Introspectus AI is a generative AI-based system designed to enhance self-reflection and support positive behavior change. By leveraging multimodal information from users' daily life recordings, it provides personalized and detailed feedback, aiming to deepen self-awareness and facilitate positive behavioral adjustments. This study explores the short-term and long-term impacts of interacting with Introspectus AI, focusing on its potential to enhance reflective practices and improve the acceptance of generative AI tools. Following the user experience was defined through an initial round of workshops with four experts. The resulting system was evaluated through a long-term study involving 64 participants. The results demonstrate that AI-supported interventions significantly improved engagement in self-reflection, the need for reflection, and insight, while also increasing user acceptance of generative AI over time. These findings underscore the potential of generative AI as a practical tool for self-improvement, offering insights into its broader applicability in promoting well-being and personal growth.

This paper presents a novel array allocation method for radar and communication integration. Firstly, the math model between the array resource and tracking and communication performance is built and analyzed. The primary objectives of radar and communication are then designed respectively to minimize tracking error and bit error rate in the presence of multiple target tracking and channel communication. Furthermore, we transfer both problems to convex problems which are solved by checking the conditions of Karush-Kuhn-Tucker(KKT). Simulation results on array allocation performance of the proposed method and conventional one are conducted finally, which also prove effectiveness of the proposed method.