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Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Anhui Agricultural Sciences is the property of Journal of Anhui Agricultural Sciences Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aspect-based sentiment analysis is a fine-grained sentiment analysis task, which aims to align aspects with the corresponding emotion words for aspect specific emotion polarity reasoning. In recent years, the graph neural network sentiment classification method based on syntactic dependent information has become a research hotspot in this field. However, due to the flexibility of comment sentences in content expression and syntactic structure, the modeling method using only syntactic dependent information still has some shortcomings. In order to enhance the comment sentences by affective knowledge and structural semantic information, a convolutional network model(DualSyn-GCN) of two channel knowledge enhancement graph is proposed. On one hand, the syntactic dependency adjacency matrix is enhanced according to the implicit relationship between aspect and aspect as well as aspect and context. On the other hand, the emotional dependency of aspect is learned from external emotional knowledge, and then the two different enhanced representations are fused to realize the sharing and complementarity between different representations. The experimental results show that, compared with the classical aspect based graph convolutional network model (ASGCN), this model improves the accuracy and MF1 value on LAP14 data set by 2.34% and 3.26% respectively. [ABSTRACT FROM AUTHOR]

Aspect-level sentiment classification aims to discriminate the sentiment polarity of a specific aspect in a sentence. Although attention-based recurrent neural network models perform well among existing solutions, they are not ideal for processing "semantically ambiguous" sentences that are short and contain many neologisms and polysemous words. Therefore, this paper proposes a neural network model based on knowledge graph and attention mechanism. The basic idea is to use a knowledge base to obtain a relevant concept set of aspect words and integrate external information to enhance the semantic representation of the text. Firstly, the output of bidirectional long short-term memory network is combined with self-attention mechanism to generate context representation. Then, the upper and lower context representations are combined to use dual attention to obtain external knowledge from the knowledge graph and obtain knowledge vectors related to aspect words. Finally, the two parts of content are input together into a fully connected network to calculate the aspect-level sentiment tendency. Experimental results show that compared with other models, the proposed model significantly improves classification performance. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Progress in Modern Biomedicine is the property of Publishing House of Progress in Modern Biomedicine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Hydrocyclone is a common solid-liquid separation equipment, but due to its own structural limitations, the classification accuracy in the cyclone is not high. As the primary channel for multiphase flow entering the cyclone, the feed body has a significant impact on the classification performance of the cyclone. However, the problems of turbulent diffusion caused by high turbulent flow field in the feed body and low particle classification accuracy caused by collision and compression between particles urgently need to be solved. Therefore, a composite curve feed structure cyclone has been designed, which reduces the proportion of fine particles entering the bottom flow by regulating the internal flow field and particle motion in the feed body, thereby achieving the goal of improving classification accuracy. The numerical analysis method was used to explore the flow field characteristics and classification performance characteristics of five types of hydrocyclones with different feed-body structure: linear feed body, involute feed body, vortex feed body, linear guide feed body, and composite curve feed body. The superiority of the hydrocyclone with composite curve feed body was highlighted, and comprehensive exploration was conducted from the aspects of pressure field, velocity field, classification efficiency, and so on. Finally, experimental exploration was conducted on it. The results show that the composite curve feed body structure has better fine particle classification performance compared to other feed body structures, and the internal flow field is more stable. At the same time, the isobaric surface distribution shows regular changes, and the particle movement is more stable. The tangential velocity of the composite curve feed body is significantly higher than that of other feed body structures, and the particles obtain greater centrifugal driving force, making them easier to classify. The experimental results show that the separation efficiency of the compound curve feed body hydrocyclone is 46.32%, which is 7.32% higher than that of the conventional feed body hydrocyclone, and the content of -24 µm particles in the compound curve underflow is 15.07% lower than that of the conventional one. The content of -24 jum particles in the overflow is 3.39% higher than that in the conventional type, and the particle classification accuracy is greatly improved. [ABSTRACT FROM AUTHOR]

Copyright of Power Generation Technology is the property of Power Generation Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The goal of aspect level affective analysis is to identify the affective polarity of specific aspect words in a given sentence. At present, most of the research combining graph convolution neural network and syntactic dependency tree focuses on learning the relationship between context and aspect words according to the sentence dependency tree, but does not focus on the construction of syntactic dependency tree, so it can't make full use of the information on the dependency tree, and will introduce noise. To solve the above problems, this paper proposes a graph convolution network model based on multi-fusion adjacency matrix algorithm. Firstly, external knowledge is used to enhance the role of emotional words in sentences, and the part-of-speech is used for information filtering to remove redundant dependencies in sentences to obtain pruned syntactic dependency trees. The two are combined by multi-fusion adjacency matrix algorithm to obtain syntactic information. The syntactic information and the semantic information extracted from the BiLSTM layer are input into the simplified graph convolution network for feature fusion. Experimental results on five datasets show that the proposed method is effective and can significantly improve the performance of the model. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Pesticide Science / Nongyaoxue Xuebao is the property of Chinese Journal of Pesticide Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Railway Signalling & Communication Engineering is the property of Railway Signalling & Communication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Fine-grained aspect-based sentiment analysis involves aspect term extraction and aspect sentiment classification. Most existing research methods address them in an independent fashion, which lack a mechanism to account for the relevant information between each other, resulting in training redundancy and waste of resources. To solve the above problems, a joint model based on position embedding and graph convolutional network (PE-GCN) under the framework of multi-task learning is proposed, which is an end-to-end approach to the overall solution of aspect term extraction and aspect sentiment classification. Firstly, the model learns the semantic feature representation of sentence through a bidirectional gated recurrent unit network. Then, it exploits positional embedding to enhance the recognition of aspect terms in sentence, and uses the graph convolutional network to generate a contextual representation containing syntactic information. Finally, interactive attention network is used to model the semantic relationship between context and aspect terms, and the sentiment polarity of aspect terms is output through softmax. Experimental results on the SemEval-2014 public datasets show that the performance of the proposed model has a significant improvement compared with other existing models. [ABSTRACT FROM AUTHOR]

