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Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, 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.)

Complex dynamic network data, such as social networks, protein interaction networks, and infectious disease transmission networks, are prevalent in the real world, consisting of numerous nodes and edges. Effective mining and utilization of such data for accurate prediction have become a key task. Dynamic graph neural network link prediction is an important branch of deep learning research, which aims to analyze the intrinsic laws of network evolution over time and predict potential future linkages, providing valuable information and basis for decision-making in various fields. This paper first reviews the development of dynamic graph neural networks, then introduces the modeling methods and training processes of dynamic graphs. Based on this, the paper categorizes dynamic graph neural network link prediction models into two main types according to the granularity of time: discrete dynamic graph models and continuous dynamic graph models, and provides an overview of the modeling methods used by current mainstream models in each category. In addition, it also introduces commonly used data sets, evaluation indicators and some application scenarios in dynamic graph link prediction research. Finally, the future development trends in this field are discussed prospectively. [ABSTRACT FROM AUTHOR]

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research 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.)

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Copyright of Pearl River is the property of Pearl River 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 Low-Carbon Chemistry & Chemical Engineering is the property of Low-Carbon Chemistry & Chemical Engineering 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 Petroleum Geology & Oilfield Development in Daqing is the property of Editorial Department of Petroleum Geology & Oilfield Development in Daqing 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 manufacturing process of composites is crucial for ensuring structural efficiency and application reliability of their products. Computer-based process simulation plays a significant role in improving the manufacturing quality of composite components and reducing the manufacturing cost. Traditional process simulation relies on physical and chemical mechanisms in manufacturing of composites with the mathematical equations solved by numerical methods such as finite element/finite volume analysis and computer-aided design methods e.g. computer graphics. At present, it has been widely used in simulations of the lay-up of reinforcements/prepregs, the infiltration flows of resin, the curing behaviours of thermosetting resin, the heat transmission and exchange, and the nonlinear mechanics including residual stress and curing deformation predictions. Recently, artificial intelligence (AI) technologies have rapidly developed, its technical basis machine learning(ML), in combination with artificial neural networks (ANN), has been used in the field of lay-up process of fiber reinforcements, liquid molding processes, and autoclave processes, which aimed for data mining and developing reduced-order models. The former can establish relationships between process conditions and the curing quality or mechanical properties of the composite parts, while the latter can improve computational efficiency of the process simulation. However, due to the complexity, immeasurability, and high cost of manufacturing fiber-reinforced resin matrix composites at the beginning of the AI age, it is difficult to meet the requirements of ML only by relying on the amount of data obtained by experiments. Also, data-driven AI technology faces uncertain issues regarding the representativeness, generalisability, and interpretability of the models. Therefore, traditional process simulation based on physicochemical mechanisms can provide a large amount of reliable data for data-driven ML simulation, and then through AI, more quantitative models describing the composite process can be established to expand the computable scope of process simulation. At the same time, as AI technology enhances the computational efficiency, the process simulation that meets real-time requirements can evolve into digital twins (DT) of the composite manufacturing process, which can provide new technical support for reducing the composite costs and improving the scientific whole-life cycle management. [ABSTRACT FROM AUTHOR]

Copyright of Acta Materiae Compositae Sinica is the property of Acta Materiea Compositae Sinica 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 Food & Fermentation Industries is the property of Food & Fermentation Industries 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 Tissue Engineering Research / Zhongguo Zuzhi Gongcheng Yanjiu is the property of Chinese Journal of Tissue Engineering Research 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 Plastics Science & Technology / Suliao Ke-Ji is the property of Plastics Science & 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.)

Copyright of Plastics Science & Technology / Suliao Ke-Ji is the property of Plastics Science & 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.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays 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 Materials Engineering / Cailiao Gongcheng is the property of Journal of Materials Engineering 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 Journal of Henan University of Technology Natural Science Edition is the property of Henan University of Technology Journal 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 Guangdong University of Technology is the property of Journal of Guangdong University of Technology 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 Automotive Digest is the property of Automotive Digest 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 Electric Power is the property of Electric Power 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 Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics 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 Tissue Engineering Research / Zhongguo Zuzhi Gongcheng Yanjiu is the property of Chinese Journal of Tissue Engineering Research 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 Food & Fermentation Industries is the property of Food & Fermentation Industries 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 Bulletin of the Chinese Ceramic Society is the property of Bulletin of the Chinese Ceramic Society 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 Cancer Research on Prevention & Treatment is the property of Cancer Research on Prevention & Treatment 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 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 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.)

