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The purpose of this study was to validate the feasibility of the proposed microstructure-based model by comparing the simulation results with experimental data. The study also aimed to investigate the relationship between the orientation of graphite flakes and the failure behavior of the material under compressive loads as well as the effect of image size on the accuracy of stress–strain behavior predictions.

This paper presents a microstructure-based model that utilizes the finite element method (FEM) combined with representative volume elements (RVE) to simulate the hardening and failure behavior of ferrite-pearlite matrix gray cast iron under uniaxial loading conditions. The material was first analyzed using optical microscopy, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD) to identify the different phases and their characteristics. High-resolution SEM images of the undeformed material microstructure were then converted into finite element meshes using OOF2 software. The Johnson–Cook (J–C) model, along with a damage model, was employed in Abaqus FEA software to estimate the elastic and elastoplastic behavior under assumed plane stress conditions.

The findings indicate that crack initiation and propagation in gray cast iron begin at the interface between graphite particles and the pearlitic matrix, with microcrack networks extending into the metal matrix, eventually coalescing to cause material failure. The ferritic phase within the material contributes some ductility, thereby delaying crack initiation.

This study introduces a novel approach by integrating microstructural analysis with FEM and RVE techniques to accurately model the hardening and failure behavior of gray cast iron under uniaxial loading. The incorporation of high-resolution SEM images into finite element meshes, combined with the J–C model and damage assessment in Abaqus, provides a comprehensive method for predicting material performance. This approach enhances the understanding of the microstructural influences on crack initiation and propagation, offering valuable insights for improving the design and durability of gray cast iron components.

Smart libraries are the result of the application of smart technologies in the era of digital intelligence. The establishment and improvement of its service evaluation system serve as indicators for evaluating the growth of smart libraries.

This study introduces and improves the capability maturity model (CMM), creatively constructs a service maturity model specifically designed for smart libraries and combines the Delphi method with the analytic hierarchy process (AHP) to establish a service maturity evaluation system for smart libraries while calculating indicator weights. Finally, two representative smart libraries are selected as case studies, and an empirical application is conducted using the fuzzy comprehensive evaluation method.

The empirical study shows that the developed smart libraries service maturity evaluation system holds significant theoretical and practical value in evaluating smart libraries.

Enhances the CMM and creatively constructs a service maturity model for smart libraries. Combines the Delphi method with AHP to establish a service maturity evaluation system while calculating indicator weights. Uses a fuzzy comprehensive evaluation method to evaluate two representative smart libraries. Demonstrates that the smart library services maturity evaluation system holds significant theoretical and practical value.

The recent COVID-19 pandemic underscores the need for educational innovation and digitalization during crises where many educational institutions were forced to shift to online and remote learning. This chapter is a discursive review of problems and solutions and key trends that have emerged because of crises. One of the key benefits of educational innovation and digitalization during crises is the ability to continue providing education and learning opportunities to students even when traditional face-to-face learning is not possible. Educational institutions need to invest in building resilient online learning ecosystems to ensure that education can continue uninterrupted even in the face of unforeseen disruptions. This ecosystem should include robust technological infrastructure, a diverse range of digital resources and tools, and training and support for educators. Ultimately, a resilient online learning ecosystem is not just a response to crises but a proactive investment in the future of education.

This study aims to explore the relationship between supply chain financing (SCF) and the business risks of core enterprises, the economic value of SCF for core enterprises and the motivation for core enterprises to participate in SCF. The authors also examine the mediating effects of financing constraints.

This study analyzes the panel data of 393 companies listed on the main board of the A-share market in China from 2011 to 2014 using fixed-effect and intermediary-effect models.

The development of SCF in core enterprises can significantly reduce business risk by alleviating financing constraints.

The study sample is from China’s A-share market, which may limit the ability to generalize results. The indicators used to measure SCF primarily consider commercial credit, which may have affected the accuracy of the study.

This study provides a new basis for core enterprise managers in the manufacturing sector to conduct SCF and control business risks. SCF with small and medium-sized upstream and downstream enterprises can reduce business risks and enhance competitiveness, especially under financing constraints.

This study focuses on core enterprises, a topic less explored in academia. It examines the impact of SCF on their performance and the mediating role of financing constraints. This study offers a novel perspective on the SCF transmission mechanism of supply chain finance and provides new insights for core enterprises.

The purpose of this study is to identify seminal research works on distance and online learning that have had significant impact on the domain.

The authors used the SCOPUS database for this study as the data source, and a well-defined search strategy retrieved the items for analysis. First, the authors identified the h-index (n = 207) of the discipline to determine the threshold for listing the top works. The authors critically analysed these classic publications using several bibliometric parameters to present the analysis. To understand the primary focus of the classic research works, the authors also carried out a keyword cluster analysis using VOSviewer.

While the USA produced maximum classic research, authors from Canada have maximum research visibility in terms of citations (n = 474.06). Canada also received the highest value of RCI (1.30), followed by Taiwan and Australia. The majority of the classics are published in 67 scientific journals. Of these, Computers and Education published the highest number with a quarter of the total citations (n = 19,403). Although e-learning was the nucleus of the research theme, the authors observed that students, learning systems, online learning, blended learning, learning management systems and computer-aided instructions dominated their influence in the research cluster.

To the best of the authors’ knowledge, this is the first of its kind work in the field of distance and online learning. Findings of this study would be useful to faculty, researchers and students in the discipline to focus on the seminal works and understand their implications better in the context of the growing significance of the discipline.

