Preparation and Characterization of Bio-Based PLA/PEG/g-C3N4 Low-Temperature Composite Phase Change Energy Storage Materials.

As energy and environmental issues become more prominent, people must find sustainable, green development paths. Bio-based polymeric phase change energy storage materials provide solutions to cope with these problems. Therefore, in this paper, a fully degradable polyethylene glycol (PEG20000)/polylactic acid (PLA)/g-C3N4 composite phase change energy storage material (CPCM) was obtained by confinement. The CPCM was characterized by FTIR and SEM for compatibility, XRD and nanoindentation for mechanical properties and DSC, LFA, and TG for thermal properties. The results showed that the CPCM was physical co-mingling; when PLA: PEG: g-C3N4 was 6:3:1, the consistency was good. PEG destroys the crystallization of PLA and causes the hardness to decrease. When PLA: PEG: g-C3N4 was 6: 3: 1, it had a maximum hardness of 0.137 GPa. The CPCM had a high latent enthalpy, and endothermic and exothermic enthalpies of 106.1 kJ/kg and 80.05 kJ/kg for the PLA: PEG: g-C3N4 of 3: 6: 1. The CPCM showed an increased thermal conductivity compared to PLA, reaching 0.30 W/(m·K),0.32 W/(m·K) when PLA: PEG: g-C3N4 was 6: 3: 1 and when PLA: PEG: g-C3N4 was 3: 6: 1, respectively. Additionally, the CPCM was stable within 250 °C, indicating a wide appliable temperature range. The CPCM can be applied to solar thermal power generation, transportation, and building construction.

Learning Feature Recovery Transformer for Occluded Person Re-Identification.

One major issue that challenges person re-identification (Re-ID) is the ubiquitous occlusion over the captured persons. There are two main challenges for the occluded person Re-ID problem, i.e. , the interference of noise during feature matching and the loss of pedestrian information brought by the occlusions. In this paper, we propose a new approach called Feature Recovery Transformer (FRT) to address the two challenges simultaneously, which mainly consists of visibility graph matching and feature recovery transformer. To reduce the interference of the noise during feature matching, we mainly focus on visible regions that appear in both images and develop a visibility graph to calculate the similarity. In terms of the second challenge, based on the developed graph similarity, for each query image, we propose a recovery transformer that exploits the feature sets of its k -nearest neighbors in the gallery to recover the complete features. Extensive experiments across different person Re-ID datasets, including occluded, partial and holistic datasets, demonstrate the effectiveness of FRT. Specifically, FRT significantly outperforms state-of-the-art results by at least 6.2% Rank- 1 accuracy and 7.2% mAP scores on the challenging Occluded-Duke dataset.