Search

近年来，不断发射的空基观测台持续传送回海量日面图像及日地间气象数据，为采用人工智能技术对太阳活动进行预报预警提供了数据基础。但是，极端天气爆发少，样本量较少；中等程度爆发稍多，样本量较多；常规无爆发天气常见，样本较为集中，样本不均衡状况严重影响机器学习方法在空间天气领域的广泛应用。本文面向多源多通道多尺度日面图像信息，构建了来自SOHO和SDO的1996－2015年日面活动区图像数据集；针对数据分布的不平衡，对太阳活动区图像作耀斑分级与预报。在对比分析元学习算法的基础上，设计了结合分类头设计和卷积核初始化的生成式模型；在使网络轻量化的基础上，能够将M和X级耀斑预报的检测率指标相较于普通的深度学习模型和无监督度量式模型分别提升10%和7%。

Copyright of Acta Prataculturae Sinica is the property of Acta Prataculturae Sinica 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 Chinese Journal of Tissue Engineering Research / Zhongguo Zuzhi Gongcheng Yanjiu is the property of Chinese Journal of Tissue Engineering Research 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 Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering 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 Food Research & Development is the property of Food Research & Development Editorial Department 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 observe the clinical effect of neurodynamic techniques combined with McKenzie diagnosis and therapy on patients with cervical spine radiculopathy, and to explore a better clinical treatment method for patients with cervical spine radiculopathy. Method: Thirty-three patients with cervical spine radiculopathy who met the inclusion and matched the baseline characteristics were randomly divided into the two groups: the McKenzie treatment group (n=16) and the combined treatment group (n=17) according to the random number table. The patients in both groups received conventional physical treatment and Mechanical diagnosis and therapy, while the combined treatment group received neurodynamic techniques additionally. Before treatment and 2 weeks after treatment, the cervical range of motion (ROM) was measured, and the visual analogue scale (VAS) was used to evaluate the patients' pain level and the neck disability index (NDI)were used to assess the functional status and treatment outcome. Result: Before treatment, there were no significant differences in cervical ROM, VAS and NDI between two groups (P>0.05). After two weeks of treatment, the outcome of the combination group showed significant improvements cervical ROM for flexion (42.65±3.12)°, extension (40.88±4.05)°, left lateral flexion (41.18±4.52)°, right lateral flexion (42.06±4.70)°, left rotation (73.53±5.80)°, and right rotation (74.71±7.39)°, the VAS score was 0.76±0.75, NDI score was 3.82±3.47, compared with both the baseline and the control group (P<0.05). Conclusion: Neurodynamic techniques combined with McKenzie diagnosis and therapy provide more effective relief of pain and ROM limitations in patients with cervical spine radiculopathy, accelerating the recovery of cervical function. The combination therapy is a useful clinical treatment option. [ABSTRACT FROM AUTHOR]

[Objectives] To address the issue of increased time cost due to separate processing of lettuce classification, segmentation, and fresh weight estimation, this paper proposed an end-to-end non-destructive fresh weight estimation model for lettuce, called LettuceNet. [Methods] LettuceNet estimated lettuce fresh weight by analyzing top-view images. The model integrated Swin Transformer-Tiny (Swin-T) and UPerNet to effectively extract features from lettuce canopy images. A K-Net-based segmentation head was designed for semantic segmentation, while a regression head was used for fresh weight estimation. The regression head combined the features extracted by Swin-T with the results of the segmentation head for lettuce classification and canopy area measurement, allowing LettuceNet to efficiently handle both semantic segmentation and fresh weight estimation tasks. [Results] Experiments on two datasets showed that LettuceNet achieved mean pixel accuracy(MPA)of 98.01% and 98.75%, and mean intersection over union (MIoU) of 96.02% and 97.63%, respectively, for the semantic segmentation task. For fresh weight estimation, the coefficient of determination (R²) was 0.898 and 0.919, the mean squared error(RMSE)was 0.865 and 30.814 g, and the mean absolute percentage error (MAPE) was 1.894% and 18.194%, respectively. [Conclusions] By inputting lettuce canopy images, LettuceNet can perform real-time, non-destructive lettuce classification, segmentation, and fresh weight estimation. The model enables rapid quantitative analysis of lettuce growth, providing data support for intelligent management in plant factories. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Architecture & Civil Engineering is the property of Chang'an Daxue Zazhishe 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 Architecture & Civil Engineering is the property of Chang'an Daxue Zazhishe 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 Science Technology & Engineering is the property of Chinese Society of Technology Economics 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 Science & Technology of Food Industry is the property of Science & Technology of Food Industry 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.)

