18397 research outputs found
Sort by
A Dying Day: A novel and exegesis [novel]
No full textThis practice-led research asks `how can the Althusserian concepts of the Repressive State Apparatus and the Ideological State Apparatus be used to inform depictions of vigilantism in a crime fiction narrative'. It comprises two elements - a crime novel titled A Dying Day and an accompanying exegesis that critically situates the author's creative practice in the field and explores issues related to the production of the narrative. Its contributions include extending scholarship on the representation of the vigilante in fiction and demonstrating how Althusser's ideas can be tested through a crime novel.</p
Ultrasonic B and C-Scan Image Dataset for Virgin and CNT doped Carbon Fibre/Epoxy Laminates (Pre and Post Lightning Strike Damage)
No full textThis dataset contains Ultrasonic B and C-scans of carbon fibre panels before and after exposure to simulated lightning strike damage under 50kA and 100kA impulse currents. The dataset consists of Ultrasonic C-scan images for a total of 6 carbon fibre/epoxy panels. The Ultrasonic C-scan images presented for each laminate configuration pertain to the backside, reflector, internal, time of flight and thickness scans. Additionally, B-scans obtained from the center of all 6 panels are presented.The following panel configurations are presented within the image data set:2 x Virgin Carbon Fibre/Epoxy panels2 x Carbon Fibre/Epoxy panels interleaved with 8 GSM thermoplastic veil doped at 2 wt.% CNT concentration2 x Carbon Fibre/Epoxy panels interleaved with 20 GSM thermoplastic veil doped at 2 wt.% CNT concentrationThe data can be used as a baseline assessment of panel quality with regards to the identification of porosity and inclusions sustained during laminate manufacturing of neat and interleaved panels. The Ultrasonic C-scan images may also serve benefit in training models that are used to assess laminate quality and defect detection. Further to this, the post lighting strike damage C-scans may be useful in developing tools for the analysis and evaluation of lightning strike damage within carbon fibre composites based of ultrasonic analysis.</p
Feature Selection for High-Dimensional Imbalanced Class Datasets Using Harmony Search and Kullback-Leibler Divergence
No full textHigh-dimensional imbalanced datasets pose significant challenges in pattern recognition, often leading to overfitting and classifier bias toward majority classes. While numerous feature selection algorithms exist, most struggle to effectively address both high dimensionality and class imbalance simultaneously. This paper introduces Harmony Search Kullback–Leibler (HKL), a novel feature selection algorithm that integrates Kullback–Leibler divergence with the Harmony Search metaheuristic to specifically address these dual challenges. HKL establishes an information-theoretic foundation by employing KL divergence as a statistical framework to evaluate feature subsets based on their ability to separate minority and majority classes. Unlike existing Harmony Search variants that operate as class-blind optimizers treating feature selection as a generic optimization problem, HKL fundamentally shifts the paradigm by incorporating direct class distribution awareness into the optimization process. The algorithm implements a dual optimization approach that simultaneously balances classification performance metrics with class distribution divergence measures. This design specifically enhances minority class discrimination by prioritizing features that maximize the divergence between class distributions, ensuring that selected features provide discriminative power for underrepresented classes rather than simply favoring the majority class. Experimental validation across multiple high-dimensional biomedical datasets demonstrates that HKL consistently outperforms existing state-of-the-art methods in terms of AUC and G-mean metrics, with particular improvements for minority class classification. The algorithm achieves optimal performance while using substantially reduced feature subsets, often requiring only a quarter to half of the original features to maintain or exceed baseline classification accuracy. Statistical significance testing confirms that these performance improvements represent genuine algorithmic advantages rather than random variation. The proposed approach offers an effective solution to both dimensionality reduction and class imbalance challenges, providing a valuable tool for complex classification tasks across various domains.</p
Fibre as a Potential Reinforcement to Continuously Reinforced Concrete Pavements
