Physical Sciences and Mathematics Commons^™

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 …

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 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 …

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 …

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 …

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 …

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 …

Active Polar Liquid Crystal Channel Flows: Analyzing The Roles Of Nematic Strength And Activation Parameter, Lacey Schenk, Ruhai Zhou

College of Sciences Posters

Suspensions of active polar liquid crystalline polymers (APLC) exhibit complex phenomena such as spontaneous flows, pattern formations and defects. Using the Kinetic Model, which couples the Smoluchowski Equation and the Navier-Stokes Equations, we conduct numerical simulations of APLC in a microfluidic channel to investigate the competitive effect among different material constants, such as the nematic concentration (the strength of the potential for nematic order) and active strength (the individual nano-rods strength of their individual movement) with and without a pressure gradient. Both Dirichlet and Neumann boundary conditions on the mathematical model are employed. Steady states, including isotropic and nematic states, …

A Machine Learning Approach To Denoising Particle Detector Observations In Nuclear Physics, Polykarpos Thomadakis, Angelos Angelopoulos, Gagik Gavalian, Nikos Chrisochoides

College of Sciences Posters

With the evolution in detector technologies and electronic components used in the Nuclear Physics field, experimental setups become larger and more complex. Faster electronics enable particle accelerator experiments to run with higher beam intensity, providing more interactions per time and more particles per interaction. However, the increased beam intensities present a challenge to particle detectors because of the higher amount of noise and uncorrelated signals. Higher noise levels lead to a more challenging particle reconstruction process by increasing the number of combinatorics to analyze and background signals to eliminate. On the other hand, increasing the beam intensity can provide physics …

Clostridioides Difficile Spore Production In Response To Antibiotic And Immune Stress, Adenrele Oludiran, Erin B. Purcell

College of Sciences Posters

Clostridioides (Clostridium) difficile, an anaerobic, spore-forming Gram-positive pathogenic bacterium, is a major cause of hospital-acquired infections and can persist as surface-attached biofilms for protection from antibiotic and immune stress. C. difficile can form biofilms as a single species or with other anaerobic intestinal bacteria. The environmental signals that cause individual cells to secrete toxins, form biofilms, or develop into spores that can spread the infection to new patients are unknown. In these studies, we investigate bacterial responses to different stress. Antimicrobial host-defense peptides (HDPs) produced by animal immune systems are promising candidates to develop novel therapies for bacterial infection …

Lattice Optics Optimization For Recirculatory Energy Recovery Linacs With Multi-Objective Optimization, Isurumali Neththikumara, Todd Satogata, Alex Bogacz, Ryan Bodenstein, Arthur Vandenhoeke

College of Sciences Posters

Beamline optics design for recirculatory linear accelerators requires special attention to suppress beam instabilities arising due to collective effects. The impact of these collective effects becomes more pronounced with the addition of energy recovery (ER) capability. Jefferson Lab’s multi-pass, multi-GeV ER proposal for the CEBAF accelerator, ER@CEBAF, is a 10- pass ER demonstration with low beam current. Tighter control of the beam parameters at lower energies is necessary to avoid beam break-up (BBU) instabilities, even with a small beam current. Optics optimizations require balancing both beta excursions at high-energy passes and overfocusing at low-energy passes. Here, we discuss an optics …

A Spatially And Temporally Second Order Method For Solving Parabolic Interface Problems, Kumudu Gamage, Yan Peng

College of Sciences Posters

Parabolic interface problems have many applications in physics and biology, such as hyperthermia treatment of cancer, underground water flow, and food engineering. Here we present an algorithm for solving two-dimensional parabolic interface problems where the coefficient and the forcing term have a discontinuity across the interface. The Crank-Nicolson scheme is used for time discretization, and the direct immersed interface method is used for spatial discretization. The proposed method is second order in both space and time for both solution and gradients in maximum norm.

