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As supervisor incivility and its negative effect may impact employees’ psychological health and even the sustainable development of hospitality enterprises, this study aims to explore the channels through which it affects employee turnover intention in China’s hospitality industry and suggest possible mitigation measures.

This study adopted exploratory factor analysis, measurement model analysis and the mediation and moderation model and used SPSS and PROCESS for the analysis.

This study found that the impact of supervisor incivility on the employees’ turnover intention would be through employees’ ego depletion and revealed that organizational support would alleviate such a negative effect. However, organizational support might not mitigate the impact of supervisor incivility on the employees’ ego depletion, which is inconsistent with previous studies. This study inferred that organizational support might be somewhat related to organizational pressure, thereby enhancing the impact of supervisor incivility on the employees’ ego depletion.

This study not only enriches incivility literature but also suggests new insights into the mixed role of organizational support.

Unlike previous studies that mainly focused on workplace pressure from colleagues or customers, this study broadens our understanding of the employees’ turnover intention affected by supervisors’ workplace incivility and the mixed role of organizational support.

This paper aims to investigate the influence of laser energy density on microstructure and mechanical properties of the selective laser melted (SLMed) Ti6Al4V to complement the existing knowledge in additive manufacturing of Ti6Al4V for future application of selective laser melting (SLM) in fabricating Ti6Al4V parts.

Ti6Al4V alloy is fabricated by SLM by adopting various energy densities. Microstructures and mechanical properties of the Ti6Al4V deposited using different energy densities are characterized.

Both high relative densities and microhardness can be obtained in the optimized processing window. The decrease of martensite width and spacing can improve the microhardness on both XOY and XOZ sections when the applied E_V (defined as the laser energy per unit volume) increases. The width of the columnar grain increases with E_V, resulting in a stronger anisotropy in microhardness between XOY and XOZ sections. Residual tensile stresses exist in the SLMed Ti6Al4V and increase with an increasing E_V. A tensile strength of 1,268 MPa, a yield strength of 1,030 MPa, and an elongation of 4% can be obtained by using the optimized range of E_V.

The microstructure of SLMed Ti6Al4V is quantitatively analysed by measuring the size of columnar grains and the martensites. The anisotropy of microstructures and properties in SLMed Ti6Al4V is characterized and its dependence on laser energy density is established. The residual stress in SLMed Ti6Al4V is characterized and its dependence on laser energy density is established. An optimized processing window to deposit Ti6Al4V alloy by SLM is proposed.

High residual stress caused by the high temperature gradient brings undesired effects such as shrinkage and cracking in selective laser melting (SLM). The purpose of this study is to predict the residual stress distribution and the effect of process parameters on the residual stress of selective laser melted (SLMed) Inconel 718 thin-walled part.

A three-dimensional (3D) indirect sequentially coupled thermal–mechanical finite element model was developed to predict the residual stress distribution of SLMed Inconel 718 thin-walled part. The material properties dependent on temperature were taken into account in both thermal and mechanical analyses, and the thermal elastic–plastic behavior of the material was also considered.

The residual stress changes from compressive stress to tensile stress along the deposition direction, and the residual stress increases with the deposition height. The maximum stress occurs at both ends of the interface between the part and substrate, while the second largest stress occurs near the top center of the part. The residual stress increases with the laser power, with the maximum equivalent stress increasing by 21.79 per cent as the laser power increases from 250 to 450 W. The residual stress decreases with an increase in scan speed with a reduction in the maximum equivalent stress of 13.67 per cent, as the scan speed increases from 500 to 1,000 mm/s. The residual stress decreases with an increase in layer thickness, and the maximum equivalent stress reduces by 33.12 per cent as the layer thickness increases from 20 to 60µm.

The residual stress distribution and effect of process parameters on the residual stress of SLMed Inconel 718 thin-walled part are investigated in detail. This study provides a better understanding of the residual stress in SLM and constructive guidance for process parameters optimization.

It is a crucial issue to eliminate cracks for selective laser melting (SLM) 7xxx series aluminum alloy. This paper aims to study the effect of silicon content on the cracking behavior and the mechanism of eliminating crack of SLMed Al7050 alloy.

Six different silicon contents were added to the Al7050 powder. The crack density and crack count measuring from optical micrographs were utilized to judge the cracking susceptibility. The low melting phases analyzing from Jmatpro and the microstructure observing by EPMA and SEM were used to discuss the mechanism of eliminating the crack.

The cracking susceptibility of SLMed Al7050 alloy decreases with the increase of adding silicon content. When adding silicon, two new low-melting phases appeared: Mg2Si and Al5Cu2Mg8Si6. These low-melting phases offer much liquid feeding along the grain boundary and decrease the cracking susceptibility. Moreover, the grains are obviously refined after adding silicon. The fine grain can increase the total surface area of the grain boundary, which can reinforce the matrix and decrease the cracking susceptibility. High silicon content results in more low-melting phases and fine grains, which decreases the cracking susceptibility.

The investigation results can help to obtain crack-free SLMed Al7050 parts and deep knowledge on eliminating cracking mechanism of high-strength aluminum alloy fabricated by SLM.

Twitter fake accounts refer to bot accounts created by third-party organizations to influence public opinion, commercial propaganda or impersonate others. The effective identification of bot accounts is conducive to accurately judge the disseminated information for the public. However, in actual fake account identification, it is expensive and inefficient to manually label Twitter accounts, and the labeled data are usually unbalanced in classes. To this end, the authors propose a novel framework to solve these problems.

