Mason Journals (George Mason Univ.)
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Optimizing Beamforming for Efficient User Equipment Association with Base Stations within an Open RAN Compliant Network
No full textIn the rapidly evolving field of fifth-generation (5G) and beyond wireless communication, optimizing signal reception and beamforming is crucial for enhancing network efficiency. Despite advancements in this domain, there remains a gap in effectively predicting and managing signal strength across different user locations and frequencies. This project aims to develop an efficient Open Radio Access Network (O-RAN) xApp using machine learning (ML) models to connect users to the strongest beam. Specifically, the models recommend the four strongest beams between the two closest towers, dynamically adjusting to the movements of users. Utilizing a dataset with 50 original user locations, 11 frequency bands, and two beam stations, the models were trained to predict the optimal signal pathways. The K-Means clustering combined with Random Forest (RF) model achieved a high silhouette score of 172.34 and a cluster accuracy of 94.55%, with a mean squared error (MSE) of 0.00134. In comparison, the K-Nearest Neighbors (K-NN) model yielded a silhouette score of 0.368 and demonstrated rapid clustering and search times. These results highlight the superior accuracy of the K-Means + RF model, while the K-NN model excels in computational efficiency. The findings suggest that incorporating advanced ML techniques can significantly enhance signal optimization processes in 5G and beyond wireless communication networks, offering a practical tool for real-time network management and improved user experiences within the O-RAN architecture
Considering Evidence-Based Open Access Policies
Get PDFAccording to the world’s most influential open access policies, only certain types of information outputs are genuinely open. In practice, however, there are actually many types of open access outcomes and solutions. A more flexible, evidence-based approach to creating open access policy will better meet researchers’ requirements and also reduce the unintended consequences of our current policies
Utilizing Novel Reaction Setups to Develop Lipid Nanoparticles for mRNA Delivery
No full textChallenges associated with mRNA vaccines include adverse reactions, poor organ targeting upon injection, and mRNA viability in the cell. The solution lies in optimizing the vaccine’s delivery mechanism using lipid nanoparticles (LNPs) to effectively transport mRNA into cells and express immunogens post-injection while enhancing adjuvant activity. Several lipid molecules were designed and synthesized through standard procedures based on lipids such as Moderna (SM-102), Pfizer-BioNTech (ALC-0315), and Onpattro (MC3). Novel lipid molecules with polyamine heads (cyclic, acyclic, or both) and linkers (donating alcohols and withdrawing esters) were designed and synthesized to understand the chelating effect on mRNA encapsulation and stability. Their functionality was predicted and compared to the lipid MC3. Physico-chemical properties such as size, Z-average, and PDI are measured by Dynamic light scattering (DLS). The lipids that were tested and synthesized, 666 through 670, had particularly low pKa values, whereas epoxide-opening lipids had a pKa of around 5.5-6.2. To increase the pKa, reactions with epoxide tails and lower equivalence ratios were implemented to obtain pKa values between 6.3-6.7. This research found that using epoxide-based and lower-equivalence reactions could allow for lipid synthesis at a higher pKa, enabling appropriate adjuvant allocation with similar efficacy
The influence of time intervals between test attempts on student performance – an ML-focused analysis of synthetic data
No full textThe practice of providing students with multiple attempts on assessments is common in many educational settings. However, the optimal time interval between retake attempts and its impact on student learning outcomes remains relatively unexplored. Existing research has shown varying results on the influence of the time interval, with some studies indicating longer intervals leading to increasing performance while others suggest no significant effect. This study analyzes synthetic data generated based on an undergraduate computing course at George Mason University, consisting of two formative tests and one summative test from the second week of classes. A statistical analysis reveals a larger mean time interval in hours between attempts for formative assessments (7.31 and 7.05) compared to summative assessments (4.52) but shows no consistent trend between score gain and time interval. A Random Forest Regression machine learning model was also employed to predict score gain based on time interval. Using data from over 900 unique students, the model generated high mean squared error values along with R-squared values of -0.02399, -0.04447, and -0.07393, indicating very little variance in score gain based on time interval. Other factors, like study methods, should be analyzed alongside time intervals in future research on optimizing academic assessment
The Impact of Peer Learning and the Flipped Classroom Model on Large Classrooms – A Literature Review
