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高效化焊接技术是当前焊接领域的热点研究。阐述的海上风电五桩导管架焊接工艺试验,母材为Q345E-Z25钢,用GMAW+SAW和FCAW两种焊接方法进行了详细试验,并根据试验结果进行了对比分析。结果表明:GMAW+SAW和FCAW两种焊接方法的力学性能均满足工况使用要求,同时"GMAW+SAW"比传统的"SAW+清根+SAW"方法具有降低成本、提高生产效率和降低工人劳动强度等优点,场外的FCAW用1.4mm焊丝比常用1.2mm焊丝熔化速度快且提高生产效率。实现了车间流水线上的高效节能焊接和场外总装手工气保焊全位置灵活性焊接。

Copyright of China Tropical Medicine is the property of China Tropical Medicine Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Forest Engineering is the property of Forest Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ecology & Rural Environment is the property of Journal of Ecology & Rural Environment Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Educational Studies (1673-1298) is the property of Journal of Educational Studies Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Wildland Fire Science is the property of Journal of Wildland Fire Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The ripeness of Camellia oleifera fruits is closely related to their oil yield and tea oil quality. Manual one-time harvesting is the primary harvesting for Camellia oleifera at present. However, the uneven ripeness levels among fruits harvested in the same batch can significantly reduce the overall quality of the fruits. Furthermore, manual harvesting cannot fully meet the large-scale Camellia oleifera industry, such as low efficiency and high costs. Therefore, it is very necessary to implement intelligent harvesting for Camellia oleifera fruits. The maturity of Camellia oleifera fruit can be detected to determine the best maturity using deep learning. The purpose of this study is to establish the ideal ripeness of Camellia oleifera fruits in the natural environment. The harvesting period was also estimated to implement the intelligent harvesting, in order to improve the oil yield and quality of fruits. A dataset was constructed for the ripeness detection. The photographs of Camellia oleifera fruits were captured at different ripening stages in natural environments using a smartphone. The phenotypic characteristics of Camellia oleifera fruits were also determined to follow the industry standards. The ripeness was categorized into three stages: immature, mature, and over-mature. Data augmentation techniques were applied on the dataset, such as brightness adjustment, salt-and-pepper noise addition, and simulating artificial occlusion. After that, the dataset was divided into the training, validation, and testing sets with a ratio of 7:1:2. An improved YOLOv7 model was constructed to deal with the occlusion in the natural environment. A cross-attention module was added in the YOLOv7 feature extraction network. The vertical and horizontal information was calculated for each pixel in Camellia oleifera images using two attention-weighted procedures. The key features were identified to determine the ripeness of Camellia oleifera fruits, thus effectively avoiding the interference from the complex backgrounds, such as the branches and leaves. Additionally, the traditional non-maximum suppression was replaced with the distance- and intersection-based NMS. The normalized distance between the center points of two candidate boxes was selected to calculate the intersection ratio, particularly for the missed detections due to mutual occlusion of Camellia oleifera fruits, in order to detect the overlapping fruits. The YOLOv7 model was trained on 3098 images from the training set, then evaluated using 442 images from the validation set, and finally tested on 885 images from the test set. The better performance was achieved in the precision rate of 93.52%, a recall rate of 90.25%, an F1 score of 91.86%, an average precision of 94.60%, an average detection time of 0.77 s, and a model weight of 82.6 MB in the test set. The ablation experiments demonstrated that the improved model was used to effectively detect the ripeness of Camellia oleifera fruit. Compared with the original YOLOv7 model, the mean average accuracies were improved by 1.10 and 1.81 percentage points, respectively. The overall mean average accuracy was improved by 2.91 percentage points. However, the detection time and model size only increased by 0.015 s and 11.3 MB, respectively. Compared with the Faster R-CNN, EfficientDet, YOLOv3, and YOLOv5l models, the improved YOLOv7 model increased the average accuracy by 7.51, 5.89, 4.21, and 4.21 percentage points, respectively. Additionally, the detection time was reduced by 1.06, 1.12, 0.10, and 0.03 s, respectively. The maturity grade of Camellia oleifera fruits was accurately discriminated, compared with the previous. In summary, the improved YOLOv7 model was achieved in the higher accuracy with only a slight sacrifice in the detection time and model size. This finding can provide a theoretical basis to estimate the optimal harvesting period of Camellia oleifera fruits and intelligent picking under natural conditions. [ABSTRACT FROM AUTHOR]

