Career exploration has always been the key to career development, but exploring the formation mechanism of career exploration behaviour from teachers' perspective is still inadequate. A three-stage time-lagged research design is used to obtain a valid sample of 1246 college students from mainland China. Based on self-determination theory, this paper analyzes the causal mechanisms of career exploration from the dual motivation of controlled motivation of teachers' support and autonomous motivation of future work self-salience of college students to explore the mechanisms and function boundaries of career exploration of college students. The results showed that: (1) Students' perceived teacher support positively influences career exploration; (2) Future work self-salience positively moderates the mediating effect of learning engagement on the relationship between perceived teacher support and career exploration. This research contributes to how to enhance the positive effects of career exploration are proposed.

This study examines career exploration formation and helps college students develop careers using the social cognitive career theory from personality, family, and university. A multistage investigation of 980 college students from 16 universities in China discovered that: (1) proactive personality positively affects career exploration; (2) college students’ self-efficacy mediates this effect; (3) teacher support and parental support strengthen the effect between self-efficacy and career exploration; and (4) both teacher and parental support strengthen the indirect influence of proactive personality on career exploration through self-efficacy. Our findings highlight the predictive mechanisms and boundary conditions of career exploration and provide practical guidance for college students’ career construction.

The stability of collaborative organization is crucial for the successful functioning of collaborative product innovation network (CPIN), which is a typical open innovation platform. Knowledge resource loss, especially the human resource and knowledge resource, is the severe challenge for the stability of CPIN. Considering there mainly are two types of elements in CPIN, i.e., members and their knowledge, this paper builds a weighted super-network model for CPIN to integrate the two types of elements. Based on the weighted super-network model, a related nodes removal method is proposed to simulate the member loss and knowledge loss, then the quantitative analytical methods are proposed pertinently to measure and analyze the influence of member loss and knowledge loss on the CPIN stability. The case study results show the methods proposed in this paper can effectively reflect the member loss and knowledge loss, and measure and analyze the influence of their loss on the CPIN stability.

Based on the theory of career construction, a moderated mediation model is built in this paper to probe into the relationship between career exploration and career adaptability, discussing the mediating role of future work self-salience and the moderating role of perceived teacher support. With the research sample of 1101 unemployed college students, SPSS and AMOS, a structural equation modeling software is employed for modeling so as to perform the linear regression analysis of three-stage data. The research findings are about four aspects; to start with, students' career exploration is positively related to career adaptability; besides, future work self-salience plays a partial role in mediating between college students' career exploration and career adaptability; next, perceived teacher support positively moderates two kinds of positive relationship: one is between career exploration and future work self-salience of college students and the other between their future work self-salience and career adaptability. Lastly, the indirect effect of the future work self-salience between career exploration and career adaptability is moderated by perceived teacher support. As opposed to a lower level of teacher support, this moderated mediating effect is significant only at a higher level of perceived teacher support. This research clarifies the link between career exploration and career adaptability through future work self-salience and practical enlightenment about how to enhance career adaptability via perceived teacher support.

The number of patents is constantly increasing, providing an invaluable knowledge base. But retrieving this knowledge becomes ever more challenging with the increase in data. In this context, this study proposes a patent recommendation method based on knowledge graph (KG) representation. Although KG-based recommendation systems received broad attention in the literature, there is still a lack of literature that explores how it can be used for patent recommendations. First, and in terms of KG construction, the existing methods encounter issues when it comes to simultaneously accounting for accuracy and recall rate. This study proposes a combination of rule-based and deep learning methods for KG construction to solve this problem. Second, existing methods have performance and semantic association limitations when learning KG representations. This study therefore uses high-performance graph neural networks for encoding and models the semantic association limitations in graphs using traditional KG representation learning methods. Finally, existing recommendation systems usually focus on the semantic correlation between interaction items in recommendations, ignoring their direct relationships. This study therefore applies the KG representation learning method for the patent recommendation task, using KG representation learning to model the semantic association and constraints in the product–patent heterogeneous graph. Experiments based on a practical case example demonstrate that the proposed method outperforms baseline methods, and that it can guide innovative design processes. Findings have important implications for research and practice.

