Mason Journals (George Mason Univ.)
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Modeling, Analysis and Prediction of COVID-19 dynamics with interacting subpopulations and human behavior using Physics-Informed Neural Networks
No full textThe COVID-19 pandemic has underlined the importance of research in epidemiological modeling concerningadaptive mathematical models, governed by nonlinear ordinary differential equations, that account for evolvingbehavioral responses to understand and predict the spread of infectious diseases. In this paper, we consider an extendedSEIR compartmental model that incorporates two interacting subpopulations representing young and old age groups,allowing for cross-group transmission dynamics. The basic reproduction number, the average number of secondary casesof infection produced by a single primary case, is derived using the Next Generation Matrix method. Furthermore, weincorporated a face mask parameter to study the effect of the imposed face mask policy on the reproduction numberwhich allows for an analysis of the effectiveness of public health interventions. We solve the associated differentialequation system as well as estimate useful parameters in the model using Physics-Informed Neural Networks (PINNs).Our results point to how the PINNs approach offers an effective framework to predict the unique parameters of ourmodel, forecast disease progression, and determine the impact of behavioral modifications on the reproduction numberand transmission dynamics
A Streamlined Solution to Efficiently Reduce Errors and Label Geographical Images
No full textGiven the recent rise in global temperatures, the impact of global warming is becoming increasingly significant. To gain a better understanding of global warming’s effects, scientists analyze images that show changes over time with climate – such as Arctic ice. At the status quo, there is a lack of Arctic ice training data, and analyzing images is a tedious and time-consuming task, requiring scientists to manually segment and label their images. To solve this issue, Class X was developed to provide an alternative solution that could complete the same tasks in a simpler and time-efficient manner. Class X uses Python and depends heavily on initial data to train off of as well as manually segmented and labeled data, allowing Class X to autonomously segment and label images. The machine learning model uses datasets such as NASA’s IceBridge data portal to train the machine learning model. As a result, the image classification model for Class X uses aerial Arctic ice images to train along with object-based analysis (OBIA) methods and high spatial resolution data. Additionally, a crucial step in ensuring the software works is creating training data testing model results. As a result, each amenity of Class X enables the software to be a vital tool for scientists around the globe. Class X helps save time and money for companies and allows for data to be easily dissected and used for further studies
Adversarial AI Model for Fact-Checking Wildfire Chatbot Answers, Reliability, and Accuracy
No full textWildfires pose a severe threat to both the environment and human health, causing over onebillion dollars in infrastructure damage annually. Wildfire smoke contaminates the air withhazardous pollutants such as lead, exacerbating the risk of cardiovascular and respiratorydiseases. This study compares the accuracy of our wildfire prediction model with that of theexisting WildfireGPT, using actual values from the NASA Fire Radiative Power (FRP) andMODIS MOD14 Wildfire datasets. A comprehensive dataset, including features such as theFire Weather Index (FWI), Vapor Pressure Deficit (VPD), temperature (T), and pressure (P),was utilized to evaluate the accuracy of our prediction model. The analysis specificallycompares the predicted FRP values against observed data to assess the model’s performance.This research aims to significantly impact wildfire prediction by providing a detailedcomparative analysis that can guide future improvements. Accurate wildfire prediction iscrucial for saving lives and protecting natural habitats. The study highlights the ongoing needto develop and refine predictive technologies, aiming for enhanced accuracy and usability infuture applications. It also contributes to a deeper understanding of how improved wildfireprediction can advance management practices
Investigating the effect of AuCu2-xSe Nanoparticle on Photocatalytic Nitrogen Fixation (PNF)
