Thermal energy equipment, a critical component for transferring and utilizing thermal energy derived from primary energy sources, is indispensable in Industrial Control Systems (ICSs). With the deep integration of information technologies, ICSs are threatened by new cyber-physical security risks, where the physical systems could be influenced by attacks from the cyber network. While cyber-physical security risks in ICSs have been well studied in various domains, such as power systems and smart structural systems, little attention has been paid to the cyber-physical security of thermal energy equipment. In this paper, we propose a novel cyber-physical threat against thermal energy equipment, namely the Network-Energy-Stress Threat (NEST), which reveals attacks from the cyber network could induce an inhomogeneous distribution of thermal energy, thus causing remarkable thermal stress that can induce physical damage to the thermal energy equipment. From the attacker’s perspective, we propose an inherent vulnerability-based algorithm to explore the threat space of potential attack strategies and find an approximately optimal attack strategy utilizing the nonlinear NEST model. Then, we propose a cyber-physical defense method to detect anomalous states stemming from the NEST. Experimental results on a simulated Solar Power Tower (SPT) plant have validated the existence of the NEST against thermal energy equipment and demonstrated the effectiveness of the proposed algorithm and the proposed detector. Note to Practitioners— This paper is motivated by the practical threat of physical damage to Industrial Control Systems (ICSs) caused by cyber-physical attacks. While cyber-physical security risks in ICSs have been well studied across plenty of types of ICS and physical infrastructures, there is a lack of a framework to describe cyber-physical threats to thermal energy equipment. To address this issue, we propose the Network-Energy-Stress Threat (NEST) to reveal how cyber attacks can induce abnormal thermal stress, causing physical damage to thermal energy equipment—a critical component widely deployed in ICSs. The existence of the NEST has been demonstrated through experimental results on a simulated Solar Power Tower (SPT) plant.

Intelligent tutoring systems optimize the selection and timing of learning materials to enhance understanding and long-term retention. This requires estimates of both the learner's progress ("knowledge tracing"; KT), and the prerequisite structure of the learning domain ("knowledge mapping"). While recent deep learning models achieve high KT accuracy, they do so at the expense of the interpretability of psychologically-inspired models. In this work, we present a solution to this trade-off. PSI-KT is a hierarchical generative approach that explicitly models how both individual cognitive traits and the prerequisite structure of knowledge influence learning dynamics, thus achieving interpretability by design. Moreover, by using scalable Bayesian inference, PSI-KT targets the real-world need for efficient personalization even with a growing body of learners and interaction data. Evaluated on three datasets from online learning platforms, PSI-KT achieves superior multi-step **p**redictive accuracy and **s**calable inference in continual-learning settings, all while providing **i**nterpretable representations of learner-specific traits and the prerequisite structure of knowledge that causally supports learning. In sum, predictive, scalable and interpretable knowledge tracing with solid knowledge mapping lays a key foundation for effective personalized learning to make education accessible to a broad, global audience.