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Enhancing Suppression of Chain Transfer via Catalyst Structural Evolution in Ethylene(Co)Polymerization

Chinese Journal of Chemistry 2023年第12期41卷 1509-1516页

作者： Beihang Ding Lihang Jiang Xiaohui Kang Shengyu Dai Institutes of Physical Science and Information Technology Key Laboratory of Structure and Functional Regulation of Hybrid Materials of Ministry of Education Anhui University Hefei Anhui 230601 China College of Pharmacy Dalian Medical University Dalian Liaoning 116044 China School of Chemical and Environmental Engineering Anhui Polytechnic University Wuhu Anhui 241000 China

Usually,the aniline-based late-transition-metal catalysts often require bulky steric substituents on both sides of the ortho-aryl position to achieve efficient suppression of chain transfer in ethylene *** this contribution,we demonstrated thatα-diimine catalysts based on naphthylamine with only one bulky ortho-aryl substituent also demonstrated excellent capabilities to suppress the chain ***,a class ofα-diimine nickel and palladium complexes with only one o-aryl-dibenzhydryl or o-aryl-dibenzosuberyl substituent were synthesized and ***,the as-prepared naphthylamine-based nickel catalysts demonstrated outstanding activities(up to 13.02×106 g·mol–1·h–1)and yielded lightly branched(16-40/1000C)polyethylenes with very high molecular weights(445.8-854.3 kg/mol)in ethylene *** comparison,the corresponding palladium catalysts showed moderate activities(level of 104-105 g·mol–1·h–1),generating moderately branched(47-78/1000C)polyethylenes with moderate molecular weights(21.6-82.0 kg/mol).Moreover,the palladium catalysts could also copolymerize ethylene and methyl acrylate(MA),albeit in low activities(level of 103 g·mol–1·h–1),providing E-MA copolymers with low to moderate molecular weights(1.4-16.3 kg/mol)and a moderate level of incorporation ratio(2.4-7.4 mol%)and branching density(53-84/1000C).As compared with aniline-based nickel and palladium catalysts,the naphthylamine-based catalysts displayed a superior ability to suppress the chain transfer reactions and could give access to(co)polymers with orders of magnitude higher molecular weight in ethylene(co)polymerization.

关键词： α-Dimine catalysts Chain transfer High molecular weight Naphthylamine-based catalysts Ethylene(co)polymerization Copolymers Homogeneouscatalysis Conformation

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Design of a Control System for Precision Two-Dimensional Positioning Platform of Small Desktop Scanning Electron Microscope 5

Design of a Control System for Precision Two-Dimensional Pos...

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5th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2023

作者： Zhang, Hui Bi, Yicheng Wang, Zuye Zhang, Yang School of Instrument Science and Opto-electronics Engineering Hefei University of Technology Anhui Hefei China

ISBN: (纸本)9798350336030

In this paper, a control system is designed for the precision two-dimensional positioning platform in small desktop scanning electron microscope. The positioning platform is driven by a stepper motor, and the control system contains control circuit and control program. RS485 protocol or Ethernet is adopted by the system to communicate with the host. The system realizes the functions of position closed-loop control, S-sharped speed curve control and error compensation for the stepper motor to achieve the target of easy operation with a stable, rapid and high-precision position. The experimental results show that the platform that adopted this control system could get the accuracy of the X-axis is ± 7.88μm, the repeated positioning accuracy of the X-axis is ± 3.21 μm, the positioning accuracy of the Y-axis is ± 5.28 μm, and the repeated positioning accuracy of the Y-axis is ± 4 μm, which meets the requirements of precision two-dimensional worktable in small desktop scanning electron microscope. © 2023 IEEE.

关键词： Error compensation

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Anomaly Detection in Spatial Information Networks: A Security Knowledge Graph-based Approach

Anomaly Detection in Spatial Information Networks: A Securit...

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2024 International Conference on Intelligent Computing and Robotics, ICICR 2024

作者： Tan, Yayue Zhang, Yaqi Liu, Hui Tang, Yongqing College of Computer and Information Science School of Software Southwest University Chongqing China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350372274

Anomaly detection stands as a critical element in securing space information networks (SINs). This paper delves into the realm of anomaly detection within dynamic networks, shedding light on established methodologies. The focus lies on scrutinizing various anomaly detection strategies in space information networks, conducting a comprehensive assessment of their practical applications, advantages, and drawbacks. Moreover, it introduces a novel anomaly detection approach driven by deep learning principles. This innovative method merges graph convolution techniques with a network securityoriented knowledge graph, presenting a cutting-edge solution. As a final contribution, the paper extends its purview to the future landscape of anomaly detection within space information networks, offering valuable insights into potential advancements and forthcoming trends in this domain. This holistic investigation seeks to enhance the understanding of anomaly detection paradigms within the unique context of space information networks, aiming to fortify their security measures and operational resilience. ©2024 IEEE.

