Search results

Following the assumption that studies of information inequality need to be based on precise discrimination between society’s information rich and poor and against the context that a mechanism for such discrimination is still lacking, the purpose of this paper is to explore the possibility of establishing a holistic informational measurement.

It does so by developing a measurement based on the conceptualization of the individual as an information agent and his/her information world as his/her characterization. The development procedure consists of four steps: operationalization of the theoretical constructs and the initial drafting of the questionnaire instrument; revisions of the questionnaire based on pilot tests with small groups of people; weighing of the questionnaire items for the purpose of calculating index-type variable scores; formal test of validity and reliability.

The resulting measurement consists of eight variables corresponding to eight theoretical constructs of an individual’s information world, each being measured by a group of questionnaire-based items which, in turn, generate an index-type score as the variable’s value. Validity and reliability tests show that the measurement is, on the whole, able to distinguish the information poor from the information rich and to measure individuals consistently.

The study demonstrates that it is possible to distinguish the information rich and poor by informational measurement in the same way as to distinguish economic groups by income, ethnic groups by race and intelligence groups by IQ; and that such a measurement has arguably multifaceted value for information inequality research.

Drawing on attribution theory, the current paper aims to examine the effects of review content structures on online review helpfulness, focusing on three pertinent variables: review sidedness, information factuality, and emotional intensity at the beginning of a review. Moreover, the moderating roles of reviewer reputation and review sentiment are investigated.

The review sentiment of 144,982 online hotel reviews was computed at the sentence level by considering the presence of adverbs and negative terms. Then, the authors quantified the impact of variables that were pertinent to review content structures on online review helpfulness in terms of review sidedness, information factuality and emotional intensity at the beginning of a review. Zero-inflated negative binomial regression was employed to test the model.

The results reveal that review sidedness negatively affects online review helpfulness, and reviewer reputation moderates this effect. Information factuality positively affects online review helpfulness, and positive sentiment moderates this impact. A review that begins with a highly emotional statement is more likely to be perceived as less helpful.

Using attribution theory as a theoretical lens, this study contributes to the online customer review literature by investigating the impact of review content structures on online review helpfulness and by demonstrating the important moderating effects of reviewer reputation and review sentiment. The findings can help practitioners develop effective review appraisal mechanisms and guide consumers in producing helpful reviews.

As intelligent technology advances, practical applications often involve data with multiple labels. Therefore, multi-label feature selection methods have attracted much attention to extract valuable information. However, current methods tend to lack interpretability when evaluating the relationship between different types of variables without considering the potential causal relationship.

To address the above problems, we propose an ensemble causal feature selection method based on mutual information and group fusion strategy (CMIFS) for multi-label data. First, the causal relationship between labels and features is analyzed by local causal structure learning, respectively, to obtain a causal feature set. Second, we eliminate false positive features from the obtained feature set using mutual information to improve the feature subset reliability. Eventually, we employ a group fusion strategy to fuse the obtained feature subsets from multiple data sub-space to enhance the stability of the results.

Experimental comparisons are performed on six datasets to validate that our proposal can enhance the interpretation and robustness of the model compared with other methods in different metrics. Furthermore, the statistical analyses further validate the effectiveness of our approach.

The present study makes a noteworthy contribution to proposing a causal feature selection approach based on mutual information to obtain an approximate optimal feature subset for multi-label data. Additionally, our proposal adopts the group fusion strategy to guarantee the robustness of the obtained feature subset.

With the development of intelligent technology, deep learning has made significant progress and has been widely used in various fields. Deep learning is data-driven, and its training process requires a large amount of data to improve model performance. However, labeled data is expensive and not readily available.

