The unprecedented crisis of COVID‐19 posed severe negative consequences for consumers, marketers, and society at large. By investigating the effect of individuals' distance from the COVID‐19 ...epicenter (i.e., the geographical area in which COVID‐19 pandemic is currently most severe) on consumers' risk perception and subsequent behaviors, this research provides novel empirical findings that can offer practical insights for marketers. While intuitively, people expect individuals closer to the COVID‐19 epicenter to generate a greater risk perception of the pandemic, empirical evidence from four studies provides consistent results for the opposite effect. We find that a closer (vs. farther) distance to the epicenter associates with lower (vs. higher) perceived risk of the pandemic, leading to less (vs. more) irrational consumption behaviors. We refer to this phenomenon as the “distance proximity effect,” which holds for both physical and psychological distances. We further demonstrated that this effect is mediated by consumers' perception of uncertainty and moderated by individuals' risk aversion tendency. The current research contributes to the literature of consumers' risk perception and irrational consumption by highlighting a novel factor of distance proximity. It also offers some timely insights into managing and intervening COVID‐19 related issues inside and outside an epicenter.
Abstract
Size cues are increasingly common in brand names (e.g., Xiaomi and Mini Cooper), but scant research has investigated whether and how brand name size cues influence consumers’ perceptions. ...This research shows that a brand name size cue can evoke gender associations, which subsequently affect consumers’ perceived warmth and competence of the target brand. A series of seven studies provide converging evidence that brands with a size cue of smallness in the name are perceived to be warmer but less competent, while those with a size cue of bigness are perceived to be less warm but more competent. A combination of measurement-of-mediation and moderation-of-process approaches provide support for the role of gender associations underlying the effect of brand name size cues on consumers’ brand perceptions. This research also shows that brand name size cues can have diverging effects on the perceived warmth of the brand versus of the product. Finally, this research rules out alternative accounts based on perceived market power and firm size.
Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed ...features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster-Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions.
The detection of product defects is essential in quality control in manufacturing. This study surveys stateoftheart deep-learning methods in defect detection. First, we classify the defects of ...products, such as electronic components, pipes, welded parts, and textile materials, into categories. Second, recent mainstream techniques and deep-learning methods for defects are reviewed with their characteristics, strengths, and shortcomings described. Third, we summarize and analyze the application of ultrasonic testing, filtering, deep learning, machine vision, and other technologies used for defect detection, by focusing on three aspects, namely method and experimental results. To further understand the difficulties in the field of defect detection, we investigate the functions and characteristics of existing equipment used for defect detection. The core ideas and codes of studies related to high precision, high positioning, rapid detection, small object, complex background, occluded object detection and object association, are summarized. Lastly, we outline the current achievements and limitations of the existing methods, along with the current research challenges, to assist the research community on defect detection in setting a further agenda for future studies.
Image classification has always been a hot research direction in the world, and the emergence of deep learning has promoted the development of this field. Convolutional neural networks (CNNs) have ...gradually become the mainstream algorithm for image classification since 2012, and the CNN architecture applied to other visual recognition tasks (such as object detection, object localization, and semantic segmentation) is generally derived from the network architecture in image classification. In the wake of these successes, CNN-based methods have emerged in remote sensing image scene classification and achieved advanced classification accuracy. In this review, which focuses on the application of CNNs to image classification tasks, we cover their development, from their predecessors up to recent state-of-the-art (SOAT) network architectures. Along the way, we analyze (1) the basic structure of artificial neural networks (ANNs) and the basic network layers of CNNs, (2) the classic predecessor network models, (3) the recent SOAT network algorithms, (4) comprehensive comparison of various image classification methods mentioned in this article. Finally, we have also summarized the main analysis and discussion in this article, as well as introduce some of the current trends.
