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This study aims to the information about consumer product quality and safety that can easily attract public attention and become the focus of public opinion. In recent years, the fast-growing social media have become an import platform for firms for releasing product quality and safety information and for firms and governments to hear public opinion.

To explore how information about consumer product quality and safety gets disseminated and a public opinion is formed in social media, this paper proposes two information transmission models, one with government intervention and the other without government intervention, based on the theory of complex network. A simulation case study in MATLAB is conducted to verify the proposed models.

Information transmission models were constructed, one without government intervention and one with government intervention. The influence of information transmission with government intervention was analyzed. MATLAB was used to simulate the Barabasi and Albert (BA)-based model to consider event information level, government information level and possible panic population proportion. The government intervention effect was evaluated.

Based on a complex network, the derived transmission rule can provide decision-making support for monitoring and managing Web information of consumer product quality and safety.

Consulting experts is an effective way to utilize tacit resource. The purpose of the paper is to optimize the matching between panels of experts and groups of demanders to improve the efficiency of tacit knowledge sharing.

Experts and demanders express preferences using linguistic terms. The estimate method based on trust is developed to get missing ratings. Weights of demanders are determined and knowledge needs are identified. Three kinds of satisfaction are measured based on grey relational analysis. To maximize satisfaction of experts and demanders and safeguard meetings of knowledge needs as well as the workload of experts, the optimization model is constructed and the solution is optimal matching results.

The presented approach not only optimizes the matching between demanders and experts but also sets up a panel of experts in case that knowledge needs exceed a single expert’s capacity.

The approach expands research works of methods for tacit knowledge sharing. The continuous updating of matching results and the processing of the data with mixing formats need to be studied further.

The presented approach acts as a valuable reference for the development of knowledge management systems. It can be used in any scene that needs the match between experts and demanders.

The approach provides a new way of helping demanders to find appropriate experts. Both experts’ and demanders’ preferences are considered. A panel of experts is set up when needed. Expert resources are utilized more efficiently and knowledge needs are met more comprehensively.

A large number of questions are posted on community question answering (CQA) websites every day. Providing a set of core questions will ease the question overload problem. These core questions should cover the main content of the original question set. There should be low redundancy within the core questions and a consistent distribution with the original question set. The paper aims to discuss these issues.

In the paper, a method named QueExt method for extracting core questions is proposed. First, questions are modeled using a biterm topic model. Then, these questions are clustered based on particle swarm optimization (PSO). With the clustering results, the number of core questions to be extracted from each cluster can be determined. Afterwards, the multi-objective PSO algorithm is proposed to extract the core questions. Both PSO algorithms are integrated with operators in genetic algorithms to avoid the local optimum.

Extensive experiments on real data collected from the famous CQA website Zhihu have been conducted and the experimental results demonstrate the superior performance over other benchmark methods.

The proposed method provides new insight into and enriches research on information overload in CQA. It performs better than other methods in extracting core short text documents, and thus provides a better way to extract core data. The PSO is a novel method used for selecting core questions. The research on the application of the PSO model is expanded. The study also contributes to research on PSO-based clustering. With the integration of K-means++, the key parameter number of clusters is optimized.

The novel core question extraction method in CQA is proposed, which provides a novel and efficient way to alleviate the question overload. The PSO model is extended and novelty used in selecting core questions. The PSO model is integrated with K-means++ method to optimize the number of clusters, which is just the key parameter in text clustering based on PSO. It provides a new way to cluster texts.

In community question answering (CQA), people who answer questions assume readers have mastered the content in the answers. Nevertheless, some readers cannot understand all content. Thus, there is a need for further explanation of the concepts that appear in the answers. Moreover, the large number of question and answer (Q&A) documents make manual retrieval difficult. This paper aims to alleviate these issues for CQA websites.

In the paper, an algorithm for recommending explanatory Q&A documents is proposed. Q&A documents are modeled with the biterm topic model (BTM) (Yan et al., 2013). Then, the growing neural gas (GNG) algorithm (Fritzke, 1995) is used to cluster Q&A documents. To train multiple classifiers, three features are extracted from the Q&A categories. Thereafter, an ensemble classification model is constructed to identify the explanatory relationships. Finally, the explanatory Q&A documents are recommended.

The GNG algorithm shows good clustering performance. The ensemble classification model performs better than other classifiers. The both effect and quality scores of explanatory Q&A recommendations are high. These scores indicate the practicality and good performance of the proposed recommendation algorithm.

The proposed algorithm alleviates information overload in CQA from the new perspective of recommending explanatory knowledge. It provides new insight into research on recommendations in CQA. Moreover, in practice, CQA websites can use it to help retrieve Q&A documents and facilitate understanding of their contents. However, the algorithm is for the general recommendation of Q&A documents which does not consider individual personalized characteristics. In future work, personalized recommendations will be evaluated.

