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Margalefidinium Polykrikoides Cyst Resuspension In The Lafayette River, A Sub-Tributary Of The Chesapeake Bay, Gabrielle Greaney, Eduardo Perez Vega, Katherine Crider, Dreux Chappell, Kimberly Powell, Richard Hale, Peter Bernhardt, Margaret Mulholland

Undergraduate Research Symposium

Harmful Algal Blooms are a collection of algae in a body of water that can cause serious environmental issues and health problems in both people and aquatic organisms. Dinoflagellates are microscopic, unicellular, and eukaryotic organisms that are well known for forming harmful algal blooms because of eutrophication. Coastal Virginia suffers from HABs in the Chesapeake Bay and its tributaries. A common species of dinoflagellate, known as Margalefidinium polykrikoides exists in the Chesapeake Bay. The purpose of this study is to determine if sediment resuspension produced by wind generated surface gravity waves cause cysts (dinoflagellate resting stages) to be suspended into …

Partnering To Advance Equity In Coastal Resilience: Lessons Learned For Research And Practice Collaborations For On-The-Ground Projects, Marina Saitgalina, Jennifer Whytlaw, Nicole Hutton, Tom Allen, Joshua Behr, Wie Yusuf

Presentations, Lectures, Posters, Reports

No abstract provided.

Advancing An Agenda For Social Equity And Coastal Resilience Through A Multi-Sectoral, Multi-Disciplinary And Applied Research Lens, Wie Yusuf, Marina Saitgalina, Jennifer Whytlaw, Nicole Hutton, Tom Allen, Joshua Behr

Presentations, Lectures, Posters, Reports

No abstract provided.

The Rise And Risks Of Internet Of Things, Diamond E. Hicks

Cybersecurity Undergraduate Research Showcase

Internet of Things (IoT) has become a necessary part of our everyday lives. IoT is the network in which many different devices communicate, connect, and share data. Though how IoT got to where it is today, the issues it faced, and how it affects our lives today is not common knowledge. Despite the fact that IoT has advanced our technology to what it is today, people do not completely understand what it does.

Partnering To Advance Equity In Coastal Resilience: Lessons Learned For Research And Practice Collaborations For On-The-Ground Projects, Ren-Neasha Blake Gilmore, Marina Saitgalina, Jennifer Whytlaw, Nicole Hutton, Tom Allen, Joshua Behr, Wie Yusuf

Presentations, Lectures, Posters, Reports

No abstract provided.

Monarch Science Observer, Volume 16, College Of Sciences, Old Dominion University

College of Sciences Newsletter

Spring 2023, issue of Monarch Science Observer, ODU Colleges of Sciences Newsletter.

The Use Of Artificial Intelligence To Detect Students Sentiments And Emotions In Gross Anatomy Reflections, Krzysztof J. Rechowicz, Carrie A. Elzie

VMASC Publications

Students' reflective writings in gross anatomy provide a rich source of complex emotions experienced by learners. However, qualitative approaches to evaluating student writings are resource heavy and timely. To overcome this, natural language processing, a nascent field of artificial intelligence that uses computational techniques for the analysis and synthesis of text, was used to compare health professional students' reflections on the importance of various regions of the body to their own lives and those of the anatomical donor dissected. A total of 1365 anonymous writings (677 about a donor, 688 about self) were collected from 132 students. Binary and trinary …

Ddr, Cassandra Jagroop

Graduate Research Conference (GSIS)

Disarmament, Demobilisation and Reintegration (DDR) has been a facet of post-conflict resolution since the 1980s. DDR seeks to address a wide range of issues varying from security to human rights, law, elections, and governance. One of the major issues arising from a conflict region are armed groups involved in the fighting and how to handle them. These armed groups represent the deep insecurity and lack of faith the population has in the state mechanism (Rondeau 2011, 654), thus in order to move ahead in the post-conflict situation the issue of armed groups needs to be tackled. The evolution of warfare …

R-Process Nucleosynthesis: Identifying The Significant Nuclear Properties, Sabina Gaia Tomasicchio

2023 REYES Proceedings

We provide a theoretical overview of r-process nucleosynthesis. We then identify the nuclear properties that have the greatest astrophysical impact according to recent sensitivity studies as: nuclear masses, β decays and neutron capture rates. Finally, we briefly discuss how the NuCRL model can enhance the performance of the relevant simulations.

