Bending mechanics test and parameters calibration of ramie stalks.

Research and development of ramie harvesting equipment is a key link to revitalize ramie industry, problems such as the tendency of stalks to tangle and clog the machine are very problematic, seriously affect the quality and fluency of the harvester. The structure of ramie stalk is complex, and the mechanical properties of each component vary greatly, collision between stalk and machine creates complex stress relationship. By building a finite element model, it is possible to analyze the stress state of the stalk during bending from a microscopic perspective, and to analyze the complex stress-strain situation within the stalk. The purpose of this paper is to establish a standard ramie stalk bending finite element model to provide a theoretical basis for the subsequent kinematics and dynamics. Firstly, material experiments were carried out on ramie straw. The structural and mechanical parameters of the straw components were obtained through measurement and calculation tests, and the force-deformation curves for straw bending were obtained. Bending finite element simulations were carried out on the basis of mechanical tests, and the parameters such as dynamic friction coefficient, wood Poisson's ratio and bast Poisson's ratio were determined by the central combination design. Then established an accurate bending finite element simulation model of ramie stalk, the accuracy of the model was verified at the end. In this paper, the key parameters of the ramie stalk model were calibrated through a combination of material tests and simulations. All parameters of the ramie stalk model were finally obtained, and the bending mechanical properties of the ramie stalk were analysed by applying finite element analysis. This bending mechanics simulation model can be used for kinematic and dynamics simulation analysis of conveying and baling to provide a theoretical basis for the structural design of the harvester. The methods explored here can be applied to other slender straw crops.

Pseudo high-frequency boosts the generalization of a convolutional neural network for cassava disease detection.

Frequency is essential in signal transmission, especially in convolutional neural networks. It is vital to maintain the signal frequency in the neural network to maintain the performance of a convolutional neural network. Due to destructive signal transmission in convolutional neural network, signal frequency downconversion in channels results into incomplete spatial information. In communication theory, the number of Fourier series coefficients determines the integrity of the information transmitted in channels. Consequently, the number of Fourier series coefficients of the signals can be replenished to reduce the information transmission loss. To achieve this, the ArsenicNetPlus neural network was proposed for signal transmission modulation in detecting cassava diseases. First, multiattention was used to maintain the long-term dependency of the features of cassava diseases. Afterward, depthwise convolution was implemented to remove aliasing signals and downconvert before the sampling operation. Instance batch normalization algorithm was utilized to keep features in an appropriate form in the convolutional neural network channels. Finally, the ArsenicPlus block was implemented to generate pseudo high-frequency in the residual structure. The proposed method was tested on the Cassava Datasets and compared with the V2-ResNet-101, EfficientNet-B5, RepVGG-B3g4 and AlexNet. The results showed that the proposed method performed [Formula: see text] in terms of accuracy, 1.2440 in terms of loss, and [Formula: see text] in terms of the F1-score, outperforming the comparison algorithms.

Detection of Pb2+ in Tea Using Aptamer Labeled with AIEgen Nanospheres Based on MOFs Sensors.

Tea is an important economic crop and health beverage in China. The presence of heavy metal ions in tea poses a significant threat to public health. Here, we prepared an aptamer biosensor labelled with AIEgen nanospheres to detect Pb2+ in tea. The dsDNA modified by amino and phosphoric acid was combined with the carboxylated AIEgen NPs to form AIEgen-DNA with a fluorescence group, which was then fixed to the surface of Zr-MOFs to quench the fluorescence of AIEgen NPs. At the same time, PEG was added to remove nonspecific adsorption. Then Pb2+ was added to cut the DNA sequences containing the cutting sites, and AIEgen NPs and part of the DNA sequences were separated from the Zr-MOFs surface to recover the fluorescence. By comparing the fluorescence changes before and after adding Pb2+, the detection limit of Pb2+ can reach 1.70 nM. The fluorescence sensor was applied to detect Pb2+ in tea, and the detection results showed that the tea purchased on the market did not contain the concentration of Pb2+ within the detection range. This study provides new insights into monitoring food and agriculture-related pollutants based on fluorescent biosensors.

Biocomposites from Organic Solid Wastes Derived Biochars: A Review.

The replacement of natural fiber with biochars to prepare biocomposites has attracted widespread attention recently. Biochar has unique properties, including the porous structure, large specific surface area, high thermal stability, good conductivity, renewable and abundant feedstock source, and environmental friendliness, which provide excellent properties, environmental benefits, and low production costs for biochar-based composites. Biocomposites from organic solid waste-derived biochars show good prospects worldwide in terms of positive social, environmental, and economic impacts. This paper reviews current biochars, elucidates the effects of biochars on the characteristics and performance of biochar composites, and points out the challenges and future development prospects of biochar composites.