Solution regeneration helps to save resources and protect the environment. Therefore, the principle, structure, characteristics and research status of air regeneration, electrodialysis regeneration, membrane distillation regeneration and mechanical vapor compression regeneration among solution regeneration technologies are reviewed・ The air regeneration technology can complete the high concentration solution regeneration, processing temperature is not high, can make full use of grade waste heat, but this method of processing capacity is small, high energy consumption; electrodialysis regeneration technology can be used for two different salts of solution regeneration; membrane distillation regeneration technology can be low-grade heat or waste heat reuse; mechanical vapor compression regeneration technology can be completed in a higher concentration of solution regeneration, processing capacity is large, compared with the traditional compared with the traditional single-effect evaporation, the energy consumption is greatly reduced. [ABSTRACT FROM AUTHOR]

Multiplex network community detection methods based on graph neural network face two main challenges. Firstly, how to effectively utilize the node content information of multiplex network; and secondly, how to effectively utilize the interlayer relationships in multiplex networks. Therefore, this paper proposed the multiplex network community detection model AGCFN. Firstly, the autoencoder independently extracted the node content information of each network layer and passed the extracted node content information to the graph autoencoder for fusing the node content information of the current network layer with the topology information through the transfer operator to obtain the representation of each node of the current network layer, which made full use of the node content information of the network and the topology information of the network. The modularity maximization module and graph decoder optimized the obtained node representation. Secondly, the multilayer information fusion module fused the node representations extracted from each network layer to obtain a comprehensive representation of each node. Finally, the model under went training, and it achieved community detection results through a self-training mechanism. Comparison with six models on three datasets demonstrate improvements in both ACC and NMI evaluation metrics, thereby validating the effectiveness of AGCFN. [ABSTRACT FROM AUTHOR]

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 Atomic Energy Science & Technology is the property of Editorial Board of Atomic Energy 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 Acta Materiae Compositae Sinica is the property of Acta Materiea Compositae Sinica Editorial Department 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 Agricultural Science & Technology (1008-0864) is the property of Journal of Agricultural 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 Acta Prataculturae Sinica is the property of Acta Prataculturae Sinica 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.)

As known, ruminants have poor nitrogen efficiency. The balance between soluble carbohydrates and soluble protein may help to improve nitrogen efficiency. This review outlines the metabolic process of soluble carbohydrates and soluble protein in the rumen, indicators to evaluate the balance between soluble carbohydrates and soluble protein, the metabolic impact of balance of soluble carbon and soluble nitrogen in ruminants, and measures for regulating the balance of soluble carbon and solub protein. [ABSTRACT FROM AUTHOR]

The images of crop roots collected by the minirhizotron method have a complex soil background, and roots occupy a relatively small proportion. Common semantic segmentation models used for root extraction may misclassify pixels at the edge of roots as soil when the receptive field is small or multi-scale feature fusion is inadequate. Moreover, the long image acquisition cycle and initial difficulty in collecting a large number of valid samples with the minirhizotron method hindered the rapid deployment of root extraction models. To improve the accuracy of root phenotyping measurements and optimize extraction model deployment strategies, this study devised an in-situ automatic root imaging system to acquire minirhizotron images of crops in real time. A full-scale skip feature fusion mechanism is constructed for the U²-Net model with rich receptive fields for the effective classification of root pixels in minirhizotron images. Integrating data augmentation and fine-tuning method of transfer learning methods to achieve rapid deployment of root extraction models for target species. The full-scale skip feature fusion mechanism involved fusing the output features of the upper encoder and lower decoder of the U²-Net model across all scales, thereby serving as input features for a certain decoder layer and effectively retaining more feature information to enhance the decoder's information restoration capability. In terms of model deployment, this study compared fine tuning method of transfer learning with mixed training to address the issue of model training with limited samples. Experimental materials included self-developed in-situ automatic root imaging systems for collecting garlic sprout root system images and the publicly available minirhizotron dataset PRMI(plant root minirhizotron imagery). The experimental design included performance metric comparisons on the PRMI(plant root minirhizotron imagery)dataset, followed by analysis and validation on garlic sprout data. The control group comprised pre-improvement and post-improvement versions of the U²-Net, U-Net, SegNet, and DeeplabV3+_Resnet50 models. Finally, the predictive effects of fine-tuning and mixed training methods on the garlic sprout test set were compared to identify effective strategies for rapid model deployment. The experimental results demonstrate that on the garlic sprout dataset, the improved U²-Net model achieves average F1 and IoU scores of 86.54% and 76.28%, respectively. Compared to the pre-improvement version, U-Net, SegNet, and DeeplabV3+_Resnet50 models, the average F1 increases by 0.66、5.51、8.67 and 2.84 percentage points, respectively, while the average IoU increases by 1.02、8.18、12.52 and 4.31 percentage points, respectively. Practical segmentation shows enhanced recognition capability for root system edges, significantly reducing over-segmentation and under-segmentation phenomena. In the garlic sprout dataset with limited samples, fine-tuning of transfer learning outperformed mixed data and training, with model performance metrics F1 and IoU improving by 2.89 and 4.45 percentage points, respectively. For root length, area, and average diameter, the determination coefficient R² values between the result of the model's predicted images and manually labeled images reached 0.965, 0.966, and 0.830, respectively. This study offers a reference for enhancing the accuracy of root phenotype measurement in minirhizotron images and the rapid deployment of root extraction models. [ABSTRACT FROM AUTHOR]

