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In this paper, we propose an innovative approach for transforming 2D human pose estimation into 3D models using Single Input–Single Output (SISO) Ultra-Wideband (UWB) radar technology. This method addresses the significant challenge of reconstructing 3D human poses from 1D radar signals, a task traditionally hindered by low spatial resolution and complex inverse problems. The difficulty is further exacerbated by the ambiguity in 3D pose reconstruction, as multiple 3D poses may correspond to similar 2D projections. Our solution, termed the Radar PoseLifter network, leverages the micro-Doppler signatures inherent in 1D radar echoes to effectively convert 2D pose information into 3D structures. The network is specifically designed to handle the long-range dependencies present in sequences of 2D poses. It employs a fully convolutional architecture, enhanced with a dilated temporal convolutions network, for efficient data processing. We rigorously evaluated the Radar PoseLifter network using the HPSUR dataset, which includes a diverse range of human movements. This dataset comprises data from five individuals with varying physical characteristics, performing a variety of actions. Our experimental results demonstrate the method's robustness and accuracy in estimating complex human poses, highlighting its effectiveness. This research contributes significantly to the advancement of human motion capture using radar technology. It presents a viable solution for applications where precision and reliability in motion capture are paramount. The study not only enhances the understanding of 3D pose estimation from radar data but also opens new avenues for practical applications in various fields. [ABSTRACT FROM AUTHOR]

This study presents a multi-objective optimization model for the urban electric transit network problem with the aim of simultaneously designing the layout of bus routes, the frequency and the location and size of charging stations by making a tradeoff between two inconsistent objectives from the perspectives of passengers and operators. A Pareto artificial fish swarm algorithm (PAFSA) embedded with the genetic algorithm (GA) is developed to solve the proposed model. The PAFSA is designed to iteratively search for the proper network configuration satisfying two conflicting objectives. During which, the demand assignment with real-time transit information is performed to update the frequency of each newly designed route. The GA embedded into the PAFSA iteratively decides the locations of charging stations and the number of chargers to be installed in each charging station. A case study of the transit network in an urban region of a city in China is implemented, revealing that the proposed approach is able to rationally design a relatively large-scaled transit network with searching for the best fits between two inconsistent objectives. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the adaptation of different lithium disilicate glass-ceramic crown and whether different fabrication processes affect the adaptation of crowns., Methods: Thirty epoxy dies crowns were divided into 3 groups. They were cemented to domestic lithium disilicate glass-ceramic crowns in Group A, to IPS e.max CAD crowns in Group B and to IPS e.max Press crowns in Group C respectively. All crowns were cut by cutting machine. A confocal laser scanning microscope was used to measure the gap between crown tissue surface and die., Results: There were significant differences in the three groups of measurement points(P<0.05). Within the groups, specimens in group A showed in the lowest marginal fit([46.8±9.1] μm). Those in group B showed the lowest shoulder suitability([59.3±7.9] μm), axial plane([50.5±3.6] μm) and occlusal surface([87.6±11.6 μm]. Those in group C demonstrated the lowest axial plane angle[(84.4±10.1) μm]. In addition to the axial plane angle, the CAD/CAM system exhibited good accuracy of fit., Conclusions: The three groups of lithium disilicate glass-ceramic crowns appeared to have clinically acceptable fit.