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Given the increasing reliance on UAS in sensitive applications, ensuring the confidentiality, integrity, and availability of their data streams is paramount. Traditional encryption methods often fail to balance performance and security under real-time constraints. This paper addresses this gap by proposing a hybrid adaptive encryption framework that integrates rule-based (RL) logic and machine learning (ML) to dynamically adjust encryption protocols based on data sensitivity, bandwidth, and CPU load. The experimental results demonstrate improved responsiveness and security under varied conditions using real-time simulations. The effectiveness of the system is benchmarked through execution time analysis, classification accuracy, and adaptive decision precision, highlighting its potential for secure and efficient UAS communications. 

The proposed integration of the Generalized Intelligent Framework for Tutoring (GIFT) into Curiosity Games, our Mars-based VR math game, represents a transformative approach to STEM education and training. Designed to engage middle and high school students, the game immerses learners in a dynamic educational experience while fostering skills critical for future careers in defense, aerospace, and STEM fields. Developed by the U.S. Army and its collaborators, GIFT provides advanced adaptive learning capabilities, real-time assessments, and robust monitoring tools that align seamlessly with the game’s structured classroom and exploratory open-world design. Students are able to use the game using VR or a desktop based application. The classroom component, set within a Martian observatory, will leverage GIFT to support a traditional adaptive course design. Students will follow a structured step-by-step process aligned with the 5E model: Math Conceptual Exercises (Engage and Explore), Apply Arithmetic (Explain and Elaborate), Test Questions (Evaluate), and culminating in Hands-On Activities (Elaborate). Progression is tied to performance, with students required to achieve a passing score of 80% or higher to move to the next stage. GIFT will issue credits as students successfully complete activities, tying in-game rewards to academic achievement.GIFT Integration Highlights:1. Real-Time Adaptive Support:-GIFT will provide tiered intervention to assist students who struggle with tasks, increasing teacher efficiency by prioritizing resources. These tiers include:-The AutoTutor Support is used first for immediate assistance.-Peer-to-Peer Support, where GIFT identifies proficient students to mentor peers, is the next intervention employed.-Small Group Support, where GIFT groups students with similar needs and facilitates teacher-hosted sessions, can be further employed.-One-on-One Teacher Support, dynamically pairing individual students with teachers based on real-time data to optimize outcomes, is the final intervention employed after the previous more time efficient options have been exhausted.2. Game Master Interface:-Teachers monitor progress, control adaptive exercises, and provide feedback through Objectives, Assessment, and Teams and Roles panels.3. After-Action Reviews (AAR):-Time-synced playback and video panels enable detailed reviews and targeted feedback.4. Open-World Exploration:-Students use credits to build Martian civilizations, solve tactical scenarios, and complete engineering challenges, with adaptive difficulty based on performance.Expected Outcomes:The integration of GIFT will enhance learning outcomes through personalized, adaptive support, improve teacher efficiency by optimizing resource allocation, and inspire the next generation of STEM professionals. This project aligns with the Department of Defense’s goals of fostering a technically skilled workforce and demonstrates the potential of integrating intelligent tutoring systems into immersive educational platforms.Lessons Learned:Leveraging GIFT’s existing tools minimizes development time for features such as teacher dashboards, multiplayer support, and scenario authoring. Utilizing these resources allows for efficient implementation and scalability, ensuring maximum return on investment. 

Structural Health Monitoring (SHM) uses wireless sensor network (WSN) to monitor a civil construction’s conditions remotely and constantly for its sustainable usage. Security in WSN for SHM is essential to safeguard critical transportation infrastructure such as bridges. While WSN offers cost-effective solutions for Bridge SHM, its wireless nature expands attack surfaces, making security a significant concern. Despite progress in addressing security issues in WSN for Bridge SHM, challenges persist in device authentication due to the unique placement of sensor nodes and their resource constraints, particularly in energy conservation requirements to extend the system’s lifetime. To overcome these limitations, this paper proposes an innovative authentication scheme with deep learning at the physical layer. Our approach steers away from conventional device authentication methods: no challenge-response protocol with heavy communication overhead and no cryptography of intensive computation. Instead, we use radio frequency (RF) fingerprinting to authenticate sensor nodes. Deep learning is chosen for its ability to discover patterns in large datasets without manual feature engineering. We model our scheme on IEEE 802.11ah, Wi-Fi HaLow of long-range communication and low-power consumption for machine-to-machine (M2M) applications. Simulations and experiments using universal software radio peripheral (USRP) demonstrate the effectiveness of the proposed scheme. By integrating security into Cyber-Physical System/the Internet-of-Things (CPS/IoT) design of WSN for Bridge SHM, our work contributes to critical infrastructure protection. 

Wireless Sensor Network (WSN) becomes the dominate last-mile connection to cyber-physical systems and Internet-of-Things. However, WSN opens new attack surfaces such as black holes, where sensing information gets lost during relay towards base stations. Current defense mechanisms against black hole attacks require substantial energy consumption, reducing the system's lifetime. This paper proposes a novel approach to detect and recover from black hole attacks using an improved version of Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol. LEACH is an energy-efficient routing protocol for groups of battery-operated sensor nodes in hierarchy. A round of selection for cluster heads is scheduled in a set time. We propose to improve LEACH with Anomaly Report Cycling (ARC-LEACH), tradeoff between security strength and energy cost. ARC-LEACH absorbs an attack when it occurs by rotating cluster heads to reestablish communication and then sending a message from the base station to coordinate all nodes against the malicious nodes. ARC-LEACH actively blocks malicious nodes while leveraging the resilience of LEACH for stronger resistance to blackhole attacks. ARC-LEACH can provide more defense capability when under attack from multiple malicious nodes that would otherwise be defenseless by LEACH, with only minor increase in energy consumption. 

