Integrating two theoretical frameworks, the product level theory and the experience economy model, this research analyzed and compared robotic technology applications and customer experiences in selected case robot restaurants in the United States and China. Guided by the product level theory, we first analyzed in which product/service levels were robots applied in each case restaurant in Study 1. Then in study 2, guided by the experience economy model, we further explored customers’ dining experiences and compared if customers’ experience differs due to variations in product/service levels that robot applied. The study first contributes to the product level theory by extending its application to the context of robotic restaurants. It also contributes to the experience economy literature, and in particularly, whether applications of robotic technologies at different product levels matter in customers’ dining experience. The study included case restaurants both from the United States and China, presenting findings with cultural implications. Given the challenges presented by COVID-19 and the industry is exploring alternative ways for service delivery and food production, such a study is particularly meaningful.

Maintaining high quality of water resources is essential to the physical health of mankind and sustainable development of society. Accordingly, it is necessary to detect anomalies in water quality variations, which may be caused by pollution. However, prompt anomaly detection is a challenging task, either demanding a lot of human effort or yielding low accuracy, due to the nonlinear and non-stationary characteristics of water quality data. In this paper, we present an efficient real-time anomaly detection scheme which boosts detection accuracy while mitigating human effort. The scheme takes a prediction–detection–verification approach in which a deep learning prediction model is built from historical data and is used to predict future values. The predicted values are compared with the actual measurements, and the residuals are inspected by a detection model. An alarm is sent to field engineers for verification for each anomaly detected by the detection model, and the verification result is analyzed by the scheme to maintain high prediction and detection accuracy. Experiments on multiple water quality datasets show that the proposed scheme achieves significantly higher recall rates and lower false alarm rates in almost all test scenarios, compared with schemes that do not utilize verification.