Physical Sciences and Mathematics Commons^™

Monarch Science Observer, Volume 12, College Of Sciences, Old Dominion University

College of Sciences Newsletter

Winter 2021 issue of Monarch Science Observer, ODU College of Sciences Newsletter.

Monarch Science Observer, Volume 11, College Of Sciences, Old Dominion University

College of Sciences Newsletter

Fall 2021 issue of Monarch Science Observer, ODU College of Sciences Newsletter.

Circulation, Volume 26, No. 3, Center For Coastal Physical Oceanography, Old Dominion University

CCPO Circulation

Summer 2021 issue of CCPO Circulation featuring the article "LARRY ATKINSON: Scientist, Leader and Mentor," by John Klinck.

Monarch Science Observer, Volume 10, College Of Sciences, Old Dominion University

College of Sciences Newsletter

Summer 2021 issue of Monarch Science Observer, ODU College of Sciences Newsletter.

Circulation, Volume 26, No.2, Center For Coastal Physical Oceanography, Old Dominion University

CCPO Circulation

Spring 2021 issue of CCPO Circulation featuring the article, "Reconciling Models and Everyday Life," by Pierre St-Laurent.

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 …

Monarch Science Observer, Volume 9, College Of Sciences, Old Dominion University

College of Sciences Newsletter

Spring 2021 issue of Monarch Science Observer, ODU College of Sciences Newsletter.

Institutional Context Drives Mobility: A Comprehensive Analysis Of Academic And Economic Factors That Influence International Student Enrollment At United States Higher Education Institutions, Natalie Cruz

College of Education & Professional Studies (Darden) Posters

International student enrollment (ISE) has become a hallmark of world-class higher education institutions (HEIs). Although the U.S. has welcomed the largest numbers of international students since the 1950s, ISE shrunk by 10% in the previous three years from an all-time high of 903,127 students in 2016/2017 (IIE, 2019). Research studies about international student mobility and enrollment highlights the significant role that academic and economic rationales play for international students. This quantitative, ex post facto study focused on the influence of ranking, tuition, Optional Practical Training, Gross Domestic Product, and the unemployment rate on ISE at 2,884 U.S. HEIs from 2008 …

Data-Limited Domain Adaptation And Transfer Learning For Learning Latent Expression Labels Of Child Facial Expression Images, Megan Witherow, Winston Shields, Manar Samad, Khan Iftekharuddin

College of Engineering & Technology (Batten) Posters

While state-of-the-art deep learning models have demonstrated success in adult facial expression classification by leveraging large, labeled datasets, labeled data for child facial expression classification is limited. Due to differences in facial morphology and development in child and adult faces, deep learning models trained on adult data do not generalize well to child data. Recent deep domain adaptation approaches have improved the generalizability of models trained on a source domain to a target domain with few labeled samples. We propose that incorporating steps of deep transfer learning, e.g. weights initialization from the pre-trained source model and freezing model layers, may …

Unsupervised Multivariate Time Series Clustering, Md Monibor Rahman, Lasitha Vidyaratne, Alex Glandon, Khan Iftekharuddin

College of Engineering & Technology (Batten) Posters

Clustering is widely used in unsupervised machine learning to partition a given set of data into non-overlapping groups. Many real-world applications require processing more complex multivariate time series data characterized by more than one dependent variables. A few works in literature reported multivariate classification using Shapelet learning. However, the clustering of multivariate time series signals using Shapelet learning has not explored yet. Shapelet learning is a process of discovering those Shapelets which contain the most informative features of the time series signal. Discovering suitable Shapelets from many candidates Shapelet has been broadly studied for classification and clustering of univariate time …