In the proposed framework, the authors introduce the concept of semi-supervised self-training learning and apply it to the real Twitter account data set from Kaggle. Specifically, the authors first train the classifier in the initial small amount of labeled account data, then use the trained classifier to automatically label large-scale unlabeled account data. Next, iteratively select high confidence instances from unlabeled data to expand the labeled data. Finally, an expanded Twitter account training set is obtained. It is worth mentioning that the resampling technique is integrated into the self-training process, and the data class is balanced at the initial stage of the self-training iteration.

The proposed framework effectively improves labeling efficiency and reduces the influence of class imbalance. It shows excellent identification results on 6 different base classifiers, especially for the initial small-scale labeled Twitter accounts.

This paper provides novel insights in identifying Twitter fake accounts. First, the authors take the lead in introducing a self-training method to automatically label Twitter accounts from the semi-supervised background. Second, the resampling technique is integrated into the self-training process to effectively reduce the influence of class imbalance on the identification effect.

Dunhuang murals are rich in cultural and artistic value. The purpose of this paper is to construct a novel mobile visual search (MVS) framework for Dunhuang murals, enabling users to efficiently search for similar, relevant and diversified images.

The convolutional neural network (CNN) model is fine-tuned in the data set of Dunhuang murals. Image features are extracted through the fine-tuned CNN model, and the similarities between different candidate images and the query image are calculated by the dot product. Then, the candidate images are sorted by similarity, and semantic labels are extracted from the most similar image. Ontology semantic distance (OSD) is proposed to match relevant images using semantic labels. Furthermore, the improved DivScore is introduced to diversify search results.

The results illustrate that the fine-tuned ResNet152 is the best choice to search for similar images at the visual feature level, and OSD is the effective method to search for the relevant images at the semantic level. After re-ranking based on DivScore, the diversification of search results is improved.

This study collects and builds the Dunhuang mural data set and proposes an effective MVS framework for Dunhuang murals to protect and inherit Dunhuang cultural heritage. Similar, relevant and diversified Dunhuang murals are searched to meet different demands.

The purpose of this paper is to build a mobile visual search service system for the protection of Dunhuang cultural heritage in the smart library. A novel mobile visual search model for Dunhuang murals is proposed to help users acquire rich knowledge and services conveniently.

First, local and global features of images are extracted, and the visual dictionary is generated by the k-means clustering. Second, the mobile visual search model based on the bag-of-words (BOW) and multiple semantic associations is constructed. Third, the mobile visual search service system of the smart library is designed in the cloud environment. Furthermore, Dunhuang mural images are collected to verify this model.

The findings reveal that the BOW_SIFT_HSV_MSA model has better search performance for Dunhuang mural images when the scale-invariant feature transform (SIFT) and the hue, saturation and value (HSV) are used to extract local and global features of the images. Compared with different methods, this model is the most effective way to search images with the semantic association in the topic, time and space dimensions.

Dunhuang mural image set is a part of the vast resources stored in the smart library, and the fine-grained semantic labels could be applied to meet diverse search needs.

The mobile visual search service system is constructed to provide users with Dunhuang cultural services in the smart library. A novel mobile visual search model based on BOW and multiple semantic associations is proposed. This study can also provide references for the protection and utilization of other cultural heritages.

The purpose of this paper is to report the influence of the peak laser power on laser micro sintering 4-μm nickel powder using Q-switched 1064-nm Nd:YAG laser.

Experimental study has been performed. Nickel powder with grain size of 4 μm has been utilized. A Q-switching duration of 20-25 μs and rate of 20-40 kHz have been used.

The peak power intensity is so high that the metal particles and molten pool are blown away, leading to laser micro sintering not being successfully proceeded. The scanning line obtained by continuous-wave (CW) laser looks like a rod owing to balling effect. Using a suitable peak power intensity, a good-shaped sintering line can be obtained because the plasma can protect the molten metal from oxidation, and improve the wettability of the system. In addition, the plasma flattening effect may also contribute to the form of the good-shaped sintering line in pulsed laser sintering regime.

The role of plasma induced by pulsed laser with high peak power intensity has been found during pulsed laser sintering under an ambient environment.

The purpose of this paper is to study the properties of the NGM (1,1,k) prediction model with multiplication transformation and reduce its modeling complexity.

The authors improved this model by putting forward a formula to solve its parameters, building an algorithm for optimizing the NGM (1,1,k) model in terms of the least modeling error and designing a key technology for the implementation of this algorithm. The optimized NGM (1,1,k) model is built accordingly. The parameter characteristics of the two models under multiple transformations and its effect of the simulation value and forecasting value are analyzed by studying the properties of multiple transformation of the two models.

The research finding shows that the modeling accuracies of the NGM (1,1,k) model and the optimized NGM (1,1,k) model are all in no relation to multiple transformations.

The above results imply that the data level can be reduced; the process of building the NGM (1,1,k) model and the optimized NGM (1,1,k) model can be simplified; but the simulative and predictive accuracy of the two models remain unchanged.

The paper succeeds in realising the properties of NGM (1,1,k) model and the optimized NGM (1,1,k) model by using the method of multiplication transformation, which is helpful for understanding the modeling mechanism and expanding the application range of the NGM (1,1,k) model.