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Construction schedule delays and quality problems caused by construction errors are common in the field of prefabricated buildings. The effective monitoring of the construction project process is one of the key factors for the success of a project. How to effectively monitor the construction process of prefabricated building construction projects is an urgent problem to be solved. Aiming at the problems existing in the monitoring of the construction process of prefabricated buildings, this paper proposes a monitoring method based on the feature extraction of point cloud model.

This paper uses Trimble X7 3D laser scanner to complete field data collection experiments. The point cloud data are preprocessed, and the prefabricated component segmentation and geometric feature measurement are completed based on the PCL platform. Aiming at the problem of noisy points and large amount of data in the original point cloud data, the preprocessing is completed through the steps of constructing topological relations, thinning, and denoising. According to the spatial position relationship and geometric characteristics of prefabricated frame structure, the segmentation algorithm flow is designed in this paper. By processing the point cloud data of single column and beam members, the quality of precast column and beam members is measured. The as-built model and as-designed model are compared to realize the visual monitoring of construction progress.

The experimental results show that the dimensional measurement accuracy of beam and column proposed in this paper is more than 95%. This method can effectively detect the quality of prefabricated components. In the aspect of progress monitoring, the visualization of real-time progress monitoring is realized.

This paper proposed a new monitoring method based on feature extraction of the point cloud model, combined with three-dimensional laser scanning technology. This method allows for accurate monitoring of the construction process, rapid detection of construction information, and timely detection of construction quality errors and progress delays. The treatment process based on point cloud data has strong applicability, and the real-time point cloud data transfer treatment can guarantee the timeliness of monitoring.

Owing to dual constraints including liability of foreignness and liability of origin when emerging multinationals internationalize, they inevitably face the challenge of overseas legitimation. However, few studies have explored how latecomers cross the threshold of legitimacy in the dynamic context of transnational operation. The purpose of this paper is to unravel the evolution process, triggers and specific strategies of overseas legitimacy threshold crossing of emerging multinationals.

Through the longitudinal case study of Haier Group and Goldwind Sci & Tech Co., Ltd, this study investigates the periodical characteristics of overseas legitimacy threshold crossings and the co-evolution among critical factors influencing the legitimation process in the host country.

First, it summarizes that the legitimacy threshold in the host country experiences a sequential process from pragmatic legitimacy to normative legitimacy, and finally cognitive legitimacy. It is an inevitable choice for emerging multinational enterprises to realize and sustain legitimation from passive adaptation to active creation. Second, it reveals that the triggers for crossing the threshold of overseas legitimacy include periodically dynamic factors – international network linkage and resource system reconfiguration, as well as cross-stage spiral interaction effects. Third, it determines the specific strategies for crossing the threshold of overseas legitimacy, namely, replacement, upgrading and reconstruction of organizational identity, and reveals the important role of insisting on the country-of-origin Facebook in promoting the legitimation.

This study enriches the legitimacy threshold crossing literature from an evolutional perspective, especially the traditional static legitimacy research. This study also reveals the key impacting factors – international network linkage and resource system reconfiguration – and their evolution process interacted with the legitimation process.

The emerging multinationals should break the stereotypes from developed markets in that only creating new cognitive patterns through active legitimate strategies can they truly cross the legitimacy threshold in the host country. The emerging multinationals also need to retain their own home country legitimacy traits – Facebook and balance the relation between the image of the home country and the image of host country.

This paper investigates the process of overseas legitimacy threshold crossing for emerging multinationals in a dynamic context of transnational operation, particularly with respect to the evolutionary role played by international network linkage and resource system reconfiguration.

This paper comprehensively addresses the influence of chopped strand mat glass fiber-reinforced polymer (GFRP) patch configurations such as geometry, dimensions, position and the number of layers of patches, whether a single or double patch is used and how well debonding the area under the patch improves the strength of the cracked aluminum plates with different crack lengths.

Single-edge cracked aluminum specimens of 150 mm in length and 50 mm in width were tested using the tensile test. The cracked aluminum specimens were then repaired using GFRP patches with various configurations. A three-dimensional (3D) finite element method (FEM) was adopted to simulate the repaired cracked aluminum plates using composite patches to obtain the stress intensity factor (SIF). The numerical modeling and validation of ABAQUS software and the contour integral method for SIF calculations provide a valuable tool for further investigation and design optimization.

The width of the GFRP patches affected the efficiency of the rehabilitated cracked aluminum plate. Increasing patch width WP from 5 mm to 15 mm increases the peak load by 9.7 and 17.5%, respectively, if compared with the specimen without the patch. The efficiency of the GFRP patch in reducing the SIF increased as the number of layers increased, i.e. the maximum load was enhanced by 5%.

This study assessed repairing metallic structures using the chopped strand mat GFRP. Furthermore, it demonstrated the superiority of rectangular patches over semicircular ones, along with the benefit of using double patches for out-of-plane bending prevention and it emphasizes the detrimental effect of defects in the bonding area between the patch and the cracked component. This underlines the importance of proper surface preparation and bonding techniques for successful repair.

