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With the customers’ increasing expectation on the product value, manufacturing enterprises around the world have made significant efforts to provide high value-added services in addition to their traditional product development and manufacturing business. For this reason, it is of great importance to research product service system. The purpose of this paper is to research on the key problem of integrated product service system (IPSS) design.

A value-oriented IPSS is developed, which is set up based on “requirements-functions-processes-structures” mapping model to give full consideration to customer value and service functions. An extended product-service blueprint, which stems from the service blueprint, is developed to describe product behaviors, service deliver processes, stakeholders’ activities and supporting activities. An ontology-based design support system is proposed to improve design efficiency and help designers making better-informed decisions. A computer-aided prototype system has been developed, and an initial attempt has been made to demonstrate the role of IPSS in the aerospace industry.

Many traditional design methods cannot effectively address the objects and processes integration problem of products and services. Moreover, both product and service should be considered in IPSS design, and both of them extremely depend on designers’ own experience and knowledge. Thus, a broader range of knowledge is required to understand product-service system (PSS) design.

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

A customer value-oriented IPSS is presented. Customer requirements are considered during the design phase of PSS as well as both product and service knowledge.

With the continuous upgrading of the production mode of the manufacturing system, the characteristics of multi-variety, small batch and mixed fluidization are presented, and the production environment becomes more and more complex. To improve the efficiency of solving multi-objective flexible job shop scheduling problem (FJSP), an improved hybrid particle swarm optimization algorithm (IH-PSO) is proposed.

After reviewing literatures on FJSP, an IH-PSO algorithm for solving FJSP is developed. First, IH-PSO algorithm draws on the crossover and mutation operations of genetic algorithm (GA) algorithm and proposes a new method for updating particles, which makes the offspring particles inherit the superior characteristics of the parent particles. Second, based on the improved simulated annealing (SA) algorithm, the method of updating the individual best particles expands the search scope of the domain and solves the problem of being easily trapped in local optimum. Finally, analytic hierarchy process (AHP) is used in this paper to solve the optimal solution satisfying multi-objective optimization.

Through the benchmark experiment and the production example experiment, it is verified that the proposed algorithm has the advantages of high quality of solution and fast speed of convergence.

This method does not consider the unforeseen events that occur during the process of scheduling and cause the disruption of normal production scheduling activities, such as machine breakdown.

IH-PSO algorithm combines PSO algorithm with GA and SA algorithms. This algorithm retains the advantage of fast convergence speed of traditional PSO algorithm and has the characteristic of inheriting excellent genes. In addition, the improved SA algorithm is used to solve the problem of falling into local optimum.

This research provides an efficient scheduling method for solving the FJSP problem.

This research proposes an IH-PSO algorithm to solve the FJSP more efficiently and meet the needs of multi-objective optimization.

Three-dimensional digital design and manufacturing technology are changing the current manufacturing pattern and have become the core of enterprise competition. However, the research and application of three-dimensional digital technology in the present phase have a strong bias toward the design of three-dimensional model and focus little on process planning. It restricts the development of manufacturing industry. Therefore, this paper aims to present a design scheme of three-dimensional digital process planning.

A three-dimensional digital process design method is developed by combining model-based definition technology and knowledge engineering technology. Model-based definition technology is used to display the process information. And knowledge engineering technology is used for process decision; meanwhile, ontology technology is introduced to describe process knowledge. And taking shaft part as an example, this paper establishes the general ontology of manufacturing process and the special ontology of shaft. This research provides an available method for the three-dimensional digital process planning.

Traditional process planning mainly is based on two-dimensional engineering drawing, which leads to the low efficiency and quality of process planning. Moreover, it cannot achieve effective mining and management of knowledge. Thus, applying an effective knowledge management technology into a three-dimensional process system is necessary.

This paper contributes to an available method for three-dimensional digital process planning.

The introduction of model-based definition technology makes process information display in three-dimensional environment. And ontology technology achieves sematic reference and efficient management of process knowledge.

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.

Citation contexts have been found useful in many scenarios. However, existing context-based recommendations ignored the importance of diversity in reducing the redundant issues and thus cannot cover the broad range of user interests. To address this gap, the paper aims to propose a novelty task that can recommend a set of diverse citation contexts extracted from a list of citing articles. This will assist users in understanding how other scholars have cited an article and deciding which articles they should cite in their own writing.

