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Emotion recognition based on brain-computer interface (BCI) has attracted important research attention despite its difficulty. It plays a vital role in human cognition and helps in making the decision. Many researchers use electroencephalograms (EEG) signals to study emotion because of its easy and convenient. Deep learning has been employed for the emotion recognition system. It recognizes emotion into single or multi-models, with visual or music stimuli shown on a screen. In this article, the convolutional neural network (CNN) model is introduced to simultaneously learn the feature and recognize the emotion of positive, neutral, and negative states of pure EEG signals single model based on the SJTU emotion EEG dataset (SEED) with ResNet50 and Adam optimizer. The dataset is shuffle, divided into training and testing, and then fed to the CNN model. The negative emotion has the highest accuracy of 94.86% fellow by neutral emotion with 94.29% and positive emotion with 93.25% respectively. With average accuracy of 94.13%. The results showed excellent classification ability of the model and can improve emotion recognition.

Metallic sodium (Na) has been regarded as one of the most attractive anodes for Na-based rechargeable batteries due to its high specific capacity, low working potential, and high natural abundance. However, several important issues hinder the practical application of the metallic Na anode, including its high reactivity with electrolytes, uncontrolled dendrite growth, and poor processability. Metal nitrates are common electrolyte additives used to stabilize the solid electrolyte interphase (SEI) on Na anodes, though they typically suffer from poor solubility in electrolyte solvents. To address these issues, a Na/NaNO3 composite foil electrode was fabricated through a mechanical kneading approach, which featured uniform embedment of NaNO3 in a metallic Na matrix. During the battery cycling, NaNO3 was reduced by metallic Na sustainably, which addressed the issue of low solubility of an SEI stabilizer. Due to the supplemental effect of NaNO3, a stable SEI with NaNxOy and Na3N species was produced, which allowed fast ion transport. As a result, stable electrochemical performance for 600 h was achieved for Na/NaNO3||Na/NaNO3 symmetric cells at a current density of 0.5 mA cm-2 and an areal capacity of 0.5 mAh cm-2. A Na/NaNO3||Na3V2(PO4)2O2F cell with active metallic Na of ∼5 mAh cm-2 at the anode showed stable cycling for 180 cycles. In contrast, a Na||Na3V2(PO4)2O2F cell only displayed less than 80 cycles under the same conditions. Moreover, the processability of the Na/NaNO3 composite foil was also significantly improved due to the introduction of NaNO3, in contrast to the soft and sticky pure metallic Na. Mechanical kneading of soft alkali metals and their corresponding nitrates provides a new strategy for the utilization of anode stabilizers (besides direct addition into electrolytes) to improve their electrochemical performance.

In natural farmland ecosystems, cadmium (Cd) pollution in rice has attracted increasing attention because of its high toxicity, relative mobility, and high water solubility. This study aims to develop a spectral index for detecting Cd stress in rice on a regional scale. Three experimental sites are selected in Zhuzhou City, Hunan Province. The hyperspectral data, chlorophyll (Chl) content, leaf area index, average leaf angle, Cd concentration in soil, and Sentinel-2A images from 2017 and 2018 are collected. A new spectral index sensitive to Cd stress in rice is established based on the global sensitivity analysis of the radiative transfer model PROSPECT + SAIL (commonly called PROSAIL) model with the auxiliary of the field-measured data. The heavy metal Cd stress-sensitive spectral index (HCSI) is devised as an indicator of the degree of Cd stress in rice. Results indicate that (1) the HCSI developed based on Chl is a good indicator of rice damage caused by Cd stress, that is, low values of HCSI occur in rice subject to relatively high pollution, (2) compared with common spectral indices, such as red-edge position and red-edge Chl index, HCSI is more sensitive to Chl content with higher Pearson correlation coefficients with respect to Chl content, ranging from 0.85 to 0.95, (3) HCSI is successfully applied in Sentinel-2A images from the two different years of monitoring rice Cd stress on a regional scale. Cd stress levels in rice stabilized, and the largest area percentage of each pollution levels of Cd decreased in the following order: No pollution (i.e., 40%), low pollution (i.e., 35%), and high pollution (i.e., 25%). This study indicates that a combination of simulation data from the PROSAIL model and measured data appears to be a promising method for establishing a sensitivity spectral index to heavy metal stress, which can accurately detect regional Cd stress in crops.

The rice-cropping system in southern China experienced a major change in the past few decades. Rice-cropping systems not only affect the comprehensive utilization intensity of agricultural resources, food security, and the ecological environment but also reveal the conditions of the agricultural policy, regional economy, and rural labor force. Consequently, accurate detection of rice-cropping systems is an essential issue. We present a method for classifying rice-cropping systems using a pretrained convolutional neural network (CNN) that involves extracting deep features of spatial and spectral trajectories. The study area is a major rice-growing region located in Zhuzhou City, Hunan Province, China. Multitemporal Sentinel-2 satellite images were collected to obtain time series curves of texture (spatial trajectories) and the first derivative vegetation index (spectral trajectories) as inputs of the hierarchical classification model. The classification results of land cover and rice-cropping systems were acquired using the pretrained CNN, and the method achieved overall accuracy of 94.78% and 94.87%, respectively. This method was then compared with the support vector machine (SVM). The accuracy of rice-cropping systems using the pretrained CNN was 7.11% higher than that of the SVM. The adaptability of the model was also investigated using the time series curve in another year with relatively insufficient data. The model obtained satisfactory performance with the overall accuracy of land cover types and rice-cropping systems of 93.52% and 93.23%, respectively. We suggest that the pretrained CNN can improve the accuracy of rice-cropping system mapping by extracting deep features of spatial and spectral trajectories during the rice growth cycle and such method may be applied in other fine crop classifications.