Empirically Adjusted Weighted Ordered P-Values Method, Wimarsha Jayanetti, Sinjini Sikdar, N. Rao Chaganty

College of Sciences Posters

Recent advancements in high-throughput technologies have enabled simultaneous inference of thousands of genes. With the abundance of public databases, it is now possible to rapidly access the results of several genomic studies, each of which includes the significance testing results of a large number of genes. Researchers frequently aggregate genomic data from multiple studies in the form of a meta-analysis. Most traditional meta-analysis methods aim at combining summary results to find signals in at least one of the studies. However, often the goal is to identify genes that are differentially expressed in a consistent pattern across multiple studies. Recently, a …

Analysis Of An Existing Method In Refinement Of Protein Structure Predictions Using Cryo-Em Images, Maytha Alshammari, Jing He, Willy Wriggers, Jiangwen Sun

College of Sciences Posters

Protein structure prediction produces atomic models from its amino acid sequence. Three-dimensional structures are important for understanding the function mechanism of proteins. Knowing the structure of a given protein is crucial in drug development design of novel enzymes. AlphaFold2 is a protein structure prediction tool with good performance in recent CASP competitions. Phenix is a tool for determination of a protein structure from a high-resolution 3D molecular image. Recent development of Phenix shows that it is capable to refine predicted models from AlphaFold2, specifically the poorly predicted regions, by incorporating information from the 3D image of the protein. The goal …

Physics-Informed Neural Networks (Pinns) For Dvcs Cross Sections, Manal Almaeen, Jake Grigsby, Joshua Hoskins, Brandon Kriesten, Yaohang Li, Huey-Wen Lin, Simonetta Liuti, Sorawich Maichum

College of Sciences Posters

We present a physics informed deep learning technique for Deeply Virtual Compton Scattering (DVCS) cross sections from an unpolarized proton target using both an unpolarized and polarized electron beam. Training a deep learning model typically requires a large size of data that might not always be available or possible to obtain. Alternatively, a deep learning model can be trained using additional knowledge gained by enforcing some physics constraints such as angular symmetries for better accuracy and generalization. By incorporating physics knowledge to our deep learning model, our framework shows precise predictions on the DVCS cross sections and better extrapolation on …

A Direct Method For Modeling And Simulations Of Elliptic And Parabolic Interface Problems, Kumudu Gamage, Yan Peng

College of Sciences Posters

Interface problems have many applications in fluid dynamics, molecular biology, electromagnetism, material science, heat distribution in engines, and hyperthermia treatment of cancer. Mathematically, interface problems commonly lead to partial differential equations (PDE) whose in- put data are discontinuous or singular across the interfaces in the solution domain. Many standard numerical methods designed for smooth solutions poorly work for interface problems as solutions of the interface problems are mostly non-smoothness or discontinuous. Moving interface problems depends on the accuracy of the gradient of the solution at the interface. Therefore, it became essential to derive a method for interface problems that gives …

Alexandrium In The Arctic: Are Harmful Algae Spreading As The Arctic Warms?, Sveinn Einarsson, Kate Lowry, Robert Pickart, Karin Ashjian, P. Dreux Chappell

College of Sciences Posters

Alexandrium tamerense is a well-studied dinoflagellate known for its ability to produce the neurotoxin that causes paralytic shellfish poisoning. Until 1970 Alexandrium tamerense was only found in Europe, North America, and Japan but has been increasingly found all over the globe. Alexandrium is characteristically found in temperate and subtropical regions and as the Arctic warms, there is considerable concern that it may be expanding into the Arctic. We found Alexandrium tamerense during a research expedition to the Alaskan Beaufort Sea shelf to study upwelling. Upwelling events are known to support seasonal blooms of phytoplankton, which are important primary producers at …

Defining The Environmental Niche Of The Two Main Clades Of Trichodesmium: A Study On The West Florida Shelf, Kristina Confesor, Corday Selden, Kimberly Powell, Angela Knapp, Kristen Buck, Laura Donahue, Dreux Chappell

College of Sciences Posters

Dinitrogen (N₂) fixation is the process of taking widely abundant but mostly biologically inaccessible N₂ gas and converting it into more biologically accessible forms of the essential macronutrient nitrogen. Only a small fraction of organisms known as diazotrophs can perform biological N₂ fixation. Trichodesmium is one such genus of N₂-fixing marine cyanobacteria that is commonly observed in waters along the West Florida Shelf (WFS). We hypothesize that the two main Trichodesmium clades (T. erythraeum and T. thiebautii) occupy distinct environmental niches, one being more coastal and one being more oceanic. To test …

