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Prior studies have mainly attributed customer incivility to dispositional characteristics, whereas little attention has been paid to exploring service employees' role in triggering or reducing customer incivility. The purpose of the present study is to propose and test a model in which service employees' emotional labor strategies affect customer incivility via influencing customers' self-esteem threat, as well as examine the moderating role of customer's perception of service climate.

Based on a matched sample consisting of 317 employee-customer dyads in China, multiple regression analysis and indirect effect tests were employed to test our model.

The study shows that employee surface acting is positively related to customer incivility, whereas deep acting is negatively associated with customer incivility. Moreover, customer self-esteem threat mediates the relationship between both types of emotional labor and customer incivility. Customer perception of service climate moderates the relationship between deep acting and customer self-esteem threat.

The current research broadens the antecedents of customer incivility from the employee perspective and sheds more light on the role of customer self-esteem in the interactions between employees and customers. It also demonstrates a complementary relationship between service climate and individual employees' emotional labor strategies, thereby expanding the existing understanding of the management of employees' emotional labor.

Nurses' work engagement is critical for the service quality of the hospital. Thus, investigation on the influencing factors of nurses' work engagement has become an important issue. This study addresses this issue by exploring the effect of daily family-to-work conflict (FWC) on next-day work engagement among Chinese nurses.

The theoretical model was tested using 555 experience sampling data from 61 nurses collected for 10 workdays in China.

Nurses' daily FWC is associated with their next-day ego depletion. Moreover, increased ego depletion ultimately reduces their next-day work engagement. In addition, a between-individual factor of frequency of perceived patient gratitude mitigates the effect of FWC on ego depletion and the indirect effect on work engagement via ego depletion.

This study is important to the management of health-care organizations as it carries significant implications for theory and practice toward understanding the influence of FWC among nurses. On the one hand, the authors apply the job demands-resources (JD-R) model as the overarching theoretical framework, which contributes to the authors’ understanding of how FWC impairs work engagement. On the other hand, the authors extend extant theoretical models of FWC by identifying the frequency of perceived patient gratitude as an important contextual factor that counteracts the negative effects of FWC among nurses. Moreover, organizations could encourage patients to express their gratitude to nurses by providing more channels, such as thank-you notes, to offer nurses some support for overcoming the destructive effect of FWC.

In this article, the elementary notions and aggregation operations of single-valued neutrosophic parameterized complex fuzzy hypersoft set (sv-NPCFHSS) are characterized initially. Then by using matrix version of sv-NPCFHSS, a decision-support system is constructed for the evaluation of real estate residential projects by observing various risk factors.

Two approaches are utilized in this research: set-theoretic approach and algorithmic approach. The first approach is used to investigate the notions of sv-NPCFHSS and its some aggregations whereas the second approach is used to propose an algorithm for designing its decision-support system by using the aggregation operations like reduced fuzzy matrix, decision matrix, etc. of sv-NPCFHSS. The adopted algorithm is validated in real estate scenario for the selection of residential project by observing various risk factors to avoid any expected investment loss.

The proposed approach is more flexible and reliable as it copes with the shortcomings of literature on sv-neutrosophic set, sv-neutrosophic soft set and other fuzzy soft set-like structures by considering hypersoft setting, complex setting and neutrosophic setting collectively.

It has limitations for complex intuitionistic fuzzy hypersoft set, complex neutrosophic hypersoft set and other complex neutrosophic hypersoft set-like models.

The scope of this research may cover a wide range of applications in several fields of mathematical sciences like artificial intelligence, optimization, MCDM, theoretical computer science, soft computing, mathematical statistics etc.

The proposed model bears the characteristics of most of the relevant existing fuzzy soft set-like models collectively and fulfills their limitations.

This paper aims to investigate the effect of Cu content and T6 heat treatment on the mechanical properties and the tribological performance of SiCp/Al-Si-Cu-Ni-Mg hybrid composites at an elevated temperature.

The stir casting method was used to synthesize SiCp/Al-12Si-xCu-1Ni-1Mg (x = 2, 3, 3.5, 4, 4.5, 5 Wt.%) composites containing 20 vol% SiC. The hardness and tensile strength of the aluminum matrix composites (AMCs) at room temperature and elevated temperature were studied, and the wear mechanism was investigated using scanning electron microscopic and energy dispersive spectroscopy.

