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生物柴油低温过滤阻塞性能差是阻碍生物柴油发展的重要原因之一。为了加强生物柴油行业规范化，促进生物柴油产业健康发展，综述了生物柴油低温过滤阻塞性能的影响因素和评价方法，并根据不同影响因素提出了建议。生物柴油中饱和脂肪酸甲酯、脂肪酸甘油酯、游离甘油、甾醇糖苷均会影响生物柴油低温过滤阻塞性能。可通过调变生物柴油分子结构，添加流动性改进剂和冷冻分离饱和脂肪酸甲酯等方法，减少饱和脂肪酸甲酯对生物柴油低温过滤阻塞性能的负面影响。ASTM D7501是评价生物柴油低温过滤阻塞性能的有效方法。在ASTM D7501基础上，制定的适用于我国生物柴油低温过滤阻塞性能评价的试验方法，对于促进我国生物柴油产品品质提升具有现实意义。 Poor biodiesel low-temperature filtration blocking performance is one of the important reasons hindering the development of biodiesel. In order to strengthen the standardization of biodiesel industry and promote the healthy development of biodiesel industry, the influencing factors and evaluation methods of low-temperature filtration blocking performance of biodiesel were reviewed, and suggestions were made according to different influencing factors. Saturated fatty acid methyl esters, fatty acid glycerides, free glycerol, sterol glycosides in biodiesel can affect the low-temperature filtration blocking performance of biodiesel. The negative effect of saturated fatty acid methyl esters on the low-temperature filtration blocking performance of biodiesel can be reduced by modifying the molecular structure of biodiesel, adding flow improver and freezing separation of saturated fatty acid methyl esters. ASTM D7501 is an effective method to evaluate the low-temperature filtration blocking performance of biodiesel. The standard method of evaluating the low-temperature filtration blocking performance of biodiesel in China developed on the basis of ASTM D7501, is of practical significance to promote the quality improvement of biodiesel products in China.

为了制备新型柴油抗磨剂，以油酸甲酯、马来酸酐和甲醇为原料，合成了油酸甲酯基琥珀酸单甲酯。采用电喷雾电离质谱、傅里叶红外光谱、核磁共振氢谱、核磁共振碳谱等对油酸甲酯基琥珀酸单甲酯的结构进行表征，采用热重分析法对其热稳定性进行分析，同时采用高频往复试验机法（HFRR）考察其在低硫柴油中的抗磨效果。结果表明：油酸甲酯基琥珀酸单甲酯化合物具有典型的—CC—、—COOH以及—COOCH3结构特征以及良好的热稳定性，可有效改善低硫柴油的润滑性能；在加剂量为200 μg/g时即可使低硫柴油满足润滑性使用要求，在低硫柴油中的加剂量为200~1 500 μg/g范围时，可使所用的两种空白柴油的润滑性能分别提高35.4%~59.6%、33.9%~54.9%。 In order to prepare a new lubricity additive of diesel, methyl oleate, maleic anhydride and methanol were used as raw materials to synthesize methyl oleate monomethyl succinate (OM). The structure of OM was characterized by electrospray ionization mass spectrometry(ESI-MS), Fourier infrared spectroscopy(FT-IR), proton nuclear magnetic resonance spectroscopy(1H NMR), and carbon nuclear magnetic resonance spectroscopy(13 C NMR), and its thermal stability was evaluated by thermogravimetric anaylsis. In addition, the lubricity property of OM was specifically investigated in low sulfur diesel by high-frequency reciprocating rig (HFRR). The results showed that OM had the typical structure of —CC—, —COOH and —COOCH3，and good thermal stability. The OM could effectively improve the lubricity properties of low sulphur diesel. The lubricity of low sulfur diesel at a dosage of 200 μg/g OM could meet the requirement of lubricity. Whats more, when the OM was added to two low sulfur diesel at a dosage of 200-1 500 μg/g, the lubricity properties of blank diesels increased by 35.4%-59.6% and 33.9%-54.9%, respectively.

