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The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to discuss the accuracy and stability of improved empirical mode decomposition (EMD) algorithm in bearing fault diagnosis.

This paper adopts the improved adaptive complementary ensemble empirical mode decomposition (ICEEMD) to process the nonlinear and nonstationary signals. Two data sets including a multistage centrifugal fan data set from the laboratory and a motor bearing data set from the Case Western Reserve University are used to perform experiments. Furthermore, the proposed fault diagnosis method, combined with intelligent methods, is evaluated by using two data sets. The proposed method achieved accuracies of 99.62% and 99.17%. Through the experiment of two data, it can be seen that the proposed algorithm has excellent performance in the accuracy and stability of diagnosis.

According to the review papers, as one of the effective decomposition methods to deal with nonlinear nonstationary signals, the method based on EMD has been widely used in bearing fault diagnosis. However, EMD is often used to figure out the nonlinear nonstationarity of fault data, but the traditional EMD is prone to modal confusion, and the white noise in signal reconstruction is difficult to eliminate.

In this paper only the top three optimal intrinsic mode functions (IMFs) are selected, but IMFs with less correlation cannot completely deny their value. Considering the actual working conditions of petrochemical units, the feasibility of this method in compound fault diagnosis needs to be studied.

Different from traditional methods, ICEEMD not only does not need human intervention and setting but also improves the extraction efficiency of feature information. Then, it is combined with a data-driven approach to complete the data preprocessing, and further carries out the fault identification and classification with the optimized convolutional neural network.

Sustainable urban rail transit requires noise barriers. However, these barriers’ durability varies due to the differing aerodynamic impacts they experience. The purpose of this paper is to investigate the aerodynamic discrepancies of trains when they meet within two types of rectangular noise barriers: fully enclosed (FERNB) and semi-enclosed with vertical plates (SERNBVB). The research also considers the sensitivity of the scale ratio in these scenarios.

A 1:16 scaled moving model test analyzed spatiotemporal patterns and discrepancies in aerodynamic pressures during train meetings. Three-dimensional computational fluid dynamics models, with scale ratios of 1:1, 1:8 and 1:16, used the improved delayed detached eddy simulation turbulence model and slip grid technique. Comparing scale ratios on aerodynamic pressure discrepancies between the two types of noise barriers and revealing the flow field mechanism were done. The goal is to establish the relationship between aerodynamic pressure at scale and in full scale.

The aerodynamic pressure on SERNBVB is influenced by the train’s head and tail waves, whereas for FERNB, it is affected by pressure wave and head-tail waves. Notably, SERNBVB's aerodynamic pressure is more sensitive to changes in scale ratio. As the scale ratio decreases, the aerodynamic pressure on the noise barrier gradually increases.

A train-meeting moving model test is conducted within the noise barrier. Comparison of aerodynamic discrepancies during train meets between two types of rectangular noise barriers and the relationship between the scale and the full scale are established considering the modeling scale ratio.

Construction workers’ safety behavior has been proven to be crucial in preventing occupational injuries and improving workplace safety, and organizational safety support provides essential resources to promote such behavior. However, the specific mechanisms of how organizational safety support affects safety behavior have not been thoroughly explored. Therefore, this study explored the relationship between workers’ perceived organizational safety support (perceived supervisor/coworker safety support) and safety behavior (safety task/contextual behavior), while considering the mediating effects of safety motivation, emotional exhaustion, and the moderating effect of psychosocial safety climate.

Based on the quantitative research method, the hypothesis was tested. The data were collected from 500 construction workers using a structured questionnaire. Observed variables were tested using confirmatory factor analysis, and the path coefficient of fitted model was then analyzed including the associated mediating and moderating effects.

The study found that (1) safety support from both supervisors and coworkers directly forecasted both types of safety behavior, (2) safety motivation was primarily predicted by perceived supervisor safety support, and perceived coworker safety support better predicted emotional exhaustion. Safety motivation mediated the relationship between perceived supervisor safety support and safety contextual behavior, and emotional exhaustion mediated the relationship between both types of safety support and both types of safety behavior, (3) psychosocial safety climate moderated the pathway relationships mediated by safety motivation and emotional exhaustion, respectively.