No full textAmmonia’s significance as a compound spans across various applications from the making of fertilisers in food production to different industries. Conventionally, ammonia has been industrially produced through the Haber-Bosch process, which has led to 1.6% of total global carbon dioxide emissions proving it to not be eco-friendly. Photocatalytic Nitrogen Fixation (PNF) has emerged as one of the key eco-friendly methods to produce ammonia through the passage of nitrogen. In order to appeal to human needs though, PNF has to be enhanced to make it faster, efficient, and produce more ammonia to sustain our population. In this research, we aim to figure out whether the Copper Selenide doped Gold nanoparticle (AuCu2-xSe) can be an effective method to enhance PNF catalytically. We first synthesised the nanoparticle by adding 500 μl of the already prepared Au nanoparticle to 1 ml of 5 mM hexadecyltrimethylammoniumbromide (CTAB) to stabilise the dumbbell-shaped nanoparticle. Then, we added 50 μl of Selenium Oxide and 100 μl of 0.1 mol Ascorbic Acid. Once we centrifuged the solution, we further added 1.5 ml of CTAB, 10 μl of 0.2 mol copper sulphate (CuSO4) and 100 μl of 0.1 mol Ascorbic Acid and ensured the formation of AuCu2-xSe nanoparticle by visualising through the TEM microscope. Then, we set up a glass reactor with the nanoparticle and performed PNF by passing nitrogen gas while maintaining a stable temperature. We took out the solution from the glass reactor every 2 hours and measured the concentration of ammonia through 2 tests: adding Nessler’s Reagent and Indophenol-blue solution. Through a UV-vis spectrophotometer, we obtained the absorbance level of this solution. We compared this solution with a calibration curve obtained from a set of known concentrations of NH4Cl to calculate the concentration of ammonia. Through the results, we imply that the AuCu2-xSe nanoparticle could be effective in enhancing the process of PNF
Seven-decades Of Roadway Fatalities Caused by Windblown Dust Events in the United States
No full textWindblown dust events, including dust storms, haboobs, and dust devils, pose a significant danger to vehicles and their drivers. Despite these hazards, such events receive far less coverage than severe weather conditions like thunderstorms or tornadoes. These natural phenomena cause low visibility and reduce road surface traction, leading to vehicle crashes. Windblown events often claim more lives than some well-known extreme weather phenomena like winter storms and hurricanes. However, due to their locality and seemingly low hazard perception, the number of fatalities and injuries caused by windblown dust have not been systematically studied.
This study developed a new dataset by merging data from the NOAA Storm Data Events Database(SED) and the Department of Transportation Fatality Analysis Reporting System(FARS), following the same method developed by Tong et al. (2023). This combined approach aims to quantify and analyze the fatalities associated with these events from 1950 to 2024, providing a more accurate and comprehensive understanding of their impact. The most important conclusion achieved by this study is the establishment of a reliable method for tracking and analyzing dust storm-related fatalities, providing important data that helps raise awareness about the dangers of dust storms in the US
Visualizing Interfacial Charging Activity of Bipolar Organic Electrodes in Proton Batteries
No full textRechargeable batteries are utilized in various areas such as electric vehicles, portable devices, and grid-scale energy storage. Organic compounds are emerging as promising cathode materials due to their light weight, low cost, safety, and high energy density. Their applications in proton batteries present a promising alternative to conventional batteries. The charging rate of a battery, a crucial parameter affecting its performance and usability, is determined by the kinetics of the electrochemical reaction between the organic materials and electrolyte ions. In this study, we conducted an electrochemical investigation of a bi-functional organic electrode material synthesized from the hydrocondensation reaction of diphenylbenzene-1,4-diamine (DPA) and perylenetetracarboxylic dianhydride (PTCDA). Scanning electrochemical cell microscopy (SECCM) was employed to examine the local reactivity of the organic electrode during the charging/discharging process. High-resolution electrochemical mapping allowed us to visualize the interfacial charging activity across the sample surface. Localized cyclic voltammetry measurements revealed interfacial charging kinetics at a single particle level. Our SECCM results with various electrolytes indicated the critical role of anions in determining electrode reactivity. Specifically, perchlorate ions exhibited the fastest charging rates, while chloride ions showed the slowest. The insights gained from our nanoscale measurements will guide the design of fast-charging aqueous battery systems
An Islamic World Trade Simulation
Get PDFThis artice provides an overview of a classroom activity in which world history students play the role of merchants working within the pre-modern Islamic world. Sufficient detail is presented that anyone reading the article would be able to run the simulation in their own classroom. 