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 …

Nanopore Guided Regional Assembly, Eleni Adam, Desh Ranjan, Harold Riethman

College of Sciences Posters

The telomeres are the “caps” of the chromosomes and their vital role is to protect them. Possible telomere dysfunction caused by telomere rearrangements can be fatal for the cell and result in age-related diseases, including cancer. The telomeres and subtelomeres are regions that are hard to investigate. The current technology cannot provide their complete sequence, instead the DNA is given in multiple pieces. Current methods of assembling the pieces of these regions are not accurate enough due to the region’s high variability and complex repeated patterns. We propose a hybrid assembly method, the NPGREAT, which utilizes two of the latest …

Combine Cryo-Em Density Map And Residue Contact For Protein Structure Prediction: A Case Study, Maytha Alshammari, Jing He

College of Sciences Posters

Although atomic structures have been determined directly from cryo-EM density maps with high resolutions, current structure determination methods for medium resolution (5 to 10 Å) cryo-EM maps are limited by the availability of structure templates. Secondary structure traces are lines detected from a cryo-EM density map for α-helices and β-strands of a protein. A topology of secondary structures defines the mapping between a set of sequence segments in 1D and a set of traces of secondary structures in 3D. In order to enhance the accuracy in ranking secondary structure topologies, we propose a method that combines three sources of information …

End-To-End Physics Event Generator, Yasir Alanazi, N. Sato, Tianbo Liu, W. Melnitchouk, Michelle P. Kuchera, Evan Pritchard, Michael Robertson, Ryan Strauss, Luisa Velasco, Yaohang Li

College of Sciences Posters

We apply generative adversarial network (GAN) technology to build an event generator that simulates particle production in electron-proton scattering that is free of theoretical assumptions about underlying particle dynamics. The difficulty of efficiently training a GAN event simulator lies in learning the complicated pat- terns of the distributions of the particles physical properties. We develop a GAN that selects a set of transformed features from particle momenta that can be generated easily by the generator, and uses these to produce a set of augmented features that improve the sensitivity of the discriminator. The new Feature-Augmented and Transformed GAN (FAT-GAN) is …

Examining Arctic Melt Pond Dynamics Via High Resolution Satellite Imagery, Austin Abbott, Victoria Hill

College of Sciences Posters

The Arctic Ocean is a rapidly changing environment, and a key observational system for monitoring climate change. The Arctic is going under a rapid transition from thicker, multi-year ice, to thinner first-year ice, that may have many potential consequences. As first year Arctic sea ice begins to retreat in the spring and early summer, melting snow and ice form ponds on the surface- “melt ponds”. These melt ponds increase light transmission to the water column, resulting in warming and increased primary production under the ice. Recent advances in high resolution satellite imagery now allow us to monitor the development and …

Design, Commissioning And Preliminary Results Of A Magnetic Field Scanning System For Superconducting Radiofrequency Cavities, Ishwari Parajuli, Jeffrey Nice, Gianluigi Ciovati, William Clemens, Jean Delayen, Alex Gurevich

College of Sciences Posters

Superconducting radiofrequency (SRF) cavities are one of the building blocks of modern particle accelerators. Such cavities are typically made of bulk niobium, operate at liquid helium temperature (2 - 4 K) and have some of the highest quality factors found in Nature. One of the leading source of residual losses, which limits the quality factor in SRF cavities, is trapped magnetic flux from either residual ambient magnetic field or thermoelectric currents. The flux trapping mechanism depends on different niobium surface preparations and cool-down conditions. Suitable diagnostic tools are not yet available to study the effects of such conditions on magnetic …

Nb3Sn Coating Of Complex Srf Cavity Structures, Jayendrika Tiskumara, Uttar Pudasaini, Grigory Eremeev, Charlie Reece, Jean Delayen

College of Sciences Posters

In the modern SRF research, Thin films coated niobium cavities are used for the low cost and increased quality factor. Among the potential thin film materials applied on the niobium, performances demonstrated by the Nb3Sn cavities makes this material attractive for SRF accelerator applications giving higher critical temperature and higher accelerating gradients. While the majority of research efforts are currently focused on the development of elliptical single-cell and multi-cell cavities, the potential of this material is evident to other cavity types, which may have complex geometries. We are working towards the development of Nb₃Sn-coated Half-wave resonator and twin …

