Spillover of distrust from domestic brands to foreign brands during an industry-wide scandal is an important but overlooked issue in the international marketing domain. Drawing on assimilation/contrast and attribution theories, the authors develop a general model of trust erosion in a multibrand product-harm crisis involving three brand categories: focal (domestic) brands, contrasting (imported) brands, and hybrid (manufactured domestically but with foreign brand names) brands. Assimilative associations in an epidemic-like crisis erode trust throughout the entire product category, whereas contrastive associations protect or even benefit a competing brand category from the brand damage. A survey of 2,156 consumers in nine metropolitan centers in China measured trust in brands of dairy products after the 2008 milk contamination crisis. This study reveals the roles of category distinctness, focal versus contrasting categories, category hybridity, brand prototypicality, perceived crisis severity, and perceived category typicality in moderating the assimilation/ contrast effects in such a crisis. These findings have important managerial implications: international marketers should be wary of the risks in having their brand associated with products manufactured domestically in a market such as China.

No abstract

No abstract

Statistical model is a traditional safety diagnostic model for dam seepage. It can hardly display the nonlinear relationship between dam seepage and the load sets and has the disadvantage of poor extension prediction. In this paper, the theories of Back Propagation Neural Network (BPNN) combined with Genetic Algorithm (GA) are applied to the seepage prediction model. Taking a typical dam in China as an example, the prediction results of BPNN-GA model and statistical model are compared with the monitoring values. The results show that the improved dam seepage model enhances the ability of nonlinear mapping and generalization and makes the seepage prediction more accurate and reasonable in the near future. According to the established criterion, the safety state of the dam in flood season is evaluated.

Recent works propose neural network-(NN-) inspired analog-to-digital converters (NNADCs) and demonstrate their great potentials in many emerging applications. These NNADCs often rely on resistive random-access memory (RRAM) devices to realize the NN operations and require high-precision RRAM cells (6∼12-bit) to achieve a moderate quantization resolution (4∼8-bit). Such optimistic assumption of RRAM resolution, however, is not supported by fabrication data of RRAM arrays in large-scale production process. In this paper, we propose an NN-inspired super-resolution ADC based on low-precision RRAM devices by taking the advantage of a co-design methodology that combines a pipelined hardware architecture with a custom NN training framework. Results obtained from SPICE simulations demonstrate that our method leads to robust design of a 14-bit super-resolution ADC using 3bit RRAM devices with improved power and speed performance and competitive figure-of-merits (FoMs). In addition to the linear uniform quantization, the proposed ADC can also support configurable high-resolution nonlinear quantization with high conversion speed and low conversion energy, enabling future intelligent analog-to-information interfaces for nearsensor analytics and processing.