Search Results (53)

The transmission of high-frequency signals over long distances depends on the ionosphere’s reflective properties, with the selection of operating frequencies being closely tied to variations in the ionosphere. The accurate prediction of ionospheric critical frequency foF2 and other parameters in low latitudes is of great significance for understanding ionospheric changes in high-frequency communications. Currently, deep learning algorithms demonstrate significant advantages in capturing characteristics of the ionosphere. In this paper, a state-of-the-art hybrid neural network is utilized in conjunction with a temporal pattern attention mechanism for predicting variations in the foF2 parameter during high- and low-solar activity years. Convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM), which is capable of extracting spatiotemporal features of ionospheric variations, are incorporated into a hybrid neural network. The foF2 data used for training and testing come from three observatories in Brisbane (27°53′S, 152°92′E), Darwin (12°45′S, 130°95′E) and Townsville (19°63′S, 146°85′E) in 2000, 2008, 2009 and 2014 (the peak or trough years of solar activity in solar cycles 23 and 24), using the advanced Australian Digital Ionospheric Sounder. The results show that the proposed model accurately captures the changes in ionospheric foF2 characteristics and outperforms International Reference Ionosphere 2020 (IRI-2020) and BiLSTM ionospheric prediction models. Full article

The current methods for diagnosing Alzheimer’s Disease using Magnetic Resonance Imaging (MRI) have significant limitations. Many previous studies used 2D Transformers to analyze individual brain slices independently, potentially losing critical 3D contextual information. Region of interest-based models often focus on only a few brain regions despite Alzheimer’s affecting multiple areas. Additionally, most classification models rely on a single test, whereas diagnosing Alzheimer’s requires a multifaceted approach integrating diverse data sources for a more accurate assessment. This study introduces a novel methodology called the Multiple Inputs and Mixed Data 3D Vision Transformer (MIMD-3DVT). This method processes consecutive slices together to capture the feature dimensions and spatial information, fuses multiple 3D ROI imaging data inputs, and integrates mixed data from demographic factors, cognitive assessments, and brain imaging. The proposed methodology was experimentally evaluated using a combined dataset that included the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Australian Imaging, Biomarker, and Lifestyle Flagship Study of Ageing (AIBL), and the Open Access Series of Imaging Studies (OASIS). Our MIMD-3DVT, utilizing single or multiple ROIs, achieved an accuracy of 97.14%, outperforming the state-of-the-art methods in distinguishing between Normal Cognition and Alzheimer’s Disease. Full article

The prevalence of the invasive species African Lovegrass (Eragrostis curvula, ALG thereafter) in Australian landscapes presents significant challenges for land managers, including agricultural losses, reduced native species diversity, and heightened bushfire risks. Uncrewed aerial system (UAS) remote sensing combined with AI algorithms offer a powerful tool for accurately mapping the spatial distribution of invasive species and facilitating effective management strategies. However, segmentation of vegetations within mixed grassland ecosystems presents challenges due to spatial heterogeneity, spectral similarity, and seasonal variability. The performance of state-of-the-art artificial intelligence (AI) algorithms in detecting ALG in the Australian landscape remains unknown. This study compared the performance of four supervised AI models for segmenting ALG using multispectral (MS) imagery at four sites and developed segmentation models for two different seasonal conditions. UAS surveys were conducted at four sites in New South Wales, Australia. Two of the four sites were surveyed in two distinct seasons (flowering and vegetative), each comprised of different data collection settings. A comparative analysis was also conducted between hyperspectral (HS) and MS imagery at a single site within the flowering season. Of the five AI models developed (XGBoost, RF, SVM, CNN, and U-Net), XGBoost and the customized CNN model achieved the highest validation accuracy at 99%. The AI model testing used two approaches: quadrat-based ALG proportion prediction for mixed environments and pixel-wise classification in masked regions where ALG and other classes could be confidently differentiated. Quadrat-based ALG proportion ground truth values were compared against the prediction for the custom CNN model, resulting in 5.77% and 12.9% RMSE for the seasons, respectively, emphasizing the superiority of the custom CNN model over other AI algorithms. The comparison of the U-Net demonstrated that the developed CNN effectively captures ALG without requiring the more intricate architecture of U-Net. Masked-based testing results also showed higher F1 scores, with 91.68% for the flowering season and 90.61% for the vegetative season. Models trained on single-season data exhibited decreased performance when evaluated on data from a different season with varying collection settings. Integrating data from both seasons during training resulted in a reduction in error for out-of-season predictions, suggesting improved generalizability through multi-season data integration. Moreover, HS and MS predictions using the custom CNN model achieved similar test results with around 20% RMSE compared to the ground truth proportion, highlighting the practicality of MS imagery over HS due to operational limitations. Integrating AI with UAS for ALG segmentation shows great promise for biodiversity conservation in Australian landscapes by facilitating more effective and sustainable management strategies for controlling ALG spread. Full article

