Physical Sciences and Mathematics Commons^™

Seasonal Weather And Climate Prediction Over Area Burned In Grasslands Of Northeast China, Ali Hassan Shabbir, Jiquan Zhang, John W. Groninger, Eddie J. B. Van Etten, Samuel Asumadu Sarkodie, James A. Lutz, Carlos Valencia

Research outputs 2014 to 2021

© 2020, The Author(s). Grassland fire dynamics are subject to myriad climatic, biological, and anthropogenic drivers, thresholds, and feedbacks and therefore do not conform to assumptions of statistical stationarity. The presence of non-stationarity in time series data leads to ambiguous results that can misinform regional-level fire management strategies. This study employs non-stationarity in time series data among multiple variables and multiple intensities using dynamic simulations of autoregressive distributed lag models to elucidate key drivers of climate and ecological change on burned grasslands in Xilingol, China. We used unit root methods to select appropriate estimation methods for further analysis. Using the …

Interpreting Health Events In Big Data Using Qualitative Traditions, Roschelle L. Fritz, Gordana Dermody

Research outputs 2014 to 2021

© The Author(s) 2020. The training of artificial intelligence requires integrating real-world context and mathematical computations. To achieve efficacious smart health artificial intelligence, contextual clinical knowledge serving as ground truth is required. Qualitative methods are well-suited to lend consistent and valid ground truth. In this methods article, we illustrate the use of qualitative descriptive methods for providing ground truth when training an intelligent agent to detect Restless Leg Syndrome. We show how one interdisciplinary, inter-methodological research team used both sensor-based data and the participant’s description of their experience with an episode of Restless Leg Syndrome for training the intelligent agent. …

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 …

Distribution And Evolution Of Fukushima Dai-Ichi Derived 137cs, 90sr, And 129i In Surface Seawater Off The Coast Of Japan, Jennifer A. Kenyon, Ken O. Buesseler, Núria Casacuberta, Maxi Castrillejo, Shigeyoshi Otosaka, Pere Masqué, Jessica A. Drysdale, Steven M. Pike, Virginie Sanial

Research outputs 2014 to 2021

© 2020 American Chemical Society. The Fukushima Dai-ichi Nuclear Power Plants (FDNPPs) accident in 2011 led to an unprecedented release of radionuclides into the environment. Particularly important are 90Sr and 137Cs due to their known health detriments and long half-lives (T1/2 ≈ 30 y) relative to ecological systems. These radionuclides can be combined with the longer-lived 129I (T1/2 = 15.7 My) to trace hydrologic, atmospheric, oceanic, and geochemical processes. This study seeks to evaluate 137Cs, 90Sr, and 129I concentrations in seawater off the coast of Japan, reconcile the sources of contaminated waters, and assess the application of 137Cs/90Sr, 129I/137Cs, and …

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.

Self-Supervised Learning To Detect Key Frames In Videos, Xiang Yan, Syed Zulqarnain Gilani, Mingtao Feng, Liang Zhang, Hanlin Qin, Ajmal Mian

Research outputs 2014 to 2021

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Detecting key frames in videos is a common problem in many applications such as video classification, action recognition and video summarization. These tasks can be performed more efficiently using only a handful of key frames rather than the full video. Existing key frame detection approaches are mostly designed for supervised learning and require manual labelling of key frames in a large corpus of training data to train the models. Labelling requires human annotators from different backgrounds to annotate key frames in videos which is not only expensive and time consuming but …

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 …

Assessing The Efficacy Of Tnf-Alpha Inhibitors In Preventing Emergency And Emergent Colectomies, Ruben Rajan, Matthew W. Trinder, Johnny Lo, Mary Theophilus

Research outputs 2014 to 2021

Background and Aim: Severe ulcerative colitis (UC) is potentially life threatening and is associated with significant morbidity. TNF-∝ inhibitors (Infliximab) were introduced in Australia for the management of medically resistant, acute, severe flares of UC in 2008. The aim of this study is to assess the efficacy of Infliximab in preventing emergent and emergency colectomies for patients with moderate to severe UC by comparing colectomy rates before and after its introduction at our institution. Methods: This was a retrospective cohort study of all patients who were admitted to the Royal Perth Hospital with a flare of UC between 2002 and …

Rdtids: Rules And Decision Tree-Based Intrusion Detection System For Internet-Of-Things Networks, Mohammad Amine Ferrag, Leandros Maglaras, Ahmed Ahmim, Makhlouf Derdour, Helge Janicke

