University of Technology Sydney

Despite the digital transformation of society, handwritten documents continue to be collected in various investigations, such as fraud investigations and drug trafficking. Recent research highlighted that handwriting offers not only comparative value (i.e. helping address a source question), but it also has the potential to infer a writer's background profile (i.e. helping address other questions than source). This study compared binary logistic regression (BLR) and classification & regression tree (CRT) models to infer a writer's cultural background based on handwriting features. An experimental two-step modelling approach was employed to distinguish Australian, Korean, and Vietnamese writers (N = 196) using categorical handwriting features coded from scanned handwritten texts. The first step was classifying Australian from non-Australian writers, and the second step was further specifically classifying non-Australians into Korean and Vietnamese. The results demonstrated how a two-step modelling framework could be operationalised for early-stage writer classification and highlighted its practical strengths and limitations. The BLR model provided statistical depths for detailed interpretation, and it achieved higher classification accuracy, 93.4% and 97.8% in each step. The CRT model also achieved a high accuracy rate, but lower than BLR with 86.7% and 94.2%. Furthermore, blind test results reflected the practical challenges and strengths of each model. The CRT model correctly classified six out of seven blind specimens while the BLR correctly classified three out of seven blind specimens. Each model presented distinct strengths as the BLR model provided rich detailed statistical outputs, such as odds ratios and significance levels, while the CRT model offered greater accessibility and usability for non-statistical experts. These findings suggest that model selection should balance interpretability, robustness and accuracy. Although more work is required until such models can be applied in practice, this study highlights the potential to extract operational insights from handwriting beyond traditional comparison methods, supporting intelligence-led workflows even when no comparison material is available.

The quinone-based cathode material, tri-(pyrene-4,5,9,10-tetraone)-di-(tetraamino-p-benzoquinone) (TPDT), for aqueous zinc batteries incorporates extended π-conjugation, cation-radical states, and dynamic tautomerization, resulting in continuous charge-transfer pathways. Therefore, the TPDT composite cathode shows excellent electrochemical performance with only 5% KB additive. Organic small molecules are promising cathode materials for aqueous zinc batteries due to their structural tunability and sustainability. However, their intrinsically poor electrical and ionic conductivities seriously hinder the rate capability. To compensate, organic electrode preparation usually requires the addition of a large amount of carbon agents, typically ≥30%, which adds processing complexity and limits the practical energy density of the system. To address these very important limitations, we design and synthesize a fully conjugated quinone molecule, tri-(pyrene-4,5,9,10-tetraone)-di-(tetraamino-p-benzoquinone) (TPDT), featuring multiple redox-active sites with unique dynamic tautomerization. Experimental and theoretical analyses reveal that TPDT possesses extended π-conjugation, cationic radical stabilization, and π-π stacking interactions, which together promote efficient through-plan and through-space charge transport. Remarkably, the partially reduced TPDT isomers with different C=O or C=N reaction sites exhibit low energy differences, enabling dynamic tautomerization at room temperature. This equilibrium facilitates charge redistribution and continuous charge-transfer pathways across the molecular framework. As a result, TPDT exhibits excellent electrical and ionic conductivities and outstanding electrochemical performance. The composite with graphene oxide further ensures stable cycling. With only 5% Ketjen black (KB) additive, the composite cathode delivers a high capacity of 313 mAh g −1 at 0.1 A g −1 and retains 81 mAh g −1 at 50 A g −1 together with 92% capacity retention after 20,000 cycles. At the low temperature of −20 °C, it still maintains 128 mAh g −1 at 5 A g −1 over 5400 cycles. This work highlights the importance of structural dynamics in designing organic cathodes and has a guiding significance for developing high-performance zinc-organic batteries.

The increasing release of greenhouse gases (GHGs) like CO₂, CH₄, N₂O, and industrial contaminants (indirect GHGs) such as SO₂ and H₂S has prompted significant global worries due to their role in climate change, air pollution, and harm to the environment. Ionic liquids (ILs) as green solvents have emerged as promising alternatives to traditional solvents because of their minimal volatility, high thermal stability, and adjustable physicochemical characteristics. Yet, limited gas solubility data in ILs is hindering their applications in carbon capture and air pollution control. Machine learning (ML) is a powerful tool for modeling and simulating the solubility of polluting gases in ILs. This research aims to critically review recent progress in ML modeling of pollutant gas removal by ILs. More importantly, a new ML model of genetic programming (GP) was developed to generate an explicit and accurate mathematical equation to predict the solubility of SO 2 , CH 4 , N 2 O, CO, H 2 S and CO 2 in ILs, using a large dataset (3209) for different gas-IL systems. Using temperature, pressure, and structural related parameters of ILs and gases as input parameters, the model achieved a high accuracy (R 2 > 0.97). Finally, a simple Excel method for calculating gas solubility has been created for prediction and modeling purposes.

Reactive oxygen species play an integral role in physiological signaling but contribute to pathology when dysregulated. Elevated ROS levels in diseased tissues such as tumors, inflamed sites, and ischemic regions present unique biochemical triggers for targeted drug delivery. Furthermore, strategies utilizing exogenously generated ROS (e.g., via photodynamic action) provide an alternative route for spatiotemporal control. This review summarizes recent advances in ROS-responsive systems, beginning with the chemistry of ROS-cleavable linkers and the design of nanocarrier platforms capable of spatiotemporally controlled release. The integration of these carriers with theranostic functions is highlighted as a strategy to enhance selectivity and reduce systemic toxicity. Applications across oncology, inflammatory disorders, ischemia-reperfusion injury, and regenerative medicine illustrate the breadth of therapeutic potential. Key design considerations including sensitivity thresholds, payload compatibility, and surface functionalization are discussed alongside translational challenges such as stability, reproducibility, and scalability. Emerging opportunities, notably patient-specific redox profiling and biosensor-guided adaptive delivery are identified as promising routes to clinical translation. By bridging redox biology with materials science and nanomedicine, ROS-triggered drug delivery platforms demonstrate the capacity to exploit endogenous oxidative cues for improved therapeutic precision and safety, positioning them as a transformative approach in the development of next-generation controlled release systems.

