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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.

To enrich the research on the economic consequences of enterprise digital development from the perspective of capacity utilization.

Using a sample of listed firms from 2010 to 2020, this paper exploits text analysis of annual reports to construct a proxy for enterprise digital development.

Results show that enterprise digital development not only improves their own capacity utilization but also generates a positive spillover effect on the capacity utilization of peer firms and firms in the supply chain. Next, based on the incomplete information about market demand and potential competitors when making capacity-building decisions, the mechanism tests show that improving the accuracy of market forecasts and reducing investment surges are potential channels behind the baseline results. Cross-sectional tests show the baseline result is more pronounced when industries are highly homogeneous and when firms have access to less information.

This paper contributes to the research related to the economic consequences of digital development. With the development of the digital economy, the real effects of enterprise digital development have also triggered extensive interest and exploration. Existing studies mainly examine the impact on physical operations, such as specialization division of labor, innovation activities, business performance or total factor productivity (Huang, Yu, & Zhang, 2019; Yuan, Xiao, Geng, & Sheng, 2021; Wang, Kuang, & Shao, 2017; Li, Liu, & Shao, 2021; Zhao, Wang, & Li, 2021). These studies measure the economic benefits from the perspective of the supply (output) side but neglect the importance of the supply system to adapt to the actual market demand. In contrast, this paper focuses on capacity utilization, aimed at estimating the net economic effect of digital development by considering the supply-demand fit scenario. Thus, our findings enrich the relevant studies on the potential consequences of digital development.

In the digital age, personalized services and accurate recommendations enhance the customer experience and streamline shopping. However, increasing concerns about personal privacy have led to resistance from consumers, necessitating a balance between providing high-quality online services and safeguarding personal data. The aim of this paper is to offer a comprehensive review of the fragmented literature on consumer privacy decision-making and to identify key issues worth exploring in future research.

Although previous studies have analyzed the antecedents and outcomes of privacy decisions, they have often been conducted in a fragmented manner. There remains a lack of a holistic understanding of the factors influencing privacy decisions, including their boundaries. Therefore, we build on the Theory of Planned Behavior to combine consumer privacy decision-making with a graphically conceptual framework used in a similar scoping methodology. We attempt to dissect the antecedent, moderator and outcome variables that influence consumer privacy decision-making, ultimately providing a comprehensive framework for understanding these dynamics.

Based on the Theory of Planned Behavior, we analyze the entire process of consumer privacy decision-making in terms of antecedent, moderating and outcome variables. The results indicate that consumer privacy decision-making is not an isolated behavior or a single choice but a complex, multi-level dynamic process. The factors influencing consumer privacy decisions primarily encompass five aspects: individual characteristics, information, organization, platform and interaction management, leading to various outcomes in both behavioral and perceptual dimensions. Furthermore, the process is constrained by multiple moderating variables, such as information sensitivity, platform knowledge and prior experience.

We build on the Theory of Planned Behavior to combine consumer privacy decision-making with a graphically conceptual framework used in a similar scoping methodology. We dissect the antecedent, moderator and outcome variables that influence consumer privacy decision-making, aiming to provide a comprehensive framework for understanding these processes.

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.

The inclusion of esports as an official event in the Hangzhou Asian Games is an important step towards the institutionalisation of esports. The significance of this event marks that Asia once again takes a lead in the global esportisation. This chapter investigates a series of history events in the inclusion process of esports into the comprehensive Games in Asia using process sociology and actor network theory (ANT). This study will analyse the type characteristics of esports events in Hangzhou Asian Games, whilst examining how key stakeholders' interact and balance in the network composed of international sports organisations, host of the event, emerging esports organisations and esports game companies. The chapter also examines the functions of global game industrial economic geography, local cultural politics, esports geopolitics and Olympic values in esports sportization, aiming to reveal the implications of esports inclusion in the Asian Games on the debate of whether esports meets the criteria to be classified as a ‘sport’ and its enlightenment of digital strategy to the inclusion esports in the Olympics.

Digitalization and supply chain collaboration are central to the successful servitization of manufacturing firms. However, how digitalization interacts with supply chain structure to affect servitization decisions in manufacturing firms has been understudied. In this study, we bridge resource dependence theory (RDT) and information processing theory (IPT) to examine how supply chain concentration interacts with digitalization to affect servitization decisions in manufacturing firms.

We tested the hypotheses using a panel dataset of 1,261 publicly listed machinery manufacturing firms in China. We addressed the endogeneity concerns using the control function approach and conducted multiple tests to ensure the robustness of the results.

