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With the development of data mining technology, diverse and broader domain knowledge can be extracted automatically. However, the research on applying knowledge mapping and data visualization techniques to genealogical data is limited. This paper aims to fill this research gap by providing a systematic framework and process guidance for practitioners seeking to uncover hidden knowledge from genealogy.

Based on a literature review of genealogy's current knowledge reasoning research, the authors constructed an integrated framework for knowledge inference and visualization application using a knowledge graph. Additionally, the authors applied this framework in a case study using “Manchu Clan Genealogy” as the data source.

The case study shows that the proposed framework can effectively decompose and reconstruct genealogy. It demonstrates the reasoning, discovery, and web visualization application process of implicit information in genealogy. It enhances the effective utilization of Manchu genealogy resources by highlighting the intricate relationships among people, places, and time entities.

This study proposed a framework for genealogy knowledge reasoning and visual analysis utilizing a knowledge graph, including five dimensions: the target layer, the resource layer, the data layer, the inference layer, and the application layer. It helps to gather the scattered genealogy information and establish a data network with semantic correlations while establishing reasoning rules to enable inference discovery and visualization of hidden relationships.

Grey target decision making is one of the important problems of decision-making theory. It is critical to express uncertain information effectively and depose them in a reasonable and simple way. The purpose of this paper is to solve the grey target problem by the grey potential degree method without whiten value and without distribution function. Furthermore, this new approach has an advantage of realizing both comparing and standardization work during the process of treating the data.

First, this paper makes a brief overview of the existing method for grey target decision. Then, the conception of a grey potential degree system is introduced and the conception of standard grey potential degree is build up, and a new grey potential-based method based on the grey target multiple attribute decision method is proposed. At the same time, the standard grey potential and its application in multiple resource data are studied.

At the same time the standard grey potential and its application in multiple resource data are studied. Standard grey potential is presented by means of three examples together with the comparison with the existing method to demonstrate that the grey potential-based method could be used to solve the problem of grey target decision conveniently and effectively.

It is very important to compare grey numbers to obtain scientific and reasonable results for a grey target decision-making problem. However, in the actual application of grey numbers, it is difficult to find out the probability density function or the whiten function of grey numbers. When grey numbers are compared and deposed through the whiten value, much information regarding grey numbers will be lost and, at the same time, the value of grey numbers in uncertainty is partly lost. The method discussed in this paper is reasonable and feasible.

This paper aims to construct a spatio-temporal emotional framework (STEF) for digital humanities from a quantitative perspective, applying knowledge extraction and mining technology to promote innovation of humanities research paradigm and method.

The proposed STEF uses methods of information extraction, sentiment analysis and geographic information system to achieve knowledge extraction and mining. STEF integrates time, space and emotional elements to visualize the spatial and temporal evolution of emotions, which thus enriches the analytical paradigm in digital humanities.

The case study shows that STEF can effectively extract knowledge from unstructured texts in the field of Chinese Qing Dynasty novels. First, STEF introduces the knowledge extraction tools – MARKUS and DocuSky – to profile character entities and perform plots extraction. Second, STEF extracts the characters' emotional evolutionary trajectory from the temporal and spatial perspective. Finally, the study draws a spatio-temporal emotional path figure of the leading characters and integrates the corresponding plots to analyze the causes of emotion fluctuations.

The STEF is constructed based on the “spatio-temporal narrative theory” and “emotional narrative theory”. It is the first framework to integrate elements of time, space and emotion to analyze the emotional evolution trajectories of characters in novels. The execuability and operability of the framework is also verified with a case novel to suggest a new path for quantitative analysis of other novels.

In this paper we appraise the ways in which use of closed-system proprietary product architectures versus open-system modular product architectures is likely to influence the dynamics and trajectory of new product market formation. We compare the evolutions of new markets in China for gas-powered two-wheeled vehicles (G2WVs) based (initially) on closed-system proprietary architectures and for electric-powered two-wheeled vehicles (E2WVs) based on open-system modular architectures. We draw on this comparison to suggest ways in which the use of the two different kinds of architectures as the basis for new kinds of products may result in very different patterns and speeds of new market formation. We then suggest some key implications of the different dynamics of market formation associated with open-system modular architectures for both the competence-based strategic management (CBSM) of firms and for technology and economic development policies of governments.

Specifically, we suggest how the use of open-system modular product architectures as the basis for new products is likely to result in dynamics of new market formation that call for new approaches to the strategic management of innovation and product creation. We also suggest technology and economic development policies favoring use of open-system modular architectures may stimulate new market formation and related economic development by providing platforms for accelerating technology development and dissemination, facilitating the formation of an industrial base of assemblers and component suppliers, assisting new firms in building customer relationships, enabling more geographically diffused economic development within countries, and facilitating development of export markets. We also suggest directions for further research into the potential for open-system modular product architectures to enable bottom-of-the-pyramid innovation processes, frugal engineering in developing economies, and development of low-cost product variations more generally.

The purpose of this article is to prepare graphene/polyimide composite materials for use as bearing cage materials, improving the friction and wear performance of bearing cages.

