Physical Sciences and Mathematics Commons^™

Gut Microbial Communities Of Hybridising Pygmy Angelfishes Reflect Species Boundaries, Megan J. Huggett, Jean-Paul A. Hobbs, Federico Vitelli, Michael Stat, Tane H. Sinclair-Taylor, Michael Bunce, Joseph D. Dibattista

Research outputs 2022 to 2026

Hybridisation and introgression of eukaryotic genomes can generate new species or subsume existing ones, with direct and indirect consequences for biodiversity. An understudied component of these evolutionary forces is their potentially rapid effect on host gut microbiomes, and whether these pliable microcosms may serve as early biological indicators of speciation. We address this hypothesis in a field study of angelfishes (genus Centropyge), which have one of the highest prevalence of hybridisation within coral reef fish. In our study region of the Eastern Indian Ocean, the parent fish species and their hybrids cohabit and display no differences in their diet, behaviour, …

Multi-Scale Mapping Of Australia’S Terrestrial And Blue Carbon Stocks And Their Continental And Bioregional Drivers, Lewis Walden, Oscar Serrano, Mingxi Zhang, Zefang Shen, James Z. Sippo, Lauren T. Bennett, Damien T. Maher, Catherine E. Lovelock, Peter I. Macreadie, Connor Gorham, Anna Lafratta, Paul S. Lavery, Luke Mosley, Gloria M. S. Reithmaier, Jeffrey J. Kelleway, Sabine Dittmann, Fernanda Adame, Carlos M. Duarte, John B. Gallagher, Pawel Waryszak, Paul Carnell, Sabine Kasel, Nina Hinko-Najera, Rakib Hassan, Madeline Goddard, Alice R. Jones, Raphael A. Viscarra Rossel

Research outputs 2022 to 2026

The soil in terrestrial and coastal blue carbon ecosystems is an important carbon sink. National carbon inventories require accurate assessments of soil carbon in these ecosystems to aid conservation, preservation, and nature-based climate change mitigation strategies. Here we harmonise measurements from Australia’s terrestrial and blue carbon ecosystems and apply multi-scale machine learning to derive spatially explicit estimates of soil carbon stocks and the environmental drivers of variation. We find that climate and vegetation are the primary drivers of variation at the continental scale, while ecosystem type, terrain, clay content, mineralogy and nutrients drive subregional variations. We estimate that in the …

Three-Dimensional Rgo/Cnt/G-C3n4 Macro Discs As An Efficient Peroxymonosulfate Activator For Catalytic Degradation Of Sulfamethoxazole, Rajan Arjan Kalyan Hirani, Abdul Hannan, Nasir Rafique, Lei Shi, Wenjie Tian, Haitao Wang, Hongqi Sun

Research outputs 2022 to 2026

Over the past few years, advanced oxidation processes (AOPs) have shown promising efficiencies for wastewater remediation. Carbocatalysis, in particular, has been exploited widely thanks to its sustainable and economical properties but has an issue of recovery and reusability of the catalysts. To address this, three-dimensional (3D) binary and ternary graphene-based composites in the form of macro discs were created to activate peroxymonosulfate (PMS) for catalytic oxidation of sulfamethoxazole (SMX). Graphene oxide served as the base, while graphitic carbon nitride (g-C3N4) and/or single-walled carbon nanotubes (SWCNTs) were added. Among the various discs synthesized, rGNTCN discs (ternary composite) were proven to be …

Pymaivar: An Open-Source Python Suit For Audio-Image Representation In Human Action Recognition, Muhammad B. Shaikh, Douglas Chai, Syed M. S. Islam, Naveed Akhtar

Research outputs 2022 to 2026

We present PyMAiVAR, a versatile toolbox that encompasses the generation of image representations for audio data including Wave plots, Spectral Centroids, Spectral Roll Offs, Mel Frequency Cepstral Coefficients (MFCC), MFCC Feature Scaling, and Chromagrams. This wide-ranging toolkit generates rich audio-image representations, playing a pivotal role in reshaping human action recognition. By fully exploiting audio data's latent potential, PyMAiVAR stands as a significant advancement in the field. The package is implemented in Python and can be used across different operating systems.

