Physical Sciences and Mathematics Commons^™

Construct Validity And Invariance Assessment Of The Social Impacts Of Occupational Heat Stress Scale (Siohss) Among Ghanaian Mining Workers, Victor F. Nunfam, Ebenezer Afrifa-Yamoah, Kwadwo Adusei-Asante, Eddie J. Van Etten, Kwasi Frimpong, Isaac Adjei-Mensah, Jacques Oosthuizen

Research outputs 2014 to 2021

Heat exposure studies over the last decade have shown little attention in assessing and reporting the psychometric properties of the various scales used to measure impacts of occupational heat stress on workers. A descriptive cross-sectional survey including 320 small- and large-scale mining workers was employed to assess the construct validity of the social impacts of occupational heat stress scale (SIOHSS) in the Western Region of Ghana in 2017. A confirmatory factor analysis (CFA) and invariance analysis were carried out using AMOS version 25 and statistical product and service solutions (SPSS) version 26 to examine the model fit and establish consistency …

Sub-Micron Moulding Topological Mass Transport Regimes In Angled Vortex Fluidic Flow, Thaar M. D. Alharbi, Matt Jellicoe, Xuan Luo, Kasturi Vimalanathan, Ibrahim K. Alsulami, Bediea S. Al Harbi, Aghil Igder, Fayed A. J. Alrashaidi, Xianjue Chen, Keith A. Stubbs, Justin M. Chalker, Wei Zhang, Ramiz A. Boulos, Darryl B. Jones, Jamie S. Quinton, Colin L. Raston

Research outputs 2014 to 2021

Shear stress in dynamic thin films, as in vortex fluidics, can be harnessed for generating non-equilibrium conditions, but the nature of the fluid flow is not understood. A rapidly rotating inclined tube in the vortex fluidic device (VFD) imparts shear stress (mechanical energy) into a thin film of liquid, depending on the physical characteristics of the liquid and rotational speed,ω, tilt angle,θ, and diameter of the tube. Through understanding that the fluid exhibits resonance behaviours from the confining boundaries of the glass surface and the meniscus that determines the liquid film thickness, we have established specific topological mass transport regimes. …

Proteome And Nutritional Shifts Observed In Hordein Double-Mutant Barley Lines, Utpal Bose, Angéla Juhász, Ronald Yu, Mahya Bahmani, Keren Byrne, Malcolm Blundell, James A. Broadbent, Crispin A. Howitt, Michelle L. Colgrave

Research outputs 2014 to 2021

Lysine is the most limiting essential amino acid in cereals, and efforts have been made over the decades to improve the nutritional quality of these grains by limiting storage protein accumulation and increasing lysine content, while maintaining desired agronomic traits. The single lys3 mutation in barley has been shown to significantly increase lysine content but also reduces grain size. Herein, the regulatory effect of the lys3 mutation that controls storage protein accumulation as well as a plethora of critically important processes in cereal seeds was investigated in double mutant barley lines. This was enabled through the generation of three hordein …

Deep Learning For 3d Ear Detection: A Complete Pipeline From Data Generation To Segmentation, Md Mursalin, Syed Mohammed Shamsul Islam

Research outputs 2014 to 2021

The human ear has distinguishing features that can be used for identification. Automated ear detection from 3D profile face images plays a vital role in ear-based human recognition. This work proposes a complete pipeline including synthetic data generation and ground-truth data labeling for ear detection in 3D point clouds. The ear detection problem is formulated as a semantic part segmentation problem that detects the ear directly in 3D point clouds of profile face data. We introduce EarNet, a modified version of the PointNet++ architecture, and apply rotation augmentation to handle different pose variations in the real data. We demonstrate that …

The Open Maritime Traffic Analysis Dataset, Martin Masek, Chiou Peng Lam, Travis Rybicki, Jacob Snell, Daniel Wheat, Luke Kelly, Damion Glassborow, Cheryl Smith-Gander