The Internet of Things (IoT) has significantly advanced the application of Wireless Sensor Networks (WSNs) in Structural Health Monitoring (SHM), particularly for civil engineering infrastructure. While unmanned aerial vehicles (UAVs) are commonly employed for data collection, this paper proposes a novel approach using Bluetooth Low Energy (BLE) for synchronization and data gathering in SHM systems. Unlike traditional methods that may suffer from compromised network security and increased energy demands, the BLE-based system ensures that individual sensor nodes operate autonomously, providing inherent security benefits and improved battery longevity. Each sensor node acts independently, minimizing the risk to the overall network if a single node is compromised. We present a synchronization scheme that leverages BLE's low-power consumption to enhance the SHM of bridges, supported by a prototype developed using a PASCO bridge kit with wireless load cells and accelerometers. The proposed BLE protocol, to the best of the authors' knowledge, represents an unexplored avenue in SHM, promising increased safety and efficiency in sensor networks. 

Reactive carbonyl and oxygen species (RCS/ROS), often generated as metabolic byproducts, particularly under conditions of pathology, can cause direct damage to proteins, lipids, and nucleic acids. Glyoxal oxidases (Gloxs) oxidize aldehydes to carboxylic acids, generating hydrogen peroxide (H₂O₂). Although best characterized for their roles in lignin degradation, Glox in plant fungal pathogens are known to contribute to virulence, however, the mechanism underlying such effects are unclear. Here, we show that Glox in the insect pathogenic fungus,Metarhizium acridum, is highly expressed in mycelia and during formation of infection structures (appressoria), with the enzyme localizing to the cell membrane.MaGloxtargeted gene disruption mutants showed RCS and ROS accumulation, resulting in cell toxicity, induction of apoptosis and increased autophagy, inhibiting normal fungal growth and development. The ability of theMaGloxmutant to scavenge RCS was significantly reduced, and the mutant exhibited increased susceptibility to aldehydes, oxidative and cell wall perturbing agents but not toward osmotic stress, with altered cell wall contents. The ΔMaGloxmutant was impaired in its ability to penetrate the host cuticle and evade host immune defense resulting in attenuated pathogenicity. Overexpression ofMaGloxpromoted fungal growth and conidial germination, increased tolerance to H₂O₂, but had little to other phenotypic effects. Transcriptomic analyses revealed downregulation of genes related to cell wall synthesis, conidiation, stress tolerance, and host cuticle penetration in the ΔMaGloxmutant. These findings demonstrate thatMaGlox-mediated scavenging of RCS is required for virulence, and contributes to normal fungal growth and development, stress resistance. 

Over the past decade, the educational landscape has experienced a surge of online learning and instruc-tional platforms (Liu et al., 2020). This remarkable surge can be attributed to a confluence of factors, including the rising demand for higher education opportunities, the shortage of available teaching staff, and the rapid advancements in information technology and artificial intelligence capabilities. Artificial Intelligence (AI) remained a niche area of research with limited practical applications in education for over half a century (Bhutoria, 2022; Chen et al., 2020; Roll & Wylie, 2016) from 1950 to 2010. Howev-er, in recent years, the advent of Big Data and advancements in computing power have propelled AI into the educational mainstream (Alam, 2021; Chen et al., 2020; Hwang et al., 2020). Today, the rise of machine learning, deep learning, automation, together with advances in big data analysis has sparked novel perspectives and explorations around the potential of enhancing personalized learning, a long-term educational vision of technology-enhanced course options to meet student needs (Grant & Basye, 2014). Fostering personalized learning necessitates the development of digital learning environments that dynamically adapt to individual learners' knowledge, prior experiences, and interests, while effectively and efficiently guiding them towards achieving desired learning outcomes (Spector, 2014, 2016). AI-powered technologies have made it possible to analyze data generated by learners and provide instruc-tion that matches their learning performance. Through learning analytics and data mining techniques, large datasets collected are analyzed and processed to uncover learners' unique learning characteristics, often referred to as learner profiling (Tzouveli et al., 2008). Subsequently, leveraging artificial intelli-gence algorithms, the learning content is tailored, and personalized learning paths are designed to align with each learner's identified needs and preferences, thereby facilitating personalized learning experienc-es. 

This paper presents an innovative courseware project based on the Adaptive Distributed Learning (ADL) Initiative’s Total Learning Architecture (TLA [1]), which encompasses a technical framework for education and training based on a data strategy built around open standards to support interoperability across diverse organizations and products ([2]). This framework includes definitions of a set of policies, specifications, and standards that enable a future learning ecosystem to facilitate lifelong learning principles promoting personalized and flexible learning environments that include both formal and informal activities [3]. In Fall 2023, a TLA- inspired course framework was implemented in a data visualization course for senior undergraduates and graduate students, using Moodle and a Learning Record Store (LRS) to track over 200,000 learning records. This system allowed instructors to visually monitor online learning activities for the whole class as well as selected individual learners. As future work, the course will expand to 10 STEM courses across 11 universities in the next three years as part of an existing NSF commitment.