关键词： Knowledge graph

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Predicting transcription factor binding sites by dual-stream multiple instance learning network

Predicting transcription factor binding sites by dual-stream...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Song, Ruqun Du, Xiuquan Anhui University School of Computer Science and Technology Hefei China

ISBN: (纸本)9781665468190

The discovery of transcription factor binding sites(TFBSs) is important for modeling potential binding mechanisms and subsequent cellular functions. In recent years, there have been many deep learning methods that have achieved good results in predicting transcription factor binding sites. However, these methods usually follow the fully supervised approach and ignore the weakly supervised information in DNA sequences. In contrast, the currently proposed multiple instance learing(MIL) methods based on weakly supervised learning usually divide the DNA sequence into multiple overlapping subsequences and model each instance separately. These methods do not take into account the connections between overlapping subsequences, and these methods destroy the global information of the sequences in the process of dividing them into overlapping subsequences. In addition, deep learning methods generally perform poorly when there is less training data. We, therefore, propose a new deep learning method, DS-SSB. More specifically, DS-SSB combines the dual-stream multiple instance network with multiple features. First, we combine sequence features and shape features after feature extraction at the instance level to enhance the feature representation of instances. Then, the instance embeddings are aggregated into bag embedding through the dual-stream multiple instance network, and the relationships between the instances are considered in the aggregation process. Finally, the instance features fused into bag features are fused together with the BERT features of the whole sequence at the bag level for the final prediction. Experiments conducted on 690 ChIP-seq datasets showed that DS-SSB achieved good performance in predicting TFBSs. Also, experiments on four datasets further show that our method has an advantage on small datasets as well © 2022 IEEE.

关键词： Embeddings

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Adaptable Individualized Investment Recommendation System

Adaptable Individualized Investment Recommendation System

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Sabiyath Fatima, N. Noor Alleema, N. Mahesh, C. Umanesan, R. Senthil, K. Santhosh Kumar, P. B.S.Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Information Technology Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Computer Science and Engineering Ramapuram Chennai India

ISBN: (纸本)9798331522988

Internetization has played a massive role in the rapid development and growth of the financial investment industry. Due to the rapid development, there is a demand for more financial knowledge services to help make well-informed investments. Accessing and conducting investments through online financial platforms has made the field highly accessible. This has made the investment recommendation system a popular research area. Personalized recommendations will help the user make investments that are safe for their financial situation and also suit their particular needs and interests. This work proposes a method where investment recommendations are given based on three key factors. The user interest sectors, risk appetite-based clustering, and market sentiment of the stock. These factors will be calculated by the use of Lasso regression, SVM, stepwise regression, generalized boosted regression, random forest, ridge regression and fuzzy clustering. Based on these three factors, the proposed method results in investment recommendations made for users that are personalized to the specific user. This allows existing investors to gain new opportunities and new investors to start investing with lower amount of risk using the information available. © 2024 IEEE.

关键词： Decentralized finance

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Bio-Inspired Optimal Dispatching of Wind Power Consumption Considering Multi-Time Scale Demand Response and High-Energy Load Participation

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computer Modeling in engineering & sciences 2023年第2期134卷 957-979页

作者： Peng Zhao Yongxin Zhang Qiaozhi Hua Haipeng Li Zheng Wen School of Information Technology Luoyang Normal UniversityLuoyang471022China Computer School Hubei University of Arts and ScienceXiangyang441000China Capinfo Company Ltd.Beijing100010China School of Fundamental Science and Engineering Waseda UniversityTokyo169-8050Japan

Bio-inspired computer modelling brings solutions fromthe living phenomena or biological systems to engineering *** overcome the obstruction problem of large-scale wind power consumption in Northwest China,this paper constructs a bio-inspired computer *** is an optimal wind power consumption dispatching model of multi-time scale demand response that takes into account the involved high-energy ***,the principle of wind power obstruction with the involvement of a high-energy load is examined in this *** this step,highenergy load model with different regulation characteristics is ***,considering the multi-time scale characteristics of high-energy load and other demand-side resources response speed,a multi-time scale model of coordination optimization is *** improved bio-inspired model incorporating particle swarm optimization is applied to minimize system operation and wind curtailment costs,as well as to find the most optimal energy configurationwithin the ***,we take an example of regional power grid in Gansu Province for simulation *** demonstrate that the suggested scheduling strategy can significantly enhance the wind power consumption level and minimize the system’s operational cost.

关键词： Biological system multi-time scale wind power consumption demand response bio-inspired computermodelling particle swarm optimization

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Reversible Data Hiding in Shared Images with Separate Cover Image Reconstruction and Secret Extraction

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IEEE Transactions on Cloud Computing 2024年第1期12卷 186-199页

作者： Xiong, Lizhi Han, Xiao Yang, Ching-Nung Shi, Yun-Qing Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China National Dong Hwa University Department of Computer Science & Information Engineering Hualien974301 Taiwan New Jersey Institute of Technology ECE Department NewarkNJ07102 United States

