Physical Sciences and Mathematics Commons^™

Dna-Templated Synthesis Of Cationic Styryl Dyes, Kriangsak Faikhruea

Chulalongkorn University Theses and Dissertations (Chula ETD)

Styryl dye is a class of pi-conjugated dye that shows several desirable optical properties, such as high photostability, solvatochromic characteristics, and high fluorescence quantum yields. Due to the responsiveness to the binding with the target biomolecules such as nucleic acids, some of these dyes have been widely employed for biomolecular detection and cellular imaging. These dyes are typically synthesized via an aldol-type reaction between a methyl heterocycle and an aromatic aldehyde in the presence of acid or base as a catalyst. However, the conventional method of synthesizing these dyes is inefficient, time-consuming, and involves the use of excessive chemicals, as …

A One-Dimensional Field Dislocation Mechanics Model Using Discontinuous Galerkin Method, Ja'nya Breeden, Dow Drake, Jay Gopalakrishnan, Saurabh Puri

Mathematics and Statistics Faculty Publications and Presentations

A numerical solution strategy for a one-dimensional field dislocation mechanics (FDM) model using the Discontinuous Galerkin (DG) method is developed. The FDM model is capable of simulating the dynamics of discrete, nonsingular dislocations using a partial differential equation involving a conservation law for the Burgers vector content with constitutive input for nucleation and velocity. Modeling of individual dislocation lines with an equilibrium compact core structure in the context of this continuum elastoplastic framework requires a non-convex stored energy density. Permanent deformation and stress redistribution caused by the dissipative transport of dislocations is modeled using thermodynamics-based constitutive laws. A DG method …

Periodic Points Of Polynomials Over Finite Fields, Derek Garton

Mathematics and Statistics Faculty Publications and Presentations

Fix an odd prime p. If r is a positive integer and f is a polynomial with coefficients in Fpr, let Pp,r⁡(f) be the proportion of P1⁢(Fpr) that is periodic with respect to f. We show that as r increases, the expected value of Pp,r⁡(f), as f ranges over quadratic polynomials, is less than 22/(log⁡log⁡pr). This result follows from a uniformity theorem on specializations of dynamical systems of rational functions over residually finite Dedekind domains. The specialization theorem generalizes previous work by Juul et al. that holds for rings of integers of number fields. Moreover, under stronger hypotheses, we effectivize …

Power, Passion And Politics: A Grounded Theory Study Of Academic Experiences In Policy Development And Implementation In Higher Education, Marie Brennan

Doctoral

This research study is designed to investigate the lived experiences and perspectives of the academic community on policy development in Irish higher education. A review of the current policy landscape in Ireland and Europe and in particular empirical studies concerning policy implementation provided the focus of the study. The initial study focused on contemporary literature on policy development practices affected by globalisation and subsequently the issues at a national level. Where the gaps in knowledge were identified were in terms of policy implementation at the local level and where the study is aligned. The participants in the study are academics …

Identification Of Nanotechnology Skill Shortages In Ireland’S Agri-Food Sector: Towards The Safe, Innovative And Sustainable Development Of Nano-Food Technology, Eileen Mccarron

Doctoral

The purpose of this research was to assess the nanotechnology skill and capacity shortages in Irelands Agri-food sector. In 2008 the Food Safety Authority of Ireland (FSAI) published its statement on ‘The Relevance for Food Safety of Applications of Nanotechnology in the Food and Feed Industries’. The importance of the food sector to the Irish economy was clearly emphasised by the FSAI’s statement. The report identified the urgent need for focused research programs into the potential of nanotechnology in the agri-food sector and it highlighted the need for a multi-organisation approach between state agencies, industry and academia to ensure safe …

Trapped Under Ice: Spatial And Seasonal Dynamics Of Dissolved Organic Matter Composition In Tundra Lakes, Martin R. Kurek, Karen E. Frey, François Guillemette, David C. Podgorski, Amy Townsend-Small, Christopher D. Arp, Anne M. Kellerman, Robert G.M. Spencer

