotiating the real: Culture and fantastical fiction 1843-1973
Get PDFThis dissertation examines the growth and practice of two distinct reading techniques, with reference to fantastical fiction from і 843 to 1973. While acknowledging that specific reading practices are not exclusive to particular groups or individuals, it is proposed, broadly, that readers fall into two categories: those who tend to be distanced from the text and approach it analytically; those who tend to embrace the text and immerse themselves in its narrative. These two groups, critical readers and experience readers, have their reading habits determined by basic philosophical assumptions. One aim of the dissertation is to explore the link between this division and divisions within the literary hierarchy, articulating a methodology/typology of reading. Criticism of texts in this dissertation involves discussion of the above hypothesis, assessing the value assigned to literary works by each group of reader and considering how the texts themselves investigate the hypothesis. Various theories and critical concepts are engaged with, including those of Marxist aesthetics, psychoanalysis, liberal humanism, cultural studies, and postmodernism. The aim is to demonstrate the practice of both reading techniques and to draw conclusions concerning their respective psychological and social significance. The dissertation argues that fantastical fiction is often a site of interaction between such binary opposites as realism/fantasy, high/popular, ideas/escape, and polemic/amusing. The struggle between these opposites may provide a dialectic of ''critical'" and ''experience" reading
Machine learning methods to predict sea surface temperature and marine heatwave occurrence: a case study of the Mediterranean Sea
Get PDFMarine heatwaves (MHWs) have significant social and ecological impacts, necessitating the prediction of these extreme events to prevent and mitigate their negative consequences and provide valuable information to decision-makers about MHW-related risks. In this study, machine learning (ML) techniques are applied to predict sea surface temperature (SST) time series and marine heatwaves in 16 regions of the Mediterranean Sea. ML algorithms, including the random forest (RForest), long short-term memory (LSTM), and convolutional neural network (CNN), are used to create competitive predictive tools for SST. The ML models are designed to forecast SST and MHWs up to 7 d ahead. For each region, we performed 15 different experiments for ML techniques, progressively sliding the training and the testing period window of 4 years from 1981 to 2017. Alongside SST, other relevant atmospheric variables are utilized as potential predictors of MHWs. Datasets from the European Space Agency Climate Change Initiative (ESA CCI SST) v2.1 and the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis from 1981 to 2021 are used to train and test the ML techniques. For each area, the results show that all the ML methods performed with minimum root mean square errors (RMSEs) of about 0.1 degrees C at a 1 d lead time and maximum values of about 0.8 degrees C at a 7 d lead time. In all regions, both the RForest and LSTM consistently outperformed the CNN model across all lead times. LSTM has the highest predictive skill in 11 regions at all lead times. Importantly, the ML techniques show results similar to the dynamical Copernicus Mediterranean Forecasting System (MedFS) for both SST and MHW forecasts, especially in the early forecast days. For MHW forecasting, ML methods compare favorably with MedFS up to 3 d lead time in 14 regions, while MedFS shows superior skill at 5 d lead time in 9 out of 16 regions. All methods predict the occurrence of MHWs with a confidence level greater than 50 % in each region. Additionally, the study highlights the importance of incoming solar radiation as a significant predictor of SST variability along with SST itself
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