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2021

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Determinants Of Non-Revenue Water, Haslinda Ab Malek, Mohamad Hafizi Zakaria, Muhammad Luqman Zulkifli, Nur Farahin Roslan Jan 2021

Determinants Of Non-Revenue Water, Haslinda Ab Malek, Mohamad Hafizi Zakaria, Muhammad Luqman Zulkifli, Nur Farahin Roslan

Malaysian Journal of Computing (MJoC)

Water is one of the most essential needs in human daily life. Water losses or Non-Revenue Water (NRW) refers to the treated water that has been produced from water plant which did not reach to the customer. This waste of water has caused the company to suffer losses and hence, burdens the people with increasing water tariff. Moreover, it becomes one of the challenges for commercial water system management because the water company must fulfil the demand from the society which keep increasing day by day. In addition, the demand for water is increasing, as the population is growing. Despite …

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Automated Rhythmic Humanisation As A Structural Marker In Music, Linda Adams, Liam O’Sullivan, Charlie Cullen Jan 2021

Automated Rhythmic Humanisation As A Structural Marker In Music, Linda Adams, Liam O’Sullivan, Charlie Cullen

Research Outputs: 2025-Present

No abstract provided.

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Evaluation Of Postoperative Refractive Error Correction After Cataract Surgery, Ellen Konadu Antwi-Adjei, Emmanuel Owusu, Emmanuel Kobia-Acquah, Emmanuella Esi Dadzie, Emmanuel Anarfi, Seth Wanye Jan 2021

Evaluation Of Postoperative Refractive Error Correction After Cataract Surgery, Ellen Konadu Antwi-Adjei, Emmanuel Owusu, Emmanuel Kobia-Acquah, Emmanuella Esi Dadzie, Emmanuel Anarfi, Seth Wanye

Articles

Suboptimal cataract surgery outcomes remain a challenge in most developing countries. In Ghana, about 2 million people have been reported to be blind due to cataract with about 20% new cases being recorded yearly. The aim of this study was to evaluate postoperative correction of refractive errors after cataract surgery in a selected eye hospital in Ashanti Region, Ghana. This was a retrospective study where medical records of patients (aged 40–100) who reported to an eye hospital in Ghana from 2013–2018 were reviewed. Included in the study were patients aged ≥40 years and patients with complete records. Data on patient …

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Big Data: Potential As An Ocular Epidemiology And Public Health Tool, Michael Moore Jan 2021

Big Data: Potential As An Ocular Epidemiology And Public Health Tool, Michael Moore

Doctoral

Refractive error is a significant cause of vision impairment both through the limited access to correction in some areas and the associated ocular diseases for which refractive errors are risk factors. Having timely, regular access to population level estimates of refractive error and vision impairment is necessary to adequately plan public health resources and resource appropriate interventions. A lack of access to current and regularly updated refractive error and vision impairment prevalence data has been identified as a significant limitation in predicting future population trends with many countries lacking any prevalence data or available data being outdated. This project addresses …

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The Design And Evaluation Of An Educational Software Development Process For First Year Computing Undergraduates, Catherine Higgins Jan 2021

The Design And Evaluation Of An Educational Software Development Process For First Year Computing Undergraduates, Catherine Higgins

Doctoral

First year, undergraduate computing students experience a series of well-known challenges when learning how to design and develop software solutions. These challenges, which include a failure to engage effectively with planning solutions prior to implementation ultimately impact upon the students’ competency and their retention beyond the first year of their studies. In the software industry, software development processes systematically guide the development of software solutions through iterations of analysis, design, implementation and testing. Industry-standard processes are, however, unsuitable for novice programmers as they require prior programming knowledge. This study investigates how a researcher-designed educational software development process could be created …

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Just-In-Time Pastureland Trait Estimation For Silage Optimization, Under Limited Data Constraints, Patricia O'Byrne Jan 2021

Just-In-Time Pastureland Trait Estimation For Silage Optimization, Under Limited Data Constraints, Patricia O'Byrne

Doctoral

To ensure that pasture-based farming meets production and environmental targets for a growing population under increasing resource constraints, producers need to know pastureland traits. Current proximal pastureland trait prediction methods largely rely on vegetation indices to determine biomass and moisture content. The development of new techniques relies on the challenging task of collecting labelled pastureland data, leading to small datasets. Classical computer vision has already been applied to weed identification and recognition of fruit blemishes using morphological features, but machine learning algorithms can parameterise models without the provision of explicit features, and deep learning can extract even more abstract knowledge …

