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Visual feedback control is a promising solution for robots work in unstructured environments, and this is accomplished by estimation of the time derivative relationship between the image features and the robot moving. While some of the drawbacks associated with most visual servoing (VS) approaches include the vision–motor mapping computation and the robots’ dynamic performance, the problem of designing optimal and more effective VS systems still remains challenging. Thus, the purpose of this paper is to propose and evaluate the VS method for robots in an unstructured environment.

This paper presents a new model-free VS control of a robotic manipulator, for which an adaptive estimator aid by network learning is proposed using online estimation of the vision–motor mapping relationship in an environment without the knowledge of statistical noise. Based on the adaptive estimator, a model-free VS schema was constructed by introducing an active disturbance rejection control (ADRC). In our schema, the VS system was designed independently of the robot kinematic model.

The various simulations and experiments were conducted to verify the proposed approach by using an eye-in-hand robot manipulator without calibration and vision depth information, which can improve the autonomous maneuverability of the robot and also allow the robot to adapt its motion according to the image feature changes in real time. In the current method, the image feature trajectory was stable in the camera field range, and the robot’s end motion trajectory did not exhibit shock retreat. The results showed that the steady-state errors of image features was within 19.74 pixels, the robot positioning was stable within 1.53 mm and 0.0373 rad and the convergence rate of the control system was less than 7.21 s in real grasping tasks.

Compared with traditional Kalman filtering for image-based VS and position-based VS methods, this paper adopts the model-free VS method based on the adaptive mapping estimator combination with the ADRC controller, which is effective for improving the dynamic performance of robot systems. The proposed model-free VS schema is suitable for robots’ grasping manipulation in unstructured environments.

This paper aims to cover mental health research related to tourism and hospitality, starting in 1984, and track its development until 2020. Relevant research published between 2020 and 2023 during the COVID-19 pandemic is also reviewed to determine how this research stream is evolving.

A detailed search of Scopus and Google Scholar yielded 4,790 mental health studies in tourism and hospitality; 102 were ultimately retained for systematic review. VOSviewer was used to visualize cluster analysis results.

Research on mental health in the context of tourism and hospitality is limited and can be classified into four themes. The most prominent involves mental health in relation to COVID-19. Thematic differences between studies published before and after the onset of the pandemic are also specified. The findings inform a critical reflection on the conceptual framework linking tourism and mental health, as well as potential research avenues, covering research populations, topics, methods, data sources and outcome measures.

This in-depth analysis of the extant literature provides a foundation for stakeholders to better understand, address and promote mental health in tourism and hospitality. Such insights can steer future research and enlighten industry practitioners, thus contributing to sustainable industry development.

This paper represents a pioneering effort to systematically review mental health studies in tourism and hospitality. It offers a holistic perspective and unique insights, bridging substantial knowledge gaps. This paper is also meant to prompt academics and practitioners to contemplate mental health-related research and practice.

本文系统性地回顾了自1984年以来与旅游和酒店业相关的心理健康研究, 以追踪该领域在2020年之前的研究进展。本研究还审视了在2020年至2023年COVID-19大流行期间发表的相关文章, 以洞察这一研究流派的演变趋势。

通过对Scopus和Google Scholar数据库进行详尽的搜索, 我们发现了4,790篇有关旅游和酒店业的心理健康研究。经过严格筛选, 我们最终保留了102篇文章进行系统性综述。为了更清晰地呈现聚类分析的结果, 我们采用了VOSviewer对相关研究主题进行可视化。

目前有关旅游与酒店业心理健康的研究十分有限, 主要分为四个主题。其中, 与COVID-19相关的心理健康研究是最突出的主题。此外, 本文还详细比较了大流行病爆发前后发表的相关研究之间存在的主题差异。我们创造性地提出了连接旅游和心理健康的概念模型, 并对未来研究方向进行了批判性反思, 涉及研究人群、主题、方法、数据来源和测量指标等方面。

本文对现有文献的深入分析为关键利益相关者更好地理解、应对和促进旅游和酒店业中的心理健康提供了基础。这些见解可以指导未来的学术研究, 并启示业界从业者, 从而促进行业的可持续发展。

本文是旅游和酒店业心理健康研究的一项开创性工作。它通过系统的文献回顾提供了全面的视角和独特的见解, 填补了这一领域的重要知识空白。本文还旨在促使学术界和从业者思考与心理健康相关的研究和实践。

Esta revisión sistemática abarca la investigación en salud mental relacionada con el turismo y la hostelería, a partir de 1984, para seguir su evolución hasta 2020. También se revisan las investigaciones pertinentes publicadas entre 2020 y 2023, durante la pandemia de COVID-19, para determinar cómo evoluciona esta corriente de investigación.