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. Full article

In 2022, the National Museum of Australia launched an immersive virtual exhibition of Australian Aboriginal art: Connection: Songlines from Australia’s First Peoples, which was created and produced by Grande Experiences, the same team that produced the multisensory experience Van Gogh Alive. The exhibition employs large-scale projections and cutting-edge light and sound technology to offer a mesmerizing glimpse into the intricate network of Australian Aboriginal art, which is an ancient pathway of knowledge that traverses the continent. Serving as the gateway to the Songlines universe, the exhibition invites visitors to delve into the profound spiritual connections with the earth, water, and sky, immersing them in a compellingly rich and thoroughly captivating narrative with a vivid symphony of sound, light, and color. This article examines Connection as a digital storytelling platform by exploring the Grande Experiences company’s approach to the digital replication of Australian Aboriginal art, with a focus on the connection between humans and nature in immersive exhibition spaces. Full article

This study’s main goal is to create a useful software application for finding and classifying fine art photos in museums and art galleries. There is an increasing need for tools to swiftly analyze and arrange art collections based on their artistic styles as a result of the digitization of art collections. To increase the accuracy of the style categorization, the suggested technique involves two parts. The input image is split into five sub-patches in the first stage. A DCNN that has been particularly trained for this task is then used to classify each patch individually. A decision-making module using a shallow neural network is part of the second phase. Probability vectors acquired from the first-phase classifier are used to train this network. The results from each of the five patches are combined in this phase to deduce the final style classification for the input image. One key advantage of this approach is employing probability vectors rather than images, and the second phase is trained separately from the first. This helps compensate for any potential errors made during the first phase, improving accuracy in the final classification. To evaluate the proposed method, six various already-trained CNN models, namely AlexNet, VGG-16, VGG-19, GoogLeNet, ResNet-50, and InceptionV3, were employed as the first-phase classifiers. The second-phase classifier was implemented as a shallow neural network. By using four representative art datasets, experimental trials were conducted using the Australian Native Art dataset, the WikiArt dataset, ILSVRC, and Pandora 18k. The findings showed that the recommended strategy greatly surpassed existing methods in terms of style categorization accuracy and precision. Overall, the study assists in creating efficient software systems for analyzing and categorizing fine art images, making them more accessible to the general public through digital platforms. Using pre-trained models, we were able to attain an accuracy of 90.7. Our model performed better with a higher accuracy of 96.5 as a result of fine-tuning and transfer learning. Full article

Using rock art conservation as a focus, this paper outlines the levels of legislated protection afforded to designated natural and cultural areas/sites in Australia and describes the co-management approach adopted in 1998 in relation to Mutawintji National Park in western New South Wales. The park encompasses four different protection categories: a Historic Site, a Nature Reserve, a National Park, and a State Conservation Area. Known for more than a century, the Historic Site is a major area of rock art containing Aboriginal engravings, paintings and stencils. Management of the Historic Site is a key concern, given the tourist interest and associated potential for accelerated deterioration of cultural heritage. The Mutawintji Plan of Management pointed to the importance of Mutawintji for Aboriginal people to connect with the country, and the co-management model encouraged tourism development as a means of providing employment opportunities as Aboriginal guides. No special legislative requirements in relation to rock art conservation, beyond those already in existence, were applied to the co-management system. Using field knowledge involving rock art research and early guide training programs at Mutawintji and literature sources, this paper suggests possible future approaches to rock art conservation in the Mutawintji Lands. Full article