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Differential Etiology: Inferring Specific Causation In The Law From Group Data In Science, Joseph Sanders, David L. Faigman, Peter B. Imrey, Philip Dawid Jan 2021

Differential Etiology: Inferring Specific Causation In The Law From Group Data In Science, Joseph Sanders, David L. Faigman, Peter B. Imrey, Philip Dawid

Faculty Scholarship

In every toxic-tort case, the plaintiff must prove that the defendant exposed the plaintiff to something that caused an injury. The causal proof is in two parts: proof of general causation and proof of specific causation. General causation addresses the question of whether the exposure can cause injury in anyone. In the area of toxic torts, the evidence available to answer this question comes in the form of groupbasedstudies in the fields of toxicology, epidemiology, and genetics—studies that search for the effects of causes. Proof of general causation, however, is not enough. Because court cases focus on the individual, the …

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Ancestral Breed Grouping For Improving Animal Modeling In Admixed Populations And Its Use In Leptin (Genetic And Hormone) Association Studies With Performance Traits In Commercial Beef Cow Herd, Nayan Bhowmik Jan 2021

Ancestral Breed Grouping For Improving Animal Modeling In Admixed Populations And Its Use In Leptin (Genetic And Hormone) Association Studies With Performance Traits In Commercial Beef Cow Herd, Nayan Bhowmik

Animal Sciences

The first study investigated American Aberdeen (AD) cattle's genetic structure and its genetic relationships with five American taurine beef breeds and the genetic population structure of a related admixed cattle herd for improved statistical modeling. The last two studies investigated the role of leptin (LEP) c.73C>T polymorphism, leptin diplotype (LEPD), and leptin hormone (LEPH) concentration on reproductive, growth, size, feeding, and carcass ultrasound traits in beef cows and heifers. High levels of polymorphism were observed in admixed, Red Angus, and Gelbvieh (GV) populations. The lowest level of polymorphism was found in AD followed by undetermined parentage. Pairwise fixation index …

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Biochemical And Cellular Characterization Of Replication Factor A (Rfa) During Meiosis And The Dna Damage Response In Saccharomyces Cerevisiae, Angela Marie Adsero Jan 2021

Biochemical And Cellular Characterization Of Replication Factor A (Rfa) During Meiosis And The Dna Damage Response In Saccharomyces Cerevisiae, Angela Marie Adsero

Cellular and Molecular Biology

Replication Factor A (RFA) is an essential heterotrimeric single-stranded DNA (ssDNA) binding complex, comprised of Rfa1, Rfa2, and Rfa3 in Saccharomyces cerevisiae. RFA is required for DNA replication, repair, recombination, and cell cycle regulation. RFA acts as a sensor of ssDNA, a common intermediate of these processes, and coordinates these processes through recruitment of proteins. For example, during the DNA damage response (DDR), RFA-coated ssDNA is necessary for the recruitment and activation of the sensor kinase Mec1. Additional checkpoint proteins, also recruited by RFA, are necessary for the downstream recruitment and activation of the effector kinase Rad53 that ultimately leads …

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Building Envelope Containing Phase Change Materials For Energy-Efficient Buildings, Mingli Li Jan 2021

Building Envelope Containing Phase Change Materials For Energy-Efficient Buildings, Mingli Li

Civil and Environmental Engineering

Energy consumption in the building sector has increased dramatically over the past two decades. The incorporation of phase change materials (PCMs) into building envelopes is considered as effective thermal energy storage to improve building thermal performance and reduce space heating/cooling load. Despite significant efforts in PCMs technologies and their application to buildings, how to select proper PCMs for buildings and maximize the activation of their latent heat to effectively improve building energy efficiency still post great challenges. The lack of systematic and comprehensive studies in these gaps hinders their broad applications in the building sector. This study aims to develop …

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The United States And Brazil - Spatial And Logistical Interdependence For Soybean Shipments To China, Gwen Marie Scheresky Jan 2021

The United States And Brazil - Spatial And Logistical Interdependence For Soybean Shipments To China, Gwen Marie Scheresky

