Mason Journals (George Mason Univ.)
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ImageJ analysis of morphological changes in mud crabs from the Chesapeake Bay infected with castrating, zombie barnacle parasite
No full textMale mud crabs infected with an invasive endoparasite (Loxothylaucus panopaei or loxo) in the Chesapeake Bay are speculated to undergo morphological changes, altering their typical long and narrow aprons to a wider and broader apron seen in female crabs. Here, we analyze these changes in two common mud crabs, Eurypanopeus deressus (e.d.) and Rhithropanopeus harrisii (r.h.), using imageJ, an image processing software. From the data collected, we calculated the relative abdominal width (RAW), a ratio between the widths of apron segments 2 and 5. Low RAW’s are a result of broader aprons, while high RAW’s are a result of a taper being present. Out of 293 crabs, 111 were females and 182 were males. 84% of males were uninfected, while 26% were infected. 62% of females were uninfected, and 48% were infected. An ANOVA test was done comparing the RAW and infection status relationship between genders. The P value between infected males and uninfected males was 1.38e-11, showing there is a significant gap with an average difference of 0.44. The P value between infected females and uninfected females was 0.0108, showing there is a slight difference with an average gap of 0.17. Uninfected males had the highest RAW, followed by infected males, uninfected females, and infected females. These findings show that loxo alters the apron shape drastically in infected males but only slightly in females. While the underlying cause of loxo feminization is still unknown, these results will provide better insight into the morphological changes of this crab-parasite relationship
Learning Agent-Based Modeling Trough Virtual Reality-Based Simulations
No full textAgent-Based Modeling (ABM) provides a multitude of application areas due to their perspective to modeling systems, which are made up of interacting and autonomous agents. For instance, pedestrian movement in a metro station can be modeled with a digital representation of the station where humans are represented as individual agents. Not only do these applications stay in the line of theoretical research, but also for real world applications for businesses. However, due to the required level of knowledge needed to understand and create these models, getting into ABM has been mostly difficult for students with no prior coding experience as these models are usually created in 2D. To combat this barrier, we created three 3D VR visualizations of popular agent-based models from different sources. All models were made using Unity’s gameEngine, in which Unity’s XR toolkit was used to set up necessary functions like locomotion and user interactions. Alongside these tasks, many scripts that handled the interactions and attributes of agents were made in the C# language. These scripts range in functionality, as some are important for user experience, and some are necessary for the simulation to work. All in all, these VR-based models provide a visual and interactive representation of the example models, making it easy to grasp how the agency concept work. Future study will involve measuring the efficacy of VR-based models against typical 2D models with respect ABM education
Investigating Governance of Decentralized Projects Through GitHub Archive
No full textDecentralized applications and services are of great importance to the crypto industry. Our work looks at over 200 such companies in our study and investigates the development history for each on Github Archive with the goal of finding patterns in the data to reveal more about the governance of decentralized corporations. The Github data for each company was pulled using a SQL script to obtain various details about the project activity from 2013 to 2023. These details include the number of watches, pull requests, pushes, commits, branch creations, and more which altogether create a picture of the project’s history. Next, our team researched each company to find what type of project the company was. For example, it could be a crypto wallet, a Dapp, a L2 network, or a token or stablecoin. We are looking to find trends between Github activities and the type of project. For instance, we look to see if Dapps have more commits on average than an L2 network, or if a characteristic of a token project turns out that they usually have many branches. The hypothesis that different projects could have different Github activity frequencies is still in the process of being tested.
