Physical Sciences and Mathematics Commons^™

Statically Controlled Synchronized Lane Architectures, Scott K. Pomerville

Dissertations, Master's Theses and Master's Reports

Modern superscalar processors dominate the field of computing. While dynamic execution allows for versatility in code, these processors are complex. Statically scheduled code has historically enabled simpler processor designs, but static scheduling cannot account for variables that are unknown at compile time. Furthermore, static scheduling has many inefficiencies, such as the need to insert a large number of nops for code in traditional Very Long Instruction Word (VLIW) processors. In this dissertation, we explore a novel architectural approach for statically scheduled code by breaking the code into several synchronous instruction streams. By representing code in a fundamentally new way, we …

Hydrologic Pathways Of A Northern Hardwood Catchment In The Great Lakes Basin Using End Member Mixing Analysis, Cory C. Burkwald

Dissertations, Master's Theses and Master's Reports

This study investigated the seasonality of hydrologic pathways, the role of wetlands in streamflow generation, and how hydrologic pathways affect the export of nutrients by streamflow in the Sturgeon River using endmember mixing analysis. The results indicated that streamflow consists of three main components: shallow subsurface water from wetland soils, shallow groundwater, and surface runoff from precipitation. Snowmelt plays a dominating role in regulating stream water chemistry (~80% volume at peak) during the snowmelt season and likely also after that. Wetland soils store snowmelt, together with rainwater though the fraction is unknown, and then gradually release through summer in the …

Structural And Functional Studies Of The Human Members Of The Macrophage Migration Inhibitory Factor Family, Andrew Parkins

University of the Pacific Theses and Dissertations

Macrophage migration inhibitory factor (MIF) and D-dopachrome tautomerase (D-DT) are the two human members of the MIF superfamily, which are implicated in an array of autoimmune disorders, inflammatory diseases, and cancer via their pleiotropic functionality. Despite only sharing 34% sequence identity, MIF and D-DT have high structural homology and overlapping functional traits, including activation of the type II cell surface receptor CD74 and keto-enol tautomerase activity. The MIF and/or D-DT-induced activation of CD74 leads to signaling cascades pivotal for cell growth, proliferation, and inhibition of apoptosis. Such characteristics make MIF and D-DT attractive molecular targets for drug discovery.

Currently, all …

Sequential Infiltration Synthesis Of Silicon Dioxide In Polymers With Ester Groups─Insight From In Situ Infrared Spectroscopy, Mahua Biswas, Vepa Rozyyev, Anil U. Mane, Amelia Korveziroska, Uttam Manna, Jeffrey W. Elam

Faculty publications – Physics

New strategies to synthesize nanometer-scale silicon dioxide (SiO2) patterns have drawn much attention in applications such as microelectronic and optoelectronic devices, membranes, and sensors, as we are approaching device dimensions shrinking below 10 nm. In this regard, sequential infiltration synthesis (SIS), a two-step gas-phase molecular assembly process that enables localized inorganic material growth in the targeted reactive domains of polymers, is an attractive process. In this work, we performed in situ Fourier transform infrared spectroscopy (FTIR) measurements during SiO2 SIS to investigate the reaction mechanism of trimethylaluminum (TMA) and tri(tert-pentoxy) silanol (TPS) precursors with polymers having ester functional groups (poly(methyl …

Diastereoselective Synthesis Of The Hiv Protease Inhibitor Darunavir And Related Derivatives Via A Titanium Tetrachloride-Mediated Asymmetric Glycolate Aldol Addition Reaction, Jordan M. Witte, Emmanuel Ayim, Christopher J. Sams, Jasmine B. Service, Caitlyn C. Kant, Lillian Bambalas, Daniel Wright, Austin Carter, Kelly Moran, Isabella G. Rohrig, Gregory M. Ferrence, Shawn R. Hitchcock