Research outputs 2014 to 2021

Ships traverse the world’s oceans for a diverse range of reasons, including the bulk transportation of goods and resources, carriage of people, exploration and fishing. The size of the oceans and the fact that they connect a multitude of different countries provide challenges in ensuring the safety of vessels at sea and the prevention of illegal activities. To assist with the tracking of ships at sea, the International Maritime Organisation stipulates the use of the Automatic Identification System (AIS) on board ships. The AIS system periodically broadcasts details of a ship’s position, speed and heading, along with other parameters corresponding …

Led Illumination Spectrum Manipulation For Increasing The Yield Of Sweet Basil (Ocimum Basilicum L.), Md Momtazur Rahman, Mikhail Vasiliev, Kamal Alameh

Research outputs 2014 to 2021

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. Manipulation of the LED illumination spectrum can enhance plant growth rate and development in grow tents. We report on the identification of the illumination spectrum required to significantly enhance the growth rate of sweet basil (Ocimum basilicum L.) plants in grow tent environments by controlling the LED wavebands illuminating the plants. Since the optimal illumination spectrum depends on the plant type, this work focuses on identifying the illumination spectrum that achieves significant basil biomass improvement compared to improvements reported in prior studies. To be able to optimize the illumination spectrum, several …

Soil Carbon Stocks Vary Across Geomorphic Settings In Australian Temperate Tidal Marsh Ecosystems, Connor Gorham, Paul Lavery, Jeffrey J. Kelleway, Cristian Salinas, Oscar Serrano

Research outputs 2014 to 2021

Tidal marshes rank among the ecosystems with the highest capacity to sequester and store organic carbon (C_org) on earth. To inform conservation of coastal vegetated ecosystems for climate change mitigation, this study investigated the factors driving variability in carbon storage. We estimated soil C_org stocks in tidal marshes across temperate Western Australia and assessed differences among geomorphic settings (marine and fluvial deltas, and mid-estuary) and vegetation type (Sarcocornia quinqueflora and Juncus kraussii) linked to soil biogeochemistry. Soil C_org stocks within fluvial and mid-estuary settings were significantly higher (209 ± 14 and 211 ± 20 …

Green Underwater Wireless Communications Using Hybrid Optical-Acoustic Technologies, Kazi Y. Islam, Iftekhar Ahmad, Daryoush Habibi, M. Ishtiaque A. Zahed, Joarder Kamruzzaman

Research outputs 2014 to 2021

Underwater wireless communication is a rapidly growing field, especially with the recent emergence of technologies such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs). To support the high-bandwidth applications using these technologies, underwater optics has attracted significant attention, alongside its complementary technology – underwater acoustics. In this paper, we propose a hybrid opto-acoustic underwater wireless communication model that reduces network power consumption and supports high-data rate underwater applications by selecting appropriate communication links in response to varying traffic loads and dynamic weather conditions. Underwater optics offers high data rates and consumes less power. However, due to the severe …

Infrequent Pattern Detection For Reliable Network Traffic Analysis Using Robust Evolutionary Computation, A. N. M. Bazlur Rashid, Mohiuddin Ahmed, Al-Sakib K. Pathan

Research outputs 2014 to 2021

While anomaly detection is very important in many domains, such as in cybersecurity, there are many rare anomalies or infrequent patterns in cybersecurity datasets. Detection of infrequent patterns is computationally expensive. Cybersecurity datasets consist of many features, mostly irrelevant, resulting in lower classification performance by machine learning algorithms. Hence, a feature selection (FS) approach, i.e., selecting relevant features only, is an essential preprocessing step in cybersecurity data analysis. Despite many FS approaches proposed in the literature, cooperative co-evolution (CC)-based FS approaches can be more suitable for cybersecurity data preprocessing considering the Big Data scenario. Accordingly, in this paper, we have …

Breeding And Feeding Habitat Selection By An Island Endemic Bird May Increase Its Vulnerability To Climate Change [Dataset], Paul Radley, Eddie Van Etten, David Blake, Robert Davis

Research Datasets

Characterising 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 …

Gearing Up Impact Assessment As A Vehicle For Achieving The Un Sustainable Development Goals, Angus Morrison-Saunders, Luis E. Sánchez, Francois Retief, John Sinclair, Meinhard Doelle, Megan Jones, Jan-Albert Wessels, Jenny Pope

