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The multi-objective optimization configuration strategy is proposed due to the configuration of EMAs in fault-tolerant control of active magnetic bearing with redundant electromagnetic actuators involving high-dimensional, nonlinear, conflicting goals.

A multi-objective optimization model for bias current coefficients is established based on the nonlinear model of active magnetic bearings with redundant electromagnetic actuators. Based on the non-dominated sorting genetic algorithm III, a numerical method is used to obtain feasible and non-inferior sets for the bias current coefficient.

(1) The conflicting relationship among the three optimization objectives was analyzed for various failure modes of EAMs. (2) For different EMAs' failure modes, the multi-objective optimization configuration strategy can simultaneously achieve the optimal or sub-optimal effective EMF, flux margins, and stability of EMF. Moreover, the characteristics of the optimal Pareto front are consistent with the physical properties of the AMB. (3) Compared with the feasible configuration of C0, the non-inferior configurations can significantly improve the performance of AMB, and the advantages of the multi-objective optimization configuration strategy become more prominent as the asymmetry of the residual supporting structure intensifies.

i) Considering the variation of the rotor displacement during the support reconstruction, a decision-making model that can accurately characterize the dynamic performance of AMB is presented. (ii) The interaction law between AMB and rotor under different failure modes of EMAs is analyzed, and the configuration principles for redundant EMAs are proposed. (iii) Based on the dynamic characteristics of AMB during the support reconstruction, effective EMF, energy consumption, and the Pearson correlation coefficient between the desired EMFs and the decoupled control currents are used as objective functions. iv. The NSGA-III is combined with the decision-making model to address the multi-objective optimization configuration problem of C0.

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 research is to explore the influence of the pandemic on manoeuvring consumption decisions towards goods and encouraging the trial of local food goods in a developing country, Jordan; primarily by taking an internal look into country image from a local perspective. Given the lack of studies analysing the impact of crises on consumption decisions, this research highlights the hidden benefits of the pandemic in shifting the perceptions of local food goods among Jordanian consumers.

This study adopts an exploratory approach to obtain rich, descriptive data to aid in the understanding of the shift in country image perceptions after the COVID-19 crisis and associated influences on purchase intentions. Using a qualitative open-ended approach eliminates the boundaries of closed-end methods of experimental research. Due to the nature of the phenomena being explored in this research, this study adopts the approach of responsive interviews with 26 participants.

Findings indicate that participants’ perceptions of country image and local goods and their consumption changed responding to COVID-19 for different reasons, creating new norms and perceptions of country image and local food goods. The findings precisely indicate a shift from negative to positive perceptions of country image and local food goods due to the pandemic. Results reveal that there are inconspicuous benefits associated with the role of the pandemic in shifting perceptions of country image and local food goods in Jordan.

Consumers’ perceptions and consumption decisions continue reciprocally to respond to and reflect on the COVID-19 crisis. Adjusting to the new normal is now the focus of research to understand the variance in consumption decisions across the world, including in emerging markets such as Jordan. Results also extend research on cue theory, as crisis seems to have a moderating role in the extent of influence cue theory has on perceptions of goods.

Assisting local brands in improving their marketing strategies, by identifying the barriers that hinder the “desire to try” phase among Jordanian consumers.

To the best of the authors’ knowledge, this study is unique and first of its kind, as it investigates perceptions of Jordanian consumers of their country’s image and whether the perceptual change in their country image would also stimulate a shift of perceptions in local food goods concerning the COVID-19 crisis. The results provide new insights into understanding consumer behaviour and preferences in crises; and the inconspicuous benefits that a crisis may have on local goods.

This chapter focuses on how alternative data can change the nature of financial forecasting through improved short-term forecasting techniques and decreased informativeness from longer term sources. Increased use of social media data leads the charge in transforming this transition. Alternative data are data not from standard financial statements or formal reports. This chapter looks at alternative data from new sources (e.g., social media, Internet of Things [IoT], and digital footprints) and alternative data from new collection methods like web scraping for textual analysis, image analysis, and vocal analysis). It first discusses standard data in financial forecasting. Next, this chapter examines alternative data in financial forecasting. Finally, it discusses alternative data used in studying finance more broadly.