The oil absorption and discharge tests were conducted to evaluate the oil content properties of the materials, while the mechanical properties were analyzed through cross-sectional morphology examination. Investigation into the tribological behavior and wear mechanisms encompassed characterization and analysis of wear trace morphology in PPI-based materials. Consequently, the influence of varied graphene nanoplatelets (GN) concentrations on the oil content, mechanical and tribological properties of PPI-based materials was elucidated.

The composites exhibit excellent oil-containing properties due to the increased porosity of PPI-GN composites. The robust formation of covalent bonds between GN and PPI amplifies the adhesive potency of the PPI-GN composites, thereby inducing a substantial enhancement in impact strength. Notably, the PPI-GN composites showed enhanced lubrication properties compared to PPI, which was particularly evident at a GN content of 0.5 Wt.%, as evidenced by the minimization of the average coefficient of friction and the width of the abrasion marks.

This paper includes implications for elucidating the wear mechanism of the polyimide composites under frictional wear conditions and then to guide the optimization of oil content and tribological properties of polyimide bearing cage materials.

In this paper, homogeneously dispersed PPI-GN composites were effectively synthesized by introducing GN into a polyimide matrix through in situ polymerization, and the lubrication mechanism of the PPI composites was compared with that of the PPI-GN composites to illustrate the composites’ superiority.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2023-0415

This paper aims to find the best method to predict the friction coefficient of textured 45# steel by comparing different machine learning algorithms and analytical calculations.

Five machine learning algorithms, including K-nearest neighbor, random forest, support vector machine (SVM), gradient boosting decision tree (GBDT) and artificial neural network (ANN), are applied to predict friction coefficient of textured 45# steel surface under oil lubrication. The superiority of machine learning is verified by comparing it with analytical calculations and experimental results.

The results show that machine learning methods can accurately predict friction coefficient between interfaces compared to analytical calculations, in which SVM, GBDT and ANN methods show close prediction performance. When texture and working parameters both change, sliding speed plays the most important role, indicating that working parameters have more significant influence on friction coefficient than texture parameters.

This study can reduce the experimental cost and time of textured 45# steel, and provide a reference for the widespread application of machine learning in the friction field in the future.

This study aims to analyze how bed and breakfast (B&B) experience influences customer value cocreation (CVCC) through emotional psychological factors under the social servicescape (SSC).

A moderated chain mediation model was constructed based on the SSC framework. This study collected 722 valid questionnaires through convenience sampling in Xiamen, China, and tourists with B&B experiences were taken as samples. A structural equation model analysis was used to test relevant hypotheses.

Results reveal that B&B experience is the core factor to stimulate CVCC. In this process, the individual emotional psychological factors [pleasant arousal (PAL) and place attachment (PAT)] play the critical chain mediating role. In addition, localness positively moderates the reinforcing effect of B&B experience on PAL and strengthens the positive effect of PAL on PAT.

The findings can provide useful CVCC strategies for B&B operators. They can achieve CVCC by enhancing customer experiences, strengthening emotional interactions and building local culture, thereby improving the competitiveness of B&Bs.

Current research lacks an in-depth analysis on the internal relationship and logic between B&B experience and CVCC, especially from emotional psychological perspectives. To the best of the authors’ knowledge, this study is one of the first studies to reveal the mechanism of the CVCC process of B&Bs under the SSC from an emotional psychological perspective. It provides a new theoretical model for research on the CVCC of experience-dominant logic.

本研究旨在分析社会服务场景下, 民宿体验如何通过情感心理影响顾客的价值共创。

基于社会服务场景框架, 本研究构建了一个有调节的链式中介模型。研究者针对具有民宿住宿体验的游客进行调查, 在中国厦门采用便利抽样技术收集了722份有效问卷, 并采用结构方程模型检验提出的相关假设。

在社会服务场景下, 民宿体验是激发顾客价值共创的核心因素。在此过程中, 个体情感心理因素（愉悦唤醒和地方依恋）发挥了重要的中介作用, 有效衔接了民宿体验到顾客价值共创的实现过程。此外, 地方性是独特的地方文化体验因子, 正向调节了民宿体验对愉悦唤醒的强化作用, 也增强了愉悦唤醒对地方依恋的积极作用。

研究结论为民宿管理者如何提升顾客价值共创贡献了经营策略。民宿管理者可以通过提升顾客体验、加强情感互动和打造地方文化来实现价值共创, 进而提升民宿竞争力。

当前关于民宿体验与顾客价值共创之间的内在关系及逻辑缺乏深入分析, 仍缺乏情感心理视角的探索。本研究首次从情感心理视角揭示了社会服务场景下民宿顾客价值共创的过程机理, 为以顾客体验为主导逻辑的价值共创研究提供了新的理论模型。

Este estudio pretende analizar cómo la experiencia en un B&B influye en la cocreación de valor para el cliente (CVCC) a través de factores psicológicos emocionales en el marco social del servicescape.