A review of the contemporary Australian church reveals a spiritual malaise in which passive learning has become the main staple for many church members or attendees. This sense is heightened by demographic trends over the last fifty years that reflect a sustained decline in Australians identifying as religious. Although commitment to Christianity is seemingly softening, this sociodemographic picture is contraindicated by other research that reflects a growing hunger for spirituality among many Australians. Given this disparity, there is an opportunity to re-examine pertinent understandings of spiritual growth. In the literature, notions of spiritual growth are conceptualised by a variety of definitions and operationalised by a range of tools and practices. Analysis suggests that many models are limited by linearity, passivity, and reductionism and do not adequately resonate with the complexities inherent in spiritual growth. This literature review extends previous research by examining the state of the art in relation to spiritual growth. The paper converges around the synthesis that heutagogy and coaching are effective twin strategies that may direct self-determined learning towards enhanced spiritual growth. This paper conceptualises opportunities for future research and thereby lays the foundation for an important emergent research agenda. This article charts pertinent perspectives and prospects. Full article

Rose Selwyn (1824–1905) was a first wave Australian feminist and public speaker. The poetry, art, and scraps of writing Rose left in her archive allow the reader to piece together an intellectual history, a genealogy of the making of self. Rose attained her way of being through several contemporary influences—the mysticism of Tractarianism, a concern with death and its meanings, an interest in the literary edges of the world, a concern with the suffering body, and a passion for women and a woman-centred world. From these tangled contemporary concerns, she made a feminism for all non-Aboriginal women apparent in her speeches. Her role as a colonising woman in a violent landscape created a complex relationship with Aboriginal people where she may be seen to be criticising her elite landholding (squatter) peers and introducing concepts such as an Aboriginal parliament. Full article

The Alice Springs sculptor Patricia Elvins created a number of busts of Indigenous Australian men, women, and children, which were distributed as casts for the gift and souvenir market. Produced between the early-1960s and the early-1990s, these varnished casts exist with four different artists’ signatures, representing collaboration with different production potters who produced the casts. Macroscopic analysis shows significant differences in weight between casts of the same bust. CT scanning was carried out to understand the make-up of these casts and to illuminate differences in production techniques. The scanning revealed that all figurines were cast, but that casting techniques varied not only between production potters but also among figurines of the same potter. It revealed differences in the densities of the casting material, both between and within specimens, suggesting that production was not standardized but occurred in smaller batches, possibly on demand of low-volume sales stock. The study has shown the potential of non-destructive CT scanning to go beyond this and serve as a tool to examine the casting process itself as well as to contribute to an understanding of the nature of the plasters used. Full article

Australia experiences a variety of climate extremes that result in loss of life and economic and environmental damage. This paper provides a first evaluation of the performance of state-of-the-art Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate models (GCMs) in simulating climate extremes over Australia. Here, we evaluate how well 37 individual CMIP6 GCMs simulate the spatiotemporal patterns of 12 climate extremes over Australia by comparing the GCMs against gridded observations (Australian Gridded Climate Dataset). This evaluation is crucial for informing, interpreting, and constructing multimodel ensemble future projections of climate extremes over Australia, climate-resilience planning, and GCM selection while conducting exercises like dynamical downscaling via GCMs. We find that temperature extremes (maximum-maximum temperature -TXx, number of summer days -SU, and number of days when maximum temperature is greater than 35 °C -Txge35) are reasonably well-simulated in comparison to precipitation extremes. However, GCMs tend to overestimate (underestimate) minimum (maximum) temperature extremes. GCMs also typically struggle to capture both extremely dry (consecutive dry days -CDD) and wet (99th percentile of precipitation -R99p) precipitation extremes, thus highlighting the underlying uncertainty of GCMs in capturing regional drought and flood conditions. Typically for both precipitation and temperature extremes, UKESM1-0-LL, FGOALS-g3, and GCMs from Met office Hadley Centre (HadGEM3-GC31-MM and HadGEM3-GC31-LL) and NOAA (GFDL-ESM4 and GFDL-CM4) consistently tend to show good performance. Our results also show that GCMs from the same modelling group and GCMs sharing key modelling components tend to have similar biases and thus are not highly independent. Full article

Background: Students in the United States gain weight significantly during their first year of university, however limited data are available for Australian students. Methods: This 12-month observational study was conducted to monitor monthly body weight and composition, as well as quarterly eating behaviours, dietary intake, physical activity, sedentary behaviours, and basal metabolic rate changes amongst first-year Australian university students. Participants were first-year university students over 18 years. Results: Twenty-two first-year university students (5 males and 17 females) completed the study. Female students gained weight significantly at two, three, and four-months (+0.9 kg; +1.5 kg; +1.1 kg, p < 0.05). Female waist circumference (2.5 cm increase at three-months, p = 0.012), and body fat also increased (+0.9%, p = 0.026 at three-months). Intakes of sugar, saturated fat (both >10% of total energy), and sodium exceeded recommended levels (>2000 mg) at 12-months. Greater sedentary behaviours were observed amongst male students throughout the study (p <0.05). Conclusions: Female students are at risk of unfavourable changes in body composition during the first year of university, while males are at risk of increased sedentary behaviours. High intakes of saturated fat, sugars, and sodium warrant future interventions in such a vulnerable group. Full article

