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The burden of disease attributable to obesity is rapidly increasing and becoming a public health challenge globally. Using a nationally representative sample in Australia, this study aims to examine the association of obesity with healthcare service use and work productivity across outcome distributions. We used Household, Income and Labour Dynamics Australia (HILDA) Wave 17 (2017–2018), including 11,211 participants aged between 20 and 65 years. Two-part models using multivariable logistic regressions and quantile regressions were employed to understand variations in the association between obesity levels and the outcomes. The prevalence of overweight and obesity was 35.0% and 27.6%, respectively. After adjusting for socio-demographic factors, low socioeconomic status was associated with a higher probability of overweight and obesity (Obese III: OR = 3.79; 95% CI 2.53–5.68) while high education group was associated with a lower likelihood of being high level of obesity (Obese III OR = 0.42, 95% CI 0.29–0.59). Higher levels of obesity were associated with higher probability of health service use (GP visit Obese, III: OR = 1.42 95% CI 1.04–1.93,) and work productivity loss (number of paid sick leave days, Obese III: OR = 2.40 95% CI 1.94–2.96), compared with normal weight. The impacts of obesity on health service use and work productivity were larger for those with higher percentiles compared to lower percentiles. Overweight and obesity are associated with greater healthcare utilisation, and loss in work productivity in Australia. Australia's healthcare system should prioritise interventions to prevent overweight and obesity to reduce the cost on individuals and improve labour market outcomes. [ABSTRACT FROM AUTHOR]

Energy forecasting plays a vital role in mitigating challenges in data rich smart grid (SG) systems involving various applications such as demand‐side management, load shedding, and optimum dispatch. Managing efficient forecasting while ensuring the least possible prediction error is one of the main challenges posed in the grid today, considering the uncertainty in SG data. This paper presents a comprehensive and application‐oriented review of state‐of‐the‐art forecasting methods for SG systems along with recent developments in probabilistic deep learning (PDL). Traditional point forecasting methods including statistical, machine learning (ML), and deep learning (DL) are extensively investigated in terms of their applicability to energy forecasting. In addition, the significance of hybrid and data pre‐processing techniques to support forecasting performance is also studied. A comparative case study using the Victorian electricity consumption in Australia and American electric power (AEP) datasets is conducted to analyze the performance of deterministic and probabilistic forecasting methods. The analysis demonstrates higher efficacy of DL methods with appropriate hyper‐parameter tuning when sample sizes are larger and involve nonlinear patterns. Furthermore, PDL methods are found to achieve at least 60% lower prediction errors in comparison to other benchmark DL methods. However, the execution time increases significantly for PDL methods due to large sample space and a tradeoff between computational performance and forecasting accuracy needs to be maintained. [ABSTRACT FROM AUTHOR]

This article presents a longitudinal case study of five second-generation Vietnamese-Australian males experiencing the transition from boyhood to manhood. The participants reside in the same peri-urban region in Australia and come from families working in factories and restaurants or on farms and all aspire to pursue more middle-class forms of employment. Our investigation compels us to reflect on gendered identities as intercultural where place-based experiences open up opportunities for young men to gain recognition and status. Central to the analysis is how masculine subjectivities are embodied and performed as these young men navigate through different places. We draw on Bourdieu's concepts (habitus, capital and field) to decipher how their experiences with place shape their sense of who they are as well as who they want to become. This case study complements scholarship which addresses intra-group differences in Vietnamese-Australian cultures, and how these differences are deeply influenced by access to and operationalisation of capital through sustained contact with various fields. [ABSTRACT FROM AUTHOR]

Operational performance in the low-voltage distribution network (LVDN) can be undermined by its inherent unbalances, which may become worse as the penetration of rooftop solar continuously increases. To address this issue, load balancing via phase-reconfiguration devices (PRDs), which can change phase positions of residential customers as required, provides a cost-efficient option. However, most reported approaches to control PRDs require that demands of all residential customers are available, which are not viable for many LVDNs without smart meters or advanced metering infrastructure (AMI) installed. To bridging the gap in this field, this paper proposes a novel method to control PRDs purely based on measurable data from PRDs, and its controller. Based on limited information, sensitivity analysis in the network with PRDs is studied, followed by the optimization model that comprehensively considers operational requirements in the network. Moreover, slack variables are introduced to the model, and penalized in the objective function to assure either a strategy that is secure or with minimized violations can always be provided. The model is a challenging mixed-integer non-convex programming (MINCP) problem, which is reformulated as an efficient solvable mixed-integer second-order cone programming (MISOCP) based on exact reformulations or accurate linear approximations. Simulations based on two modified IEEE systems, and a real system in Australia demonstrate that an efficient strategy can be provided to mitigate unbalances in the network. [ABSTRACT FROM AUTHOR]

