Carbon dioxide (CO₂) enrichment in an agricultural environment has been shown to enhance the efficiency of crop photosynthesis, increasing crop yield and product quality. There is a problem of the excessive use of CO₂ gas when the CO₂ is enriched for crops, such as soybean and other field crops. Given the application of micro-nanobubbles (MNBs) in agricultural production, this research takes CO₂ as the gas source to prepare the micro-nanobubble water by the dissolved gas release method, and the response surface methodology is used to optimize the preparation process. The results show that the optimum parameters, which are the gas–liquid ratio, generator running time, and inlet water temperature for the preparation of CO₂ micro-nanobubble water, are 2.87%, 28.47 min, and 25.52 °C, respectively. The CO₂ content in the MNB water prepared under the optimum parameters is 7.64 mg/L, and the pH is 4.08. Furthermore, the particle size of the bubbles is mostly 255.5 nm. With the extension of the storage time, some of the bubbles polymerize and spill out, but there is still a certain amount of nanoscale bubbles during a certain period. This research provides a new idea for using MNB technology to increase the content and lifespan of CO₂ in water, which will slow the release and increase the utilization of CO₂ when using CO₂ enrichment in agriculture.

Cloud image prediction is a spatio-temporal sequence prediction task, similar to video prediction. Spatio-temporal sequence prediction involves learning from historical data and using the learned features to generate future images. In this process, the changes in time and space are crucial for spatio-temporal sequence prediction models. However, most models now rely on stacking convolutional layers to obtain local spatial features. In response to the complex changes in cloud position and shape in cloud images, the prediction module of the model needs to be able to extract both global and local spatial features from the cloud images. In addition, for irregular cloud motion, more attention should be paid to the spatio-temporal sequence features between input cloud image frames in the temporal sequence prediction module, considering the extraction of temporal features with long temporal dependencies, so that the spatio-temporal sequence prediction network can learn cloud motion trends more accurately. To address these issues, we have introduced an innovative model called SAM-Net. The self-attention module of this model aims to extract an inner image frame’s spatial features of global and local dependencies. In addition, a memory mechanism has been added to the self-attention module to extract interframe features with long temporal and spatial dependencies. Our method shows better performance than the PredRNN-v2 model on publicly available datasets such as MovingMNIST and KTH. We achieved the best performance in both the 4-time-step and 10-time-step typhoon cloud image predictions. On a cloud dataset consisting of 10 time steps, we observed a decrease in MSE of 180.58, a decrease in LPIPS of 0.064, an increase in SSIM of 0.351, and a significant improvement in PSNR of 5.56 compared to PredRNN-v2.

In recent years, the scale of greenhouse grows rapidly and steadily in China, which promotes the development of greenhouse technology and the demand for standardized technology. However, the standardization process of greenhouse industry in China has been facing with the problem of unbalanced development with the practical standard system for many years. To solve these problems, the most characteristic greenhouse designs used in China contributing to this work are presented in detail. And the development of standard system and the standardization efforts of them are summarized and analyzed in relationship to the data presented in this paper. In this way the origin of greenhouse standard system in China and context of the system development and standardization of greenhouse structure are clarified. It was also found that the development and iterative update of greenhouse standard system are under the influences of enterprises which as the participation of standard constitutors were proved to play an important role in promoting the standardization development of greenhouse. The outcome of this work may be utilized to perfect the greenhouse standardization system and improve the greenhouse standardization efficiency that dominate the facility agriculture in China. Keywords: greenhouse, standardization, standard system, informationization, greenhouse technology DOI: 10.25165/j.ijabe.20181101.3857 Citation: Ren Y Z, Wang M J, Saeeda I A, Chen X R, Gao W L. Progress, problems and prospects for standardization of greenhouse-related technologies. Int J Agric & Biol Eng, 2018; 11(1): 40–48.

Knowledge extraction aims to identify entities and extract relations between them from unstructured text, which are in the form of triplets. Analysis of the fruit nutrition domain corpus revealed many overlapping triplets, that is, multiple correspondences between a subject and multiple objects or the same subject and object. The current relevant methods mainly target the extraction of ordinary triplets, which cannot accurately identify overlapping triplets. To solve this problem, a deep learning based model for overlapping triplet extraction is proposed in this study. The relation is modeled as a function that maps a subject to an object. The hybrid information of the subject is entered into the relation-object extraction model to detect the object and relation. The experimental results show this model outperforms existing extraction models and achieves state-of-the-art performance on the manually labeled fruit nutrition domain dataset. In terms of application value, the proposed work can obtain a high-quality and structured fruit nutrition knowledge base, which provides application fundamentals for downstream applications of nutrition matching.

A nonparametric variable step-size subband adaptive filtering algorithm for acoustic echo cancellation

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