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management 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 Journal of Materials Engineering / Cailiao Gongcheng is the property of Journal of Materials Engineering 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 Journal of Materials Engineering / Cailiao Gongcheng is the property of Journal of Materials Engineering 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 Automobile Technology is the property of Automobile 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.)

Copyright of Journal of Functional Polymers is the property of Journal of Functional Polymers 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.)

Objective: The study was designed to test solvent extracts of Rubus suavissimus (RS) for compounds that regulate lipid metabolism and to measure their relative activity as quantitative markers of lipid metabolism-regulating activities based on the spectrum-effect relationship. Method: RS was extracted with 95% ethanol, the different extraction parts were successively extracted with petroleum ether, methylene chloride, ethyl acetate and n-butanol, respectively. The solution of different extraction parts was applied to the induced differentiated 3T3-L1 preadipocytes, and the oil red O staining and triglyceride release from the cells were determined to screen the best metabolism-regulating site. Analysis of different extraction sited by UPLC-Q-TOF-MS/MS technique to separate and identify the components. Based on specific assays, image intensity analysis, grey correlation and partial least squares regressions, the spectrum-effect relationship of the most abundant active components and their ability to regulate lipid metabolism were determined, and the quality markers were screened. Results: Ethyl acetate and n-butanol extraction site was the best metabolism-regulating site. Four components, including ellagic acid, centaurin-3-O-rutinoside, rubusoside and enantio-kauri-16-ene-19-carboxylic acid-13-O-β-D-glucoside were highly associated with metabolism-regulating effects. It could be used as quality markers for regulating lipid metabolism. Conclusion: Investigation of the quality markers of RS extracts based on the spectrum-effect relationship is of great importance for elucidating the pharmacodynamics, screening the core quality markers for therapeutic activity, and ensuring the safety and rational application of traditional medicines. [ABSTRACT FROM AUTHOR]

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research 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 Natural Gas Geoscience is the property of Natural Gas Geoscience 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.)

Gaze estimation is a technique for predicting the gaze position or gaze direction of the human eye and plays an important role in human-computer interaction and computer vision applications. The recent development of deep learning has revolutionized many computer vision tasks, and using deep learning for appearance-based gaze estimation has also become a hot topic. Focusing on the training process of the deep learning model, this paper analyzes state-of-the-art gaze estimation methods from four perspectives: gaze data preprocessing, gaze feature extraction, gaze learning strategies, and deep gaze model structures. In addition, the mainstream public datasets are summarized, and the performance evaluation and analysis of 2D and 3D gaze estimation methods are carried out on several popular datasets. Finally, the challenges faced by the existing gaze estimation methods are discussed, and the future development directions are prospected. [ABSTRACT FROM AUTHOR]

Knowledge graphs are widely used and semantically rich data representations, which is increasingly becoming a crucial technology in the field of knowledge engineering. However, real-world knowledge graphs often suffer from incompleteness and ambiguity, hindering their application performance. Knowledge graph completion techniques aim to enrich the content of knowledge graphs by predicting missing entities or relations, has been a hot research topic in recent years. In particular, embedding-based approaches have made remarkable progress in knowledge graph completion tasks. It reviews recent embedding- based static knowledge graph completion methods, categorizing them based on approaches such as translation- based models, tensor factorization, neural network models, and pre- trained language models. These methods achieve an improved semantic representation and inferential capabilities by embedding entity relations into continuous vector spaces. At the same time, it has potential advantages in capturing complex relationships between entities and utilizing graph structural information. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality 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 Shanghai Land & Resources / Shanghai Guotu Ziyuan is the property of Editorial Board of Shanghai Land & Resources 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 Foundry Technology (1000-8365) is the property of Foundry 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.)