As residential PV generation penetrates in the low-voltage distribution network (LVDN), the unbalance issue may be intensified due to the asymmetry of generations/loads in different phases. Phase-reconfiguration device (PRD), which can reconfigure connected phases of residential customers, provides an effective method to address this issue. Noting that although the benefit brought by PRDs can vary if they are placed at different locations in the network, little literature has been reported on this topic. To bridge the research gap, this paper presents a novel method to optimally place PRDs in an LVDN aiming at minimizing the power unbalance running through the distribution transformer. The problem considers both installation, operational constraints, and boils down to a challenging mixed-integer non-convex programming problem, which is then reformulated as an efficient solvable mixed-integer linear programming problem. Moreover, operational constraints are relaxed with slack variables penalized in the objective function, which makes sure a feasible solution is always available without or with minimal operational violations. Case studies based on a modified IEEE system and a practical system in Australia demonstrate that an efficient strategy can be provided to address the unbalance issue while improving the network's power supply qualities. [ABSTRACT FROM AUTHOR]

Deploying the cloud energy storage system (CESS) is an economic and efficient way to store excess photovoltaic generation and participate in demand response without personal investment on pricy energy storage equipment. It is a shared battery energy storage system (BESS) for local residential and small commercial consumers, which is designed and controlled by the CESS operator. Based on the profit purpose, the CESS operator not only pursues the most economic operating strategy, but also tries to minimize the total investment on the design stage. This paper considers the investment on the batteries, power conversion system, reactive power compensation equipment and the cost including battery degradation cost and operation cost. The electricity price uncertainty and the voltage deviation of the CESS node caused by power exchange are also considered. Moreover, the cases of a largely centralized energy storage system and multiple distributed energy storage systems are all modelled. Finally, an original robust cooptimization model is transferred to a mixed integer linear programming model (MILP) and solved in GAMS. Numerical results based on historical data from 300 residential consumers in Australia present that the battery degradation cost and price uncertainty can't be neglected. [ABSTRACT FROM AUTHOR]

As rooftop solar PVs installed by residential customers penetrate in low voltage distribution network (LVDN), some issues, e.g. over/under voltage and unbalances, which may undermine the network's operational performance, need to be adequately addressed. To mitigate unbalances in LVDN, phase-switching devices (PSDs) and static var compensator (SVC) are two equipment that is cost-effective and efficient. However, most existing research on operating PSDs is based on inflexible heuristic algorithms or without considering the network formulation, which may lead to strategies that violate operational requirements. Moreover, few pieces of literature have been reported on mitigating unbalances in LVDN via SVC and PSDs together. This paper formulates the decision-making process as a mixed-integer non-convex programming (MINCP) problem after developing an SVC model for dispatch purpose. Compared with existing work, the proposed method aims at minimizing current unbalance based on their phasor values and takes the network's operational requirements into account. To efficiently solve the challenging problem, the MINCP is reformulated as a mixed-integer second order-cone programming (MISOCP) problem based on either exact reformulations or accurate approximations, making it possible to employ efficient off-the-shelf solvers. Simulations based on two modified IEEE systems and a practical Australian LVDN demonstrates the efficiency of the proposed method in mitigating unbalances in LVDN. [ABSTRACT FROM AUTHOR]

Due to the rapid development of distributed renewable generation, an effective risk assessment and early warning mechanism for active distribution networks is of great significance to maintain the system reliability and enhance energy grid resilience. In this paper, a novel risk assessment model is proposed to assess the probability of potential disturbances to the grid and provide accurate advice for trading prosumers’ renewable energy. The model can compute node failure probability (FP) for transmission networks as well as the area FP for distribution networks, while combining the two perspectives by topology analysis. A weather threshold value is first derived to define the extreme weather condition. Then the FP of transmission lines is calculated by joint probability models under four instances of extreme climate. For distribution networks, the weather influence is obtained by applying the Rare Events Logistic Regression model initially. Then the equipment fault related to the geographical feature is captured using feeder taxonomy and hierarchical clustering. Furthermore, the accidental factor as a new parameter is introduced to evaluate the vandalism, vegetation, and operating fault to the grid. Finally, the warning information and advice for customers will be presented after fault chain analysis. The FP for a specific area in Australia is analyzed in case studies to verify the proposed model. [ABSTRACT FROM AUTHOR]