Partner selection plays a meaningful role in gaining and maintaining the competitive advantage of knowledge alliance. The knowledge spillover effect impacts the competitiveness of members, thereby affecting the partner selection strategy of the knowledge alliance. Therefore, this paper will explore the impact of knowledge spillover effects on partner selection in knowledge alliances based on benefit distribution. We construct a biform game model of partner selection for a knowledge alliance composed of core and collaborative members. The clique solution is employed to divide the alliance's overall benefits after the introduction of the variables such as innovation efficiency, knowledge input, and marginal loss. Then the influence of the knowledge spillover effect on partner selection for knowledge alliance is explored. Under certain conditions, the core member and members with whom they have cooperated are at the advantage of benefit distribution in the knowledge alliance, while the rest of the members are at the disadvantage of benefit distribution. Enhancing the alliance's overall interests while boosting its competitiveness is made possible by the growth of innovation efficiency and knowledge input. Also, knowledge spillover helps organizations identify and select potential partners, which is conducive to exerting the positive knowledge spillover effect to the fullest, especially for collaborative members who lack cooperation experience, which can significantly enhance their competitiveness. The results offer a useful guide for choosing suitable knowledge alliance partners to fully exploit the knowledge spillover effect.

That customer value should drive product development and production is a basic tenet of the Toyota Production System and Lean. Traditional means to extract what the customer wants often focus on customer surveys. But surveys are time consuming and costly. At the same time, there exists a large amount of customer comments in online reviews that is easily accessible, whilst the advances of data science, for example as part of Lean Automation, provide new means to extract information from this data. In this context, a new approach to fine-grained sentiment analysis of Chinese consumer data is developed. The new approach integrates pre-training language model, conditional random field model and linguistic knowledge model. The new approach is shown to outperform traditional approaches in a comparison experiment, while an ablation experiment shows that our new approach is parsimonious, i.e., all three constituting components are needed. Finally, a use case is presented that exemplifies how our new approach can support managers in identifying customer value (through positive evaluations), and most importantly guide Lean improvement, through detailed information on characteristics that are evaluated negatively, ranked according to customer importance. Findings have important implications for research and practice.

Knowledge sharing among teachers is one of the important ways to improve their teaching and research ability. From the perspectives of teachers’ knowledge sharing willingness, knowledge sharing ability, knowledge sharing environment, knowledge sharing technology support, knowledge sharing effect, and so forth, this paper constructs a teacher knowledge sharing behavior evaluation model, develops a knowledge sharing behavior evaluation index system, and proposes a comprehensive teacher’s knowledge sharing behavior evaluation method based on the improved entropy-TOPSIS method. This is a comprehensive evaluation method combining subjective and objective weights, which avoids the subjectivity of traditional expert evaluation methods and other multilevel and multi-index weight determination methods and makes the evaluation results more objective, accurate, and more realistic. Finally, by taking the example of evaluating knowledge sharing behavior of teachers in a university of Chongqing, China, this paper verifies the feasibility and practicability of the proposed comprehensive teachers’ knowledge sharing behavior evaluation system and method.

Among the inherent problems in recommendation systems are data sparseness and cold starts; the solutions to which lie in the introduction of knowledge graphs to improve the performance of the recommendation systems. The results in previous research, however, suffer from problems such as data compression, information damage, and insufficient learning. Therefore, a DeepFM Graph Convolutional Network (DFM-GCN) model was proposed to alleviate the above issues. The prediction of the click-through rate (CTR) is critical in recommendation systems where the task is to estimate the probability that a user will click on a recommended item. In many recommendation systems, the goal is to maximize the number of clicks so the items returned to a user can be ranked by an estimated CTR. The DFM-GCN model consists of three parts: the left part DeepFM is used to capture the interactive information between the users and items; the deep neural network is used in the middle to model the left and right parts; and the right one obtains a better item representation vector by the GCN. In an effort to verify the validity and precision of the model built in this research, and based on the public datasets ml1m-kg20m and ml1m-kg1m, a performance comparison experiment was designed. It used multiple comparison models and the MKR and FM_MKR algorithms as well as the DFM-GCN algorithm constructed in this paper. Having achieved a state-of-the-art performance, the experimental results of the AUC and f1 values verified by the CTR as well as the accuracy, recall, and f1 values of the top-k showed that the proposed approach was excellent and more effective when compared with different recommendation algorithms.