The samples of this study were mostly immersed in eastern culture and the construction industry, and the cultural and industry diversity of the samples deserves further consideration to enhance the universality of the results. The cross-sectional approach may have some impact on the accuracy of the results. In addition, other potential mediating variables deserve to be explored in future studies.

This study provides a new basis for extending current theoretical frameworks of organizational safety support and safety behavior by using a moderated mediation model. Some practical insights on construction safety management have also been proposed based on the research findings. It is recommended that practitioners should further raise awareness of the critical role of supervisor-worker and worker-coworker relationships, as high levels of safety support from the supervisor/worker respectively effectively encourage safety motivation, alleviate emotional exhaustion, and thus improve workers’ safety performance. Meanwhile, the psychosocial health conditions of workers should also receive further attention.

This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.

This study explores the impact of ethical and authentic leadership on employees' workplace perceptions, focusing on organisational citizenship behaviour (OCB), trust in leader, commitment, employee voice and empowerment in independent hotels across two contrasting Global Leadership and Organizational Behaviour Effectiveness (GLOBE) clusters: Germanic and Middle-Eastern clusters. It examines how national culture influences these relationships in the hospitality industry.

Data were collected from 1,678 employees in independent hotels in the Germanic European cluster (Germany and the Netherlands) and the Middle-Eastern cluster (Qatar and Turkey) using selective and snowball sampling techniques. Hypotheses were tested using two-stage structural equation modelling.

Ethical leadership significantly affects employee voice in Germany and the Netherlands but not in Qatar and Turkey. Authentic leadership positively influences employee voice in Qatar, Turkey and Germany but does not significantly impact trust in leader in any of the four countries. The study underscores the role of cultural dimensions, particularly power distance, in shaping these relationships.

This research contributes to the literature by investigating the effects of ethical and authentic leadership on key organisational variables in culturally diverse contexts within the hospitality industry. The findings highlight the necessity of considering national culture in leadership practices and suggest practical implications for independent hotels to adapt their leadership approaches to enhance employee outcomes. Future research should explore cultural dimensions as moderators in organisational relationships.

Along with the development of the robotics industry, service robots have been gradually used in the hospitality industry. Nevertheless, service robot categorization and the fulfillment of the cognitive and emotional needs of consumers by hotel service robots have yet to be fully explored. Hence, the purpose of this study are to categorize hotel service robots, to explore consumers’ robot hotel experience, to identify the consumers’ preference of hotel service robot in general, to reveal consumers’ preference for hotel service robots based on their fulfillment of emotional needs and to examine the completion of cognitive–analytical and emotional–social tasks.

Through in-depth interviews with technology managers and questionnaire survey among consumers who have and have not had robot hotel stay experience to achieve the aforementioned research objectives.

Findings of in-depth interviews show that service robots can be categorized as check-in/out robots, artificial intelligence (AI) robots and service delivery robots. Results of questionnaire survey indicate that consumers prefer non-humanoid robots (n = 213, p = 47.87%) among check-in/out robots, the Xiaodu Smart Display (n = 163, p = 36. 63%) among the AI robots and the machine-shaped robot porter (I) (n = 178, p = 40.00%) among the service delivery robots.

This study provides implications, such as the adoption of robot-shaped AI with a screen display, to hotel managers to meet the needs of consumers regarding the completion of cognitive–analytical and emotional–social tasks of robots.

This study extends uncanny valley theory by identifying preference for the shape and functions of different categories of service robots and contributes to the limited literature on hotel robots.

The purpose of this review paper is to provide a review of the most recent advances in the field of manufacturing composite sandwich panels along with their advantages and limitations. The other purpose of this paper is to familiarize the researchers with the available developments in manufacturing sandwich structures.

The most recent research articles in the field of manufacturing various composite sandwich structures were reviewed. The review process started by categorizing the available sandwich manufacturing techniques into nine main categories according to the method of production and the equipment used. The review is followed by outlining some automatic production concepts toward composite sandwich automated manufacturing. A brief summary of the sandwich manufacturing techniques is given at the end of this article, with recommendations for future work.