This abstract is part of a collection in which the overarching large project under Dr. Jiasun Li was subdivided into discrete critical tasks that were carried out by multiple individuals or smaller teams. Abstracts in this collection read similarly given the shared project goals, but represent distinct tasks completed by the abstract authors towards finalizing the described analysis
Improved Classical and Quantum Algorithms for the Shipment Rerouting Problems
No full textWe study a shipment rerouting problem, which generalizes many NP-hard routing problems and packing problems. This problem has ample and practical applications in vehicle scheduling and transportation logistics. Given a network of hubs, a set of shipments needs must be delivered from their sources to their respective destinations by trucks. The objective is to select a set of transportation means and schedule travel routes so that the total cost is minimized. This problem is NP-hard and only classical approximation algorithms were known to have been studied for some of its NP-hard variants. Prior to our research, a quantum algorithm, based on the Ising model, generates an exact solution for a variant of this problem. In this work, we design classical exact and approximation algorithms as well as a quantum algorithm for this problem. The algorithms that we design use novel mathematical programming formulations and/or new insights on solving packing and routing problems simultaneously. Such algorithms take advantage of the network infrastructure, the shipments, and transportation means. We give provable running time bounds. We conduct extensive experiments and show that our classical solutions are empirically better than the up-to-date quantum algorithms in the noisy intermediate-scale quantum (NISQ) era
Analyzing the Connection Between GitHub Activity of Open Source Software Projects and Relevant Funding and Governance Models
No full textOpen-Sourced Software, or OSS, is a type of software that allows those besides its initial authors to edit it. Examples include NFTs, blockchains, cryptocurrencies, and various more. By releasing the code to the public, the software becomes more developed and advanced. Despite the recent boom of OSS projects in the tech industry, the effects of different funding and governance models in relation to GitHub activity remains unclear. This research analyzes if there is a link or correlation factor between an OSS’s GitHub Development Activity and its relative funding and governance model. Through a Big Query script and SQL script on various GitHub OSS blockchain projects, we were able to collect data on the user activity of several terabytes of blockchain projects. By identifying project metrics, and categorizing various project types, the study aims to find patterns in trends and development activity. The findings and study will provide valuable insight to project elements that influence OSS project success, and the effects of difference governance structures in the process.
This abstract is part of a collection in which the overarching large project under Dr. Jiasun Li was subdivided into discrete critical tasks that were carried out by multiple individuals or smaller teams. Abstracts in this collection read similarly given the shared project goals, but represent distinct tasks completed by the abstract authors towards finalizing the described analysis
Innovating and prototyping an AI-powered chatbot to track governance policies in the Space Force: Wingman
No full textEmployees in the Space Force can spend up to 50 man hours a week maintaining governance, which is an extreme time burden on this aspect of their job. The scientists, composed of a functional and technical team, worked to address this issue by innovating aprototype of a new system to track governance. Interviews with beneficiaries in the federal defense industry revealed more insight into the issue, highlighting the overwhelming amount of searchable governance without an automated system to retrieve the most relevant data. Using this information, the teams innovated a prototype of an AI-powered chatbot, Wingman, which successfully responds to queries in the form of a checklist extracting information from over 100 governance PDFs from the Air Force E-publishing Website and other external terminology. We find that the use of Natural Language Processing (NLP), which uses machine learning (ML) to rank and tokenize the extracted terms, in conjunction with an open AI key, GPT 3.5- turbo, to access and generate a text response from the data, helps efficiently code this prototype, allowing it to run successfully. While the team found general success in the ability of the chatbot, the team needs more time to assess its full accuracy in ranking and sorting through the PDF terms. Future optimization includes improving the source for more practicality as many documents in the federal defense space are highly classified. Overall, the scientists were able to successfully plan, innovate, and code their chatbot Wingman to improve the current methods of maintaining governance, demonstrating the potential of AI and ML to improve various time-burdening processes
Using Physics-Informed Neural Networks to Model the Dynamics of the Opioid Epidemic
