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The purposes of this paper are to solve the low-efficiency problem caused by large search space in global localization and develop an efficient global localization method requiring only one 2D-LiDAR scan to match against the prior map for indoor mobile robots.

This paper solves the global localization problem using phase correlation as the underlying registration method. To obtain accurate rotation parameter, this paper exhaustively pre-rotates the prior map by a certain angle stride in advance. Then the input scan is matched against the pre-rotated maps one by one using phase correlation to determine translation parameters, and this paper constructs an orientation histogram by the correlation coefficients. The map rotation angle and corresponding translation parameters of the maximal peak value in the orientation histogram constitute the global pose. This paper applies a divide-and-conquer method to reduce the time consumption of single phase correlation and determines promising angle ranges where the maximal peak value may appear based on the periodicity of 90º in the orientation histogram with the signal-to-noise ratio (SNR) to reduce execution times of phase correlation.

Both simulated and real experimental results reveal that the proposed method achieves a high enough success rate and efficient (processing time in a second) global localization.

The proposed method constructs an orientation histogram to improve the global localization success rate and applies a divide-and-conquer method with SNR to improve efficiency, which will benefit the indoor mobile robots equipped with 2D-LiDAR.

This paper aims to investigate, in China stock market, whether the reputation loss of a firm caused by financial restatements will lead to significant economic consequences such as financial distress and how a firm should respond to such a crisis.

This paper uses Chinese A-share listed firms from 2004 to 2013 as research samples to test research hypotheses using regression analyses.

This paper finds a significant relationship between restatements and financial distress, and such a relationship will be affected by both the type and the magnitude of restatements. More importantly, we find joint effects of restatements and state ownership on financial distress, which provides a unique contribution to the extant literature in restatement, financial distress and crisis management using Chinese stock markets data. It shows that ownership structure, affecting the firm reputation and crisis responses strategies, plays a significant role in consequences of restatements, and it is more important for state-owned enterprises (SOEs) to undertake an appropriate crisis response strategy to reduce the negative impact of restatements.

The results suggest that the damage to a firm’s reputation caused by restatements is affected by restatement type and state ownership. To reduce the negative consequences and avoid financial distress, firms should consider both the restatement type and their firm characteristics when deciding different actions to respond to restatements. In particular, SOEs should act in a more timely manner and take reputation-rebuilding actions such as taking the responsibility and making apologies and taking prompt remedial actions after restatements to regain the public trust and avoid more serious economic consequences. The Chinese government should strengthen their supervisions of SOEs and put more effort to help SOEs reduce administrative procedures, and to improve the efficiency of the implementation of recovery plans after restatements to reinstate firm credibility.

First, this paper is among the first to link financial restatement, including the type and magnitude of restatements, with financial distress, and the authors find a significant relationship between restatement type and financial distress in China stock markets. Second, this paper is the first to examine whether there is a joint effect of state ownership and restatements on financial distress. Third, this study examines how the magnitude and pervasiveness of restatements influence financial distress and find that both result in an increase of financial distress. Finally, this paper is among the first to connect crisis management and accounting literature to explain how a reputation loss caused by financial restatement may damage a firm’s value and subsequent performance, and based on which to suggest crisis-responses strategies.

This study aims to examine the empirical linkages among emotional experiences, national identity and behavioural intentions for red tourism in China.

Partial least squares structural equation modelling was applied to data from two rounds of surveys of 1,095 on-site visitors to Zunyi and Yan’an, two well-known red tourism cities in China.

Emotional experiences significantly affect national identity, with positive emotions significantly affecting national identity. Positive emotions also significantly influenced behavioural intentions, national identity positively influenced behavioural intentions and national pride mediated the relationship of “positive emotion → behavioural intentions”. In addition, previous experience played a moderating role in the relationship of “positive emotion → national pride”.

This research produced new insights on red tourism emotional experiences by clarifying its types, connotations and influences. It contributes to a better understanding of national identity antecedents and outcomes in red tourism. Findings revealed the significant role of red tourism in cultivating national identity and provide implications for academic research and practical development of red tourism.