To investigate the relationship between the polymorphism of the nlrc3 gene in grass carp （Ctenopharyngodon idella） and resistance to Ichthyophthirius multifiliis, susceptible and resistant groups were selected from grass carp populations infected with I. multifiliis. The single nucleotide polymorphism （SNP） sites of the nlrc3 gene in the two groups were analyzed and compared. High-resolution melting （HRM） and Sanger sequencing techniques were used to validate the genotypes of the SNP sites, and the obtained SNP sites were analyzed for their association with the disease resistance traits in grass carp. The results showed that four SNPs sites were identified from the nlrc3 gene in grass carp, and three SNP sites （SNP1, SNP3, and SNP4） were successfully genotyped. Among them, SNP1 and SNP3 exhibited two genotypes each （AA and AG for SNP1, AA and GG for SNP3） and were significantly associated with resistance to I. multifiliis. The analysis of haplotype combinations and their correlation with disease resistance revealed that the genotype composed of SNP1 AG and SNP3 AA （D1） showed 100% disease resistance, while the genotype composed of SNP1 AG and SNP3 GG （D2） showed 95% disease resistance, both representing advantageous genotypes for disease resistance. The study demonstrated the existence of nlrc3 gene polymorphism in the entire population of I. multifiliis resistant grass carp, and different genotypes were significantly correlated with the ability to resist I. multifiliis infection. Among them, the D1 genotype with SNP1 AG and SNP3 AA combination exhibited the strongest resistance. These findings provide important theoretical and technical support for further breeding and prevention of I. multifiliis disease in grass carp. [ABSTRACT FROM AUTHOR]

Copyright of Oil Drilling & Production Technology / Shiyou Zuancai Gongyi is the property of Shiyou Zuancai Gongyi Bianjibu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Dalian Ocean University is the property of Journal of Dalian Ocean University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The traditional manual control measures for apple disease primarily rely on subjective experience, which can lead to arbitrariness and bias. This approach is also prone to pesticide waste and misuse. As apple leaves are one of the high-incidence areas of diseases, achieving natural environment-based identification of apple leaf diseases can provide effective guidance for disease prevention and control. To address the challenge of accurate identification of apple leaf diseases in natural settings, a DenseNet121+EfficientNet model with Focal Loss and Label Smoothing (DEFL) is designed. Given that apple leaf disease lesions are small and exhibit varying spatial characteristics for different diseases, accurately localizing and identifying disease regions is challenging. The DEFL model utilizes two parallel networks: EfficientNet-B0 and DenseNet121 for feature extraction. It combines the semantic and positional information extracted from these networks to enhance the model's finegrained feature extraction capability. To overcome difficulties in identifying apple leaf samples due to the similarity in features among different diseases, a Focal Loss function, combined with label smoothing strategies, is introduced. This prevents the model from being overly confident about the class of apple leaf samples, focusing on those samples with challenging decision boundaries during training, thereby improving the model's ability to recognize boundary cases and enhancing its robustness and generalization. The DEFL model proposed in this study was tested with 3906 apple leaf disease images in natural scenes. The results show that the overall recognition accuracy of the model proposed in this study is 99.13%, and the mean average accuracy is 98.47%. The results of ablation experiments show that the mean average precision of the model is improved by 7.99% after adding DenseNet121 feature extraction branch to EfficientNet-B0. And, after introducing the focus loss function combined with label smoothing strategy to EfficientNet-B0, the mean average precision of the model is improved by 3.15%. After the combination of the two improvements, the mean average precision of the model increased by 12.29%. Visualization results using Gradient-weighted class activation mapping (Grad-CAM) heatmaps show that the model accurately localizes and focuses on disease regions. The image feature information used by the model for apple leaf disease recognition is reliable. Uniform manifold approximation and projection (UMAP) feature dimension reduction visualization results indicate that the Focal Loss function with label smoothing effectively enhances the model's feature extraction capability, resulting in more distinctive feature information and improved disease recognition. The comparative experimental results show that compared with mainstream recognition models such as ResNet50, Inception V3, and ResNeXt, as