Radio Frequency (RF) sensing has emerged as a pivotal technology for non-intrusive human perception in various applications. However, the challenge of collecting extensive labeled RF data hampers the scalability and effectiveness of machine learning models in this domain. Our prior work introduced innovative generative AI frameworks - RF-Artificial Intelligence Generated Content using conditional generative adversarial networks and RF-Activity Class Conditional Latent Diffusion Model employing latent diffusion models - to synthesize high-quality RF sensing data across multiple platforms. Building upon this foundation, we explore future directions that leverage generative AI for enhanced 3D human pose estimation and beyond. Specifically, we discuss our recent advances in pose completion using latent diffusion transformers and propose additional research avenues: cross-modal generative models for RF sensing, real-time adaptive generative AI incorporating evolutionary learning for dynamic environments, and addressing security and privacy concerns in intelligent cyber-physical systems. These directions aim to further exploit the capabilities of generative AI to overcome challenges in RF sensing, paving the way for more robust, scalable, and secure applications.

The extensive use of artificial intelligence (AI) tools in education and their effectiveness and explicability have become an essential area of investigation because of their scope for ethical and fair use. Therefore, this innovative approach proposed a multimodal machine learning and explainable AI (XAI) approach to predict how ChatGPT’s usage impacts students’ academic outcomes, including satisfaction and performance. Three machine learning models, XGBoost, Random Forest, and Support Vector Machine, were used to predict students’ performance and level of satisfaction. The XAI techniques, SHapley Additive exPlanations and Local Interpretable Model-Agnostic Explanations, were used to investigate how models make decisions with ethical standards and ensure models are reliable and fair in their usage. A custom-created dataset, ChatGPT Survey Data, was utilized for the experiment. All three models gave the promised classification, with Support Vector Machine having the highest accuracy at 89% and the receiver operating characteristic curve and the area under this curve (AUC-ROC) at 92%. Random Forest has 87% accuracy and an AUC-ROC of 90%. XGBoost showed the best accuracy with 92% (R²). XAI analysis revealed ChatGPT usage and satisfaction as key predictors of academic performance, advancing AI’s role in education.

Human spaceflight has been a key element of exploration for more than 70 years. Human presence in space and exploration of low Earth orbit and planetary sojourns are possible because of complex systems. As humans are involved in all aspects of this endeavor it is important to have constructive dialogue across diverse disciplines. This Editorial highlights the interaction between different disciplines in support of the complex systems for human spaceflight. It highlights the establishment of the University of Cincinnati’s Armstrong Institute for Space, Technology and Research (ASTRO) and the efforts by faculty and students in fundamental and translational research. Space exploration has many facets and includes a wide variety of complex systems to support launch systems, human habitats, and opportunities in low Earth orbit and human health.

The ongoing electrification of the transportation sector requires the deployment of multiple Electric Vehicle (EV) charging stations across multiple locations. However, the EV charging stations introduce significant cyber-physical and privacy risks, given the presence of vulnerable communication protocols, such as the Open Charge Point Protocol (OCPP). Meanwhile, the Federated Learning (FL) paradigm showcases a novel approach for improved intrusion detection results that utilize multiple sources of Internet of Things data, while respecting the confidentiality of private information. This paper proposes an FL-based intrusion detection system, which leverages OCPP 1.6 network flows to detect OCPP 1.6 cyberattacks. The evaluation results showcase high detection performance of the proposed FL-based solution.