本研究旨在考察中国红色旅游者的情绪体验、国家认同和行为意向之间的影响关系及其作用机制。

采用偏最小二乘法结构方程模型(PLS-SEM), 对来自中国红色旅游圣地——遵义和延安的1095名现场游客的问卷调查数据进行了统计分析。

情绪体验对国家认同会产生显著影响。其中, 积极情感会显著正向影响国家认同。同时, 积极情感也会对行为意向产生显著正向影响。此外, 国家认同对行为意向具有积极影响, 且国家自豪感在“积极情感→行为意图”的影响关系中充当中介作用。最后, 研究还发现旅游经历在“积极情感→国家自豪感”的影响关系中具有显著的调节作用。

本研究通过阐明红色旅游情绪体验的类型、内涵及其影响关系, 为深入理解红色旅游情绪体验提供了一个全新的视角。同时, 文章还进一步厘清了红色旅游情境中国家认同感的形成前因和影响结果的作用机理。研究结论揭示了红色旅游在培育国家认同感方面的重要价值, 并为红色旅游的学术研究和实践发展提供了重要启示。

Esta investigación examinó los vínculos empíricos entre las experiencias emocionales, la identidad nacional y las intenciones de comportamiento para del turismo rojo en China.

Se aplicó el análisis de ecuaciones estructurales basado en mínimos cuadrados parciales (PLS - SEM) a los datos recogidos mediante dos rondas de encuestas realizadasa 1.095 visitantes in situ en Zunyi y Yan'an, dos conocidas ciudades de turismo rojo de China.

Las experiencias emocionales afectan significativamente a la identidad nacional, y las emociones positivas influyen significativamente en la identidad nacional. Las emociones positivas también influyeron significativamente en las intenciones de comportamiento; la identidad nacional influyó positivamente en las intenciones de comportamiento y el orgullo nacional medió la relación “emoción positiva → intenciones de comportamiento”. Además, la experiencia previa desempeñó un papel moderador en la relación “emoción positiva → orgullo nacional”.

Esta investigación aportó nuevos hallazgos sobre las experiencias emocionales del turismo rojo al aclarar sus tipos, connotaciones e influencias. Contribuye a una mejor comprensión de los antecedentes y resultados de la identidad nacional en el turismo rojo. Los resultados de la investigación revelan el importante papel del turismo rojo en el cultivo de la identidad nacional y ofrecen implicaciones para la investigación académica y el desarrollo práctico del turismo rojo.

This paper aims to make a systematic review of the manpower prediction model of the construction industry. It aims to determine the forecasting model's development trend, analyse the use limitations and applicable conditions of each forecasting model and then identify the impact indicators of the human resource forecasting model from an economic point of view. It is hoped that this study will provide insights into the selection of forecasting models for governments and groups that are dealing with human resource forecasts.

The common search engine, Scopus, was used to retrieve construction manpower forecast-related articles for this review. Keywords such as “construction”, “building”, “labour”, “manpower” were searched. Papers that not related to the manpower prediction model of the construction industry were excluded. A total of 27 articles were obtained and rated according to the publication time, author and organisation of the article. The prediction model used in the selected paper was analysed.

The number of papers focussing on the prediction of manpower in the construction industry is on the rise. Hong Kong is the region with the largest number of published papers. Different methods have different requirements for the quality of historical data. Most forecasting methods are not suitable for sudden changes in the labour market. This paper also finds that the construction output is the economic indicator with the most significant influence on the forecasting model.

The research results discuss the problem that the prediction results are not accurate due to the sudden change of data in the current prediction model. Besides, the study results take stock of the published literature and can provide an overall understanding of the forecasting methods of human resources in the construction industry.

Through this study, decision-makers can choose a reasonable prediction model according to their situation. Decision-makers can make clear plans for future construction projects specifically when there are changes in the labour market caused by emergencies. Also, this study can help decision-makers understand the current research trend of human resources forecasting models.

Although the human resource prediction model's effectiveness in the construction industry is affected by the dynamic change of data, the research results show that it is expected to solve the problem using artificial intelligence. No one has researched this area, and it is expected to become the focus of research in the future.

The purpose of this study is to test the impact of time and price sensitivity on consumer satisfaction and purchase intention on online-to-offline (O2O) takeout platforms and explore the moderating effect of purchase preference on time sensitivity and satisfaction, as well as price sensitivity and satisfaction, in order to guide market pricing.