With increasingly severe problems of energy consumption and environmental pollution, under the guidance of fuel consumption and emission regulations, how to further reduce fuel consumption has become the top priority of the development of the automobile industry. With a consideration of both power and economy, Plug-in Hybrid Electric Vehicles (PHEV) have lower fuel consumption than traditional fuel vehicles and have multiple power drive modes in their design. Compared with pure electric vehicles, they do not have the problem of endurance mileage anxiety, and become the focus of the current automobile industry. However, fuel economy of PHEV requires a careful consideration of various factors. How to improve fuel economy has been the focus and difficulty of the automotive industry. The PHEV based on the P2.5 hybrid system configuration integrates drive motors into a particular input shaft of transmission, with high drive and braking efficiency. It can drive directly through the clutch and transmission and work together with the engine. Compared with other hybrid models, the P2.5 configuration PHEV has higher integration, less space, a smooth connection between oil and electricity, higher drive and braking efficiency, and superior fuel economy. Therefore, this paper takes the P2.5 configuration PHEV as the research object and proposes a rule-based energy management strategy to improve fuel economy of the P2.5 configuration PHEV. First of all, the power system of PHEV with P2.5 configuration is analyzed, and, combined with the advantages of this configuration, it is divided into five working modes : pure electric driving mode, pure engine driving mode, light load charging mode, hybrid driving mode, and regenerative braking mode. Secondly, based on the idea of the logical threshold value of the state of charge (SOC) of the power battery, a multi-phase energy management control strategy of CD-CS is proposed. According to the change trajectory of the SOC value of the battery, the active phase of PHEV is divided into two phases, namely charge-depleting (CD) and charge-sustaining (CS), and corresponding rules are formulated according to the SOC value of the battery to switch the two modes. On the premise of meeting the power, the vehicle is operated to use up the battery energy as much as possible to reduce fuel consumption within the driving range. Finally, according to the idea of the forward simulation model, the driver model, engine model, drive motor model, power battery model, vehicle dynamics model and other vital components of PHEV are simulated under different working conditions in Matlab/Simulink software based on experimental data. The results show that the vehicle model established in this paper and the proposed rule-based energy management strategy are accurate and effective. Compared with the pure engine mode, fuel savings of 100 km under WLTC, NEDC, UDDS and HWFET conditions increases by 34.6%, 38.8%, 63.3%, and 30.9%respectively. The simulation results show that fuel savings under all conditions increase by more than 30%, with a maximum increase of 63.3 %. This fully demonstrates that the rule-based energy management strategy can effectively improve fuel economy of the engine. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Ocean University is the property of Editorial Department of Journal of Shanghai Ocean University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Andrology is the property of Chinese Journal of Andrology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Andrology is the property of Chinese Journal of Andrology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Basic Sciences Journal of Textile Universities / Fangzhi Gaoxiao Jichu Kexue Xuebao is the property of Basic Sciences Journal of Textile Universities and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of TRANS: Revista de Traductología is the property of TRANS: Revista de Traductologia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Modern Laboratory Medicine is the property of Journal of Modern Laboratory Medicine Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The goat hair and chicken feather was steam exploded, frozen and disintegrated and further used for the adsorption of reactive blue 19 (RB19) dye. The effects of steam explosion,freezing and disintegration on the adsorption capacity of goat hair and chicken feather and their power were studied. As a result, the raw chicken hair (175 mg/g) had higher adsorption capacity than that of goat hair (55.24 mg/g); After steam flash explosion, freezing and disintegration, the scales of goat hair were broken, The crystallization indexes of EGHP and ECFP decreased, which were 8. 6% and 13.1%, respectively, its specific surface area, BJH adsorption pore cumulative specific surface area and pore volume are 1.64, 2.74, and 2.65 times of ECFP, respectively, their adsorption amount are 426. 7 mg/g and 440 mg/g. The combined treatment of steam flash explosion, frozen and disintegration improved the adsorption performance of goat hair and chicken feather effectively on RB19, and its adsorption capacity for goat hair is improved significantly. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Modern Medicine & Health is the property of Journal of Modern Medicine & Health and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Northern Agriculture / Beifang Nongye Xuebao is the property of Journal of Northern Agriculture Editorial and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)