Direct Visualization Of 3-Dimensional Force And Energy Map Of A Single Molecular Switch, Abeykoon Mudiyanselage Shashika Darshani Wijerathna, Zaw Myo Win, K. Z. Latt, Yang Li, A. T. Ngo, L. Curtiss, R. Zhang, S. W. Hla, Y. Zhang

College of Sciences Posters

Mechanical properties of molecules adsorbed on materials surfaces are increasingly vital for the applications of molecular thin films. Here, we conduct a fundamental research to induce conformational change mechanically on a single molecule and quantify the driving force needed for such molecular shape switch via a low temperature (~ 5K) Scanning Tunneling Microscope (STM) and Qplus Atomic Force Microscope (Q+AFM). Our measurement maps a three-dimensional landscape for mechanical potential and force at single molecule level with high spatial resolution in all three dimensions of a few angstrom (10^-10 m).

Molecule TBrPP-Co (a cobalt porphyrin) deposited on an atomically clean …

The Magnetic Field Penetration Measurement Of Thin Film And Multilayered Superconductors For Srf Cavities, Iresha Harshani Senevirathne, Jean Delayen

College of Sciences Posters

Radio Frequency (RF) Cavities are used in particle accelerators and they are typically formed from or coated with superconducting materials. High purity niobium is the material of choice for SRF cavities and niobium cavities operate at their theoretical field limits. SRF researchers have begun a significant R&D effort to develop alternative materials to continue to keep up with the demands of new accelerator facilities. To achieve high performance with high accelerating gradient, cavity material should have an ability to persist in superconducting state under high magnetic field without magnetic flux penetration through the cavity wall. Therefore, the magnetic field at …

Using Torchattacks To Improve The Robustness Of Models With Adversarial Training, William S. Matos Díaz

Cybersecurity: Deep Learning Driven Cybersecurity Research in a Multidisciplinary Environment

Adversarial training has proven to be one of the most successful ways to defend models against adversarial examples. This process consists of training a model with an adversarial example to improve the robustness of the model. In this experiment, Torchattacks, a Pytorch library made for importing adversarial examples more easily, was used to determine which attack was the strongest. Later on, the strongest attack was used to train the model and make it more robust against adversarial examples. The datasets used to perform the experiments were MNIST and CIFAR-10. Both datasets were put to the test using PGD, FGSM, and …

Solving Relativistic Three-Body Integral Equations In The Presence Of Bound States And Resonances, Taylor R. Powell, Raúl A. Briceño, Andrew W. Jackura

Physics: Accelerator and Nuclear Physics at the Thomas Jefferson National Accelerator Facility in Newport News, Virginia

Three-body interactions play an important role throughout modern-day particle, nuclear, and hadronic physics; many experimentally observed reactions of interest for testing the Standard Model result in final states composed of three particles or more. Due to these issues, a full description of three-body interactions from Quantum Chromodynamics is required. The focus of this project was to extend previous results for a two-body subsystem with a bound state to include resonance channels. We first derived a novel single-variable observable, denoted as an intensity distribution, which is proportional to the probability density of the three-body scattering amplitude. We explored this distribution in …

Measurement Study Of Energy Impact On Blockchain Technologies: Cryptocurrency Mining, Qaylin Holliman

Cybersecurity: Deep Learning Driven Cybersecurity Research in a Multidisciplinary Environment

Blockchain technology facilitates the flow of information and the speed of information through a faster and more decentralized network. It has its advantages as compared to more centralized networks and legacy networks. With the evolution of mainstream technology, blockchains is predicted to be more effective and sufficient to consumers and commercial companies. In this paper, blockchains will be scaled to cryptocurrency mining, where cryptocurrencies utilize blockchain technology to record transactions and orders. Mining will also be examined through energy consumption, the algorithms behind some cryptocurrencies, their sustainability issue, and resolutions to combat high energy consumption. While the pace of energy …