Agribusiness and Applied Economics

Analyzing the United States/Brazil competition for market share of Chinese soybean imports requires a detailed, stochastic approach. Using data from 2013 to 2019, this thesis attempts to model and explain the change in market share for each producing country as different variables fluctuate or enter the equation. Using five origins in each country, the PNW, USG, Santos, Paranaguá, and the northern arc of ports in Brazil, various transportation routes from origin to port, congestion and quality metrics, and ocean freight, a least-cost optimized Monte Carlo simulation is performed using time-series forecasting distributions for monthly variables. A distribution of outcomes for …

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Implementing High-Throughput Phenotyping At The Ndsu Dry Bean (Phaseolus Vulgaris L.) Breeding Program Using Unmanned Aerial Systems, Diego Jose Gris Jan 2021

Implementing High-Throughput Phenotyping At The Ndsu Dry Bean (Phaseolus Vulgaris L.) Breeding Program Using Unmanned Aerial Systems, Diego Jose Gris

Agribusiness and Biosystems Engineering

Plant breeding has led to considerable yield gains to several crops. However, it alone might not be able to keep up with the growing demand for food. In this study, data extracted from UAS-collected RGB and multispectral images were assessed on their ability to estimate four agronomic traits in three market classes of dry beans in a breeding program. The results showed that (i) seed yield, 100-seed weight, stem diameter and days to flowering can be estimated within the same market class with variable accuracy; (ii) aggregating data from several flights yielded better results than using a single flight; (iii) …

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An Investigation Of Student Understanding And Acceptance Of Evolution, Rachel Leigh Salter Harding Jan 2021

An Investigation Of Student Understanding And Acceptance Of Evolution, Rachel Leigh Salter Harding

Biological Sciences

Evolution is central to biology education and yet, it is often one of the most misunderstood and controversial topics that biology educators must teach. Research spanning the last four decades has shown that students continue to struggle, even with direct instruction, to understand the process of evolution by natural selection. In my first chapter, I found that students enrolled in non-majors geology course did not increase in their understanding of evolution, even after instruction. This followed similar findings from research occurring over 30 years in the past. Discipline-based education researchers have theorized that students’ persistent difficulties understanding evolution may stem …

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Recent Trends In Heat-Related Mortality In The United States: An Update Through 2018, Scott C. Sheridan, P. Grady Dixon, Adam J. Kalkstein, Michael J. Allen Jan 2021

Recent Trends In Heat-Related Mortality In The United States: An Update Through 2018, Scott C. Sheridan, P. Grady Dixon, Adam J. Kalkstein, Michael J. Allen

Political Science & Geography Faculty Publications

Much research has shown a general decrease in the negative health response to extreme heat events in recent decades. With a society that is growing older, and a climate that is warming, whether this trend can continue is an open question. Using eight additional years of mortality data, we extend our previous research to explore trends in heat-related mortality across the United States. For the period 1975–2018, we examined the mortality associated with extreme-heat-event days across the 107 largest metropolitan areas. Mortality response was assessed over a cumulative 10-day lag period following events that were defined using thresholds of the …

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A Global Ecological Classification Of Coastal Segment Units To Complement Marine Biodiversity Observation Network Assessments, Roger Sayre, Kevin Butler, Keith Van Graafeiland, Sean Breyer, Dawn Wright, Charlie Frye, Deniz Karagulle, Madeline Martin, Jill Cress, Tom Allen, Rebecca J. Allee, Rost Parsons, Bjorn Nyberg, Mark J. Costello, Peter Harris, Frank E. Muller-Karger Jan 2021

A Global Ecological Classification Of Coastal Segment Units To Complement Marine Biodiversity Observation Network Assessments, Roger Sayre, Kevin Butler, Keith Van Graafeiland, Sean Breyer, Dawn Wright, Charlie Frye, Deniz Karagulle, Madeline Martin, Jill Cress, Tom Allen, Rebecca J. Allee, Rost Parsons, Bjorn Nyberg, Mark J. Costello, Peter Harris, Frank E. Muller-Karger

Political Science & Geography Faculty Publications

A new data layer provides Coastal and Marine Ecological Classification Standard (CMECS) labels for global coastal segments at 1 km or shorter resolution. These characteristics are summarized for six US Marine Biodiversity Observation Network (MBON) sites and one MBON Pole to Pole of the Americas site in Argentina. The global coastlines CMECS classifications were produced from a partitioning of a 30 m Landsat-derived shoreline vector that was segmented into 4 million 1 km or shorter segments. Each segment was attributed with values from 10 variables that represent the ecological settings in which the coastline occurs, including properties of the adjacent …