Sustainable development is an important strategy promoted by the United Nations World Tourism Organization (UNWTO) and governments of many destinations. However, the seeming contradiction about the ...environment vis‐à‐vis business interests may result in noncompliant responses to the environmental policies and regulations and hinder implementation of strategies for improved sustainability. Using resource‐based theory, we empirically test the influence of environmental competitiveness on tourism growth. Using a biennial dataset from 130 destinations between 2009 and 2017, the current work applies the fixed effect (FE) and threshold models to identify a strong link for developed destinations but a weak link for less developed regions. Several indicators of environmental competitiveness significantly affect tourism demand for destinations with high GDP per capita; the effects are not significant, however, for low GDP per capita destinations. These findings demonstrate that environmental factors are important influencers of destination competitiveness and are positively linked to economic performance of developed destinations. The connection between environmental competitiveness and tourism growth in less developed destinations seems weak and insignificant. A possible reason could be that such destinations lack the capital guarantee to translate environmental competitiveness into tourism demand. This article extends the resource‐based theory from the corporate level into the destination level and theoretically contributes to the sustainable tourism literature. Our findings provide the UNWTO and destination governments with empirical evidence to support their sustainable strategies and with suggestions concerning how to strengthen the environment‐performance link for less developed destinations.
A major challenge in new product development is how to increase consumers' adoption of new offerings. Using the socio‐ecological perspective, we reveal a largely ignored but important social factor ...that alters consumers' new product adoption. Specifically, we propose that residential mobility—the frequency with which individuals change their residence—acts as an antecedent of new product adoption. Six studies—using different operationalizations of residential mobility—provide convergent evidence that residential mobility (vs. stability) augments new product adoption in both laboratory settings and real business settings. This effect is driven by openness to new experiences and moderated by the voluntariness of moving. Our findings add to the literature on new product adoption and residential mobility. Moreover, marketers should take residential mobility into full consideration when designing marketing strategies for new products and presenting contextual cues to activate consumers' residential mobility mindset to enhance their acceptance of new products.
Metal–organic frameworks (MOFs) are a class of coordination polymers, consisting of metal ions or clusters linked together by chemically mutable organic groups. In contrast to zeolites and porous ...carbons, MOFs are constructed from a building block strategy that enables molecular level control of pore size/shape and functionality. An area of growing interest in MOF chemistry is the synthesis of MOF-based composite materials. Recent studies have shown that MOFs can be combined with biomacromolecules to generate novel biocomposites. In such materials, the MOF acts as a porous matrix that can encapsulate enzymes, oligonucleotides, or even more complex structures that are capable of replication/reproduction ( i.e. , viruses, bacteria, and eukaryotic cells). The synthetic approach for the preparation of these materials has been termed “biomimetic mineralization”, as it mimics natural biomineralization processes that afford protective shells around living systems. In this Perspective, we focus on the preparation of MOF biocomposites that are composed of complex biological moieties such as viruses and cells and canvass the potential applications of this encapsulation strategy to cell biology and biotechnology.
This paper focuses on bearing fault diagnosis with limited training data. A major challenge in fault diagnosis is the infeasibility of obtaining sufficient training samples for every fault type under ...all working conditions. Recently deep learning based fault diagnosis methods have achieved promising results. However, most of these methods require large amount of training data. In this study, we propose a deep neural network based few-shot learning approach for rolling bearing fault diagnosis with limited data. Our model is based on the siamese neural network, which learns by exploiting sample pairs of the same or different categories. Experimental results over the standard Case Western Reserve University (CWRU) bearing fault diagnosis benchmark dataset showed that our few-shot learning approach is more effective in fault diagnosis with limited data availability. When tested over different noise environments with minimal amount of training data, the performance of our few-shot learning model surpasses the one of the baseline with reasonable noise level. When evaluated over test sets with new fault types or new working conditions, few-shot models work better than the baseline trained with all fault types. All our models and datasets in this study are open sourced and can be downloaded from https://mekhub.cn/as/fault_diagnosis_with_few-shot_learning/ .
A major challenge in materials design is how to efficiently search the vast chemical design space to find the materials with desired properties. One effective strategy is to develop sampling ...algorithms that can exploit both explicit chemical knowledge and implicit composition rules embodied in the large materials database. Here, we propose a generative machine learning model (MatGAN) based on a generative adversarial network (GAN) for efficient generation of new hypothetical inorganic materials. Trained with materials from the ICSD database, our GAN model can generate hypothetical materials not existing in the training dataset, reaching a novelty of 92.53% when generating 2 million samples. The percentage of chemically valid (charge-neutral and electronegativity-balanced) samples out of all generated ones reaches 84.5% when generated by our GAN trained with such samples screened from ICSD, even though no such chemical rules are explicitly enforced in our GAN model, indicating its capability to learn implicit chemical composition rules to form compounds. Our algorithm is expected to be used to greatly expand the range of the design space for inverse design and large-scale computational screening of inorganic materials.
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