Correction to: I Scientific Reports i https://doi.org/10.1038/s41598-023-33389-4, published online 24 April 2023 In the original version of this Article Tianxin Pan was incorrectly affiliated with 'School of Population and Global Health, Nossal Institute for Global Health, University of Melbourne, Melbourne, Australia'. Health Economics Unit, Centre for Health Policy, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia The original Article has been corrected. [Extracted from the article]

Owing to the technological development of natural gas exploration and the increasing penetration of gas-fired power generation, gas and power systems inevitably interact with each other from both physical and economic points of view. In order to effectively assess the two systems' interdependency, this paper proposes a systematic modeling framework and constructs simulation platforms for coupled gas and power systems in an energy market environment. By applying the proposed approach to the Australian national electricity market (NEM) and gas market, the impacts of six types of market and system factors are quantitatively analyzed, including power transmission limits, gas pipeline contingencies, gas pipeline flow constraints, carbon emission constraints, power load variations, and non-electric gas load variations. The important interdependency and infrastructure weakness for the two systems are well studied and identified. Our work provides a quantitative basis for grid operators and policy makers to support and guide operation and investment decisions for electric power and natural gas industries. [ABSTRACT FROM AUTHOR]

Emission trading is widely considered to be the most effective policy to minimize the overall costs for CO2 abatement. However, the political feasibility of an emission trading scheme may crucially depend on the free initial allocation of emission permits to carbon-intensive industries in order to offset the reduction in profits.This paper aims to analyze these potential profit impacts and the possible compensation to affected generation compaflies through modeling the Australian National Electricity Market under a potential emission trading scheme. Historical emissionbased and historical generation-based allocation approaches are used in this paper to calculate and compare the percentages of carbon permits that should be freely allocated. Two carbon permit price scenarios are -used to analyze the sensitivity of the optimal percentage of free allocation to carbon permit price. [ABSTRACT FROM AUTHOR]

Based on extensive research work on the application of energy trading models, energy sharing strategies are applied to smart homes. This paper addresses an energy sharing platform as an energy storage solution for renewable distributed generation (RDG) integration in microgrids. An energy-sharing service platform for renewable distributed generation smart home applications is proposed. This platform aims to suit various kinds of energy users, energy generation and energy storage devices. It is based on a decentralized approach, which means a central operator is not required. In the proposed energy management model, the up-reserve (UR) is defined as the maximum state of charge (SOC) value in every participant. The conditional Value-at-Risk (CVaR) method is used to undertake risk analysis and UR calculation. In a shared strategy, the Conditional Value at Risk (CVaR) method is used for risk analysis and energy operations. Moreover, this energy sharing platform refers to a trading mechanism called the call-auction method, which is similar to the opening auction trading in the stock market. The objective of the trading principle is to achieve the maximum trading volume; thus the platform facilitates an increase in the utilization of RDG. This paper details the rigorous proof of the energy sharing strategy used by the algorithm in the energy trading model. A rigorous proof of the algorithm is also given. Furthermore, the proposed energy sharing strategy has been compared with existing offerings of the frequency control ancillary services (FCAS). The historical data in Australia's electricity market is used to verify the proposed approach. Moreover, the proposed energy sharing platform compares with the FCAS provision. Simulation results show that the surplus energy can be shared among participants who hold different quantities of demand and generation/storage. Therefore, the proposed approach is a cost-effective energy storage solution, especially when energy storage capital cost is high. • An energy sharing platform is presented for renewable distributed generation. • An independent energy management strategy can be customized for each participant. • The call auction model of stock exchange is cited in the energy sharing platform. • The maximum transaction volume is applied in the call auction model. • The award of energy sharing can be self-ruled by participants. [ABSTRACT FROM AUTHOR]