It has been found that several composite sandwich manufacturing techniques were proposed in the literature. The diversity of the manufacturing techniques arises from the variety of the materials as well as the configurations of the final product. Additive manufacturing techniques represent the most recent trend in composite sandwich manufacturing.

This work is valuable for all researchers in the field of composite sandwich structures to keep up with the most recent advancements in this field. Furthermore, this review paper can be considered as a guideline for researchers who are intended to perform further research on composite sandwich structures.

This study aims to investigate the effect of seasonality caused by fasting as a religious practice on trading activity.

The authors use an unbiased sample of daily trading by individuals and institutions on the Boursa Kuwait. The authors use panel data on trading activities and Tobit regression models to examine the effect of Muslims’ religious practice of fasting during the holy month of Ramadan on trading behavior.

The authors find that during the holy month of Ramadan, Muslims’ religious practice of fasting leads to a decline in the frequency of both overall stock market trading and the ratio of individual trading volume to total trading volume. The authors find a significant decrease in individual buy-side trading as a proportion of total trading volume and simultaneously a significant increase in institutional buy-side trading.

This study’s findings have important implications for the main players in stock markets of countries with a Muslim majority. Market-makers should be aware of the significant increase in the proportion of institutional buy-side trading volume to total trading volume to minimize the cost of trading with better-informed traders (adverse selection).

To the best of the authors’ knowledge, this is the first study that investigates individuals’ trading activity during Ramadan.

Assembly action recognition plays an important role in assembly process monitoring and human-robot collaborative assembly. Previous works overlook the interaction relationship between hands and operated objects and lack the modeling of subtle hand motions, which leads to a decline in accuracy for fine-grained action recognition. This paper aims to model the hand-object interactions and hand movements to realize high-accuracy assembly action recognition.

In this paper, a novel multi-stream hand-object interaction network (MHOINet) is proposed for assembly action recognition. To learn the hand-object interaction relationship in assembly sequence, an interaction modeling network (IMN) comprising both geometric and visual modeling is exploited in the interaction stream. The former captures the spatial location relation of hand and interacted parts/tools according to their detected bounding boxes, and the latter focuses on mining the visual context of hand and object at pixel level through a position attention model. To model the hand movements, a temporal enhancement module (TEM) with multiple convolution kernels is developed in the hand stream, which captures the temporal dependences of hand sequences in short and long ranges. Finally, assembly action prediction is accomplished by merging the outputs of different streams through a weighted score-level fusion. A robotic arm component assembly dataset is created to evaluate the effectiveness of the proposed method.

The method can achieve the recognition accuracy of 97.31% and 95.32% for coarse and fine assembly actions, which outperforms other comparative methods. Experiments on human-robot collaboration prove that our method can be applied to industrial production.

The author proposes a novel framework for assembly action recognition, which simultaneously leverages the features of hands, objects and hand-object interactions. The TEM enhances the representation of dynamics of hands and facilitates the recognition of assembly actions with various time spans. The IMN learns the semantic information from hand-object interactions, which is significant for distinguishing fine assembly actions.

In 2016, the Chinese central government decentralized the responsibilities of housing market regulation to the municipal level. This paper aims to assess whether the decentralized market regulation is effective.

This study first investigates the fundamental drivers of urban housing prices in China. Taking into consideration the factors driving housing prices, the authors further investigate the effectiveness of decentralized housing market regulation by a pre- and post-policy comparison test using a panel data set of 35 major cities for the years from 2014 to 2019.

The results reveal heterogenous policy effects on housing price growth among cities with a one-year lag in effectiveness. With the decentralized housing market regulation, cities with fast price growth are incentivized to implement tightening measures, while cities with relatively low housing prices and slow price growth are more likely to do nothing or deregulate the markets. The findings indicate that the shift from a centralized housing market regulation to a decentralized one is more appropriate and effective for the individual cities.

Few policy evaluation studies have been done to examine the effects of decentralized housing market regulation on the performance of urban housing markets in China. The authors devise a methodology to conduct a policy evaluation that is important to inform public policy and decisions. This study helps enhance the understanding of the fundamental factors in China’s urban housing markets and the effectiveness of municipal government interventions.