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The purpose of this paper is to propose a consensus method for multi-attribute group decision-making (MAGDM) problems based on preference-approval structure and regret theory, which can improve the efficiency of decision-making and promote the consensus level among individuals.

First, a new method to obtain the reference points based on regret theory and expert weighting method is proposed. Second, a consensus reaching method based on preference-approval structure is proposed. Then, an adjustment mechanism to further improve the consensus level between individuals is designed. Finally, an example of the assessment of elderly care institutions is used to illustrate the feasibility and effectiveness of the proposed method.

The feasibility and validity of the proposed method are verified by comparing with the advanced two-stage minimum adjustment method. The compared results show that the proposed method is more consistent with the actual situation.

This paper presents a consensus reaching method for MAGDM based on preference-approval structure, which considers the avoidance behaviors of individuals and reference points. Decision makers (DMs) can use this approach to rank and categorize alternatives while further increasing the level of consensus among them. This can further help determine the optimal alternative more efficiently.

A new MAGDM problem based on the combination of regret theory and individual reference points is proposed. Besides, a new method of obtaining experts' weights and a consensus reaching method for MAGDM based on preference-approval structure are designed.

The aging and deterioration of engineering building structures present significant risks to both life and property. Fiber Bragg grating (FBG) sensors, acclaimed for their outstanding reusability, compact form factor, lightweight construction, heightened sensitivity, immunity to electromagnetic interference and exceptional precision, are increasingly being adopted for structural health monitoring in engineering buildings. This research paper aims to evaluate the current challenges faced by FBG sensors in the engineering building industry. It also anticipates future advancements and trends in their development within this field.

This study centers on five pivotal sectors within the field of structural engineering: bridges, tunnels, pipelines, highways and housing construction. The research delves into the challenges encountered and synthesizes the prospective advancements in each of these areas.

The exceptional performance of FBG sensors provides an ideal solution for comprehensive monitoring of potential structural damages, deformations and settlements in engineering buildings. However, FBG sensors are challenged by issues such as limited monitoring accuracy, underdeveloped packaging techniques, intricate and time-intensive embedding processes, low survival rates and an indeterminate lifespan.

This introduces an entirely novel perspective. Addressing the current limitations of FBG sensors, this paper envisions their future evolution. FBG sensors are anticipated to advance into sophisticated multi-layer fiber optic sensing networks, each layer encompassing numerous channels. Data integration technologies will consolidate the acquired information, while big data analytics will identify intricate correlations within the datasets. Concurrently, the combination of finite element modeling and neural networks will enable a comprehensive simulation of the adaptability and longevity of FBG sensors in their operational environments.

Despite the growing interest in micro-celebrities in destination marketing, their role in transferring cognitive, emotional and behavioral outcomes to destination psychological ownership (DPO) is underexplored. This study aims to address this void by investigating how the perceived characteristics of micro-celebrities influence travel intentions through interactive engagement, perceived information quality and DPO. It highlights three pathways for fostering DPO.

A survey-based design was developed with 302 samples collected and analyzed using partial least squares structural equation modeling and artificial neural network to examine the hypothesized model.

Findings reveal that the expertise and attractiveness of micro-celebrities enhance their perceived personal trustworthiness. This perception encourages social media users to view travel information from micro-celebrities as higher quality and to engage more with them, leading to DPO. Consequently, when social media users experience this psychological ownership, they show a stronger intention to travel to the destination, influenced partly by micro-celebrity marketing.

This study provides a psychological–developmental perspective on micro-celebrity marketing-induced travels. It underscores the importance of fostering micro-celebrity-induced DPO to establish a sustained, mutually beneficial relationship between tourists and destinations.

尽管微名人在目的地营销中的作用已引起学术界的关注, 但他们在传递认知、情感和行为结果至目的地心理所有权中的作用尚未得到充分探索。本研究通过调查微名人的感知特征如何通过互动参与、感知信息质量及目的地心理所有权影响旅行意图, 填补了这一研究空白。此外, 本研究还强调了促进目的地心理所有权的三条途径。

本研究采用基于调查的设计, 收集并分析了302个样本, 并使用偏最小二乘结构方程模型（PLS-SEM）和人工神经网络（ANN）对假设模型进行了检验。

研究结果表明, 微名人的专业知识和吸引力增强了其感知个人可信度。这一感知促使社交媒体用户将微名人分享的旅行信息视为更高质量的信息, 并与他们进行更频繁的互动, 从而促进了目的地心理所有权的形成。由此, 当社交媒体用户体验到这种心理所有权时, 他们表现出更强烈的旅行意图, 这在一定程度上受到微名人营销的影响。

本研究从心理发展的角度探讨了微名人营销引发的旅行意图, 强调了通过微名人激发目的地心理所有权的重要性, 以建立游客与目的地之间持续且互利的关系。

A pesar del creciente interés por las micro-celebridades en el marketing de destinos, su papel en la transferencia de resultados cognitivos, afectivos y conductuales a la apropiación psicológica del destino (DPO) está poco explorado. Esta investigación aborda esta laguna de investigación analizando cómo las características percibidas de las micro-celebridades influyen en las intenciones de viaje a través del compromiso interactivo, la calidad de la información percibida y la DPO. Se destacan tres vías para fomentar la DPO.

