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In this study, machine learning (ML) algorithms were employed to predict the shear capacity and behavior of DCSWs.

In this study, ML algorithms were employed to predict the shear capacity and behavior of DCSWs. Various ML techniques, including linear regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN), were utilized. The ML models were trained using a dataset of 462 numerical and experimental samples. Numerical models were generated and analyzed using the finite element (FE) software Abaqus. These models underwent push-over analysis, subjecting them to pure shear conditions by applying a target displacement solely to the top of the shear walls without interaction from a frame. The input data encompassed eight survey variables: geometric values and material types. The characterization of input FE data was randomly generated within a logical range for each variable. The training and testing phases employed 90 and 10% of the data, respectively. The trained models predicted two output targets: the shear capacity of DCSWs and the likelihood of buckling. Accurate predictions in these areas contribute to the efficient lateral enhancement of structures. An ensemble method was employed to enhance capacity prediction accuracy, incorporating select algorithms.

The proposed model achieved a remarkable 98% R-score for estimating shear strength and a corresponding 98% accuracy in predicting buckling occurrences. Among all the algorithms tested, XGBoost demonstrated the best performance.

In this study, for the first time, ML algorithms were employed to predict the shear capacity and behavior of DCSWs.

This conceptual paper aims to contribute to a better understanding of value creation and value capture with smart resources in the Internet of Things (IoT)-driven business models against the backdrop of an increasingly networked and connectivity-based environment. More specifically, the authors screen strategic management theories and adapt them to the specificities of new types of smart resources by focusing on a conceptual analysis of isolating mechanisms that enable value creation and value capture based upon different types of smart resources.

By adapting the state of the art of the contemporary resource-based discussion (resource-based view, dynamic capabilities view, relational view, resource-based view for a networked environment) to the context of IoT-driven business models, the paper typifies valuable intra- and inter-organisational resource types. In the next step, a discursive discussion on the evolution of isolating mechanisms, which are assumed to enable the translation of value creation into value appropriation, adapts the resource-based view for a networked environment to the context of IoT-driven business models.

The authors find that connectivity shapes both opportunities and challenges for firms, e.g. focal firms, in such business models, but it is notably social techniques that help to generate connectivity and transform inter-organisational ties into effective isolating mechanisms.

This paper lays a foundation for a theoretically underpinned understanding of how IoT can be exploited through designing economically sustainable business models. In this paper, research propositions are established as a point of departure for future research that applies strategic management theories to better understand business models that work with the digitisation and connectivity of resources on different levels.

Thin cracks on the surface, such as those found in nuclear power plant concrete structures, are difficult to identify because they tend to be thin. This paper aims to design a novel segmentation network, called U-shaped contextual aggregation network (UCAN), for better recognition of weak cracks.

UCAN uses dilated convolutional layers with exponentially changing dilation rates to extract additional contextual features of thin cracks while preserving resolution. Furthermore, this paper has developed a topology-based loss function, called ℓ_cl Dice, which enhances the crack segmentation’s connectivity.

This paper generated five data sets with varying crack widths to evaluate the performance of multiple algorithms. The results show that the UCAN network proposed in this study achieves the highest F1-Score on thinner cracks. Additionally, training the UCAN network with the ℓ_cl Dice improves the F1-Scores compared to using the cross-entropy function alone. These findings demonstrate the effectiveness of the UCAN network and the value of incorporating the ℓ_cl Dice in crack segmentation tasks.

In this paper, an exponentially dilated convolutional layer is constructed to replace the commonly used pooling layer to improve the model receptive field. To address the challenge of preserving fracture connectivity segmentation, this paper introduces ℓ_cl Dice. This design enables UCAN to extract more contextual features while maintaining resolution, thus improving the crack segmentation performance. The proposed method is evaluated using extensive experiments where the results demonstrate the effectiveness of the algorithm.

This study examines the role of digital transformation intentions in enhancing the perceived resilience of firms, with a focus on China’s manufacturing sector. It investigates the mediating role of knowledge sharing and the moderating impact of transformational leadership.

A quantitative approach was employed, collecting data from 347 manufacturing firms. Participants included managers and MBA students involved in digital transformation projects. The study utilized statistical analysis to explore the relationships between digital transformation intentions, knowledge sharing, transformational leadership and perceived firm resilience.

