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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.

This chapter covers two behaviors that greatly affect college students’ mental health and wellbeing: eating and sleeping. The chapter begins with a definition of eating disorders and distinguishes clinically disordered eating from other forms of problematic eating. The chapter describes common eating disorders among college students: anorexia nervosa, bulimia nervosa, avoidant restrictive food intake disorder (ARFID) and binge eating disorder. The chapter then discusses measures of problematic eating among college students, including the SCOFF, the Eating Disorder Inventory and the Eating Concerns subscale of the Counseling Center Assessment of Psychological Symptoms (CCAPS). Next, the chapter discusses the prevalence of problematic eating among college students. Cultural considerations are described, with particular attention paid to gender, sexual orientation and ethnicity. Causes of problematic eating among college students are discussed, and the consequences of problematic eating are explored, from shame to medical complications to death. Treatment options are detailed, as are barriers to seeking professional help. The chapter follows a similar structure in covering healthy and problematic sleep behaviors among college students. In particular, the chapter explores measures of sleep quality, the prevalence of problematic sleep among college students, their causes and consequences, as well as strategies for correcting poor sleep and interventions for promoting healthy sleep habits.

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.

The main objective of this research is to analyze the dynamics of academic research on digital entrepreneurship and innovation, unveiling the main research trends developed in the last decade.

The analysis has been carried out using a dual methodological approach comprising bibliometric methods and an innovative three-level systematic review, involving the analysis of the most used keywords, the co-occurrence network of keywords and a traditional review of the abstracts of the 1713 peer-reviewed articles that make up the database under study, with the aim of revealing and discussing the most important research trends developed in the period analyzed.

The results reveal seven major research trends that underscore the fundamental role of digital platforms in democratizing innovation, enabling a more inclusive and collaborative environment wherein startups, established firms, academia and government entities converge to drive technological advancements and societal progress. Additionally, the pressing need for a holistic understanding of digital technologies' potential to spur economic growth, societal progress and sustainable development is highlighted, as well as the challenges posed by privacy, security and the digital divide.

This research has been developed using an innovative methodology, and contributes to the academic discourse by providing a comprehensive overview of the current state and prospects of digital entrepreneurship and innovation for policymakers, entrepreneurs and researchers, urging a collaborative effort to harness the benefits of digital transformation in an equitable and responsible manner, fostering an environment of innovation that is conducive to the sustainable growth of the digital economy.

Researchers continue to address the concept of self-disclosure because it is foundational for helping social networking sites (SNS) function and thrive. Nevertheless, the authors' literature review indicates that uncertainty remains around the underlying mechanisms and factors involved in the self-disclosure process. The purpose of this research is to better understand the self-disclosure process from the lens of dual-process theory (DPT). The authors consider both the controlled factors (i.e. self-presentation and reciprocity) and an automatic factor (i.e. social influence to use an SNS) involved in self-disclosure and broaden The authors proposed a model to include the interactive facets of enjoyment.

The proposed model was empirically validated by conducting a survey among users of WeChat Moments in China.

As hypothesized, this research confirms that enjoyment and automatic processing (i.e. social influence to use an SNS) are complementary in the SNS self-disclosure process and enjoyment negatively moderates the positive relationship between controlled factor (i.e. self-presentation) and self-disclosure.

Theoretically, this study offers a new perspective on explaining SNS self-disclosure by adopting DPT. Specifically, this study contributes to the extant SNS research by applying DPT to examine how the controlled factors and the automatic factor shape self-disclosure processes and how enjoyment influences vary across these processes – enriching knowledge about SNS self-disclosure behaviors. Practically, the authors provide important design guidelines to practitioners concerning devising mechanisms to foster more automatic-enjoyable value-added functions to improve SNS users' participation and engagement.

The authors contribute to the literature on leadership by investigating how characteristics of principal investigators (PIs) affect innovation performance, and how collaborative and non-collaborative projects moderate this relationship within the context of inter-organisational research projects.

The authors analysed panel data from the National Science and Technology Information Service on 171 research projects within a biomedical and regenerative medicines programme overseen by the Korea Health Industry Development Institute. The authors used a hierarchical regression model, based on the ordinary least squares method, to examine the relationship between PI characteristics and performance, considering both quantity and quality.

