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Product-service system (PSS) has been attracting attentions of global manufacturing to providing high-value added services in addition to their traditional product development and manufacturing business. For this reason, it is of great importance to research PSS. The purpose of this paper is to establish a systematic strategy and a system tool for PSS design.

A requirement-driven product-service system (RdPPS) is developed using requirements analysis and knowledge management technologies. A framework is proposed to support RdPPS by providing tools and methods for requirement analysis and processing, formalization of PSS by ontology-based knowledge representation, reasoning method for PSS solution finding, and solution optimizing and assessing. Finally, the design support strategies for RdPPS are investigated to demonstrate the usability and functioning of the developed system.

Many conventional design methods did not consider the influence of customer requirements (CRs) during the planning phase of PSS design. Moreover, a broader range of knowledge is required to PSS design, since both products and services are considered.

This research provides a solid foundation for PSS, and promotes an effective means for PSS design.

A RdPSS is presented. CRs are considered during the design phase of PSS as well as both product and service knowledge.

This paper aims to tackle the challenge topic of continuum structural layout in the presence of random loads and to develop an efficient robust method.

An innovative robust topology optimization approach for continuum structures with random applied loads is reported. Simultaneous minimization of the expectation and the variance of the structural compliance is performed. Uncertain load vectors are dealt with by using additional uncertain pseudo random load vectors. The sensitivity information of the robust objective function is obtained approximately by using the Taylor expansion technique. The design problem is solved using bi-directional evolutionary structural optimization method with the derived sensitivity numbers.

The numerical examples show the significant topological changes of the robust solutions compared with the equivalent deterministic solutions.

A simple yet efficient robust topology optimization approach for continuum structures with random applied loads is developed. The computational time scales linearly with the number of applied loads with uncertainty, which is very efficient when compared with Monte Carlo-based optimization method.

This paper aims to amplify the retrieval and utilization of historical newspapers through the application of semantic organization, all from the vantage point of a fine-grained knowledge element perspective. This endeavor seeks to unlock the latent value embedded within newspaper contents while simultaneously furnishing invaluable guidance within methodological paradigms for research in the humanities domain.

According to the semantic organization process and knowledge element concept, this study proposes a holistic framework, including four pivotal stages: knowledge element description, extraction, association and application. Initially, a semantic description model dedicated to knowledge elements is devised. Subsequently, harnessing the advanced deep learning techniques, the study delves into the realm of entity recognition and relationship extraction. These techniques are instrumental in identifying entities within the historical newspaper contents and capturing the interdependencies that exist among them. Finally, an online platform based on Flask is developed to enable the recognition of entities and relationships within historical newspapers.

This article utilized the Shengjing Times·Changchun Compilation as the datasets for describing, extracting, associating and applying newspapers contents. Regarding knowledge element extraction, the BERT + BS consistently outperforms Bi-LSTM, CRF++ and even BERT in terms of Recall and F1 scores, making it a favorable choice for entity recognition in this context. Particularly noteworthy is the Bi-LSTM-Pro model, which stands out with the highest scores across all metrics, notably achieving an exceptional F1 score in knowledge element relationship recognition.

Historical newspapers transcend their status as mere artifacts, evolving into invaluable reservoirs safeguarding the societal and historical memory. Through semantic organization from a fine-grained knowledge element perspective, it can facilitate semantic retrieval, semantic association, information visualization and knowledge discovery services for historical newspapers. In practice, it can empower researchers to unearth profound insights within the historical and cultural context, broadening the landscape of digital humanities research and practical applications.