Geography

Arctic lakes store, modify, and transport large quantities of carbon from terrestrial environments to the atmosphere; however, the spatial and temporal relationships between quantity and composition of dissolved organic matter (DOM) have not been well characterized across broad arctic regions. Moreover, most arctic lake DOM compositions have been examined during the ice-free summer, whereas DOM cycling between the ice-covered winter months and summer have not been addressed. To resolve these spatial and seasonal uncertainties in DOM cycling, we sampled a series of arctic lakes from the North Slope of Alaska across a latitudinal gradient in the winter and summer over …

Secure Storage Model For Digital Forensic Readiness, Avinash Singh, Richard Adeyemi Ikuesan, Hein Venter

All Works

Securing digital evidence is a key factor that contributes to evidence admissibility during digital forensic investigations, particularly in establishing the chain of custody of digital evidence. However, not enough is done to ensure that the environment and access to the evidence are secure. Attackers can go to extreme lengths to cover up their tracks, which is a serious concern to digital forensics – particularly digital forensic readiness. If an attacker gains access to the location where evidence is stored, they could easily alter the evidence (if not remove it altogether). Even though integrity checks can be performed to ensure that …

Loguad: Log Unsupervised Anomaly Detection Based On Word2vec, Jin Wang, Changqing Zhao, Shiming He, Yu Gu, Osama Alfarraj, Ahed Abugabah

All Works

System logs record detailed information about system operation and are important for analyzing the system's operational status and performance. Rapid and accurate detection of system anomalies is of great significance to ensure system stability. However, large-scale distributed systems are becoming more and more complex, and the number of system logs gradually increases, which brings challenges to analyze system logs. Some recent studies show that logs can be unstable due to the evolution of log statements and noise introduced by log collection and parsing. Moreover, deep learning-based detection methods take a long time to train models. Therefore, to reduce the computational …

Autonomous Driving And Connected Mobility Modeling: Smart Dynamic Traffic Monitoring And Enforcement System For Connected And Autonomous Mobility, Dimitrios Zavantis, Fatma Outay, Youssef El-Hansali, Ansar Yasar, Elhadi Shakshuki, Haroon Malik

All Works

In recent years, autonomous vehicles (AVs), connected vehicles (CVs) and all relative technology have been in the spotlight, being intensively researched and developed. There is high anticipation on the benefits of automation and the overall reform it will bring to the transport sector, with some optimistic estimates considering it as a reality within the next few years. Evidently, AVs and CVs are attracting considerable attention and are developed very rapidly, cultivating great expectations for traffic safety improvements. While their potential is enormous and undeniable, benefits are not automatically guaranteed as there are parameters that currently appear unforeseen. This paper investigates …

The Effect Of Climate Change, Human Activity, And Water Management Interventions On Environmental And Socio-Economic Outcomes In The Mekong Delta Region, Huynh Truong Gia Nguyen

Theses, Dissertations and Culminating Projects

The Vietnam Mekong Delta (VMD) is the southernmost part of the Mekong River watershed basin and plays a critical role in Vietnam’s socio-economic and ecological wellbeing. Because of both climate change and anthropogenic activities, such as hydropower dam construction and overwhelming water extraction, the area has recently experienced severe droughts, changing rainfall patterns, and decreasing water resources, collectively heightening disaster risks. Understanding the interactions among these evolving factors is key to preserving resources in the VMD and similarly afflicted regions. Therefore, this dissertation included three objectives. The first objective was to estimate potential impacts of changes in the available water …

The Privacy Leakage Of Ip Camera Systems, Lee R. Castro

Theses, Dissertations and Culminating Projects

For in-home security, intelligent operations like top individual recognition and minimizing losses due to home break-ins, emergencies, and fraud are keys to success. This application integrates the closed-circuit television (CCTV) camera and the deep learning algorithms used to process these images. Automated intrusion detection alerts, real-time fire alerts, smart checkout, and potentially fraudulent point of sale (POS) transactions are its main features. Dynamic intrusion with machine learning is a software program in which the price of certain products changes over time through an algorithm that considers a variety of pricing variables. The face locator is a part of the algorithm …

Distributed Energy Resources : An Assessment Of New Jersey’S Clean Energy Future, Mary Elizabeth Kenny