well as EfficientNet-B0 models that fuse these three models, the mean average precision of the proposed model has increased by 14.53, 13.17, 14.61, 6.4, 7.71, and 8.91 percentage points, respectively, and the model size has decreased by 18.73, 7.7, 12.2, 83.62, 69.6, and 60.09 Mb, respectively. Comparative experiments have shown that the proposed model has the best recognition performance. In practical applications, the DEFL model achieved an overall recognition accuracy of 97.73% and a mean average precision of 95.82% when tested with 1501 apple leaf images collected from apple plantations. In conclusion, the DEFL model proposed in this study enables accurate and rapid identification of apple leaf diseases, providing valuable guidance for apple disease prevention and control. [ABSTRACT FROM AUTHOR]

The number of trees is essential information for modern forestry management, which affects managers' formulation of development strategies. At present, tree inventory mainly relies on manual counting, which is costly, time-consuming and labor-intensive. Using unmanned aerial vehicle (UAV) to count targets has become increasingly popular in agriculture and forestry due to its low cost, ease of operation, and flexibility of use. This study focused on using UAV images to count spruce numbers. A total of 603 images with an average canopy density of 81% and an average plant density of 6 667 plants/hm² were selected for the dataset, each containing an average of 354 spruce trees. Among these, 205 images had interference factors such as weeds. The images were divided into a training set and a test set at a ratio of 7:3. The training set was expanded by randomly flipping the images to improve the robustness to different flight attitudes of UAV. After data augmentation, 844 training images and 181 test images were obtained. Aiming at the problem of dense spruces in natural environments including severe adhesion and background interference such as weeds with similar characteristics to spruces, we selected IntegrateNet, a model known for its strong performance in dense target counting tasks as baseline model for spruces. This study then worked to improve the IntegrateNet model to achieve a more accurate counting of dense spruce that is closer to real-world conditions. First of all, this paper used the self-calibrated convolutions (SCConv) to replace the ordinary convolutional layers at 1/8, 1/16 feature maps and density maps in the baseline model to expand the convolution receptive field. Secondly, in order to deal with background interference problems such as weeds and the serious adhesion of the target, this paper added the criss-cross attention mechanism (CCA) to the feature fusion of the IntegrateNet model. It can consider the horizontal and vertical context information of each pixel to generate richer semantic features to improve the contextual information extraction ability of the model. The mean counting accuracy(MCA), mean absolute error(MAE), root mean square error(RMSE), and the coefficient of determination R² are used as evaluation indicators. The ablation experiments and comparative experiments are designed to verify the performance of the proposed model. The ablation experiments show that the improved methods proposed in this paper can effectively improve the counting accuracy of the model. The comparative experiments show that the improved IntegrateNet model proposed in this study has MCA, MAE and RMSE reached 98.32%, 8.99 plants and 13.79 plants respectively, and the R² was 0.99.Compared with TasselNetv3_lite, TasselNetv3_seg, and IntegrateNet models, the MCA of the improved IntegrateNet model increased by 16.44, 10.55, and 9.26 percentage points, the MAE of the improved IntegrateNet model decreased by 25.62, 10.45, and 6.99 plants, and the RMSE of the improved IntegrateNet model decreased by 48.25, 13.84, and 12.52 plants. In summary, the improved IntegrateNet model proposed in this study demonstrated a significant increase in accuracy for counting dense spruce in natural environments. Moreover, our research is not limited to spruce, as the methodology can be applied to other tree species as well. It can providing technical support for subsequent forestry intelligent statistics. [ABSTRACT FROM AUTHOR]

