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The just‐in‐time (JIT) production system is notable for its emphasis on employee involvement and participation. However, we suggest that the role of participation that is most typically described in the organizational behavior (OB) literature does not match the type of participation practiced in JIT. We introduce a theoretical framework that accounts for these different perspectives: whereas the OB approach treats participation as an intervention and hence as an independent variable, the JIT approach sees it as a side effect of the JIT production system and hence a dependent variable. Understanding of the differences is essential in JIT implementation in the workplace if we are to avoid miscommunication, stress, and disbelief in the system.

Impressed by the remarkable improvement in productivity by Japanese manufacturing companies, many leading American counterparts are implementing their Just‐in‐time (JIT) systems. There are still several key issues to be answered for successful implementation. The results of a field survey on the implementation of JIT systems in US firms are described and some major findings are discussed.

Managerial practices and perceptions in the implementation of just‐in‐time (JIT) principles and concepts are significantly different in the United Kingdom compared to the United States. In a study of 99 companies located throughout both countries, several distinct differences between the US and the UK were found. US companies tend to provide a greater range of training and education for their employees than do UK companies. Incentives for employees are more prevalent in the US than the UK. The UK lags behind the US in taking JIT to the shopfloor. Respondents in both countries indicated comparable dissatisfaction with the implementation process. Problems in implementation included: lack of top management commitment and support, poor communication, lack of cross‐functional planning teams, data accuracy, and supplier participation. In conclusion, JIT tends to be viewed more often as a technique by UK managers whereas their US counterparts practise JIT from a philosophical perspective.

This paper aims to realize an in-situ quality inspection system rapidly for new injection molding (IM) tasks via transfer learning (TL) approach and automation technology.

The proposed in-situ quality inspection system consists of an injection machine, USB camera, programmable logic controller and personal computer, interconnected via OPC or USB communication interfaces. This configuration enables seamless automation of the IM process, real-time quality inspection and automated decision-making. In addition, a MobileNet-based deep learning (DL) model is proposed for quality inspection of injection parts, fine-tuned using the TL approach.

Using the TL approach, the MobileNet-based DL model demonstrates exceptional performance, achieving validation accuracy of 99.1% with the utilization of merely 50 images per category. Its detection speed and accuracy surpass those of DenseNet121-based, VGG16-based, ResNet50-based and Xception-based convolutional neural networks. Further evaluation using a random data set of 120 images, as assessed through the confusion matrix, attests to an accuracy rate of 96.67%.

The proposed MobileNet-based DL model achieves higher accuracy with less resource consumption using the TL approach. It is integrated with automation technologies to build the in-situ quality inspection system of injection parts, which improves the cost-efficiency by facilitating the acquisition and labeling of task-specific images, enabling automatic defect detection and decision-making online, thus holding profound significance for the IM industry and its pursuit of enhanced quality inspection measures.

An emergency shelter design as an immediate response to assist people in disaster situations was evaluated in this study. The design experiment concerns the use of recyclable paper tubes and simple construction techniques so that unskilled labor can quickly and readily assemble the shelter where required. The design exercise is a result of a joint effort of a group of architecture students at Inha University with the help of an architect, Shigeru Ban, who is renowned for his innovative work with paper tubes. In the first phase, five different design alternatives for a short term recovery option are proposed and in the second phase, a design was selected and developed on the full scale to demonstrate its design and assembly technique.

The advent of social media has led to the emergence of influencers and the domain of influencer marketing (IM). Despite being a popular and evolving research domain, it still lacks a comprehensive and systematic bibliometric analysis in the field. This study attempts to fill this critical gap by mapping the influence of IM to understand the intellectual structure and propose a future research agenda for scholars working in the domain.

The authors have used VOSviewer and Biblioshiny packages for conducting a variety of analyses such as citation analysis, bibliographic coupling, keyword analysis and three-field plot analysis.

The main findings include identifying six clusters in the domain, i.e. “mechanism of IM,” “measuring the impact of IM,” “persuasive cues in IM,” “likability factors of influencers,” “authenticity of influencers” and “understanding followers.” Future research guidelines have been proposed in each of the six clusters, followed by research and managerial implications, conclusion and limitations.

