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In order to solve the decision-making problem that the attributive weight and attributive value are both interval grey numbers, this paper tries to construct a multi-attribute grey decision-making model based on generalized greyness of interval grey number.

Firstly, according to the nature of the generalized gresness of interval grey number, the generalized weighted greyness distance between interval grey numbers is given, and the transformation relationship between greyness distance and real number distance is analyzed. Then according to the objective function that the square sum of generalized weighted greyness distances from the decision scheme to the best scheme and the worst scheme is the minimum, a multi-attribute grey decision-making model is constructed, and the simplified form of the model is given. Finally, the grey decision-making model proposed in this paper is applied to the evaluation of technological innovation capability of 6 provinces in China to verify the effectiveness of the model.

The results show that the grey decision-making model proposed in this paper has a strict mathematical foundation, clear physical meaning, simple calculation and easy programming. The application example shows that the grey decision model in this paper is feasible and effective. The research results not only enrich the grey system theory, but also provide a new way for the decision-making problem that the attributive weights and attributive values are interval grey numbers.

The decision-making model proposed in this paper does not need to seek the optimal solution of the attributive weight and the attributive value, and can save the decision-making labor and capital investment. The model in this paper is also suitable for the decision-making problem that deals with the coexistence of interval grey numbers and real numbers.

The paper succeeds in realizing the multi-attribute grey decision-making model based on generalized gresness and its simplified forms, which provide a new method for grey decision analysis.

Talent is a key resource for urban development, and building and disseminating urban brands have an important impact on attracting talent. This paper explores what kind of urban brand ecology (UBE) can effectively enhance urban talent attraction (UTA). We explore this question using a novel grey quantitative configuration analysis (GQCA) model.

To develop the GQCA model, grey clustering is combined with qualitative configuration analysis (QCA). We conducted comparative configuration analysis of UTA using fuzzy set QCA (fsQCA) and the proposed GQCA.

We find that the empirical results of fsQCA may contradict the facts, and that the proposed GQCA effectively solves this problem.

Based on the theory of UBE, we identify bottleneck factors for improving UTA at different stages. Seven configuration paths are described for cities to enhance UTA. Theoretically, this study expands the application boundaries of UBE.

The proposed GQCA effectively solves the problem of inconsistent analysis and facts caused by the use of a binary threshold by the fsQCA. In practical case studies, the GQCA significantly improves the reliability of configuration comparisons and the sensitivity of QCA to cases, demonstrating excellent research performance.

The purpose of this paper is to construct a grey incidence model for panel data that can reflect the incidence direction and degree between indicators.

Firstly, this paper introduces the concept of a negative matrix and preprocesses the data of each indicator matrix to eliminate differences in dimensions and magnitudes between indicators. Then a model is constructed to measure the incidence direction and degree between indicators, and the properties of the model are studied. Finally, the model is applied to a practical problem.

The grey-directed incidence degree is 1 if and only if corresponding elements between the feature indicator matrix and the factor indicator matrix have a positive linear relationship. This degree is −1 if and only if corresponding elements between the feature indicator matrix and the factor indicator matrix have a negative linear relationship.

The example shows the number of days with good air quality is negatively correlated with the annual average concentration of each pollutant index. PM_2.5, PM₁₀ and O₃ are the main pollutants affecting air quality in northern Henan.

This paper introduces the negative matrix and constructs a model from the holistic perspective to measure the incidence direction and level between indicators. This model can effectively measure the incidence between the feature indicator and factor indicator by integrating information from the point, row, column and matrix.

The selection of sensitive temperature measurement points is the premise of thermal error modeling and compensation. However, most of the sensitive temperature measurement point selection methods do not consider the influence of the variability of thermal sensitive points on thermal error modeling and compensation. This paper considers the variability of thermal sensitive points, and aims to propose a sensitive temperature measurement point selection method and thermal error modeling method that can reduce the influence of thermal sensitive point variability.

Taking the truss robot as the experimental object, the finite element method is used to construct the simulation model of the truss robot, and the temperature measurement point layout scheme is designed based on the simulation model to collect the temperature and thermal error data. After the clustering of the temperature measurement point data is completed, the improved attention mechanism is used to extract the temperature data of the key time steps of the temperature measurement points in each category for thermal error modeling.

By comparing with the thermal error modeling method of the conventional fixed sensitive temperature measurement points, it is proved that the method proposed in this paper is more flexible in the processing of sensitive temperature measurement points and more stable in prediction accuracy.

The Grey Attention-Long Short Term Memory (GA-LSTM) thermal error prediction model proposed in this paper can reduce the influence of the variability of thermal sensitive points on the accuracy of thermal error modeling in long-term processing, and improve the accuracy of thermal error prediction model, which has certain application value. It has guiding significance for thermal error compensation prediction.

This paper aims to test the effectiveness of the Haryono Umar (HU)-model used in corruption prevention strategies through corruption detection as a tool for detecting corruption because the mode of corruption is increasingly dynamic and complex by focusing on the causes of corruption: pressure, opportunity, rationalization, capability and lack of integrity.