Atomically Dispersed Cu-N3 On Hollow Spherical Carbon Nitride For Acetaminophen Degradation: Generation Of 1o2 From H2o2, Shuoyuan Tian, Yu Yin, Mengxuan Liu, Lei Shi, Shu Zhang, Abdul Hannan Asif, Xuan Li, Mengqiong Liu, Xiaoguang Duan, Shaobin Wang, Hongqi Sun

Research outputs 2022 to 2026

Discharge of recalcitrant pharmaceuticals into aquatic systems has caused severe impacts on public health and ecosystem. Advanced oxidation processes (AOPs) are effective for eliminating these refractory pollutants, for which single-atom catalysts (SACs) become the state-of-the-art materials owing to the maximized exposure of active metal sites. In this work, hollow spherical graphitic carbon nitride (hsCN) was fabricated to incorporate copper species to develop Fenton-like catalysts for acetaminophen (ACT) removal. Through pyrolysis of supramolecular assemblies derived from melamine-Cu complex and cyanuric acid, single atom Cu-N3 sites were anchored on hsCN by N-coordination to obtain SACu-hsCN. In virtue …

An Improved Shallow Water Temperature Model For An Australian Tidal Wetland Environment Using Publicly Available Data, Kerry Staples, Steven Richardson, Peter J. Neville, Jacques Oosthuizen

Research outputs 2022 to 2026

Larval mosquito development is directly impacted by environmental water temperature. Shallow water less than 1 m deep is a common larval mosquito habitat. Existing mathematical models estimate water temperature using meteorological variables, and they range in complexity. We developed a modification of an existing one-layer heat balance model for estimating hourly water temperature and compared its performance with that of a model that uses only air temperature and water volume as inputs and that uses air temperature itself as an indicator of water temperature. These models were assessed against field measurements from a shallow tidal wetland—a known larval habitat—in southwest …

Aesthetically Appealing Building Integrated Photovoltaic Systems For Net-Zero Energy Buildings. Current Status, Challenges, And Future Developments—A Review, Mohammad Khairul Basher, Mohammad Nur-E-Alam, Md Momtazur Rahman, Kamal Alameh, Steven Hinckley

Research outputs 2022 to 2026

With the sharp increase in global energy demand, industrial and residential buildings are responsible for around 40% of the energy consumed with most of this energy portion being generated by non-renewable sources, which significantly contribute to global warming and environmental hazards. The net-zero energy building (NZEB) concept attempts to solve the global warming issue, whereby a building will produce, on-site, its required energy demand throughout the year from renewable energy sources. This can be achieved by integrating photovoltaic (PV) building materials, called building-integrated photovoltaic (BIPV) modules, throughout the building skin, which simultaneously act as construction materials and energy generators. Currently, …

Fabrication And Optimization Of Nafion As A Protective Membrane For Tin-Based Ph Sensors, Shimrith P. Shylendra, Magdalena Wajrak, Kamal Alameh

Research outputs 2022 to 2026

In this study, a solid-state modified pH sensor with RF magnetron sputtering technology was developed. The sensor consists of an active electrode consisting of a titanium nitride (TiN) film with a protective membrane of Nafion and a reference glass electrode of Ag/AgCl. The sensitivity of the pH sensor was investigated. Results show a sensor with excellent characteristics: sensitivity of 58.6 mV/pH for pH values from 2 to 12, very short response time of approximately 12 s in neutral pH solutions, and stability of less than 0.9 mV in 10 min duration. Further improvement in the performance of the TiN sensor …

Long Future Frame Prediction Using Optical Flow Informed Deep Neural Networks For Enhancement Of Robotic Teleoperation In High Latency Environments, Md Moniruzzaman, Alexander Rassau, Douglas Chai, Syed M. S. Islam