Faculty Publications – Chemistry

Darunavir is a potent HIV protease inhibitor that has been established as an effective tool in the fight against the progression of HIV/AIDS in the global community. The successful application of this drug has spurred the development of derivatives wherein strategic regions (e.g., P1, P1’, P2, and P2’) of the darunavir framework have been structurally modified. An alternate route for the synthesis of darunavir and three related P1 and P1’ derivatives has been developed. This synthetic pathway involves the use of a Crimmins titanium tetrachloride-mediated oxazolidine-2-thione-guided asymmetric glycolate aldol addition reaction. The resultant aldol adduct introduces the P1 fragment of …

A Data-Driven Machine Learning Approach For Electron-Molecule Ionization Cross Sections, Allison Harris, Josh Nepomuceno

Faculty publications – Physics

Despite their importance in a wide variety of applications, the estimation of ionization cross sections for large molecules continues to present challenges for both experiment and theory. Machine learning (ML) algorithms have been shown to be an effective mechanism for estimating cross section data for atomic targets and a select number of molecular targets. We present an efficient ML model for predicting ionization cross sections for a broad array of molecular targets. Our model is a 3-layer neural network that is trained using published experimental datasets. There is minimal input to the network, making it widely applicable. We show that …

Antibody-Driven Assembly Of Plasmonic Core–Satellites To Increase The Sensitivity Of A Sers Vertical Flow Immunoassay, Eunice Ebbah, Anthony Amissah, Jun-Hyun Kim, Jeremy D. Driskell

Faculty Publications – Chemistry

Here, we describe a SERS-based vertical flow assay as a platform technology suitable for point-of-care (POC) diagnostic testing. A capture substrate is constructed from filter paper embedded with spherical gold nanoparticles (AuNPs) and functionalized with an appropriate capture antibody. The capture substrate is loaded into a filtration device and connected to a syringe to rapidly and repeatedly pass the sample through the sensor for efficient antigen binding. The antigen is then labeled with a SERS-active detection probe. We show that only a few Raman reporter molecules, exclusively located adjacent to the plasmonic capture substrate, generate detectible signals. To maximize the …

Immobilization Of Thiol-Modified Horseradish Peroxidase On Gold Nanoparticles Enhances Enzyme Stability And Prevents Proteolytic Digestion, Faith E. Breausche, Annelise Somerlot, Jason Walder, Kwame Osei, Samuel Okyem, Jeremy D. Driskell

Faculty Publications – Chemistry

The specificity and efficiency of enzyme-mediated reactions have the potential to positively impact many biotechnologies; however, many enzymes are easily degraded. Immobilization on a solid support has recently been explored to improve enzyme stability. This study aims to gain insights and facilitate enzyme adsorption onto gold nanoparticles (AuNPs) to form a stable bioconjugate through the installation of thiol functional groups that alter the protein chemistry. In specific, the model enzyme, horseradish peroxidase (HRP), is thiolated via Traut’s reagent to increase the robustness and enzymatic activity of the bioconjugate. This study compares HRP and its thiolated analog (THRP) to deduce the …

Quantum Interference Enhancement Of The Spin-Dependent Thermoelectric Response, Runa X. Bennett, Joshua R. Hendrickson, Justin P. Bergfield

Faculty publications – Physics

We investigate the influence of quantum interference (QI) and broken spin-symmetry on the thermoelectric response of node-possessing junctions, finding a dramatic enhancement of the spin-thermopower (Ss), figure-of-merit (ZsT), and maximum thermodynamic efficiency (ηsmax) caused by destructive QI. Using many-body and single-particle methods, we calculate the response of 1,3-benzenedithiol and cross-conjugated molecule-based junctions subject to an applied magnetic field, finding nearly universal behavior over a range of junction parameters with Ss, ZsT, and reaching peak values of 2𝜋/ √3(𝑘/𝑒)2𝜋/3(𝑘/𝑒), 1.51, and 28% of Carnot efficiency, respectively. We also find that the …

A Technical Perspective On Integrating Artificial Intelligence To Solid-State Welding, Sambath Yaknesh, Natarajan Rajamurugu, Prakash K. Babu, Saravanakumar Subramaniyan, Sher A. Khan, C. Ahamed Saleel, Mohammad Nur-E-Alam, Manzoore E. M. Soudagar