Hedonic shopping is a growing phenomenon designed to enhance gamers’ virtual content shopping experience with increasing economic significance, yet limited attention has been dedicated to this area. Our study explores key hedonic motivations of virtual content shopping and how hedonic shopping value builds trust (trust in virtual content and trust in virtual retailers) that enhances the intention to pay for premium.

This research adopts a mixed-methods approach. Study 1 is qualitative; 19 semi-structured interviews were conducted with virtual game retail platform users. Study 2, based on the literature review and qualitative inquiry findings (obtained from Study 1), proposes a research model empirically validated by analyzing survey data administered to 437 online gamers from gaming zones, cybercafés and e-sports centers.

The results show that in-game shopping-related adventure-, gratification-, role- and idea-seeking motivations significantly influence gamers' perceived hedonic shopping value. In turn, perceived shopping value has a significant indirect effect through trust on gamers’ intention to pay for premium.

This research contributes to gaming literature by offering a comprehensive model that elucidates the role of hedonic shopping in increasing gamers’ trust, which explains purchase behavior in the virtual game retail context. The findings deepen the understanding of the game retailing landscape and offer strategies to build gamers’ trust, increase premium usage and retain existing spenders.

This article aims to develop and validate a theoretical model via survey data to identify the affordances and challenges influencing metaverse adoption. We specifically examine the impact of immersion on users' adoption decisions and identify which affordances predict this immersion. Additionally, this paper assesses the importance of perceived risks in users' decision-making processes regarding future metaverse engagement.

Using regression models applied to 198 survey responses, we tested our proposed model. To deepen our insights, we also conducted a qualitative analysis.

The findings confirm that users' perceptions of immersion and perceived risks are critical determinants in adoption decisions. Social presence, influenced by factors such as ubiquity and interoperability, emerges as a key component of immersion. From the qualitative data, we identified two potential strategies to enhance metaverse immersion: technical improvements and offline device-assisted strategies.

Our study contributes to the literature on information systems (IS) adoption and provides practical insights for practitioners on crucial considerations in metaverse design.

This study explores the deep integration of digital technology and cultural heritage to promote the preservation and inheritance of cultural heritage. Focusing on Digital Cultural Heritage (DCH), this research investigates its key role in activating theoretical research and practical applications in cultural heritage.

This study conducted an extensive bibliometric analysis utilizing VOSviewer and Bibliometrix visualization software to meticulously examine DCH research. Insights were gleaned from a dataset comprising 2,997 DCH-related publications harvested from the Web of Science database.

The bibliometric analysis reveals several notable findings: driven by active contributions from Italy, China, Spain, and the USA, the number of DCH publications shows a linear upward trend. Consiglio Nazionale delle Ricerche in Italy emerges as a prominent institution, while the Journal of Cultural Heritage stands out as the most influential journal in the DCH field. Scholars such as Remondino, Guidi, Barazzetti, and Carrozzino have significantly impacted DCH research. Furthermore, an in-depth analysis of keyword co-occurrence networks elucidates six major research trajectories in the DCH field, covering various aspects from cultural heritage digitization to digital humanities.

The study emphasizes the value of global knowledge exchange, interdisciplinary collaboration, innovative technology applications, and digital content provision practices in advancing DCH research.

By delving into the multifaceted landscape of DCH research, this study brings forth original insights into the escalating trends, pivotal contributors, and burgeoning research directions.

This paper explores the challenges of using cable-driven parallel robots on high-altitude, large-span facades, where redundancy in multicable systems and the elastic deformation of the cables are significant issues. This study aims to improve the accuracy and stability of the work platform through enhanced control strategies. These strategies address the redundancy in multicable systems and reduce the risks associated with cable deformation and mechanical failures during large-span movements.

The paper proposes a dynamic model for a four-rope parallel robot designed explicitly for large-span applications. The study introduces a position–force control strategy incorporating kinematic inverse solutions and a rope dynamics model to account for rope elasticity and its effects. This approach increases the number of system equations to match the unknowns, effectively solving the redundancy problem inherent in multicable systems. In addition, the tension changes of ropes and the stability of the working platform are examined under different motion distances (X = 50 m and X = 100 m) and varying Young’s modulus values (K = 5000 MPa and K = 8000 MPa).

