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This paper examines the design and evaluation of Large Language Model (LLM) tutors for Python programming, focusing on personalization that accommodates diverse student backgrounds. It highlights the challenges faced by socioeconomically disadvantaged students in computing courses and proposes LLM tutors as a solution to provide inclusive educational support. The study explores two LLM tutors, Khanmigo and CS50.ai, assessing their ability to offer personalized learning experiences. By employing a focus group methodology at a public minority-serving institution, the research evaluates how these tutors meet varied educational goals and adapt to students’ diverse needs. The findings underscore the importance of advanced techniques to tailor interactions and integrate programming tools based on students' progress. This research contributes to the understanding of educational technologies in computing education and provides insights into the design and implementation of LLM tutors that effectively support equitable student success. 

Collisionless plasma systems are often studied using fully kinetic simulations, where protons and electrons are treated as particles. Due to their computational expense, it is necessary to reduce the ion-to-electron mass ratio $m_i/m_e$or the ratio between plasma and cyclotron frequencies in simulations of large systems. In this Letter we show that when electron-scale waves are present in larger-scale systems, numerical parameters affect their amplitudes and effects on the larger system. Using lower-hybrid drift waves during magnetic reconnection as an example, we find that the ratio between the wave electric field and the reconnection electric field scales as $\sqrt{m_i/m_e}$, while the phase relationship is also affected. The combination of these effects means that the anomalous drag that contributes to momentum balance in the reconnection region can be underestimated by an order of magnitude. The results are relevant to the coupling of electron-scale waves to ion-scale reconnection regions, and other systems such as collisionless shocks. Published by the American Physical Society2024 

As the digital world gets increasingly ingrained in our daily lives, cyberattacks—especially those involving malware—are growing more complex and common, which calls for developing innovative safeguards. Keylogger spyware, which combines keylogging and spyware functionalities, is one of the most insidious types of cyberattacks. This malicious software stealthily monitors and records user keystrokes, amassing sensitive data, such as passwords and confidential personal information, which can then be exploited. This research introduces a novel browser extension designed to effectively thwart keylogger spyware attacks. The extension is underpinned by a cutting-edge algorithm that meticulously analyzes input-related processes, promptly identifying and flagging any malicious activities. Upon detection, the extension empowers users with the immediate choice to terminate the suspicious process or validate its authenticity, thereby placing crucial real-time control in the hands of the end user. The methodology used guarantees the extension's mobility and adaptability across various platforms and devices. This paper extensively details the development of the browser extension, from its first conceptual design to its rigorous performance evaluation. The results show that the extension considerably strengthens end-user protection against cyber risks, resulting in a safer web browsing experience. The research substantiates the extension's efficacy and significant potential in reinforcing online security standards, demonstrating its ability to make web surfing safer through extensive analysis and testing. 

Against the backdrop of the ever-evolving IT industry, this comparative study explores the differences among various project management methods, highlighting key distinctions between Agile and traditional approaches by evaluating the benefits of Agile and the drawbacks of not adopting agile methods. Agile practices have gained recognition for their adaptability and efficiency, in addressing dynamic industry demands. Our multifaceted approach, which examines the pros and cons of Agile methodologies across various industries employs different machine learning algorithms—logistic regression, linear regression, and decision tree regressor. The study quantitatively measures Agile’s impact compared to other methodologies using prediction probabilities, classifications, confusion metrics, R-squared, and Mean Squared Error (MSE) for performance analysis. Results highlight that linear regression outperforms other models with 71% accuracy and 82% precision. These findings offer valuable insights into understanding Agile’s impact on IT industries, encouraging further exploration and refinements to make informed decisions on project management strategies and fostering future research to enhance IT project success rates. 

Abstract Numerous structures conducive to magnetic reconnection are frequently observed in the turbulent regions at quasi-parallel shocks. In this work, we use a particle-in-cell simulation to study 3D magnetic reconnection in shock turbulence. We identify and characterize magnetic null points, and focus on reconnection along the separator between them. We identify a reconnection region with strong parallel current, a finite parallel potential, and counterrotating electron flows. Electrons are shown to be accelerated by the parallel electric field before being scattered at the null.