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Meaning in Life (MIL) is a protective factor that buffers the impact of COVID-19 epidemic on emotions. Our study aimed to explore whether photographic intervention based on MIL could increase Positive Affect (PA) and mitigate Negative Affect (NA), and whether Future Self-Continuity (FSC) functioned as a mediator between them. In this study, 90 college students were randomly divided into an intervention group or a control group. Participants in the intervention group were asked to take a photo and describe it every 2 days lasting 2 weeks. All the participants in the two groups were measured by the Meaning in Life Questionnaire, Positive and Negative Affect Scale, and Future Self-Continuity Scale before and after the intervention. The results showed that: (1) Before the intervention, there were no significant differences in baseline levels of MIL, PA and NA, and FSC between intervention and control groups. (2) In the intervention group, compared to pre-test, the scores of MIL, PA, and FSC of post-test increased significantly, and the score of NA of post-test decreased significantly. (3) After the intervention, the scores of MIL, PA, and FSC in the intervention group were significantly higher than those in the control group; NA score in the intervention group was significantly lower than that in the control group. (4) In terms of the difference score (post-test minus pre-test), FSC was a mediator between MIL and PA. Our study demonstrated that photographic intervention could effectively improve college students’ MIL, PA, and FSC while mitigating NA. Moreover, MIL could significantly enhance PA by the mediating role of FSC.

Sparse representation (SR) has shown great potential in multishot person reidentification. However, conventional SR methods usually encode each probe image individually, which ignores the prior knowledge shared by similar probe images with the same identity. Considering that probe images with the same identity are similar and can be complementary to each other, we propose a joint group sparse representation (JGSR) method to simultaneously encode a set of probe person images with the same identity. Additionally, we consider the fact that prior knowledge is also contained in the set of images with the same identity in the gallery. In JGSR, probe images with the same identity are coded on the same dictionary atoms and shared similar coefficients. Furthermore, the person reidentification performance is also improved by incorporating JGSR with k-means clustering and kernel local Fisher discriminant analysis into a unified framework. Extensive experiments on three publicly available iLIDS-VID, PRID 2011, and SAIVT-SoftBio multishot benchmark datasets were conducted and demonstrated the superior performance of the JGSR method in comparison with current state-of-the-art methods.

Quantum capacitance is a fundamental quantity that can directly reveal many interactions among electrons and is expected to play a critical role in nanoelectronics. One of many tantalizing recent physical revelations about quantum capacitance is that it can posses a negative value, hence allowing for the possibility of enhancing the overall capacitance in some particular material systems beyond the scaling predicted by classical electrostatics. Using detailed quantum mechanical simulations, we find an intriguing result that mechanical strains can tune both signs and values of quantum capacitance. We use a small coaxially-gated carbon nanotube as a paradigmatical capacitor system and show that, for the range of mechanical strain considered, quantum capacitance can be adjusted from very large positive to very large negative values (in the order of plus/minus hundreds of at-to farads), compared with the corresponding classical geometric value (0.31035 aF). We elucidate the mechanisms underpinning the switching of the sign of quantum capacitance due to strain. This finding opens novel avenues in designing quantum capacitance for applications in nanosensors, energy storage, and nanoelectronics.