A structural equation model (SEM) of customer purchase intention was constructed, and the relationships between the variables (time sensitivity, price sensitivity, satisfaction and purchase intention) were examined. The completed questionnaires of 349 respondents were collected from the Questionnaire Star platform in China. The research model and hypotheses were then tested. Analytic hierarchy procedure was used to determine the moderating effect of purchase preference. Finally, the study proposes a pricing strategy for customer-active selective services.

Satisfaction positively influences purchase intention, and price sensitivity significantly increases satisfaction and further increases purchase intention; however, time sensitivity negatively affects satisfaction. Specifically, purchase preference has strongly moderated the relationship between time, price sensitivity and satisfaction. In addition, the findings show that when purchase preference is high, the effect of price sensitivity on satisfaction is stronger, suggesting the importance of purchase preference in strengthening purchase intentions. The research work recommends a pricing strategy involving value-added pricing primarily for time-sensitive customers, which can help build a high-end brand image and reduce price competition. Reduced pricing is mainly for price-sensitive customers, which is conducive to stimulating consumption within a specific time. This pricing strategy is important for adjusting market sensitivity and flexibility.

This research provides new ideas for related disciplines and guidance for the differentiated pricing and promotion of takeout platforms, as well as a theoretical basis for the diversified development of takeout platforms, improvement of personalized service quality and enhancement of customer stickiness. This study fills gaps in the existing literature on the moderating effect of purchase preference on time sensitivity and satisfaction and price sensitivity and satisfaction.

This paper aims to explore the effectiveness of customer inoculation strategies in the context of AI service failures in the hospitality and tourism industries. Furthermore, it examines how these strategies can enhance customer complaint behavior and satisfaction with service recovery, thereby improving the overall service experience.

Four distinct studies were conducted: Study 1 investigated the influence of customer inoculation on complaint behavior post-AI service failure. Study 2 assessed the impact of service remedies on customer satisfaction. Study 3 explored the implications of initial purchase and usage intentions. Finally, Study 4 validated the findings using a large-scale online survey.

The results indicated that customer inoculation significantly increases customer complaint behavior and satisfaction with service remedies following AI service failures. They also showed that this relationship is mediated by psychological distance. Furthermore, customer inoculation positively affects initial purchase and usage intentions, demonstrating effectiveness at various customer engagement stages.

This study enriches the literature on AI hospitality service failure and recovery by introducing the novel concept of customer inoculation. Additionally, it significantly contributes to the inoculation theory literature, which covers diverse fields. Practically, this study proposes an efficient and low-cost strategy for marketers.

This study introduces the concept of customer inoculation in the context of AI service failures, a novel approach in the hospitality and tourism literature. It provides empirical evidence of the efficacy of the strategy, bridging a crucial gap in understanding customer behavior in the face of technological disruptions.

Current flexible printed circuit (FPC) assembly relies heavily on manual labor, limiting capacity and increasing costs. Small FPC size makes automation challenging as terminals can be visually occluded. The purpose of this study is to use 3D tactile sensing to mimic human manual mating skills for enabling sensing offset between FPC terminals (FPC-t) and FPC mating slots (FPC-s) under visual occlusion.

The proposed model has three stages: spatial encoding, offset estimation and action strategy. The spatial encoder maps sparse 3D tactile data into a compact 1D feature capturing valid spatial assembly information to enable temporal processing. To compensate for low sensor resolution, consecutive spatial features are input to a multistage temporal convolutional network which estimates alignment offsets. The robot then performs alignment or mating actions based on the estimated offsets.

Experiments are conducted on a Redmi Note 4 smartphone assembly platform. Compared to other models, the proposed approach achieves superior offset estimation. Within limited trials, it successfully assembles FPCs under visual occlusion using three-axis tactile sensing.

A spatial encoder is designed to encode three-axis tactile data into feature maps, overcoming multistage temporal convolution network’s (MS-TCN) inability to directly process such input. Modifying the output to estimate assembly offsets with related motion semantics overcame MS-TCN’s segmentation points output, unable to meet assembly monitoring needs. Training and testing the improved MS-TCN on an FPC data set demonstrated accurate monitoring of the full process. An assembly platform verified performance on automated FPC assembly.

This paper aims to introduce a joint DEMATEL and NK methodology to develop a process model for introducing and implementing relational supply chain practices for low-carbon supply chains. Using this process model as a guide, insights into specific practices and how to implement these relational practices to achieve competitive advantage across organizations are introduced.

