The recent developments in the research area indicate a strong focus on enhancing domain adaptation techniques, particularly in scenarios where data from the source domain is unavailable or inaccessible due to privacy concerns. A notable trend is the shift towards source-free domain adaptation (SFDA), which allows models to adapt to new target domains without requiring access to the original source data. This approach is particularly relevant in fields like visual emotion recognition and sleep staging, where individual differences and privacy issues are critical. Additionally, there is a growing emphasis on improving the efficiency and accuracy of neural architecture search (NAS) methods, with a particular interest in few-shot NAS techniques that reduce computational costs while maintaining or improving performance. These methods often leverage novel strategies for splitting search spaces and balancing supernet training to achieve state-of-the-art results. Furthermore, the integration of explainable and interpretable models, such as fuzzy neural networks, is gaining traction, especially in complex design space exploration tasks, offering better insights and control over decision-making processes. Overall, the field is moving towards more personalized, efficient, and interpretable solutions that address real-world challenges in domain adaptation and NAS.