Research outputs 2022 to 2026

High latency in teleoperation has a significant negative impact on operator performance. While deep learning has revolutionized many domains recently, it has not previously been applied to teleoperation enhancement. We propose a novel approach to predict video frames deep into the future using neural networks informed by synthetically generated optical flow information. This can be employed in teleoperated robotic systems that rely on video feeds for operator situational awareness. We have used the image-to-image translation technique as a basis for the prediction of future frames. The Pix2Pix conditional generative adversarial network (cGAN) has been selected as a base network. Optical …

Seagrass Posidonia Escarpments Support High Diversity And Biomass Of Rocky Reef Fishes, Oscar Serrano Gras, Karina Inostroza, Glenn Hyndes, Alan M. Friedlander, Eduard Serrano, Caitlin Rae, Enric Ballesteros

Research outputs 2022 to 2026

Although seagrass meadows form a relatively homogenous habitat, escarpments, which form three-dimensional structures and originate from the erosion of seagrass peat, can provide important habitat for reef fishes. Here, we compare fish assemblages and habitat structural complexity among seagrass Posidonia australis escarpments and canopies, as well as limestone reef habitats, to understand the role of seagrass escarpments as reef fish habitat in Shark Bay, Western Australia. The total number of fish species, fish biomass, and top predator biomass were significantly higher in seagrass escarpments and reef habitats than in seagrass canopies due to lower habitat structural complexity and thus becoming …

Heterogeneous Activation Of Persulfate By Macroscopic Nitrogen-Doped Graphene Oxide Cubes For The Degradation Of Antibiotic Contaminants In Water, Rajan A. K. Hirani, Abdul H. Asif, Nasir Rafique, Lei Shi, Shu Zhang, Martin Saunders, Wenjie Tian, Shaobin Wang, Hongqi Sun

Research outputs 2022 to 2026

As a sustainable and green approach, carbocatalysis, a metal-free strategy, has shown exceptional results in advanced oxidation processes (AOPs). Nonetheless, the recovery of these catalysts has been a major shortcoming over the years. Herein, three-dimensional nitrogen-doped graphene macrostructures (3D-NGMs), in the form of macro cubes, were synthesised by a simple cross-linking and thermal annealing procedure, after which they were employed in the activation of peroxydisulfate (PS) for the degradation of sulfamethoxazole (SMX). The catalytic cubes were loaded with different amounts of nitrogen precursor to investigate the role of nitrogen configuration in the sp2 hybridised carbon network on AOPs. NGC3 cubes …

A Review Of Multi-Factor Authentication In The Internet Of Healthcare Things, Tance Suleski, Mohiuddin Ahmed, Wencheng Yang, Eugene Wang

Research outputs 2022 to 2026

Objective: This review paper aims to evaluate existing solutions in healthcare authentication and provides an insight into the technologies incorporated in Internet of Healthcare Things (IoHT) and multi-factor authentication (MFA) applications for next-generation authentication practices. Our review has two objectives: (a) Review MFA based on the challenges, impact and solutions discussed in the literature; and (b) define the security requirements of the IoHT as an approach to adapting MFA solutions in a healthcare context. Methods: To review the existing literature, we indexed articles from the IEEE Xplore, ACM Digital Library, ScienceDirect, and SpringerLink databases. The search was refined to combinations …

A Survey On Artificial Intelligence-Based Acoustic Source Identification, Ruba Zaheer, Iftekhar Ahmad, Daryoush Habibi, Kazi Y. Islam, Quoc Viet Phung

Research outputs 2022 to 2026

The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we …

Improving Accuracy And Efficiency In Seagrass Detection Using State-Of-The-Art Ai Techniques, Md Kislu Noman, Syed M. S. Islam, Jumana Abu-Khalaf, Seyed M. J. Jalali, Paul Lavery

Research outputs 2022 to 2026

Seagrasses provide a wide range of ecosystem services in coastal marine environments. Despite their ecological and economic importance, these species are declining because of human impact. This decline has driven the need for monitoring and mapping to estimate the overall health and dynamics of seagrasses in coastal environments, often based on underwater images. However, seagrass detection from underwater digital images is not a trivial task; it requires taxonomic expertise and is time-consuming and expensive. Recently automatic approaches based on deep learning have revolutionised object detection performance in many computer vision applications, and there has been interest in applying this to …

