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Although the importance and variety of emotions have been emphasized in existing literature, studies on discrete emotions remain limited. This study aims to propose a method for more precise recognition and calculation of emotions in massive amounts of online data on attraction visitor experiences and behaviour, by using discrete emotion theory.

Using HowNet’s word similarity calculation technique, this study integrated multiple generic dictionaries, including the sentiment vocabulary ontology database of the Dalian University of Technology, the National Taiwan University Sentiment Dictionary and the Boson Dictionary. Word2vec algorithm filters emotion words unique to hospitality and tourism in 1,596,398 texts from Sogou News, Wikipedia and Ctrip reviews about attractions, and 1,765,691 reviews about attractions in China.

The discrete sentiment dictionary developed in this study outperformed the original dictionary in identifying and calculating emotions, with a total vocabulary extension of 12.07%, demonstrating its applicability to tourism.

The developed new dictionary can be used by researchers and managers alike to quickly and accurately evaluate products and services based on online visitor reviews.

To the best of the authors’ knowledge, this study is the first to construct a sentiment dictionary based on discrete emotion theory applicable to hospitality and tourism in the Chinese context. This study extended the applicability of affective psychology to hospitality and tourism using discrete emotion theory. Moreover, the study offers a methodological framework for developing a domain-specific sentiment dictionary, potentially applicable to other domains in hospitality.

Tolerance simulation’s reliability depends on the concordance between the input probability distribution and the real variation. The prescribed clamp force introduced changes in parts’ variation, which should be reflected in the input probability distribution for the tolerance simulation. The paper aims to present a tolerance analysis process of the composite wingbox assembly considering the preloading-modified distribution and especially focuses on the spring-in deviation of the thin-walled C-section composite beam (TC2B).

Based on finite element analysis model of TC2B, the preloading-modified probability distribution function (PDF) of the spring-in deviation is obtained. Thickness variations of the TC2B are obtained from the data of the downscaled composite wingbox. These variations are input to the computer-aided tolerance tools, and the final assembly variations are obtained. The assembly of the downscaled wingbox illustrates the effect of preloading on the probability distribution of the spring-in deviation.

The results have shown that the final assembly variations estimated with the modified probability distribution is more reliable than the variation of the initial normal distribution.

The tolerance simulation work presented in the paper will enhance the understanding of the composite parts assembling with spring-in deviations, improve the chance to choose assembling processes that allow specifications to be met and help with tolerance allocation in composites assembly.

This study aims to investigate the current state of research using deep learning methods for text classification in the tourism and hospitality field and to propose specific guidelines for future research.

This study undertakes a qualitative and critical review of studies that use deep learning methods for text classification in research fields of tourism and hospitality and computer science. The data was collected from the Web of Science database and included studies published until February 2022.

Findings show that current research has mainly focused on text feature classification, text rating classification and text sentiment classification. Most of the deep learning methods used are relatively old, proposed in the 20th century, including feed-forward neural networks and artificial neural networks, among others. Deep learning algorithms proposed in recent years in the field of computer science with better classification performance have not been introduced to tourism and hospitality for large-scale dissemination and use. In addition, most of the data the studies used were from publicly available rating data sets; only two studies manually annotated data collected from online tourism websites.

The applications of deep learning algorithms and data in the tourism and hospitality field are discussed, laying the foundation for future text mining research. The findings also hold implications for managers regarding the use of deep learning in tourism and hospitality. Researchers and practitioners can use methodological frameworks and recommendations proposed in this study to perform more effective classifications such as for quality assessment or service feature extraction purposes.

The paper provides an integrative review of research in text classification using deep learning methods in the tourism and hospitality field, points out newer deep learning methods that are suitable for classification and identifies how to develop different annotated data sets applicable to the field. Furthermore, foundations and directions for future text classification research are set.

Existing Simultaneous Localization and Mapping (SLAM) algorithms have been relatively well developed. However, when in complex dynamic environments, the movement of the dynamic points on the dynamic objects in the image in the mapping can have an impact on the observation of the system, and thus there will be biases and errors in the position estimation and the creation of map points. The aim of this paper is to achieve more accurate accuracy in SLAM algorithms compared to traditional methods through semantic approaches.

