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PAINTING OBJECT (shelves/wall painting) #4 [4th Australian Biennale of Reductive Arts (ABORA – 4)]
No full textsingle artwork in a group exhibition with accompanying published catalogu
IGARSS 2025 - 2025 IEEE International Geoscience and Remote Sensing Symposium
No full textUnderground mapping and identification of exposed rock mass structures or discontinuity sets is critical in challenging mine regions like stopes for stability analysis and plays a key role in terms of both the economic and safety aspects of an excavation site. However, utilisation of traditional mapping equipment is not feasible in a stope due to the challenges posed by limited accessibility, complex geometries of stopes, unavailability of global navigation satellite system (GNSS), and safety risks. Recent advances in unmanned aerial vehicles (UAV), portable mobile laser scanning (MLS) LiDAR technology, and simultaneous localization and mapping (SLAM) have led to the development of UAV laser scanners, allowing engineering personnel to remotely obtain 3D point clouds from such challenging regions. This study investigates the use of a UAV laser scanning system for acquiring 3D point cloud of an underground metal mining stope to perform automated mapping of discontinuity sets in the exposed stope surface
IGARSS 2025 - 2025 IEEE International Geoscience and Remote Sensing Symposium
No full textThe eradication of Giant Rat Tail (GRT) grass (Sporobolus natalensis and Sporobolus pyramidalis) in southeastern and central Queensland is essential for maintaining agricultural productivity. These invasive weeds significantly deplete soil nutrients, thereby rendering the land unsuitable for crop cultivation. Moreover, their high silica, phenolic compound, and lignin content make them unpalatable to livestock, further diminishing the utility of infested pastures. This study proposes a novel methodology that integrates drone-based imaging with machine learning techniques to classify and manage GRT grass infestations. Highresolution RGB imagery of pastureland was captured using a DJI Phantom 4 Pro UAV. The collected data were subjected to square patch segmentation, resulting in over 315574 image segments categorized into GRT and non-GRT classes. For each segment, an extensive set of 37 features, encompassing texture, radiometric, runlength characteristics and color, was extracted. Subsequent data pre-processing included the removal of tiles having no data value and the partitioning of the dataset into training and testing subsets. Five tree based machine learning classifiers-Decision Tree Classifier, Random Forest Classifier, AdaBoost Classifier, XGBoost Classifier, and XGBoost Random Forest Classifier-were developed and assessed. The classification performance was optimized by retraining the XGBoost classifier using the top 21 features identified based on their importance as determined by the already trained XGBoost classifier. Among the evaluated models, the XGBoost classifier with top 21 features achieved the highest accuracy (95.5 %), along with robust performance metrics for GRT classification, including a precision of 95.3 %, recall of 94.2 %, and F1 score of 94.7 %. These findings underscore the potential of UAV-assisted remote sensing, coupled with advanced machine learning methodologies, as a scalable and costefficient solution for the precise detection of invasive weed species, thereby facilitating their effective management and enhancing agricultural sustainability
It's hard to describe what it feels like to become a mum, but it has a name: matrescence
No full textGenomic analysis of two all stage stripe rust resistance genes in the Vavilov wheat landraces AGG40807WHEA1
No full textThe ongoing occurrence and spread of wheat stripe rust, caused by the fungal pathogen Puccinia striiformis f. sp. tritici, threatens the global food security. Cultivation of varieties with effective sources of resistance is often followed by the appearance of virulent pathotypes at various times after their introduction. This requires an ongoing search for new sources. Tests of 296 accessions from the Vavilov wheat landrace collection identified numerous lines with broadly effective all-stage stripe rust resistance. Genetic analysis of one of these accessions (Australian Grains Genebank number AGG40807WHEA1) identified two all-stage resistance genes, temporarily named YrV1 and YrV2. The YRV1 and YRV2 loci were mapped to 3.48–3.98 and 730.2–731.2 Mb intervals in the short arm of chromosome 3B and the long arm of chromosome 7B, respectively. A comparative genomic analysis of the YRV1 locus in the Chinese Spring and the 10 + wheat pangenome databases revealed genomic rearrangements and lack of sequences encoding a nucleotide-binding and leucine-rich repeat (NLR) domain protein. Sequences belonging to NLR-like genes were present in the YRV2 region. Kompetitive allele-specific PCR (KASP) markers designed from SNPs IWB71814 and IWB69562, located at 0.4 cM and 0.5 cM distal to YrV1 and YrV2, respectively, were validated for marker-assisted selection using 123 hexaploid and 15 tetraploid wheat and 14 triticale cultivars. YrV1 and YrV2 genes are potentially valuable resources, and use of the closely linked molecular markers will expedite their deployment in breeding
Enhancing Spatial Awareness and Collaboration: A Guide to VR-Ready Survey Data Transformation
Get PDFSurveying and spatial science are experiencing a paradigm shift from traditional data outputs to more immersive and interactive formats, driven by the rise in Virtual Reality (VR). This study addresses the challenge of transforming UAV (Unmanned Aerial Vehicle)-acquired photogrammetry data into VR-compatible surfaces while preserving the accuracy and quality crucial to professional surveying. The study leverages Blender, an open-source 3D creation tool, to develop a procedural guide for creating VR-ready models from high-quality survey data. The case study focuses on silos located in Yelarbon, Southeast Queensland, Australia. UAV mapping is utilised to gather the data necessary for 3D modelling with a few minor alterations in the photo capturing angle and processing. Key findings reveal that while Blender excels as a visualisation tool, it struggles with geospatial precision, particularly when handling large numbers coming from coordinate systems, leading to rounding errors seen within the VR model. Blender’s strength lies in creating immersive experiences for public engagement but is constrained by its lack of capability to hold survey metadata, hindering its applicability for professional survey-grade outputs. The results highlight the need for further development into possible Blender plugins that integrate geospatial accuracy with VR outputs. This study underscores the potential of VR to enhance how survey data are visualised, offering opportunities for future innovations in both the technical and creative aspects of the surveying profession
Labor settled the ‘funding wars’ just before the election. Here are 4 big issues schools still face
Get PDFTrajectories of Supportive Care Needs for People Who Travel to Receive Cancer Treatment: A Longitudinal Study in Australia
Get PDFObjective
To describe trajectories of change in unmet supportive care needs over a two-year period among people diagnosed with cancer and assess whether these trajectories vary as a function of sociodemographic and clinical characteristics.