Research outputs 2014 to 2021

This article reflects on the potential for impact assessment (IA) to be a major vehicle for implementing the UN Sustainable Development Goals (SDGs). While it is acknowledged that the SDGs are intended to deliver broader outcomes than IA currently does, we nevertheless argue there is significant convergence between IA and the SDGs, which we explore utilising the key dimensions of sustainability assessment: comprehensiveness, strategicness and integratedness. We conclude that ‘geared up’ IA might be used as a major vehicle to facilitate achievement of the SDGs. However, IA must become more comprehensive and integrated, such that the full suite of SDGs …

Investigation Of Enhanced Double Weight Code In Point To Point Access Networks, Hesham A. Bakarman, Ali Z. Ghazi Zahid, M. H. Mezher, Al Aboud Wahed Al-Isawi, Feras N. Hasoon, Saad H. Al-Isawi, Hussein A. Rasool, Sahbudin Shaari, Maitham Al-Alyawy, S. T. Yousif, Jaber K. Taher, Ali H. Mezher, Hala Musawy, W. Y. Chong, R. Zakaria

Research outputs 2014 to 2021

© 2020 Published under licence by IOP Publishing Ltd. In this paper, an investigation and evaluation to enhanced double weight (EDW) code is performed, a new technique for code structuring and building using modified arithmetical model has been given for the code in place of employing previous technique based on Trial Inspections. Innovative design has been employed for the code into P2P networks using diverse weighted EDW code to be fitting into optical CDMA relevance applications. A new developed relation for EDW code is presented, the relation is based on studying and experimenting the effect of input transmission power with …

Denial Of Service Attack Detection Through Machine Learning For The Iot, Naeem Firdous Syed, Zubair Baig, Ahmed Ibrahim, Craig Valli

Research outputs 2014 to 2021

Sustained Internet of Things (IoT) deployment and functioning are heavily reliant on the use of effective data communication protocols. In the IoT landscape, the publish/subscribe-based Message Queuing Telemetry Transport (MQTT) protocol is popular. Cyber security threats against the MQTT protocol are anticipated to increase at par with its increasing use by IoT manufacturers. In particular, IoT is vulnerable to protocol-based Application layer Denial of Service (DoS) attacks, which have been known to cause widespread service disruption in legacy systems. In this paper, we propose an Application layer DoS attack detection framework for the MQTT protocol and test the scheme on …

Pollen Adaptation To Ant Pollination – A Case Study From The Proteaceae, Nicola Delnevo, Eddie Van Etten, Nicola Clemente, Luna Fogu, Evelina Pavarani, Margaret Byrne, William Stock

Research outputs 2014 to 2021

Background and aims:

Ant-plant associations are widely diverse and distributed throughout the world, leading to antagonistic and/or mutualistic interactions. Ant pollination is a rare mutualistic association and reports of ants as effective pollinators are limited to a few studies. Conospermum (Proteaceae) is an insect-pollinated genus well represented in the south-western Australia biodiversity hotspot, and here we aimed to evaluate the role of ants as pollinators of C. undulatum.

Methods:

Pollen germination after contact with several species of ants and bees was tested for C. undulatum and five co-flowering species for comparison. We then sampled the pollen load of floral …

Trade-Off Assessments Between Reading Cost And Accuracy Measures For Digital Camera Monitoring Of Recreational Boating Effort, Ebenezer Afrifa-Yamoah, Stephen M. Taylor, Ute Mueller

Research outputs 2014 to 2021

Digital camera monitoring is increasingly being used to monitor recreational fisheries. The manual interpretation of video imagery can be costly and time consuming. In an a posteriori analysis, we investigated trade-offs between the reading cost and accuracy measures of estimates of boat retrievals obtained at various sampling proportions for low, moderate and high traffic boat ramps in Western Australia. Simple random sampling, systematic sampling and stratified sampling designs with proportional and weighted allocation were evaluated to assess trade-offs in terms of bias, accuracy, precision, coverage rate and cost in estimating the annual total number of powerboat retrievals in 10,000 jackknife …

