Search Results (5)

The Australian Royal Commission into Aged Care Quality and Safety has highlighted the chronic shortages of labour to provide care for those aged 65 and over in rural and remote areas of Australia. This descriptive cross-sectional study compares the availability of care provision in metropolitan regions with that in rural and remote regions. We analysed the 2021 Australian Census, grouped according to Aged-Care-Planning Region (ACPR), and investigated the numbers of people aged 65 years and over with different levels of care need, both in residential care and in-home. The available workforce in each ACPR was also examined in detail, using occupational classifications reported in the Census, and shortages of doctors, nurses, allied health and other care workers were identified. Overall, an additional 492,416 care hours were needed per week (or 12,958 full-time equivalent (FTE) care workers) in order to bring remote community ACPRs to parity with provision in metropolitan ACPRs. A further 95,342 FTE workers were needed in rural ACPRs to bring these areas to parity with metropolitan ACPRs. Our findings underscore the ongoing disparities in aged care workforce availability between metropolitan, rural, and remote regions of Australia. Addressing these workforce shortages is crucial to ensuring equitable access to care for older Australians, regardless of their geographical location. The implementation of targeted strategies to enhance workforce recruitment, retention, and training in these underserved areas is essential to bridge the gap and improve the quality of care provided to older adults in rural and remote communities. Such strategies could include targeted recruitment campaigns and incentives for professionals to relocate; further capacity for clinical placements and supervision in rural areas; tailoring funding and employment models for rural needs; and strengthening vocational education in regional areas. Full article

Urban Agriculture (UA) is widely presented as a feature of sustainable cities, with various claims around economic, social, and/or environmental benefits. However, the extent to which these different benefits may reinforce or compete with one another is not clear. This paper presents an integrated modelling framework using proxy measures for economic benefit (the net margin, NM), social benefit (the full-time farmer employment equivalent (FTE) per consumer) and environmental benefit (reduction in carbon dioxide emissions, CO₂). The model is applied in two divergent development scenarios, including Adelaide, Australia, and the Kathmandu Valley, Nepal, to study the characteristic features of UA in different settings. Two-stage optimisation is used to explore trade-offs and synergies when pursuing different objectives (NM, FTE and CO₂). The model seeks the optimal farming area and selects from three levels of mechanisation (non-mechanised, garden tiller and garden cultivator), two purposes (gardening and commercial), two crop value categories (mixed and mid- to high-value vegetables) and two market mechanisms (wholesale vs. retail). The results of the optimisation provide insights into the key features of a UA system depending on the objective(s) being pursued, which we believe is a novel approach to justify UA research. For instance, the model favours a commercial UA form (in which both land and labour are costed) with a larger area when pursuing an economic objective, whereas it favours a gardening form of UA when aiming to maximise participation in the food system, with the preferred area depending on the extent to which either the economic or environmental objective is also being pursued. In Adelaide, the model favours commercial UA for the best-case profit and carbon emissions, and gardening for FTE maximisation. In the Kathmandu Valley, the model chooses the gardening UA within the given model assumptions. Full article

The growing demand for the integration of surface mount design (SMD) antennas into miniaturized electronic devices has imposed increasing limitations on the structure dimensions. Examples include embedded antennas in applications such as on-board devices, picosatellites, 5G communications, or implantable and wearable devices. The demands for size reduction while ensuring a satisfactory level of electrical and field performance can be managed through constrained numerical optimization. The reliability of optimization-based size reduction requires utilization of full-wave electromagnetic (EM) analysis, which entails significant computational costs. This can be alleviated by incorporating surrogate modeling techniques, adjoint sensitivities, or the employment of sparse sensitivity updates. An alternative is the incorporation of multi-fidelity simulation models, normally limited to two levels, low and high resolution. This paper proposes a novel algorithm for accelerated antenna miniaturization, featuring a continuous adjustment of the simulation model fidelity in the course of the optimization process. The model resolution is determined by factors related to violation of the design constraints as well as the convergence status of the algorithm. The algorithm utilizes the lowest-fidelity model for the early stages of the optimization process; it is gradually refined towards the highest-fidelity model upon approaching convergence, and the constraint violations improve towards the preset tolerance threshold. At the same time, a penalty function approach with adaptively adjusted coefficients is applied to enable the precise control of constraints, and to increase the achievable miniaturization rates. The presented procedure has been validated using five microstrip antennas, including three broadband, and two circularly polarized structures. The obtained results corroborate the relevance of the implemented mechanisms from the point of view of improving the average computational efficiency of the optimization process by 43% as compared to the single-fidelity adaptive penalty function approach. Furthermore, the presented methodology demonstrates a performance that is equivalent or even superior to its single-fidelity counterpart in terms of an average constraint violation of 0.01 dB (compared to 0.03 dB for the reference), and an average size reduction of 25% as compared to 25.6%. Full article

Croatian energy strategy defines ambitious goals aimed at achieving energy transition toward a sustainable low-carbon society. Achieving those goals requires significant investments in the renewable energy sources and improved energy efficiency of buildings. The purpose of this paper is to estimate and compare the economic effects of the energy transition on the renewable energy supply and demand side. The estimation of the energy transition effects in Croatia in the period 2020–2050 is based on the input-output model, which identifies direct, indirect and induced effects of investments in renewable energy sources and energy efficient buildings renovation. Results of the study reveal relatively higher gross value added and employment effects induced by investments in building retrofits, but the effects of investments in renewable energy are also significant. Investments in sustainable, efficient and environmentally effective energy system could significantly contribute to Croatian GDP. While GVA effects range from 0.4% to 0.6% of annual GDP, the share of full-time equivalent jobs induced by energy transition could reach 0.5% to 1% of total employment in Croatia. Investments in RES plants and energy efficient building reconstruction are not substitutes but complements which ensure a smooth energy transition if undertaken together. Full article

This article explores the impact of the novel coronavirus (COVID-19) upon Australia’s education industry with a particular focus on universities. With the high dependence that the revenue structures of Australian universities have on international student tuition fees, they are particularly prone to the economic challenges presented by COVID-19. As such, this study considers the impact to total Australian university revenue and employment caused by the significant decline in the number of international students continuing their studies in Australia during the current pandemic. We use a linear regression model calculated from data published by the Australian Government’s Department of Education, Skills, and Employment (DESE) to predict the impact of COVID-19 on total Australian university revenue, the number of international student enrolments in Australian universities, and the number of full-time equivalent (FTE) positions at Australian universities. Our results have implications for both policy makers and university decision makers, who should consider the need for revenue diversification in order to reduce the risk exposure of Australian universities. Full article