Intolerance of uncertainty and anxiety among college students during the re-emergence of COVID-19: Mediation effects of cognitive emotion regulation and moderation effects of family function.

OBJECTIVE: In June 2021, the COVID-19 spread again in the community, and residents had to face the impact of the outbreak again after 276 days, none of the local cases in Guangdong Province, China. The purpose of this study was to investigate the mechanisms underlying the relationship between intolerance of uncertainty (IU) and anxiety in college students in non-epidemic area during the periods of re-emergence of COVID-19. METHODS: A survey was conducted among 86,767 college students in Guangdong Province, China from 10 to 18 June 2021, information on the Intolerance of Uncertainty Scale (IUS), General Anxiety Disorder-7 (GAD-7), Cognitive Emotion Regulation Questionnaire (CERQ) and Family APGAR Index were collected. Five moderation and mediation models were analyzed using latent moderated structural equations. RESULTS: The results showed that IU was positively related to anxiety (r = 0.42, p < 0.000). After controlling for age and gender, latent moderated structural equations indicated that catastrophizing mediated the relationship between IU and anxiety, and family function acted as a moderator in this relationship. Further analyses indicated that IU directly affected anxiety and had indirect effects on anxiety by catastrophizing. This relationship was weaker among college students who reported lower family function. CONCLUSION: This study provides practical implications for designing intervention strategies to reduce anxiety in college students when the epidemic re-emerges.

All-printed soft human-machine interface for robotic physicochemical sensing.

Ultrasensitive multimodal physicochemical sensing for autonomous robotic decision-making has numerous applications in agriculture, security, environmental protection, and public health. Previously reported robotic sensing technologies have primarily focused on monitoring physical parameters such as pressure and temperature. Integrating chemical sensors for autonomous dry-phase analyte detection on a robotic platform is rather extremely challenging and substantially underdeveloped. Here, we introduce an artificial intelligence-powered multimodal robotic sensing system (M-Bot) with an all-printed mass-producible soft electronic skin-based human-machine interface. A scalable inkjet printing technology with custom-developed nanomaterial inks was used to manufacture flexible physicochemical sensor arrays for electrophysiology recording, tactile perception, and robotic sensing of a wide range of hazardous materials including nitroaromatic explosives, pesticides, nerve agents, and infectious pathogens such as SARS-CoV-2. The M-Bot decodes the surface electromyography signals collected from the human body through machine learning algorithms for remote robotic control and can perform in situ threat compound detection in extreme or contaminated environments with user-interactive tactile and threat alarm feedback. The printed electronic skin-based robotic sensing technology can be further generalized and applied to other remote sensing platforms. Such diversity was validated on an intelligent multimodal robotic boat platform that can efficiently track the source of trace amounts of hazardous compounds through autonomous and intelligent decision-making algorithms. This fully printed human-machine interactive multimodal sensing technology could play a crucial role in designing future intelligent robotic systems and can be easily reconfigured toward numerous practical wearable and robotic applications.