Unmasking Deception In Vanets: A Decentralized Approach To Verifying Truth In Motion, Susan Zehra, Syed R. Rizvi, Steven Olariu

College of Sciences Posters

VANET, which stands for "Vehicular Ad Hoc Network," is a wireless network that allows vehicles to communicate with each other and with infrastructure, such as Roadside Units (RSUs), with the aim of enhancing road safety and improving the overall driving experience through real-time exchange of information and data. VANET has various applications, including traffic management, road safety alerts, and navigation. However, the security of VANET can be compromised if a malicious user alters the content of messages transmitted, which can harm both individual vehicles and the overall trust in VANET technology. Ensuring the correctness of messages is crucial for the …

Architecture Of Heptagonal Metallo-Macrocycles Via Embedding Metal Nodes Into Its Rigid Backbone, A.M.Shashika D. Wijerathna, He Zhao, Qiangqiang Dong, Qixia Bai, Zhiyuan Jiang, Jie Yuan, Jun Wang, Mingzhao Chen, Markus Zirnheld, Rockwell T. Li, Yuan Zhang, Yiming Li, Pingshan Wang

College of Sciences Posters

Metal-organic macrocycles have received increasing attention not only due to their versatile applications such as molecular recognition, compounds encapsulation, anti-bacteria and others, but also for their important role in the study of structure-property relationship at nano scale. However, most of the constructions utilize benzene ring as the backbone, which restricts the ligand arm angle in the range of 60, 120 and 180 degrees. Thus, the topologies of most metallo-macrocycles are limited as triangles and hexagons, and explorations of using other backbones with large angles and the construction of metallo-macrocycles with more than six edges are very rare.

In this study, …

Robots Still Outnumber Humans In Web Archives In 2019, But Less Than In 2012, Himarsha R. Jayanetti, Kritika Garg, Sawood Alam, Michael L. Nelson, Michele C. Weigle

College of Sciences Posters

To identify robots and human users in web archives, we conducted a study using the access logs from the Internet Archive’s (IA) Wayback Machine in 2012 (IA2012), 2015 (IA2015), and 2019 (IA2019), and the Portuguese Web Archive (PT) in 2019 (PT2019). We identified user sessions in the access logs and classified them as human or robot based on their browsing behavior. In 2013, AlNoamany et al. [1] studied the user access patterns using IA access logs from 2012. They established four web archive user access patterns: single-page access (Dip), access to the same page at multiple archive times (Dive), access …

Adhd Prediction Through Analysis Of Eye Movements With Graph Convolution Network, Gavindya Jayawardena, Sampath Jayarathna, Yi He

College of Sciences Posters

Processing speech with background noise requires appropriate parsing of the distorted auditory signal, fundamental language abilities as well as higher signal-to-noise ratio. Adolescents with ADHD have difficulty processing speech with background noise due to reduced inhibitory control and working memory capacity. In this study we utilize Audiovisual Speech-In-Noise performance and eye-tracking measures of young adults with ADHD compared to age-matched controls, and generate graphs for ADHD evaluation using the eye-tracking data. We form graphs utilizing the eight eye-tracking features (fixation count, average, total, and standard deviation of fixation duration, max and min saccade peak velocity, min, average, and standard deviation …

Evaluation Of Scalable Quantum And Classical Machine Learning For Particle Tracking Classification In Nuclear Physics, Polykarpos Thomadakis, Emmanuel Billias, Nikos Chrisochoides

The Graduate School Posters

Future particle accelerators will exceed by far the current data size (10¹⁵) per experiment, and high- luminosity program(s) will produce more than 300 times as much data. Classical Machine Learning (ML) likely will benefit from new tools based on quantum computing. Particle track reconstruction is the most computationally intensive process in nuclear physics experiments. A combinatorial approach exhaustively tests track measurements (“hits”), represented as images, to identify those that form an actual particle trajectory, which is then used to reconstruct track parameters necessary for the physics experiment. Quantum Machine Learning (QML) could improve this process in multiple ways, …

Analysis Of Ab Initio Protein Structure Prediction Methods, Maytha Alshammari, Jing He

College of Sciences Posters

Protein structure prediction produces atomic models of three-dimensional structure of a protein from its amino acid sequence. Understanding the function mechanism of proteins requires knowledge of three-dimensional structures. When developing new enzymes and drugs, it's essential to understand the structure of the target protein. In this study, we analyze models predicted using two ab initio protein structure prediction methods, trRosetta and Quark. A set of thirty protein chains was used to evaluate the effectiveness of the methods. The thirty chains were collected from Protein Data Bank (June – November, 2020). The length and the relative position of the predicted secondary …

A Bootstrap Test For Informative Intra-Cluster Group Sizes In Clustered Data, Hasika K. Wickrama Senevirathne, Sandipan Dutta

College of Sciences Posters

Clustered data are frequently observed in various domains of scientific and social studies. In a typical clustered data, units within a cluster are correlated while units between different clusters are independent. An example of such clustered data can be found in dental studies where individuals are treated as clusters and the teeth in an individual are the units within a cluster. While analyzing such clustered data, it has been observed that the number of units present in a cluster can be informative in terms of being associated with the outcome from that cluster. Specifically, when the aim is to compare …