In this paper, the effects of three postharvest treatments (aerobic, vacuum, vacuum+1-MCP) on browning, active oxygen metabolism and saccharides during storage of deastringent persimmon were researched. Meanwhile, the correlations between browning and other indexes were analyzed. BI of vacuum and vacuum+1-MCP was lower than that in aerobic group, and the browning degree in vacuum+1-MCP group was the lowest. The SOD and APX activities of the three kinds of persimmon treated after harvest decreased and increased respectively, and the SOD activities of the vacuum group were the lowest, the APX activities under vacuum and vacuum+1-MCP conditions were higher than that in aerobic group. At vacuum and vacuum+1-MCP conditions, the increase of CAT activity of persimmon was inhibited, and the accumulation of H2O2 was reduced, with lower ability to inhibit hydroxyl radical and ascorbic acid content. The reducing sugar content of persimmon in vacuum and vacuum+1-McP groups was lower than that in aerobic group, and the increase in vacuum+1-MCP group was the slowest, from 14.10% at the beginning of storage to 14.87% on 12 d. The pectin content of persimmon in vacuum+1-MCP group decreased slowly. Correlation analysis showed that the correlations between browning and various indexes under different postharharvest treatments were different. The persimmon BI was always negatively correlated with pectin, meanwhile positively correlated with reducing sugar. It can be concluded that vacuum and vacuum+1-MCP conditions are more conducive to inhibiting the browning of persimmon after deastringent. [ABSTRACT FROM AUTHOR]

The classification of pulmonary nodule is one of the important issues in early detection and diagnosis of lung cancer. To address the problem of information redundancy in multi-scale feature fusion and lack of discriminative feature representation in existing lung nodule classification methods, a multi-scale feature complementation and aggregate constraint (MFCAC) pulmonary nodule classification network is proposed. A multi-scale feature complementation module is proposed to learn the difference information of adjacent scale features, thereby avoiding information redundancy in the feature fusion process. Meanwhile, aggregate constraint loss is introduced into the network feature layer to achieve aggregation of similar features and improve the discriminative feature representation ability of the network. The two modules are integrated into the encoder-decoder architecture to form MFCAC, which can achieve efficient classification. Comparative experiments are conducted on the LIDC-IDRI dataset, and ablation experiments are used to analyze the contributions and effects of each component in this method. The results show that MFCAC has better performance in lung nodule classification compared to the compared algorithms. [ABSTRACT FROM AUTHOR]

With the rapid development of the e-commerce industry, RMFS is prevalent in the modern logistic center. As for this advanced system, robots can apply two following strategies: all-time following and non-following in a task. Alltime following strategy means that robot follows the picking rack until the task is completed, and non-following strategy refers to that robot will leave the rack to perform other tasks when the rack is queued at the workstation, and the robot will be dispatched when the rack needs to be moved. For the robot scheduling problem under two kinds of following strategies, aiming at the shortest total time for the robot to complete the wave tasks, the robot scheduling models under two kinds of following strategies are designed. The simulation experimental platform under two kinds of following strategies are designed based on genetic algorithm, and the system performance of all-time following strategy and non- following strategy is compared through simulation experiments. The decision-making curve of the following strategy based on the shortest total time is obtained, and it is proved that selecting the appropriate following strategy, which is combined with the specific working conditions, can effectively improve the operation efficiency of the picking system. The application following strategy curve can help enterprises to choose robot following strategy. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs), due to their mobility and flexibility, are widely used in tasks such as search and tracking. The large amount of data generated during task execution can be significantly leveraged by machine learning (ML) algorithms to improve the intelligence of UAV clusters. Federated learning (FL), as a distributed machine learning architecture, is more suitable for UAV networks with limited bandwidth and energy budget, as it only requires model parameter transmission during training. To fully exploit the advantages of FL in UAV networks, this paper models factors affecting training energy consumption, such as bandwidth, computational frequency, FL accuracy, and training delay. A joint optimization algorithm is proposed to minimize the overall training energy consumption of FL by jointly optimizing training parameter settings, UAV trajectories, resource allocation of communication and computation in UAV networks. The proposed joint optimization algorithm decomposes the mixed integer nonlinear integer programming problem (MINLP) into three sub-problems, and transforms the non-convex sub-problems into convex sub-problems through methods such as successive convex approximation (SCA) and block coordinate descent to obtain suboptimal solutions. Simulation results show that, based on the constraints of training accuracy, UAV movement, and FL delay, the proposed algorithm reduces the overall training energy consumption of unmanned aerial vehicles by more than 15% compared to the existing joint training and resource optimization schemes. [ABSTRACT FROM AUTHOR]

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research 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.)