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The use of neural networks for plant disease identification is a hot topic of current research. However, unlike the classification of ordinary objects, the features of plant diseases frequently vary, resulting in substantial intra-class variation; in addition, the complex environmental noise makes it more challenging for the model to categorize the diseases. In this paper, an attention and multidimensional feature fusion neural network (AMDFNet) is proposed for Camellia oleifera disease classification network based on multidimensional feature fusion and attentional mechanism, which improves the classification ability of the model by fusing features to each layer of the Inception structure and enhancing the fused features with attentional enhancement. The model was compared with the classical convolutional neural networks GoogLeNet, Inception V3, ResNet50, and DenseNet121 and the latest disease image classification network DICNN in a self-built camellia disease dataset. The experimental results show that the recognition accuracy of the new model reaches 86.78% under the same experimental conditions, which is 2.3% higher than that of GoogLeNet with a simple Inception structure, and the number of parameters is reduced to one-fourth compared to large models such as ResNet50. The method proposed in this paper can be run on mobile with higher identification accuracy and a smaller model parameter number.

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In this paper, three kinds of micro-nano bamboo powder (MBP) and alkyl ketene dimer (AKD) were added to the polyvinyl alcohol/cellulose nanofiber (PVA/CNF) coating to prepare PVA/CNF/MBP coated paper and PVA/CNF/M-MBP/AKD coated paper. The results showed that MBP improved the oleophobicity of PVA/CNF coating, and the grease resistance grade of PVA/CNF/B-MBP and PVA/CNF/M-MBP coated papers reached the highest level, with a kit number of 12. Among the PVA/CNF/MBP coated papers, the PVA/CNF/M-MBP coated paper has the best hydrophobic properties, with the water contact angle and Cobb value of 74° and 21.3 g/m2, respectively. In addition, when the AKD dosage was 0.2% in the PVA/CNF/M-MBP/AKD coating, the kit number of the coated paper was 11, the Cobb value was 15.2 g/m2, the water contact angle was 103°, and the tensile strength was found to increase slightly. Therefore, compared with PVA/CNF coated paper, PVA/CNF/M-MBP/AKD coated paper has good strength and excellent hydrophobic and oleophobic properties.

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The effect of cellulose nanofibers (CNFs)/polyvinyl alcohol (PVA) coating on the hydrophobic, oleophobic, and strength properties of paper were investigated. The results showed that the size of bamboo fibers (BFs) decreased significantly and the crystallinity increased significantly after biological enzyme treatment. The average length of CNFs obtained by high pressure homogenization was 2.4 µm, the diameter was 28.7 nm, and the crystallinity was 63.63%. When the coating weight of PVA/CNF was 2.0 g/m2 and the CNF dosage was increased from 0.0% to 3.0%, the paper grease resistance grade was increased from 7 to 9, the Cobb value was decreased from 22.68 ± 0.29 g/m2 to 18.37 ± 0.63 g/m2, the contact angle was increased from 67.82° to 93.56°, and the longitudinal and transverse tensile index were increased from 67.72 ± 0.21 N m/g and 37.63 ± 0.25 N m/g to 68.61 ± 0.55 N m/g and 40.71 ± 0.78 N m/g, respectively. When the CNF dosage was 3.0% and the coating weight of PVA/CNF was 4.0 g/m2, the grease resistance grade of the paper was 12, the Cobb value was 21.80 ± 0.39 g/m2, and the longitudinal and transverse tensile indices were 72.11 ± 0.43 N m/g and 42.58 ± 0.48 N m/g, respectively. In summary, the increase of CNFs can effectively improve the lipophobicity, hydrophobicity and tensile strength of the PVA coated paper.

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Hypoglycemia can cause rapid and severe brain damage. We studied the impact of hypoglycemic brain damage in the insulin-induced hypoglycemic rats. Thirty male rats were divided into normal blood sugar control group (group A), the blank group (group B), and the experimental group which was further divided into four groups according to the level of blood glucose reperfusion i.e., blood glucose ≤ 3 mmol/L (Group C), ≤ 6 mmol/L (Group D), ≤ 9 mmol/L (Group E), and >9 mmol/L (Group F). Each groups had five rats. TUNEL and FJB staining were used to observe the apoptosis and necrosis in the rat hippocampus CA1 and DG regions and transmission electron microscopy for ultra-structures. We observed that neuronal apoptosis and necrosis of group A and B were not obvious. The apoptotic and necrotic neuron cell densities in the hippocampus CA1 and DG regions were moderately detected in group C, D, and E, while we found it maximum in group F. No significant difference was found in apoptotic and necrotic neuron cell density in the hippocampus CA1 and DG regions in group A and B. Apoptotic and necrotic cell density was significantly increased in all experimental groups as compared to the control group. Moreover, the apoptotic and necrotic cell density was significantly higher in group F than other experimental groups (group C, D, and E). However, apoptosis and necrosis in hippocampus CA1 and DG regions was not differed significantly among groups C, D, and E. All results were well supported by transmission electron microscopy. In conclusion, under the condition of the same blood glucose level, the degree of brain damage related to the blood glucose level with hypoglycemia and rapid blood glucose increased after hypoglycemia could cause more significant brain damage.

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Soft tissues are anisotropic materials yet a majority of mechanical property tests have been uniaxial, which often failed to recapitulate the tensile response in other directions. This paper aims to study the feasibility of determining material parameters of anisotropic tissues by uniaxial extension with a minimal loss of anisotropic information. We assumed that by preselecting a certain constitutive model, we could give the constitutive parameters based on uniaxial extension data from orthogonal strip samples. In our study, the Holzapfel-Weizsäcker type strain energy density function (H-W model) was used to determine the material parameters of arterial walls from two fresh donation bodies. The key points we applied were the relationships between strain components in uniaxial tensile tests and the methods of stochastic optimisation. Further numerical experiments were taken. The estimate-effect ratio, defined by the number of data with the precision of estimation less than 0.5% over whole size of data, was calculated to demonstrate the feasibility of our method. The material parameters for Chinese aorta and pulmonary artery were given with the maximum root mean square (RMS) errors 0.042, and the minimal estimate-effect ratio in numerical experiments was 90.79%. Our results suggest that the constitutive parameters of arterial walls can be determined from uniaxial extension data, given the passive mechanical behaviour governed by H-W model. This method may apply to other tissues using different constitutive models.

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