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Mastering Enterprise Networks-Security Textbook
Get PDFEffective cybersecurity education requires theoretical knowledge and hands-on experience to prepare students for real-world challenges . While foundational concepts are presented in textbooks, many lack practical applications, resulting in insufficient preparation for troubleshooting and securing complex network systems. Additionally, as cybersecurity tools rapidly evolve, educational resources must be continuously updated to support relevance. To address this issue, Mastering Enterprise Networks 2nd Edition is being developed as an updated and expanded replacement aimed at enhancing cybersecurity education . The new edition builds upon the first version, which served as a comprehensive guide to building, securing, and attacking enterprise networks. A hands-on learning approach is emphasized, with 50 lab-based chapters designed to reinforce theoretical knowledge through real-world applications. Security topics are examined from multiple perspectives, covering both fundamental and advanced concepts. including firewalls, intrusion detection systems, and vulnerability scanning. By modernizing the textbook and integrating practical exercises, the gap between traditional cyber security instruction and real-world requirements is addressed. Students will be provided with both knowledge and the practical skills necessary to troubleshoot and defend enterprise networks effectively. With the cybersecurity landscape continuously evolving, Mastering Enterprise Networks 2nd Edition ensures that students are equipped with the latest tools and practices, allowing them to still be prepared for industry challenges
Reward Shaping For Video Game Playing Agents Based on Human Motivations
No full textVideo games have been used for machine learning research for decades because of their complex yet controlled environments. This project uses Quantic Foundry’s gamer motivation model to develop reward functions for multiple reinforcement learning agents tasked with playing the video game Pokémon Red. By basing the agents’ training on empirically derived motivations of human players, the agents exhibit more human-like behavior than previous approaches to game playing AI. We trained 9 RL agents, each representing one of the Quantic Foundry gamer types: Acrobat, Gardener, Slayer, Skirmisher, Gladiator, Ninja, Bounty Hunter, Architect, and Bard. The agents use the same proximal policy optimization algorithm, but different reward functions. Rewards are given for performing actions that correspond to the twelve gamer motivations: destruction, excitement, competition, community, challenge, strategy, completion, power, fantasy, story, design, and discovery. The amount of reward per action is weighted based on the weight of the corresponding motivation in the gamer type model. We have observed distinguishable behavior among the agents: Gardener progresses the furthest in-game and is able to earn two gym badges. Ninja progresses the least and rarely earns a single badge. These results are consistent with the respective motivation profiles of the gamer types. The data generated by this project provides insight into how human motivations can be used to train AI to perform tasks in certain ways. It is becoming increasingly relevant for AI systems to be able to behave and think more like humans. Our process and results may be extrapolated to more practical applications of RL agents, such as robotics and autonomous vehicle
Development of a Multi Design Point Optimization Framework for Gas Turbine Cycles
No full textThis thesis presents the development and validation of an in-house engine performance modeling tool, CY23, as a foundation for multi-design point optimization of aero gas turbines. While the original intent was to construct a complete optimization framework, the primary contribution of this work lies in the creation, enhancement, and rigorous verification of CY23 as a reliable platform for turbofan cycle analysis. CY23 was designed to support both design-point and off-design performance simulations, enabling students and researchers to evaluate gas turbine behavior across a range of operating conditions. A key focus of this thesis was the construction of a baseline two-spool, unmixed-flow turbofan model using GasTurb, which served as a reference for validating CY23. The comparison showed strong agreement in critical parameters such as mass flow, pressure ratio, and turbine entry temperature, while also identifying areas requiring further refinement, particularly in map scaling and solver convergence during off-design scenarios. Although limited progress was made in implementing a whole multi-design point optimization routine, the initial integration of CY23 with OpenMDAO was completed, and the framework is now structurally capable of handling mission-level fuel burn optimization tasks. This work highlights CY23\u27s technical capabilities and the practical challenges of achieving robust optimization across multiple design points. It lays the groundwork for future enhancements, including surrogate modeling, uncertainty quantification, and expanded solver strategies, to evolve CY23 into a comprehensive research and teaching tool for gas turbine performance and optimization studies
AI and Prompt Engineering for Library Discovery Services
Get PDFWe have seen the rise of generative artificial intelligence in the form of Large Language Models (LLMs) to provide answers to users’ queries. Services such as ChatGPT, Copilot, and Gemini have quickly become accepted and adopted in the research process. Now library vendors are adding artificial intelligence (AI) to their discovery services to allow for natural language queries to produce generative results. However, the AI model used for discovery services differs from normal LLMs in a significant way that has several positive benefits, but it affects how prompts are written. Library discovery services use a model called Retrieval- Augmented Generation to enhance the normal keyword searching function
Innovative Bird Strike Mitigation Strategies: Integrating Drones and Cutting-Edge Technologies to Enhance Airport Safety