The Ability Of Ruppia Polycarpa To Regenerate From Seed Depends On Seasonal Porewater Salinity Dynamics And Declining Winter Rainfall Could Delay Recruitment, Chanelle L. Webster, Paul S. Lavery, Simone Strydom, Jessica Billinghurst, Kathryn Mcmahon

Research outputs 2022 to 2026

For many plants, regeneration from seed is vital for population recovery. Climate change is modifying the global hydrological cycle and a primary factor controlling germination of marine plants: salinity. How altered salinity regimes, and especially altered porewater salinity regimes, will regulate early life history stages of estuarine seagrasses is poorly understood. Here, we quantified the porewater salinity dynamics in two ephemeral estuaries that are dominated by the cosmopolitan genus Ruppia. Seedbank, germlings (germinated seeds) and seedlings were found in salinities ranging from 5 to 110 over an annual cycle. To understand the germination ecology of the dominant species, Ruppia polycarpa, …

Photocatalytic Reforming Of Lignocellulose: A Review, Xinyuan Xu, Lei Shi, Shu Zhang, Zhimin Ao, Jinqiang Zhang, Shaobin Wang, Hongqi Sun

Research outputs 2022 to 2026

Biomass has been considered as a promising energy resource to combat the exhaustion of fossil fuels, as it is renewable, sustainable, and clean. Photocatalytic reforming is a novel technology to utilize solar energy for upgrading biomass in relatively mild conditions. This process efficiently reforms and recasts biomass into hydrogen and/or valuable chemicals. To date, lignocellulose, including cellulose, hemicellulose and lignin, has attracted extensive studies in facile photocatalytic valorisation. This review summarizes and analyzes the most recent research advances on photoreforming of lignocellulose to provide insights for future research, with a particular emphasis on the reformation of lignin because of its …

Evaluating Staff Attitudes, Intentions, And Behaviors Related To Cyber Security In Large Australian Health Care Environments: Mixed Methods Study, Martin Dart, Mohiuddin Ahmed

Research outputs 2022 to 2026

Background: Previous studies have identified that the effective management of cyber security in large health care environments is likely to be significantly impacted by human and social factors, as well as by technical controls. However, there have been limited attempts to confirm this by using measured and integrated studies to identify specific user motivations and behaviors that can be managed to achieve improved outcomes.

Objective: This study aims to document and analyze survey and interview data from a diverse range of health care staff members, to determine the primary motivations and behaviors that influence their acceptance and application of cyber …

Expressiveness Of Real-Time Motion Captured Avatars Influences Perceived Animation Realism And Perceived Quality Of Social Interaction In Virtual Reality, Alan D. Fraser, Isabella Branson, Ross C. Hollett, Craig P. Speelman, Shane L. Rogers

Research outputs 2022 to 2026

Using motion capture to enhance the realism of social interaction in virtual reality (VR) is growing in popularity. However, the impact of different levels of avatar expressiveness on the user experience is not well understood. In the present study we manipulated levels of face and body expressiveness of avatars while investigating participant perceptions of animation realism and interaction quality when disclosing positive and negative experiences in VR. Moderate positive associations were observed between perceptions of animation realism and interaction quality. Post-experiment questions revealed that many of our participants (approximately 40 %) indicated the avatar with the highest face and body …

Enhanced Heart Rate Prediction Model Using Damped Least-Squares Algorithm, Angela An, Mohammad Al-Fawa’Reh, James Jin Kang

Research outputs 2022 to 2026

Monitoring a patient’s vital signs is considered one of the most challenging problems in telehealth systems, especially when patients reside in remote locations. Companies now use IoT devices such as wearable devices to participate in telehealth systems. However, the steady adoption of wearables can result in a significant increase in the volume of data being collected and transmitted. As these devices run on limited battery power, they can run out of power quickly due to the high processing requirements of the device for data collection and transmission. Given the importance of medical data, it is imperative that all transmitted data …

Anomaly Detection In Cybersecurity Datasets Via Cooperative Co-Evolution-Based Feature Selection, Bazlur A. N. M. Rashid, Mohiuddin Ahmed, Leslie F. Sikos, Paul Haskell-Dowland