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Anticipating And Adapting To The Future Impacts Of Climate Change On The Health, Security And Welfare Of Low Elevation Coastal Zone (Lecz) Communities In Southeastern Usa, Thomas Allen, Joshua Behr, Anamaria Bukvic, Ryan S.D. Calder, Kiki Caruson, Charles Connor, Christopher D'Elia, David Dismukes, Robin Ersing, Rima Franklin, Jesse Goldstein, Jonathon Goodall, Scott Hemmerling, Jennifer Irish, Steven Lazarus, Derek Loftis, Mark Luther, Leigh Mccallister, Karen Mcglathery, Molly Mitchell, William Moore, Charles Reid Nichols, Karinna Nunez, Matthew Reidenbach, Julie Shortridge, Robert Weisberg, Robert Weiss, Lynn Donelson Wright, Meng Xia, Kehui Xu, Donald Young, Gary Zarillo, Julie C. Zinnert Jan 2021

Anticipating And Adapting To The Future Impacts Of Climate Change On The Health, Security And Welfare Of Low Elevation Coastal Zone (Lecz) Communities In Southeastern Usa, Thomas Allen, Joshua Behr, Anamaria Bukvic, Ryan S.D. Calder, Kiki Caruson, Charles Connor, Christopher D'Elia, David Dismukes, Robin Ersing, Rima Franklin, Jesse Goldstein, Jonathon Goodall, Scott Hemmerling, Jennifer Irish, Steven Lazarus, Derek Loftis, Mark Luther, Leigh Mccallister, Karen Mcglathery, Molly Mitchell, William Moore, Charles Reid Nichols, Karinna Nunez, Matthew Reidenbach, Julie Shortridge, Robert Weisberg, Robert Weiss, Lynn Donelson Wright, Meng Xia, Kehui Xu, Donald Young, Gary Zarillo, Julie C. Zinnert

Political Science & Geography Faculty Publications

Low elevation coastal zones (LECZ) are extensive throughout the southeastern United States. LECZ communities are threatened by inundation from sea level rise, storm surge, wetland degradation, land subsidence, and hydrological flooding. Communication among scientists, stakeholders, policy makers and minority and poor residents must improve. We must predict processes spanning the ecological, physical, social, and health sciences. Communities need to address linkages of (1) human and socioeconomic vulnerabilities; (2) public health and safety; (3) economic concerns; (4) land loss; (5) wetland threats; and (6) coastal inundation. Essential capabilities must include a network to assemble and distribute data and model code to …

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Integrating The Balanced Scorecard (Bsc) And The Data Envelopment Analysis (Dea) Approaches For An Enhanced Police Performance Measurement System, Myungwoo Lee Jan 2021

Integrating The Balanced Scorecard (Bsc) And The Data Envelopment Analysis (Dea) Approaches For An Enhanced Police Performance Measurement System, Myungwoo Lee

Criminal Justice and Political Science

An effective performance measurement system is an integral part of modern police management. Police agencies have measured their performance against a very restricted set of crime-focused indicators, such as crime rates, arrests, response times, and clearance rates. Police performance should be measured across multiple dimensions to capture public values produced by modern police agencies. This study set out to present an enhanced performance measurement system for police agencies by integrating the Balanced Scorecard (BSC) and Data Envelopment Analysis (DEA) approaches. The BSC provides the theoretical foundation for building a comprehensive performance measurement framework, while the DEA provides the analytical tool …

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Conceptual Cost Estimation Models For Bridge Projects, Adikie Esinam Essegbey Jan 2021

Conceptual Cost Estimation Models For Bridge Projects, Adikie Esinam Essegbey

Construction Management and Engineering

Conceptual cost estimating is typically completed early in the project lifecycle when little design work has been completed. Because little information is known at this early stage, the estimate usually deviates substantially from the actual construction cost. Therefore, the objective of this research is to develop a conceptual cost estimate model for bridge infrastructure projects. In this study, a systematic literature review and meta-analysis of Mean Average Percentage Errors (MAPEs) of cost models was undertaken to identify the various cost estimation methods, the input variables that have adopted in the development of models and determine the impact of cost estimation …