To address the above problem, researchers have integrated semi-supervised and deep learning, using a limited number of labeled data and many unlabeled data to train models. In this paper, Generative Adversarial Networks (GANs) are analyzed as an entry point. Firstly, we discuss the current research on GANs in image super-resolution applications, including supervised, unsupervised, and semi-supervised learning approaches. Secondly, based on semi-supervised learning, different optimization methods are introduced as an example of image classification. Eventually, experimental comparisons and analyses of existing semi-supervised optimization methods based on GANs will be performed.

Following the analysis of the selected studies, we summarize the problems that existed during the research process and propose future research directions.

This paper reviews and analyzes research on generative adversarial networks for image super-resolution and classification from various learning approaches. The comparative analysis of experimental results on current semi-supervised GAN optimizations is performed to provide a reference for further research.

The authors examined how the identification mechanism of the innovation performance of knowledge employees is affected by empowering leadership by influencing the organizational identification and the moderating effect of leaders on the role expectation of knowledge employees as an essential innovation subject.

The authors employed a mixed-method research approach. The authors collected data from 378 knowledge employees and managers in 20 companies in China's Yangtze River Delta cities. The authors analyzed data using multiple regression analysis forecasting methods.

The authors found that there was an inverted U-shaped relationship between empowering leadership and the innovation performance of knowledge employees; organizational identity played a partial mediating role between empowering leadership and the innovation performance of knowledge employees; role expectation of leaders on the innovation behavior of employees regulated the relationship between the organizational identity and innovation performance of knowledge employees.

This study extends the literature on empowering leadership and innovation performance. This study empirically examines the mediating effect of organizational identity between empowering leadership and innovation performance. In addition, this study empirically examines how empowered leaders' expected innovation level moderates the association between organizational identity and innovation performance.

Numerical and experimental results for different oncoming base-flow conditions indicate that nonuniform trailing edge blowing (NTEB) can expand the performance range of compressors and reduce the thrust on the rotor, while the efficiency of the compressor can be improved by more than 2 per cent.

Relevant aerodynamic parameters, such as total pressure, ratio of efficiency and axial thrust, are calculated and analyzed under conditions with and without NTEB. Measurements are performed downstream of two adjacent stator blades, at seven equidistantly spaced reference locations. The experimental measurement of the interstage flow field used a dynamic four-hole probe with phase lock technique.

An axial low-speed single-stage compressor was established with flow field measurement system and nonuniform blowing system. NTEB was studied by means of numerical simulations and experiments, and it is found that the efficiency of the tested compressor can be improved by more than 2 per cent.

Unlike most of the previous research studies which mainly focused on the rotor/stator interaction and trailing edge uniform blowing, the research results summarized in the current paper on the stator/rotor interaction used inlet guide vanes for steady and unsteady calculations. An active control of the interstage flow field in a low-speed compressor was used to widen the working range and improve the performance of the compressor.

Automatic optical inspection (AOI) systems have been widely used in many fields to evaluate the qualities of products at the end of the production line. The purpose of this paper is to propose a local-to-global ensemble learning method for the AOI system based on to inspect integrated circuit (IC) solder joints defects.

In the proposed method, the locally statistically modeling stage and the globally ensemble learning stage are involved to tackle the inspection problem. At the former stage, the improved visual background extraction–based algorithm is used for locally statistically modeling to grasp tiny appearance differences between the IC solder joints to achieve potential defect images for the subsequent stage. At the latter stage, mean unqualified probability is introduced based on a novel ensemble learning, in which an adaptive weighted strategy is proposed for revealing different contributions of the base classifier to the inspection performance.

Experimental results demonstrate that the proposed method achieves better inspection performance with an acceptable inspection time compared with some state-of-the-art methods.

The approach is a promising method for IC solder joint inspection, which can simultaneously grasp the local characteristics of IC solder joints and reveal inherently global relationships between IC solder joints.

Global supply chains experienced unprecedented changes in 2020 and the relationship between domestic and global markets needed adjustments considering the long-term impacts of the changes that are unfolding around these markets. China has become the first country to announce a formal strategy – “Dual Circulation” Strategy (DCS) – to guide its self-reliant economic development in the post-COVID era. However, what exactly is the DCS and what drove China to publicize this strategy is not yet clear. This study aims to answer these questions.