The research uses multiple regression methods, classification and regression trees and the HU-model application system developed by researchers. The research sample uses secondary data from financial reports on the Indonesia stock exchange according to organizational clustering (such as red, grey and green areas).

The research result showed that of the 470 sample companies, there were 445 companies, or 98.9%, in the red cluster (indicated corruption), 19 companies, or 4.04, in the green clusters or not indicated corruption and six companies, or 1.28%, were included in the grey cluster or potential corruption. By knowing the cluster of an organization, efforts to prevent corruption can be made effective and efficient. Implementing the HU-model proves that the amount of pressure, the abundance of opportunities, the ease of rationalization and the high level of position and authority strengthen the drive for corruption if there is a lack of integrity.

Each internal organization can use this model independently and find conditions related to corruption so that they can immediately take action to prevent it.

The application of the HU-model is a discovery in preventing corruption by focusing on the possibility of corruption occurring in each organization through organizational clustering.

This paper aims to optimize the air pressure regulation scheme of the aeroengine pressure test bench.

Based on the requirements of pressure regulation process and the operating mechanism of aeroengine pressure test bench, a grey performance evaluation index system is constructed. The combination of principal component analysis and grey theory is employed to assign weights to grey indexes. The grey target evaluation model is introduced to evaluate the performance of historical regulation processes, and the evaluation results are analyzed to derive optimization mechanism for pressure regulating schemes.

A case study based on monitoring data from nearly 300 regulation processes verifies the feasibility of the proposed method. On the one hand, the improved principal component analysis method can achieve rational weighting for grey indexes. On the other hand, the method comparison intuitively shows that the proposed method performs better.

The pressure test bench is a fundamental technical equipment in the aviation industry, serving the development and testing of aircraft engines. Due to the complex system composition, the pressure and flow adjustment of the test bench heavily rely on manual experience, leading to issues such as slow adjustment speed and insufficient accuracy. This paper proposes a performance evaluation method for the regulation process of pressure test bench, which can draw knowledge from historical regulation processes, provide guidance for the pressure regulation of test benches, and ultimately achieve the goal of reducing equipment operating costs.

This study aims to provide an overview, the state-of-the-art “research fronts”, the emerging themes of investigation and a research agenda of crisis communication for destinations’ image.

This research is conducted with a bibliographic coupling study, complemented with an H-Classic classification and a thematic analysis of the articles included in the four clusters provided by the bibliometric methodology (papers dating from 2017 to 2021, both years included).

Based on the bibliometric analysis, four thematic clusters were identified. Two of these clusters supply the “research fronts”, the most current themes in a scientific field: Cluster 1 addresses communication related to tourists’ safety, and cluster 2 enhances the role of stakeholders’ collaboration to create destinations resilience in crisis communication. The other two clusters highlight emerging themes for future investigation: Cluster 3 focuses on recovery marketing communication strategies for a post-crisis era, and cluster 4 analyses how crisis communication strategies contribute to reduce tourists’ risk perception and boosting travel intention. Finally, a future research agenda is proposed, based on the emerging themes from this study.

To the best of the authors’ knowledge, this is the first bibliometric study to analyse crisis communication for destinations’ image (pre-crisis, crisis and post-crisis). This study, which covers the most recent academic literature in this field, provides insights of communication strategies from recent crises and disasters within the “research fronts”. Besides, a research agenda useful for future scholar investigation is proposed with its emerging themes. These rising topics and learnings from past events could be used by destination marketing organisations in crisis communication for destination image recovery in the current post-pandemic scenario or in upcoming crises or disasters.

本研究提供了目的地形象危机沟通的概述、最先进的“研究前沿”、新兴的研究主题以及研究议程。

本文进行了书目耦合研究, 辅以 H-经典分类和对文献计量方法提供的四个集群中包含的文章的主题分析 (论文日期为 2017–2021 年间)。

根据文献计量分析, 确定了四个主题集群。 其中两个集群提供“研究前沿”, 这是科学领域的最新主题：集群 一 解决与游客安全相关的沟通, 集群 二 加强利益相关者合作的作用, 以在危机沟通中创造目的地恢复力。 其他两个集群突出了未来调查的新兴主题：集群 三 侧重于后危机时代的复苏营销传播策略, 集群 四分析危机传播策略如何有助于降低游客的风险感知和提高旅行意愿。最后, 本文提出了基于新兴主题的未来研究议程。

据我们所知, 这是第一个分析了目的地形象的危机传播（危机前、危机和危机后）的, 独特的文献计量研究。该研究涵盖了该领域最新的学术文献, 通过其“研究前沿”提供了有关近期危机和灾难的沟通策略的见解。此外, 本文还提出了具有新兴主题的研究议程。这些新兴话题以及从过去事件中吸取的教训, 可以被目的地营销组织 (DMO) 用来进行灾难沟通, 以便在当前的大流行后情景或未来的危机或灾难中恢复目的地的形象。

Este estudio proporciona una perspectiva general, los “research fronts”- los temas más actuales de una disciplina científica-, los temas emergentes y una agenda de investigación sobre comunicación de crisis de la imagen de los destinos turísticos.

