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Brand storytelling is an effective marketing tool. However, when choosing whether to tell more or tell less, it remains unclear which of these two narrative styles is most effective. This study aims to examine whether blank-leaving(less) leads to favourable brand attitudes and compares its effects on consumers’ story immersion, to non-blank-leaving(more).

Two experiments were conducted to test the hypotheses. In Study 1, a single-factorial design was used with 252 participants assigned at random to one of two narrative conditions: blank-leaving or non-blank-leaving. Study 2 replicated Study 1 and investigated the moderating role of implicit mindsets.

The results show that a blank-leaving narrative style increases favourable brand attitudes. Consumers present deeper immersion in the brand story that uses blank-leaving, as compared to non-blank-leaving, leading to a more favourable brand attitude. Furthermore, this effect is stronger for individuals with growth mindsets.

Telling the brand story using a blank-leaving narrative style is more effective in catching consumers’ attention than non-blank-leaving. In particular, a blank-leaving narrative is a good approach for targeting consumers who have a growth mindset.

To the best of the authors’ knowledge, this research is the first to investigate and compare the effects of blank-leaving and non-blank-leaving narrative styles on brand attitudes in the context of storytelling marketing.

Sharing and disseminating debunking information are critical to correcting rumours and controlling disease when dealing with public health crises. This study investigates the factors that influence social media users' debunking information sharing behaviour from the perspective of persuasion. The authors examined the effects of argument adequacy, emotional polarity, and debunker's identity on debunking information sharing behaviour and investigated the moderating effects of rumour content and target.

The model was tested using 150 COVID-19-related rumours and 2,349 original debunking posts on Sina Weibo.

First, debunking information that contains adequate arguments is more likely to be reposted only when the uncertainty of the rumour content is high. Second, using neutral sentiment as a reference, debunking information containing negative sentiment is shared more often regardless of whether the government is the rumour target, and information containing positive sentiment is more likely to be shared only when the rumour target is the government. Finally, debunking information published by government-type accounts is reposted more often and is enhanced when the rumour target is the government.

The study provides a systematic framework for analysing the behaviour of sharing debunking information among social media users. Specifically, it expands the understanding of the factors that influence debunking information sharing behaviour by examining the effects of persuasive cues on debunking information sharing behaviour and the heterogeneity of these effects across various rumour contexts.

In the era of big data, there is doubt about the significance of causal inference as a paramount scientific task in the social sciences. Meanwhile, data-mining techniques rooted in big data and artificial intelligence (AI) have infiltrated numerous aspects of social science research. This study aims to expound the criticality of discerning causal relationships – beyond mere correlations – and scrutinizes the ramifications of big data and AI in the identification of causality.

This study discusses the challenges and opportunities for causality identification in the era of big data under the framework of potential outcomes model and structural causal model.

First, even in the age of big data, correlations that lack interpretability, robustness and feasibility cannot substitute causality. Second, the richness of the sample size does not help solve the problem of systematic bias in the process of causal inference. Furthermore, current AI research targets correlations rather than causality, thus creating difficulties in advancing from observations to counterfactuals.

This study provides insights into the impact of big data era on causal inference in the social sciences, with a view toward enhancing the pool of theoretical concepts available to researchers in relevant fields and accurately guiding the direction of scientific research in these fields.

This paper aims to investigate an efficient approach to model the electromagnetic behaviors and predict stray-field loss inside the magnetic steel plate under 3D harmonic magnetization conditions so as to effectively prevent the structural components from local overheating and insulation damage in electromagnetic devices.

An experimental setup is applied to measure all the magnetic properties of magnetic steel plate under harmonic excitations with different frequencies and phase angles. The measurement and numerical simulation are carried out based on the updated TEAM Problem 21 Model B+ (P210-B+), under the 3D harmonic magnetization conditions. An improved method to evaluate the stray-field loss is proposed, and harmonic flux distribution in the structural components is analyzed.

The influence of the harmonic order and phase angle on the stray-field loss in magnetic steel components are noteworthy. Based on the engineering-oriented benchmark models, the variations of stray-field losses and magnetic field distribution inside the magnetic components under harmonic magnetization conditions are presented and analyzed in detail.

The capacity of the multi-function harmonic source, used in this work, was not large enough, which limits the magnetization level. Up to now, further improvements to increase the harmonic source capacity and investigations of the electromagnetic behaviors of magnetic steel components under multi-harmonic and DC-AC hybrid excitations are in progress.

To accurately predict the stray-field loss in magnetic steel plate, the improved method based on the combination of magnetic measurement and numerical simulation is proposed. The effects of the frequency and phase angle on the stray-field loss are analyzed.

Conventional image super-resolution reconstruction by the conventional deep learning architectures suffers from the problems of hard training and gradient disappearing. In order to solve such problems, the purpose of this paper is to propose a novel image super-resolution algorithm based on improved generative adversarial networks (GANs) with Wasserstein distance and gradient penalty.