User preference mining is an application of data mining that attracts increasing attention. Although most of the existing user preference mining methods achieved significant performance improvement, the sentiment tendencies of users were seldom considered. This paper proposes fine-grained sentiment analysis for preference mining. The powerful feature representation capabilities of deep neural networks have significantly improved the performance of fine-grained sentiment analysis. But two main challenges remain when using deep neural network models: incomplete user feature extraction and insufficient interaction. In response, a pre-training language model is employed to encode user features to fully explore potential interests of users, a linguistic knowledge model is introduced to assist the encoding, a multi-scale convolution neural network is adopted to capture text features at different scales and fully utilize the text information, and the fine-grained sentiment analysis task is modeled as a sequence labeling problem to explore the sentiment polarity of user evaluation. Experiments on a user review data set are used to verify the new approach. Experimental results of precision, recall rate and F1-value show that the proposed approach performs better, and is more effective than baseline models. For example, the F1-value is increased by 4.27% compared to the best performing baseline model. Findings have important implications for research and practice.

This study challenges the negative assumption that older customers struggle to adapt to e-service by exploring the positive influence of their participation on transformative value co-creation. A two-phase mixed-methods approach was adopted to develop and validate the research model. The results found that customer participation, including information sharing, responsible behaviors, and in-role feedback, promotes older customers' digital skills and basic needs satisfaction, leading to higher appreciation of digital affordances and well-being. Additionally, online employees' customer orientation positively moderates the above relationships. This study provides insight for service providers to include older customers in e-services.

With the development of specialization, coordination and intelligence in the manufacturing service process, the issue of how to quickly extract potential resources or capabilities for distributed manufacturing service requirements, and how to carry out resource matching for manufacturing service requirements with correlated mapping characteristics, have become the critical issues to be addressed in the cloud manufacturing environment. Through the combination of the characteristics of relevance, synergy and diversity of manufacturing service tasks on the intelligent cloud platform, a matching decision method for manufacturing service resources is proposed in this paper based on multidimensional information fusion. On the basis of integrating multidimensional information data in cloud manufacturing resource, the information entropy and rough set theory are applied to classify the importance of manufacturing service tasks, while the matching capability are analyzed by using a hybrid collaborative filtering (HCF) algorithm. Then, the information of function attribute, reliability and preference is employed to match and push manufacturing service resources or capabilities actively, so as to realize the matching decision of manufacturing service resources with precise quality, stable service and maximum efficiency. At last, a case study of resources matching decision for body & chassis manufacturing service in a new energy automobile enterprise is presented, in which the experimental results show that the proposed approach is more accuracy and effective compared with other different recommendation algorithms.

Customer participation is crucial for online service providers to gain and maintain competitive advantages through service recovery. Focusing on the interactive nature of customer participation, this study explored the relationships among employees' politeness strategies, customer membership, perceived co-recovery, and online post-recovery satisfaction. We found that perceived co-recovery was significant for online post-recovery satisfaction. Compared to non-members costumers, members' perceived co-recovery was more positive. Finally, member customers preferred employees to adopt positive politeness strategies, whereas non-member customers preferred negative ones. This study provides valuable suggestions for online service providers to improve online recovery performance.

With the rapid development of social economy and information technology, the credit risk and financial risk of my country’s financial enterprises are also facing severe challenges. In financial enterprises, credit is related to the survival of the enterprise. As the business volume and scale of financial enterprises continue to expand, financial risks are correspondingly increased. Therefore, the research on financial enterprise credit and financial risks is of great significance. The research on the credit and financial risks of financial enterprises is helpful to help financial enterprises handle financial risks well and perform evasive operations on them. In addition, it can also enhance the credit awareness of enterprises and reduce the default rate in the financial industry. This paper studies and analyzes the financial enterprise credit and financial risk measurement based on the PSM model. First, it uses the literature method to study the PSM model, corporate credit, financial risk and other theoretical knowledge, and then establish a fuzzy neural network model for risk assessment. And the establishment of a PSM model to conduct a questionnaire survey experiment design, analyze the price sensitivity changes and acceptable price ranges under the PSM model, and get the optimal pricing of new financial products issued by financial companies. Finally, it analyzes the relationship between the default rate of corporate credit and internal finance. The conclusion is that when this financial product is priced at 45 yuan, the proportion of reserved recipients is the largest, reaching 66%; when the price is 75 yuan, the acceptable proportion is 23%, which is the acceptable number of people in the three price ranges. The proportion is the largest; if the price is 100 yuan, the unacceptable proportion is the largest, reaching 45%. This shows that the pricing of a new financial product is directly related to its sales. The reasonableness of the product pricing directly determines whether people are willing to pay for it and accept it.