Hydrodynamics And Sediment Transport In The Tidally Influenced James River, Ollie Gilchrest, Rip Hale

The Graduate School Posters

The tidally influenced James River is an important economic, ecologic, and cultural resource for VA residents. Tidal rivers have been historically understudied, however they are critical transition zones, the dynamics of which have implications for freshwater supply and sediment trapping. Globally, estimates suggest that >30% of fluvial sediment is trapped in the tidal zone, the location and dynamics of which are actively changing due to sea level rise and saltwater encroachment. In addition, analysis of historical water levels on the James River has shown a decrease in the tidal range since 1940. The present study combines >1-year’s worth of hydrographic …

Organic Matter Content And Grain Size Analysis In Seagrass Sediments, Adriana Amrhein, Rip Hale

College of Sciences Posters

Anthropogenic stresses including increased water temperatures, decreased water quality, sea level rise, ocean acidification and sediment biogeochemical processes have caused a significant loss in seagrass meadow acreage. Seagrass meadows provide coastal protection from storms and recently have been emphasized for their importance in sequestering and storing “Blue Carbon” from the atmosphere and ocean. Seagrass meadows can trap this blue carbon in their sediment as organic carbon, and it can be stored for hundreds to thousands of years. Restoration efforts of seagrass meadows in the Virginia coast started in the 1990s and effects of increasing seagrass density can be observed in …

Dust Deposition To The Bermuda Region: A Comparison Of Estimates Using Seasonally-Resolved Measurements Of Aluminum In Water-Column, Aerosol, And Rain Samples, Tara Williams, Peter Sedwick, Bettina Sohst, Joe Resing, Kristen Buck, Salvatore Caprara, Rod Johnson, Dan Ohnemus, Ben Twining, Alessandro Tagliabue

College of Sciences Posters

Dust deposition is a major source of bioactive trace elements to the surface ocean, yet this flux remains difficult to constrain. Previously, time-averaged dust flux has been estimated using surface ocean dissolved aluminum (DAl) concentrations, assumed values for aerosol aluminum solubility (%Al_S), and the residence time of DAl in the surface mixed layer (SML). We apply this method to estimate dust deposition in the Bermuda Atlantic Time-series Study (BATS) region using water-column DAl data from cruises in 2019, which is compared with direct flux estimates from contemporaneous measurements of aluminum in aerosols and rain collected on Bermuda. Seasonal …

Copula-Based Models For Bivariate And Multivariate Zero-Inflated Count Time Series Data, Dimuthu Fernando, Norou Diawara

College of Sciences Posters

Count time series data have multiple applications. The applications can be found in areas of finance, climate, public health and crime data analyses. In some scenarios, count time series come as multivariate vectors that exhibit not only serial dependence within each time series but also with cross correlation among the series. When considering these observed counts, analysis presents crucial challenges when a value, say zero, occurs more often than usual. There is presence of zero-inflation in the data.

In this presentation, we mainly focus on modeling bivariate zero-inflated count time series model based on a joint distribution of the two …

Ml-Based Surrogates And Emulators, Tareq Alghamdi, Yaohang Li, Nobuo Sato

College of Sciences Posters

No abstract provided.

A Novel Parking Management In Smart City Vehicular Datacenters, Syed Rizvi, Susan Zehra, Steven Olariu

College of Sciences Posters

Researchers have shown that most vehicles spend the majority of their time parked in parking garages, lots, or driveways. During this time, their computing resources are unused and untapped. This has led to substantial interest in Vehicular Cloud, an area of research in which each vehicle acts as a computation node. The main difference between traditional cloud computing and vehicular cloud computing is the availability of nodes. In traditional clouds, nodes are available 24/7, while in vehicular clouds, nodes (vehicles) are only available while parked in parking lots. This creates a dynamic environment as vehicles enter and exit parking garages …

Investigating The Impacts Of Connected Vehicles On Driving Aggressiveness And Situational Awareness In Highway Crash Scenarios: A Driving Simulator Study, Abdalziz Alruwaili

College of Engineering & Technology (Batten) Posters

Psychological factors such as aggressiveness and situational awareness can impact driving performance. Connected vehicles (CV), equipped with advanced sensors and able to communicate safety messages to drivers, have the potential to influence driving performance by altering drivers’ aggressiveness and situational awareness. This paper aims to investigate the impacts of the CVs on driving aggressiveness and situational awareness in highway crash scenarios where a primary crash has already occurred, and a second crash may occur as a result. To achieve this goal, a driving simulator experiment was conducted, and questionnaires focused on driving aggressiveness and CV effectiveness were distributed. Structural equation …

Scalable Quantum Edge Detection Method For D-Nisq Imaging Simulations: Use Cases From Nuclear Physics And Medical Image Computing, Emmanuel Billias, Nikos Chrisochoides