No full textLearning from mistakes is a crucial part of a student’s education. Larger classroom sizes make it significantly more difficult for students to learn from their mistakes because of the lack of personalized instruction. To allow more time for personalized teaching, the flipped learning model was introduced, which consists of teachers assigning lecture materials to be studied outside of the classroom and students applying the knowledge during the class. Studies have proven that flipped learning has not only produced higher test scores than traditional learning practices, but it has also increased students’ critical thinking skills. Another issue with larger classroom sizes is that it is nearly impossible for all students’ questions and mistakes to be given attention to. To combat this issue, a peer learning strategy was introduced, in which students learn from and with other students. Researchers found that classrooms that engage in peer learning have increased test scores compared to classrooms without, and that using peer learning students have increased social skills and are more open minded towards new ideas. Both the flipped classroom model and peer learning should be adapted to more classrooms since they both help students learn from their mistakes despite the constraints of large classrooms
Determining Context Factors Used When Selecting Web Development Debugging Strategies
No full textWhen debugging, developers have many strategies they may choose from to proceed. A programming strategy is a series of steps developers take to accomplish a goal. Depending on the context, developers may choose between different strategies. Developers with more effective strategies, or better ways to choose between strategies, are able to debug substantially faster. To understand these context factors, we are designing a study to investigate the strategies developers may choose when debugging and the context factors which impact these choices. Developers will be given a list of common debugging strategies and common debugging problems. Developers will then be asked to identify what debugging strategy they would use to address each problem, what context factors influenced their choice in strategy, and what strategies would not be effective, based on their personal experiences. We hope this study will provide valuable insights that help developers select more effective debugging strategies
Observational Constraints on Circumplanetary Material Orbiting a Young Exoplanet
No full textAstronomers' study of the formation and evolution of exoplanets has led them to explore the connection between disks of debris around stars and the presence of planetary companions, particularly orbiting M dwarfs. Among these fascinating systems, the AU Mic b exoplanet stands out as a potential target for studying circumplanetary material, which has not been conclusively directly observed before. This research aims to investigate the role of secondary thermal eclipses in assessing the presence of exoplanets with circumplanetary debris, to observationally constrain the relationship between planetary formation and circumstellar debris around young stars. The study of secondary eclipses, occurring when an exoplanet moves behind its parent star, presents a unique chance to directly assess the exoplanet's thermal radiation. Moreover, young stars with planetary companions will exhibit a unique thermal pattern during secondary eclipses, reflecting the presence of circumplanetary material surrounding the exoplanet. This is achieved by obtaining measurements of the emitted flux directly from the illuminated dayside of the planet during secondary eclipses, along with phase curves captured at various illumination angles. By comparing and contrasting the thermal characteristics of the primary and secondary eclipses, we expect to observe distinct thermal signals associated with the presence of circumplanetary debris. This research will pave the way for future understanding of exoplanetary systems and their formation. 
Measuring and Calculating Velocity of Material Along Solar Coronal Magnetic Loops
No full textA perplexing nature of the sun is that although the core of the sun, where nuclear fusion occurs, is the hottest region – at 15 million K - and the surface, the photosphere, of the sun is much cooler - at 5000 K - the outer magnetic loops jump in temperature to 2 million K. Magnetic loops are the result of strong magnetic fields protruding through the sun’s atmosphere as arcs. Scientists are investigating the cause of the sudden increase in temperature from the surface to the solar coronal magnetic loops. They have developed computer models to simulate the heat conditions. Using data from the EIS EUV-imaging spectrometer on the Hinode spacecraft, the velocities of particles have been measured along three different magnetic loops. Velocity is a critical component of theoretical predictions and computer models. Several studies have already taken place, however, they were left inconclusive as they contradict computer models. Increasing observations and data collection lead to a better understanding of the velocity profile in the coronal loops. To make these measurements, new computer programs in Python are used. Tools from EIS and built-in Python libraries are used to first select points on the three chosen magnetic loops. These velocities are measured at several different spectral lines. Subsequently, programs measuring the velocities based on the observed wavelength vs the lab wavelength are used. The graphs of three different magnetic loops measured at different wavelengths will be presented.