Moisture sorption isotherms were determined for defatted canola meal at 16, 22, and 34ЊC. The isotherms were fitted to the Guggenheim-Anderson-deBoer (GAB) sorption equation. This equation was then used to develop a packaging model that predicted the changes of moisture content of canola meal under stated environmental and packaging conditions. The model was tested using Melinex 813 (12 µm) and Propafilm C (28 µm) packaging films at 86% relative humidity and 23ЊC. The GAB equation provided a good fit to experimental data (Ͻ3% RMS). The monolayer moisture content of the meal was 9.5%. The enthalpy of sorption of the monolayer at 22ЊC was 84.61 KJ/mol. The model predicted the time required by packaged canola to attain a selected moisture content ‫5.0ע‬ days.

Benefiting from the excellent ability of neural networks on learning semantic representations, existing studies for entity linking (EL) have resorted to neural networks to exploit both the local mentionto-entity compatibility and the global interdependence between different EL decisions for target entity disambiguation. However, most neural collective EL methods depend entirely upon neural networks to automatically model the semantic dependencies between different EL decisions, which lack of the guidance from external knowledge. In this paper, we propose a novel end-to-end neural network with recurrent random-walk layers for collective EL, which introduces external knowledge to model the semantic interdependence between different EL decisions. Specifically, we first establish a model based on local context features, and then stack random-walk layers to reinforce the evidence for related EL decisions into high-probability decisions, where the semantic interdependence between candidate entities is mainly induced from an external knowledge base. Finally, a semantic regularizer that preserves the collective EL decisions consistency is incorporated into the conventional objective function, so that the external knowledge base can be fully exploited in collective EL decisions. Experimental results and indepth analysis on various datasets show that our model achieves better performance than other stateof-the-art models. Our code and data are released at https://github.com/DeepLearnXMU/RRWEL.

Common spatial pattern (CSP) has been proved to be one of the most efficient feature-extracting methods for brain-computer interfaces (BCIs), especially for motor imagery BCI. However, CSP is a supervised method and performs poorly when there are not enough labeled data. This paper aims to construct a minimum-training BCI, which means there are only a few labeled data, even none labeled data for target subjects. Under this condition, conventional CSP cannot work well. Therefore, source data (related labeled data from other subjects) are exploited and common filters across subjects are obtained using a clustering method. After that, features are extracted and a semi-supervised support vector machine which transfers knowledge across subjects is proposed. The experiments illustrate the effectiveness of our algorithm. When there are none labeled data for target subjects, our algorithm outperforms two state-of-the-art algorithms in semi-supervised learning field, and as the amount of unlabeled data for target subjects becomes larger, the performance of our algorithm grows better and better, which is suitable for online use. When the amount of labeled data for target subjects (denoted as M in this paper) is small, our algorithm also shows its strength compared with corresponding outstanding algorithms. For dataset IVa of BCI competition III, our algorithm performs the best for all the subjects excluding ''aw'' when M = 20. Compared with counterparts, our method outperforms 6.6% for ''aa,'' 19.3% for ''al,'' 11.8% for ''av,'' and 9.7% for ''ay'' on average, respectively. When M = 40, our method performs the best for ''al'' and ''av''. It averagely outperforms 8.0% for ''al'' and 15.3% for ''av,'' respectively. For GigaDataset, averagely our method outperforms 4.4% for ''sbj2,'' 5.0% for ''sbj4,'' and 7.1% for ''sbj5'' when M = 20, respectively. When M = 40, our algorithm performs the best only for ''sbj5.'' It averagely outperforms 8.6% compared with counterparts. Although the performances of our method are not the best for all the conditions, its performances are very robust and competitive.

Real-time detection and identification of orchard pests is related to the economy of the orchard industry. Using lab picture collections and pictures from web crawling, a dataset of common pests in orchards has been created. It contains 24,748 color images and covers seven types of orchard pests. Based on this dataset, this paper combines YOLOv5 and GhostNet and explains the benefits of this method using feature maps, heatmaps and loss curve. The results show that the mAP of the proposed method increases by 1.5% compared to the original YOLOv5, with 2× or 3× fewer parameters, less GFLOPs and the same or less detection time. Considering the fewer parameters of the Ghost convolution, our new method can reach a higher mAP with the same epochs. Smaller neural networks are more feasible to deploy on FPGAs and other embedding devices which have limited memory. This research provides a method to deploy the algorithm on embedding devices.

QR code payment plays an indispensable role in the mobile payment market, and the security of scanning codes has always been a problem in the field of information security. Static QR codes are easily copied and replaced, and there are huge security loopholes. The QR code payment in a closed system still faces security challenges. In order to solve the security problem of QR code payment, we have studied dynamic QR code payment system that supports SM2, SM3, and SM4 cryptographic algorithms, which can realize QR code scanning and scanned transactions, UnionPay cloud QuickPass transactions, etc., and generate dynamic QR code information in real time during the transaction process, one order and one code. Through dynamic algorithm distribution, the randomness and uniqueness of QR code generation are guaranteed, and it is suitable for multi-scene application transactions. The algorithm correctness test result shows that the system has achieved the expected effect. The performance test results show that the hardware of the security module implements the algorithm flow and improves the payment performance. Compared with some other algorithms, the processing time is shorter, the running speed is faster, and the system is more secure.