Based on an extensive review of literature and media reports, a background has been constructed that justifies the DCS as a long-overdue historic necessity.

A novel definition of “Dual Circulation” is introduced. A novel construct to visualize the domestic circulation in light of international and domestic markets and international circulation has been presented. The study argues that maintaining optimum levels of consumption and saving rates is crucial to the DCS’s success.

The study pioneers the first scientific definition of the “Dual Circulation” that will pave way for future debate on the topic. Also, it is the first time an academic study on the DCS has been executed.

As the grey systems theory has been widely used in the field of sustainable development (SD) research, in the following, a short literature overview will be put forward, starting from the usage of these theories in the economic development, social inclusion and environmental protection contributions to the evolving process of SD during 2011–2021. The purpose of this paper is to identify some key studies from all the SD areas in which the grey systems can be used in order to open and to bring the researchers to new domains in which they can reveal their interest and in which they can successfully use the methods offered by the grey systems theory.

Using the search engine offered by the Google Scholar and the Web of Science (WoS), a literature review has been performed for the grey systems applications on SD research on both grey relational analysis (GRA) and grey forecasting. In addition, some grey evaluation theories – clustering evaluation models and grey target decision models – have also been presented.

Many grey models are widely used in the field of SD. Compared with other methods such as grey prediction, grey evaluation and decision-making model, GRA technology is the most used method, and the research using this method is more than three times that of all other methods.

The present paper identifies some of the most representative examples in which the grey system theory (GST) has been used, but, in the same time, there are a lot of studies that have not been mentioned here due to the lack of space.

The present paper focuses on the SD applications in which GST has been successfully used, bringing to the reader a general overview on this field and, in the same time, enables new research perspectives.

The purpose of this paper is to identify most favorable (or quasi-preferred) industry characteristics of remanufacturing industry and most favorable (or quasi-preferred) industry factors which have an effect on these characteristics so as to improve these factors.

Grey system theory has prominent advantage of using few data and uncertainty information to analyze many factors. Therefore, it is more suited for system analysis than traditional statistical analysis methods like regression analysis, variance analysis and principal component analysis, which require massive data, certain probability distribution in the data and few variant factors. So in this paper, grey incidence analysis method, which is an important part of grey system theory, is used to identify industry characteristics and key industry factor of remanufacturing industry in China and then put forward appropriate industrial policies and countermeasures to improve these industry factors.

According to the results of this study, it reveals that there are no most favorable industry characteristics and no most favorable industry factors in remanufacturing industry of China. “Annual sale of remanufacturing industry” is identified as quasi-preferred industry characteristic, and “total number of employees with master degree or above in remanufacturing enterprise” is identified as the quasi-preferred industry factor. “Total building area of remanufacturing enterprise” is referred as the most unfavorable industry factors.

Judging from the findings of this study, four practical implications are summarized as follows: “annual sale of remanufacturing industry” should be given great importance because it is a quasi-preferred industry characteristic. “Total number of employees with master degree or above in remanufacturing enterprise” and “total number of research institution and university participated in remanufacturing” should be further strengthened by establishing an industry-university-research institute collaboration network, due to the fact that they are the top two quasi-preferred industry factors. “Total investment of remanufacturing industry” and “total annual R&D expenditures” have not played their due role in improving remanufacturing industry, so they should be moderately controlled so as to reduce waste of investment. “Total building area of remanufacturing enterprise” must be strictly controlled because of its little impact on remanufacturing industry.

In this research, grey incidence analysis is applied to identify key industry factors of remanufacturing industry for the first time. It helps in finding industry factors which are in urgent need of improvement and assists in making appropriate industrial policies and countermeasures to improve them by studying relationships between industry characteristic and industry factors.