In this paper, we report a flame deposition method to prepare carbon nanoparticles (CNPs) from coconut oil. The CNPs were further modified with a piranha solution to obtain surfacecarboxylated carbon nanoparticles (c-CNPs). When used as an anode for sodium-ion batteries, the CNPs and c-CNPs respectively delivered discharge capacities of 277 and 278 mAhg-1 in the second cycle at a current density of 100 mAg-1. At the 20 th cycle, the capacities of CNP and c-CNPs were 217 and 206 mAhg-1 respectively. The results suggest that modification of the CNPs with the piranha solution improved neither the charge storage capacity nor the stability against cycling in a sodium-ion battery. When the CNP and c-CNP were used an anode in a lithium-ion battery, 2 nd-cycle discharge capacities of 741 and 742 mAhg-1 respectively at a current density of 100 mAg-1 were obtained. After 20 cycles the capacities of CNP and c-CNP became 464 and 577 mAhg-1 respectively, showing the cycling stability of the CNPs was improved after modification. The excellent cycling performance, high capacity and good rate capability make the present material as highly promising anodes for both sodium-ion and lithium-ion batteries.

The technology used for road extraction from remote sensing images plays an important role in urban planning, traffic management, navigation, and other geographic applications. Although deep learning methods have greatly enhanced the development of road extractions in recent years, this technology is still in its infancy. Because the characteristics of road targets are complex, the accuracy of road extractions is still limited. In addition, the ambiguous prediction of semantic segmentation methods also makes the road extraction result blurry. In this study, we improved the performance of the road extraction network by integrating atrous spatial pyramid pooling (ASPP) with an Encoder-Decoder network. The proposed approach takes advantage of ASPP’s ability to extract multiscale features and the Encoder-Decoder network’s ability to extract detailed features. Therefore, it can achieve accurate and detailed road extraction results. For the first time, we utilized the structural similarity (SSIM) as a loss function for road extraction. Therefore, the ambiguous predictions in the extraction results can be removed, and the image quality of the extracted roads can be improved. The experimental results using the Massachusetts Road dataset show that our method achieves an F1-score of 83.5% and an SSIM of 0.893. Compared with the normal U-net, our method improves the F1-score by 2.6% and the SSIM by 0.18. Therefore, it is demonstrated that the proposed approach can extract roads from remote sensing images more effectively and clearly than the other compared methods.

Predicting the collective motion of a group of pedestrians (a crowd) under the vehicle influence is essential for the development of autonomous vehicles to deal with mixed urban scenarios where interpersonal interaction and vehiclecrowd interaction (VCI) are significant. This usually requires a model that can describe individual pedestrian motion under the influence of nearby pedestrians and the vehicle. This study proposed two pedestrian trajectory datasets, CITR dataset and DUT dataset, so that the pedestrian motion models can be further calibrated and verified, especially when vehicle influence on pedestrians plays an important role. CITR dataset consists of experimentally designed fundamental VCI scenarios (front, back, and lateral VCIs) and provides unique ID for each pedestrian, which is suitable for exploring a specific aspect of VCI. DUT dataset gives two ordinary and natural VCI scenarios in crowded university campus, which can be used for more general purpose VCI exploration. The trajectories of pedestrians, as well as vehicles, were extracted by processing video frames that come from a down-facing camera mounted on a hovering drone as the recording equipment. The final trajectories of pedestrians and vehicles were refined by Kalman filters with linear point-mass model and nonlinear bicycle model, respectively, in which xy-velocity of pedestrians and longitudinal speed and orientation of vehicles were estimated. The statistics of the velocity magnitude distribution demonstrated the validity of the proposed dataset. In total, there are approximate 340 pedestrian trajectories in CITR dataset and 1793 pedestrian trajectories in DUT dataset. The dataset is available at GitHub 1 .

Social distancing (SD) is an effective measure to prevent the spread of the infectious Coronavirus Disease 2019 (COVID-19). However, a lack of spatial awareness may cause unintentional violations of this new measure. Against this backdrop, we propose an active surveillance system to slow the spread of COVID-19 by warning individuals in a region-of-interest. Our contribution is twofold. First, we introduce a vision-based real-time system that can detect SD violations and send non-intrusive audio-visual cues using state-of-the-art deep-learning models. Second, we define a novel critical social density value and show that the chance of SD violation occurrence can be held near zero if the pedestrian density is kept under this value. The proposed system is also ethically fair: it does not record data nor target individuals, and no human supervisor is present during the operation. The proposed system was evaluated across real-world datasets.

Pyromellitic dianhydride-based polyimides [C 16 H 6 O 4 N 2 ] n with different crystallinity and morphology were synthesised by simple one-step hydrothermal method. The electrochemical performance and sodium storage mechanism of the polyimide-based organic electrode as anode for sodium-ion batteries were investigated.