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Artificial intelligence (AI) is not only disrupting industries and businesses, particularly the ones have fallen behind the adoption, but also significantly impacting public life as well. This calls for government authorities pay attention to public opinions and sentiments towards AI. Nonetheless, there is limited knowledge on what the drivers behind the public perception of AI are. Bridging this gap is the rationale of this paper. As the methodological approach, the study conducts an online public perception survey with the residents of Sydney, Melbourne, and Brisbane, and explores the collected survey data through statistical analysis. The analysis reveals that: (a) the public is concerned of AI invading their privacy, but not much concerned of AI becoming more intelligent than humans; (b) the public trusts AI in their lifestyle, but the trust is lower for companies and government deploying AI; (c) the public appreciates the benefits of AI in urban services and disaster management; (d) depending on the local context, public perceptions vary; and (e) the drivers behind the public perception include gender, age, AI knowledge, and AI experience. The findings inform authorities in developing policies to minimise public concerns and maximise AI awareness. [ABSTRACT FROM AUTHOR]

Understanding housing preferences is critical for successful compact city development. However, there is limited research on understanding preference heterogeneity in dwelling type choices. Using the Household Income and Labour Dynamics in Australia survey, this paper identifies the key housing and built environment characteristics associated with changes in dwelling type choice from detached houses to high-density. A latent class choice model captures the heterogeneity of dwelling type preferences within a traditionally low-density city, Brisbane, Australia. Findings reveal six household classes with distinct dwelling preferences: Class 1 (senior households without children with other family members) and Class 2 (couple families with children) in inner-city areas, Class 3 (high-income young households) and Class 4 (low-income households without children) in middle-city areas, Class 5 (low-income families with children) and Class 6 (middle-income young families without children) in outer-city areas. Residential environments with better access to educational facilities encourage Classes 3 and 6 to change to high-density living. Greater land use diversity encourages Classes 2, 3, and 6 to move towards high-density living. The findings can be used to design and improve high-density housing for targeted population groups across inner-, middle- and outer-city areas. [ABSTRACT FROM AUTHOR]

Prediction of ionospheric parameters, such as ionospheric F2 layer critical frequency (foF2) at low latitude regions is of significant interest in understanding ionospheric variation effects on high-frequency communication and global navigation satellite system. Currently, deep learning algorithms have made a striking accomplishment in capturing ionospheric variability. In this paper, we use the state-of-the-art hybrid neural network combined with a quantile mechanism to predict foF2 parameter variations under low and high solar activity years (solar cycle-24) and space weather events. The hybrid neural network is composed of a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), in which CNN and BiLSTM networks extracted spatial and temporal features of ionospheric variation, respectively. The proposed method was trained and tested on 5 years (2009–2014) of ionospheric foF2 observation data from Advanced Digital Ionosonde located in Brisbane, Australia (27°53′S, 152°92′E). It is evident from the results that the proposed model performs better than International Reference Ionosphere 2016 (IRI-2016), long short-term memory (LSTM), and BiLSTM ionospheric prediction models. The proposed model extensively captured the variation in ionospheric foF2 feature, and better predicted it under two significant space weather events (29 September 2011 and 22 July 2012). [ABSTRACT FROM AUTHOR]

Background: Suicide in young people is a leading cause of death. Interventions that are reflexive, tailored, and developed in concert with this at-risk population are needed. This study aimed to integrate lived-experience into the design of a suicide prevention intervention delivered by phone to young people post-discharge from an emergency department (ED) for suicide risk or self-harm. Methods: Qualitative study was conducted at the Queensland Children's Hospital, Brisbane Australia. Four focus groups with young people with lived-experience, parents or carers and ED mental health clinicals were conducted. In total 5 young people with lived-experience of suicidality (17–21 years, Mage = 19.20), 3 parents and carers with a lived-experience of caring for a young person with mental illness, and 10 ED mental health clinicians participated in focus groups. The first phase of qualitative analysis involved a phenomenological analysis and second phase included a deductive content analysis. The paper is following the Consolidated Criteria for Reporting Qualitative Research. Results: First phase, a phenomenological analysis identified three foundational themes to structure future follow-up phone interventions: a person-centred focus, the phone-call dynamic, and the phone-call purpose. Second phase, a deductive content analysis found that participants preferred an intervention that was structured, consistent, and finite. Moreover, an intervention that was authentic, able to facilitate and empower growing independence, and achievable of young people after an ED presentation was desired. Conclusions: Participants expressed their desire for a responsive, structured, and clearly focused phone call that would recognise the young person and parent/carer's needs while providing tailored support to ease transition from the ED to available community and family led care. [ABSTRACT FROM AUTHOR]

This paper examines the role of local weather conditions in explaining variations in assault, in sub-tropical Brisbane, Australia. It details the extent to which local variations in weather are important in shaping the necessary preconditions for assault to take place. Results suggest that higher daily temperatures are associated with an increased propensity for assault at the neighborhood level after controlling for seasonal effects. Assaults occur significantly less frequently in summer than in spring and there is a greater propensity for assaults to occur on weekends compared to weekdays. Neighborhood disadvantage, ethnic diversity, and the presence of risky facilities such bars, schools, or shops increased the propensity for assault above and beyond the effect of temperature. Findings are important in their capacity to isolate the effect of the prevailing local weather conditions whilst controlling for seasonal variations, land use, and the socio-economic and demographic context within which assaults took place. [ABSTRACT FROM AUTHOR]

[Display omitted] Pre-cooling is a method to shift peak air conditioning demand to off-peak and shoulder hours by utilizing energy storage in the thermal mass of buildings. This paper evaluates the pre-cooling potential of a range of residential buildings broadly representative of the Australian housing stock. Pre-cooling in 2-, 6-, and 8-star buildings with light, medium, and heavy construction weights in the cities of Adelaide, Brisbane, Melbourne, and Sydney is simulated, and its impacts on household bills, thermal comfort and peak air-conditioning demand reduction are assessed. Star rating is the method for rating Australian buildings based on their annual heating and cooling demand. The housing assessment by both star rating and construction weight highlights the varied potential of pre-cooling. Pre-cooling sensitivity to air conditioning size is also evaluated by simulating 5 k W th , 15 k W th , and 25 k W th air conditioning sizes representing typical small, medium, and large units in the Australian air conditioning market. Using an adaptive thermal comfort model, the impact of people's thermal comfort expectations on pre-cooling is also considered. The results highlight that compared with 2-star buildings, the percentage of daily cost savings from pre-cooling is considerably higher in 6-star and 8-star buildings. Moreover, with a small air conditioner, 2-star buildings are prone to negative seasonal cost savings as the air conditioner is not able to achieve thermostat temperature settings and hence runs longer. However, pre-cooling does offer considerable discomfort reduction to household members. A maximum summer seasonal cost savings of around $260 is observed in a 6-star light-weight building in Adelaide, and a 2-star light-weight building in Brisbane. Households that accept wider temperature variations with pre-cooling may not necessarily see cost savings. These households do provide greater peak air conditioning demand reductions. In conclusion, the value of pre-cooling for households and network peak demand reduction depends significantly on the housing type and location but can be highly valuable in many cases. [ABSTRACT FROM AUTHOR]