Research outputs 2022 to 2026

Anomaly detection from Big Cybersecurity Datasets is very important; however, this is a very challenging and computationally expensive task. Feature selection (FS) is an approach to remove irrelevant and redundant features and select a subset of features, which can improve the machine learning algorithms’ performance. In fact, FS is an effective preprocessing step of anomaly detection techniques. This article’s main objective is to improve and quantify the accuracy and scalability of both supervised and unsupervised anomaly detection techniques. In this effort, a novel anomaly detection approach using FS, called Anomaly Detection Using Feature Selection (ADUFS), has been introduced. Experimental analysis …

Ransomware 2.0: An Emerging Threat To National Security, Mohiuddin Ahmed, Sascha Dominik Dov Bachmann, Abu Barkat Ullah, Shaun Barnett

Research outputs 2022 to 2026

The global Covid-19 pandemic has seen the rapid evolution of our traditional working environment; more people are working from home and the number of online meetings has increased. This trend has also affected the security sector. Consequently, the evolution of ransomware to what is now being described as ‘Ransomware 2.0’ has governments, businesses and individuals alike rushing to secure their data.

Design, Development, And Characterization Of Highly Efficient Colored Photovoltaic Module For Sustainable Buildings Applications, Mohammad Khairul Basher, Mohammad Nur-E-Alam, Momtazur Rahman, Steven Hinckley, Kamal Alameh

Research outputs 2022 to 2026

The building integrated photovoltaic (BIPV) system is one of the contributors which has enormous potential to reach the goal of net-zero energy buildings (NZEB) that significantly reduce the use of fossil fuels that contribute to global warming. However, the limitations of the visual and aesthetic appearance of current BIPV systems make this aspiration unlikely. This study investigates the limitations of the single-color-based PV modules that are dull in appearance and have low photo-conversion efficiency (PCE). In order to solve this issue, we designed, developed, and characterized micro-patterned-based multicolored photovoltaic (MPCPV) modules which are applicable to net-zero building and development. Our …

Development And Validation Of An Astronomy Self-Efficacy Instrument For Understanding And Doing, Rachel Freed, David Mckinnon, Michael Fitzgerald, Christina M. Norris

Research outputs 2022 to 2026

This paper presents a new astronomy self-efficacy instrument, composed of two factors; one relating to learning astronomy content, which we call astronomy personal self-efficacy, and the other relating to the use of astronomical instrumentation, specifically the use of remote robotic telescopes for data collection. The latter is referred to as the astronomy instrumental self-efficacy factor. The instrument has been tested for reliability and construct validity. Reliability testing showed that factor 1 had a Cronbach's α of 0.901 and factor 2 had a Cronbach's α of 0.937. Construct validity was established by computing one-way analyses of variances, with the p value …

Ecu-Ioft: A Dataset For Analysing Cyber-Attacks On Internet Of Flying Things, Mohiuddin Ahmed, David Cox, Benjamin Simpson, Aseel Aloufi

Research outputs 2022 to 2026

There has been a significant increase in the adoption of unmanned aerial vehicles (UAV) within science, technology, engineering, and mathematics project-based learning. However, the risks that education providers place their student and staff under is often unknown or undocumented. Low-end consumer drones used within the education sector are vulnerable to state-of-the-art cyberattacks. Therefore, datasets are required to conduct further research to establish cyber defenses for UAVs used within the education sector. This paper showcases the development of the ECU-IoFT dataset, documenting three known cyber-attacks targeting Wi-Fi communications and the lack of security in an affordable off-the-shelf drone. At present, there …

A Low-Cost Machine Learning Based Network Intrusion Detection System With Data Privacy Preservation, Jyoti Fakirah, Lauhim Mahfuz Zishan, Roshni Mooruth, Michael L. Johnstone, Wencheng Yang

