Keqin Wang

Small- and medium-sized enterprises (SMEs) play a vital role in the global economy, driving innovation and economic growth, despite constraints on their financial and operational resources. In the competitive landscape of modern markets, continuous product design improvement has become essential for SMEs to meet dynamic user requirements, enhance satisfaction, and maintain competitiveness. Online reviews have emerged as valuable sources of user feedback, offering real-time, large-scale insights into user preferences. However, existing methods for leveraging online reviews in product design improvement have significant limitations, including insufficient attention paid to the hierarchical structure of different attributes when extracting product improvement attributes and a lack of quantitative attribute prioritization strategies. These shortcomings often result in suboptimal improvement and inefficient resource allocation, particularly for SMEs with limited resources. To address these challenges, this study proposed a novel online review-assisted method for product design improvement tailored to the needs of SMEs. The proposed method incorporates a hierarchical latent Dirichlet allocation model to extract and organize product attributes hierarchically, thereby enabling a comprehensive understanding of user requirements. Furthermore, a marginal utility-based approach is employed to prioritize product improvement attributes quantitatively, ensuring that the most impactful attributes are addressed efficiently. The effectiveness of the proposed method was demonstrated through a case study on the design improvement of a robotic vacuum cleaner developed using a typical SME in robotic cleaning solutions.

Evaluation methods based on data-driven techniques and artificial intelligence for the sustainable enrollment plan configurations of Chinese universities have become a research hotspot in the field of higher education teaching reform. Enrollment, education, and employment constitute the three key pillars of talent cultivation in universities. However, due to an unclear understanding of their interconnection, universities have yet to establish robust quantitative relationship models, hindering the formation of an evaluation mechanism for sustainable enrollment plan configurations. This study begins by constructing a relevant indicator system and utilizing real enrollment data from a specific university. Through statistical methods such as correlation analysis, it systematically sorts out key variables and identifies seven effective indicators, including average admission score and first-time graduation rate. Subsequently, by using the increase or decrease in enrollment quotas for each major as the experimental target, evaluation models for sustainable enrollment plan configurations aimed at enhancing the advanced education rate are constructed using naïve Bayes networks and tree-augmented Bayesian networks; these are compared with three other classic machine learning methods. The accuracy of these models is evaluated through confusion matrices and receiver operating characteristic curves. Additionally, the Birnbaum importance analysis method is utilized to prioritize remaining variables, ultimately identifying the optimal combination strategy of indicators conducive to the sustainable development of the advanced education rate. The results indicate that the average admission score, transfer rate, and student/teacher ratio are the top 3 prognostic factors affecting the advanced education rate, with the TAN model achieving an accuracy of 96.49%, thus demonstrating good reliability.

Solar radiation governs biological, chemical and other processes in the atmosphere, hydrosphere, biosphere and lithosphere. As the source of heat and energy of forest ecosystem, solar radiation is the original power to maintain the normal operation and develop the ecosystem, and has important effects on plant photosynthesis, transpiration and carbon exchange. Gongga Mountain is located in southeast edge of Tibetan Plateau, and is a typical and representative alpine ecosystem. According to the protocols for standard radiation observation and measurement of Chinese Ecosystem Research Network (CERN) and National Ecosystem Research Network of China (CNERN), the Alpine Ecosystem Observation and Experiment Station of Gongga Mountain, Chinese Academic of Sciences has been carrying out long-term radiation monitoring practice. In this dataset, we report 22 radiation indices (total file size, 97 KB) collected from the automatic radiation system after data processing and quality control and assessment during 1998–2018. The dataset can provide basic data for carbon cycle, water cycle and energy cycle of alpine ecosystem under global change.

Purpose: Visual Distinctive Language (VDL)-based iconic tags are structured visual information annotation. They explicate the content and organization of tagged information by graphical and symbolic features in order to improve the vocabulary problems of textual tags. The purpose of this paper is to investigate how these special icons help in tagged-based user information searching. Design/methodology/approach: A two-stage experiment was designed and conducted so as to follow and quantify the searching process in specific searching target case and no specific searching target case when using VDL-based iconic tags. Findings: The experimental results manifested that VDL-based iconic tags enhanced the role of tag in information searching. They could make user better understand tag clusters, which, in turn, provide global structure of involved topics. Also, VDL-based iconic tags helped user to find out searching target more quickly with higher accuracy by taking advantages of visual representation of tag categories and symbolic signification of tag content. Originality/value: This study is one of the first to verify how structured icons work in information searching and how user’s graphical cognition impacts on tag-based information searching process. The research findings are dedicated to the theory of VDL-based iconic tags, as well as to a new visualization method for search user interface design.

Nowadays, companies have to improve their practices in the management of green supply chain with increased awareness of environmental issues worldwide. Selecting the optimum green supplier is crucial for green supply chain management, which is a challenging multi-criteria decision making (MCDM) problem. Moreover, while evaluating the performance of alternative suppliers, decision makers tend to determine their assessments using linguistic descriptors due to experts’ vague knowledge and information deficiency. Therefore, this paper develops an integrated MCDM model based on the cloud model and QUALIFLEX (qualitative flexible multiple criteria method) approach to assess the green performance of companies under economic and environmental criteria. For the introduced model, the linguistic terms, expressed in normal clouds, are utilized to assess alternatives against each selection criterion. A linear programming model is established to compute the weights of criteria with unknown or incompletely known weight information. An extended QUALIFLEX approach is proposed and used to select the most suitable green supplier. Finally, the proposed green supplier selection method is demonstrated by an empirical example of an auto manufacturer to confirm its rationality and effectiveness.

Finding information from manufacturing quality information to support product design decisions is necessary for improving design quality. Based on the review of group technology and quality bill of material, the classification and coding of manufacturing quality information is proposed in which combines the manufacturing quality information families with part families. Then the application of classified and coded manufacturing quality information is illustrated for further use to support design decisions