ORCID Profile
0000-0001-8894-8282
Current Organisation
Monash University
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Publisher: ACM
Date: 19-04-2023
Publisher: ACM
Date: 06-05-2021
Publisher: Zenodo
Date: 2020
Publisher: IEEE
Date: 03-2022
Publisher: IEEE
Date: 10-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Publisher: ACM
Date: 22-06-2021
Publisher: Elsevier BV
Date: 06-2022
Publisher: Frontiers Media SA
Date: 28-03-2022
DOI: 10.3389/FPSYG.2022.812677
Abstract: This study analyzed and explored the cognitive load of Australian energy market operators managing one of the longest inter-connected electrical networks in the world. Each operator uses a workstation with seven screens in an active control room environment, with a large coordination screen to show information and enable collaboration between different control centers. Cognitive load was assessed during both training scenarios and regular control room operations via the integration of subjective and physiological measures. Eye-tracking glasses were also used to analyze the operators gaze behavior. Our results indicate that different events (normal or unexpected), different participants for the same session, and different periods of one session all have varying degrees of cognitive load. The system design was observed to be inefficient in some situations and to have an adverse affect on cognitive load. In critical situations for instance, operator collaboration was high and the coordination screen was used heavily when collaborating between two control centers, yet integration with the system could be improved. Eye tracking data analysis showed that the layout of applications across the seven screens was not optimal for many tasks. Improved layout strategies, potential combination of applications, redesigning of certain applications, and linked views are all recommended for further exploration in addition to improved integration of procedures and linking alarms to visual cues.
Publisher: Elsevier BV
Date: 12-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: IEEE
Date: 04-2021
Publisher: Springer Science and Business Media LLC
Date: 26-02-2021
DOI: 10.1038/S41598-021-84198-6
Abstract: Understanding human movement patterns at local, national and international scales is critical in a range of fields, including transportation, logistics and epidemiology. Data on human movement is increasingly available, and when combined with statistical models, enables predictions of movement patterns across broad regions. Movement characteristics, however, strongly depend on the scale and type of movement captured for a given study. The models that have so far been proposed for human movement are best suited to specific spatial scales and types of movement. Selecting both the scale of data collection, and the appropriate model for the data remains a key challenge in predicting human movements. We used two different data sources on human movement in Australia, at different spatial scales, to train a range of statistical movement models and evaluate their ability to predict movement patterns for each data type and scale. Whilst the five commonly-used movement models we evaluated varied markedly between datasets in their predictive ability, we show that an ensemble modelling approach that combines the predictions of these models consistently outperformed all in idual models against hold-out data.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: ACM
Date: 30-05-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 31-01-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: ACM
Date: 22-10-2023
Publisher: Cambridge University Press (CUP)
Date: 2022
DOI: 10.1017/DAP.2021.33
Abstract: Data governance is an emerging field of study concerned with how a range of actors can successfully manage data assets according to rules of engagement, decision rights, and accountabilities. Urban studies scholarship has continued to demonstrate and criticize lack of community engagement in smart city development and urban data governance projects, including in local sustainability initiatives. However, few move beyond critique to unpack in more detail what community engagement should look like. To overcome this gap, we develop and test a participatory methodology to identify approaches to empowering community engagement in data governance in the context of the Monash Net Zero Precinct in Melbourne, Australia. Our approach uses design for social innovation to enable a small group of “precinct citizens” to co-design prototypes and multicriteria mapping as a participatory appraisal method to open up and reveal a ersity of perspectives and uncertainties on data governance approaches. The findings reveal the importance of creating deliberative spaces for pluralising community engagement in data governance that consider the erse values and interests of precinct citizens. This research points toward new ways to conceptualize and design enabling processes of community engagement in data governance and reflects on implementation strategies attuned to the politics of participation to support the embedding of these innovations within specific socio-institutional contexts.
Publisher: ACM
Date: 22-06-2021
Publisher: ACM
Date: 12-06-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2021
Publisher: ACM
Date: 22-06-2021
Publisher: Springer International Publishing
Date: 2020
Publisher: IEEE
Date: 04-2019
Publisher: IEEE
Date: 04-2021
Publisher: ACM
Date: 29-11-2021
Publisher: ACM
Date: 19-04-2023
Publisher: ACM
Date: 19-04-2023
Publisher: ACM
Date: 02-12-2019
Publisher: Elsevier BV
Date: 03-2010
Publisher: IEEE
Date: 10-2012
Publisher: IEEE
Date: 10-2012
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Sarah Goodwin.