This study aims to provide a comprehensive analysis of the current privacy concerns in the tourism industry by uncovering the key factors leading to such concerns (i.e. smart public services, cyber security issues, consumer behaviour and governance). Using papers from multiple sources, the relationship between technology advancements and tourist’s privacy concerns has been established.

This study adopted a machine learning-based systematic literature review approach to find out the clusters. The study analysed 68 papers using the topic modelling approach. A four-cluster solution was considered to be most representative of the extant literature identified using bibliographic coupling. Finally, content analysis of the selected literature has been performed.

This study identified four factors majorly leading to privacy concerns amid increasing technological advancements. Moreover, these factors were found to have a dyadic relationship with technological advancements. To everyone’s amazement, sustainable tourism was also found to have led to privacy concerns among tourists along with a lack of governance and cyber security issues. Furthermore, cluster-wise future research directions are provided based on the content analysis.

This study contributes to the literature by systematically reviewing and identifying the four dimensions leading to privacy concerns. To the best of the authors’ knowledge, the study done is the only attempt to synthesize the extant literature on tourists’ privacy concerns using an unbiased scientific approach.

本研究对旅游业当前的隐私问题进行了全面分析, 以揭示导致此类担忧的关键因素（即智能公共服务、网络安全问题、消费者行为和治理）。 通过考察来自多方渠道的现有文献, 本研究确立了技术进步与游客隐私问题之间的关系。

本研究采用基于机器学习的系统文献综述方法来找出聚类。 该研究使用主题建模方法分析了 68 篇文章。四聚类解决方案是现有文献中最具代表的 文献分类方法。最后, 本研究对所选文献进行了内容分析。

这项研究确定了在技术进步不断增加的情况下主要导致隐私问题的四个因素。 此外, 这些因素被发现与技术进步有二元关系。 本研究还发现可持续旅游业引发了游客对隐私的担忧以及缺乏治理和网络安全问题。 此外, 通过基于内容的分析, 本研究提供了未来的聚类研究方向。

本研究对现有文献的贡献在于通过系统地文献综述和分析以确定导致隐私问题的四个维度。该研究是基于公正的科学方法综合现有游客隐私问题文献的首度尝试。

Este estudio proporciona un análisis exhaustivo de la actual preocupación por la privacidad en el sector turístico, poniendo al descubierto los factores clave que la generan (los servicios públicos inteligentes, los problemas de ciberseguridad, el comportamiento de los consumidores y la gobernanza). Gracias al uso de artículos de múltiples fuentes, se ha establecido la relación entre los avances tecnológicos y la preocupación de los turistas por la privacidad.

Este estudio adoptó un enfoque de revisión sistemática de la literatura basado en el aprendizaje automático para descubrir los conglomerados. El estudio analizó sesenta y ocho artículos utilizando el enfoque de modelización de temas. Se consideró que una solución de cuatro conglomerados era la más representativa de la literatura existente identificada mediante el acoplamiento bibliográfico. Por último, se realizó un análisis de contenido de la bibliografía seleccionada.

En este estudio se identificaron cuatro factores principales que suscitan inquietud por la privacidad en medio de los crecientes avances tecnológicos. Además, se descubrió que estos factores tienen una relación diádica con dichos avances. Para sorpresa de todos, se halló que el turismo sostenible, junto con la falta de gobernanza y los problemas de ciberseguridad, también suscitan entre los turistas preocupaciones por su privacidad. Finalmente, el análisis de contenido ofrece orientaciones para futuras investigaciones.

Este estudio contribuye a la literatura haciendo una revisión sistemática e identificando las cuatro dimensiones que conducen a la preocupación por la privacidad. Este estudio es el único intento de sintetizar la bibliografía existente sobre la preocupación de los turistas por su privacidad utilizando un enfoque científico imparcial.

This paper aims to improve the wear resistance of titanium alloy using a high-hardness boride layer, which was fabricated on Ti6Al4V by a high-temperature boronizing process.

The boride layers on Ti6Al4V were obtained at 1000°C for 5–15 h. Scanning electron microscopy, energy dispersive analysis and X-ray diffractometer were used to characterize the properties of the boride layer. The tribological performance of the boride layer at room and elevated temperatures was investigated.

The X-ray diffraction analysis showed that the boride layers were a dual-phase structure of TiB and TiB₂. When the boronizing time increased from 5 h to 15 h, the microhardness increased from 1192 HV_0.5 to 1619.8 HV_0.5. At 25°C and elevated temperatures, the friction coefficients of the boride layers were higher than that of Ti6Al4V. The wear track areas of T-5 at 200°C and 400°C were 2.5 × 10^–3 and 1.1 × 10^–3 mm², respectively, which were 6.1% and 2.6% of that of Ti6Al4V, indicating boride layer exhibited a significant wear resistance. The wear mechanisms of the boride layer transformed from slight peeling to oxidative wear and abrasive wear as the temperature was raised.

The findings provide an effective strategy for improving the wear resistance of Ti6Al4V and have important implications for the application of titanium alloy in a high-temperature field.