Se desarrolló un diseño basado en encuestas, recogiéndose y analizándose 302 observaciones. Se utilizó modelización de ecuaciones estructurales por mínimos cuadrados parciales y redes neuronales artificiales para analizar el modelo propuesto.

Los resultados revelan que la experiencia y el atractivo de las micro-celebridades mejoran su credibilidad personal percibida. Esta percepción anima a los usuarios de redes sociales a considerar la información sobre viajes proporcionada por las micro-celebridades como de mayor calidad y a interactuar más con ellas, lo que conduce a la formación de la apropiación psicológica del destino. Como resultado, cuando los usuarios de redes sociales experimentan esta apropiación psicológica, muestran una mayor intención de viajar al destino, influenciados en parte por el marketing de micro-celebridades.

Este estudio aporta una perspectiva de desarrollo psicológico sobre los viajes inducidos por el marketing de micro-celebridades. Subraya la importancia de fomentar la propiedad psicológica del destino inducida por micro-celebridades para establecer relaciones sostenidas y mutuamente beneficiosas entre los turistas y los destinos.

The safety and reliability of high-speed trains rely on the structural integrity of their components and the dynamic performance of the entire vehicle system. This paper aims to define and substantiate the assessment of the structural integrity and dynamical integrity of high-speed trains in both theory and practice. The key principles and approaches will be proposed, and their applications to high-speed trains in China will be presented.

First, the structural integrity and dynamical integrity of high-speed trains are defined, and their relationship is introduced. Then, the principles for assessing the structural integrity of structural and dynamical components are presented and practical examples of gearboxes and dampers are provided. Finally, the principles and approaches for assessing the dynamical integrity of high-speed trains are presented and a novel operational assessment method is further presented.

Vehicle system dynamics is the core of the proposed framework that provides the loads and vibrations on train components and the dynamic performance of the entire vehicle system. For assessing the structural integrity of structural components, an open-loop analysis considering both normal and abnormal vehicle conditions is needed. For assessing the structural integrity of dynamical components, a closed-loop analysis involving the influence of wear and degradation on vehicle system dynamics is needed. The analysis of vehicle system dynamics should follow the principles of complete objects, conditions and indices. Numerical, experimental and operational approaches should be combined to achieve effective assessments.

The practical applications demonstrate that assessing the structural integrity and dynamical integrity of high-speed trains can support better control of critical defects, better lifespan management of train components and better maintenance decision-making for high-speed trains.

This paper aims to propose an incremental graph indexing method based on probabilistic inferences in Bayesian network (BN) for approximate nearest neighbor search (ANNS) that adds unindexed queries into the graph index incrementally.

This paper first uses the attention mechanism based graph convolutional network to embed a social network into the low-dimensional vector space, which could improve the efficiency of graph index construction. To add the unindexed queries into the graph index incrementally, this study proposes to learn the rule-based BN from social interactions. Thus, the dependency relations of unindexed queries and their neighbors are represented, and the probabilistic inferences in BN are then performed.

Experimental results demonstrate that the proposed method improves the search precision by at least 5% and search efficiency by 10% compared to the state-of-the-art methods.

This paper proposes a novel method to construct the incremental graph index based on probabilistic inferences in BN, such that both indexed and unindexed queries in ANNS could be addressed efficiently.

The purpose of this paper is to explore what organizational adaptability means in the digitized context and to discuss how manufacturing companies achieve organizational adaptability during the digital transformation process.

By conducting semi-structured interviews and acquiring archive data from a typical Chinese manufacturing company, this paper gathers extensive data. Based on this, a single-case study methodology is used to investigate organizational adaptability in digital transformation.

This study identifies the process by which companies achieve organizational adaptability during digital transformation and deconstructs organizational adaptability into three dimensions: structural adaptability, operational adaptability and governance adaptability. This study also explores how organizational adaptability is affected by digital capabilities.

This study proposes a process model to demonstrate how organizational adaptability may be attained during digital transformation and redefines organizational adaptability in the context of digitization.

This paper aims to contribute to the performance improvement and the broader application of hot-dip galvanized coating.

