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In recent years, mine site closure and rehabilitation have emerged as significant global challenges. The escalating number of abandoned mines, exemplified by over 60,000 in Australia in 2017, underscores the urgency. Growing public concerns and governmental focus on environmental issues are now jeopardising sustainable mining practices. This paper assesses the role of unmanned aerial vehicles (UAVs) in mine closure, exploring sensor technology, artificial intelligence (AI), and mixed reality (MR) applications. Prior research validates UAV efficacy in mining, introducing various deployable sensors. Some studies delve into AI's use for UAV data analysis, but a comprehensive review integrating AI algorithms with MR methods for mine rehabilitation is lacking. The paper discusses data acquisition methods, repeatability, and barriers toward fully autonomous monitoring systems for mine closure projects. While UAVs prove adaptable with various sensors, constraints such as battery life and payload capacity impact effectiveness. Although UAVs hold potential for AI testing in mine closure studies, these applications have been overlooked. AI algorithms are pivotal for creating autonomous systems, reducing operator intervention. Moreover, MR's significance in mine closure is evident, emphasising its application in the mining industry. Ultimately, a hybrid UAV–AI–MR technology is not only viable but essential for achieving successful mine closure and sustainable mining practices in the future. [ABSTRACT FROM AUTHOR]

This article examines the experience and effects of a trauma-responsive program that uses creative methods to address the ongoing psychosocial impacts of transgenerational trauma and youth suicide, which disproportionately affect First Nations people in Australia. Our aim is to understand how the aesthetic (sensory-affective) dimensions of such a program serve to promote experiences of growth after trauma, manifesting in a sense of connection to both self and community. The paper focuses on the second of two immersive, experiential workshops delivered seven months apart in the regional town of Warwick in Queensland, Australia. In the light of self-reports of growth and personal transformation following the initial workshop, the paper examines the key drivers of such growth, focusing in particular on how trauma-related experience is metabolised through cultural containment. It builds on Bion's concept of container/contained, combining analysis of the affordances of immersion. Framed in cultural rather than medical terms, the larger goal of the paper is to establish how cultural programs fill a gap in trauma informed support, facilitating the processing of trauma. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast cancer is one of the leading causes of cancer-related death in women. The early detection of breast cancer with screening mammograms plays a pivotal role in reducing mortality rates. Although population-based double-reading screening mammograms have reduced mortality by over 31% in women with breast cancer in Europe, continuing this program is difficult due to the shortage of radiologists. Artificial intelligence (AI) is an emerging technology that has provided promising results in medical imaging for disease detection. This study investigates the performance of AI models on an Australian mammographic database, demonstrating how transfer learning from a USA mammographic database to an Australian one, contrast enhancement on mammographic images and the quality of training data according to radiologists' concordance can improve breast cancer diagnosis. Our proposed methodology offers a more efficacious approach for AI to contribute to radiologists' decision making when interpreting mammography images. This paper investigates the adaptability of four state-of-the-art artificial intelligence (AI) models to the Australian mammographic context through transfer learning, explores the impact of image enhancement on model performance and analyses the relationship between AI outputs and histopathological features for clinical relevance and accuracy assessment. A total of 1712 screening mammograms (n = 856 cancer cases and n = 856 matched normal cases) were used in this study. The 856 cases with cancer lesions were annotated by two expert radiologists and the level of concordance between their annotations was used to establish two sets: a 'high-concordances subset' with 99% agreement of cancer location and an 'entire dataset' with all cases included. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of Globally aware Multiple Instance Classifier (GMIC), Global-Local Activation Maps (GLAM), I&H and End2End AI models, both in the pretrained and transfer learning modes, with and without applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. The four AI models with and without transfer learning in the high-concordance subset outperformed those in the entire dataset. Applying the CLAHE algorithm to mammograms improved the performance of the AI models. In the high-concordance subset with the transfer learning and CLAHE algorithm applied, the AUC of the GMIC model was highest (0.912), followed by the GLAM model (0.909), I&H (0.893) and End2End (0.875). There were significant differences (p < 0.05) in the performances of the four AI models between the high-concordance subset and the entire dataset. The AI models demonstrated significant differences in malignancy probability concerning different tumour size categories in mammograms. The performance of AI models was affected by several factors such as concordance classification, image enhancement and transfer learning. Mammograms with a strong concordance with radiologists' annotations, applying image enhancement and transfer learning could enhance the accuracy of AI models. [ABSTRACT FROM AUTHOR]