Results indicate that the hardness and tensile strength of the AMCs are affected significantly by T6 heat treatment and Cu content. The high-temperature friction and wear mechanism of AMCs is the composite wear mechanism of oxidation wear, adhesion wear, abrasive wear, peeling wear, high-temperature softening and partial melting. Among them, adhesion wear, high-temperature matrix softening and local melting are the main wear mechanisms.

The influence mechanism of Cu content on the hardness, tensile strength and high temperature resistance of AMCs was explained by microstructure. And the results further help to explore the application of AMCs in high temperature.

The light detection and ranging sensor has been widely deployed in the area of simultaneous localization and mapping (SLAM) for its remarkable accuracy, but obvious drift phenomenon and large accumulated error are inevitable when using SLAM. The purpose of this study is to alleviate the accumulated error and drift phenomenon in the process of mapping.

A novel light detection and ranging SLAM system is introduced based on Normal Distributions Transform and dynamic Scan Context with switch. The pose-graph optimization is used as back-end optimization module. The loop closure detection is only operated in the scenario, while the path satisfies conditions of loop-closed.

The proposed algorithm exhibits competitiveness compared with current approaches in terms of the accumulated error and drift distance. Further, supplementary to the place recognition process that is usually performed for loop detection, the authors introduce a novel dynamic constraint that takes into account the change in the direction of the robot throughout the total path trajectory between corresponding frames, which contributes to avoiding potential misidentifications and improving the efficiency.

The proposed system is based on Normal Distributions Transform and dynamic Scan Context with switch. The pose-graph optimization is used as back-end optimization module. The loop closure detection is only operated in the scenario, while the path satisfies condition of loop-closed.

The purpose of this paper is to present a Big Data solution as a methodological approach to the automated collection, cleaning, collation, and mapping of multimodal, longitudinal data sets from social media. The paper constructs social information landscapes (SIL).

The research presented here adopts a Big Data methodological approach for mapping user-generated contents in social media. The methodology and algorithms presented are generic, and can be applied to diverse types of social media or user-generated contents involving user interactions, such as within blogs, comments in product pages, and other forms of media, so long as a formal data structure proposed here can be constructed.

The limited presentation of the sequential nature of content listings within social media and Web 2.0 pages, as viewed on web browsers or on mobile devices, do not necessarily reveal nor make obvious an unknown nature of the medium; that every participant, from content producers, to consumers, to followers and subscribers, including the contents they produce or subscribed to, are intrinsically connected in a hidden but massive network. Such networks when mapped, could be quantitatively analysed using social network analysis (e.g. centralities), and the semantics and sentiments could equally reveal valuable information with appropriate analytics. Yet that which is difficult is the traditional approach of collecting, cleaning, collating, and mapping such data sets into a sufficiently large sample of data that could yield important insights into the community structure and the directional, and polarity of interaction on diverse topics. This research solves this particular strand of problem.

The automated mapping of extremely large networks involving hundreds of thousands to millions of nodes, encapsulating high resolution and contextual information, over a long period of time could possibly assist in the proving or even disproving of theories. The goal of this paper is to demonstrate the feasibility of using automated approaches for acquiring massive, connected data sets for academic inquiry in the social sciences.

The methods presented in this paper, together with the Big Data architecture can assist individuals and institutions with a limited budget, with practical approaches in constructing SIL. The software-hardware integrated architecture uses open source software, furthermore, the SIL mapping algorithms are easy to implement.

The majority of research in the literature uses traditional approaches for collecting social networks data. Traditional approaches can be slow and tedious; they do not yield adequate sample size to be of significant value for research. Whilst traditional approaches collect only a small percentage of data, the original methods presented here are able to collect and collate entire data sets in social media due to the automated and scalable mapping techniques.

This paper aims at the problem of attaching the data of doctors, patients and the real-time sensor data of the exoskeleton to the cloud in intelligent rehabilitation applications. This study designed the exoskeleton cloud-brain platform and validated its safety assessment.

According to the dimension of data and the transmission speed, this paper implements a three-layer cloud-brain platform of exoskeleton based on Alibaba Cloud's Lambda-like architecture. At the same time, given the human–machine safety status detection problem of the exoskeleton, this paper built a personalized machine-learning safety detection module for users with the multi-dimensional sensor data cloned by the cloud-brain platform. This module includes an abnormality detection model, prediction model and state classification model of the human–machine state.

These functions of the exoskeleton cloud-brain and the algorithms based on it were validated by the experiments, they meet the needs of use.

This thesis innovatively proposes a cloud-brain platform for exoskeletons, beginning the digitalization and intelligence of the exoskeletal rehabilitation process and laying the foundation for future intelligent assistance systems.