First, the ability to provide barrier protection, galvanic protection, and corrosion product protection provided by hot-dip galvanized coating is introduced. Then, according to the varying Fe content, the growth process of each sublayer within the hot-dip galvanized coating, as well as their respective microstructures and physical properties, is presented. Finally, the electrochemical corrosion behaviors of the different sublayers are analyzed.

The hot-dip galvanized coating is composed of η-Zn sublayer, ζ-FeZn13 sublayer, δ-FeZn10 sublayer, and Γ-Fe3Zn10 sublayer. Among these sublayers, with the increase in Fe content, the corrosion potential moves in a noble direction.

There is a lack of research on the corrosion behavior of each sublayer of hot-dip galvanized coating in different electrolytes.

It provides theoretical guidance for the microstructure control and performance improvement of hot-dip galvanized coatings.

The formation mechanism, coating properties, and corrosion behavior of different sublayers in hot-dip galvanized coating are expounded, which offers novel insights and directions for future research.

This paper aims to develop a robust navigation enhancement framework to handle one of the most urgent needs for real applications of autonomous vehicles nowadays, as these corner cases act as the most commonly occurred risks in potential self-driving accidents.

In this paper, the main idea is to fully exploit the consistent features among spatio-temporal data and thus detect the anomalies and build residual channels to reconstruct the abnormal information. The authors first develop an anomaly detection algorithm, then followed by a corresponding disturbed information reconstruction network which has strong robustness to address both the nature disturbances and external attacks. Finally, the authors introduce a fully end-to-end resilient navigation performance enhancement framework to improve the driving performance of existing self-driving models under attacks and disturbances.

Comparison results on CARLA platform and real experiments demonstrate strong resilience of the authors’ approach which enhances the navigation performance under disturbances and attacks.

Reliable and resilient navigation performance under various nature disturbances and even external attacks is one of the most urgent needs for real applications of autonomous vehicles nowadays, as these corner cases act as the most commonly occurred risks in potential self-driving accidents. The information reconstruction approach provides a resilient navigation performance enhancement method for existing self-driving models.

Social media marketing (SMM) denotes attaining website traffic or attention through social media platforms. The paper aims to focus on examining the viewpoint of library professionals from different universities along with the user respondents from different disciplines and universities on the role of social media tools to market university libraries.

It is a survey-based study that used the questionnaire as its chief data collection instrument designed to be administered to library professionals (n = 100) and users (n = 1,189) from eight universities. The study also adopted the general observation and interview methods to supplement the data. The data was analyzed using SPSS software, and Chi-square test and ANOVA were applied to ascertain the significant variations in viewpoints of the library professionals and the users.

The research study showed that library professionals (from different universities) and users (from different disciplines and universities) felt that social media could be an appropriate marketing tool for libraries in the future. Observations during data collection highlighted a lack of confidence and unwillingness among library professionals to implement any changes in the present time.

The present study provides some significant insights for improving the current situation of the libraries under study in terms of increasing awareness among the students and adopt social media tools for marketing of library facilities and services in future. It is important to note that a few issues that came into light during data collection were a marked lack of willingness and prevalence of less confidence among the library professionals to execute or implement the social media tools to market library resources and services among the users.

SMM programs focus on creating content that attracts attention and encourages readers to use it. This study attempts to fill the gap in of marketing in libraries through social media. The paper offers insights into the use of marketing tools for promoting library resources and services as per the needs of library users. The research work differs from other studies undertaken on library marketing related to social media as it has included both library professionals and users together to obtain a better picture in this regard.

The discrepancy between the content of an article and its title is a key characteristic of fake news. Current methods for detecting fake news often ignore the significant difference in length between the content and its title. In addition, relying solely on textual discrepancies between the title and content to distinguish between real and fake news has proven ineffective. The purpose of this paper is to develop a new approach called semantic enhancement network with content–title discrepancy (SEN–CTD), which enhances the accuracy of fake news detection.

The SEN–CTD framework is composed of two primary modules: the SEN and the content–title comparison network (CTCN). The SEN is designed to enrich the representation of news titles by integrating external information and position information to capture the context. Meanwhile, the CTCN focuses on assessing the consistency between the content of news articles and their corresponding titles examining both emotional tones and semantic attributes.

The SEN–CTD model performs well on the GossipCop, PolitiFact and RealNews data sets, achieving accuracies of 80.28%, 86.88% and 84.96%, respectively. These results highlight its effectiveness in accurately detecting fake news across different types of content.

The SEN is specifically designed to improve the representation of extremely short texts, enhancing the depth and accuracy of analyses for brief content. The CTCN is tailored to examine the consistency between news titles and their corresponding content, ensuring a thorough comparative evaluation of both emotional and semantic discrepancies.