In this paper, the semantic segmentation of dynamic objects is realized based on U-Net semantic segmentation network, followed by motion consistency detection through motion detection method to determine whether the segmented objects are moving in the current scene or not, and combined with the motion compensation method to eliminate dynamic points and compensate for the current local image, so as to make the system robust.

Experiments comparing the effect of detecting dynamic points and removing outliers are conducted on a dynamic data set of Technische Universität München, and the results show that the absolute trajectory accuracy of this paper's method is significantly improved compared with ORB-SLAM3 and DS-SLAM.

In this paper, in the semantic segmentation network part, the segmentation mask is combined with the method of dynamic point detection, elimination and compensation, which reduces the influence of dynamic objects, thus effectively improving the accuracy of localization in dynamic environments.

The purpose of this paper is to examine the relationship between servant leadership and customer service behaviors by probing the mediating role of promotion focus and the moderating role of internal locus of control.

The authors hypothesized an indirect relationship between servant leadership and customer service behaviors through promotion focus. Also, the authors predicted that the positive relationship between servant leadership and promotion focus would be stronger for employees with low internal locus of control. The authors tested the theoretical model with data gathered across two phases over three months from 280 supervisor-subordinate dyads.

Results indicated that servant leadership was positively related to customer service behaviors via promotion focus. Results also showed that internal locus of control moderated the relationship between servant leadership and promotion focus, such that the relationship was stronger for employees low on internal locus of control. Furthermore, this moderated mediated model was supported. As predicted, the indirect effect was stronger when internal locus of control was low.

This study extends the regulatory focus theory to the service context to investigate how and when servant leadership enhances customer service behaviors. The authors suggested promotion focus as a key mediating mechanism and revealed internal locus of control as a boundary condition for the effectiveness of servant leadership.

This study highlights the importance role of promotion focus in fostering customer service behaviors and provides novel theoretical insight regarding when servant leadership enhances customer service behaviors.

This paper aims to study different morphology Cu₆Sn₅ effect on Babbitt alloy tribological properties.

Different morphology Cu₆Sn₅ of Babbitt was conducted by different cooling modes. Bare Babbitt was marked by Babbitt-0, Babbitt modified by first cooling mode (marked by Babbitt-1) and Babbitt modified by second cooling mode (marked by Babbitt-2). The microstructure and microhardness of specimens were tested. Then, tribological properties of Babbitt-0, Babbitt-1 and Babbitt-2 were performed by reciprocating mode under lubricated condition.

The results showed that shape Cu₆Sn₅ of Babbitt was changed from mixed needle and star-like shape to short rod-like or granular shape. The microhardness of Babbitt-1 was highest than that of Babbitt-0 and Babbitt-2. Compared with Babbitt-0 and Babbitt-2, tribological properties of Babbitt-1 were better under lubricated condition due to short rod-like and sparse distribution of Cu₆Sn₅. Moreover, the simulation result of strain and stress of Babbitt-1 was lowest than that of Babbitt-0 and Babbitt-2.

Different morphology (shape and distributed) of Cu₆Sn₅ was obtained by different cooling modes. Modulated different forms of Cu₆Sn₅ around SnSb was beneficial to improve Babbitt alloy tribological properties.

Employees’ bootlegging innovation behavior is common and plays an important role in enterprise management. Based on the resource conservation theory and self-regulation theory, the purpose of this study is to explore the influence mechanism of leaders’ abusive supervision on employees’ bootlegging innovation behavior, with psychological safety as a mediator and mindfulness at workplace as a moderator.

Survey data were gathered from 591 employees’ self-assessment questionnaires in China. Hierarchical regression analysis was used to test the research model through SPSS and AMOS.

This study found that the leaders’ abusive supervision negatively affects employees’ bootlegging innovation behavior; employees’ psychological safety completely mediates the negative effect of leaders’ abusive supervision on employees’ bootlegging innovation behavior; and mindfulness at work moderates the influence of leaders’ abusive supervision on employee’ bootlegging innovation behavior, as well as the influence of leaders’ abusive supervision on employees’ psychological safety.

