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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.

The purpose of this paper is to study the importance of image data in hotel selection-recommendation using different types of cognitive features and to explore whether there are reinforcing effects among these cognitive features.

This study represents user-generated images “cognitive” in a knowledge graph through multidimensional (shallow, middle and deep) analysis. This approach highlights the clustering of hotel destination imagery.

This study develops a novel hotel selection-recommendation model based on image sentiment and attribute representation within the construction of a knowledge graph. Furthermore, the experimental results show an enhanced effect between different types of cognitive features and hotel selection-recommendation.

This study enhances hotel recommendation accuracy and user satisfaction by incorporating cognitive and emotional image attributes into knowledge graphs using advanced machine learning and computer vision techniques.

This study advances the understanding of user-generated images’ impact on hotel selection, helping users make better decisions and enabling marketers to understand users’ preferences and trends.

This research is one of the first to propose a new method for exploring the cognitive dimensions of hotel image data. Furthermore, multi-dimensional cognitive features can effectively enhance the selection-recommendation process, and the authors have proposed a novel hotel selection-recommendation model.

This chapter examines the significant role of non-fungible tokens (NFTs) and blockchain technology in fostering a sustainable economy in the metaverse. Blockchain allows the saving and transfer of decentralized and secure data. As a primary component of the metaverse economy, NFTs are distinct and secure virtual assets saved on the blockchain. These assets facilitate possessing, trading, and monetizing digital assets. These advancing technologies have also revolutionized the method by which creators and artists test and exchange their digital work, introducing a novel period of ownership and value in the digital realm. However, the negative environmental effects of some blockchain technologies constitute a considerable constraint, pushing a shift to a sustainable economy. Platforms like The Sandbox have implemented initiatives to address environmental concerns. As a case study, The Sandbox play-to-earn model with tokenized assets showcases its ability to create value and encourage user participation. It shows the ability of NFTs and blockchain to support a sustainable economy.

This study aims to describe the current conceptualisation of self-management of dementia by family carers in the literature and from the views of dementia professionals and family carers, and to establish a more comprehensive concept of self-management of dementia by family carers.

A hybrid concept analysis included three phases: the theoretical phase reviewed the literature on self-management of dementia by family carers; the fieldwork phase interviewed professionals and family carers; and the analytical phase synthesised and discussed the findings from the previous two phases.

The findings revealed that self-management of dementia by family carers encompasses four domains: supporting care recipients, self-care, sustaining a positive relationship with care recipients, and personal characteristics and skills.

The findings highlighted the essential elements of the construct of self-management of dementia by family carers. The findings can be used as a conceptual framework of self-management and are useful in designing and evaluating self-management support interventions for family carers.

In many instances, the data exhibits periodic and trend characteristics. However, indices like the Digital Economy Development Index (DEDI), which pertains to science, technology, policy and economy, may occasionally display erratic behaviors due to external influences. Thus, to address the unique attributes of the digital economy, this study integrates the principle of information prioritization with nonlinear processing techniques to accurately forecast rapid and anomalous data.

The proposed method utilizes the new information priority GM(1,1) model alongside an optimized BP neural network model achieved through the gradient descent technique (GD-BP). Initially, the provincial Digital Economic Development Index (DEDI) is derived using the entropy weight approach. Subsequently, the original GM(1,1) time response equation undergoes alteration of the initial value, and the time parameter is fine-tuned using Particle Swarm Optimization (PSO). Next, the GD-BP model addresses the residual error. Ultimately, the prediction outcome of the grey combination forecasting model (GCFM) is derived by merging the findings from both the NIPGM(1,1) model and the GD-BP approach.

Using the DEDI of Jiangsu Province as a case study, researchers demonstrate the effectiveness of the grey combination forecasting model. This model achieves a mean absolute percentage error of 0.33%, outperforming other forecasting methods.

First of all, due to the limited data access, it is impossible to obtain a more comprehensive dataset related to the DEDI of Jiangsu Province. Secondly, according to the test results of the GCFM from 2011 to 2020 and the forecasting results from 2021 to 2023, it can be seen that the results of the GCFM are consistent with the actual development situation, but it cannot guarantee the correctness of the long-term forecasting, so the combination forecasting model is only suitable for short-term forecasting.

This article proposes a grey combination prediction model based on the principles of new information priority and nonlinear processing.

