A cross-cultural study on the persuasive effectiveness of fear appeals messages in advertising : an empirical investigation on Canadian and Chinese subjects

This exploratory study investigated the effects of cultural differences on persuasion of fear appeals communication. Based on Rogers' Protection Motivation model, the framework of the study was developed by incorporating type of fear as an independent variable and culture as a moderating variable. An experiment was conducted using 12 anti-smoking ads with three levels of fear appeals (high, moderate, and low) and two types of fear appeals (physical and social) on 173 Canadian and 180 Chinese subjects. The findings indicated that the Canadian subjects experienced attitude change toward smoking after viewing the anti-smoking ads. For the physical fear ads, the Canadian subjects had more negative attitude toward smoking and higher behavior intention to quit. No significant difference was found for the social fear ads between the two cultural groups. As for the level of fear, findings indicated that increasing fear arousal resulted in an ad attitude change and an increase in behavior intention in the future for both Canadian and Chinese subjects but not in the attitude toward smoking. Further exploration of the proposed framework found that self-efficacy was an important cognitive variable to change attitude for the two cultural groups. Coping response efficacy was effective in changing attitudes for the Canadian subjects, while severity had more influence for the Chinese subjects. Fear-persuasion models for the Canadians and Chinese were proposed

Rapid Determination of Saponins in the Honey-Fried Processing of Rhizoma Cimicifugae by Near Infrared Diffuse Reflectance Spectroscopy.

ObjectiveA model of Near Infrared Diffuse Reflectance Spectroscopy (NIR-DRS) was established for the first time to determine the content of Shengmaxinside I in the honey-fried processing of Rhizoma Cimicifugae.MethodsShengmaxinside I content was determined by high-performance liquid chromatography (HPLC), and the data of the honey-fried processing of Rhizoma Cimicifugae samples from different batches of different origins by NIR-DRS were collected by TQ Analyst 8.0. Partial Least Squares (PLS) analysis was used to establish a near-infrared quantitative model.ResultsThe determination coefficient R² was 0.9878. The Cross-Validation Root Mean Square Error (RMSECV) was 0.0193%, validating the model with a validation set. The Root Mean Square Error of Prediction (RMSEP) was 0.1064%. The ratio of the standard deviation for the validation samples to the standard error of prediction (RPD) was 5.5130.ConclusionThis method is convenient and efficient, and the experimentally established model has good prediction ability, and can be used for the rapid determination of Shengmaxinside I content in the honey-fried processing of Rhizoma Cimicifugae

Iterative Geometry-Aware Cross Guidance Network for Stereo Image Inpainting

Currently, single image inpainting has achieved promising results based on deep convolutional neural networks. However, inpainting on stereo images with missing regions has not been explored thoroughly, which is also a significant but different problem. One crucial requirement for stereo image inpainting is stereo consistency. To achieve it, we propose an Iterative Geometry-Aware Cross Guidance Network (IGGNet). The IGGNet contains two key ingredients, i.e., a Geometry-Aware Attention (GAA) module and an Iterative Cross Guidance (ICG) strategy. The GAA module relies on the epipolar geometry cues and learns the geometry-aware guidance from one view to another, which is beneficial to make the corresponding regions in two views consistent. However, learning guidance from co-existing missing regions is challenging. To address this issue, the ICG strategy is proposed, which can alternately narrow down the missing regions of the two views in an iterative manner. Experimental results demonstrate that our proposed network outperforms the latest stereo image inpainting model and state-of-the-art single image inpainting models.Comment: Accepted by IJCAI 202