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This review aims to give a critical view of the wheel/rail high frequency vibration-induced vibration fatigue in railway bogie.

Vibration fatigue of railway bogie arising from the wheel/rail high frequency vibration has become the main concern of railway operators. Previous reviews usually focused on the formation mechanism of wheel/rail high frequency vibration. This paper thus gives a critical review of the vibration fatigue of railway bogie owing to the short-pitch irregularities-induced high frequency vibration, including a brief introduction of short-pitch irregularities, associated high frequency vibration in railway bogie, typical vibration fatigue failure cases of railway bogie and methodologies used for the assessment of vibration fatigue and research gaps.

The results showed that the resulting excitation frequencies of short-pitch irregularity vary substantially due to different track types and formation mechanisms. The axle box-mounted components are much more vulnerable to vibration fatigue compared with other components. The wheel polygonal wear and rail corrugation-induced high frequency vibration is the main driving force of fatigue failure, and the fatigue crack usually initiates from the defect of the weld seam. Vibration spectrum for attachments of railway bogie defined in the standard underestimates the vibration level arising from the short-pitch irregularities. The current investigations on vibration fatigue mainly focus on the methods to improve the accuracy of fatigue damage assessment, and a systematical design method for vibration fatigue remains a huge gap to improve the survival probability when the rail vehicle is subjected to vibration fatigue.

The research can facilitate the development of a new methodology to improve the fatigue life of railway vehicles when subjected to wheel/rail high frequency vibration.

Drawing on the theory of memory-dominant logic, this study aims to examine how the substantive staging of the servicescape, experience co-creation, experiential satisfaction and experience intensification affect experience memorability and hedonic well-being in the case of unmanned smart hotels.

An online survey was used, with the target respondents being hotel guests people aged 18 years and older who had been recent guests of the FlyZoo Hotel in Hangzhou, China. Data were collected online from 429 guests who had stayed in the hotel between April and June 2023. Data analysis was undertaken using structural equation modelling.

The results suggest that all the proposed four constructs are positive drivers of a memorable unmanned smart hotel experience. The relationship between the memorability of the hotel experience and hedonic well-being was found to be significant and positive.

Unmanned smart hotels should ensure that all smart technologies function effectively and dependably and offer highly personalised services to guests, allowing them to co-create their experiences. This will lead to the guest receiving a satisfying and memorable experience. To enable experience co-creation using smart technologies, unmanned smart hotels could provide short instructional videos for guests, as well as work closely with manufacturers and suppliers to ensure that smart technology systems are regularly updated.

This study investigates the antecedents and outcomes of a novel phenomenon and extends the concept of memorable tourism experiences to the context of unmanned smart hotels.

This paper investigates the interrelationships of destination social responsibility (DSR), emotional attachment, self-congruity, experiential satisfaction and environmentally responsible behavior (ERB).

Using a sample of 294 visitors to the Guangzhou Zoo in China, this study tested the aforementioned relationships via structural equation modeling.

Emotional attachment mediates the effect of DSR on experiential satisfaction, while emotional attachment and experiential satisfaction mediate the effect of DSR on ERB sequentially. Moreover, self-congruity moderates the relationship between DSR and emotional attachment.

The management of zoos should use DSR communication strategies more proactively to make visitors become well-aware of their economic, philanthropic, environmental and social activities in the host community. This will result in many positive consequences, including visitors’ ERBs.

The study adds to the DSR literature by introducing multiple mediation mechanisms and paths that lead to visitors’ ERBs.