Theses, Dissertations and Culminating Projects

The demand for renewable energy in New Jersey will continue to grow as economic opportunities and community support drive development. The effective integration of Distributed Energy Resources (DERs) will transform energy production, storage, and use. To achieve sustainable energy production, the current reliance on fossil fuels must be reduced and replaced with less carbon-intensive energy sources that optimize the electric grid. DERs help pioneer the path to a clean energy transition where the implementation of new renewable energy projects will diversify New Jersey’s energy portfolio and provide a more resilient, equitable, and independent energy source. This thesis investigates different perspectives …

Development And On-Road Applications Of A 1/10-Scale Autonomous Vehicle, Laura Cornejo Paulino

Theses, Dissertations and Culminating Projects

Autonomous vehicles (AVs) are a key component in the creation of the new transportation infrastructure. Over the last several decades, nations across the world have experienced an increase in traffic congestion, environmental deprecation due to greenhouse gas emissions and an increase in time loss and productivity. A key factor in these components is the increase in numbers of vehicles on the road, a number that continues to increase gradually every year. In addition, the continued increase in vehicles on the road poses a threat to human and environmental safety. There is strong evidence to support that accidental vehicular deaths and …

A Divide & Concur Approach To Collaborative Goal Modeling With Merge In Early-Re: Supplemental Material, Kathleen R. Hablutzel, Anisha Jain, Alicia M. Grubb

Computer Science: Faculty Publications

Supplemental material for the paper:
"A Divide & Concur Approach to Collaborative Goal Modeling with Merge in Early-RE"
This paper proposes a formal approach to the problem of merging the attributes of intentions and actors, once these elements have been matched.

Three Major Instructional Approaches For Requirements Engineering, Marian Daun, Alicia M. Grubb, Bastian Tenbergen

Computer Science: Faculty Publications

In this talk, we report on our findings from the paper A Survey of Instructional Approaches in the Requirements Engineering Education Literature [DGT21], which has been accepted at and published in the proceedings of the 2021 IEEE International Conference on Requirements Engineering. The paper reports the findings of a systematic literature review to define and investigate the current state of research on requirements engineering education.

Novel Primitive Decompositions For Real-World Physical Reasoning, Mackie Zhoou, Bridget Duah, Jamie C. Macbeth

Computer Science: Faculty Publications

In this work, we are concerned with developing cognitive representations that may en- hance the ability for self-supervised learning systems to learn language as part of their world explorations. We apply insights from in-depth language understanding systems to the problem, specifically representations which decompose language inputs into language-free structures that are complex combinations of primitives representing cognitive abstractions such as object permanence, movement, and spatial relationships. These decompositions, performed by a system traditionally called a conceptual analyzer, link words with complex non-linguistic structures that engender the rich relations between language expressions and world exploration that are a familiar aspect of …

Interleaving A Symbolic Story Generator With A Neural Network-Based Large Language Model, Jingwen Xiang, Zoie Zhao, Mackie Zhou, Megan Mckenzie, Alexis Kilayko, Jamie C. Macbeth, Scott Carter, Katharine Sieck, Matthew Klenk

Computer Science: Faculty Publications

Research in deep learning has recently produced models of natural language that are capable of generating natural language output which, at a glance, has strong similarities to that written by intelligent humans. However, the texts produced by deep learning-based large language models (LLMs), upon deeper examination, reveal the challenges that they have in producing outputs that maintain logical coherence. One specific application area of interest for LLMs is in fictional narrative generation, a mode of operation in which stories are generated by the model in response to a prompt text that indicates the start of a story or the desired …

The Future Of Ai Accountability In The Financial Markets, Gina-Gail S. Fletcher, Michelle M. Le

Faculty Scholarship

Consumer interaction with the financial market ranges from applying for credit cards, to financing the purchase of a home, to buying and selling securities. And with each transaction, the lender, bank, and brokerage firm are likely utilizing artificial intelligence (AI) behind the scenes to augment their operations. While AI’s ability to process data at high speeds and in large quantities makes it an important tool for financial institutions, it is imperative to be attentive to the risks and limitations that accompany its use. In the context of financial markets, AI’s lack of decision-making transparency, often called the “black box problem,” …

A Framework For Inland Cities To Prevent Marine Debris: A Case Study From Durham, North Carolina, Nancy Lauer, Michelle B. Nowlin

