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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 …

Comparative Study Of Green And Synthetic Polymers For Enhanced Oil Recovery, Nasiru Salahu Muhammed, Md. Bashirul Haq, Dhafer Al-Shehri, Mohammad Mizanur Rahaman, Alireza Keshavarz, S. M. Zakir Hossain

Research outputs 2014 to 2021

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Several publications by authors in the field of petrochemical engineering have examined the use of chemically enhanced oil recovery (CEOR) technology, with a specific interest in polymer flooding. Most observations thus far in this field have been based on the application of certain chemicals and/or physical properties within this technique regarding the production of 50–60% trapped (residual) oil in a reservoir. However, there is limited information within the literature about the combined effects of this process on whole properties (physical and chemical). Accordingly, in this work, we present a clear distinction …

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. …

A Review On Green Caching Strategies For Next Generation Communication Networks, M. Ishtiaque A. Zahed, Iftekhar Ahmad, Daryoush Habibi, Quoc Viet Phung, Md Munjure Mowla, Muhammad Waqas

Research outputs 2014 to 2021

© 2020 IEEE. In recent years, the ever-increasing demand for networking resources and energy, fueled by the unprecedented upsurge in Internet traffic, has been a cause for concern for many service providers. Content caching, which serves user requests locally, is deemed to be an enabling technology in addressing the challenges offered by the phenomenal growth in Internet traffic. Conventionally, content caching is considered as a viable solution to alleviate the backhaul pressure. However, recently, many studies have reported energy cost reductions contributed by content caching in cache-equipped networks. The hypothesis is that caching shortens content delivery distance and eventually achieves …

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 …

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Table A2c. Bpsou 2020 Wet Weather Water Quality Results - General Chemistry, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Project-Based Learning: Lessons Learned With Teaching The Non-Communication Majors, Sarah Leblanc

Journal of Communication Pedagogy

Family communication, as an upper-level communication course, attracts communication majors and students studying in other disciplines. As such, instructors employ pedagogies that appeal to both majors and non-majors. This essay reflects on how I used project-based learning (PBL) in a family communication course filled with mostly non-majors. The essay highlights my rationale for choosing PBL, provides an explanation of the PBL activity, describes how PBL addresses two key problems I experienced in teaching the family communication course, and offers conclusions regarding lessons learned.

Association Rules Patterns Discovery From Mixed Data, Welendawa Acharige Charith A. Elson

College of Graduate Studies: Theses & Dissertations

Finding Association Rules has been a popular unsupervised learning technique for dis covering interesting patterns in commercial data for well over two decades. The method seeks groups of data attributes and their values where their probability density of these attributesattherespectivevaluesismaximized. Therearecurrentlywell-establishedmeth ods for tackling this problem for data with categorical (discrete) attributes. However, for the cases of data with continuous variables, the techniques are largely focusing on cate gorizing continuous variables into intervals of interest and then relying on the categorical data methods to address the problem. We address the problem of finding association rules patterns in mixed data by …

Multiple Imputation Using Influential Exponential Tilting In Case Of Non-Ignorable Missing Data, Kavita Gohil

College of Graduate Studies: Theses & Dissertations

Modern research strategies rely predominantly on three steps, data collection, data analysis, and inference. In research, if the data is not collected as designed, researchers may face challenges of having incomplete data, especially when it is non-ignorable. These situations affect the subsequent steps of evaluation and make them difficult to perform. Inference with incomplete data is a challenging task in data analysis and clinical trials when missing data related to the condition under the study. Moreover, results obtained from incomplete data are prone to biases. Parameter estimation with non-ignorable missing data is even more challenging to handle and extract useful …

A Numerical And Experimental Study On Effect Of Composition Of Ar-N2 Shielding Gas On The Weld Bead Profile And Its Prediction For Hot Wire Arc Additive Manufacturing, Niraj Pudasaini

College of Graduate Studies: Theses & Dissertations

Wire arc additive manufacturing is a process of making three-dimensional metal parts in a layer-by-layer approach using a feed wire and electric arc as a heat source. Wire arc additive manufacturing (WAAM) is becoming more popular due to its ability to create complex 3D parts, less build time, high deposition rate, and significant cost savings. Out of the many parameters used in WAAM, one of the important parameters is shielding gas which plays a significant role in material quality, properties, and defects. In this study, a controlled amount of Argon (Ar) and Nitrogen (N₂) shielding gases are used …