Sam-Sos: A Stochastic Software Architecture Modeling And Verification Approach For Complex System-Of-Systems, Ahmad Mohsin, Naeem Khalid Janjua, Syed M. S. Islam, Muhammad Ali Babar

Research outputs 2014 to 2021

A System-of-Systems (SoS) is a complex, dynamic system whose Constituent Systems (CSs) are not known precisely at design time, and the environment in which they operate is uncertain. SoS behavior is unpredictable due to underlying architectural characteristics such as autonomy and independence. Although the stochastic composition of CSs is vital to achieving SoS missions, their unknown behaviors and impact on system properties are unavoidable. Moreover, unknown conditions and volatility have significant effects on crucial Quality Attributes (QAs) such as performance, reliability and security. Hence, the structure and behavior of a SoS must be modeled and validated quantitatively to foresee any …

Cooperative Co-Evolution For Feature Selection In Big Data With Random Feature Grouping, A.N.M. Bazlur Rashid, Mohiuddin Ahmed, Leslie F. Sikos, Paul Haskell-Dowland

Research outputs 2014 to 2021

© 2020, The Author(s). A massive amount of data is generated with the evolution of modern technologies. This high-throughput data generation results in Big Data, which consist of many features (attributes). However, irrelevant features may degrade the classification performance of machine learning (ML) algorithms. Feature selection (FS) is a technique used to select a subset of relevant features that represent the dataset. Evolutionary algorithms (EAs) are widely used search strategies in this domain. A variant of EAs, called cooperative co-evolution (CC), which uses a divide-and-conquer approach, is a good choice for optimization problems. The existing solutions have poor performance because …

Correction To: Cooperative Co‑Evolution For Feature Selection In Big Data With Random Feature Grouping (Journal Of Big Data, (2020), 7, 1, (107), 10.1186/S40537-020-00381-Y), A. N.M.Bazlur Rashid, Mohiuddin Ahmed, Leslie F. Sikos, Paul Haskell‑Dowland

Research outputs 2014 to 2021

© 2020, The Author(s). Following publication of the original article [1], the author reported that the 2nd author affiliation was incorrect. It should only be “School of Science, Edith Cowan University, Joondalup, WA, Australia”. The affiliation is presented correctly in this correction article. The original article [1] has been corrected.

Development Of An Infrared Pollution Index To Identify Ground-Level Compositional, Particle Size, And Humidity Changes Using Himawari-8, M. Sowden, D. Blake, D. Cohen, A. Atanacio, Ute Mueller

Research outputs 2014 to 2021

Speciated air quality data informs health studies and quantitates impacts. However, monitoring is concentrated around populated regions whilst, large remote and rural regions remain unmonitored despite risks of dust-storms or wild-fires. Sub-hourly, infrared, geostationary data, such as the 10-min data from Himawari 8, could potentially be used to quantify regional air quality continually. Monitoring of Aerosol Optical Depth (AOD) is restricted to visible spectra (i.e. daytime only), while newer quantification methods using geostationary infrared (IR) data have focused on detecting the presence, or absence, of an event. Limited attention has been given to the determination of particle size and aerosol …

Seagrass Losses Since Mid‐20th Century Fuelled Co2 Emissions From Soil Carbon Stocks, Cristian Salinas, Carlos M. Duarte, Paul Lavery, Pere Masque´, Ariane Arias-Ortiz, Javier X. Leon, David Callaghan, Gary A. Kendrick, Oscar Serrano

Research outputs 2014 to 2021

Seagrass meadows store globally significant organic carbon (C_org) stocks which, if disturbed, can lead to CO₂ emissions, contributing to climate change. Eutrophication and thermal stress continue to be a major cause of seagrass decline worldwide, but the associated CO₂ emissions remain poorly understood. This study presents comprehensive estimates of seagrass soil C_org erosion following eutrophication‐driven seagrass loss in Cockburn Sound (23 km² between 1960s and 1990s) and identifies the main drivers. We estimate that shallow seagrass meadows ( < 5 m depth) had significantly higher C_org stocks in 50 cm thick soils (4.5 ± 0.7 kg C_org/m²) …