Copyright of Electronic Components & Materials is the property of Electronic Components & Materials 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 Acta Geoscientica Sinica is the property of Acta Geoscientica Sinica 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 Railway Standard Design is the property of Railway Standard Design 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 Hydrogeology & Engineering Geology / Shuiwendizhi Gongchengdizhi is the property of Hydrogeology & Engineering Geology 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 Precious Metals / Guijinshu is the property of Precious Metals 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 Chongqing University of Technology (Natural Science) is the property of Chongqing 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 Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She 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 China Mining Magazine is the property of China Mining Magazine Co., Ltd. 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 utilization efficiency of ruminants for protein feed is low compared with that of monogas‐ tric animals, how to improve the nitrogen utilization efficiency of ruminants to ensure the healthy growth of animals is particularly important, this paper reviews the main rumen nitrogen utilization microorgan‐ isms as well as the impact of nitrogen utilization microbial preparations on the growth and health of rumi‐ nants, with a view to improving the nitrogen utilization rate of ruminants to provide more reference basis. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Anhui Agricultural Sciences is the property of Journal of Anhui Agricultural Sciences 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 Food & Fermentation Industries is the property of Food & Fermentation Industries 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.)

This paper aims to reveal the inherent interaction mechanism between stress path and dilatancy equation for rockfill materials. The stress-strain response relationships among isotropic compression tests, conventional triaxial tests, and constant p test is analyzed. By employing theoretical analysis and experimental verification methods, a new method for constructing dilatancy equation is proposed within the framework of an elastoplastic constitutive model and was validated through triaxial tests of rockfill materials under various stress paths. The prediction results show that the proposed dilatancy equation can capture stress path effect, especially the effect for constant stress ratio loading paths. The conclusion reveals the importance of isotropic compression tests and constant p-tests. It is suggested to use these two types of tests when establishing dilatancy equation and constitutive model that consider stress path effect. [ABSTRACT FROM AUTHOR]

Since the measurement of oil quantity in aviation engine lubrication systems is susceptible to multiple parameters, it is difficult to calculate and predict oil consumption rates. We propose an improved method for extracting oil quantity data and predicting oil consumption rates. Engine data are cleansed using density-based clustering algorithms, including density based spatial clustering of applications with noise (DBSCAN), to obtain stable oil quantity data during steady flight conditions. By the least squares method, oil consumption rates are derived with an average fitting goodness of 0.86. Subsequently, a multi-layer perception (MLP) is employed to establish the relationship between oil consumption rates and flight status parameters, resulting in a predicted average absolute percentage error of 1.15%, compared to actual values. The proposed method meets practical engineering requirements, providing a reliable reference for assessing the health status of aviation engine lubrication systems. [ABSTRACT FROM AUTHOR]

Copyright of Acta Agronomica Sinica is the property of Crop Science Society of China 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 Chinese Journal of Network & Information Security is the property of Beijing Xintong Media Co., Ltd. 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 China Condiment is the property of China Condiment 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 Architectural Design & Research is the property of Omniscient Pte. Ltd. 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 Guangdong Agricultural Sciences is the property of South China Agricultural University, Guangdong Academy of Agricultural Sciences 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 Nuclear Safety is the property of Nuclear & Radiation Safety Center 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 Xinjiang Agricultural Sciences is the property of Xinjiang Agricultural Sciences Editorial Department 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.)