Research outputs 2014 to 2021

This paper proposes a novel intrusion detection system (IDS), named RDTIDS, for Internet-of-Things (IoT) networks. The RDTIDS combines different classifier approaches which are based on decision tree and rules-based concepts, namely, REP Tree, JRip algorithm and Forest PA. Specifically, the first and second method take as inputs features of the data set, and classify the network traffic as Attack/Benign. The third classifier uses features of the initial data set in addition to the outputs of the first and the second classifier as inputs. The experimental results obtained by analyzing the proposed IDS using the CICIDS2017 dataset and BoT-IoT dataset, attest …

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) …

Macroinvertebrates And Microbes (Archaea, Bacteria) Offer Complementary Insights Into Mine-Pit Lake Ecology, Melanie L. Blanchette, Richard Allcock, Jahir Gonzalez, Nina Kresoje, Mark Lund

Research outputs 2014 to 2021

The broad objective of this research was to determine the environmental drivers of macroinvertebrate and microbial assemblages in acidic pit lakes. This is important because pit lake ecosystem development is influenced by prevailing environmental characteristics. Three lakes (Stockton, Kepwari, WO5H) within a larger pit-lake district in Collie, Western Australia were surveyed for spatial variability of benthic macroinvertebrate and microbe (Archaea, Bacteria) assemblage composition as well as potential environmental drivers (riparian condition, aquatic habitat, sediments, and aquatic chemistry) of assemblages. With the exception of sediment chemistry, biophysical variables were significantly different across lakes and reflected riparian condition and groundwater chemistry. Microbial …

Provenance-Aware Knowledge Representation: A Survey Of Data Models And Contextualized Knowledge Graphs, Leslie F. Sikos, Dean Philp

Research outputs 2014 to 2021

Expressing machine-interpretable statements in the form of subject-predicate-object triples is a well-established practice for capturing semantics of structured data. However, the standard used for representing these triples, RDF, inherently lacks the mechanism to attach provenance data, which would be crucial to make automatically generated and/or processed data authoritative. This paper is a critical review of data models, annotation frameworks, knowledge organization systems, serialization syntaxes, and algebras that enable provenance-aware RDF statements. The various approaches are assessed in terms of standard compliance, formal semantics, tuple type, vocabulary term usage, blank nodes, provenance granularity, and scalability. This can be used to advance …

A Vision-Based Machine Learning Method For Barrier Access Control Using Vehicle License Plate Authentication, Kh Tohidul Islam, Ram Gopal Raj, Syed Mohammed Shamsul Islam, Sudanthi Wijewickrema, Md Sazzad Hossain, Tayla Razmovski, Stephen O’Leary

Research outputs 2014 to 2021

Automatic vehicle license plate recognition is an essential part of intelligent vehicle access control and monitoring systems. With the increasing number of vehicles, it is important that an effective real-time system for automated license plate recognition is developed. Computer vision techniques are typically used for this task. However, it remains a challenging problem, as both high accuracy and low processing time are required in such a system. Here, we propose a method for license plate recognition that seeks to find a balance between these two requirements. The proposed method consists of two stages: detection and recognition. In the detection stage, …

Navigating Coasts Of Concrete: Pervasive Use Of Artificial Habitats By Shorebirds In The Asia-Pacific, Micha V. Jackson, Chi-Yeung Choi, Tatsuya Amano, Sora M. Estrella, Weipan Lei, Nial Moores, Taej Mundkur, Danny I. Rogers, Richard A. Fuller

Research outputs 2014 to 2021

Loss and degradation of wetlands has occurred worldwide, impacting ecosystems and contributing to the decline of waterbirds, including shorebirds that occur along the heavily developed coasts of the East Asian-Australasian Flyway (EAAF). Artificial (i.e. human-made) wetlands are pervasive in the EAAF and known to be used by shorebirds, but this phenomenon has not been systematically reviewed. We collated data and expert knowledge to understand the extent and intensity of shorebird use of coastal artificial habitats along the EAAF. We found records of 83 species, including all regularly occurring coastal migratory shorebirds, across 176 artificial sites with eight different land uses. …

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 …

Riverine Biota As Environmental Indicators Of Artisanal Small-Scale And Large-Scale Gold Mining Impacts On Riverine Ecosystems In Brong Ahafo Region, Ghana, K. F. Macdonald, M. Lund, E. Van Etten

Research outputs 2014 to 2021

A study on two adjacent small ephemeral river systems in the upper Tano River Basin in Brong Ahafo, Ghana; one impacted by ASGM and the other by a modern large gold mining (LSM), showed that impacts of mining on river sediment and water quality and quantity were reflected in the macroinvertebrate and microbial communities. This study investigated the impacts of ASGM on the ecology of the Surow River and that of a large-scale mining (LSM, the Ahafo mine) on the Subri River between February 2013 and April 2014 Macroinvertebrate communities responded to the shift in river water and sediment qualities, …

