Abstract
The integration of students with intellectual and developmental disabilities into STEAM education presents ongoing challenges, particularly in engineering disciplines where both technical and social competencies are essential. Robotics and active learning methodologies have emerged as promising solutions to address these challenges by offering adaptive, interactive, and student-centered learning environments. This study conducts a systematic literature review to examine how these technologies and methodologies are applied to support students with Intellectual and Developmental Disabilities. A total of 34 high-quality studies published over the past ten years were selected through a rigorous process of database searching, inclusion/exclusion filtering, and quality assessment. The analysis reveals that robotics is particularly effective in fostering academic development, cognitive skills, social-behavioral interaction, and emotional regulation, while active learning promotes social responding, role understanding, and collaborative skills. Together, these approaches not only enhance individual learning outcomes but also facilitate the broader inclusion of students with disabilities within engineering education.

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Abstract
Car sharing, car clubs and short-term rentals could support the transition toward net zero but their success depends on them being financially sustainable for service providers and attractive to end users. Dynamic pricing could support this by incentivizing users while balancing supply and demand. We describe the usage of a round trip car sharing fleet by a continuous time Markov chain model, which reduces to a multi-server queuing model where hire duration is assumed independent of the hourly rental price. We present analytical and simulation optimization models that allow the development of dynamic pricing strategies for round trip car sharing systems; in particular identifying the optimal hourly rental price. The analytical tractability of the queuing model enables fast optimization to maximize expected hourly revenue for either a single fare system or a system where the fare depends on the number of cars on hire, while accounting for stochasticity in customer arrival times and durations of hire. Simulation optimization is used to optimize prices where the fare depends on the time of day or hire duration depends on price. We present optimal prices for a given customer population and show how the expected revenue and car availability depend on the customer arrival rate, willingness-to-pay distribution, dependence of the hire duration on price, and size of the customer population. The results provide optimal strategies for pricing of car sharing and inform strategic managerial decisions such as whether to use time-or state-dependent pricing and optimizing the fleet size.

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Abstract
This paper investigates the relationship between school principals' managerial practices and two key mensions of school performance: students' cognitive outcomes and school climate. School performance assessed using a classical Data Envelopment Analysis (DEA) framework, complemented by both unconditional robust and conditional robust models to evaluate the influence of managerial practices on school efficiency. We introduce a methodological innovation that allows for a nuanced analysis of how contextual variables-specifically, principals' managerial practices-affect performance, both individually and through their interactions. The analysis is based on 2019 INVALSI data from a nationally representative sample of 8th grade students in Italian schools. The findings show that principals' practices, as well as the ways in which these practices interact, play a significant role in shaping school efficiency, particularly by promoting a positive supportive school climate.

Abstract
The increasing reliance on Networked Microgrids (NMGs) for decentralized energy management introduces unprecedented cybersecurity risks, particularly in the context of False Data Injection Attacks (FDIA). While traditional FDIA studies have primarily focused on network-based intrusions, this work explores a novel cyber-physical attack vector leveraging Uncrewed Aerial Vehicles (UAVs) to execute sophisticated cyberattacks on microgrid operations. UAVs, equipped with communication jamming and data spoofing capabilities, can dynamically infiltrate microgrid communication networks, manipulate sensor data, and compromise power system stability. This paper presents a multi-objective optimization framework for UAV-assisted FDIA, incorporating Non-dominated Sorting Genetic Algorithm III (NSGA-III) to maximize attack duration, disruption impact, stealth, and energy efficiency. A comprehensive mathematical model is formulated to capture the intricate interplay between UAV operational constraints, cyberattack execution, and microgrid vulnerabilities. The model integrates flight path optimization, energy consumption constraints, signal interference effects, and adaptive attack strategies, ensuring that UAVs can sustain long-duration cyberattacks while minimizing detection risk. Results indicate that UAV-assisted cyberattacks can induce power imbalances of up to 15%, increase operational costs by 30%, and cause voltage deviations exceeding 0.10 p.u.. Furthermore, analysis of attack success rates vs. detection mechanisms highlights the limitations of conventional rule-based anomaly detection, reinforcing the need for adaptive AI-driven cybersecurity defenses. The findings underscore the urgent necessity for advanced intrusion detection systems, UAV tracking technologies, and resilient microgrid architectures to mitigate the risks posed by airborne cyber threats.