We find that both supplier and customer concentration are negatively related to servitization, indicating that concentrated supplier and customer bases are hindrances to manufacturing servitization. Digitalization weakens the negative impact of customer concentration on servitization, but it strengthens the negative impact of supplier concentration on servitization.

The findings extend our understanding of supply chain structure and digitalization as determinants of servitization. This research also offers a nuanced view of how digitalization mitigates the negative impacts of supply chain concentration.

This study aims to explore scientific discourses on vaccination in YouTube comments using the Connectivism theory as a foundational guide in the inquiry of understanding knowledge seeking and sharing. The authors sought to understand how individuals share and seek information by using external sources through URL links to validate their arguments.

Using content analysis, the authors extracted and analysed 584 random comments with URL links from eight YouTube videos scientifically addressing the purpose of vaccines. The comments were coded by stance (pro, anti, and neutral) and the type of resource to observe how their links were used.

The results showed that URL links were composed of quotes, questions, and opinions. Many sources came from research papers, conspiracy websites, or other videos. Some of the comments did not accurately reflect the information from research papers and showed little scientific reasoning. This suggests the need for critical evaluation among individuals when finding information online.

The findings can be expanded to explore different types of information literacy practices in the comment section of social media for both informal and formal environments.

YouTube is useful in fostering scientific discourse and information-seeking/sharing practices among individuals. However, considering the inaccuracy of content deliverance, educators and individuals will need to consider how to teach/conduct information literacy skills when implementing social media for educational purposes.

Only a few studies have conducted research on comments using URL links, the originality of sources and how the sources were used in argumentation.

Fast fashion brands in Asia are increasingly incorporating corporate social responsibility (CSR) into their business strategies in response to the mounting environmental and social concerns. The study aims to provide insights into the current landscape of CSR practices and communication within the Asian context through focusing on two prominent Asian brands to understand how these fashion brands articulate and convey their CSR activities on their respective websites.

This study used a qualitative content analysis to critically evaluate the CSR communication used by Uniqlo and Shein, with the incorporation of signalling theory as a theoretical framework to discuss the observed elements on their websites.

The results indicate that the two Asian fast fashion brands actively engage in CSR’s environmental and social dimensions. Notably, the social dimension is more prominently expressed on both brands’ websites than on other dimensions. Through a nuanced interplay of colour, imagery and keywords, these Asian fast fashion brands strategically signify their commitment to environmental stewardship and stakeholder well-being.

This study fills a gap in the literature by examining CSR programs on Asian-owned fast fashion brands’ websites through the application of signalling theory. The research contributes to the theoretical foundation of CSR communication outcome research, offering actionable insights for marketers entering the Asian fashion market to enhance brand reputation and legitimacy.

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 research investigates how to optimize storage location assignment to decrease the order picking time and the waiting time of orders in the staging area of low-temperature logistics centers, with the goal of reducing food loss caused by temperature abuse.

The authors applied ABC clustering to the products in a simulated database of historical orders modeled after the actual order pattern of a large cold logistics company; then, the authors mined the association rules and calculated the sales volume correlation indices of the ordered products. Finally, the authors generated three different simulated order databases to compare order picking time and waiting time of orders in the staging area under eight different storage location assignment strategies.

All the eight proposed storage location assignment strategies significantly improve the order picking time (by up to 8%) and the waiting time of orders in the staging area (by up to 22%) compared with random placement.

The results of this research are based on a case study and simulated data, which implies that, if the best performing strategies are applied to different environments, the extent of the improvements may vary. Additionally, the authors only considered specific settings in terms of order picker routing, zoning and batching: other settings may lead to different results.

A storage location assignment strategy that adopts dispersion and takes into consideration ABC clustering and shipping frequency provides the best performance in minimizing order picker's travel distance, order picking time, and waiting time of orders in the staging area. Other strategies may be a better fit if the company's objectives differ.

Previous research on optimal storage location assignment rarely considered item association rules based on sales volume correlation. This study combines such rules with several storage planning strategies, ABC clustering, and two warehouse layouts; then, it evaluates their performance compared to the random placement, to find which one minimizes the order picking time and the order waiting time in the staging area, with a 30-min time limit to preserve the integrity of the cold chain. Order picking under these conditions was rarely studied before, because they may be irrelevant when dealing with temperature-insensitive items but become critical in cold warehouses to prevent temperature abuse.