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, …

Towards Geostatistical Learning For The Geosciences: A Case Study In Improving The Spatial Awareness Of Spectral Clustering, Hassan Talebi, L. J. M. Peeters, Ute Mueller, R. Tolosana-Delgado, K. G. Van Den Boogaart

Research outputs 2014 to 2021

The particularities of geosystems and geoscience data must be understood before any development or implementation of statistical learning algorithms. Without such knowledge, the predictions and inferences may not be accurate and physically consistent. Accuracy, transparency and interpretability, credibility, and physical realism are minimum criteria for statistical learning algorithms when applied to the geosciences. This study briefly reviews several characteristics of geoscience data and challenges for novel statistical learning algorithms. A novel spatial spectral clustering approach is introduced to illustrate how statistical learners can be adapted for modelling geoscience data. The spatial awareness and physical realism of the spectral clustering are …

Energy Efficiency Of A Flat-Plate Solar Collector Using Thermally Treated Graphene-Based Nanofluids: Experimental Study, Omer A. Alawi, Haslinda Mohamed Kamar, Hussein A. Mohammed, A. R. Mallah, Omar A. Hussein

Research outputs 2014 to 2021

© The Author(s) 2020. A covalent functionalization approach was utilized for the preparation of highly dispersed pentaethylene glycol-thermally treated graphene-water as the absorbing material inside a flat-plate solar collector. Four mass fractions of nanofluids were prepared (0.025, 0.05, 0.075, and 0.1 wt% pentaethylene glycol-thermally treated graphene-water). Graphene nanoparticles were characterized by energy dispersive X-ray analysis with a scanning electron microscope. Measurements of the thermophysical properties were subsequently carried out for the nanosuspensions. The raw investigation data were collected from an indoor flat-plate solar collector test setup. The experimental procedure included different sets of variables such as input temperatures of 303, …

Integrated Assessment—How Does It Help Unpack Water Access By Marginalized Farmers?, Serena H. Hamilton, Wendy S. Merritt, Mahanambrota Das, M. Wakilur Rahman, Sumana S. Bhuiya, Lucy Carter, Michaela Cosijn, Christian H. Roth, Sambhu Singha, Geoffrey J. Syme

Research outputs 2014 to 2021

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Water is critical to the lives and livelihoods of rural communities in developing countries; however, access to water can be inequitable within communities. This paper uses a generalized integrated assessment approach to explore the determinants of water access by marginalized farmers in two villages in coastal Bangladesh, before and after the setup of local water institutions. The study was part of a broader project aimed at promoting socially inclusive agricultural intensification. An integrative framework was developed in this study to capture and link the diverse range of factors that influence the …

Ontology‐Driven Perspective Of Cfraas, Victor R. Kebande, Nickson M. Karie, Richard A. Ikuesan, Hein S. Venter

Research outputs 2014 to 2021

A Cloud Forensic Readiness as a Service (CFRaaS) model allows an environment to preemptively accumulate relevant potential digital evidence (PDE) which may be needed during a post‐event response process. The benefit of applying a CFRaaS model in a cloud environment, is that, it is designed to prevent the modification/tampering of the cloud architectures or the infrastructure during the reactive process, which if it could, may end up having far‐reaching implications. The authors of this article present the reactive process as a very costly exercise when the infrastructure must be reprogrammed every time the process is conducted. This may hamper successful …

A Review Of Nyoongar Responses To Severe Climate Change And The Threat Of Epidemic Disease—Lessons From Their Past, Francesca Robertson, Jason Barrow

Research outputs 2014 to 2021

Nyoongar people have lived in the South West of Western Australia for at least 50,000 years. During that time, they experienced significant climate change, including wide variations in temperature and rainfall, and hundreds of metres’ difference in sea levels. Nyoongar people have a long memory, and climate change is described in their stories and in the knowledge they hold about how life was lived in earlier times. There are artifacts and places that have been manipulated to be productive despite severe drought. COVID-19 disrupted the writing of this article, and the authors felt it appropriate to include Nyoongar responses to …

Biplots For Compositional Data Derived From Generalized Joint Diagonalization Methods, Ute Mueller, R. Tolosana Delgado, E. C. Grunsky, J. M. Mckinley

Research outputs 2014 to 2021

Biplots constructed from principal components of a compositional data set are an established means to explore its features. Principal Component Analysis (PCA) is also used to transform a set of spatial variables into spatially decorrelated factors. However, because no spatial structures are accounted for in the transformation the application of PCA is limited. In geostatistics and blind source separation a variety of different matrix diagonalization methods have been developed with the aim to provide spatially or temporally decorrelated factors. Just as PCA, many of these transformations are linear and so lend themselves to the construction of biplots. In this contribution …