Methods
This analysis used data from a longitudinal study of people in Queensland, Australia who travelled largely from regional and remote areas to metropolitan centres to receive cancer care (N = 784). Supportive care needs were measured at baseline, then at 3-, 12-, and 24-month post-baseline across five domains (‘psychological’, ‘physical and daily living’, ‘health systems and information’, ‘patient care and support’, ‘sexuality’) using the Supportive Care Needs Survey-Short Form. Latent Curve Growth Analysis was performed to examine trajectories of change in unmet needs and assess whether these trajectories were influenced by participant characteristics.
Results
Significant linear slopes indicated a modest decrease in unmet supportive care needs for all domains, except sexuality. For most domains, significant variance in intercepts but not slopes indicated individual differences in needs at baseline but not in trajectories over time. At baseline, the proportion of unmet needs was highest for the ‘physical and daily living’ (M = 44.2%, SD = 39.1%) and ‘psychological’ domains (M = 37.8%, SD = 36.3%). Unmet needs at baseline were consistently higher among participants who were younger, had a higher education level, and who reported poorer QoL.
Conclusions
The proportion of unmet supportive care needs reported by people living with cancer may decrease over time, largely irrespective of sociodemographic and clinical characteristics. Despite this, unmet needs remain prevalent, particularly for physical and psychological support
Australian Paramedic Students' Reports of Clinical Placement Experiences: A Snapshot From two Cohorts
Get PDFClinical placements are a core requirement of paramedicine undergraduate degrees in Australia. Registered paramedics are
expected to participate in teaching and mentoring of undergraduate students during placement. Additionally, universities and paramedic clinical placement providers are required to actively participate in providing 360° feedback to ensure ongoing quality of clinical placements. Most students look forward to attending clinical placement and experiencing what it is really like to be a paramedic; however, at times, students' expectations differ greatly to their experiences. In this study, we provide students' reporting of their experiences and feedback verbatim and, where possible, grouped into four themes of challenges common across placements: staffing/supervisor challenges, clinical challenges, occupational hazards and balancing competing demands. We welcome further discussion and recommend collaborations between universities, clinical placement providers and regulatory bodies to improve clinical placement experiences of paramedicine students on clinical placements, clinical supervision and, potentially, patient safety outcomes
Chameleon swarm algorithm with Morlet wavelet mutation for superior optimization performance
Get PDFMetaheuristic algorithms play a vital role in addressing a wide range of real-world problems by overcoming hardware and computational constraints. The Chameleon Swarm Algorithm (CSA) is a modern metaheuristic algorithm that uses how chameleons act. To improve the capabilities of the CSA, this work proposes a modified version of the Chameleon Swarm Algorithm to find better optimal solutions applicable to various application areas. The effectiveness of the proposed algorithm is assessed using 97 typical benchmark functions and three real-world engineering design problems. To validate the efficacy of the proposed algorithm, it has been compared to a number of well-known and widely-used classical algorithms, the Gravitational Search Algorithm, the Earthworm Optimization. The proposed modified Chameleon Swarm Algorithm using Morlet wavelet mutation and Lévy flight (mCSAMWL) is superior to existing algorithms for both unimodal and multimodal functions, as demonstrated by Friedman’s mean rank test as well as three real world engineering design problems. Five performance metrics—average energy consumption, total energy consumption, total residual energy, dead node and cluster head frequency are taken into consideration when evaluating the performances against state-of-the-art algorithms. For nine different simulation scenarios, the proposed algorithm mCSAMWL outperforms the Atom Search Optimization (ASO), Hybrid Particle Swarm Optimization and Grey Wolf Optimization (PSO-GWO), Bald Eagle Search Algorithm (BES), the African Vulture Optimization Algorithm (AVOA), and the Chameleon Swarm Algorithm (CSA) in terms of average energy consumption and total energy consumption by 50.9%, 52.6%, 45%, 42.4%, 50.1% and 51.4%, 53.3%, 45.6%, 42.4%, 50.7%