Demographic characteristics such as race and ethnicity have long been shown to affect individuals' decision-making and can be associated with various behavioral outcomes. In this paper, we examine the association between the ethnicity of a chief financial officer (CFO) and financial reporting conservatism in a large sample of US public firms. We find that firms headed by CFOs of nonwhite ethnicities exhibit less conservative financial reporting than firms headed by white CFOs; however, this effect is attenuated for firms facing greater external scrutiny. Moreover, nonwhite CFOs in our sample recognize a higher level of discretionary accruals than white CFOs. Our study contributes to the literature on financial reporting and answers the call for more studies on top manager ethnicity effects. More importantly, our findings hold implications for both regulators and investors, given the prevalence and significance of diversity initiatives in today's globalized business environment.

The Metaverse, an emergent Web 3.0 platform, offers users immersive virtual reality experiences. This study employs a case study approach to explore the concept of sustainability within the Metaverse. It examines the environmental, social, and economic implications of virtual interactions and the role of sustainable technologies in shaping user behavior and virtual economies. Through selected case studies, the research provides insights into the potential and challenges of integrating sustainable practices in the Metaverse, with implications for stakeholders ranging from policymakers to end-users.

In this chapter, we explore the dark side of the metaverse and the need for user protection. While the metaverse offers multiple opportunities it also poses significant risks for users, such as privacy concerns, addiction, harassment, and cyberbullying. First, we discuss the various threats that users may encounter such as online harassment, assaults, cyberbullying, hate speech, identity theft, and virtual property theft. As per the Center for Countering Digital Hate, an incident of violation occurs every seven minutes within VRChat, a popular virtual reality game. The level of misconduct in the metaverse can surpass the extent of internet harassment. Virtual reality gaming has been associated with various health issues like sleep deprivation, and insomnia as well as mental health concerns such as depression, anger, and anxiety. We examine how these issues may impact user’s physical and mental health. The sensors and devices used in the metaverse collect a vast amount of user biometric data and spatial data. Interactions between users and metaverse could be leaked. We examine different methods that improve user protection, including everyone from enhanced security protocols via the application of privacy-enhanced technology to several avatars, two-factor authentication, and user educational and awareness programs. Moreover, we explore how the newest technologies, like blockchain and artificial intelligence, play a role in making user safety more important. We finished the course with the study of the case of Second Life, the virtual reality gaming platform, and pointing out some of the problems that exist within it.

Public sector employees’ contributions play a crucial role in improving public service quality and promoting the image of public organizations. The aim of this research is to unravel how and when human resource (HR) flexibility activates citizen-oriented boundary-spanning behaviors among public sector employees.

The data were collected from 427 public sector employees and 102 supervisors working for governmental agencies from the districts of a major city in Vietnam. Multilevel structural equation modeling (MSEM) was employed to analyze the data.

The positive associations between HR flexibility and the three dimensions of citizen-oriented boundary-spanning behaviors were supported. Harmonious passion demonstrated a mediating path for such linkages. Employee perceptions of normative public values were found to exert a positive moderating effect on the positive link between HR flexibility and harmonious passion, as well as their indirect link via harmonious passion.

This study advances the literature by identifying how and when HR flexibility shapes citizen-oriented boundary-spanning behaviors among public sector employees.

Given that a prerequisite for COVID-19 transmission is the interaction between individuals, it is reasonable to suspect that transportation networks may have contributed to the spread of COVID-19. This study uses the air transportation network to quantify the risk of COVID-19 spread in the United States. The proposed model is applied at the county level and identifies the risk of importing COVID-19-infected passengers into a given county. We also undertake an examination of the factors influencing the spread of COVID-19 in relation to air travel. Utilizing an extensive dataset encompassing various socioeconomic, demographic, and healthcare-related variables, our results indicate a positive relationship between these factors and the relative risk of COVID-19 spread, highlighting the pronounced impact of population density, air travel volume, and larger household sizes on increasing travel-related risk. Conversely, greater healthcare capacity, particularly in terms of hospital and intensive care unit (ICU) beds, is associated with reduced risk. We provide estimates of expected relative risk for each county and a ranking that can be useful for informing public health policies to stem the spread of the virus by devoting resources such as screening and enhanced travel protocols to airports located in at-risk counties.