The analysis reveals that knowledge sharing is a critical mediating factor between digital transformation intentions and perceived firm resilience. Additionally, transformational leadership significantly strengthens this relationship, highlighting its importance in the successful implementation of digital initiatives.

The study is geographically and sectorally limited to China’s manufacturing sector, which may affect the generalizability of the findings. Future research could explore other sectors and regions to validate and extend the results.

The findings underscore the necessity of integrating digital transformation initiatives with effective leadership and knowledge management practices. Firms that foster transformational leadership and facilitate knowledge sharing are better equipped to enhance their resilience in the face of global disruptions.

This research offers a deep understanding of how digital transformation intentions, mediated by knowledge sharing and supported by transformational leadership, contribute to perceived firm resilience. It provides valuable insights for both academic research and practical applications in the field of management.

This study aims to provide a comprehensive review of the existing literature on uncertainty in underground mining operations, using a bibliometric and systematic analysis covering the period from 1975 to 2024.

To achieve this, the following questions were addressed using a mixed-method approach involving bibliometrics, text mining and content analysis: How has the field of uncertainty research in underground mining operations evolved? What are the most prominent research topics and trends in uncertainty in underground mining operations? and What are the possible directions for future research on uncertainty in underground mining operations?

As a result, bibliometric networks of 327 journal articles from the Scopus database were created and examined, the main research topics were underground mining management; rock mechanics; operational optimization; and stochastic systems. Finally, the inclusive investigation of uncertainty in underground mining operations and its prominent patterns can serve as a basis for real-time direction for new research and as a tool to improve underground mining activities by implementing advanced technology for innovative practices and optimizing operational efficiency. This is fundamental to identify unknown variables that impair the planning, operation, safety and economic viability of underground mines.

This research is 100% original because there is no review research on the uncertainty present in underground mining operations.

The lubricating fluid stored in the porous matrix will spontaneously exude to supplement the lubricating film in the damaged area, thus ensuring the long-term self-lubricating function of the porous surface. To reveal the repair mechanism of oil film, it is necessary to understand the flow characteristics of oil in micropores. The purpose of this study guides the design of micropore structure to realize the rapid exudation of oil to the porous surface and the rapid repair of the lubricating film.

In this paper, cylindrical orifice, convergent orifice and divergent orifice were studied. The numerical model of lubricating oil exudation in micropores was established. The distribution characteristics of oil pressure, velocity and three-phase contact line in the process of oil exudation were investigated. The effects of different orifice shapes and orifice structure parameters on the pinning and spreading characteristics of oil droplet were analyzed. Then the internal mechanisms of oil droplet formation and spread on the orifice surface were summarized.

The results show that during the process of oil exudation, the three-phase contact line of the oil drop is pinned once at the edge of the cylindrical and convergent orifice. Compared with the three orifice structures, the inlet pressure of the oil drop is low, and the oil velocity at the pinning point is stable in the divergent orifice. Resulting in favorable oil exudation. It is easier for oil droplet to depin by appropriately reducing the wall wetting angle, increasing the aperture or controlling the wall inclination angle. Ensure the self-healing and long-lasting lubrication film of porous oil-bearing surfaces.

The effect of pore structure on the flow behavior of lubricating fluid has always been concerned. But the mechanism by which different orifice shape affect the pinning behavior of oil droplets is not yet clear, which is crucial for understanding the self-healing mechanism of oil films on porous surfaces. It is meaningful to analyze the mechanism of oil exudation and spreading on the porous surface of oil in the special orifice, to optimize the design of the orifice structure.

Orifice shape has influence on internal flow field parameters. There is no report on the influence of orifice shape on the film formation process of oil seepage and diffusion from pores. The effects of different orifice shapes and orifice structure parameters on the characteristics of oil droplet pinning and diffusion were studied.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0118/

Traditional library book recommendation methods are mainly based on association rules and user profiles. They may help to learn about students' interest in different types of books, e.g., students majoring in science and engineering tend to pay more attention to computer books. Nevertheless, most of them still need to identify users' interests accurately. To solve the problem, the authors propose a novel embedding-driven model called InFo, which refers to users' intrinsic interests and academic preferences to provide personalized library book recommendations.