La investigación está basada en un análisis bibliográfico coupling, complementado con una clasificación h-Classics y un análisis temático de todos los artículos examinados con esta metodología bibliométrica (artículos fechados entre 2017 y 2021, ambos años incluidos).

Con este análisis bibliométrico, se identifican cuatro clusters temáticos. Dos de ellos, presentan los “research fronts”, los temas más vigentes de un campo científico: el cluster 1 se refiere a la comunicación realizada para transmitir el concepto de seguridad a los turistas, y el cluster 2 destaca la relevancia de la colaboración de todos los agentes turísticos para crear resiliencia en los destinos en la comunicación de crisis. Los otros dos clusters recogen los temas emergentes de investigación futura: el cluster 3 se centra en las estrategias de marketing para la época de postcrisis y el cluster 4 analiza cómo la comunicación contribuye a reducir la percepción de riesgo de los turistas y, por tanto, a potenciar su intención de viaje. Por último, el artículo propone una agenda de investigación basada en estos temas emergentes.

Hasta donde tenemos conocimiento, éste es el primer estudio bibliométrico especialmente enfocado a la comunicación de crisis para la imagen de los destinos turísticos (con sus tres etapas, precrisis, crisis y poscrisis). Esta investigación, que analiza la literatura más reciente en este campo, proporciona conocimiento sobre la comunicación de las crisis y desastres más recientes, a través de sus “research fronts”. Asimismo, propone una agenda con nuevos temas que están surgiendo en esta disciplina, útil para futuras investigaciones académicas. Dichos temas, junto con los aprendizajes de incidentes pasados, pueden ser usados por las Organizaciones de Marketing de Destinos (DMO, en sus siglas en inglés) para incorporarlos en su comunicación de crisis destinada a la recuperación de la imagen de los destinos turísticos en el actual escenario post pandemia o en futuras crisis o desastres.

The current research elucidates the role of empathy in design of artificial intelligence (AI) systems in healthcare context, through a structured literature review, analysis and synthesis of academic literature published between 1990 and 2024.

This study aims to advance the domain of empathy in AI by adopting theory constructs context method approach using the PRISMA 2020 framework.

The study presents a current state-of-the-art literature to review the connections between empathy and AI and identifying four clusters showing the emerging trajectories in the field of AI and empathy in healthcare setting.

Despite a rise in empirical research, the potential pathways enhancing AI accountability by incorporation of empathy is unclear. The research aims to contribute to the existing literature on AI and empathy in the healthcare sector by carving out four distinct clusters depicting the future research avenues.

This paper aims to propose a stable and precise recognition and pose estimation method to deal with the difficulties that industrial parts often present, such as incomplete point cloud data due to surface reflections, lack of color texture features and limited availability of effective three-dimensional geometric information. These challenges lead to less-than-ideal performance of existing object recognition and pose estimation methods based on two-dimensional images or three-dimensional point cloud features.

In this paper, an image-guided depth map completion method is proposed to improve the algorithm's adaptability to noise and incomplete point cloud scenes. Furthermore, this paper also proposes a pose estimation method based on contour feature matching.

Through experimental testing on real-world and virtual scene dataset, it has been verified that the image-guided depth map completion method exhibits higher accuracy in estimating depth values for depth map hole pixels. The pose estimation method proposed in this paper was applied to conduct pose estimation experiments on various parts. The average recognition accuracy in real-world scenes was 88.17%, whereas in virtual scenes, the average recognition accuracy reached 95%.

The proposed recognition and pose estimation method can stably and precisely deal with the difficulties that industrial parts present and improve the algorithm's adaptability to noise and incomplete point cloud scenes.

The proliferation of industry platforms has disrupted several industries. Firms adopting a platform business model have experienced a substantial expansion in size and scale, positioning themselves as the foremost valuable entities in market capitalization. Over the past two decades, there has been a substantial expansion in the body of literature dedicated to platforms, and different streams of research have emerged. Despite considerable efforts and the significant progress made in recent years toward a comprehensive understanding of industry platforms, there is still room for further harnessing the field’s diversity. As a result, the aim of this article is to examine the field’s structure, identify research concerns and provide suggestions for future research, thereby enhancing the overall understanding of industry platforms.

We conducted a thorough examination of 458 articles on the topic using bibliometric methods and systematic review techniques.

Through co-citation analysis, we identified five distinct clusters rooted in various bodies of literature: two-sided markets, industry platforms, digital platforms, innovation platforms and two-sided networks. Furthermore, the examination of these five clusters has revealed three key areas that demand further consideration: (1) terminologies, (2) classifications and (3) perspectives.

While previous reviews have provided valuable insights into the topic of industry platforms, none have explored the structure of the field so far. Consequently, as a first step toward advancing the field, we uncover the structure of the literature, identifying three major areas of concern. By addressing these concerns, our goal is to converge different clusters, thereby harnessing the diversity in the field and enhancing the overall understanding of industry platforms.