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Real-time dynamic monitoring of orchard grape leaf diseases can greatly improve the efficiency of disease control and is of great significance to the healthy and stable development of the grape industry. Traditional manual disease-monitoring methods are inefficient, labor-intensive, and ineffective. Therefore, an efficient method is urgently needed for real-time dynamic monitoring of orchard grape diseases. The classical deep learning network can achieve high accuracy in recognizing grape leaf diseases; however, the large amount of model parameters requires huge computing resources, and it is difficult to deploy to actual application scenarios. To solve the above problems, a cross-channel interactive attention mechanism-based lightweight model (ECA-SNet) is proposed. First, based on 6,867 collected images of five common leaf diseases of measles, black rot, downy mildew, leaf blight, powdery mildew, and healthy leaves, image augmentation techniques are used to construct the training, validation, and test set. Then, with ShuffleNet-v2 as the backbone, an efficient channel attention strategy is introduced to strengthen the ability of the model for extracting fine-grained lesion features. Ultimately, the efficient lightweight model ECA-SNet is obtained by further simplifying the network layer structure. The model parameters amount of ECA-SNet 0.5× is only 24.6% of ShuffleNet-v2 1.0×, but the recognition accuracy is increased by 3.66 percentage points to 98.86%, and FLOPs are only 37.4 M, which means the performance is significantly better than other commonly used lightweight methods. Although the similarity of fine-grained features of different diseases image is relatively high, the average F1-score of the proposed lightweight model can still reach 0.988, which means the model has strong stability and anti-interference ability. The results show that the lightweight attention mechanism model proposed in this paper can efficiently use image fine-grained information to diagnose orchard grape leaf diseases at a low computing cost.

The accurate identification of apple leaf diseases is of great significance for controlling the spread of diseases and ensuring the healthy and stable development of the apple industry. In order to improve detection accuracy and efficiency, a deep learning model, which is called the Coordination Attention EfficientNet (CA-ENet), is proposed to identify different apple diseases. First, a coordinate attention block is integrated into the EfficientNet-B4 network, which embedded the spatial location information of the feature by channel attention to ensure that the model can learn both the channel and spatial location information of important features. Then, a depth-wise separable convolution is applied to the convolution module to reduce the number of parameters, and the h-swish activation function is introduced to achieve the fast and easy to quantify the process. Afterward, 5,170 images are collected in the field environment at the apple planting base of the Northwest A&F University, while 3,000 images are acquired from the PlantVillage public data set. Also, image augmentation techniques are used to generate an Apple Leaf Disease Identification Data set (ALDID), which contains 81,700 images. The experimental results show that the accuracy of the CA-ENet is 98.92% on the ALDID, and the average F1-score reaches .988, which is better than those of common models such as the ResNet-152, DenseNet-264, and ResNeXt-101. The generated test dataset is used to test the anti-interference ability of the model. The results show that the proposed method can achieve competitive performance on the apple disease identification task.

Barium is a heavy divalent alkaline earth metal that has been known as a muscle poison. Barium can cause human toxicity, which may lead to significant hypokalemia and have serious consequences. This paper reports a case of unprecedented barium intoxication in which the patient, who suffered from depression, swallowed at least 3.0 g barium chloride to commit suicide. On admission, the patient presented with nausea, vomiting, stomach burning feeling, dizziness, and weakness. Emergency biochemical testing showed that the patient was suffering from severe hypokalemia (K+ 1.7 mmol/L). His electrocardiogram (ECG) prompted atrioventricular blocking, ventricular tachycardia, prolongation of PR interval, ST segment depression with U waves, and T wave inversion. Intravenous potassium supplements were given immediately to correct hypokalemia and regular monitoring of vital signs and fluid balance was arranged. After all-out rescue of our hospital personnel, the condition of the patient is currently stable and he is gradually recovering. This case exemplifies the weaknesses of the management of toxic substances and the lack of mental health education for young people. We hope to get more attention for the supervision of toxic substances and the healthy development of young people.

The objective of this study was to investigate the relationship between ammonium (NH4 +-N) dynamic sorption capacity and physicochemical properties of modified biochars. Biochars, producing from three species of agricultural wastes of cornstalk (A), corncob (B) and sawdust (C) at 550°C, 600°C and 650°C, were modified by four methods of NaOH plus microwave (NaM), KOH (K), FeCl3 (Fe) and HNO3 (H). The static and dynamic adsorption experiments were conducted to investigate sorption characteristics of modified biochars on NH4 +-N in slurry of piggery manure anaerobic digestate. Four modified biochars with better NH4 +-N adsorption rates were selected through static kinetics adsorption tests, which showed that chemical reactions might have occurred during the NH4 +-N sorption process and the maximum NH4 +-N removal rates of B-550-Fe, A-550-NaM, A-550-K and C-600-NaM were 66.64%, 57.50%, 52.39% and 45.20%, respectively. The dynamic adsorption column experiment was conducted in a three-stage adsorption column packed with the selected three modified biochars. NH4 +-N dynamic adsorption depended on the slurry inflow flow rate, the type of biochar and the depth of packed biochar. The optimal adsorption process was elected by the method of orthogonal experiment. The data showed that using deeper packed biochar and applying lower flow rates could be a better strategy to increase NH4 +-N adsorption. The maximum NH4 +-N removal rate in the slurry could reach 85.60% in the three-stage adsorption process. It concluded that NH4 +-N adsorption in three-stage adsorption process could be an effective method to recover nitrogen from piggery manure anaerobic digestate. [ABSTRACT FROM AUTHOR]