The results show that the characteristics of PIs have diverse effects on the quantity and quality of innovation performance. Gender diversity within PIs negatively affects the quality of innovation performance, while the capacity of PIs positively influences it. Moreover, the degree of PI’s engagement is positively associated with the quantity of innovation performance but does not have a significant relationship with the quality of performance. In terms of moderating effects, collaborative projects with multiple leaders seem less reliant on PI capacity than non-collaborative projects led by a single leader, in terms of innovation performance.

The results contribute significantly to the literature on innovation management by examining the role of leadership in collaborative environments to enhance innovation performance, addressing the need for empirical evidence in this area. Analyses of PI characteristics in government R&D management can lead to improved team performance, more efficient processes and effective resource allocation, ultimately fostering innovation.

The purpose of this study is to develop a straightforward method for creating a non – toxic metal oxide sensor capable of detecting melamine in milk at ambient temperature.

A low temperature, straightforward solution-based approach, specifically the hydrothermal method was utilized to apply the active sensing layer onto the substrate. Subsequently, analysis of the cyclic voltammetry (CV) profile was conducted to detect the concentration of melamine and determine its sensitivity.

An experimental analysis was performed on a nontoxic metal oxide-based sensor fabricated for detecting melamine sensitivity using the CV profile. The sensor’s performance was evaluated under three different concentrations of melamine (0.1 mmol, 0.2 mmol and 0.3 mmol). The results demonstrated a satisfactory sensitivity of 0.0297 µAmM⁻¹cm⁻² with a response time of 2 s.

The primary innovation of this research lies in the creation of a nontoxic and environmentally friendly sensor. The synthesis method employed featured significantly lower temperatures compared to existing literatures. Furthermore, the sensor achieved enhanced sensitivity along with rapid response times.

The aim of this study is to present an overview of sustainable practices in the development of waterproof breathable fabrics for garments. It aims to provide insights into the current state of academic research in this domain and identify and analyze major sustainable trends in the field.

This study conducts a thorough examination of research publications sourced from the Scopus database spanning the years 2013–2023 by employing a systematic approach. The research utilizes both descriptive analysis and content analysis to identify trends, notable journals and leading countries in sustainable waterproof breathable fabric development.

The study reveals a notable increase in studies focusing on sustainable approaches in the development of waterproof breathable fabrics for garments. Descriptive analysis highlights the most prominent journal and leading country in terms of research volume. Content analysis identifies four key trends: minimizing chemical usage, developing easily degradable materials, creating fabrics promoting health and well-being and initiatives to reduce energy consumption.

The main limitation of this research lies in its exclusive reliance on the Scopus database.

The insights derived from this study offer practical guidance for prospective researchers interested in investigating sustainable approaches to developing waterproof breathable fabric for garments. The identified trends provide a foundation for aligning research endeavors with contemporary global perspectives, facilitating the integration of sustainable methodologies into the garment industry.

This systematic literature review contributes original insights by synthesizing current research trends and outlining evolving sustainable practices in the development of waterproof breathable fabrics. The identification of key focus areas adds a novel perspective to existing knowledge.

The demand for the cold chain logistics of agricultural products was investigated through demand forecasting; targeted suggestions and countermeasures are provided. This paper aims to discuss the aforementioned statement.

A Markov-optimised mean GM (1, 1) model is proposed to forecast the demand for the cold chain logistics of agricultural products. The mean GM (1, 1) model was used to forecast the demand trend, and the Markov chain model was used for optimisation. Considering Guangxi province as an example, the feasibility and effectiveness of the proposed method were verified, and relevant suggestions are made.

Compared with other models, the Markov-optimised mean GM (1, 1) model can more effectively forecast the demand for the cold chain logistics of agricultural products, is closer to the actual value and has better accuracy and minor error. It shows that the demand forecast can provide specific suggestions and theoretical support for the development of cold chain logistics.

This study evaluated the development trend of the cold chain logistics of agricultural products based on the research horizon of demand forecasting for cold chain logistics. A Markov-optimised mean GM (1, 1) model is proposed to overcome the problem of poor prediction for series with considerable fluctuation in the modelling process, and improve the prediction accuracy. It finds a breakthrough to promote the development of cold chain logistics through empirical analysis, and give relevant suggestions based on the obtained results.