Applying Artificial Intelligence To Medical Data, Shaikh Shiam Rahman

College of Graduate Studies: Theses & Dissertations

Machine learning, data mining, and deep learning has become the methodology of choice for analyzing medical data and images. In this study, we implemented three different machine learning techniques to medical data and image analysis. Our first study was to implement different log base entropy for a decision tree algorithm. Our results suggested that using a higher log base for the dataset with mostly categorical attributes with three or more categories for each attribute can obtain a higher accuracy. For the second study, we analyzed mental health data tuning the parameters of the decision tree (splitting method, depth and entropy). …

A Novel Data-Driven Scheme For The Ship Wake Identification On The 2-D Dynamic Sea Surface, Yanming Zhang, Lijun Jiang

Electrical and Computer Engineering Faculty Research & Creative Works

Because the ship wake spreads into an area much larger than that of the ship hull in the sea surface, it has been widely used in the ship detection. However, due to the complex sea wave motion and the high sea state, the ship wake detection is still a challenging task. In this paper, we propose a novel data-driven method based on dynamic mode decomposition (DMD) to detect, reconstruct, and locate the Kelvin wake on the two-dimensional dynamic sea surface. Through the proposed method, the sea's dynamic characteristics including the oscillation frequency and decay/growth rate of ship wakes and the …

Numerical Methods For Electromagnetic Modeling Of Graphene: A Review, Kaikun Niu, Ping Li, Zhixiang Huang, Li (Lijun) Jun Jiang, Hakan Bagci

Electrical and Computer Engineering Faculty Research & Creative Works

Graphene's remarkable electrical, mechanical, thermal, and chemical properties have made this the frontier of many other 2-D materials a focus of significant research interest in the last decade. Many theoretical studies of the physical mechanisms behind these properties have been followed by those investing the graphene's practical use in various fields of engineering. Electromagnetics, optics, and photonics are among these fields, where potential benefits of graphene in improving the device/system performance have been studied. These studies are often carried out using simulation tools. To this end, many numerical methods have been developed to characterize electromagnetic field/wave interactions on graphene sheets …

Cybersecurity Behavioural Model For Students In The Tertiary Institutions, Boluwatife Fatokun Faith

Student Works (2020-2029)

Humans are majorly identified as the weakest link in cybersecurity. Tertiary institution student’s face lot of cybersecurity issues due to their increased Internet exposure, however cybersecurity behavioural studies focusing on tertiary students is limited. This study focused on investigating tertiary institutions students’ cybersecurity behaviour, via validated cybersecurity factors, Perceived Vulnerability (PV); Perceived Barriers (PBr); Perceived Severity (PS); Security Self-Efficacy (SSE); Response Efficacy (RE); Cues to Action (CA); Peer Behaviour (PBhv); Computer Skills (CS); Internet Skills (IS); Prior Experience with Computer Security Practices (PE); Perceived Benefits (PBnf); and a newly added factor, Familiarity with Cyber-Threats (FCT), to explore the factors relationship …

Feature Extraction And Description For Retinal Fundus Image Registration, Ramli Roziana

Student Works (2020-2029)

Retinal fundus image registration (RIR) is performed to align two or more fundus images. A general framework of a feature-based RIR technique comprises of preprocessing, feature extraction, feature descriptor, matching and estimating geometrical transformation. The RIR is mainly performed for super-resolution, image mosaicking and longitudinal study applications to assist diagnosis and monitoring retinal diseases. Registering image pair from these applications involve a combination of challenges such as overlapping area and rotation between images. The challenges of the overlapping area and rotation can be addressed at feature extraction and feature descriptor stages of the feature-based RIR technique, respectively. To address the …

An Intra-Severity Classification And Adaptation Technique To Improve Dysarthric Speech Recognition Accuracy, Al-Qatab Bassam Ali Qasem

Student Works (2020-2029)

Dysarthria is a motor speech impairment at the neurological and/or muscular levels that caused difficulty in pronouncing words clearly. Automatic speech recognition (ASR) system is increasingly applied as assistive technology to aid an individual with physical disability particularly the speech impaired community such as dysarthria speakers. However, the development of an effective ASR system is hindered by the data sparsity, either in the coverage of the language or the size of the existing speech databases. The speaker adaptation (SA) technique is one of the solutions to overcome the data sparsity issue of ASR for dysarthric speakers. Our proposed method introduces …

The Evolving Fuzzy Clustering Approach For Discriminating Neutron And Gamma-Ray Pulses, Shirkhorshidi Ali Seyed

Student Works (2020-2029)