This study has significant implications in passive leadership that affect employees’ innovation. Authors found that leaders’ abusive supervise, mindfulness at workplace play a crucial role in employees’ bootleg innovation through psychological safety.

Theoretically, this study has enriched the antecedent research on employees’ bootlegging innovation behavior from the perspective of negative leadership behavior and employee psychology. And this study considered mindfulness at workplace as a boundary condition.

Previous research has yielded inconsistent findings of the effects that task conflict has on creative outcomes, with some research finding a negative relationship but others holding a positive or even no significant relationship. Drawing on the too-much-of-a-good-thing effect approach, this paper aims to investigate the curvilinear relations between task conflict and creative idea generation as well as the mediating role of task reflexivity and the moderating role of task complexity.

Two studies were carried out to test the proposed relationship. In Study 1, multisource and lagged data collected from 533 employees and 140 corresponding supervisors were used to test the curvilinear relationship between task conflict and creative idea generation as well as the moderating effect of task complexity. In Study 2, the authors extended the findings by exploring the mediating effect of task reflexivity using a matched sample of 350 employees and 99 corresponding supervisors.

Task conflict had an inverted U-shaped relationship with creative idea generation, and task reflexivity partially mediated this relationship. Besides, this association was moderated by task complexity such that the curvilinear relationship was more pronounced for tasks with lower complexity.

This study was more or less contaminated by common method variance because some variables were derived from the same sources. Also, task conflict might be necessitated to differentiate and more situational variables should be considered to draw a complete picture.

Managers should undertake conflict management according to the levels of task conflict and task complexity. At a lower degree of task conflict, managers might motivate employees to think more about task-related issues; at higher levels of task conflict, managers should act as conflict mediators to reduce the underlying negative effects, especially for simple tasks.

These findings could help us understand the boundary conditions under, and the underlying mechanisms by, which task conflict has an impact on creative idea generation.

This study aims to establish a model for rapid and accurate emotion recognition in restaurant online reviews, thus advancing the literature and providing practical insights into electronic word-of-mouth management for the industry.

This study elaborates a hybrid model that integrates deep learning (DL) and a sentiment lexicon (SL) and compares it to five other models, including SL, random forest (RF), naïve Bayes, support vector machine (SVM) and a DL model, for the task of emotion recognition in restaurant online reviews. These models are trained and tested using 652,348 online reviews from 548 restaurants.

The hybrid approach performs well for valence-based emotion and discrete emotion recognition and is highly applicable for mining online reviews in a restaurant setting. The performances of SL and RF are inferior when it comes to recognizing discrete emotions. The DL method and SVM can perform satisfactorily in the valence-based emotion recognition.

These findings provide methodological and theoretical implications; thus, they advance the current state of knowledge on emotion recognition in restaurant online reviews. The results also provide practical insights into intelligent service quality monitoring and electronic word-of-mouth management for the industry.

This study proposes a superior model for emotion recognition in restaurant online reviews. The methodological framework and steps are elucidated in detail for future research and practical application. This study also details the performances of other commonly used models to support the selection of methods in research and practical applications.

The purpose of this paper is to analyze the liquid sloshing behaviors in two-dimensional tanks with various porous baffles under the external excitation.

Adopting the finite element method (FEM) and control variable method to study the impacts of the height, length, number, location, shape, porous-effect parameter of the porous baffle, the external load frequency and the shape of the tank on the liquid sloshing response.

The amplitude of the free surface can be reduced effectively when the baffle opening is appropriate. The anti-sway ability of the system increases in pace with the baffle’s height growing. Under the same conditions, the shapes of the baffles have an important effect on improving the anti-sway ability of the system.

As there exist the differences of the velocity potential between each side of the porous baffle, which means that there are two different velocity potentials at a point on the porous baffle, the conventional finite element modeling technologies are not suitable to be applied here. To deal with this problem, the points on the porous baffle are regarded as two nodes with the same coordinate to model and calculate.