Copyright of Modern Educational Technology is the property of Editorial Board of Modern Educational Technology, Tsinghua University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Journal of Wildland Fire Science is the property of Journal of Wildland Fire Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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The extensive range of olive cultivars available in the market exhibit minor differences in their phenotypic traits. Nonetheless, their quality attributes, particularly the oil content and fatty acid composition, significantly differ among distinct cultivars, resulting in the emergence of use iinferior products as superior products in the market. The accurate and quick identification of olive cultivars holds significant importance in enhancing the production and quality of olives. As such, delving into the study of olive cultivar identification is crucial for the advancement of the olive industry. This study presents a novel approach to address the challenge of identifying olive cultivars in natural conditions. Specifically, a bilinear attentional EfficientNet model is proposed, which incorporates the bilinear network design concept and attention mechanism. The model is trained and evaluated using four commonly planted olive cultivars (i.e., Frantoio, Leccino, Picholine, and Ezhi 8) in Longnan, Gansu. The experimental results demonstrate the effectiveness of the proposed model in accurately and quickly identifying different olive cultivars. A bilinear network has been suggested to comprehensively extract feature information from olive images, for the limited phenotypic differences across different olive cultivars. In light of this, the selection of a feature extraction network has been made with consideration for both speed and accuracy, leading to the selection of the EfficientNetB0 network. To tackle the challenge of identifying olive cultivars under natural conditions which are prone to intricate background interferences, a novel approach combining convolutional block attention module (CBAM) with bilinear network has been proposed. This approach facilitates the model in selectively focusing on the salient features responsible for cultivar identification during the feature extraction process of olive images. Upon conducting experiments, the bilinear attention EfficientNet model presented in this study has exhibited an overall accuracy of 90.28% and an inference time of 9.15 ms in identifying four distinct olive cultivars. These experiment results demonstrate that the proposed model better achieved better rapaid and accurate identification of olive cultivars under natural conditions. The present study proposes a bilinear attention EfficientNet model for identifying olive cultivars and utilizes gradient-weighted class activation mapping (Grad-CAM) to analyse its performance. The results demonstrate that the proposed model exhibits a greater attention towards fruit regions, as well as some leaf regions, within olive images. These findings are in agreement with the expert knowledge and experience of human practitioners. The analysis of heat maps generated from misidentified olive images revealed that inadequate focus on the fruit and leaf regions, which are pivotal for successful identification of cultivars, was the primary contributing factor to misidentification. The outcomes of the ablation experiments indicated that the bilinear network and the CBAM exhibited a positive impact on the precision of olive variety recognition. To ascertain the efficacy of the method elucidated in this manuscript, a set of comparative experiments has been formulated. The primary objective of these experiments is to juxtapose the proposed bilinear attention EfficientNet model against the conventional cultivar identification models, including bilinear ResNet34, EfficientNet-SE attention, bilinear ResNet18, bilinear VGG16, and bilinear GoogLeNet. The experimental results obtained from the comparison analysis provide that the proposed bilinear attention EfficientNet model exhibits superior performance in terms of overall accuracy for the identification of olive cultivars. The bilinear attention EfficientNet model's accuracy for the identification of olive cultivars exceeded that of bilinear ResNet34, EfficientNet-SE attention, bilinear ResNet18, bilinear VGG16, and bilinear GoogLeNet models by 12.78%, 11.53%, 11.11%, 10.70%, and 5.00%, respectively. The present study establishes a foundation for resolving the challenge of accurately identifying olive cultivars in natural conditions. Moreover, it offers a valuable point of reference for the identification of cultivars of diverse crops. [ABSTRACT FROM AUTHOR]

High voltage direct current (HVDC) transmission lines have an attenuation effect on most frequency bands of fault transient voltage signals, while the physical boundary of HVDC will have a strong attenuation effect on high-frequency components and amplify low-frequency components. According to the above characteristics, the paper proposes a single-ended protection method for HVDC transmission lines based on variational mode decomposition (VMD)-sparrow search algorithm ( SSA) and Multi-band Hilbert energy amplitude. The VMD parameters was optimized by using SSA to decompose the fault transient voltage signal. Then, the Hilbert transform of each intrinsic mode function (IMF) after decomposition was performed to obtain the Hilbert energy of 200 ~ 1 200 Hz low-frequency band and 8 ~ 12 kHz high-frequency band, and the fault segment was identified by using the ratio of low-band and high-frequency Hilbert energy, and the fault pole was judged by the ratio of the sum of the Hilbert energy of the high and low frequency bands in the positive and negative poles. Simulation experiments show that the proposed method can effectively avoid the phenomenon of modal aliasing when using empirical mode decomposition (EMD) fault signals, accurately identify fault areas and fault poles, have good transition resistance, and effectively protect the full length of the line. [ABSTRACT FROM AUTHOR]