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Conceptual Cost Estimation Of Highway Bid Items Using Geostatistical Interpolation, Bright Awuku Jan 2021

Conceptual Cost Estimation Of Highway Bid Items Using Geostatistical Interpolation, Bright Awuku

Construction Management and Engineering

Challenges associated with ensuring the accuracy and reliability of cost estimation of highway bid items, especially during the conceptual phase of a project, are of significant interest to state highway agencies. Even with the existing research undertaken on the subject, the problem of inaccurate estimation of highway bid items still exists. A systematic literature review was performed to determine research trends, identify, categorize the factors influencing highway unit prices, and assess the performance of conceptual cost prediction models. This research proposes a geographic information system (GIS)–based methodology that leverages vast historical bid data for unit-price estimation and the robust GIS …

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Mining Approximate Frequent Dense Modules From Multiple Gene Expression Datasets, San Ha Seo Jan 2021

Mining Approximate Frequent Dense Modules From Multiple Gene Expression Datasets, San Ha Seo

Computer Science

Large amount of gene expression data has been collected for various environmental and biological conditions. Extracting dense modules that are recurrent in multiple gene coexpression networks has been shown to be promising in functional gene annotation and biomarkers discovery. In this thesis, we propose a biclustering-based approach for mining approximate frequent dense modules. This approach reports a large number of modules with many duplicate modules. Thus, we build on this approach and propose two extended approaches for mining dense modules, which mine set of representative patterns using post-processing and on-line pattern summarization methods. The extended approaches report smaller number of …

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Addressing Challenges In Data Privacy And Security: Various Approaches To Secure Data, Sayantica Pattanayak Jan 2021

Addressing Challenges In Data Privacy And Security: Various Approaches To Secure Data, Sayantica Pattanayak

Computer Science

Emerging neural networks based machine learning techniques such as deep learning and its variants have shown tremendous potential in many application domains. However, the neural network models raise serious privacy concerns due to the risk of leakage of highly privacy-sensitive data. In this dissertation, we propose various techniques to hide the sensitive information and also evaluate the performance and efficacy of our proposed models. In our first research work we propose a model, which can both encrypt and decrypt a ciphertext. Our model is based on symmetric key encryption and back propagation neural network. Our model takes the decimal values …

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Decision-Making For Self-Replicating 3d Printed Robot Systems, Andrew Burkhard Jones Jan 2021

Decision-Making For Self-Replicating 3d Printed Robot Systems, Andrew Burkhard Jones

Computer Science

This work addresses decision-making for robot systems that can self-replicate. With the advent of 3D printing technology, the development of self-replicating robot systems is more feasible to implement than it was previously. This opens the door to various opportunities in this area of robotics. A major benefit of having robots that are able to make more robots is that the survivability of the multi-robot system increases dramatically. A single surviving robot that has the necessary capabilities to self-replicate could prospectively repopulate an entire ‘colony’ of robots, given sufficient resources and time. This gives robots an opportunity to take more risks …

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Deep Learning Applied To Public Company Valuation For Value Investing, Abram Paul Haich Jan 2021

Deep Learning Applied To Public Company Valuation For Value Investing, Abram Paul Haich

Computer Science

Value investing is an investing approach that seeks to discover and take advantage of price discrepancies between the market price and the actual value of a company (intrinsic value). The purpose of this work is to measure the intrinsic value of companies using an approach that has had success in the broad field of Artificial Intelligence, Deep Learning. Finding patterns in large amounts of data is what Deep Learning can be used for. Typically for value investing an investor will seek to find conservative estimates on the current value of a company by analyzing fundamental data. Our method attempts to …

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Image Registration And Fusion Application, Shreya Singh Jan 2021

Image Registration And Fusion Application, Shreya Singh

Computer Science

Image Registration and Fusion is a process of manipulating two or more input images through a combination of geometric transformation operations and fusion rules to obtain the information from the input images into one fused image. Currently, most existing image registration and fusion techniques are appropriate for grayscale images. There are a few techniques which support colored images but cannot be used for a large input data set. The aim of this research is to propose an image registration and fusion technique that supports large input data sets of both grayscale and colored images. To this end, we explored various …

