Small and Medium-Sized City: The Main Battle Field of the New Urbanization Construction

Small and medium-sized cities have played important roles in China’s new urbanization strategies. Small and medium-sized cities can not only help large cities avert over-concentration of population, but also avoid excessively decentralizing rural industrialization in small towns. To develop small and medium-sized cities, characteristic industries and vocational education are required so that people can get employed in the cities and become residents easily. City infrastructure and public services are necessary for urban residents. Public-Private Partnership may solve the problems of fund shortage that the local government is facing during the construction

StereoVoxelNet: Real-Time Obstacle Detection Based on Occupancy Voxels from a Stereo Camera Using Deep Neural Networks

Obstacle detection is a safety-critical problem in robot navigation, where stereo matching is a popular vision-based approach. While deep neural networks have shown impressive results in computer vision, most of the previous obstacle detection works only leverage traditional stereo matching techniques to meet the computational constraints for real-time feedback. This paper proposes a computationally efficient method that leverages a deep neural network to detect occupancy from stereo images directly. Instead of learning the point cloud correspondence from the stereo data, our approach extracts the compact obstacle distribution based on volumetric representations. In addition, we prune the computation of safety irrelevant spaces in a coarse-to-fine manner based on octrees generated by the decoder. As a result, we achieve real-time performance on the onboard computer (NVIDIA Jetson TX2). Our approach detects obstacles accurately in the range of 32 meters and achieves better IoU (Intersection over Union) and CD (Chamfer Distance) scores with only 2% of the computation cost of the state-of-the-art stereo model. Furthermore, we validate our method's robustness and real-world feasibility through autonomous navigation experiments with a real robot. Hence, our work contributes toward closing the gap between the stereo-based system in robot perception and state-of-the-art stereo models in computer vision. To counter the scarcity of high-quality real-world indoor stereo datasets, we collect a 1.36 hours stereo dataset with a Jackal robot which is used to fine-tune our model. The dataset, the code, and more visualizations are available at https://lhy.xyz/stereovoxelnet

The operation modal analysis of the structure crack fault diagnosis based on pseudo-successive data

In order to monitor the crack propagation of the structure in the working state for a long time, an operation modal analysis method based on pseudo-successive data is proposed. The vibration response signals of the cantilever beam under white noise excitation are collected and the modal parameters are extracted by the time-frequency operation modal analysis method based on the complex Morlet wavelet. In comparison with the experimental modal analysis results of hammering method, it is revealed that the error of the time-frequency operation modal analysis method is less than 10 %. By setting cracks of different lengths on the cantilever beam, the vibration response signals are extracted, and the modal parameters are extracted by the operation modal analysis method separately. By comparing those modal parameters above, it is found that the natural frequencies of the second, the fourth and the sixth orders decrease with the increase of the crack depth, and the changes of natural frequencies show the monotonicity. So, it can be used as an index for quantitative identification of crack damage. The pseudo continuous data monitoring signals of crack propagation can be constructed by means of “first discrete, then continuous”. The modal parameters changes of the whole crack propagation can be observed in one time plane by means of the operation modal analysis method. Therefore, the effective monitoring and diagnosis of the structure can be completed in case of excessive data of long-time vibration monitoring signals

A Fully Discrete Discontinuous Galerkin Method for Nonlinear Fractional Fokker-Planck Equation

The fractional Fokker-Planck equation is often used to characterize anomalous diffusion. In this paper, a fully discrete approximation for the nonlinear spatial fractional Fokker-Planck equation is given, where the discontinuous Galerkin finite element approach is utilized in time domain and the Galerkin finite element approach is utilized in spatial domain. The priori error estimate is derived in detail. Numerical examples are presented which are inline with the theoretical convergence rate

Cortical Neural Stem Cell Lineage Progression Is Regulated by Extrinsic Signaling Molecule Sonic Hedgehog.

Neural stem cells (NSCs) in the prenatal neocortex progressively generate different subtypes of glutamatergic projection neurons. Following that, NSCs have a major switch in their progenitor properties and produce γ-aminobutyric acid (GABAergic) interneurons for the olfactory bulb (OB), cortical oligodendrocytes, and astrocytes. Herein, we provide evidence for the molecular mechanism that underlies this switch in the state of neocortical NSCs. We show that, at around E16.5, mouse neocortical NSCs start to generate GSX2-expressing (GSX2+) intermediate progenitor cells (IPCs). In vivo lineage-tracing study revealed that GSX2+ IPC population gives rise not only to OB interneurons but also to cortical oligodendrocytes and astrocytes, suggesting that they are a tri-potential population. We demonstrated that Sonic hedgehog signaling is both necessary and sufficient for the generation of GSX2+ IPCs by reducing GLI3R protein levels. Using single-cell RNA sequencing, we identify the transcriptional profile of GSX2+ IPCs and the process of the lineage switch of cortical NSCs

A Life-Cycle Energy and Inventory Analysis of Adiabatic Quantum-Flux-Parametron Circuits

The production process of superconductive integrated circuits is complex and consumes significant amounts of resources and energy. Therefore, it is crucial to evaluate the environmental impact of this emerging technology. An attractive option for the next generation of superconductive technology is Adiabatic Quantum-Flux-Parametron (AQFP) devices. This study is the first to present a comprehensive process-based life-cycle assessment (LCA) and inventory analysis of AQFP integrated circuits. To generate relevant outcomes, we conduct a comparative LCA that included the bulk CMOS technology. The inventory analysis considered the manufacturing, assembly, and use phases of the circuits. To ensure a fair assessment, we choose the 32-bit AQFP RISC-V single-core processor as the reference functional unit and compare its performance with that of a CMOS counterpart. Our findings reveal that the AQFP processor consumes several orders of magnitude less energy during the use phase than its CMOS counterpart. Consequently, the total life cycle energy (which encompasses manufacturing and assembly energies) of AQFP integrated circuits improves at least by two orders of magnitude