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The picking maturity of olive fruit varies greatly in different uses. However, the commonly-used manual picking cannot fully meet the picked fruit in the best picking period, due to the low picking efficiency and high cost during olive fruit picking. The purpose of this study was to determine the distribution areas of fruits in the different maturity under the natural environment, in order to realize the automatic picking of olive fruits. Firstly, the phenotypic characteristics of olive fruit at maturity were determined to develop the new standard of ripeness, according to the recommendations of the International Olive Council and China's forestry industry standards. Secondly, mobile phones were utilized to capture the olive images at the different maturity periods under natural conditions. An olive image dataset was then constructed. A series of operations were implemented to adjust the contrast, the image size, salt-pepper noise, and artificial occlusion, in order to expand the olive images to four times the original ones. The training and test set was built at the ratio of 9:1. Thirdly, an improved EfficientDet was proposed to detect the ripeness of olive fruits, the inconspicuous changes of the adjacent ripeness features, and the mutual occlusion between the fruits. The main advantages of this model were as follows: 1) A module of convolution block attention was introduced into the feature extraction network of the EfficientDet model. The weight distribution of features was then calculated in the channel and spatial domain. The improved model was then used to accurately locate the target area, thereby eliminating the interference of background information. The phenotypic characteristics of olive fruit were fully extracted for the key features to distinguish maturity categories. 2) A cross-level data flow was added to fuse the features of the bottom nodes into the high-level nodes for common learning. As such, the weighted bidirectional feature pyramid network of the EfficientDet model fully utilized the semantic relationship and location information of the high-level and bottom layers. Finally, the improved EfficientDet model was verified with the 623 olive test images. The experimental results showed that the precision, recall, and mean average precision of the improved EfficientDet model under the test set were 92.89%, 93.59%, and 94.60%, respectively, where the average detection time was only 0.337s, and the model size was only 32.4M. The mean average precision was improved by 4.77 percentage points, but the average detection time and model size were reduced by 0.028 s and 14 M, respectively, compared with the original. The mean average precision of the improved EfficientDet model increased by 7.85, 3.73, and 1.04 percentage points, respectively, compared with the SSD, YOLOv3, and Faster R-CNN model. Correspondingly, the model sizes were reduced by 60.8, 202.6, and 75.6 M, respectively. The mean average precision increased by 1.15 percentage points, while the model size increased by 4.9M, compared with the YOLOv5s model. Therefore, the improved EfficientDet model presented a higher detection accuracy with a smaller memory size, fully meeting the actual needs of promotion and application under natural conditions. [ABSTRACT FROM AUTHOR]

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In order to solve the problem that continuous commutation failure in high voltage direct current ( HVDC) transmission system has a serious impact on the operation of HVDC system, the mechanism of commutation failure of HVDC system was analyzed, and the concept of virtual capacitor was introduced. The virtual capacitor was used as the carrier of DC voltage change rate, and by re-ducing the current command value and compensating the DC voltage during the fault, the continuous commutation failure of HVDC transmission system can be restrained. The method of introducing virtual capacitor can reflect the dynamic characteristics of DC voltage during fault and recovery process. Through the electromagnetic transient model of HVDC system, it is verified that the proposed method can effectively suppress the continuous commutation failure of HVDC system under single-phase fault and three-phase fault, and accelerate the recovery of the system after fault. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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A seedling quantity is a key indicator to predict the actual production, supply, and demand for the operation and management of a nursery. The manual visualization has still dominated the statistics for the number of seedlings in complete plots. However, the application needs cannot be fully met in recent years, such as high cost, low efficiency, and slow data update. Therefore, it is necessary to fast and accurately estimate the number of seedlings in the whole plots. Taking the spruce as the research object, this study aims to propose a quantity statistics approach under Unmanned Aerial Vehicle (UAV) aerial videos using YOLOv3 and SORT. The specific procedure included the data acquisition, YOLOv3 detection model, SORT tracking, and cross-line counting. Two areas were divided for the image and video acquisition, each with 6 complete test plots. In the stage of data acquisition, 558 images and 6 videos were captured by a DJI Phantom 4 (UAV). The quantity statistics dataset was then constructed with the acquired images and videos, where the training dataset contained 558 images, and the test dataset contained 6 videos. Subsequently, a YOLOv3 model was selected to detect the spruce, while a SORT model was to track the spruce, and the cross-line counting to count the number of spruce. The performance of the combined YOLOv3+SORT was also quantitatively evaluated using Mean Count Accuracy (MCA), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Frame Rate (FR). It was found that the MCA of 92.30%, MAE of 72, RMSE of 98.85, and FR of 11.5 frames/s for the test dataset in the quantity statistics. The experimental results showed that quick and accurate counting was achieved for the number of spruce in the complete plots. The YOLOv3+SORT was also compared with the SSD+SORT and Faster R-CNN+SORT, in order to further verify the performance of the model. The results showed that the YOLOv3+SORT performed over the SSD+SORT in all four evaluation indexes. Particularly, the YOLOv3+SORT was much faster with higher guaranteed accuracy, with 1.33 percentage points lower MCA, and 10.1 frames/s higher FR, compared with the Faster R-CNN+SORT. In summary, the quantity statistics using YOLOv3 and SORT can be widely expected to serve as an effective way to rapidly and accurately count the number of seedlings in the whole plots. This study can also offer promising potential support to the seedling quantity statistics from the perspective of UAV aerial videos. [ABSTRACT FROM AUTHOR]

Copyright of Shandong Medical Journal is the property of Shandong Medical Health Newspapers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Modern Food Science & Technology is the property of Editorial Office of Modern Food Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: To explore the value of multi-parameters ultrasound in predicting the delivery mode of gestational diabetes mellitus (gdm) macrosomia.Methods:156 part urients with gestational diabetes mellitus from february 2016 to september 2020 in our hospital were selected as the research subjects. The general clinical indicators and doppler ultrasound parameters were retrospectively analyzed. According to whether the newborn was a macrosomia, it was divided into macrosomia group (52 cases) and non-macrosomia group (104 cases). The differences of fetal ultrasound parameters between the two groups were compared. To evaluate the correlation between fetal biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC) and femoral length (FL) and neonatal weight, and incorporate such indicators as age, uterine height, pregnant women's abdominal circumference, pregnant women's weight, BPD, HC, AC, and FL to evaluate the univariate factors of macrosomia. Finally, multivariate Logistic regression analysis was used to explore the independent risk factors of macrosomia. Results:(1) Comparison showed that the fetal BPD, HC, AC and HL in the macrosomia group were larger than those in the non-macrosomia group (P<0.05). (2) Spearman's correlation analysis showed that BPD, HC, AC and HL were positively correlated with neonatal weight (P<0.05). (3) That analysis show that maternal uterine height, maternal abdominal circumference, weight gain during pregnancy, BPD, HC, AC and HL were the single factor affect macrosomia (P<0.05). (4) Logistic regression analysis showed that uterine height, BPD, HC and AC were the independent risk factors for macrosomia. Conclusion: Multi-parameters ultrasound during pregnancy has a good value in predicting the incidence of macrosomia in pregnant women with gestational diabetes, which can help doctors to understand the relevant indicators of the fetus and select the appropriate mode of delivery accordingly, worthy of clinical application. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Computational Mechanics / Jisuan Lixue Xuebao is the property of Chinese Journal of Computational Mechanics Editorial Office, Dalian University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)