The authors analyze the characteristics and challenges in real library book recommendations and then propose a method considering feature interactions. Specifically, the authors leverage the attention unit to extract students' preferences for different categories of books from their borrowing history, after which we feed the unit into the Factorization Machine with other context-aware features to learn students' hybrid interests. The authors employ a convolution neural network to extract high-order correlations among feature maps which are obtained by the outer product between feature embeddings.

The authors evaluate the model by conducting experiments on a real-world dataset in one university. The results show that the model outperforms other state-of-the-art methods in terms of two metrics called Recall and NDCG.

It requires a specific data size to prevent overfitting during model training, and the proposed method may face the user/item cold-start challenge.

The embedding-driven book recommendation model could be applied in real libraries to provide valuable recommendations based on readers' preferences.

The proposed method is a practical embedding-driven model that accurately captures diverse user preferences.

This chapter delves into how smart city innovations positively affect workforce efficiency, residents’ quality of life (QoL), and the delivery of services, particularly within the dynamic context of smart cities: innovation, development, transformation, and prosperity. It discusses the role of technologies like cyber-physical systems, the Internet of Things, and intelligent transport systems in creating efficient, sustainable urban spaces that benefit the workforce and the broader community. The chapter highlights strategies for improving urban environments, ensuring workforce well-being, and fostering sustainable growth by examining the interplay between these technologies and urban living. The narrative emphasizes the necessity of ongoing innovation, policy support, and workforce adaptation, underscoring the importance of tailoring smart city initiatives to regional needs for maximal impact on employee performance, QoL, and service delivery. Additionally, it introduces a comprehensive framework designed to guide the development of next-generation smart cities. This framework integrates advanced technologies for optimized urban management and service provision, directly linking to enhanced employee performance through improved urban infrastructure and services. The strategic application of this framework aims to elevate economic prosperity and societal well-being, ensuring workforce efficiency is central to the urban development agenda. The enhanced employee performance, catalyzed by smart city innovations, is pivotal in driving economic vibrancy, social inclusivity, and environmental sustainability, shaping the future of urban development. This analysis will offer valuable insights for smart cities research and development in the Gulf Region, suggesting pathways for implementing these concepts to address the region’s urbanization and development challenges.

Chinese consumers' brand preferences are shifting from foreign sportswear brands to domestic ones. This indicates an increasingly strong relationship between Chinese consumers and domestic sportswear brands. The purpose of this study is to explore the spillover effect of Chinese domestic sportswear brands’ relationship quality to uncover the psychological mechanisms driving this preference shift.

The study used a brand relationship quality scale based on Chinese Confucian yuanfen culture, considering it as a second-order reflective-formative construct. The survey generated 326 valid responses online. Due to the presence of second-order reflective-formative construct in the variables, SmartPLS 4.0 was used for hypothesis testing.

Interaction belief, intimate interaction and happiness as formative dimensions of Confucian yuanfen brand relationship quality are validated, while emotional expression and tolerance are not. The Confucian yuanfen brand relationship quality has a spillover effect on product origin image and domestic sportswear brand preference. Product origin image has a mediating role between Confucian yuanfen brand relationship quality and domestic sportswear brand preference. However, consumer xenocentrism does not moderate the spillover effect of Confucian yuanfen brand relationship quality on domestic sportswear brand preference.

This study tests brand relationship quality from Confucian yuanfen perspective as a second-order reflective-formative construct. It contributes to understanding how Chinese consumers perceive their relationships with domestic sportswear brands. The results advance the current body of knowledge on brand relationship quality and spillover effect in sports marketing, indicating that Chinese sportswear brands can explore the possibility of co-opetition to achieve mutual benefits.

The study explores how mobile governance affects the administrative burden on older adults, focusing on learning, psychological and compliance costs.

Using attribution theory, the research employs a quantitative research design, utilizing surveys to gather data from 516 older adults across three cities in China: Quzhou, Wuhan and Shanghai. The study examines how intrinsic factors and extrinsic factors of m-government interfaces impact older adults’ administrative burden.

Perceived complexity increases learning, psychological and compliance costs for older adults. Personalization and high-quality information decrease these costs, enhancing user satisfaction. Visual appeal decreases anxiety and psychological costs.

This research links attribution theory with m-government’s administrative burden on older adults, offering new insights into optimizing m-government to serve older adults better.