The task of fraud detection in blockchain requires a thorough exploration of the inherent temporal and spatial characteristics in historical transaction data. Existing fraud detection methods suffer from large prediction errors. To address this issue, this paper proposed a blockchain fraud detection method, named RGCN-SRG, based on reinforced graph convolutional network (RGCN) and spatiotemporal recurrent gate (SRG). Firstly, leveraging Bitcoin's historical transaction data for the construction of the transaction graph, the method used a reinforced graph convolutional network with different kernel sizes to comprehensively extract the graph's topology information and generate feature vectors. Additionally, considering the temporal characteristics of blockchain transactions, the method introduced a spatiotemporal recurrent gate structure that incorporated graph convolutional operations into the traditional gate structure to extract dependency information from multiple spatiotemporal dimensions of the transaction graph. Finally, it obtained the prediction results of money laundering detection through a linear layer and activation function. The proposed fraud detection method was evaluated by the constructed dataset. Compared with GCN, DEDGAT, EGT and GCN + MLP F, by the proposed method improves 18.4, 10.7, 9.2 and 4.9 percentage points, respectively; the precision improves 11.5, 11.2, 7.7 and 3.7 percentage points, respectively. [ABSTRACT FROM AUTHOR]

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Objective: To explore the effects of MOTOmed lower limb intelligent exercise training combined with motor imagination therapy on lower limb function, walking ability and trunk flexor and extensor muscle strength in stroke patients with hemiplegia. Methods: 148 stroke patients with hemiplegia who were treated in our hospital from May 2019 to May 2021 were selected. According to random number table method, they were divided into control group (n=74, combined with MOTOmed lower limb intelligent exercise training on the basis of routine rehabilitation training) and atudy group (n=74, combined with motor imagination therapy on the basis of control group). Both groups were intervened for 12 weeks. The changes of lower limb function, walking ability and trunk flexor and extensor muscle strength were compared between the two groups. Results: 12 weeks after intervention, the scores of Fugl-Meyer motor function scale (FMA), Barthel Index(BI), functional Ambulation Category (FAC) in two groups increased, and the study group was higher than the control group (P<0.05). 12 weeks after intervention, the step frequency, step speed and cross step ratio of the two groups increased, and the study group was higher than the control group (P<0.05). 12 weeks after intervention, there were no significant change in the root mean square value of surface electromyographic signals in the healthy side rectus abdominis muscle and healthy side erector spine muscle, and there was no difference between the two groups at the same time point (P>0.05). 12 weeks after intervention, the root mean square value of surface electromyographic signals in affected side rectus abdominis muscle and affected side erector spine muscle were increased, and the study group was higher than the control group(P<0.05). Conclusion: Stroke patients with hemiplegia can be rehabilitated by motor imagination therapy combined with MOTOmed lower limb intelligent exercise training, which can promote the improvement of lower limb function, improve walking ability, and improve the muscle strength of the trunk flexor and extensor muscle group on the affected side. [ABSTRACT FROM AUTHOR]

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When training graph embedding via neural network, high dimension feature vector and large scale graph cause data transferring from memory to GPU to be a bottleneck. Aimed to solve this problem, this paper proposed graph partition based graph representation learning. This method splitted graph nodes into blocks according to their degree. It stored several node feature matrices in buffer pool on GPU. Every epoch, it trained representation during several blocks which fitted into buffer pool to reduce the data transferred from memory to GPU. Based on block split, this method used blocked based sampling algorithm, cached block feature matrix in GPU buffer pool to reduce memory read and built hierarchical negative sampling table, which could sample nodes in const time complex. Compared to related work on several real world datasets, this method achieves competitive accuracy at downstream machine learning task and 2 ~ 7 times speedup on training process. The experiments show that graph representation learning based on partition can train model efficiently and generate accurate embedding vectors. In future work, it is worth to prove the deviation between partition based method and original method in theory. [ABSTRACT FROM AUTHOR]

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