Timing Recovery for Point-to-Multi-Point Coherent Passive Optical Networks

We propose a timing recovery for point-to-multi-point coherent passive optical networks. The results show that the proposed algorithm has low complexity and better robustness against the residual chromatic dispersion.Comment: The artical have been submitted to SPPCom conferenc

Design and experiments with a SLAM system for low-density canopy environments in greenhouses based on an improved Cartographer framework

To address the problem that the low-density canopy of greenhouse crops affects the robustness and accuracy of simultaneous localization and mapping (SLAM) algorithms, a greenhouse map construction method for agricultural robots based on multiline LiDAR was investigated. Based on the Cartographer framework, this paper proposes a map construction and localization method based on spatial downsampling. Taking suspended tomato plants planted in greenhouses as the research object, an adaptive filtering point cloud projection (AF-PCP) SLAM algorithm was designed. Using a wheel odometer, 16-line LiDAR point cloud data based on adaptive vertical projections were linearly interpolated to construct a map and perform high-precision pose estimation in a greenhouse with a low-density canopy environment. Experiments were carried out in canopy environments with leaf area densities (LADs) of 2.945–5.301 m2/m3. The results showed that the AF-PCP SLAM algorithm increased the average mapping area of the crop rows by 155.7% compared with that of the Cartographer algorithm. The mean error and coefficient of variation of the crop row length were 0.019 m and 0.217%, respectively, which were 77.9% and 87.5% lower than those of the Cartographer algorithm. The average maximum void length was 0.124 m, which was 72.8% lower than that of the Cartographer algorithm. The localization experiments were carried out at speeds of 0.2 m/s, 0.4 m/s, and 0.6 m/s. The average relative localization errors at these speeds were respectively 0.026 m, 0.029 m, and 0.046 m, and the standard deviation was less than 0.06 m. Compared with that of the track deduction algorithm, the average localization error was reduced by 79.9% with the proposed algorithm. The results show that our proposed framework can map and localize robots with precision even in low-density canopy environments in greenhouses, demonstrating the satisfactory capability of the proposed approach and highlighting its promising applications in the autonomous navigation of agricultural robots

The LDBC Financial Benchmark

The Linked Data Benchmark Council's Financial Benchmark (LDBC FinBench) is a new effort that defines a graph database benchmark targeting financial scenarios such as anti-fraud and risk control. The benchmark has one workload, the Transaction Workload, currently. It captures OLTP scenario with complex, simple read queries and write queries that continuously insert or delete data in the graph. Compared to the LDBC SNB, the LDBC FinBench differs in application scenarios, data patterns, and query patterns. This document contains a detailed explanation of the data used in the LDBC FinBench, the definition of transaction workload, a detailed description for all queries, and instructions on how to use the benchmark suite.Comment: For the source code of this specification, see the ldbc_finbench_docs repository on Githu

Case report: Indocyanine green fluorescence-guided imaging in laparoscope, a more sensitive detection technique of lateral lymph nodes metastases from rectal neuroendocrine tumors

BackgroundThe diagnosis and surgical strategy of lateral lymph node metastases of rectal neuroendocrine tumors are still controversial. At present, the major diagnostic means rely on imaging examinations, but will be affected by the size of lymph nodes leading to false negativity. We provide a new technique to determine lateral lymph node metastases during surgery.Clinical caseA 68-year-old man developed abdominal pain, bloating and fever for a month. Colonoscopy revealed the mass is 2.4 cm x 2.0 cm in size, with a wide stratum, poor mobility, and a rough but intact surface mucosa. Therefore, rectal neuroendocrine tumors (R-NET) were diagnosed. Multiple imaging methods, such as CT, octreotide imaging and endoscopic ultrasonography, have not found lateral lymph node metastases from rectal neuroendocrine tumors. But indocyanine green (ICG)-enhanced near-infrared fluorescence-guided imaging during surgery found left lateral lymph nodes metastases, which was proved by postoperative pathological examination.ConclusionsWe believe that applying ICG-enhanced near-infrared fluorescence-guided imaging in laparoscope can improve the detection of positive LLNs in those R-NET patients who did not reveal LNM on imaging examinations

Dynamic Succession of Microbial Communities in Soybean Paste Made with Broomcorn Millet as an Additive and Its Correlation with Flavor and Nutritional Properties during the Brewing Process

To obtain a full understanding the quality and microbial characteristics of soybean paste made from a mixture of soybean and broomcorn millet flour, its physicochemical properties (amino nitrogen and nitrite), and total phenols (TP), γ-aminobutyric acid (GABA), free amino acids (FAAs), volatile compounds, and microbial community composition were investigated. The results showed that the amino nitrogen content increased to 0.71%, and the nitrite content decreased to within the standard range (1.37 mg/kg). The contents of TP, key FAAs and volatile compounds increased significantly during the fermentation process. The core microbial communities included Enterobacter, Pseudomonas, Stenotrophomonas, Aspergillus, and Alternaria. The results of correlation analysis confirmed that bacteria (Bacillus, Knoellia, and Blastococcus) and fungi (Epicoccum and Saccharomyces) played a significant role in the bioactivity changes and flavor generation in soybean paste. This study will be of great significance for understanding the quality and flavor of novel soybean paste made with cereal flour as an additive