ABSTRACT Production data, a critical component of industrial datasets derived from production processes, is widely used to train data‐driven models for forecasting and managing industrial processes. However, shifts in data distribution, caused by changes in production environments, operating conditions, and equipment states, disrupt the consistency between the training and deployment, and lead to catastrophic forgetting and a significant deterioration in both model prediction accuracy and stability. Although existing incremental learning methods have improved adaptability and mitigated forgetting, challenges remain in balancing knowledge retention with dynamic sample selection and ensemble optimization, particularly in complex industrial settings. To address these challenges, this paper proposes an incremental learning framework that includes two key strategies: sample‐importance‐aware buffer update and elastic weight consolidation (EWC) based learner construction for knowledge retention, and performance‐driven iterative strong learner construction with multi‐objective weight optimization. The buffer update dynamically adjusts capacity according to training loss fluctuations, selects high‐information samples guided by loss rates and uncertainty estimation, and maintains diversity through K‐means clustering. EWC consolidates previously acquired knowledge to mitigate forgetting during weak learner training. The ensemble construction evaluates individual learner performance comprehensively and iteratively adjusts model weights using a multi‐objective optimization method, balancing prediction accuracy, stability, and uncertainty. Experimental results on multiple publicly available industrial datasets, complemented by an external validation on a financial dataset, demonstrate that the proposed method outperforms several representative approaches in both accuracy and stability of prediction.

Scientific and technological literature resources sharing can improve the utilization of literature resources. Understanding how scientific and technological literature resources are shared can help academia better share literature resources. Based on bibliometrics and the CiteSpace software, this paper conducts cooperative network analysis and co-occurrence network analysis of articles in the field of Life Cycle Assessment (LCA) from 1990 to 2019 in the Web of Science database. The analysis found that Jolliet O. et al., the scholar with the highest degree of central nodes, played a great bridging role in the process of LCA method development and knowledge sharing, and the research of LCA experienced a transition from methodological research to applications. Combined with the background investigation data from Wanfang Dataset, we find that such resources are generally shared in the academic community through academic exchanges, academic visits and supervisor-student relations. Academic cooperation within a same institution or organization is extremely common. International communication among scholars promotes the sharing of literature resources among international organizations, and team leaders are more likely to share their literature resources. The bibliometrics based on time series and background investigation method can more intuitively and deeply explore the sharing model of literature resources.

Product-service systems (PSSs) deploy a selection of products and services in order to cope with diverse markets, so as to achieve a higher profit than would be possible by offering physical products alone. Modular design inherently contributes to the sustainability performance of PSS by material and resource reuse through the configuration of physical product and service modules. PSS configuration design is enacted through service configuration in line with product configuration; this entails two separate yet coordinated optimization problems, enabling customer satisfaction through service configuration and manufacturers' sales profits through product configuration, respectively. Traditional multiobjective optimization approaches assume that the conflicting goals between customers and manufacturers can be aggregated into one single objective function through cooperative protocols, such as a weighted sum; in practice, this scarcely holds true. Consistent with game-theory decision-making, it is necessary to leverage the concerns of customers and manufacturers within a coherent framework of equilibrium solutions. This paper proposes a bi-level coordinated optimization framework to support PSS configuration design. An upper-level optimization problem is formulated for service configuration to act as a leader in the achievement of customer satisfaction, and a lower-level optimization problem is formulated for product configuration to act as a follower in an effort to enhance sales profits. Coordination between the upper and lower levels coincides with the tradeoffs underlying the conflicting goals that exist between customers and manufacturers. A constrained genetic algorithm is developed to solve the bi-level optimization model, and a case study of transformer PSS configuration design is reported to illustrate the feasibility and potential of bi-level coordinated configuration.

In order to incorporate both affective and cognitive factors in the decision-making process, a user experience (UX) evaluation function based on cumulative prospect theory is proposed for three different affective states and two different types of products (affect-rich versus affect-poor). In order to tackle multiple parameters involved in the UX evaluation function, a hierarchical Bayesian model is proposed with a technique called “Markov chain Monte Carlo.” It estimates parameters that represent different cognitive tendencies and affective influences for customers at the individual and group levels by generating posterior probability density functions of the parameters to incorporate inherent uncertainty. An experiment with four hypotheses was designed to test the proposed model. We found that: 1) anxious participants tend to be more risk-averse than those in joy and excitement; 2) joyful and excited participants tend to be more risk-seeking than those in anxiety in UX-related choice decision making; 3) all participants tend to be averse to unpleasant UX; and 4) participants tend to value by feeling for affect-rich products and value by calculation for affect-poor products. Furthermore, the models of five different types can predict choice decision making between product profiles with around 80% accuracy. In summary, the results explain affective-cognitive decision-making behavior in the complex domain of UX design and, thus, illustrate the potential and feasibility of the proposed method.