This study is probably the first attempt at providing a comprehensive bibliometric analysis in the domain using a combination of VOSviewer and Biblioshiny packages.

The purpose of the study is to investigate and reveal this relationship of various engineering demand parameters (EDPs) of this structural type and intensity measures (IMs) under intra-plate earthquakes.

The nonlinear finite element model used was calibrated first to the existing results of the shaking table test to verify the modeling technique.

This paper investigated the relationship between intensity measures and various engineering demand parameters of cable-stayed bridges using intra-plate earthquakes. The correlation analysis and Pearson coefficient are used to study the correlation between EDPs and IMs. The results showed that peak ground velocity (PGV)/peak ground acceleration, peak ground displacement and root-mean-square of displacement showed weak correlation with IMs. PGV, sustained maximum velocity, a peak value of spectral velocity, A95 parameter, Housner intensity and spectral acceleration at the fundamental period, the spectral velocity at the fundamental period and spectral displacement at the fundamental period were determined to be better predictors for various EDPs.

This paper investigated the correlation between the intensity measures of intra-plate earthquakes with the seismic responses of a typical long-span cable-stayed bridge in China. The nonlinear finite element model used was calibrated to the existing results of the shaking table test to verify the modeling technique. In total, 104 selected ground motions were applied to the calibrated model, and the responses of various components of the bridge were obtained. This study proposed PGV as the optimal IM.

Gives introductory remarks about chapter 1 of this group of 31 papers, from ISEF 1999 Proceedings, in the methodologies for field analysis, in the electromagnetic community. Observes that computer package implementation theory contributes to clarification. Discusses the areas covered by some of the papers ‐ such as artificial intelligence using fuzzy logic. Includes applications such as permanent magnets and looks at eddy current problems. States the finite element method is currently the most popular method used for field computation. Closes by pointing out the amalgam of topics.

This paper provides a quantitative review of the literature on the repercussions of idiosyncratic information on firms’ cost of equity (CoE) capital. In total, I review the results of 113 unique studies examining the CoE effects of information Quantity, Precision and Asymmetry. My results suggest that the association between firm-specific information and CoE is subject to moderate effects. First, the link between Quantity and CoE is moderated by disclosure types and country-level factors in that firms in comparatively weakly regulated countries tend to enjoy up to four times greater CoE benefits from more expansive disclosure—depending on the type of disclosure—than firms in strongly regulated markets. Second, a negative relationship between Precision and CoE is only significant in studies using non-accrual quality proxies for Precision and risk factor-based (RFB)/valuation model-based (VMB) proxies for CoE. Third, almost all VMB studies confirm the positive association between Asymmetry and CoE, but there is notable variation in the conclusions reached when ex post CoE measurers are used.

The purpose of this paper is to explore gas/liquid two-phase flow is widely existed in industrial fields, especially in chemical engineering. Electrical resistance tomography (ERT) is considered to be one of the most promising techniques to monitor the transient flow process because of its advantages such as fast respond speed and cross-section imaging. However, maintaining high resolution in space together with low cost is still challenging for two-phase flow imaging because of the ill-conditioning of ERT inverse problem.

In this paper, a sparse reconstruction (SR) method based on the learned dictionary has been proposed for ERT, to accurately monitor the transient flow process of gas/liquid two-phase flow in a pipeline. The high-level representation of the conductivity distributions for typical flow regimes can be extracted based on denoising the deep extreme learning machine (DDELM) model, which is used as prior information for dictionary learning.

The results from simulation and dynamic experiments indicate that the proposed algorithm efficiently improves the quality of reconstructed images as compared to some typical algorithms such as Landweber and SR-discrete fourier transformation/discrete cosine transformation. Furthermore, the SR-DDELM has also used to estimate the important parameters of the chemical process, a case in point is the volume flow rate. Therefore, the SR-DDELM is considered an ideal candidate for online monitor the gas/liquid two-phase flow.

This paper fulfills a novel approach to effectively monitor the gas/liquid two-phase flow in pipelines. One deep learning model and one adaptive dictionary are trained via the same prior conductivity, respectively. The model is used to extract high-level representation. The dictionary is used to represent the features of the flow process. SR and extraction of high-level representation are performed iteratively. The new method can obviously improve the monitoring accuracy and save calculation time.