Intolerance of uncertainty fuels preservice teachers' smartphone dependence through rumination and anxiety during the COVID-19 pandemic: A cross-sectional study.

Objectives: We aimed to explore the relationship among intolerance of uncertainty (IU), rumination, anxiety, and smartphone dependence (SPD) in preservice teachers during the COVID-19 pandemic. Methods: Two cross-sectional studies were conducted with Chinese preservice teachers, using questionnaires on IU, rumination, anxiety, and SPD. Data were analyzed using AMOS 24.0 and SPSS 25.0, and the mediating mechanism was tested using the macro program Model 6. Study 1 recruited participants who were forcibly sequestered in a university due to an anti-epidemic policy during the COVID-19 crisis. Study 2 was surveyed online from different universities to replicate and enhance the reliability of Study 1 finding. Results: Study 1 (N = 553, Mage = 20.8 ± 2.3, 30.0% female) and Study 2 (N = 1610, Mage = 21.1 ± 2.1, 51.4% female) both found that IU affected SPD through the independent mediators of rumination and anxiety, as well as the chain mediation of rumination→ anxiety. In Study 1, the indirect effect of IU on SPD was significant through rumination (ß = 0.16, 95% CI [0.03, 0.06]), anxiety (ß = 0.11, 95% CI [0.03, 0.06]), and the chain mediation (ß = 0.02, 95% CI [0.01, 0.04]); in Study 2, the indirect effect of IU on SPD was significant through rumination (ß = 0.08, 95% CI [0.05, 0.11]), anxiety (ß = 0.10, 95% CI [0.08, 0.13]), and the chain mediation (ß = 0.02, 95% CI [0.02, 0.03]). Conclusion: Two cross-sectional studies found that preservice teachers' SPD is indirectly connected to IU, mediated by rumination and anxiety, and weakly mediated by the chain mediation of rumination and anxiety. Our findings may help educators understand the impact of anti-epidemic policies on preservice teachers and possible inclusive later interventions.

Integrated Multifunctional Electronic Skins with Low-Coupling for Complicated and Accurate Human-Robot Collaboration.

Multifunctional capability and low coupling electronic skin (e-skin) is of great significance in advanced robot systems interacting with the human body or the external environment directly. Herein, a multifunctional e-skin system via vertical integrated different sensing materials and structures is presented. The multifunctional e-skin has capacity sensing the proximity, pressure, temperature, and relative humidity simultaneously, with scope of 100-0 mm, 0-30 N, 20-120 °C and 20-70%, respectively. The sensitivity of the four kinds of sensors can be achieved to 0.72 mm-1 , 16.34 N-1 , 0.0032 °C-1 , and 15.2 pF/%RH, respectively. The cross-coupling errors are less than 1.96%, 1.08%, 2.65%, and 1.64%, respectively, after temperature compensation. To be state-of-the-art, a commercial robot is accurately controlled via the multifunctional e-skin system in the complicated environment. The following and safety controlling exhibit both accuracy and high dynamic features. To improve the sensing performance to the insulating objects, machine learning is employed to classify the conductivity during the object approaching, leading to set the threshold in dynamic. The accuracy for isolating the insulator increases from 18% to 88%. Looking forward, the multifunctional e-skin system has broader applications in human-machine collaboration and industrial safety production technology.