Although previous research has recognized the pivotal role of mobile social media in knowledge sharing among project members, little is known about what factors affect knowledge sharing in mobile social media groups (MSMGs). Against this background, using normative social influence theory, this study attempts to explore factors influencing knowledge sharing in MSMGs.

Data from 205 Chinese construction project members are collected and used for analysis. Ordinary least squares regression by Stata 16 is used to test the proposed hypotheses.

Concerning role norms, gender difference in knowledge sharing behavior (KSB) is found, while it is not the case for knowledge quality (KQ). Work experience is found to positively affect KQ, but shows no influence on KSB. As for group norms, the inverted-U relationship between number of members and KSB is partially supported. In addition, organizational norms generally exhibit the greatest influence on both KSB and KQ among the three forms of norms.

This study deepens the understanding of knowledge sharing factors in mobile social media environments and affords practical implications for how to make full use of social media for knowledge management within construction project teams.

This study aims to examine how incorporating gamification elements into an offline training program influences learner engagement and learning outcomes in a non-academic, organizational setting.

A randomized pretest–posttest control group experiment was designed to investigate participants’ levels of affective, behavioral and cognitive engagement (learner engagement), as well as their scores on a knowledge and skills assessment (learning outcomes) under two training conditions – traditional instructional strategy (TI) and gamification instructional strategy (GL). Training content, which was the same in both groups, included disease-related information, diagnostic expertise and product operational assistance. Participants (N = 98) were medical sales representatives from a multinational company.

Participants in the treatment group had higher levels of learner engagement in comparison to those in the control group. Additionally, participants in the GL group had outperformed their counterparts in the control group on the knowledge and skills assessment.

This is one of the first studies demonstrating how incorporating gamification elements into corporate training can improve medical sales representatives' learner engagement and learning outcomes.

Social media data from financial websites contain information related to enterprise credit risk. Mining valuable new features in social media data helps to improve prediction performance. This paper proposes a credit risk prediction framework that integrates social media information to improve listed enterprise credit risk prediction in the supply chain.

The prediction framework includes four stages. First, social media information is obtained through web crawler technology. Second, text sentiment in social media information is mined through natural language processing. Third, text sentiment features are constructed. Finally, the new features are integrated with traditional features as input for models for credit risk prediction. This paper takes Chinese pharmaceutical enterprises as an example to test the prediction framework and obtain relevant management enlightenment.

The prediction framework can improve enterprise credit risk prediction performance. The prediction performance of text sentiment features in social media data is better than that of most traditional features. The time-weighted text sentiment feature has the best prediction performance in mining social media information.

The prediction framework is helpful for the credit decision-making of credit departments and the policy regulation of regulatory departments and is conducive to the sustainable development of enterprises.

The prediction framework can effectively mine social media information and obtain an excellent prediction effect of listed enterprise credit risk in the supply chain.

Limited research exists on the perceptions of police within specific ethnic minority groups. The primary purpose of this study is to investigate the experiences of Korean and Korean American residents in the Metro Atlanta area regarding their perceptions of cooperation with the police, particularly in relation to hate crimes, along with their perceptions of police legitimacy and other relevant factors. By focusing on this specific population, the study aims to shed light on their unique perspectives and contribute to a deeper understanding of the complex dynamics between ethnic minorities and law enforcement.

The authors’ sample comprised 128 Korean residents who were asked about their demographics, victimization experiences, self-rated English proficiency and police legitimacy. Multiple linear regression analyses were employed to investigate the impact of police legitimacy, victimization experiences and English-speaking skills on the participants' level of cooperation with the police.

Police legitimacy and self-rated levels of English proficiency emerged as the most significant factors in predicting the level of cooperation among residents with the police. Furthermore, individuals who have experienced crime victimization in the past were more willing to cooperate with the police compared to those who have not. Additionally, men showed a higher tendency to cooperate with the police compared to women participants.

The findings of this study suggest important implications for the policies and strategies aimed at enhancing the relationship between the Korean American community and the police. These implications include the need for improved language support for non-English speaking community members and the importance of building trust and fostering mutual understanding to cultivate positive police-community relations. By implementing measures based on these findings, it is recommended to promote a more inclusive and effective approach to policing within the Korean American population.

Multiple topics often exist on social media platforms that compete for users' attention. To explore how users’ attention transfers in the context of multitopic competition can help us understand the development pattern of the public attention.

This study proposes the prediction model for the attention transfer behavior of social media users in the context of multitopic competition and reveals the important influencing factors of users' attention transfer. Microblogging features are selected from the dimensions of users, time, topics and competitiveness. The microblogging posts on eight topic categories from Sina Weibo, the most popular microblogging platform in China, are used for empirical analysis. A novel indicator named transfer tendency of a feature value is proposed to identify the important factors for attention transfer.

The accuracy of the prediction model based on Light GBM reaches 91%. It is found that user features are the most important for the attention transfer of microblogging users among all the features. The conditions of attention transfer in all aspects are also revealed.

The findings can help governments and enterprises understand the competition mechanism among multiple topics and improve their ability to cope with public opinions in the complex environment.