Research outputs 2022 to 2026

Network intrusion is a well-studied area of cyber security. Current machine learning-based network intrusion detection systems (NIDSs) monitor network data and the patterns within those data but at the cost of presenting significant issues in terms of privacy violations which may threaten end-user privacy. Therefore, to mitigate risk and preserve a balance between security and privacy, it is imperative to protect user privacy with respect to intrusion data. Moreover, cost is a driver of a machine learning-based NIDS because such systems are increasingly being deployed on resource-limited edge devices. To solve these issues, in this paper we propose a NIDS …

Geostatistical Tools To Assess Shifts In Recreational Fishing, Shannon J. Burchert, Glenn A. Hyndes, Karina Ryan, Ute Mueller

Research outputs 2022 to 2026

The use of geostatistical indices to examine fishery-dependent data over time is novel. We assess the value of a range of these indices for describing trends in boat-based recreational fishing in Western Australia and areas of high effort and catches for two demersal species with varying biological characteristics, life histories and distributions. The number of blocks (10- × 10-nautical mile (~19- × 19-km) area) visited decreased by 40%, while the number of fishing trips with demersal species catch increased by 15%. Spatial indices showed a south-easterly shift in demersal catch per unit effort (CPUE) across survey years. The southerly shift …

Realistic Motion Avatars Are The Future For Social Interaction In Virtual Reality, Shane L. Rogers, Rebecca Broadbent, Jemma Brown, Allan Fraser, Craig P. Speelman

Research outputs 2022 to 2026

This study evaluated participant self-reported appraisal of social interactions with another person in virtual reality (VR) where their conversational partner was represented by a realistic motion avatar. We use the term realistic motion avatar because: 1. The avatar was modelled to look like the conversational partner it represented, and 2. Full face and body motion capture was utilised so that the avatar mimicked the facial and body language of the conversational partner in real-time. We compared social interaction in VR with face-to-face interaction across two communicative contexts: 1. Getting acquainted conversation, and 2. A structured interview where the participant engaged …

Biometric Security: A Novel Ear Recognition Approach Using A 3d Morphable Ear Model, Md Mursalin, Mohiuddin Ahmed, Paul Haskell-Dowland

Research outputs 2022 to 2026

Biometrics is a critical component of cybersecurity that identifies persons by verifying their behavioral and physical traits. In biometric-based authentication, each individual can be correctly recognized based on their intrinsic behavioral or physical features, such as face, fingerprint, iris, and ears. This work proposes a novel approach for human identification using 3D ear images. Usually, in conventional methods, the probe image is registered with each gallery image using computational heavy registration algorithms, making it practically infeasible due to the time-consuming recognition process. Therefore, this work proposes a recognition pipeline that reduces the one-to-one registration between probe and gallery. First, a …

Breeding And Feeding Habitat Selection By An Island Endemic Bird May Increase Its Vulnerability To Climate Change, Paul M. Radley, Eddie J. B. Van Etten, David Blake, Robert A. Davis

Research outputs 2014 to 2021

Characterizing patterns of habitat use is an important first step for effective conservation planning. Species restricted to low-lying islands are at greatest risk from climate change-related sea level rise, and requirements for breeding and foraging habitat may determine their risk from tidal inundation. The endangered Micronesian Scrubfowl (Megapodius laperouse senex) is a model species for understanding these impacts. This species faces the cumulative challenges of tourist visitation, invasive species, and rising sea levels, yet little is understood about its habitat use in the Rock Islands Southern Lagoon Conservation Area (RISL) of Palau. We studied the habitat requirements of this mound-nesting …

Heat Exposure Effect On Ghanaian Mining Workers: A Mediated-Moderation Approach, Victor Fannam Nunfam, Ebenezer Afrifa-Yamoah

Research outputs 2014 to 2021

The lack of empirical evidence on the effect of heat exposure on the health and safety, productivity, psychological behaviour and social well-being outcomes of small- and large-scale mining workers in Africa has derailed concrete policy directions and interventions. An explanatory cross-sectional survey involving 320 small- and large-scale mining workers was used to assess this research gap. A path analysis was used to model health and safety, productivity, psychological behaviour and social well-being as a function of heat exposure, mediated and moderated by adaptation strategies and barriers, while controlling for age, gender, level of education, years of working experience and workplace …