Una búsqueda detallada en Scopus y Google Scholar arrojó 4.790 estudios sobre salud mental en turismo y hostelería; finalmente se seleccionaron 102 para la revisión sistemática. Se utilizó VOSviewer para visualizar los resultados del análisis de conglomerados.

La investigación sobre la salud mental en el contexto del turismo y la hostelería es limitada y puede clasificarse en cuatro temas. El más destacado se refiere a la salud mental en relación con COVID-19. También se especifican las diferencias temáticas entre los estudios publicados antes y después del inicio de la pandemia. Los resultados aportan una reflexión crítica sobre el marco conceptual que vincula turismo y salud mental, así como posibles vías de investigación, que abarcan poblaciones de investigación, temas, métodos, fuentes de datos y medidas de resultados.

Este análisis en profundidad de la bibliografía existente sienta las bases para que las partes interesadas comprendan, aborden y promuevan mejor la salud mental en el turismo y la hostelería. Estos conocimientos pueden orientar futuras investigaciones e ilustrar a los profesionales del sector, contribuyendo así al desarrollo sostenible de la industria.

Este artículo representa un esfuerzo pionero por revisar sistemáticamente los estudios sobre salud mental en el turismo y la hostelería. Ofrece una perspectiva holística y una visión única, cubriendo importantes lagunas de conocimiento. Este artículo también pretende incitar a académicos y profesionales a contemplar la investigación y la práctica relacionadas con la salud mental.

This study aims to provide a series of drivers that prompt the blockchain technology (BT) adoption decisions in circular supply chain finance (SCF) and also assesses their degrees of influence and interrelationships, which leads to the construction of a theoretical model depicting the influence mechanism of BT adoption decisions in circular SCF.

This study mainly uses the technology-organization-environment (TOE) framework, which focuses on the aspects based on the nature of innovation, intra-organizational characteristics and extra environmental consideration, to identify the drivers of blockchain adoption in circular SCF context, while the significance and causality of the drivers are explained using interpreting structural models (ISMs) and the decision-making trial and evaluation laboratory (DEMATEL) method.

The findings of this study indicate that government policy and technological comparative advantage are the underlying reasons for BT adoption decisions, management commitment and financial expectations are the critical drivers of BT adoption decisions while other factors are the receivers of the mechanism.

This study provides theoretical references and empirical insights that influence the technology adoption decisions of both BT and circular SCF by practitioners.

The theoretical research contributes significantly to current research and knowledge in both BT and circular SCF fields, especially by extending the existing TOE model by combining relevant enablers from technological, organizational and external environmental aspects with the financial performance objectives of circular SCF services, which refer to the optimization of the financial resources flows and financing efficiency.

Traditional visual simultaneous localization and mapping (SLAM) systems are primarily based on the assumption that the environment is static, which makes them struggle with the interference caused by dynamic objects in complex industrial production environments. This paper aims to improve the stability of visual SLAM in complex dynamic environments through semantic segmentation and its optimization.

This paper proposes a real-time visual SLAM system for complex dynamic environments based on YOLOv5s semantic segmentation, named YLS-SLAM. The system combines semantic segmentation results and the boundary semantic enhancement algorithm. By recognizing and completing the semantic masks of dynamic objects from coarse to fine, it effectively eliminates the interference of dynamic feature points on the pose estimation and enhances the retention and extraction of prominent features in the background, thereby achieving stable operation of the system in complex dynamic environments.

Experiments on the Technische Universität München and Bonn data sets show that, under monocular and Red, Green, Blue - Depth modes, the localization accuracy of YLS-SLAM is significantly better than existing advanced dynamic SLAM methods, effectively improving the robustness of visual SLAM. Additionally, the authors also conducted tests using a monocular camera in a real industrial production environment, successfully validating its effectiveness and application potential in complex dynamic environment.