No full textThis research explores how adding steel fibers to concrete can make concrete roads stronger, longer-lasting, cost-effective, and sustainable. It focuses on a type of concrete road known as continuously reinforced concrete pavement, popular for highways. By testing different fiber types and dosages, the study shows how these materials help optimize road design, resource use, and cost. The research also presents a predictive model to estimate the behavior of steel fiber reinforced concrete under flexure, reducing the need for repetitive lab testing. These findings support more sustainable and resilient infrastructure to meet the growing demand for better road networks worldwide.</p
Insights into binary pulsar dynamics through studies of the ionized interstellar medium
No full textThis thesis investigates pulsar timing and scintillation to study the ionised interstellar medium and pulsar orbital kinematics. Observations were conducted with two major Southern Hemisphere radio telescopes: Murriyang in Australia and MeerKAT in South Africa. Refined kinematic measurements were obtained for PSR J1909-3744, crucial for pulsar timing arrays, and for PSR J0737-3039, the only known double pulsar system essential for testing general relativity. Additionally, the mass of PSR J1757-5322 was constrained using combined scintillation and timing techniques. Collectively, these results demonstrate novel scientific and statistical methods for probing the ionised interstellar medium and advancing pulsar timing science.</p
Machine Learning System for the Evaluation of Tape Layup Scans
No full textThe Convolutional Neuronal Network that is programmed in this Jupiter notebook can be trained to detect defects in surface scans of fibre tape layups. The network has a U-net architecture. The "transfer learning" method is used to first train the network with artificial data and then fine-tune it with real scans. The input data must have the format described in the benchmark data set (https://doi.org/10.25916/sut.27328356).This publication contains the software as a Jupiter notebook.It was created as part of the PhD project "Data-Driven Quality Assurance of the Dry Fibre Tape Laying Process." Funding: GA51557 under the Global Innovation Linkages program Round 2 by the Department of Industry, Science and Resources of the Australian Federal Government.</p
A density functional theory supported spectroscopic analysis of a dual p38a MAPK/PDE-4 inhibitor (CBS3595)
No full textMitogen-activated protein kinase (MAPK) p38 and phosphodiesterase-4 (PDE-4) are significant therapeutic targets for treating chronic inflammatory conditions and certain cancers. CBS3595 is a novel and potent inhibitor of both p38 MAPK and PDE-4, but its binding mechanism and chemical properties are not well-documented. This thesis utilised computational chemistry methods, complemented by available experimental data on the structural and spectroscopic characteristics of CBS3595. The results highlight the importance of molecular conformation and solvent environment to the spectroscopic and electronic properties of CBS3595, identifying potential reporting properties for its binding with protein targets.</p
Discovery of MgrA Inhibitors to Suppress<i> Staphylococcus aureus </i>Virulence and Pathogenesis
No full textThis study aimed to identify compounds that reduce the virulence of drug-resistant Staphylococcus aureus by targeting MgrA, a key regulator of toxin production and biofilm formation. Computational analyses revealed potential inhibitor binding sites, and laboratory assays confirmed that several compounds, particularly E1, E10, and E11, effectively inhibited both biofilm development and hemolytic activity without significantly affecting bacterial growth. These findings highlight a promising strategy to combat resistant infections by disarming the bacteria rather than killing them, potentially minimizing the emergence of further resistance.</p
AI-Based Prediction of Wet-Period Rainfall Driven by Global Climate Teleconnections in the Spatially Diverse Australian Tropics
No full textThis research developed a new approach to forecast rainfall in Australia's Northern Territory, where rainfall greatly affects farming, water supply, and flood planning. By analyzing past climate patterns and using advanced AI-Driven models, the study improved predictions of heavy rains during the wet-period months (Jan-Feb). These more accurate forecasts can help farmers make planting decisions, support water managers in planning storage and use, and improve disaster preparedness for extreme weather. The findings offer practical tools for communities and policymakers in regions facing uncertain and changing weather patterns.</p
