| 1 | Effect of subsoil tillage depth on nutrient accumulation, root distribution, and grain yield in spring maize显示文摘A four-year field experiment was conducted to investigate the effect of subsoiling depth on root morphology, nitrogen(N), phosphorus(P), and potassium(K) uptake, and grain yield of spring maize. The results indicated that subsoil tillage promoted root development,increased nutrient accumulation, and increased yield. Compared with conventional soil management(CK), root length, root surface area, and root dry weight at 0–80 cm soil depth under subsoil tillage to 30 cm(T1) and subsoil tillage to 50 cm(T2) were significantly increased, especially the proportions of roots in deeper soil. Root length, surface area, and dry weight differed significantly among three treatments in the order of T2 > T1 > CK at the12-leaf and early filling stages. The range of variation of root diameter in different soil layers in T2 treatment was the smallest, suggesting that roots were more likely to grow downwards with deeper subsoil tillage in soil. The accumulation of N, P, and K in subsoil tillage treatment was significantly increased, but the proportions of kernel and straw were different. In a comparison of T1 with T2, the grain accumulated more N and P, while K accumulation in kernel and straw varied in different years. Grain yield and biomass were increased by 12.8% and 14.6% on average in subsoil tillage treatments compared to conventional soil treatment. Although no significant differences between different subsoil tillage depths were observed for nutrient accumulation and grain yield, lodging resistance of plants was significantly improved in subsoil tillage to 50 cm, a characteristic that favors a high and stable yield under extreme environments. | Hongguang Cai Wei Ma Xiuzhi Zhang Jieqing Ping Xiaogong Yan Jianzhao Liu Jingchao Yuan Lichun Wang Jun Ren | 2014 | The Crop Journal2014，2，5： | 42 |
| 4 | Detection method of absence seizures based on Resnet and bidirectional GRU显示文摘Background Epilepsy is a common chronic neurological disease.Its repeated seizure attacks have a great negative impact on patients’physical and mental health.The diagnosis of epilepsy mainly depends on electroencephalogram(EEG)signals detection and analysis.There are two main EEG signals detection methods for epilepsy.One is the detection based on abnormal waveform,the other is the analysis of EEG signals based on the traditional machine learning.The feature extraction method of the traditional machine learning is difcult to capture the high-dimension information between adjacent sequences.Methods In this paper,redundant information was removed from the data by Gaussian fltering,downsampling,and short-time Fourier transform.Convolutional Neural Networks(CNN)was used to extract the high-dimensional features of the preprocessed data,and then Gate Recurrent Unit(GRU)was used to combine the sequence information before and after,to fully integrate the adjacent information EEG signals and improve the accuracy of the model detection.Results Four models were designed and compared.The experimental results showed that the prediction model based on deep residual network and bidirectional GRU had the best efect,and the test accuracy of the absence epilepsy test set reached 92%.Conclusions The prediction time of the network is only 10 sec when predicting four-hour EEG signals.It can be efectively used in EEG software to provide reference for doctors in EEG analysis and save doctors’time,which has great practical value. | Lijun Li Hengxing Zhang Xiaomei Liu Jie Li Lei Li Dan Liu Jieqing Min Ping Zhu Huan Xia Shangkun Wang Li Wang | 2023 | Acta Epileptologica2023，6，1： | 0 |
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