A novel explanatory Q&A recommendation algorithm is proposed for CQA to alleviate the burden of manual retrieval and Q&A overload. The novel GNG clustering algorithm and ensemble classification model provide a more accurate way to identify explanatory Q&A documents. The method of ranking the explanatory Q&A documents improves the effectiveness and quality of the recommendation. The proposed algorithm improves the accuracy and efficiency of retrieving explanatory Q&A documents. It assists users in grasping answers easily.

In organizations, knowledge intensive activities are mainly task oriented. Finding relevant completed tasks to the new task and providing task-related knowledge to workers facilitate the knowledge reuse. However, relevant tasks are not easily found in the huge amount of completed tasks. The purpose of this paper is to assist the worker to find the required knowledge for the task at hand by reusing the knowledge related to relevant competed tasks.

First, the task profile is constructed. Relevant degrees to categories which tasks to are derived by multi-granularity fuzzy linguistic method. The stages of completed tasks are identified by the modified KNN method. Second, similar completed tasks on categories are retrieved and then the relevant tasks are selected from the retrieved similar tasks by multi-granularity fuzzy linguistic method. Third, the worker’s current task stage is derived by calculating the similarity between the rated knowledge and the knowledge in the stage of completed tasks. Finally, the knowledge is recommend based on stage relevance, relevance of the completed tasks and importance of the knowledge.

The proposed method helps the worker find the knowledge related to the task at hand by finding and reusing the completed tasks. The experimental results show that the proposed method performs well and can fulfill the worker’s’ knowledge needs. The use of the linguistic term set with preferred granularities instead of precise numbers facilitates the expression of the opinions. The recommendation stage by stage makes the knowledge recommended more precisely. The obtained linguistic weight of the knowledge makes the recommended results understood more easily than the numerical values.

In the study, the authors just focus on the codified knowledge recommendation. However, there is another kind of knowledge named tacit knowledge, which exists in the mind of the experts. The constructing and updating of the expert profile can be investigated. Meanwhile, the new recommendation method which considers more factors also needs to be studied further.

The paper includes implications for the development of the knowledge management system. The proposed approach can be applied as a tool of knowledge sharing. It facilitates the finding of the knowledge that is related to the task at hand.

The paper provides new ways to find the relevant tasks and the related knowledge to the task at hand. Meanwhile, the new method to recommend the knowledge stage by stage is also proposed. It expands the research in the knowledge sharing and knowledge recommendation.

The purpose of this paper is to explore financial quality problems, based on the dynamics of the ownership structure, in the privatization process to clarify the internal relation among the ownership’s attribution of the commercial mixed ownership company, the company’s performance and its financial relationships. This paper also examines the mixed ownership enterprise’s potential problems during the development process.

Adopting the single case study method, the authors selected the mixed ownership public company Hubei Sanxia New Building Materials Co., Ltd. (stock code: 600293) to explore, from a privatization perspective, the impact of mixed ownership on financial quality.

The study found that Sanxia experienced tight cash flow and heavy debt burdens due to the privatization and that its controlling shareholders used non-operating income to support Sanxia, thus characterizing the dual role of “the grabbing hand” and “the helping hand.” Sanxia’s privatization process highlighted the volatility of performance, the exception of monetary funds and the existence of accounting fraud rather than the prosperous development of the capital combination.

These findings provided case support that privatization negatively affects the financial quality of the company. Previous studies have indicated that there should be greater focus more on the issue that state-owned shares rebound during the process of privatization and that, with respect to commercial mixed ownership reform of state-owned enterprises, such reform must avoid the passive transfer of corporate control, ensure the fairness of the related transactions, prevent the loss of state-owned assets and preclude the controlling shareholders from seizing interests of listed companies.

In this paper, boric acid was loaded on the surface of expandable graphite (EG), polyvinyl alcohol (PVA) and silane coupling agent (KH550) served as a bridge. The purpose of this study was to improve the flame retardant properties of semi-rigid polyurethane, meanwhile, the mechanical properties of the foam got ameliorated.

PVA was dissolved in hot water. EG was added to this solution. After stirring for 0.5 h at 85°C in ultrasonic agitation, the system was put at room temperature to cool. The silane coupling agent KH550 was added dropwise into the solution system, stirring to fully hydrolyze. Boric acid was added into the system, placing it in an oven at 90°C to dry after filtration. Changing of flame retardant properties and mechanical properties of semi-rigid polyurethane adding modified EG were characterized.

The flame retardant performance of the foam with EG has been improved, whereas the tensile strength decreased with an increase in the content of EG. After adding modified EG, compared to semi-rigid polyurethane with EG, flame retardant performance and tensile strength of the foam improved.

In the study reported here, the surface of EG was modified by boric acid. The modified EG was added into semi-rigid polyurethane foam. The flame retardant performance and tensile strength of the foam after adding modified EG were discussed. Results of this research could benefit in-depth study of the influence of adding modified EG to semi-rigid polyurethane. The study could promote the application of flame-retardant polyurethane foam.

The flame retardant performance and tensile strength of the semi-rigid polyurethane were improved by adding modified EG. The effects of modified EG on the flame retardant performance and tensile strength of semi-rigid polyurethane were discussed in detail.