The Graduate School Posters

Edge Detection is one of the computationally intensive modules in image analysis. It is used to find important landmarks by identifying a significant change (or “edge”) between pixels and voxels. We present a hybrid Quantum Edge Detection method by improving three aspects of an existing widely referenced implementation, which for our use cases generates incomprehensible results for the type and size of images we are required to process. Our contributions are in the pre- and post-processing (i.e., classical phase) and a quantum edge detection circuit: (1) we use space- filling curves to eliminate image artifacts introduced by the image decomposition, …

Exploring Human Perception While Reading Fake And Real News Articles, Yasasi Abeysinghe, Gavindya Jayawardana, Autumn Woodson, Efe Bozkir, Enkelejda Kasneci, Andrew Duchowski, Sampath Jayarathna

College of Sciences Posters

With the increased spread of misinformation on online platforms and the popularity of AI-generated text, there is a critical need to detect human perception regarding the truthfulness of news. Users’ believability in a news item influences the reading and sharing of that news. Hence, in order to reduce the spread of fake news online, it is important to understand how users' engagement with fake and real news and users' perceived believability impact their behavioral and physiological factors. In this work, we study human eye movements based on the truthfulness of news and their perceived believability. Using the publicly available FakeNewsPerception …

Metaenhance: Metadata Quality Improvement For Electronic Theses And Dissertations, Muntabir H. Choudhury, Lamia Salsabil, Himarsha R. Jayanetti, Jian Wu

College of Sciences Posters

Metadata quality is crucial for digital objects to be discovered through digital library interfaces. Although DL systems have adopted Dublin Core to standardize metadata formats (e.g., ETD-MS v1.1¹), the metadata of digital objects may contain incomplete, inconsistent, and incorrect values [1]. Most existing frameworks to improve metadata quality rely on crowdsourced correction approaches, e.g., [2]. Such methods are usually slow and biased toward documents that are more discoverable by users. Artificial intelligence (AI) based methods can be adopted to overcome this limit by automatically detecting, correcting, and canonicalizing the metadata, featuring quick and unbiased responses to document metadata. …

X-Disetrac: Distributed Eye-Tracking With Extended Realities, Bhanuka Mahanama, Sampath Jayarathna

College of Sciences Posters

Humans use heterogeneous collaboration mediums such as in-person, online, and extended realities for day-to-day activities. Identifying patterns in viewpoints and pupillary responses (a.k.a eye-tracking data) provide informative cues on individual and collective behavior during collaborative tasks. Despite the increasing ubiquity of these different mediums, the aggregation and analysis of eye-tracking data in heterogeneous collaborative environments remain unexplored. Our study proposes X-DisETrac: Extended Distributed Eye Tracking, a versatile framework for eye tracking in heterogeneous environments. Our approach tackles the complexity by establishing a platform-agnostic communication protocol encompassing three data streams to simplify data aggregation and …

Nudyclr: Nuclear Dynamic Co-Learned Representations, Víctor Samuel Pérez-Díaz

2023 REYES Proceedings

NuCLR (Nuclear Co-Learned Representations) is a cutting-edge multi-task deep learning framework designed to predict essential nuclear observables, including binding energies, decay energies, and nuclear charge radii. As part of the REYES Mentorship Program, we investigated the application of dynamic loss weighting to further refine NuCLR’s predictive performance. Our findings indicate that while weighting strategies can enhance accuracy in specific tasks, such as binding energy prediction, they may underperform in others. Equal Weighting (EW), the original method employed by NuCLR, demonstrated consistent performance across multiple tasks, affirming its robustness. This report succinctly presents the developments and results of the mentorship program …

Algebraic Tunnelling, Gaurab Sedhain

2023 REYES Proceedings

We study the quantum phenomenon of tunnelling in the framework of algebraic quantum theory, motivated by the tunnelling aspects of false vacuum decay. We see that resolvent C*-algebra, proposed relatively recently by Buchholz and Grundling rather than Weyl algebra provides an appropriate framework for treating the dynamics of non-free quantum mechanical system as an algebraic automorphism. At the end, we propose to investigate false vacuum decay in algebraic quantum field theoretic setting in terms of the two-point correlation function which gives us the tunneling probability, with the corresponding C*-algebraic construction.

Spontaneous Symmetry Breaking And Goldstone Theorem, Emilia Szymańska

2023 REYES Proceedings

We discuss the concept of spontaneous symmetry breaking and illustrate it with a general example. We consider Wigner-Weyl and Nambu-Goldstone realisations of symmetry in the quantum theory. Next, we state Goldstone’s theorem and sketch its proof. We discuss why quantum chromodynamics is not realised in the Wigner-Weyl mode. We also consider different order parameters of spontaneous chiral symmetry breaking.