Having a significant amount of data is not useful unless the data can be processed for extracting knowledge and information. One of the elementary steps in crunching data is to break it down into groups. When the data is small and collected in a controlled manner, and when the training data is appropriately labelled, the trivial approach is to use supervised learning to perform the grouping. Supervised methods need training data and information about groups beforehand; however, in the current reality, with an avalanche of data, this information is not available. Nevertheless, the need for grouping data remains. Clustering, as …

Makna Numeral Hindu Arab Murid Tahun Empat, Hashmi Syaheela Atikah

Student Works (2020-2029)

Kajian ini bertujuan untuk meneroka makna numeral Hindu Arab murid Tahun Empat. Dalam kajian ini, reka bentuk kajian yang digunakan adalah kajian kes yang melibatkan empat orang murid Tahun Empat sekolah rendah di negeri Selangor yang dipilih melalui kaedah persampelan bertujuan. Data dikumpul melalui tujuh sesi temu duga klinikal dengan menggunakan pelbagai tugasan berkonteks dan juga tanpa konteks diberikan kepada peserta kajian. Dalam kajian ini, empat tugasan pelbagai format berkonteks dan juga tanpa konteks telah diberikan kepada peserta kajian semasa temu duga. Transkripsi temu duga telah di analisis secara dalam kes dan merentas kes. Keputusan analisis merentas kes menunjukkan yang …

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, …

Do We Need Stochastic Volatility And Generalised Autoregressive Conditional Heteroscedasticity? Comparing Squared End-Of-Day Returns On Ftse, David E. Allen, Michael Mcaleer

Research outputs 2014 to 2021

The paper examines the relative performance of Stochastic Volatility (SV) and Generalised Autoregressive Conditional Heteroscedasticity (GARCH) (1,1) models fitted to ten years of daily data for FTSE. As a benchmark, we used the realized volatility (RV) of FTSE sampled at 5 min intervals taken from the Oxford Man Realised Library. Both models demonstrated comparable performance and were correlated to a similar extent with RV estimates when measured by ordinary least squares (OLS). However, a crude variant of Corsi’s (2009) Heterogeneous Autoregressive (HAR) model, applied to squared demeaned daily returns on FTSE, appeared to predict the daily RV of FTSE better …

Global Database Of Ratios Of Particulate Organic Carbon To Thorium-234 In The Ocean: Improving Estimates Of The Biological Carbon Pump, Viena Puigcorbe, Pere Masque, Frederic A.C. Le Moigne

Research outputs 2014 to 2021

The ocean's biological carbon pump (BCP) plays a major role in the global carbon cycle. A fraction of the photosynthetically fixed organic carbon produced in surface waters is exported below the sunlit layer as settling particles (e.g., marine snow). Since the seminal works on the BCP, global estimates of the global strength of the BCP have improved but large uncertainties remain (from 5 to 20 Gt C yr−1 exported below the euphotic zone or mixed-layer depth). The 234Th technique is widely used to measure the downward export of particulate organic carbon (POC). This technique has the advantage of allowing a …

Assessment Of Post-Wildfire Erosion Risk And Effects On Water Quality In South-Western Australia, David Blake, Petter Nyman, Helen Nice, Frances M.L. D'Souza, Christopher R.J. Kavazos, Pierre Horwitz

Research outputs 2014 to 2021

Investigations of wildfire impact on water resources have escalated globally over the last decade owing to an awareness of climate-related vulnerabilities. Within Australia, research into post-wildfire erosion has focused on water supply catchments in the south-eastern region. Here, we examine post-wildfire erosion risk and its potential for water quality impacts in a catchment in south-western Australia. The catchment of the Harvey River, which drains from forested escarpments onto an agricultural coastal plain and into valuable coastal wetlands, was burnt by wildfire in 2016. The aims of this study were to determine erosion risk across contrasting landforms and variable fire severity, …

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 …

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 …

Missing Data Imputation Of High-Resolution Temporal Climate Time Series Data, Eben Afrifa-Yamoah, Ute A. Mueller, S. M. Taylor, A. J. Fisher

Research outputs 2014 to 2021

© 2020 The Authors. Meteorological Applications published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society. Analysis of high-resolution data offers greater opportunity to understand the nature of data variability, behaviours, trends and to detect small changes. Climate studies often require complete time series data which, in the presence of missing data, means imputation must be undertaken. Research on the imputation of high-resolution temporal climate time series data is still at an early phase. In this study, multiple approaches to the imputation of missing values were evaluated, including a structural time series model with Kalman smoothing, …

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 …

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 …

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