Memory behavior modeling seeks to predict individual recall performance and understand its underlying cognitive mechanisms. However, the dynamic and heterogeneous nature of memory data poses significant challenges to the generalization ability of models under unseen conditions. To address this challenge, we propose an invariant representation learning framework I-Mem that integrates self-supervised contrastive learning with decorrelation constraints, enabling the adaptive identification and suppression of environment-related factors in sequential behavioral data, thereby mitigating the influence of spurious features and enhancing the modeling of stable cognitive structures. Importantly, the method does not rely on explicit environment partitioning or predefined environment labels, while our theoretical analysis demonstrates that it can effectively resist environmental perturbations and facilitate the extraction of invariant structural representations, thereby ensuring adaptability and generalization. Empirical evaluations on both synthetic and real-world datasets further confirm its superiority over mainstream methods in terms of generalization performance and stable feature identification. Feature attribution analysis reveals that I-Mem extracts invariant features aligned with classical cognitive effects, and reflects short-term behavioral patterns that may indicate latent cognitive mechanisms beyond existing theories, highlighting both interpretability and discovery potential.

Digital transformation (DT) and automation technologies have significantly impacted logistics and supply chain management (LSCM), enhancing business performance. Efforts to automate processes alongside machine and robotic automation aim to improve reliability, consistency, efficiency, and cost-effectiveness. While existing research has explored specific aspects of DT and robotic process automation (RPA), the deployment and implementation of RPA as a service (RPAaaS) in LSCM are under-explored. This study focuses on RPA in LSCM and aims to improve organisational processes associated with DT. A systematic review identifies five knowledge clusters regarding RPA deployment for DT in LSCM. Real-life RPA implementation cases in a third-party logistics company highlight challenges and opportunities related to RPA deployment. Insights on the timing and choice of the RPAaaS platform to facilitate DT in enterprises are discussed. This paper provides extensive theoretical insights into RPA for DT in LSCM and offers practical guidance for streamlined implementation.