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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 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.

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.

The purpose of this paper is to explore the factors that influence migrant workers' household registration transfer willingness at both individual and urban levels and to provide empirical evidence on adjusting the household registration system to accommodate economic development and migrant workers' imbalances.

This paper adopts a hierarchical nonlinear model and examines individual and urban influencing factors of migrant workers' household registration transfer willingness, based on the data from China Migrants Dynamic Survey (CMDS) and the Urban Statistical Yearbooks.

This paper shows that: (1) multi-factors, such as age, education, marital status, household demographics, industry and migrant workers' contract coverage, have significant effects on migrant workers' household registration transfer willingness; (2) The urban public service equalization indicators, such as regional economic, educational resources, medical care and ecological quality, have significant effects on migrant workers' willingness to transfer household registration; (3) The heterogeneity of migrant workers' willingness to transfer household registration is significant in central, eastern and western China.

The authors provide a fresh perspective on population migration research in China and other countries worldwide based on the pull–push migration theory, which incorporates both individual and macro (urban) factors, enabling a comprehensive examination of the factors influencing household registration transfer willingness. This hierarchical ideology and approach (hierarchical nonlinear model) could be extended to investigate the influencing factors of various other human intentions and behaviors.

Micro approaches (individual perspective) have dominated existing studies examining the factors influencing migrant workers' household registration transfer willingness. The authors combine individual and urban perspectives and adopt a more comprehensive hierarchical nonlinear model to extend the empirical evidence and provide theoretical explanations for the above issues.

Ball grid array (BGA) package is prone to failure issues in a thermal vibration-coupled environment, such as deformation and fracture of solder joints. To predict the minimum equivalent stress of solder joints more accurately and optimize the solder joint structure, this paper aims to compare the machine learning method with response surface methodology (RSM).

This paper introduced a machine learning algorithm using Grey Wolf Optimization (GWO) Support Vector Regression (SVR) to optimize solder joint parameters. The solder joint height, spacing, solder pad diameter and thickness were the design variables, and minimizing the equivalent stress of solder joint was the optimization objective. The three dimensional finite element model of the printed circuit board assembly was verified by a modal experiment, and simulations were conducted for 25 groups of models with different parameter combinations. The simulation results were employed to train GWO-SVR to build a mathematical model and were analyzed using RSM to obtain a regression equation. Finally, GWO optimized these two methods.

The results show that the optimization results of GWO-SVR are closer to the simulation results than those of RSM. The minimum equivalent stress is decreased by 8.528% that of the original solution.

This study demonstrates that GWO-SVR is more precise and effective than RSM in optimizing the design of solder joints.

This paper aims to develop an optimization model to enhance pipeline assembly performance. It focuses on predicting the pipeline’s assembly pose while ensuring compliance with clamp constraints.

The assembly pose of the pipeline is quantitatively assessed by a proposed indicator based on joint defects. The assembly interference between the pipeline and assembly boundary is characterized quantitatively. Subsequently, an analytical mapping relationship is established between the assembly pose and assembly interference. A digital fitting model, along with a novel indicator, is established to discern the fit between the pipeline and clamp. Using the proposed indicators as the optimization objective and penalty term, an optimization model is established to predict the assembly pose based on the reinforced particle swarm optimization, incorporating a proposed adaptive inertia weight.

The optimization model demonstrates robust search capability and rapid convergence, effectively minimizing joint defects while adhering to clamp constraints. This leads to enhanced pipeline assembly efficiency and the achievement of a one-time assembly process.

The offset of the assembly boundary and imperfections in pipeline manufacturing may lead to joint defects during pipeline assembly, as well as failure in the fit between the pipeline and clamp. The assembly pose predicted by the proposed optimization model can effectively reduce the joint defects and satisfy clamp constraints. The efficiency of pipeline modification and assembly has been significantly enhanced.

This study aims to identify a value co-creation framework for live streaming through tourism scenes (LStTS). It also clarifies the value attributes of LStTS and makes an empirical test.

The study used a mixed-method approach. In Study 1, a total of 12,216 pieces of viewers’ comments and ten web news reports were coded and analyzed employing a grounded theory approach. In Study 2, data were collected from 587 Douyin e-commerce users. Exploratory factor analysis and partial least squares structural equation modeling were used to test the value co-creation framework of LStTS.

In Study 1, six value attributes in three categories were identified based on a content analysis of viewers’ comments. In Study 2, a three-order factorial model of value co-creation in LStTS was identified and tested.

Our study is limited by the preponderance of female respondents in the sample and the unique nature of the research context.

Merchants and streamers should consider whether there is a fit between the merchandise and the tourism scene when selecting the tourism scene for live streaming marketing; they can select novel and beautiful natural tourism scenes to attract viewers. Detailed and comprehensive product information should be provided in the process of live streaming marketing and sharing with consumers.

The novelty of our study lies in the provision of a new value co-creation framework in LStTS, which offers a theoretical basis for analyzing the value of the tourism scene in live streaming marketing.

We aim to provide a systematic literature review of the determinants and consequences of labor investment efficiency in an international context. First, we offer a theoretical discussion of labor investment efficiency, followed by an examination of its measurement. Next, we review the determinants of labor investment efficiency, categorizing them into firm fundamentals including financial reporting quality, governance and controls, corporate social responsibility/environmental regulation and macroeconomic determinants. Finally, we review the limited empirical literature on the consequences of labor investment efficiency. We also provide some suggestions for future research.

We perform a systematic literature review using the Preferred Reporting Items for a Systematic Review of Meta-Analysis (PRISMA) guidelines to examine archival studies investigating the determinants and consequences of labor investment efficiency. Using a Boolean search strategy on the Scopus and PRISMA selection criteria, we review 86 published archival research articles from 2014 to the end of August 2024.

Our review highlights that firm-level fundamental factors including financial reporting quality have profound implications for labor investment efficiency. Effective governance mechanisms also help mitigate agency conflicts and information asymmetries and alleviate labor investment inefficiencies. Furthermore, the influence of regulations including ESG-related regulations and macroeconomic factors play a crucial role in shaping labor investment decisions. We find very little research on the consequence of labor investment efficiency.

Our review has highlighted that well-functioning corporate governance tools are effective in mitigating inefficient labor investments. Stakeholders, therefore, should ensure that firms have effective internal governance mechanisms in place and that external governance regulations complement and where necessary act as substitutes for internal governance mechanisms to optimize labor investments.

To the best of our knowledge, this study represents the first systematic review of extant research on labor investment efficiency. Our review highlights some research gaps, particularly about the consequences of labor investment efficiency and offers some suggestions for future research.