A large proportion of photovoltaic (PV) power generation is connected to the power grid, and its volatility and stochasticity have significant impacts on the power system. Accurate PV power forecasting is of great significance in optimizing the safe operation of the power grid and power market transactions. In this paper, a novel dual-channel PV power forecasting method based on a temporal convolutional network (TCN) is proposed. The method deeply integrates the PV station feature data with the model computing mechanism through the dual-channel model architecture; utilizes the combination of multihead attention (MHA) and TCN to extract the multidimensional spatio-temporal features between other meteorological variables and the PV power; and utilizes a single TCN to fully extract the temporal constraints of the power sequence elements. The weighted fusion of the dual-channel feature data ultimately yields the ideal forecasting results. The experimental data in this study are from a 26.52 kW PV power plant in central Australia. The experiments were carried out over seven different input window widths, and the two models that currently show superior performance within the field of PV power forecasting: the convolutional neural network (CNN), and the convolutional neural network combined with a long and short-term memory network (CNN_LSTM), are used as the baseline models. The experimental results show that the proposed model and the baseline models both obtained the best forecasting performance over a 1-day input window width, while the proposed model exhibited superior forecasting performance compared to the baseline model. It also shows that designing model architectures that deeply integrate the data input method with the model mechanism has research potential in the field of PV power forecasting. [ABSTRACT FROM AUTHOR]

While there is a significant body of research concerning Catholic faith formation in a variety of educational contexts around the world, relatively little attention has been devoted to the notion of missionary discipleship as a foundation for Catholic formation. Recently, the National Catholic Education Commission, in its document Leading Formation for Mission, also elaborates a solid definition of formation and stated: 'Formation is imaginative, creative and honours adult learning principles [...] It engages the 'intellect' and is nurtured by appropriate theological content and it engages the 'Spirit' and is characterised by contemplation and action for mission' (2022). This paper seeks to contribute to a deeper understanding of formation for missionary discipleship by presenting an evaluation of the efficacy of In Altum, a student-focussed ministry and leadership program developed at the University of Notre Dame Australia in response to the developing magisterial teaching concerning the notion of 'missionary discipleship'. In particular, we examine participants' personal faith development, understanding of missionary discipleship on campus, preparation for future ministry, and understanding of contemporary challenges to discipleship. The study progresses in three parts: First, it provides a brief background to In Altum, including the philosophical and sociological context, as well as the key theological principles underpinning the formation offered within the program. Second, following an overview of the mixed-methodology approach taken in the study, which details both the use of qualitative and quantitative data derived from focus groups and an online survey, the paper presents the results of the study, which sought to investigate the efficacy of the program as it pertained to the following: (a) the building of (personal subjective assessment of) faith in participants; (b) the building of participants' understanding of, and confidence in, the task of evangelisation as missionary discipleship in a secularised context; (c) building a strengthened sense of community amongst the participants. In the third and final section, the reported strengths and weaknesses of the program are examined. The paper also comments on implications for the program on the culture of the university more broadly, including precepts of the program's findings that may be applied in light of the Australian Catholic Plenary Council's findings to enhance the future directions of formation programs in the Catholic University Chaplaincy, with possible application to Catholic secondary schools and parish youth groups. [ABSTRACT FROM AUTHOR]

Colonisation, genocide, ecocide, and climate derangement are ongoing, unfurling, global tragedies. In so-called Australia, spiritual practitioners can respond to these crises by deepening their engagement with Aboriginal perspectives/practices. This paper contends that some Eurocentric habitual categorisations subtly misinterpret Aboriginal experiences of the sacred, such as identifying creation myths as beliefs comparable to post-Enlightenment representations of the sacred and identifying the performance of sacred activity with similar characteristics to separateness and priesthood. This leads to erroneous characterisations of Aboriginal ritual practices as being based on a strong hierarchy, distinctive castes, and esotericism. We argue that an embodied and practice-based sense of sacredness guides Aboriginal spirituality. As a living culture, Aboriginal ongoing care for Country provides an enfleshed, real, palpable enactment of human spirituality. We argue that Aboriginal spirituality has been fetishised to the neglect of a call to care for Country in the most 'mundane' sense of tending to food, water, air, etc., as embodied religious experiences. Delving into dadirri and death, we elucidate contemporary cases of practical care for Country that illustrate how being on, in, and with Country can be a contemplative experience. We conclude by outlining how caring for Country 'layers' the various expressions of Aboriginal religious experience socially, psychologically, interpersonally, and ritually. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative framework for wind power prediction that focuses on the future of energy forecasting utilizing intelligent deep learning and strategic feature engineering. This research investigates the application of a state-of-the-art deep learning model for wind energy prediction to make extremely short-term forecasts using real-time data on wind generation from New South Wales, Australia. In contrast with typical approaches to wind energy forecasting, this model relies entirely on historical data and strategic feature engineering to make predictions, rather than relying on meteorological parameters. A hybrid feature engineering strategy that integrates features from several feature generation techniques to obtain the optimal input parameters is a significant contribution to this work. The model's performance is assessed using key metrics, yielding optimal results with a Mean Absolute Error (MAE) of 8.76, Mean Squared Error (MSE) of 139.49, Root Mean Squared Error (RMSE) of 11.81, R-squared score of 0.997, and Mean Absolute Percentage Error (MAPE) of 4.85%. Additionally, the proposed framework outperforms six other deep learning and hybrid deep learning models in terms of wind energy prediction accuracy. These findings highlight the importance of advanced data analysis for feature generation in data processing, pointing to its key role in boosting the precision of forecasting applications. [ABSTRACT FROM AUTHOR]