Research outputs 2022 to 2026

The implementation of artificial intelligence (AI) techniques in industrial applications, especially solid-state welding (SSW), has transformed modeling, optimization, forecasting, and controlling sophisticated systems. SSW is a better method for joining due to the least melting of material thus maintaining Nugget region integrity. This study investigates thoroughly how AI-based predictions have impacted SSW by looking at methods like Artificial Neural Networks (ANN), Fuzzy Logic (FL), Machine Learning (ML), Meta-Heuristic Algorithms, and Hybrid Methods (HM) as applied to Friction Stir Welding (FSW), Ultrasonic Welding (UW), and Diffusion Bonding (DB). Studies on Diffusion Bonding reveal that ANN and Generic Algorithms can predict outcomes …

Expanding Australia's Defence Capabilities For Technological Asymmetric Advantage In Information, Cyber And Space In The Context Of Accelerating Regional Military Modernisation: A Systemic Design Approach, Pi-Shen Seet, Anton Klarin, Janice Jones, Michael N. Johnstone, Violetta Wilk, Stephanie Meek, Summer O'Brien

Research outputs 2022 to 2026

Introduction. The aim of the project was to conduct a systemic design study to evaluate Australia'sopportunities and barriers for achieving a technological advantage in light of regional military technological advancement. It focussed on the three domains of (1) cybersecurity technology, (2) information technology, and (3) space technology.

Research process. Employing a systemic design approach, the study first leveraged scientometric analysis, utilising informetric mapping software (VOSviewer) to evaluate emerging trends and their implications on defence capabilities. This approach facilitated a broader understanding of the interdisciplinary nature of defence technologies, identifying key areas for further exploration. The subsequent survey study, engaging 828 …

Cyberbullying Text Identification: A Deep Learning And Transformer-Based Language Modeling Approach, Khalid Saifullah, Muhammad Ibrahim Khan, Suhaima Jamal, Iqbal H. Sarker

Research outputs 2022 to 2026

In the contemporary digital age, social media platforms like Facebook, Twitter, and YouTube serve as vital channels for individuals to express ideas and connect with others. Despite fostering increased connectivity, these platforms have inadvertently given rise to negative behaviors, particularly cyberbullying. While extensive research has been conducted on high-resource languages such as English, there is a notable scarcity of resources for low-resource languages like Bengali, Arabic, Tamil, etc., particularly in terms of language modeling. This study addresses this gap by developing a cyberbullying text identification system called BullyFilterNeT tailored for social media texts, considering Bengali as a test case. The …

Multimodal Fusion For Audio-Image And Video Action Recognition, Muhammad B. Shaikh, Douglas Chai, Syed M. S. Islam, Naveed Akhtar

Research outputs 2022 to 2026

Multimodal Human Action Recognition (MHAR) is an important research topic in computer vision and event recognition fields. In this work, we address the problem of MHAR by developing a novel audio-image and video fusion-based deep learning framework that we call Multimodal Audio-Image and Video Action Recognizer (MAiVAR). We extract temporal information using image representations of audio signals and spatial information from video modality with the help of Convolutional Neutral Networks (CNN)-based feature extractors and fuse these features to recognize respective action classes. We apply a high-level weights assignment algorithm for improving audio-visual interaction and convergence. This proposed fusion-based framework utilizes …

Development Of A Regional Climate Change Model For Aedes Vigilax And Aedes Camptorhynchus (Diptera: Culicidae) In Perth, Western Australia, Kerry Staples, Peter J. Neville, Steven Richardson, Jacques Oosthuizen

Research outputs 2022 to 2026

Mosquito-borne disease is a significant public health issue and within Australia Ross River virus (RRV) is the most reported. This study combines a mechanistic model of mosquito development for two mosquito vectors; Aedes vigilax and Aedes camptorhynchus, with climate projections from three climate models for two Representative Concentration Pathways (RCPs), to examine the possible effects of climate change and sea-level rise on a temperate tidal saltmarsh habitat in Perth, Western Australia. The projections were run under no accretion and accretion scenarios using a known mosquito habitat as a case study. This improves our understanding of the possible implications of sea-level …