Se construyó un modelo de mediación en cadena moderado basado en el marco social del servicescape. En este estudio se recogieron 722 cuestionarios válidos mediante un muestreo de conveniencia en Xiamen, China, y se tomaron como muestra turistas con experiencias en establecimientos Bed and Breakfast (B&B). Se utilizó un análisis de modelo de ecuaciones estructurales (SEM) para poner a prueba las hipótesis pertinentes.

Los resultados revelan que la experiencia en el B&B es el factor central para estimular la CVCC. En este proceso, los factores psicológicos emocionales individuales (excitación placentera y apego al lugar) desempeñan un papel mediador en cadena. Además, el carácter local modera positivamente el efecto de refuerzo de la experiencia de B&B sobre la excitación placentera y refuerza el efecto positivo de la excitación placentera sobre el apego al lugar.

Los resultados pueden proporcionar estrategias de CVCC útiles para los operadores de B&B. Pueden lograr la CVCC mejorando las experiencias de los clientes, reforzando las interacciones emocionales y construyendo la cultura local, mejorando así la competitividad de los B&B.

La investigación actual carece de un análisis en profundidad sobre la relación interna y la lógica entre la experiencia de los B&B y la CVCC, especialmente desde las perspectivas psicológicas emocionales. Este estudio es uno de los primeros que revela el mecanismo del proceso de CVCC de los B&B bajo el modelo social del servicescape desde una perspectiva psicológica emocional. Proporciona un nuevo modelo teórico para la investigación sobre la CVCC en la lógica dominante de la experiencia.

This study aims to examine the impact of board characteristics on firm performance while also exploring the influential mechanisms that help Chinese listed companies establish effective boards of directors and strengthen their corporate governance mechanisms.

This paper uses machine learning methods to investigate the predictive ability of the board of directors' characteristics on firm performance based on the data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges in China during 2008–2021. This study further analyzes board characteristics with relatively strong predictive ability and their predictive models on firm performance.

The results show that nonlinear machine learning methods are more effective than traditional linear models in analyzing the impact of board characteristics on Chinese firm performance. Among the series characteristics of the board of directors, the contribution ratio in prediction from directors compensation, director shareholding ratio, the average age of directors and directors' educational level are significant, and these characteristics have a roughly nonlinear correlation to the prediction of firm performance; the improvement of the predictive ability of board characteristics on firm performance in state-owned enterprises in China performs better than that in private enterprises.

The findings of this study provide valuable suggestions for enriching the theory of board governance, strengthening board construction and optimizing the effectiveness of board governance. Furthermore, these impacts can serve as a valuable reference for board construction and selection, aiding in the rational selection of boards to establish an efficient and high-performing board of directors.

The study findings unequivocally demonstrate the superiority of nonlinear machine learning approaches over traditional linear models in examining the relationship between board characteristics and firm performance in China. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. The study reveals that the predictive performance of board attributes is generally more robust for state-owned enterprises in China in comparison to their counterparts in the private sector.

Rural China has been undergoing the “religion fever” since the Reform and Opening-up. By comparing the intergenerational lock-in effects of religious belief with that of non-religious belief, the purpose of this paper is to explain why more and more peasants convert to religion especially Christianity in China.

Data used in this paper comes from a field survey conducted in villages, Funan county. The samples were obtained by the two-stage cluster probability proportional sampling method. Based on the collected survey data, econometric model on the intergenerational lock-in effects of belief was constructed and used for analysis.

Compared with non-religious believers, religious believers can transmit their beliefs more successfully. In addition, the intergenerational lock-in effects of religious beliefs is weakened by oblique socialization in contemporary rural China.

This is the first paper focussing on intergenerational transmission of belief in rural China; although many researchers did careful analyses on effects of parents’ belief or religiosity on children’s belief or religiosity in the West, few of them compared the success of intergenerational transmission between different beliefs. This paper fills this gap; as an interdisciplinary study, this paper tries to study religion in economics analysis approaches. This attempt extends research field in Economics and at the same time enriches analysis tools in Religion.

The purpose of this paper is to propose an overall deformation rolling mechanism based on double four-link mechanism. The double quadrilateral mobile mechanism (DQMM) has two switchable working modes which can be used to traverse different terrains or climb over obstacles.

The main body of the DQMM is composed of a double four-link mechanism which sharing a public link and two symmetrical steering platforms which placed at both ends of the four-link mechanism. The steering platforms give the DQMM not only steering ability but also reconnaissance ability which can be achieved by carrying sensors such as cameras on steering platforms. By controlling the deformation of the DQMM, it can switch between two working modes (tracked rolling mode and obstacle-climbing mode) to achieve the functions of rolling and obstacle-climbing. Dynamic simulation model was established to verify the feasibility.

Based on the kinematics analysis and simulation results of the DQMM, its moving function is realized by the tracked rolling mode, and the obstacle-climbing mode is used to climb over obstacles in structured terrains such as continuous stairs. The feasibility of the two working modes is verified on a physical prototype.

The work of this paper is a new exploration of applying “overall closed moving linkages mechanism” to the area of small mobile mechanisms. The adaptability of different terrains and the ability of obstacle-climbing are improved by the combination of multi-modes.