Get PDFNotwithstanding significant efforts to reduce the risk of bird strikes at and around airports, as well as advancements in technology, aviation safety continues to be threatened by this hazard. Our study of recent data shows a clear historical increase in accidents, therefore underscoring the flaws in present technologies. Although among the technologies in common use are radar, drones, cameras, and acoustic approaches, they are not always able to completely meet the challenging needs of bird strike risk mitigation. This approach creatively blends these four fundamental technologies by means of enhanced real-time data processing and targeted drone deployment. While providing more efficient and comprehensive preventive activities than present methods, this synergy helps quick and precise decision-making in crucial events. It incorporates a diagnostic system using contemporary information technology that forecasts medium- and short-term dangers, therefore enhancing the accuracy and efficacy of preventive measures. This combination of technology also makes it feasible to follow migrating birds and assemble all the gathered data in a single dashboard, therefore facilitating dynamic analysis of bird movements and improved visualization. This approach helps airports to concentrate more on administrative and preventive aspects. Thanks to more coverage and more flexibility to fit particular climatic conditions, this method covers all significant stages of flight, including takeoff, approach, and landing. It lowers false alerts, maximizes operating costs, and offers a sustainable means to prevent bird attacks, therefore enhancing aviation safety completely and efficiently
Chief Executive Officers, Accountability and Hidden Violations
Get PDFCorporate governance demands transparency, ethical leadership and accountability from Chief Executive Officers (CEOs). However, numerous cases suggest that CEOs often wield significant influence over what is reported to their boards of directors, particularly when it comes to internal misconduct, regulatory violations, and criminal activity. This lack of transparency raises serious concerns about ethical and legal obligations, as well as the effectiveness of board oversight
Evaluating the Physiological Effects of a Spacesuit While Performing CPR
Get PDFLimited research exists on the challenges of performing cardiopulmonary resuscitation (CPR) while wearing a spacesuit/pressure suit; but, during a space mission or following spacecraft egress, astronauts may need to perform Basic Life Support (BLS) on a crewmember. BLS consists of CPR and External Chest Compressions (ECC). In an emergency, astronauts may not have time to remove their intravehicular (IVA) spacesuits/pressure suits or wait for their heart rates to return to resting conditions and, therefore, would have to perform CPR while physically exerted. This study sought to evaluate the physiological exertion on rescuer performance while performing ECCs over 6 minutes while wearing a commercially available IVA spacesuit supplied by Final Frontier Design (FFD). Twelve (12) participants were divided into three groups balanced by age, gender, and body mass index (BMI). Each group performed three trials: Performing CPR without wearing the IVA spacesuit, performing CPR while wearing the IVA spacesuit, and performing CPR while wearing the IVA spacesuit following physical exertion. The sequence in which trials were performed was randomized across groups. Heart rate and blood pressure readings were taken before and after each trial. Analysis suggests that the addition of the suit alone increased heart rate and, therefore, physiological stress while performing CPR, and with physical exertion adding even more stress
Promoting Wellbeing in Collegiate Aviation Programs
Get PDFThis study explores the impact of mental wellness workshops on the mental health and resilience of collegiate aviation pilots in Part 141 training programs, with a focus on awareness, coping strategies, and stigma reduction. The high-pressure environment of aviation training often exacerbates mental health challenges, underscoring the need for effective wellness interventions. The research investigates how participation in mental wellness workshops enhances pilots\u27 awareness and understanding of mental health issues, aiming to foster a more informed and resilient cohort of future aviation professionals. It further examines the perceived benefits of these workshops in improving coping strategies and stress management skills, which are crucial for maintaining mental well-being and performance under pressure. Through the workshop intervention, pilots reported a heightened awareness of mental health concerns and a greater understanding of the impact such issues can have on their personal and professional lives. Additionally, the study explores the extent to which the workshop contributes to reducing the stigma surrounding mental health within aviation and increases help-seeking behavior among students. Participants noted a more open approach to discussing mental health, with some indicating a higher likelihood of seeking support when needed. Overall, the findings suggest that mental wellness workshops play a critical role in cultivating resilience among collegiate aviation pilots, promoting healthier attitudes toward mental health, and enhancing the coping mechanisms necessary for navigating the stresses of aviation training and careers
Resident Space Object Identification in Unresolved Optical Space Imagery via Streak Detection
Get PDFAccurately identifying resident space objects (RSOs) within optical space imagery poses a key challenge in the realm of Space Situational Awareness (SSA). For unresolved imagery, the primary issue is distinguishing RSOs from stars and other objects (or aberrations) that may be present. In this project we explore a first-principles approach to identifying RSOs based on the nature and degree of streaking in an image. Different objects in an image will streak to different extents, depending on each object’s motion relative to the observer’s platform. It is this knowledge, combined with appropriate processing of the image, that allows effective discrimination between RSOs and stars. The proposed technique is developed to operate on a single image as opposed to a multi-frame collect