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Your search keyword '"Marshall, Adele H."' showing total 149 results
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149 results on '"Marshall, Adele H."'

1. Reviewing Non-intrusive Load Monitoring Using a Pilot Study of an IoT Device to Disaggregate Energy Usage

Author

McCrory, Matthew, Marshall, Adele H., Novakovic, Aleksandar, Collins, Geoffrey, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Yang, Xin-She, editor, Sherratt, R. Simon, editor, Dey, Nilanjan, editor, and Joshi, Amit, editor

Published
2024
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3. Impact of COVID-19 on the Belfast Bike Sharing Scheme

Author

Doyle, Lucy, Novakovic, Aleksandar, Marshall, Adele H., Cheung, Darren, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Yang, Xin-She, editor, Sherratt, Simon, editor, Dey, Nilanjan, editor, and Joshi, Amit, editor

Published
2023
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4. Optimizing the Belfast Bike Sharing Scheme

Author

Demidova, Nadezda, Novakovic, Aleksandar, Marshall, Adele H., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Arai, Kohei, editor

Published
2022
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8. Knowledge Discovery of the Delays Experienced in Reporting COVID-19 Confirmed Positive Cases Using Time to Event Models

Author

Novakovic, Aleksandar, Marshall, Adele H., McGregor, Carolyn, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Soares, Carlos, editor, and Torgo, Luis, editor

Published
2021
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18. A New Data Analytics Framework Emphasising Pre-processing in Learning AI Models for Complex Manufacturing Systems

Author

Carbery, Caoimhe M., Woods, Roger, Marshall, Adele H., Barbosa, Simone Diniz Junqueira, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Li, Kang, editor, Fei, Minrui, editor, Du, Dajun, editor, Yang, Zhile, editor, and Yang, Dongsheng, editor

Published
2018
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19. Detection of Voigt Spectral Line Profiles of Hydrogen Radio Recombination Lines toward Sagittarius B2(N)

Author

von Prochazka, Azrael A., Remijan, Anthony J., Balser, Dana S., Ryans, Robert S. I., Marshall, Adele H., Schwab, Fredric R., Hollis, Jan M., Jewell, Philip R., and Lovas, Frank J.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

We report the detection of Voigt spectral line profiles of radio recombination lines (RRLs) toward Sagittarius B2(N) with the 100-m Green Bank Telescope (GBT). At radio wavelengths, astronomical spectra are highly populated with RRLs, which serve as ideal probes of the physical conditions in molecular cloud complexes. An analysis of the Hn(alpha) lines presented herein shows that RRLs of higher principal quantum number (n>90) are generally divergent from their expected Gaussian profiles and, moreover, are well described by their respective Voigt profiles. This is in agreement with the theory that spectral lines experience pressure broadening as a result of electron collisions at lower radio frequencies. Given the inherent technical difficulties regarding the detection and profiling of true RRL wing spans and shapes, it is crucial that the observing instrumentation produce flat baselines as well as high sensitivity, high resolution data. The GBT has demonstrated its capabilities regarding all of these aspects, and we believe that future observations of RRL emission via the GBT will be crucial towards advancing our knowledge of the larger-scale extended structures of ionized gas in the interstellar medium (ISM).

Published
2010
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32. The CP-ABM approach for modelling COVID-19 infection dynamics and quantifying the effects of non-pharmaceutical interventions

Author

Novakovic, Aleksandar and Marshall, Adele H.

Subjects
Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Software
Abstract

The motivation for this research is to develop an approach that reliably captures the disease dynamics of COVID-19 for an entire population in order to identify the key events driving change in the epidemic through accurate estimation of daily COVID-19 cases. This has been achieved through the new CP-ABM approach which uniquely incorporates Change Point detection into an Agent Based Model taking advantage of genetic algorithms for calibration and an efficient infection centric procedure for computational efficiency. The CP-ABM is applied to the Northern Ireland population where it successfully captures patterns in COVID-19 infection dynamics over both waves of the pandemic and quantifies the significant effects of non-pharmaceutical interventions (NPI) on a national level for lockdowns and mask wearing. To our knowledge, there is no other approach to date that has captured NPI effectiveness and infection spreading dynamics for both waves of the COVID-19 pandemic for an entire country population.