Anti-Uterine Fibroid Effect Of Standardized Labisia Pumila Var. Alata Extracts In Vitro And In Human Uterine Fibroid Cancer Xenograft Model, Norfahana Zakaria, Khamsah S. Mohd, Mohammed A. A. Saeed, Loiy E. A. Hassan, Armaghan Shafaei Darestani, Fouad S. R. Al-Suede, Abdul H. Memon, Zhari Ismail

Research outputs 2014 to 2021

Background: Uterine fibroids are a common type of solid tumor presenting in women of reproductive age. There are very few alternative treatment available from conventional treatment involving surgeries. Labisia pumila var. alata or locally known as ‘Kacip Fatimah’ was widely used as traditional medicine in Malaysia. This plant has been used to maintain a healthy female reproductive system. The present study aimed to evaluate anti fibroid potential of L. pumila extracts through in vitro apoptosis activity against uterine leiomyoma cells (SK-UT-1) and in uterine leiomyoma xenograft model. Evaluation of bioactive markers content were also carried out. Methods: Apoptotic induction of …

Morphological And Heartwood Variation Of Santalum Macgregorii In Papua New Guinea, T. Page, G. K. Jeffrey, P. Macdonell, D. Hettiarachchi, Mary C. Boyce, A. Lata, L. Oa, G. Rome

Research outputs 2014 to 2021

© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Santalum macgregorii (sandalwood), which is endemic to the southern part of Papua New Guinea (PNG), has been heavily exploited for its fragrant heartwood and is classified as threatened across its natural range. Domestication and smallholder agroforestry offer the means to preserve remaining diversity. This study was undertaken to understand the extent of remaining natural variation to support the species’s conservation and domestication. We evaluated morphological, heartwood and essential oil characters in 126 S. macgregorii trees in five populations (districts) in PNG’s Central, Gulf and Western …

A Novel Intrusion Detection System Against Spoofing Attacks In Connected Electric Vehicles, Dimitrios Kosmanos, Apostolos Pappas, Leandros Maglaras, Sotiris Moschoyinnais, Francisco J. Aparicio-Navarro, Antonios Argyriou, Helge Janicke

Research outputs 2014 to 2021

The Electric Vehicles (EVs) market has seen rapid growth recently despite the anxiety about driving range. Recent proposals have explored charging EVs on the move, using dynamic wireless charging that enables power exchange between the vehicle and the grid while the vehicle is moving. Specifically, part of the literature focuses on the intelligent routing of EVs in need of charging. Inter-Vehicle communications (IVC) play an integral role in intelligent routing of EVs around a static charging station or dynamic charging on the road network. However, IVC is vulnerable to a variety of cyber attacks such as spoofing. In this paper, …

Improving Firefighter Tenability During Entrapment And Burnover: An Analysis Of Vehicle Protection Systems, Greg Penney, Daryoush Habibi, Marcus Cattani

Research outputs 2014 to 2021

When attempting to suppress severe wildfire the possibility for firefighting crews to be overrun by wildfire, known as entrapment and burnover, remains a catastrophic and all too common occurrence. While improvements have been made to vehicle protection systems to increase the safety of firefighters caught in burnover, the potential effectiveness of these systems remains limited. This study involved systematic analysis of 62 historical entrapment and burnover reports from the USA, Australian and New Zealand from 1978 to 2020 (Phase 1), and 135 simulated wildfires encompassing the 99th percentile of Australian fire weather conditions, fuel structures and terrain (Phase 2). Analysis …

Multi-Omics Strategies For Decoding Smoke-Assisted Germination Pathways And Seed Vigour, Utpal Bose, Angéla Juhász, James A. Broadbent, Setsuko Komatsu, Michelle L. Colgrave

Research outputs 2014 to 2021

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The success of seed germination and the successful establishment of seedlings across diverse environmental conditions depends on seed vigour, which is of both economic and ecologic importance. The smoke-derived exogenous compound karrikins (KARs) and the endogenous plant hormone strigolactone (SL) are two classes of butanolide-containing molecules that follow highly similar signalling pathways to control diverse biological activities in plants. Unravelling the precise mode-of-action of these two classes of molecules in model species has been a key research objective. However, the specific and dynamic expression of biomolecules upon stimulation by these signalling …

Architectural Heritage Images Classification Using Deep Learning With Cnn, Mohammed Hamzah Abed, Muntasir Al-Asfoor, Zahir M. Hussain

Research outputs 2014 to 2021

© 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). Digital documentation of cultural heritage images has emerged as an important topic in data analysis. Increasing the size and number of images to be processed making the task of categorizing them a challenging task and may take an inordinate amount of time. This research paper proposes a solution to the mentioned challenges by classifying the subject of the image of the study using Convolutional Neural Network. Classification of available images leads to improve the management of the images dataset and …