In the early years of the discovery of Indigenous bark paintings in Australia, anthropologists regarded this artform as part of a static and unchanging tradition. Inspired by the images of Arnhem Land rock art and ceremonial body design, the bark paintings were innovatively adapted by Indigenous Australians for the bark medium. Today, this art is recognised for its dynamism and sophistication, offering a window into how the artists engaged with the world. Within the context of recent art and anthropological scholarship, the paiFntings are understood as artefacts of Indigenous ‘agency’. They are products of the intentional action of artists through which power is enacted and from which change has followed. This paper reveals how the paintings were influential to their audiences and the discourses arising from their display through the agency of the artists who made them, and the curators who selected them. It underlines how Indigenous agency associated with the aesthetic and semantics values of bark painting has been and continues to be a powerful mechanism for instigating cultural, social, economic and political change. As such, it points to the wealth of Indigenous agency yet to be documented in the other collections of bark painting that are held in institutions in Australia and throughout the world. Full article

Soil moisture (SM) is critical in monitoring the time-lagged impacts of agrometeorological drought. In Australia and several south-west Pacific Small Island Developing States (SIDS), there are a limited number of in situ SM stations that can adequately assess soil-water availability in a near-real-time context. Satellite SM datasets provide a viable alternative for SM monitoring and agrometeorological drought provision in these regions. In this study, we investigated the performance of Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Operational Products System (SMOPS), SM from the Advanced Microwave Scanning Radiometer 2 (AMSR-2) and SM from the Advanced Scatterometer (ASCAT) over Australia and south-west Pacific SIDS. Products were first evaluated in Australia, given the presence of several in-situ SM monitoring stations and a state-of-the-art hydrological model—the Australian Water Resources Assessment Landscape modelling system (AWRA-L). We further investigated the accuracy of SM satellite datasets in Australia and the south-west Pacific through Triple Collocation analysis with two other SM reference datasets—ERA5 reanalysis SM data and model data from the Global Land Data Assimilation System (GLDAS) dataset. All datasets have differing observation periods ranging from 1911-now, with a common period of observations between 2015–2021. Results demonstrated that ASCAT and SMOS were consistently superior in their performance. Analysis in the six south-west Pacific SIDS indicated reduced performance for all products, with ASCAT and SMOS still performing better than others for most SIDS with median R values ranging between 0.3–0.9. We conducted a case study of the 2015 El Niño and Positive Indian Ocean Dipole-induced drought in Papua New Guinea. It was shown that ASCAT is a valuable dataset indicative of agrometeorological drought for the nation, highlighting the value of using satellite SM products to provide early warning of drought in data-sparse regions in the south-west Pacific. Full article

The need for continued research into suicide prevention strategies is undeniable, with high global statistics demonstrating the urgency of this public health issue. In Australia, approximately 3000 people end their lives each year, with those living in rural and regional areas identified as having a higher risk of dying by suicide. Due to decreased access and support services in these areas, community-based suicide prevention initiatives provide opportunities to educate and support local communities. A scoping review was conducted to explore the literature pertaining to such programs in rural and/or regional communities in Australia. This review follows the five-stage Arksey and O’Malley (2005) framework and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist. Nine databases were searched, from which studies were considered eligible if suicide prevention programs were community-based and catered for adults (aged ≥ 18 years) in rural or regional Australia. Ten papers that met our inclusion criteria were included in this review, showcasing a variety of interventions such as workshops, a digital intervention, art therapy, and initiatives to increase education and reduce stigma around suicide. Program engagement strategies included the importance of providing culturally appropriate services, the inclusion of lived experience mentoring, and tailoring the suicide prevention program to reach its targeted audience. Overall, there is a dearth of literature surrounding community-based suicide prevention initiatives for adults in rural and regional Australia. Further evaluation of community-based projects is required to ensure quality improvement and tailored suicide prevention initiatives for rural and regional Australians. Full article