Vaim For Solving Inverse Problems, Manal Almaeen, Yasir Alanazi, Michelle Kuchera, Nobuo Sato, Wally Melnitchouk, Yaohang Li

College of Sciences Posters

In this work, we propose the Variational Autoencoder Inverse Mapper (VAIM) to solve inverse problems, where there is a demand to accurately restore hidden parameters from indirect observations. VAIM is an autoencoder-based neural network architecture. The encoder and decoder networks approximate the forward and backward mapping, respectively, and a variational latent layer is incorporated into VAIM to learn the posterior parameter distributions with respect to the given observables. VAIM shows promising results on several artificial inverse problems. VAIM further demonstrates preliminary effectiveness in constructing the inverse function mapping quantum correlation functions to observables in a quantum chromodynamics analysis of nucleon …

Seasonal Variability In Diazotroph Abundance And Gene Expression At A Coastal N2 Fixation Hotspot (Outer Banks, Nc), Katherine Crider, Corday Selden, Kimberly Powell, P. Dreux Chappell

College of Sciences Posters

Marine microbial dinitrogen (N₂) fixation, the conversion of gaseous N₂ to bioavailable species, is the primary source of new oceanic nitrogen (N). N is present in nucleic acids, amino acids, and proteins, and is essential to all life. Long considered to be a primarily oligotrophic ocean process, significant N₂ fixation rates have recently been observed in coastal environments, including along the Cape Hatteras front. To see if elevated N₂ fixation was a persistent feature in this region, N₂ fixation rates and N₂ fixer (diazotroph) abundance and gene expression were investigated through roughly monthly …

Influence Of Monovalent And Divalent Ions In The Conformational Change Of Caspase-Cleaved Par-4 (Cl-Par-4) Tumor Suppressor Protein, Krishna K. Raut, Komala Ponniah, Steven M. Pascal

College of Sciences Posters

Prostate apoptosis response-4 (Par-4) is a pro-apoptotic tumor suppressor protein. We have shown that this 38 kDa full-length Par-4 (Fl-Par-4) protein is predominantly intrinsically disordered in vitro. In vivo, Par-4 is cleaved by caspase-3 at Asp-131 to generate a 24 kDa functionally active cleaved Par-4 (cl-Par-4) fragment. The cl-Par-4 protein inhibits the NF-κB-mediated cell survival pathway and causes selective apoptosis in various tumor cells. Our laboratory is interested in how the disorder-order balance within Fl-Par-4 and cl-Par-4 may be related to the balance between cell survival and cell death. Currently, we are using biophysical techniques such as circular …

Analysis Of Reading Patterns Of Scientific Literature Using Eye-Tracking Measures, Gavindya Jayawardena, Sampath Jayarathna, Jian Wu

College of Sciences Posters

Scientific literature is crucial for researchers to inspire novel research ideas and find solutions to various problems. This study presents a reading task for novice researchers using eye-tracking measures. The study focused on the scan paths, fixation, and pupil dilation frequency of the participants. In this study, 3 participants were asked to read a pre-selected research paper while wearing an eye-tracking device (PupilLabs Core 200Hz). We specified sections of the research paper as areas of interest (title, abstract, motivation, methodology, conclusion)to analyze the eye-movements. Then we extracted eye-movements data from the recordings and processed them using an eye-movement processing pipeline. …

Multi-Dimensional Numerical Integration On Parallel Architectures, Ioannis Sakiotis, Marc Paterno, Balsa Terzic, Mohammad Zubair, Desh Ranjan

College of Sciences Posters

Multi-dimensional numerical integration is a challenging computational problem that is encountered in many scientific computing applications. Despite extensive research and the development of efficient techniques such as adaptive and Monte Carlo methods, many complex high-dimensional integrands can be too computationally intense even for state-of-the-art numerical libraries such as CUBA, QUADPACK, NAG, and MSL. However, adaptive integration has few dependencies and is very well suited for parallel architectures where processors can operate on different partitions of the integration-space. While existing parallel methods exist, most are simple extensions of their sequential versions. This results in moderate speedup and in many cases failure …