Faculty Scholarship

Land-based sources of litter are increasingly recognized as significant contributors to marine debris, and rivers can carry debris to the coast from far-inland sources. In this paper, we demonstrate the important role inland cities can play in the marine debris crisis by reducing their own marine debris contributions. Given this role, we provide a framework for inland cities to prevent plastic pollution along with the lessons learned from introducing these strategies in Durham, North Carolina, a mid-sized, inland city that drains to the ocean through the Cape Fear and Neuse River watersheds. This framework guides city officials, resource managers, and …

Blockchain And Distributed Ledger Technologies For Iot Security: A Survey Paper, Suleiman Abdulrahman, Maroning Useng

Mesopotamian Journal of Computer Science

As the Internet of Things (IoT) continues to grow, the security concerns associated with this technology become increasingly important. Blockchain and Distributed Ledger Technologies (DLTs) are emerging as potential solutions for IoT security due to their decentralized and immutable nature. In this survey paper, we provide a comprehensive overview of the state-of-the-art research in the use of blockchain and DLTs for IoT security. We begin by introducing the basic concepts of blockchain and DLTs, followed by a discussion of their potential applications in IoT security. We then review the existing literature on the topic, including both academic and industrial research. …

Current Cutting-Edge Research In Computer Science, Mohanad Ghazi Yaseen, Mohammad Aljanabi, Ahmed Hussein Ali, Saad Abbas Abd

Mesopotamian Journal of Computer Science

Computer science research is a rapidly evolving field that is shaping the future of technology. From artificial intelligence and machine learning to image processing and cybersecurity, researchers are constantly pushing the boundaries of what is possible. In this editorial, we will explore some of the current trends in computer science research and their potential impact on society. After searching the most prestigious academic databases, we settle on a list of subjects widely recognized as cutting-edge in the field of computer science.

Security And Privacy In Iot-Based Healthcare Systems: A Review, Omar Ibrahim Obaid, Saba Abdul-Baqi Salman

Mesopotamian Journal of Computer Science

IoT-based healthcare solutions have transformed patient care and improved results. These technologies capture and store sensitive patient data, raising security and privacy concerns. The paper first discusses IoT-based healthcare system security issues. It evaluates cyber threats, system unavailability, and vulnerabilities attackers can use to get patient data. The report also tackles IoT-based healthcare system privacy issues such illegal data access and sharing and patient data breach concerns. The study proposes numerous frameworks and solutions to protect patient data in IoT-based healthcare systems. Blockchain technology can increase data security and privacy by offering a transparent, decentralized, tamperresistant database. The report also …

Anomaly Detection In Encrypted Https Traffic Using Machine Learning: A Comparative Analysis Of Feature Selection Techniques, Hind Raad Ibraheem, Nashwan Dheyaa Zaki, Mohammed Ibrahim Al-Mashhadani

Mesopotamian Journal of Computer Science

With the increasing use of encryption in network traffic, anomaly detection in encrypted traffic has become a challenging problem. This study proposes an approach for anomaly detection in encrypted HTTPS traffic using machine learning and compares the performance of different feature selection techniques. The proposed approach uses a dataset of HTTPS traffic and applies various machine learning models for anomaly detection. The study evaluates the performance of the models using various evaluation metrics, including accuracy, precision, recall, F1-score, and area under the curve (AUC). The results show that the proposed approach with feature selection outperforms other existing techniques for anomaly …

Towards Exchanging Wearable-Pghd With Ehrs: Developing A Standardized Information Model For Wearable-Based Patient Generated Health Data, Abdullahi Abubakar Kawu, Dympna O'Sullivan, Lucy Hederman

Articles

Wearables have become commonplace for tracking and making sense of patient lifestyle, wellbeing and health data. Most of this tracking is done by individuals outside of clinical settings, however some data from wearables may be useful in a clinical context. As such, wearables may be considered a prominent source of Patient Generated Health Data (PGHD). Studies have attempted to maximize the use of the data from wearables including integrating with Electronic Health Records (EHRs). However, usually a limited number of wearables are considered for integration and, in many cases, only one brand is investigated. In addition, we find limited studies …

Phototrophy And Carbon Fixation In Chlorobi Postdate The Rise Of Oxygen, L. M. Ward, Patrick M. Shih