No full textThe opioid epidemic is one of the fastest growing public health crises in the United States. As a disease that spreads through complex behavior and interaction as opposed to a shared medium, addiction poses a challenge in terms of modeling as it cannot be analyzed through traditional infectious disease frameworks. Previous research struggles to incorporate the dynamic progression of the individual stages of addiction and the differences between medication and endemic-based addiction. In this study, the dynamics of the spreading opioid epidemic are modeled using coupled nonlinear Ordinary Differential Equations (ODEs). These equations employ a compartmental model composed of seven subpopulations including Susceptible, Prescribed, Exposed, Addicted, Deceased, Treated, and Recovered. The model considers the impact of human behavior and interaction of the subpopulations by incorporating both prescription-based addictions and social exposure from addicted individuals. Physics-Informed Neural Networks (PINNs) are used to numerically approximate the parameters for the governing equations based on synthetic data. Additionally, we derive the basic reproduction number to provide further information on the spread of this epidemic. By employing a data-driven interaction-based model, we can more accurately understand the spread of opioid addiction to aid in implementing policies that help mitigate its adverse impacts
Design and Development of a Novel Peptide Theranostic Agent
No full textTheranostic agents are compounds that combine both therapeutic and diagnostic functionalities for monitoring and treatment in real time. Peptides exhibit favorable properties such as high affinity to protein targets and ability to be modified, and are therefore good candidates for the design of theranostic agents. This study investigates the physical and chemical properties of a novel peptide, HFSGluc, which is designed to serve as a theranostic agent of sodium-glucose cotransporter 2 (SGLT2), a protein overexpressed in various tumor cells. This peptide aims to mimic the affinity of the SGLT2 inhibitor, empagliflozin, without exhibiting the negative effects empagliflozin has shown in other studies. The physical properties of the HFSGluc peptide were assessed via solubility and stability tests across a range of pH values and buffers. Metal binding affinity assays assess the ability of the peptide to bind to copper ions, essential for imaging and therapeutic purposes. pH tests, quantified by MALDI-MS, indicate that HFSGluc shows little to no water solubility, tested over a broad pH range (4.4-8.3) using different buffers (HEPES 50 mM, MES 50 mM, and CHES 50 mM). Preliminary computational data using AutoDock shows HDSGluc as an alternative candidate (-11.2 kcal/mol vs. -9.91 kcal/mol). This peptide was synthesized using Solid Phase Peptide Synthesis and is currently being tested for solubility and metal binding. If solubility and stability shows improvement compared to the previous peptide candidate, HDSGluc can possibly be used in the imaging and treatment of cancers linked with overexpression of SGLT2
NdFeTiO5: Synthesis and Characterization of a Polar Magnetic Compound
No full textNdFeTiO5 is an interesting compound that exhibit the magnetic ordering and polar crystal structure at low temperature (TN ~20-30 K). This material can be potentially utilized in magnetoelectric sensors. However, the magnetic ordering of this compound is very low for practical application. In this research, we aim to prepare this compound and study possible modification of chemical composition to increase the magnetic ordering temperature. The parent sample was first prepared using a solid-state synthesis reaction. Stoichiometric quantities of Nd2O3, Fe2O3 and TiO2 were ground together and pressed into a pellet and then heated at high temperature. Powder X-ray diffraction technique was used to examine the purity of the prepare sample, and the results indicate that it contains the main phase with a small amount of impurity. The elemental composition was studied using the energy dispersive X-ray, which confirms the chemical composition and the homogenous distribution of all elements in the sample
How Inclusive is Inclusive Postsecondary Education? Over- and Under-Representation of Individuals with Intellectual Disabilities in Postsecondary Education
No full textInclusive postsecondary education (IPSE) programs provide new opportunities for individuals with intellectual disabilities (ID). Despite advances, prior research has found that postsecondary education access is inequitable, and therefore it is necessary to evaluate whether advances in IPSE programs have benefitted all individuals with ID. An analysis was conducted utilizing the Vocational Rehabilitation (VR) 2018 Case Service Report (RSA-911) to determine the over- or under-representation of minoritized students in the population of individuals with ID pursuing postsecondary education. Results indicated that while women, Asian and American Indian students were significantly over-represented, Black or African American students were under-represented. In addition, students from low socio-economic backgrounds, homeless students, and foster care students were all significantly under-represented. Implications for research, policy and practice are provided based on these results