本文以等效电路模型为基础模型,结合动态气体压力模型和动态热传输模型建立了集总参数模型,在SIMULINK环境下,利用建立的模型模拟了电池启动过程,发现了电压的下冲现象,且电压的响应时间与电池温度的响应时间基本一致,说明启停过程中电池温度对电池的动态性能影响很大.进一步从温度角度对模型中决定电池输出电压大小的热力学电动势、活化过电势、欧姆过电势和浓差过电势的动态响应情况进行了分析,发现启动过程电压的下冲现象主要由电池活化过电势和欧姆过电势的过冲引起;当以阶跃信号形式输入温度时,启动过程电池输出电压响应很快且未发生下冲现象,说明提高电池温度的响应速度能够改善电池的动态性能.

Liquid water in diffusion layer of PEMFC has a significant impact on the operation and performance of the cell.In order to improve the cell performance,it is necessary to conduct research to liquid water transport in the gas diffusion layer detailedly.The composition and working principle of PEMFC were described.Especially,the results of experimental researches (transporting mechanism and influencing factors) of liquid water transport in gas diffusion layer of PEMFC were reviewed.And the results of research for scholars were summarized.

Fuel cell operation with a dead-ended systems anode reduces fuel cell system cost, weight, and volume because the anode external humidification and recirculation hardware can be eliminated. However, water accumulation is one of the key factors which influence the stability of its performance. Generally, the anode flooding of PEMFC with anodic dead-end operating mode can be avoided by using periodic purge process. But such an operating method can lower the utilization efficiency of hydrogen because small amount of hydrogen is expelled out of fuel cell and wasted. In order to improve the utilization efficiency of hydrogen, the pulsating technique at anode has been proposed. In this paper, the investigation of the hydrogen pulsation effects for PEMFC stack has been made, with only continuous hydrogen supply, with between continuous hydrogen and pulsation hydrogen at different positions, such as the inlet or outlet of anode. The results shows that the performance of a PEMFC stack has been improved significantly when the pulsation hydrogen flow introduced from the outlet at anode.

加强作风建设,是开展党的群众路线教育实践活动的核心和关键.这就要求每个党员要深刻认识到加强作风建设的重要性和着力践行改进作风的内在要求,从而大力推进作风建设常态化、长效化.

Automobile is a typical multi-stage split charging and assembly manufactured product. It can be divided into engine, body, chassis and electrical four modules functionally. Auto parts proportion is defined as the ratio of purchasing costs of a given part to the total costs. In this paper, an auto parts proportion model is proposed by means of matrix and statistical analysis, a benchmarking proportion calculating model of various vehicle types is presented by means of average and mode comparison. On the basis of these models, target costs of auto-parts, systems and modules can be calculated. These models and methodologies have been used in the processes of new product development costs controlling and developed products costs reduction in a big scale automobile company.

It analyzes in detail the influence factor of voltage of proton exchange membrane fuel cell (PEMFC) when load changes sudden by the lumped modeling developed. The partial pressure of hydrogen and oxygen can be expressed using the ideal gas law equation, and ohmic loss, activation loss and concentration losses are accounted, it is assumed that both operating factor of hydrogen and mixture ratio of hydrogen and oxygen are constant. Based on the above, the dynamic lumped model of PEMFC is proposed. The correctness of model is verified by comparing the experiment data with simulation result of model. The research about lumped model has important significance to the research of the distributed generation network in various performances.