• A private reliability service is proposed and can be traded in the electricity market. • A novel group double side auction method for market clearing is designed and verified. • Dynamic elasticities considering real-time preference are applied in this model. • The model provides the advised price interval for customers. • Real historical data of customers in Australia are used to verify the proposed model. Due to the rapid development of distributed renewable generation, emerging prosumers are encouraged to participate in the energy market. Meanwhile, blackout events, planned and unplanned outages due to weather, component failures and other causes, can also impact users' experience. To maintain reliability and improve users' experience under outage conditions, it is possible to transform the service reliability from a public good (compulsory and uniform) to a private good (self-selection). In this paper, a new restore mechanism based on private reliability service is proposed, the reliability service is no longer uniform. Customers with a higher reliability requirement are willing to pay to maintain their desired consumption in outage conditions. The customers are classified by dynamic elasticity considering historical data and real-time customer preferences. Then the advised unit price, trading amount and reliability level are provided to customer. Once they submit this information to market operator, the price and transaction amount is matched in each group, and floated average clearing price is applied to obtain the bidding results. The trading mechanism is proved to satisfy incentive compatibility, and the transaction process for a specific area in Australia is analyzed in a case study to verify the proposed model. [ABSTRACT FROM AUTHOR]

Purpose: To report the normative ocular and periocular anthropometric measurements in an Australian cohort and investigate how these may be affected age, gender, and ethnicity., Methods: Prospective study of patients presenting to the Royal Adelaide Hospital. Patient with orbital or eyelid disease, previous surgery, craniofacial abnormalities, pupil abnormalities, strabismus, and poor image quality was excluded. Standardised photographs were taken in a well-illuminated room. A green dot with a diameter of 24 mm was placed on the participant's foreheads for calibration between pixels and millimetres. Ocular and periocular landmarks were segmented to calculate the periorbital measurements. Independent sample t test was used to compare male and female subjects, Pearson's correlation was used to correlate periocular dimensions with age, and ANOVA with Bonferroni was used to compare periocular dimension between ethnic groups., Results: Seven hundred and sixty eyes from 380 participants (215 female, mean age 58 ± 18 years) were included. The mean marginal reflex distance (MRD) 1 was 3.5 mm and decreased with increasing age (r =  - 0.09, p = 0.01) and MRD 2 was 5.2 mm. Compared to Caucasians, African subjects had a significantly larger interpupillary distance and outer intercanthal distance, whereas East Asians had a significantly larger inner intercanthal distance (p < 0.05). The values of marginal reflex distance 2, palpebral fissure height, horizontal palpebral aperture, inner intercanthal distance, interpupillary distance and outer intercanthal distance were significantly higher in male subjects than female subjects (p < 0.05)., Conclusions: Normative periocular dimensions may vary according to age, gender, and ethnicity. An understanding of normal periocular dimensions is important in the evaluation of orbital disease across different ethnic groups and may serve as reference points for oculoplastic surgery and industry., (© 2023. Crown.)

Purpose: The TP53 tumor-suppressor gene is mutated in >95% of high-grade serous ovarian cancers. Detecting an autologous antibody response to TP53 that might improve early detection. Experimental Design: An immunoassay was developed to measure TP53 autoantibody in sera from 378 cases of invasive epithelial ovarian cancer and 944 age-matched healthy controls from the United States, Australia, and the United Kingdom. Serial preclinical samples from cases and controls were also assayed from the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). Results: Using a cutoff value of 78 U/mL to achieve a specificity of 97.4%, TP53 autoantibody was elevated in 30% of 50 cases from MD Anderson, 21.3% of 108 cases from the Australian Ovarian Cancer Study, and 21% of 220 cases from the UKCTOCS. Among 164 cases with rising CA125 detected with the UKCTOCS risk of ovarian cancer algorithm (ROCA), 20.7% had elevated TP53 autoantibody. In cases missed by the ROCA, 16% of cases had elevated TP53 autoantibody. Of the 34 ovarian cancer cases detected with the ROCA, TP53 autoantibody titers were elevated 11.0 months before CA125. In the 9 cases missed by the ROCA, TP53 autoantibody was elevated 22.9 months before cancer diagnosis. Similar sensitivity was obtained using assays with specific mutant and wild-type TP53. Conclusions: TP53 autoantibody levels provide a biomarker with clinically significant lead time over elevation of CA125 or an elevated ROCA value. Quantitative assessment of autoantibodies in combination with CA125 holds promise for earlier detection of invasive epithelial ovarian cancer. Clin Cancer Res; 23(19); 5912-22. ©2017 AACR ., (©2017 American Association for Cancer Research.)