This paper combines semantic segmentation algorithms with boundary semantic enhancement algorithms to effectively achieve precise removal of dynamic objects and their edges, while ensuring the system's real-time performance, offering significant application value.

This, the first chapter of this text provides an introduction to a social world that is constructed through cultural attitudes, with a long history of the so-called ‘insane’ or deviants being excluded from society. In many cases, this was due to their behaviour resulting from an addiction issue, mental ill health or as is often the case, both. The chapter begins with an introduction to what led to the conceptualisation of this text. Once this occurs, the interplay between the ‘normal’ and the deviant, as discussed above, is played through an examination of the cultural perceptions of both mental health and addiction. In addition, to support this, a brief historical timeline of mental health, addiction and dual diagnosis is described and visually depicted. Finally, the chapter concludes with an introduction to both editors of this text who then describe what will be discussed in the chapters that follow.

This study aims to explore the spatial impact of an increase in the minimum wage on the labor productivity of star-rated hotels in China.

The impact is analyzed by using the dynamic spatial Durbin model.

The authors find a U-shaped link between the increase in minimum wage and labor productivity of star-rated hotels. The long-term impact of a minimum wage increase has a greater influence on labor productivity than its short-term effects. While there is no notable spatial spillover impact observed in the sample of 31 provinces in China, the authors do identify a spatial spillover effect of the minimum wage rises on the labor productivity of star-rated hotels in the central area. Furthermore, they observe heterogeneity across China. The eastern and western regions exhibit a U-shaped relationship, whereas the central region exhibits an inverted U-shaped relationship.

The findings of this study allow government agencies to get a more comprehensive comprehension of the actual consequences of minimum wage hikes on the tourism and hospitality sector, thereby establishing a solid basis for them to develop appropriate policies. Moreover, it offers a variety of suggestions aimed at enhancing the quality and efficiency of hotel management.

Research on the effects of minimum wage standards is scant in the hospitality industry. Based on human capital investment theory, this study examines the effect of the minimum wage standard hikes on labor productivity of star-rated hotels from the spatial perspective, filling the existing research gap.

As intelligent technology advances, practical applications often involve data with multiple labels. Therefore, multi-label feature selection methods have attracted much attention to extract valuable information. However, current methods tend to lack interpretability when evaluating the relationship between different types of variables without considering the potential causal relationship.

To address the above problems, we propose an ensemble causal feature selection method based on mutual information and group fusion strategy (CMIFS) for multi-label data. First, the causal relationship between labels and features is analyzed by local causal structure learning, respectively, to obtain a causal feature set. Second, we eliminate false positive features from the obtained feature set using mutual information to improve the feature subset reliability. Eventually, we employ a group fusion strategy to fuse the obtained feature subsets from multiple data sub-space to enhance the stability of the results.

Experimental comparisons are performed on six datasets to validate that our proposal can enhance the interpretation and robustness of the model compared with other methods in different metrics. Furthermore, the statistical analyses further validate the effectiveness of our approach.

The present study makes a noteworthy contribution to proposing a causal feature selection approach based on mutual information to obtain an approximate optimal feature subset for multi-label data. Additionally, our proposal adopts the group fusion strategy to guarantee the robustness of the obtained feature subset.

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.

Integrating balance theory and social identify theory, this paper proposes a multilevel model to explain how abusive supervision climate of team impacts the relationship among team members as well as subordinates’ behavior towards their teammates, especially organizational citizenship behavior (OCB).

A survey was conducted to collect two-wave and multi-source data from 398 employees nested in 106 teams from Chinese high-technology companies. Hierarchical linear modeling was conducted to examine the theoretical model.

The results indicate that there is an inverted U-shape association between abusive supervision climate and subordinates’ OCB towards coworker; team member exchange (TMX) mediates their inverted U-shaped link. Furthermore, we confirm that coworker support plays a vitally moderating role upon the curvilinear link of abusive supervision climate (ASC)–TMX; specifically, when employees perceive low coworker support, negative relations between ASC and TMX will be stronger.

This study identifies team members’ advantageous and adverse relational response to shared threat of ASC and examines coworker support as a moderator of ASC, which provides valuable insights into when and why employees tend to cooperate with their teammates to jointly confront their leader’s abuse and highlights the importance of coworkers, thus enabling organizations to deeply understand the wider influences of ASC on interpersonal relationship between team members.