Low-carbon cooperation practices framework based on the relational view is developed. A methodology based on DEMATEL and the NK model is used to construct a sequential process model for introducing and implementing these relational practices. Empirical data from three manufacturing organizations in China are utilized to validate the model.

Initial results provide a sequence of relational practices for guiding those organizations and their suppliers for healthy and low-carbon development. Interdependencies between relational practices are analyzed and evaluated from four aspects. Insights into the broader application of the methodology and initial results from both a research and managerial perspective are presented, especially with consideration of the China, an emerging economy, context.

The methodology remains relatively abstract in nature, yet the tool can provide very useful interpretations and information for both researchers and practitioners.

This paper stipulates that in addition to internal operational practices, the relational practices between buyer and supplier may be equally important to achieve a low-carbon outcome, especially in supply chain setting. This paper also shows that not only the relational practice itself but also the implementation sequence of the relational practices can relate to performance. According to the authors’ initial results, organizations in this study should first develop product development cooperation, then exchange carbon knowledge and implement effective governance and last build a trust relationship with its suppliers for low-carbon cooperation.

This is one of the few approaches that directly evaluates and identifies the interdependencies among relational practices and to construct a process model for introducing and implementing low-carbon supply chain cooperation. It is also the first time that the NK model has been integrated with DEMATEL. Focusing on Chinese supply chain carbon emissions concerns is also a unique perspective.

The aim of this paper is to achieve a reasonable microclimate between clothing and the human body and optimize the custom dress pattern.

An interactive design method of 3D modeling, virtual try-on and heat transfer simulation are used. First, a 3D dress is designed with nonuniform rational B-splines curves and tried on virtually. After that, the heat transfer in the body-air-clothing microclimate and temperature distributions on the clothing surface are obtained. Based on the heat transfer in the body-air-clothing system, we design a method to improve the thermal comfort by optimizing the garment pattern digitally. Then, this paper utilized two heat transfer validating indexes to quantify the improvement of thermal comfort, and evaluate the modified model of dress.

The microclimate under the clothing is varied with the air gap distance, and the heat transfer on the area of the clavicle, bust point and front abandon are higher than other parts due to the narrow air gaps. In view of thermal comfort, the pattern optimization changes the distance ease and reforms the air circulating efficiency. The mean heat transfer and its standard variance are changing by about 10% and more than 20%. Thus, the heat transfer evaluation indexes are suitable to represent the heat transfer and thermal comfort in the microclimate system.

It can be concluded that the methodology proposed in this paper has the advantage of interactive design, 3D visualization and local heat transfer simulation. This technology meets the need of personalized customization and well-considered garment and has broad application prospects.

This study demonstrates that modifying the distance ease on body key girths based on heat transfer is a reliable way to improve thermal comfort. This method meets the consumers’ demand of the comfort of body-fit clothing under the condition of daily activities.

• 3D air gap distributions.

• Heat transfer varies with air gap distance.

• Thermal comfort can be improved by optimizing garment pattern.

3D air gap distributions.

Heat transfer varies with air gap distance.

Thermal comfort can be improved by optimizing garment pattern.

Fake news in cyberspace has greatly interfered with national governance, economic development and cultural communication, which has greatly increased the demand for fake news detection and intervention. At present, the recognition methods based on news content all lose part of the information to varying degrees. This paper proposes a lightweight content-based detection method to achieve early identification of false information with low computation costs.

The authors' research proposes a lightweight fake news detection framework for English text, including a new textual feature extraction method, specifically mapping English text and symbols to 0–255 using American Standard Code for Information Interchange (ASCII) codes, treating the completed sequence of numbers as the values of picture pixel points and using a computer vision model to detect them. The authors also compare the authors' framework with traditional word2vec, Glove, bidirectional encoder representations from transformers (BERT) and other methods.

The authors conduct experiments on the lightweight neural networks Ghostnet and Shufflenet, and the experimental results show that the authors' proposed framework outperforms the baseline in accuracy on both lightweight networks.

The authors' method does not rely on additional information from text data and can efficiently perform the fake news detection task with less computational resource consumption. In addition, the feature extraction method of this framework is relatively new and enlightening for text content-based classification detection, which can detect fake news in time at the early stage of fake news propagation.