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Object recognition is among the basic survival skills of human beings and other animals. To date, artificial intelligence (AI) assisted high-performance object recognition is primarily visual-based, empowered by the rapid development of sensing and computational capabilities. Here, we report a tactile-olfactory sensing array, which was inspired by the natural sense-fusion system of star-nose mole, and can permit real-time acquisition of the local topography, stiffness, and odor of a variety of objects without visual input. The tactile-olfactory information is processed by a bioinspired olfactory-tactile associated machine-learning algorithm, essentially mimicking the biological fusion procedures in the neural system of the star-nose mole. Aiming to achieve human identification during rescue missions in challenging environments such as dark or buried scenarios, our tactile-olfactory intelligent sensing system could classify 11 typical objects with an accuracy of 96.9% in a simulated rescue scenario at a fire department test site. The tactile-olfactory bionic sensing system required no visual input and showed superior tolerance to environmental interference, highlighting its great potential for robust object recognition in difficult environments where other methods fall short.

：Background: The frequency of take-out food consumption has increased rapidly among Chinese college students, which has contributed to high obesity prevalence. However, the relationships between take-out food consumption, body mass index (BMI), and other individual factors influencing eating behavior among college students are still unclear. This study explored the association of take-out food consumption with gender, BMI, physical activity, preference for high-fat and high-sugar (HFHS) food, major category, and degree level among Chinese college students. Methods: Cross-sectional data were collected from 1220 college students in Beijing, China, regarding information about take-out food consumption, physical activity, and preference for HFHS food using a self-reported questionnaire. The logistic linear regression model was used to analyze the association between take-out food consumption and personal and lifestyle characteristics. Results: Out of 1220 college students, 11.6% of college students were overweight or obese. Among the personal and lifestyle characteristics, high frequency of take-out food consumption was significantly associated with a non-medical major, high preference for HFHS food, degree level, and higher BMI, but not physical activity. Conclusion: Among Chinese college students, consumption of take-out food may be affected by major category, preference for HFHS food, degree level, and BMI. This could provide guidance on restrictions of high take-out food consumption, which contributes to high obesity prevalence and high risk for metabolic diseases.

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