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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.

This study aims to simulate and test the performance of a transmitting and receiving capacitive micro-machined ultrasonic transducer (CMUT). Aimed at detecting demand of the CMUT, a matched integrated adjustment circuit was designed through analyzing processing methods of transducer’s weak echo signal.

Based on the analysis of CMUT array structure and work principle, the CMUT units are designed and the dynamic performance analysis of SIMULINK is given according to the demand of underwater detecting. A transceiver isolation circuit is used to make transmission mode and receiving mode separate. A detection circuit is designed based on the transimpedance amplifier to achieve extraction of high-frequency and weak signal.

Through experimentation, the effectiveness of the CMUT performance simulation and the transceiver integrated adjustment circuit were verified. In addition, the test showed that CMUT with 400 kHz frequency has wider bandwidth and better dynamic characteristics than other similar transducers.

This paper provides a theoretical basis and design reference for the development and application of CMUT technology.

To understand the meaning of a sentence, humans can focus on important words in the sentence, which reflects our eyes staying on each word in different gaze time or times. Thus, some studies utilize eye-tracking values to optimize the attention mechanism in deep learning models. But these studies lack to explain the rationality of this approach. Whether the attention mechanism possesses this feature of human reading needs to be explored.

The authors conducted experiments on a sentiment classification task. Firstly, they obtained eye-tracking values from two open-source eye-tracking corpora to describe the feature of human reading. Then, the machine attention values of each sentence were learned from a sentiment classification model. Finally, a comparison was conducted to analyze machine attention values and eye-tracking values.

Through experiments, the authors found the attention mechanism can focus on important words, such as adjectives, adverbs and sentiment words, which are valuable for judging the sentiment of sentences on the sentiment classification task. It possesses the feature of human reading, focusing on important words in sentences when reading. Due to the insufficient learning of the attention mechanism, some words are wrongly focused. The eye-tracking values can help the attention mechanism correct this error and improve the model performance.

Our research not only provides a reasonable explanation for the study of using eye-tracking values to optimize the attention mechanism but also provides new inspiration for the interpretability of attention mechanism.

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.

Machine learning is having a major impact on banking, law and other organisations. The speed with which this technology is developing to undertake tasks that are not only complex and technical but also time-consuming and that are subject to constantly changing parameters is astounding. The purpose of this paper is to explore the extent to which machine learning can be used as a solution to lighten the compliance and regulatory burden on charitable organisations in the UK; so that they can comply with their regulatory duties and develop a coherent and streamlined action plan in relation to technological investment.

The subject is approached through the analysis of data, literature and domestic and international regulation. The first part of the study summarises the extent of current regulatory obligations faced by charities, these are then, in the second part, set against the potential technological solutions provided by machine learning as of July 2021.

It is suggested that charities can use machine learning as a smart technological solution to ease the regulatory burden they face in a growing and impactful sector.

The work is original because it is the first to specifically explore how machine learning as a technological advance can assist charities in meeting the regulatory compliance challenge.

This paper aims to examine the perceptions of good design attributes and propose a model to estimate their relative importance through fundamental drivers. Design activities must understand and meet customer and producer expectations and deliver products in a profitable manner. Requirements analysis is conducted to understand customer expectations, but in new product development, this information can be available too late in the development cycle. Moreover, customer needs are often unclear to designers at early stages of design, with customers often unable to articulate their requirements or unaware of how a new product may solve problems or create complications. Evaluating non-product-specific drivers to generalized good product design attributes can help designers estimate important factors in early requirements analysis.

Quantification of the weight designers place in their mental models of what makes up a good product is determined from linear regression modeling, providing a more concrete evaluation of inherently subjective perceptions. A survey is deployed using Mechanical Turk^TM to collect perceptions of good product attributes and drivers through product case studies. Data are analyzed using a utility theory framework and importance of attributes is estimated from the importance of drivers.

A generalized model that estimates good design attributes from drivers is presented. This study also demonstrates that non-product-specific attribute importance can be extracted from specific product cases. An application example demonstrating the relative importance of good design attributes is given for different types of watches.

The approach is intended to supplement ordinary product design and development processes, and is not intended to replace market research and concept testing activities. Model coefficient weights are dependent on the quality of the data that was collected, which has limitations. While the current study included confounding variables, introducing interactions into the model could make attribute importance prediction more accurate.