我们的论文调查了目的地社会责任 (DSR) 情感依恋、自我一致性、体验满意度和对环境负责的行为 (ERB) 之间的相互关系。

我们的研究以中国广州动物园的 294 名游客为样本, 通过结构方程模型测试了上述关系。

情感依恋介导 DSR 对体验满意度的影响, 而情感依恋和体验满意度依次介导 DSR 对 ERB 的影响。 此外, 自我一致性调节 DSR 与情感依恋之间的关系。

动物园的管理层应该更积极地使用 DSR 沟通策略, 让游客充分了解他们在东道社区的经济、慈善、环境和社会活动。 这将带来许多积极的后果, 包括访客的 ERB。

该研究通过引入多种调解机制和导致访客 ERB 的路径增加了 DSR 文献。

nuestro artículo investiga las interrelaciones de la responsabilidad social del destino (DSR), el apego emocional, la autocongruencia, la satisfacción experiencial y el comportamiento ambientalmente responsable (ERB).

utilizando una muestra de 294 visitantes del zoológico de Guangzhou en China, nuestro estudio probó las relaciones antes mencionadas a través del modelo de ecuaciones estructurales.

el apego emocional media el efecto de DSR en la satisfacción experiencial, mientras que el apego emocional y la satisfacción experiencial median el efecto de DSR en ERB secuencialmente. Además, la autocongruencia modera la relación entre DSR y apego emocional.

la administración de los zoológicos debe utilizar estrategias de comunicación de DSR de manera más proactiva para que los visitantes estén bien informados sobre sus actividades económicas, filantrópicas, ambientales y sociales en la comunidad anfitriona. Esto tendrá muchas consecuencias positivas, incluidos los ERB de los visitantes.

el estudio se suma a la literatura de DSR al presentar múltiples mecanismos de mediación y caminos que conducen a los ERB de los visitantes.

This study aims to investigate the influence of the reviewed establishment’s price level and the user’s social network size and reputation status on consumers’ tendency to post more visual imagery content. Furthermore, it explores the moderating effects of user experiences and geographic distance on these dynamics.

This study adopts a multi-method approach to explore both the determinants behind the sharing of user-generated photos in online reviews and their internal mechanisms. Using a comprehensive secondary data set from Yelp.com, the authors focused on restaurant reviews from a prominent tourist destination to construct econometric models incorporating time-fixed effects. To enhance the robustness of the authors’ findings, the authors complemented the big data analysis with a series of controlled experiments.

The reviewed establishments price level and the users reputation status and social network size incite corresponding motivations conspicuous display “reputation seeking” and social approval motivating users to incorporate more images in reviews. “User experiences can amplify the influence of these factors on image sharing.” An increase in the users geographical distance lessens the impact of the price level on image sharing, but it heightens the influence of the users reputation and social network size on the number of shared images.

As a result of this study, high-end establishments can increase their online visibility by leveraging user-generated visual content. A structured rewards program could significantly boost engagement by incentivizing photo sharing, particularly among users with elite status and extensive social networks. Additionally, online review platforms can enhance users’ experiences and foster more dynamic interactions by developing personalized features that encourage visual content production.

This research, anchored in trait activation theory, offers an innovative examination of the determinants of photo-posting behavior in online reviews by enriching the understanding of how the intricate interplay between users’ characteristics and situational cues can shape online review practices.

In recent years, there has been growing interest in the use of stainless steel (SS) in reinforced concrete (RC) structures due to its distinctive corrosion resistance and excellent mechanical properties. To ensure effective synergy between SS and concrete, it is necessary to develop a time-saving approach to accurately determine the ultimate bond strength τ_u between the two materials in RC structures.

Three robust machine learning (ML) models, including support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost), are employed to predict τ_u between ribbed SS and concrete. Model hyperparameters are fine-tuned using Bayesian optimization (BO) with 10-fold cross-validation. The interpretable techniques including partial dependence plots (PDPs) and Shapley additive explanation (SHAP) are also utilized to figure out the relationship between input features and output for the best model.

Among the three ML models, BO-XGBoost exhibits the strongest generalization and highest accuracy in estimating τ_u. According to SHAP value-based feature importance, compressive strength of concrete f_c emerges as the most prominent feature, followed by concrete cover thickness c, while the embedment length to diameter ratio l/d, and the diameter d for SS are deemed less important features. Properly increasing c and f_c can enhance τ_u between ribbed SS and concrete.

An online graphical user interface (GUI) has been developed based on BO-XGBoost to estimate τ_u. This tool can be utilized in structural design of RC structures with ribbed SS as reinforcement.