This study’s large-span rope force–position control strategy successfully resolves the typical nonlinear characteristics and external disturbances in multicable parallel systems. By continuously monitoring and adjusting cable tension and end positions, this strategy ensures precise control over each cable’s tension, optimizes the distribution of cable tensions and maintains the system’s stability and response speed. The analysis in this paper indicates that this control strategy significantly improves the motion accuracy of robots operating on large-span high-altitude facades.

Industry adoption: The design and control strategies developed for the four-cable-driven parallel robot can be adopted by companies specializing in facade maintenance, construction or inspection. This could lead to safer, more efficient and cost-effective operations, especially in challenging environments like high-rise buildings. Innovation in robotic solutions: The research can inspire innovation within the field of robotics, particularly in developing robots for specific applications such as large surface maintenance. It showcases how adaptive control and stability can be achieved in complex operational scenarios. Safety improvements: By demonstrating a more stable and precise control mechanism for navigating large facades, the study could contribute to significant safety improvements, reducing the risk of accidents associated with manual facade maintenance and inspection tasks.

This paper combines the force/position hybrid control method with actual robotic applications, offering a novel solution to the complex issue of controlling cable-driven parallel robots in challenging environments. Thus, it contributes to the field. The proposed method significantly enhances the precision and stability of such systems and provides robust technical support for high-precision tasks in complex mechanical settings.

Technological tools for knowledge management (KM) actively support and enhance knowledge acquisition and sharing in organizations. However, technology for KM has been understudied, especially in terms of disruptive technologies (DTs). There is a need to identify how DTs, which are becoming increasingly important in industry and society, are applied to KM and their impact. This paper aims to examine the current state of technology and DT adoption in KM.

The analysis involves four steps. First, we examine the current status of DT in academia through a keyword co-occurrence network of literature. Second, we analyze the technological convergence (TC) of KM technology through the cooperative patent classification code co-classification analysis of patents. Third, we explore the main topics of KM technologies using BERTopic, and finally, we explore the introduction of DT into KM technologies and suggest potential TC combinations for the future.

KM technologies can be categorized into four main topics (knowledge acquisition, sharing, searching, and transfer), and DT is most often applied to knowledge transfer and acquisition. The DTs that are attracting attention from academia and industry are artificial intelligence, augmented and virtual reality, and blockchain, which have applications in healthcare, supply chain management, and human resource management.

The findings provide useful insights for organizations to build a technology roadmap for KM. They can also improve the rigid mindset of organization employees toward DT adoption and innovation. By adopting a KM system that leverages DT, organizations will be able to manage and operate efficiently and systematically.

This study aims to critically evaluate the competitiveness of Transformer-based models in financial forecasting, specifically in the context of stock realized volatility forecasting. It seeks to challenge and extend upon the assertions of Zeng et al. (2023) regarding the purported limitations of these models in handling temporal information in financial time series.

Employing a robust methodological framework, the study systematically compares a range of Transformer models, including first-generation and advanced iterations like Informer, Autoformer, and PatchTST, against benchmark models (HAR, NBEATSx, NHITS, and TimesNet). The evaluation encompasses 80 different stocks, four error metrics, four statistical tests, and three robustness tests designed to reflect diverse market conditions and data availability scenarios.

The research uncovers that while first-generation Transformer models, like TFT, underperform in financial forecasting, second-generation models like Informer, Autoformer, and PatchTST demonstrate remarkable efficacy, especially in scenarios characterized by limited historical data and market volatility. The study also highlights the nuanced performance of these models across different forecasting horizons and error metrics, showcasing their potential as robust tools in financial forecasting, which contradicts the findings of Zeng et al. (2023)

This paper contributes to the financial forecasting literature by providing a comprehensive analysis of the applicability of Transformer-based models in this domain. It offers new insights into the capabilities of these models, especially their adaptability to different market conditions and forecasting requirements, challenging the existing skepticism created by Zeng et al. (2023) about their utility in financial forecasting.