Which Dual-Band Infrared Indices Are Optimum For Identifying Aerosol Compositional Change Using Himawari-8 Data?, Miles Sowden, D Blake

Research outputs 2014 to 2021

Aerosol optical depth algorithms predominantly use the visible portion of the electromagnetic spectrum. However, quantifying sporadic dust events throughout the full 24-h period requires using continuous wavelengths such as infrared (IR). Identifying aerosols, using IR from geostationary data, has relied on subtraction indices rather than normalised differences. Limited attention has been given to determining which IR indices could be suitable for identifying aerosol compositional change. Suitable IR indices could potentially result in multi-spectral data from geostationary satellites, such as Himawari, being used to separate dust from other types of aerosols.

This study evaluated three index types: subtraction (brightness temperature difference …

A Novel Penalty-Based Wrapper Objective Function For Feature Selection In Big Data Using Cooperative Co-Evolution, A.N.M. Bazlur Rashid, Mohiuddin Ahmed, Leslie F. Sikos, Paul Haskell-Dowland

Research outputs 2014 to 2021

The rapid progress of modern technologies generates a massive amount of high-throughput data, called Big Data, which provides opportunities to find new insights using machine learning (ML) algorithms. Big Data consist of many features (also called attributes); however, not all these are necessary or relevant, and they may degrade the performance of ML algorithms. Feature selection (FS) is an essential preprocessing step to reduce the dimensionality of a dataset. Evolutionary algorithms (EAs) are widely used search algorithms for FS. Using classification accuracy as the objective function for FS, EAs, such as the cooperative co-evolutionary algorithm (CCEA), achieve higher accuracy, even …

Performances Of The Lbp Based Algorithm Over Cnn Models For Detecting Crops And Weeds With Similar Morphologies, Vi Nguyen Thanh Le, Selam Ahderom, Kamal Alameh

Research outputs 2014 to 2021

Weed invasions pose a threat to agricultural productivity. Weed recognition and detection play an important role in controlling weeds. The challenging problem of weed detection is how to discriminate between crops and weeds with a similar morphology under natural field conditions such as occlusion, varying lighting conditions, and different growth stages. In this paper, we evaluate a novel algorithm, filtered Local Binary Patterns with contour masks and coefficient k (k-FLBPCM), for discriminating between morphologically similar crops and weeds, which shows significant advantages, in both model size and accuracy, over state-of-the-art deep convolutional neural network (CNN) models such as VGG-16, VGG-19, …

Imputation Of Missing Data From Time-Lapse Cameras Used In Recreational Fishing Surveys, Ebenezer Afrifa-Yamoah, Stephen M. Taylor, Aiden Fisher, Ute Mueller

Research outputs 2014 to 2021

While remote camera surveys have the potential to improve the accuracy of recreational fishing estimates, missing data are common and require robust analytical techniques to impute. Time-lapse cameras are being used in Western Australia to monitor recreational boating activities, but outages have occurred. Generalized linear mixed effect models formulated in a fully conditional specification multiple imputation framework were used to reconstruct missing data, with climatic and some temporal classifications as covariates. Using a complete 12-month camera record of hourly counts of recreational powerboat retrievals, data were simulated based on ten observed camera outage patterns, with a missing proportion of between …

Use Of Landsat Imagery To Map Spread Of The Invasive Alien Species Acacia Nilotica In Baluran National Park, Indonesia, Sutomo Sutomo, Eddie Van Etten, Rajif Iryadi

Research outputs 2014 to 2021

© 2020 Seameo Biotrop. In the late 1960s, Acacia nilotica was introduced to Baluran National Park to establish fire breaks which would prevent the spread of fire from Baluran Savanna to the adjacent teak forest. However, A. nilotica has spread rapidly and has threatened the existence of Baluran Savanna as it has caused an ecosystem transition from an open savanna to a closed canopy of A. nilotica in some areas. This study is one of the few that examines A. nilotica invasion in Baluran National Park through remote sensing. Land cover dynamics were quantified using a supervised classification approach on …