Accurate segmentation of optic disc regions in fundus images is essential for the diagnosis and monitoring ocular fundus pathology of ophthalmic diseases such as myopia, glaucoma, and diabetic retinopathy. However, challenges such as irregular disc shapes, edge detail preservation, and limited annotated datasets hinder the effectiveness of existing methods. To handle these problems, we propose FDAU-Net, an efficient, fully convolutional network for optic disc segmentation. FDAU-Net integrates the Flexible Wavelet Convolution Block (FWC Block), which combines the dynamic receptive field adjustment of AKConv with wavelet down-sampling to achieve efficient feature extraction while retaining critical edge information. Additionally, we design the Selective Dual-Attention Fusion Gate (SDA Gate) to optimize feature selection and fusion, leveraging channel and spatial attention mechanisms to substantially enhance segmentation accuracy and robustness. To further improve performance, we introduce MedAugment, an automated data augmentation method tailored for medical images, efficiently boosts data diversity in scenarios with low datasets. Experimental results on the iChallenge and IDRiD datasets demonstrate that FDAU-Net consistently achieves superior segmentation performance across key metrics, highlighting its potential for advancing clinical applications.

Heterotopic ossification (HO) within tendons leads to debilitating tissue dysfunction, primarily arising from the abnormal osteogenic differentiation of tendon-derived stem cells (TDSCs). Despite its clinical prevalence, effective pharmacological treatments remain elusive. Farnesol, a natural isoprenoid with potent anti-inflammatory properties, represents a novel but unexplored candidate for musculoskeletal repair. This study aimed to evaluate the therapeutic efficacy of Farnesol in attenuating tendon HO and to elucidate the specific molecular mechanisms driving its effects. The anti-osteogenic effects of Farnesol were assessed using invitro TDSC differentiation assays and an invivo tendon injury model. Integrated RNA sequencing and network pharmacology analysis were employed to identify potential molecular targets. The mechanism was rigorously validated through rescue experiments using a specific GSTP1 inhibitor. Farnesol treatment significantly inhibited osteogenic differentiation invitro and attenuated ectopic bone formation invivo. Mechanistic screening identified the GSTP1/MAPK signaling axis as a critical regulatory pathway. Crucially, the administration of a GSTP1 inhibitor reversed the suppressive effects of Farnesol on osteogenesis, confirming GSTP1 as the primary mediator. Farnesol effectively inhibits the progression of post-traumatic tendon HO by targeting the GSTP1/MAPK pathway. These findings provide the first evidence of Farnesol's therapeutic potential in preventing pathological ossification.

Postmortem interval (PMI) estimation is a critical component of forensic investigations as it can help establish a timeline of events and assist in reducing a pool of missing persons or potential suspects. Despite its importance, the accuracy of PMI estimation remains challenging due to the complexity of decomposition and limitations of current methods, particularly in the later postmortem period. Proteomics has shown potential as a more objective method, with current studies taking an untargeted approach to identify informative peptide targets. Previous studies in our laboratory analysed human muscle tissue and identified 12 peptide ratios, consisting of 19 individual peptides, that were correlated with decomposition time. This study aimed to optimise the detection of these 19 peptide targets using a targeted liquid chromatography triple quadrupole mass spectrometry (LC-QQQ) method suited for casework applications and instrument platforms commonly found in forensic laboratories. Of the 19 peptide targets, 17 were optimised to be reproducible. Additionally, a longitudinal analysis in authentic human samples was conducted over a 14-day period postmortem to monitor detection throughout the decomposition process. All but one of the targets produced interpretable results, and data analysis was performed to generate peptide ratios. After log2 transformation, two of the previously proposed peptide ratios showed linear trends. Overall, this study has shown that a targeted LC-QQQ method is a viable system for the detection of PMI peptide targets from human postmortem muscle tissue and has demonstrated the potential for this type of analysis to be validated and applied in routine forensic casework.

Industry 4.0 technologies, such as artificial intelligence, machine learning, and the Industrial Internet of Things (IIoT), are reshaping industries and hold considerable promise for advancing productivity and sustainable economic development. However, their adoption is fraught with risks, particularly for small and medium-sized enterprises (SMEs) that play a pivotal role in driving entrepreneurship, innovation, and the circular economy. Based on survey data collected between May and July 2020 from 307 professionals in mining, automotive manufacturing, and construction industries, this study examines critical risks across three stages of technology adoption: identification and selection; pilot testing, and full-scale implementation. Using the fuzzy analytical hierarchy process, the analysis reveals that technical risks dominate across stages and industries, while key social and environmental risks for achieving sustainability goals remain consistently deprioritized. However, risk profiles diverge by firm size and industry: SMEs are disproportionately affected by organizational and financial risks, whereas larger firms demonstrate consistent approaches through established risk management and economic policy frameworks. These findings demonstrate the necessity of a staged, context-sensitive, and sustainability-oriented adoption strategy. This study contributes to understanding how SMEs can balance economic efficiency and sustainable technology adoption in the transition toward Industry 4.0 by offering industry-specific propositions and managerial implications.