While design requirements analysis is now central to modern design practice, these estimates can be available too late in the development cycle, especially when customers have no experience with the product type. The developed model quantifies design attributes that consumers, manufacturers and society as a whole use to distinguish if a product will be considered well designed. Product designers can better focus their development resources toward good design attributes based on guidance generated from generalized drivers.

Historically, requirements analysis is undertaken specific to the product being designed. This paper provides a model to give designers early guidance in a non-product-specific framework. The framework also considers good design attributes as holistic, including societal and producer concerns. Although all of the proposed good design attributes can be associated with a well-engineered product, it is unnecessary to design a product that performs exceptionally on every attribute. This model provides identification of the handful of attributes that can make the most significant difference for design success.

This paper aims to investigate whether close auditor-client relationships affect audit quality over the tenure of the audit partner and the potential role of partner rotation in mitigating this effect.

Using the Chinese mandatory audit partner rotation setting, the authors identify the existence of a close auditor-client relationship if the audit partner tenure with a client is larger than the audit firm tenure with that client. The sample period (1998–2009) is divided into voluntary and mandatory rotation periods when examining the effects of audit partner tenure on audit quality for the normal and close auditor-client relationship subsamples, respectively. The authors also conduct a propensity score matching analysis to address a selection issue.

The paper finds that under the voluntary partner rotation regime, audit quality decreases with audit partner tenure for the subsample with close auditor-client relationships, whereas this effect is not shown in the normal relationship subsample. However, audit quality no longer declines with audit partner tenure under the mandatory partner rotation regime.

This is the first study that directly examines the effect of audit partner tenure on audit quality associated with close auditor-client relationships under the voluntary and mandatory partner rotation regimes.

Performance pressure is unavoidable in the career of any frontline employee in the service industry, yet the authors have little understanding of the dualistic nature of performance pressure. This study aims to distinguish between challenge performance pressures and hindrance performance pressure and to investigate the effect of challenge/hindrance performance pressure on in-role/extra-role service performance through distinct emotional-labor-strategy mechanisms.

Study 1 conducted exploratory and confirmatory factor analyses and developed a performance pressure scale. To test the predictive validity of this scale, Study 2 used data from 178 frontline employees based on diary and experience sampling spanning 18 consecutive calendar days.

Findings revealed that surface/deep acting mediated the relationship between hindrance/challenge performance pressure and in-role/extra-role service performance. Calling moderated the relationship between emotional labor and service performance. The relationship between surface acting and in-role service performance was weaker in the higher calling condition, whereas the relationship between deep acting and extra-role service performance was weaker in the higher calling condition.

Service organizations should motivate employees to preserve more challenge performance pressure rather than hindrance performance pressure through establishing a reasonable performance target system. Furthermore, organizations can encourage employees to provide more extra-role services for customers through establishing an emotional support system, so as to enhance customer satisfaction.

To the best of the authors’ knowledge, this study is a pioneering effort to develop a dualistic performance pressure scale and explore the impact mechanism and boundary conditions of performance pressure on service performance in the presence of emotional labor.

The purpose of this paper is to review the major systemic educational reforms that China has undertaken over the past three decades and analyze the reasons behind their different degrees of success.

Two running themes are identified among various reforms on the Chinese educational system: decentralization and marketization. The authors explore the driving forces behind these reforms and discuss whether the reforms were successful or not.

In conclusion, the authors highlight four takeaway lessons of Chinese educational reforms.

This is an original discussion of educational reforms in China in the contexts of decentralization and marketization.

Today’s business environment has the nature that challenges the decision-making processes of businesses with its many different factors including technological developments and changing consumer structures. Previous research focused on specific contexts of the business environment regarding marketing and innovation issues, however, an integrative approach can be helpful for understanding and taking actions. This study aims to present a comprehensive framework which employs sensemaking from management literature as an approach to evaluate the market environment. Following a conceptual approach for the methodology, a framework consisting of three stages: discovery, sensemaking, and prediction is included in the study. Proposed framework identifies the stages for the sensemaking of consumers and can guide marketing decision-makers for competitiveness in the digital world.