Migrating From Partial Least Squares Discriminant Analysis To Artificial Neural Networks: A Comparison Of Functionally Equivalent Visualisation And Feature Contribution Tools Using Jupyter Notebooks, Kevin M. Mendez, David I. Broadhurst, Stacey N. Reinke

Research outputs 2014 to 2021

Introduction:

Metabolomics data is commonly modelled multivariately using partial least squares discriminant analysis (PLS-DA). Its success is primarily due to ease of interpretation, through projection to latent structures, and transparent assessment of feature importance using regression coefficients and Variable Importance in Projection scores. In recent years several non-linear machine learning (ML) methods have grown in popularity but with limited uptake essentially due to convoluted optimisation and interpretation. Artificial neural networks (ANNs) are a non-linear projection-based ML method that share a structural equivalence with PLS, and as such should be amenable to equivalent optimisation and interpretation methods.

Objectives:

We hypothesise that …

Optical And Chromaticity Properties Of Metal-Dielectric Composite-Based Multilayer Thin-Film Structures Prepared By Rf Magnetron Sputtering, Mohammad Nur-E-Alam, Mohammad M. Rahman, Mohammad Khairul Basher, Mikhail Vasiliev, Kamal Alameh

Research outputs 2014 to 2021

Coated glass products, and especially the low-emissivity coatings, have become a common building material used in modern architectural projects. More recently, these material systems became common in specialized glazing systems featuring solar energy harvesting. Apart from achieving the stability of optical parameters in multilayer coatings, it is also important to have improved control over the design of visual color properties of the coated glass. We prepare metal-dielectric composite (MDC)-based multilayer thin-film structures using the radio frequency (RF)-magnetron sputtering deposition and report on their optical and chromaticity properties in comparison with these obtained using pure metal-based Dielectric/Metal/Dielectric (DMD) trilayer structures of …

A Novel Method For Detecting Morphologically Similar Crops And Weeds Based On The Combination Of Contour Masks And Filtered Local Binary Pattern Operators, Vi Nguyen Thanh Le, Selam Ahderom, Beniamin Apopei, Kamal Alameh

Research outputs 2014 to 2021

Background: Weeds are a major cause of low agricultural productivity. Some weeds have morphological features similar to crops, making them difficult to discriminate. Results: We propose a novel method using a combination of filtered features extracted by combined Local Binary Pattern operators and features extracted by plant-leaf contour masks to improve the discrimination rate between broadleaf plants. Opening and closing morphological operators were applied to filter noise in plant images. The images at 4 stages of growth were collected using a testbed system. Mask-based local binary pattern features were combined with filtered features and a coefficient k. The classification of …

Quantifiable Isovist And Graph-Based Measures For Automatic Evaluation Of Different Area Types In Virtual Terrain Generation, Andrew Pech, Chiou Peng Lam, Martin Masek

Research outputs 2014 to 2021

© 2013 IEEE. This article describes a set of proposed measures for characterizing areas within a virtual terrain in terms of their attributes and their relationships with other areas for incorporating game designers' intent in gameplay requirement-based terrain generation. Examples of such gameplay elements include vantage point, strongholds, chokepoints and hidden areas. Our measures are constructed on characteristics of an isovist, that is, the volume of visible space at a local area and the connectivity of areas within the terrain. The calculation of these measures is detailed, in particular we introduce two new ways to accurately and efficiently calculate the …

The Importance Of Catchments To Mine-Pit Lakes: Implications For Closure, Mark Lund, Eddie Van Etten, Jonas Polifka, Marylin Quintero Vasquez, Ravish Ramessur, Dechen Yangzom, Melanie L. Blanchette

Research outputs 2014 to 2021

Despite the large body of riparian literature for rivers and lakes, there are few studies on the catchments of mine pit lakes. Therefore, the broad objective of this research was to determine if catchment characteristics were related to pit lake nutrient concentrations. We hypothesised that: (1) catchment characteristics would vary among pit lakes, (2) pit lake catchments would differ from co-occurring naturally-forested catchments, and (3) connecting a pit lake (Kepwari) to a naturally-forested catchment via a river flow-through would increase C accumulation in the lake. The research was conducted in pit lakes of the Collie lake district in Western Australia …