Geosciences: Faculty Publications

While most productivity on the surface of the Earth today is fueled by oxygenic photosynthesis, for much of Earth history it is thought that anoxygenic photosynthesis—using compounds like ferrous iron or sulfide as electron donors—drove most global carbon fixation. Anoxygenic photosynthesis is still performed by diverse bacteria in niche environments today. Of these, the Chlorobi (formerly green sulfur bacteria) are often interpreted as being particularly ancient and are frequently proposed to have fueled the biosphere during late Archean and early Paleoproterozoic time before the rise of oxygenic photosynthesis. Here, we perform comparative genomic, phylogenetic, and molecular clock analyses to determine …

Lidar-Landsat Covariance For Predicting Canopy Fuels, Margaret D. Epstein

Graduate Student Theses, Dissertations, & Professional Papers

Managing wildfires in the western United States is becoming increasingly complex. Visualizing and quantifying canopy structures allows fire managers to both plan for fire and track recovery. Light detecting and ranging, or LiDAR can measure forests in three dimensions, but has limited spatial and temporal coverage. LiDAR-Landsat covariance uses machine learning to fill in the spatial and temporal gaps of LiDAR coverage with supplemental Landsat imagery. However, in order to capture real forest dynamics, a model needs to be stable enough to detect long term trends, sensitive to episodic disturbance, and general enough to work on multiple landcovers. The purpose …

Hydrogeomorphic Response Of Steep Streams Following Severe Wildfire In The Western Cascades, Oregon, David Matthew Busby

Graduate Student Theses, Dissertations, & Professional Papers

Severe wildfire may alter the morphologic resilience of steep mountain streams by increasing peak discharges, elevating inputs of sediment and wood into channels, and increasing susceptibility to landslides and debris flows. In the Pacific Northwest, where mean annual precipitation is high and mean fire return intervals range from decades to centuries, understanding of steep stream response to fire is limited. In 2020, the Western Cascade Range, Oregon, experienced wildfire of historic magnitude and severity. The objective of this study was to evaluate the hydrologic and geomorphic response of steep streams to the 2020 fires. I assessed streamflow, instream wood, and …

A Non-Deterministic Deep Learning Based Surrogate For Ice Sheet Modeling, Hannah Jordan

Graduate Student Theses, Dissertations, & Professional Papers

Surrogate modeling is a new and expanding field in the world of deep learning, providing a computationally inexpensive way to approximate results from computationally demanding high-fidelity simulations. Ice sheet modeling is one of these computationally expensive models, the model used in this study currently requires between 10 and 20 minutes to complete one simulation. While this process is adequate for certain applications, the ability to use sampling approaches to perform statistical inference becomes infeasible. This issue can be overcome by using a surrogate model to approximate the ice sheet model, bringing the time to produce output down to a tenth …

Development Of Nanodisc Affinity Capillary Electrophoresis To Study The Interaction Of Giα Protein With Cell Membranes, Bethany R. Emeigh

Graduate Student Theses, Dissertations, & Professional Papers

Guanine nucleotide-binding proteins, or G-proteins, are crucial to cell signaling by acting as molecular switches through interactions with membrane-bound receptors. G-proteins have become of greater interest due to the possibility of novel drug targets. The G-protein alpha subunit, Gα, binds GTP before localizing to the membrane to interact with other G-protein subunits and G-protein-coupled receptors (GPCRs). Gi lacks a hydrophobic region to promote localization to the membrane, but a co-translational lipidation of the N-terminal residue stabilizes this interaction.

Protein interactions with biological membranes are challenging to study due to the complex structure of membranes and the often-limited quantities of the …

Trading Financial Instruments Like A Video Game: Searching For Profit Using Deep Reinforcement Learning., Sebastian Coombs

Graduate Student Theses, Dissertations, & Professional Papers

Buying and selling Stocks, Foreign Currencies (FOREX), Commodities, and Cryptocurrencies have been a source of wealth generation, and more often, wealth loss for many brave enough to enter the financial markets. In this paper, the author builds on the work of Williams, J. 2022 and develops an agent-based method to solve this wealth generation problem with the use of neural networks. The author points out some assumptions made by Williams, J. 2022 that were sound in theory, but made the implementation of the algorithm presented in their paper diverge from the theory. The author proposes a fundamentally different algorithmic method, …