Published
2021

39. A joint likelihood approach to the analysis of length of stay data utilising the continuous-time hidden Markov model and Coxian phase-type distribution.

Author

Mitchell, Hannah J., Marshall, Adele H., and Zenga, Mariangela

Subjects
HIDDEN Markov models, MARKOV processes, MEDICAL personnel
Abstract

The Coxian phase-type distribution is a special case of phase-type distribution which represents the time to absorption of a finite Markov chain in continuous time. The distribution is able to capture subjects' flow through a system but is unable to highlight if there are different pathways caused by an underlying latent factor. Identifying these different pathways will give healthcare providers a deeper insight and understanding of patient flow and allow them to identify and change any potential issues. This paper combines the Coxian phase-type distribution with the continuous-time hidden Markov model to highlight these paths. The theory of combining the Coxian phase-type distribution with the continuous-time hidden Markov model shall be presented along with a simulation study and an application using Italian healthcare data. [ABSTRACT FROM AUTHOR]

Published
2021
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42. A two-stage approach to the joint analysis of longitudinal and survival data utilising the Coxian phase-type distribution

Author

Donnelly, Conor, McFetridge, Lisa M, Marshall, Adele H, and Mitchell, Hannah J

Abstract

The Coxian phase-type distribution is a special type of Markov model which can be utilised both to uncover underlying stages of a survival process and to make inferences regarding the rates of flow of individuals through these latent stages before an event of interest occurs. Such models can be utilised, for example, to identify individuals who are likely to deteriorate faster through a series of disease states and thus require more aggressive medical intervention. Within this paper, a two-stage approach to the analysis of longitudinal and survival data is presented. In Stage 1, a linear mixed effects model is first used to represent how some longitudinal response of interest changes through time. Within this linear mixed effects model, the individuals’ random effects can be considered as a proxy measure for the effect of the individuals’ genetic profiles on the response of interest. In Stage 2, the Coxian phase-type distribution is employed to represent the survival process. The individuals’ random effects, estimated in Stage 1, are incorporated as covariates within the Coxian phase-type distribution so as to evaluate their effect on the individuals’ rates of flow through the system represented by the Coxian. The approach is illustrated using data collected on individuals suffering from chronic kidney disease, where focus is given to an emerging longitudinal biomarker of interest – an individual’s haemoglobin level.

Published
2017

46. Education achievement and type 2 diabetes—what mediates the relationship in older adults? Data from the ESTHER study: a population-based cohort study

Author

Steele, Christopher J, primary, Schöttker, Ben, additional, Marshall, Adele H, additional, Kouvonen, Anne, additional, O'Doherty, Mark G, additional, Mons, Ute, additional, Saum, Kai-Uwe, additional, Boffetta, Paolo, additional, Trichopoulou, Antonia, additional, Brenner, Hermann, additional, and Kee, Frank, additional

Published
2017
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47. A two-stage approach to the joint analysis of longitudinal and survival data utilising the Coxian phase-type distribution.

Author

Donnelly, Conor, McFetridge, Lisa M., Marshall, Adele H., and Mitchell, Hannah J.

Subjects
MARKOV processes, KIDNEY diseases, HEMOGLOBINS, GENETICS, RANDOM effects model
Abstract

The Coxian phase-type distribution is a special type of Markov model which can be utilised both to uncover underlying stages of a survival process and to make inferences regarding the rates of flow of individuals through these latent stages before an event of interest occurs. Such models can be utilised, for example, to identify individuals who are likely to deteriorate faster through a series of disease states and thus require more aggressive medical intervention. Within this paper, a two-stage approach to the analysis of longitudinal and survival data is presented. In Stage 1, a linear mixed effects model is first used to represent how some longitudinal response of interest changes through time. Within this linear mixed effects model, the individuals' random effects can be considered as a proxy measure for the effect of the individuals' genetic profiles on the response of interest. In Stage 2, the Coxian phase-type distribution is employed to represent the survival process. The individuals' random effects, estimated in Stage 1, are incorporated as covariates within the Coxian phase-type distribution so as to evaluate their effect on the individuals' rates of flow through the system represented by the Coxian. The approach is illustrated using data collected on individuals suffering from chronic kidney disease, where focus is given to an emerging longitudinal biomarker of interest - an individual's haemoglobin level. [ABSTRACT FROM AUTHOR]

Published
2018
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