The Forest Fire Danger Index (FFDI) is the main measure used in Australia for estimating fire risk. Recent work by the authors showed that the FFDI forms stable state regimes, nominated as fire climate regimes. These regimes shifted to greater intensity in southern and eastern Australia around the year 2000 and, a decade later, further north. Reductions in atmospheric moisture were the primary contributor. These changes have not been fully incorporated into future projections. This paper compares the recent regime shifts with the most recent national projections of FFDI, published in 2015. They show that for most states and regions, the 2030 upper limit is approached or exceeded by the recent shift, except for two states with large arid zones, South Australia and Western Australia. Methods for attributing past changes, constructing projections, and the inability of climate models to reproduce the recent decreases in atmospheric moisture, all contribute to these underestimates. To address these shortcomings, we make some suggestions to modify efforts aiming to develop seamless predictions and projections of future fire risk. [ABSTRACT FROM AUTHOR]

People with innate variations of sex characteristics (also known as intersex traits or disorders or differences of sex development) have any of a wide range of innate physical traits that differ from medical and social norms for female and male bodies. Responses to these physical differences create experiences and risks of stigmatisation, discrimination, violence, and harmful medical practices intended to promote social and familial integration and conformity with gender stereotypes. As is evident globally, the Australian policy response to the existence and needs of people with innate variations of sex characteristics has been largely incoherent, variously framing the population as having disordered sex development in need of "fixing", and a third sex/gender identity group in need of recognition, with only recent engagement by intersex community-controlled civil society organisations. This paper presents an overview of the context and goals of the intersex human rights movement in Australia. Australian intersex community organisations have sought to apply human rights norms and develop new infrastructure to address key health and human rights issues, and necessitating new ways of resolving policy incoherence. Together with human rights, mental health, and public health institutions, they have called for significant changes to medical models of care and reform to research and classification systems. Intersex community organising and resourcing have made a tangible difference. The Australian Capital Territory is the first jurisdiction in the country to move ahead with reforms to clinical practice, including a legislative prohibition of certain practices without personal informed consent, oversight of clinical decision-making, and investment in psychosocial support. A national community-controlled psychosocial support service has also commenced. [ABSTRACT FROM AUTHOR]

Simple Summary: The current evidence regarding Indigenous* women and breast cancer in Australia shows lower prevalence but higher mortality rates. There are a range of reasons for this, including co-morbidities, lack of access to health services and low health information fluency. Perhaps most importantly, breast cancer health policy and service delivery practice do not meet the needs of Indigenous women in Australia, according to Indigenous women. Talking and listening to Indigenous women about breast cancer highlight that the solutions to improve breast cancer outcomes are available and that they are not complex. Indigenous women must be involved in the improvement of policy and practice in order for these outcomes to improve. *Terminology: We respectfully refer to Aboriginal and Torres Strait Islander people as "Indigenous". In Australia, the incidence rate of breast cancer is lower in Indigenous* women than non-Indigenous women; however, the mortality rate is higher, with Indigenous women 1.2 times more likely to die from the disease. This paper provides practical and achievable solutions to improve health outcomes for Indigenous women with breast cancer in Australia. This research employed the Context–Mechanism–Outcome (CMO) framework to reveal potential mechanisms and contextual factors that influence breast cancer outcomes for Indigenous women, stratified into multiple levels, namely, micro (interpersonal), meso (systemic) and macro (policy) levels. The CMO framework allowed us to interpret evidence regarding Indigenous women and breast cancer and provides nine practical ways to improve health outcomes and survival rates. [ABSTRACT FROM AUTHOR]