This research combines three semantic distance algorithms and three diversification re-ranking algorithms for the diversifying recommendation based on the CiteSeer^X data set and then evaluates the generated citation context lists by applying a user case study on 30 articles.

Results show that a diversification strategy that combined “word2vec” and “Integer Linear Programming” leads to better reading experience for participants than other diversification strategies, such as CiteSeer^X using a list sorted by citation counts.

This diversifying recommendation task is valuable for developing better systems in information retrieval, automatic academic recommendations and summarization.

The originality of the research lies in the proposal of a novelty task that can recommend a diversification context list describing how other scholars cited an article, thereby making citing decisions easier. A novel mixed approach is explored to generate the most efficient diversifying strategy. Besides, rather than traditional information retrieval evaluation, a user evaluation framework is introduced to reflect user information needs more objectively.

This study aims to evaluate a method of building a biomedical knowledge graph (KG).

This research first constructs a COVID-19 KG on the COVID-19 Open Research Data Set, covering information over six categories (i.e. disease, drug, gene, species, therapy and symptom). The construction used open-source tools to extract entities, relations and triples. Then, the COVID-19 KG is evaluated on three data-quality dimensions: correctness, relatedness and comprehensiveness, using a semiautomatic approach. Finally, this study assesses the application of the KG by building a question answering (Q&A) system. Five queries regarding COVID-19 genomes, symptoms, transmissions and therapeutics were submitted to the system and the results were analyzed.

With current extraction tools, the quality of the KG is moderate and difficult to improve, unless more efforts are made to improve the tools for entity extraction, relation extraction and others. This study finds that comprehensiveness and relatedness positively correlate with the data size. Furthermore, the results indicate the performances of the Q&A systems built on the larger-scale KGs are better than the smaller ones for most queries, proving the importance of relatedness and comprehensiveness to ensure the usefulness of the KG.

The KG construction process, data-quality-based and application-based evaluations discussed in this paper provide valuable references for KG researchers and practitioners to build high-quality domain-specific knowledge discovery systems.

The purpose of this paper is to examine the effect of psychological contract breach on destructive by developing a moderated mediation model. The model focuses on the mediating role of moral identity and moral disengagement and the moderating role of moral belief.

This study conducted a three-wave questionnaire survey and used 377 matched-sample data to test the hypotheses. PROCESS bootstrapping program in SPSS and confirmatory factor analysis in AMOS software were adopted in this study.

Results reveal that psychological contract breach has a positive effect on destructive leadership behavior, and the relationship is mediated by moral identity and moral disengagement; moral belief not only moderates the relationship between psychological contract breach and destructive leadership behavior, but also moderates the mediation effect of moral identity and moral disengagement.

First, this study enriches the destructive leadership behavior literature by verifying psychological contract breach as an antecedent. Second, this study discusses the role of morality in the formation mechanism of destructive leadership behavior by testing the mediating effect of moral identity and moral disengagement and the moderating effect of moral belief.

Facial recognition payment (FRP) has been attracting attention as an alternative payment mode. This research aims to investigate the future use of FRP for both mobile payment and point of sale payment.

The body of information on this topic is promoted by proposing the valence framework, where the authors used relative advantage, initial trust, perceived playfulness and need for uniqueness as positive valence, and perceived risk, technophobia and perceived complexity as negative valence. This study also investigated the moderating effect of personal innovativeness on consumers' behavioral intention to use FRP-based payments. The authors collected data from 392 FRP users from China to test the model. The authors used structural equation modeling (SEM) to evaluate the significant determinants influencing FRP use.

The authors found that relative advantage and privacy risk are the two most influential predictors of FRP use. The findings indicate that personal innovativeness acts as a moderator between negative valence and behavioral intention. This study provides valuable policy guidelines for the mobile or point of sale (POS) payment companies for adding FRP service into their default payment method.

FRP is a relatively new technology that has not received much research attention in information system (IS) literature. Most studies on payment investigated enablers, and less effort has been given to study both enablers and inhibitors together. Furthermore, the authors employed SEM-based analysis to identify the most important factors influencing consumers' future use decisions.