Malware Detection With Artificial Intelligence: A Systematic Literature Review, Matthew G. Gaber, Mohiuddin Ahmed, Helge Janicke

Research outputs 2022 to 2026

In this survey, we review the key developments in the field of malware detection using AI and analyze core challenges. We systematically survey state-of-the-art methods across five critical aspects of building an accurate and robust AI-powered malware-detection model: malware sophistication, analysis techniques, malware repositories, feature selection, and machine learning vs. deep learning. The effectiveness of an AI model is dependent on the quality of the features it is trained with. In turn, the quality and authenticity of these features is dependent on the quality of the dataset and the suitability of the analysis tool. Static analysis is fast but is …

Pdf Malware Detection: Toward Machine Learning Modeling With Explainability Analysis, G. M.Sakhawat Hossain, Kaushik Deb, Helge Janicke, Iqbal H. Sarker

Research outputs 2022 to 2026

The Portable Document Format (PDF) is one of the most widely used file types, thus fraudsters insert harmful code into victims' PDF documents to compromise their equipment. Conventional solutions and identification techniques are often insufficient and may only partially prevent PDF malware because of their versatile character and excessive dependence on a certain typical feature set. The primary goal of this work is to detect PDF malware efficiently in order to alleviate the current difficulties. To accomplish the goal, we first develop a comprehensive dataset of 15958 PDF samples taking into account the non-malevolent, malicious, and evasive behaviors of the …

Trends In Monitoring Of Australia’S Threatened Birds (1990–2020): Much Improved But Still Inadequate, Simon J. Verdon, Robert A. Davis, Ayesha Tulloch, Sarah M. Legge, David M. Watson, John C. Z. Woinarski, G. Barry Baker, Joris Driessen, Hayley M. Geyle, Hugh Possingham, Stephen T. Garnett

Research outputs 2022 to 2026

Monitoring is vital to conservation, enabling conservation scientists to detect population declines, identify threats and measure the effectiveness of interventions. However, not all threatened taxa are monitored, monitoring quality is variable, and the various components of monitoring are likely to differ in their rates of improvement over time. We assessed the presence of monitoring and monitoring quality, using a range of metrics, for all Australia’s threatened bird taxa from 1990 to 2020 (four assessments spanning 30 years). We used our assessments to understand decadal trends in the number of taxa monitored; monitoring quality; and the groups that conduct monitoring. The …

Advanced Techniques In Time Series Forecasting: From Deterministic Models To Deep Learning, Xue Bai

Graduate Theses, Dissertations, and Problem Reports

This dissertation discusses three instances of temporal prediction, applied to population dynamics and deep learning.

In population modeling, dynamic processes are frequently represented by systems of differential equations, allowing for the analysis of various phenomena. The first application explores modeling cloned hematopoiesis in chronic myeloid leukemia (CML) via a nonlinear system of differential equations. By tracking the evolution of different cell compartments, including cycling and quiescent stem cells, progenitor cells, differentiated cells, and terminally differentiated cells, the model captures the transition from normal hematopoiesis to the chronic and accelerated-acute phases of CML. Three distinct non-zero steady states are identified, representing …

Assessment Of Potential Impacts Of Climate Change On Hydrology And Water Resource Availability In The Passaic River Basin, New Jersey, Felix Oteng Mensah

Theses, Dissertations and Culminating Projects

Streamflow dynamics in a basin is known to be a major driver of available water resources. In the context of climate change, it is expected that global warming will accelerate the global hydrologic cycle, which will drive more intense floods and droughts leading to changes in streamflow and water resource availability. Most researchers agree that the amount and intensity of precipitation have a direct impact on runoff. Yet, there is no consensus as to how warming can affect streamflow. Evapotranspiration (ET) plays a crucial role here. However, there is a shortage of real-world observations on it. And yet, ET is …