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Development And Validation Of A Library For Iterative Window-Based Processing Of Geospatial Data, David Michael Schwartz Jan 2021

Development And Validation Of A Library For Iterative Window-Based Processing Of Geospatial Data, David Michael Schwartz

Computer Science

High-resolution spectral images and digital elevation models are widely available. With this quantity of data, it is imperative to develop fast algorithms to extract information. We present a Python library that implements a set of algorithms for aggregating data within sliding windows. The algorithms have O(log(n)) time complexity and maintain the original image resolution. They are vectorized and written with NumPy to create fast code with C-like performance. The library offers several analysis procedures, architected such that additional procedures utilizing sliding windows can easily be added. Slope, aspect, and curvature analyses exist for digital elevation models. Fractal dimensions and correlation …

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Implementation Of Parallel Programming To Improve Transaction Speed And Scalability In Blockchain Systems, Joshua Denio Jan 2021

Implementation Of Parallel Programming To Improve Transaction Speed And Scalability In Blockchain Systems, Joshua Denio

Computer Science

This thesis presents a parallel mining architecture model intended to be used in blockchain systems to improve transaction speed and network scalability while maintaining decentralization. Typical blockchain validation times are significantly slower than traditional digital transaction systems. The model presented is intended to allow devices with limited computational power to make meaningful contributions to the blockchain system by introducing parallel proof of work, managed by automated manager nodes. This will allow blockchain systems to be integrated into cloud environments and the internet of things. The presented model is also intended to address and reduce power consumption problems current blockchain systems …

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Investigating Super Learner On Healthcare Data Sets, Humaira Rahman Jan 2021

Investigating Super Learner On Healthcare Data Sets, Humaira Rahman

Computer Science

In the field of machine learning, classification is the essential task that predicts the target class or label for each sample in the data. Improving the performance of a classification model has been a challenging research problem. Researchers try to choose the proper techniques and combine several algorithms to be applied to the specific data set to get better predictions. Nowadays, researchers have used the method called super learner. The idea of super learning is that it combines multiple techniques as base learners and uses a meta-learner to get the final predictions and thus obtain more reliable results. In this …

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Development Of An Algorithm For Detection And Recovery Of Corruption In Convolutional Neural Networks Data Storage, Mohammadreza Ramzanpour Jan 2021

Development Of An Algorithm For Detection And Recovery Of Corruption In Convolutional Neural Networks Data Storage, Mohammadreza Ramzanpour

Computer Science

Computer vision based applications are commonly utilized in embedded systems. The demand for higher accuracy leads to increased complexity of convolutional neural networks (CNN) and hence, larger storage requirement for saving pre-trained networks. Even the smallest data corruption in the storage units of the embedded systems can result in drastic failures due to the propagation of the errors. This thesis proposes a new algorithm for the recovery of the data in case of single event upset (SEU) error. An association rule mining based algorithm is used to find the probability of corruption in each of the bits. The recovery algorithm …

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Comparison Of Global And Local Particle Swarm Optimization, Nazrin Ferdousi Jan 2021

Comparison Of Global And Local Particle Swarm Optimization, Nazrin Ferdousi

Computer Science

Particle swarm optimization is a computational algorithm used to optimize a solution through sequential processing of particles using a specified estimation of quality. The algorithm is inspired by biological systems such as bird and insect swarms. This paper focuses on the comparison between Local PSO and Global PSO. We have utilized eight functions to provide benchmarks to compare the optimizations provided by the two optimization strategies. This resulted in findings that indicate that global optimizations tend to be more effective than local optimizations when comparing final costs. Our research indicates that an automated approach to particle swarm optimization will benefit …

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Covid-19 Face Cover Tracker Using Amazon Web Services, Rahmanul Hoque Jan 2021

Covid-19 Face Cover Tracker Using Amazon Web Services, Rahmanul Hoque

Computer Science

Numerous studies have shown that wearing face covers and following other social distancing guidelines reduce community transmission of air borne diseases like COVID-19 during a pandemic. While public health administrators can provide guidelines on wearing masks and social distancing, it cannot be guaranteed that everyone will follow those guidelines. It is very important to have a mechanism available for the public health administration to collect data on how well the population is following the guidelines provided. This information will allow them to anticipate when there will be peak hospitalization and prepare accordingly. This paper aims to look at the feasibility …

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