Physical Sciences and Mathematics Commons^™

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 …

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

Research Datasets

The database compiles published data (in Salinas et al. 2020) on biogeochemical characteristics (density, organic carbon, stable carbon isotopes, sediment grain size) of cores from Posidonia australis and sinuosa soil in Cockburn Sound, Western Australia.

Enquiries about the dataset may be sent to Cristian Salinas: c.salinaszapata@ecu.edu.au