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Sensory encounters are an essential component of the customer experience. However, most of the research conducted thus far has used self‐reported measuring techniques to address sensory experiences, which have limitations in holistically capturing the sensory encounters. Text analysis coupled with online reviews provides valuable customer experience information and at the same time, the data contains many suggestions regarding consumers' sensory experiences. Consequently, online review data can be used to gain insights into how consumers' sensory experiences—visual, olfactory, auditory, gustatory, and tactile—are represented in the dynamics of online reviews. Using a data set of 13,575 online reviews from Trustpilot and expanding upon the vocabulary of senses, we investigate how customers' sensory experiences during grocery shopping relate to online review ratings. We discover that the interaction is more complex than one might think because the review star rating rises in the presence of visual and gustatory cues, while the effects of auditory, olfactory, and tactile cues are not demonstrated. We recommend marketers to monitor eWOM related to their own brand, as it can provide indications of what kind of sensory experiences customers have encountered. [ABSTRACT FROM AUTHOR]

Lineaments play a critical role in the study of lunar geological evolution. However, lineaments still need to be extracted manually which constrains lunar research. To fill this gap, a Markov chain-based methodology is introduced for automatically extracting vector-based non-bifurcated lineaments from images. The extracted lineaments are formed by successive nodes and line segments, where nodes denote pixel positions and line segments represented connecting lines between nodes. A modified U-net with residual shortcut connections and dilated convolutions is proposed based on the elongated shapes of lineaments to evaluate the probability of nodes belonging to lineaments. Four connection shape features are proposed to describe the connection shapes of nodes and a Gaussian Mixture Model is used to evaluate the probability of line segments belonging to the lineaments based on the proposed features. In the final stage, the lineament probabilities of both nodes and line segments to extract lineaments are considered in the Markov chain. Our method has experimented on a dataset containing 220 samples and 10-fold cross-validation was used to evaluate the performance. Both qualitative and quantitative results indicated that our method can effectively extract non-bifurcated lunar lineaments with arbitrary bending. The method sheds useful light on automatic lineament extraction. [ABSTRACT FROM AUTHOR]

This article deals with a repairable k-out-of-n: G system with retrial feature and two maintenance policies. All components in the system have two failure modes, mode a failure and mode b failure. When a component fails, the failed component will be repaired instantly if the repair equipment is idle. Otherwise, it enters into a retrial orbit and tries again after some time until it is repaired. The single repair equipment may be subject to breakdown in a busy period, and in order to improve system reliability, the idle repair equipment will be performed preventive maintenance. The Markov process theory and Laplace transform method are adopted to compute the reliability indexes of the system. Also, four optimization design models are constructed to determine the optimal repair rates of the components with two failure modes, and the minimization problems of the total expected cost function per unit time and the cost-effectiveness ratio are solved. To verify the correctness of the model, numerical analysis is executed to demonstrate the impact of each parameter on system reliability indexes, the optimal solutions of different optimization design models are obtained by using differential evolution algorithm and genetic algorithm. [ABSTRACT FROM AUTHOR]