Research on the Visualization of Railway Signal Operation and Maintenance Based on BIM + GIS.

To adapt to the "fine" and "extensive" management characteristics of railway signal equipment operation and maintenance, achieving real-time and interactive monitoring of signal equipment operation status, and developing an integrated approach to equipment operation and maintenance, this paper takes a comprehensive management perspective. To create a lightweight BIM model, the Garland folding algorithm is utilized to simplify the IFC file format. Building on this approach, the data are divided based on building component division standards to obtain separate files containing geometric information and semantic attributes. The geometric information files are converted to a 3D Tiles format, combining BIM semantic attributes with semantic attribute files through an intermediate format. Dynamic data management is achieved by setting the octree space index structure in combination with a view-frustum culling algorithm. Then, the 3D Tiles target file is imported into the Cesium platform, and Node.js is used to achieve three-dimensional visualization of railway signal operation and maintenance. The proposed method is verified using an inbound signal as an example to assess its feasibility. The results demonstrate the potential of the proposed method to achieve stable integration between BIM equipment full lifecycle maintenance and GIS geographical space display. Railway signal equipment is endowed with comprehensive one-click information query functions for equipment positioning and spatial analysis, improving the efficiency and scientific decision-making level of equipment operation and maintenance.

Molecular Mapping of Putative Genomic Regions Controlling Fruit and Seed Morphology of Watermelon.

The genetic regulatory basis of qualitative and quantitative phenotypes of watermelon is being investigated in different types of molecular and genetic breeding studies around the world. In this study, biparental F2 mapping populations were developed over two experimental years, and the collected datasets of fruit and seed traits exhibited highly significant correlations. Whole-genome resequencing of comparative parental lines was performed and detected single nucleotide polymorphism (SNP) loci were converted into cleaved amplified polymorphic sequence (CAPS) markers. The screened polymorphic markers were genotyped in segregating populations and two genetic linkage maps were constructed, which covered a total of 2834.28 and 2721.45 centimorgan (cM) genetic lengths, respectively. A total of 22 quantitative trait loci (QTLs) for seven phenotypic traits were mapped; among them, five stable and major-effect QTLs (PC-8-1, SL-9-1, SWi-9-1, SSi-9-1, and SW-6-1) and four minor-effect QTLs (PC-2-1 and PC-2-2; PT-2-1 and PT-2-2; SL-6-1 and SSi-6-2; and SWi-6-1 and SWi-6-2) were observed with 3.77-38.98% PVE. The adjacent QTL markers showed a good fit marker-trait association, and a significant allele-specific contribution was also noticed for genetic inheritance of traits. Further, a total of four candidate genes (Cla97C09G179150, Cla97C09G179350, Cla97C09G180040, and Cla97C09G180100) were spotted in the stable colocalized QTLs of seed size linked traits (SL-9-1 and SWi-9-1) that showed non-synonymous type mutations. The gene expression trends indicated that the seed morphology had been formed in the early developmental stage and showed the genetic regulation of seed shape formation. Hence, we think that our identified QTLs and genes would provide powerful genetic insights for marker-assisted breeding aimed at improving the quality traits of watermelon.

Deep learning enabled reflective coded aperture snapshot spectral imaging.

Coded aperture snapshot spectral imaging (CASSI) can acquire rich spatial and spectral information at ultra-high speed, which shows extensive application prospects. CASSI innovatively employed the idea of compressive sensing to capture the spatial-spectral data cube using a monochromatic detector and used reconstruction algorithms to recover the desired spatial-spectral information. Based on the optical design, CASSI currently has two different implementations: single-disperser (SD) CASSI and dual-disperser (DD) CASSI. However, SD-CASSI has poor spatial resolution naturally while DD-CASSI increases size and cost because of the extra prism. In this work, we propose a deep learning-enabled reflective coded aperture snapshot spectral imaging (R-CASSI) system, which uses a mask and a beam splitter to receive the reflected light by utilizing the reflection of the mask. The optical path design of R-CASSI makes the optical system compact, using only one prism as two dispersers. Furthermore, an encoder-decoder structure with 3D convolution kernels is built for the reconstruction, dubbed U-net-3D. The designed U-net-3D network achieves both spatial and spectral consistency, leading to state-of-the-art reconstruction results. The real data is released and can serve as a benchmark dataset to test new reconstruction algorithms.