The proposed algorithm first introduces the conventional GANs architecture, the Wasserstein distance and the gradient penalty for the task of image super-resolution reconstruction (SRWGANs-GP). In addition, a novel perceptual loss function is designed for the SRWGANs-GP to meet the task of image super-resolution reconstruction. The content loss is extracted from the deep model’s feature maps, and such features are introduced to calculate mean square error (MSE) for the loss calculation of generators.

To validate the effectiveness and feasibility of the proposed algorithm, a lot of compared experiments are applied on three common data sets, i.e. Set5, Set14 and BSD100. Experimental results have shown that the proposed SRWGANs-GP architecture has a stable error gradient and iteratively convergence. Compared with the baseline deep models, the proposed GANs models have a significant improvement on performance and efficiency for image super-resolution reconstruction. The MSE calculated by the deep model’s feature maps gives more advantages for constructing contour and texture.

Compared with the state-of-the-art algorithms, the proposed algorithm obtains a better performance on image super-resolution and better reconstruction results on contour and texture.

The purpose of this paper is to propose a novel approach to integrate portable document format (PDF) interface into Java-based digital library application. It bridges the gap between conducting content operation and viewing on PDF document asynchronously.

In this paper, the authors first review some related research and discuss PDF and its drawbacks. Next, the authors propose the design steps and implementation of three modes of displaying PDF document: PDF display, image display and extensible markup language (XML) display. A comparison of these three modes has been carried out.

The authors find that the PDF display is able to completely present the original PDF document contents and thus obviously superior to the other two displays. In addition, the format specification of PDF-based e-book does not perform well; lack of standardization and complex structure is exposed to the publication.

The proposed approach makes viewing the PDF documents more convenient and effective, and can be used to retrieve and visualize the PDF documents and to support the personalized function customization of PDF in the digital library applications.

This paper proposes a novel approach to solve the problem between content operation and the view of PDF synchronously, providing users a new tool to retrieve and reuse the PDF documents. It contributes to improve the service specification and policy of viewing the PDF for digital library. Besides, the personalized interface and public index make further development and application more feasible.

This review paper reveals the literature on ultrasonic, chemical-assisted ultrasonic and rotary ultrasonic machining (USM) of glass material. The purpose of this review paper is to understand and describe the working principle, mechanism of material removal, experimental investigation, applications and influence of input parameters on machining characteristics. The literature reveals that the ultrasonic machines have been generally preferred for the glass and brittle work materials. Some other non-traditional machining processes may thermally damage the work surface. Through these USM, neither thermal effects nor residual stresses have been generated on the machined surface.

Various input parameters have the significant role in machine performance characteristics. For the optimization of output response, several input parameters have been critically investigated by the various researcher.

Some advance types of glasses such as polycarbonate bulletproof glass, acrylic heat-resistant glass and glass-clad polycarbonate bulletproof glass still need some further investigation because these materials have vast applications in automobile, aerospace and space industries.

Review paper will be beneficial for industrial application and the various young researcher. Paper reveals the detail literature review on traditional ultrasonic, chemical assisted ultrasonic and rotary USM of glass and glass composite materials.

To implement a risk-based regulatory approach, this paper aims to make an assessment on customers' money laundering risk and conducts some applications.

During the transition of a regulatory approach from “rule-based” to “risk-based”, this paper considers that the area of a customer, types of business and the industries to which the customer belongs are the main indicators to judge money laundering risk of a customer. Based on the statistical analysis of 221 typical money laundering cases, first-class index weights are given by using the entropy weight method and then by combining with the membership function, this paper determines a customer’s money laundering risk levels. On the basis of the entropy weight method, this paper uses the C5.0 algorithm to construct a decision tree model and then carries out application research on customer money laundering risk assessment to verify the effectiveness of the entropy weight method and the decision tree model.

This empirical research found the weights of three key money laundering indicators: customer areas, business types and corresponding industries.

Asserting that current money laundering risk assessments of customers are marginal at best, this paper contends from the perspective of practice, and applies the entropy weight method and the decision tree model for money laundering risk assessment of customers.

Given that global advertising often originates in the West, this study aimed to investigate whether the same racial cues in advertising had different effects on US and Chinese readers who belonged to the majority group in their own country but occupied varied positions in the global racial hierarchy.

The study included two experiments that used US and Chinese college students as participants to test the effect of model race on model likeability, ad attitude, and brand attitude.

The experiments confirmed that model race affected the effectiveness of advertising, though the nature and extent of its effects were mediated by factors such as country and product category. The effects of model race were much more pronounced for the Chinese data than for the US data, and for computer than for beer.

The findings illustrate the inadequacy of the center-periphery approach to global advertising planning, expose the limitations of the social identity theory in predicting racial attitudes in international settings, and outline the ethical challenge for global advertisers to develop effective multicultural advertising.

To date, most studies on racial cues in advertising have focused on the USA, and very few have explored the topic in the context of other markets. As an initial cross-country comparative investigation, this study suggests that multicultural advertising does not have the same effect in international markets.