This study aims to provide data references for deposit cleaning, fuel formulation and lubricant formulation.

The engine disassembly procedure was carried out based on the IO540 operation manual part number 60297-10-1-7 document. Except for the normal disassembly and assembly of the engine and the measurement of parts, the main test contents included break-in run operation, cylinder pressure test, oil consumption run, pretest calibration run, post-test calibration test and the most critical endurance test. The Lycoming IO-540-V4V5 engine and the customized Hartzell CK319 propeller were selected for the test run.

The deposits were mainly composed of C, O, B, Al, Br and Pb, which were mainly sourced from fuel, lubricating oil and body materials. XPS analysis showed that the presence of O determined the richness of the deposit components, giving the deposit structure with organic and inorganic components.

This work provides certain data support and reference for subsequent aircraft maintenance, fuel formula and lubricating oil formula. The long-term stability and safety of the aircraft run can be ensured through continuous research, improvement and optimizing the operating of the engine.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2024-0300/

This paper aims to investigate how digital orientation affects digital process innovation from the dual perspectives of knowledge and capability. It also stresses the mediating effects of digital knowledge creation and strategic flexibility on this relationship, as well as the moderating effect of strategic flexibility on the relationship between digital knowledge creation and digital process innovation.

This paper adopted knowledge- and capability-based views to develop the theoretical model. A total of 193 samples from China were collected to test the model and hypotheses by the partial least squares structural equation modeling method.

The results indicate that digital orientation promotes knowledge creation and strategic flexibility respectively, which in turn facilitates digital process innovation. Also, the effect of digital knowledge creation on digital process innovation is moderated by strategic flexibility.

This study adopts the dual perspectives of knowledge and capability to deepen the relationship between digital orientation and digital process innovation by introducing digital knowledge creation and strategic flexibility as the crucial links, which responds to the call for attaching importance to digital process innovation.

The purpose of this study is to systematically investigate the novel phenomenon of rail corrugation on small radius curves with rail joints in mountainous city metros, characterized by the coexistence of short and long wavelengths (30–40 mm and 150–200 mm) on the low rail.

The finite element model of the wheel-rail system in the section with rail joint is constructed based on field surveys. The friction-coupled vibration characteristics of the wheel-rail system are studied from the perspective of friction self-excited vibration of the wheel-rail system and feedback vibration of the rail irregularity.

The rail corrugation with short wavelength is primarily induced by the friction self-excited vibration of wheel-rail system. In contrast, the rail corrugation with long wavelength is predominantly caused by the feedback vibration of rail joint irregularity. Additionally, the feedback vibration of corrugated irregularity accelerates the progression of corrugation depth without triggering the emergence of rail corrugation with new wavelength.

The research advances the understanding of the vibration inducement behind rail corrugation in mountainous city metros.

Purpose: The economies of the European Union (EU) countries are significantly affected by new developments in technology and digital transformations, requiring tailored policies to bridge gaps and boost economic development. This chapter analyses the impact of the digital economy in EU countries according to the level of economic growth.

Need for study: Assessing the interaction between economic growth and digitalisation, focussing on digital transformations, digital skills, and economic growth, this chapter designs advanced theoretical and empirical research by building on certain important research issues.

Methodology: The research framework relies on assessing the correlation between the Digital Economy and Society Index (DESI) and economic growth. Based on Eurostat data, this research employs panel econometric models to uncover causal relationships between digital policies and economic growth, incorporating macroeconomic variables and country-specific digital policies. The models are designed through the panel-corrected standard errors (PCSEs) method and robust regression with Huber iteration (RRHI) to ensure unbiased and robust estimates.

Findings: Main findings include that digitalisation coordinates and digital skills are essential for improving economic development in the EU, with benefits for economic growth. These advances affect balance and overall performance and can support policymakers in strengthening their understanding of this scientific field.

Practical implications: The degree of development and the underlying technology underlying determine how the digital economy affects economic growth. Decision-makers can utilise these results to improve digital policies within the EU, favourably impacting the economic development of EU member states.

The volume of passenger traffic at metro transfer stations serves as a pivotal metric for the orchestration of crowd flow management. Given the intricacies of crowd dynamics within these stations and the recurrent instances of substantial passenger influxes, a methodology predicated on stochastic processes and the principle of user equilibrium is introduced to facilitate real-time traffic flow estimation within transfer station streamlines.

The synthesis of stochastic process theory with streamline analysis engenders a probabilistic model of intra-station pedestrian traffic dynamics. Leveraging real-time passenger flow data procured from monitoring systems within the transfer station, a gradient descent optimization technique is employed to minimize the cost function, thereby deducing the dynamic distribution of categorized passenger flows. Subsequently, adhering to the tenets of user equilibrium, the Frank–Wolfe algorithm is implemented to allocate the intra-station categorized passenger flows across various streamlines, ascertaining the traffic volume for each.

Utilizing the Xiaozhai Station of the Xi’an Metro as a case study, the Anylogic simulation software is engaged to emulate the intra-station crowd dynamics, thereby substantiating the efficacy of the proposed passenger flow estimation model. The derived solutions are instrumental in formulating a crowd control strategy for Xiaozhai Station during the peak interval from 17:30 to 18:00 on a designated day, yielding crowd management interventions that offer insights for the orchestration of passenger flow and operational governance within metro stations.

The construction of an estimation methodology for the real-time streamline traffic flow augments the model’s dataset, supplanting estimated values derived from surveys or historical datasets with real-time computed traffic data, thereby enhancing the precision and immediacy of crowd flow management within metro stations.