Background-Based Delineation of Internal Tumor Volumes on Static Positron Emission Tomography in a Phantom Study

Objective(s): Considering the fact that the standardized uptake value (SUV) of a normal lung tissue is expressed as x±SD, x+3×SD could be considered as the threshold value to outline the internal tumor volume (ITV) of a lung neoplasm. Methods: Three hollow models were filled with 55.0 kBq/mL fluorine18- fluorodeoxyglucose (18F-FDG) to represent tumors. The models were fixed to a barrel filled with 5.9 kBq/mL 18F-FDG to characterize normal lung tissues as a phantom. The PET/CT images of the phantom were acquired at rest. Then, the barrel was moved periodically to simulate breathing while acquiring PET/CT data. Volume recovery coefficient (VRC) was applied to evaluate the accuracy of ITVs. For statistical analysis, paired t-test and analysis of variance were applied. Results: The VRCs ranged from 0.74 to 0.98 and significantly varied among gross tumor volumes for delineating ITV (P0.05), whereas VRC decreased with increasing distance in three-dimensional PET scans (

Research on Defect Detection in Automated Fiber Placement Processes Based on a Multi-Scale Detector

Various surface defects in automated fiber placement (AFP) processes affect the forming quality of the components. In addition, defect detection usually requires manual observation with the naked eye, which leads to low production efficiency. Therefore, automatic solutions for defect recognition have high economic potential. In this paper, we propose a multi-scale AFP defect detection algorithm, named the spatial pyramid feature fusion YOLOv5 with channel attention (SPFFY-CA). The spatial pyramid feature fusion YOLOv5 (SPFFY) adopts spatial pyramid dilated convolutions (SPDCs) to fuse the feature maps extracted in different receptive fields, thus integrating multi-scale defect information. For the feature maps obtained from a concatenate function, channel attention (CA) can improve the representation ability of the network and generate more effective features. In addition, the sparsity training and pruning (STP) method is utilized to achieve network slimming, thus ensuring the efficiency and accuracy of defect detection. The experimental results of the PASCAL VOC and our AFP defect datasets demonstrate the effectiveness of our scheme, which achieves superior performance

Research on the Measurement Method of Feeding Rate in Silage Harvester Based on Components Power Data

For existing problems, such as the complex interactions between a crop and a machine, the measuring difficulty and the limited measurement precision of the feeding quantity within the corn silage harvester, a method of feeding rate measurement based on key conditions data, working data cleaning, and multiple variate regression is proposed. Non-destructive rotation speed, rotation torque, and power consumption sensors are designed for the key mechanical components. The data conditions, such as rotating speed, rotating torque, power consumption, hydraulic pressure, and hydraulic flow for the key operation of parts including cutting, feeding, shredding, and throwing are monitored and collected in real-time during field harvesting. The working data are screened and preprocessed, and the Mann-Kendall boundary extraction algorithm is applied, as is multiple component time lag correction analysis, and the Grubbs exception detection method. Based on a Pearson correlation analysis results, one-factor and multiple-factor regression models are respectively developed to achieve an accurate measurement of the corn feeding rate. The field validation tests show that the working data boundary extraction results among the load-stabilizing components such as shredding roller and throwing blower are highly reliable, with a correct rate of 100%. The power monitoring data of the shredding roller and throwing blowers are significantly correlated with the crop feeding rate, with a max correlation coefficient of 0.97. The determination coefficient of the single-factor feeding rate model based on the shredding roller reaches 0.94, and the maximum absolute error of the multi-factor feeding rate model is 0.58 kg/s. The maximum relative error is ±5.84%, providing technical and data support for the automatic measuring and intelligent tuning of the feeding quantity in a silage harvester

A chromosome-level genome assembly of yellow stem borer (Scirpophaga incertulas)

Abstract The yellow stem borer Scirpophaga incertulas is the dominant pest of rice in tropical Asia. However, the lack of genomic resources makes it difficult to understand their invasiveness and ecological adaptation. A high-quality chromosome-level genome of S. incertulas, a monophagous rice pest, was assembled by combining Illumina short reads, PacBio HiFi long sequencing, and Hi-C scaffolding technology. The final genome size was 695.65 Mb, with a scaffold N50 of 28.02 Mb, and 93.50% of the assembled sequences were anchored to 22 chromosomes. BUSCO analysis demonstrated that this genome assembly had a high level of completeness, with 97.65% gene coverage. A total of 14,850 protein-coding genes and 366.98 Mb of transposable elements were identified. In addition, comparative genomic analyses indicated that chemosensory processes and detoxification capacity may play critical roles in the specialized host preference of S. incertulas. In summary, the chromosome-level genome assembly of S. incertulas provides a valuable genetic resource for understanding the biological characteristics of its invasiveness and developing an efficient management strategy

Research on the Measurement Method of Feeding Rate in Silage Harvester Based on Components Power Data

For existing problems, such as the complex interactions between a crop and a machine, the measuring difficulty and the limited measurement precision of the feeding quantity within the corn silage harvester, a method of feeding rate measurement based on key conditions data, working data cleaning, and multiple variate regression is proposed. Non-destructive rotation speed, rotation torque, and power consumption sensors are designed for the key mechanical components. The data conditions, such as rotating speed, rotating torque, power consumption, hydraulic pressure, and hydraulic flow for the key operation of parts including cutting, feeding, shredding, and throwing are monitored and collected in real-time during field harvesting. The working data are screened and preprocessed, and the Mann-Kendall boundary extraction algorithm is applied, as is multiple component time lag correction analysis, and the Grubbs exception detection method. Based on a Pearson correlation analysis results, one-factor and multiple-factor regression models are respectively developed to achieve an accurate measurement of the corn feeding rate. The field validation tests show that the working data boundary extraction results among the load-stabilizing components such as shredding roller and throwing blower are highly reliable, with a correct rate of 100%. The power monitoring data of the shredding roller and throwing blowers are significantly correlated with the crop feeding rate, with a max correlation coefficient of 0.97. The determination coefficient of the single-factor feeding rate model based on the shredding roller reaches 0.94, and the maximum absolute error of the multi-factor feeding rate model is 0.58 kg/s. The maximum relative error is ±5.84%, providing technical and data support for the automatic measuring and intelligent tuning of the feeding quantity in a silage harvester