Reversible data hiding is widely utilized for secure communication and copyright protection. Recently, to improve embedding capacity and visual quality of stego-images, some Partial Reversible Data Hiding (PRDH) schemes are proposed. But these schemes are over the plaintext domain. To protect the privacy of the cover image, Reversible Data Hiding in Encrypted Images (RDHEI) techniques are preferred. In addition, the full separability of cover image reconstruction and data restoration is also an important characteristic that cannot be achieved by most RDHEI schemes. To solve the issues, a partial and a complete Reversible Data Hiding in Shared Images with Separate Cover Image Reconstruction and Secret Extraction (RDHSI-SRE) are proposed in this paper. In the proposed schemes, the secret data is divided by Secret Sharing (SS). Then, the marked shared images are generated based on the proposed modify-and-recalculate strategy. The receiver can extract embedded data and reconstruct the image separably using k-out-of-n marked shared images. In the embedding phase of partial RDHSI-SRE (PRDHSI-SRE), the pixel values are modified according to the proposed Minimizing-Square-Errors Strategy to achieve high visual quality, and the complete RDHSI-SRE (CRDHSI-SRE) embeds data by modifying random coefficients to achieve reversibility. The experimental results and theoretical analyses demonstrate that the proposed schemes have a high embedding performance. Most importantly, the proposed schemes are fault-tolerant and completely separable. © 2013 IEEE.

关键词： Image reconstruction

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Qualitative Failure Analysis of IoT-enabled Industrial Fire Detection and Prevention System 26

Qualitative Failure Analysis of IoT-enabled Industrial Fire ...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Rahman, Md Mokhlesur Abdulhamid, Alhassan Kabir, Sohag Military Institute of Science and Technology Department of Computer Science and Engineering Bangladesh University of Bradford School of Computer Science AI and Electronics United Kingdom

ISBN: (纸本)9798350359015

The Internet of Things (IoT) has improved our lives through various applications such as home automation, smart city monitoring, environmental monitoring, intelligent farming, and a host of others. IoT is increasingly being used for environmental monitoring to prevent fire incidents and other environmental hazards. However, for IoT systems to function effectively in preventing fire incidents, they must operate in a safe, reliable, and dependable manner. The intelligent sensors and devices that constitute the system are prone to different types of failures, which can lead to unsafe or dangerous conditions. Failure of a fire prevention system can pose significant risks to Health, Safety, and the Environment (HSE). To address these concerns, it is essential to understand how component failures can contribute to the overall system failure. This paper adopts Fault Tree Analysis, a widely used framework for failure behaviour analysis in other safety-critical domains, to qualitatively analyse an intelligent fire detection system in an industrial setting. The analysis outlines the ways in which the system can fail and the necessary prevention mechanism to guard against undesired system failure. © 2023 IEEE.

关键词： fault tree analysis Internet of Things smart fire control system system failure analysis

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RealDriftGenerator: A Novel Approach to Generate Concept Drift in Real World Scenario

RealDriftGenerator: A Novel Approach to Generate Concept Dri...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lin, Borong Huang, Chao Zhu, Xiaohui Jin, Nanlin School of Advance Technology Xi'an Jiaotong-Liverpool University Department of Computer Science and Software Engineering SuZhou China University of Liverpool Department of Computer Science Liverpool United Kingdom

ISBN: (纸本)9781665410205

Concept drift refers to the probability distribution of data generation changes over time in a data stream environment. In recent years, there has been an increasing interest in drift detection models. However, due to the lack of labeled concept drift datasets, most researchers tend to using synthetic drift data generators for model training. These generators only have relatively simple feature distributions, which fail to capture the complexity found in real-world scenarios. This paper introduces a real scenario concept drift label generator (RealDriftGenerator). This generator aims to preserve the complexity and temporal correlation of real-world scenario while generating concept drifts with user defined drift positions and drift widths. The validation result show that the temporal correlation coefficients of RealDriftGenerator is significantly higher than benchmark drift generators. Additionally, the ability of RealDriftGenerator to capture the complexity in real-world scenarios is 20% higher than benchmark drift generators(measured by model performance). The source code of RealDriftGenerator has been published on https://***/sniperrifle71/realDriftGenerator. © 2024 IEEE.

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Plausible Heterogeneous Graph k-Anonymization for Social Networks

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Tsinghua science and technology 2022年第6期27卷 912-924页

作者： Kaiyang Li Ling Tian Xu Zheng Bei Hui School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu 610000China

The inefficient utilization of ubiquitous graph data with combinatorial structures necessitates graph embedding methods,aiming at learning a continuous vector space for the graph which is amenable to be adopted in traditional machine learning algorithms in favor of vector *** embedding methods build an important bridge between social network analysis and data analytics as social networks naturally generate an unprecedented volume of graph data *** social network data not only bring benefit for public health,disaster response,commercial promotion,and many other applications,but also give birth to threats that jeopardize each individual’s privacy and ***,most existing works in publishing social graph embedding data only focus on preserving social graph structure with less attention paid to the privacy issues inherited from social *** be specific,attackers can infer the presence of a sensitive relationship between two individuals by training a predictive model with the exposed social network *** this paper,we propose a novel link-privacy preserved graph embedding framework using adversarial learning,which can reduce adversary’s prediction accuracy on sensitive links while persevering sufficient non-sensitive information such as graph topology and node attributes in graph *** experiments are conducted to evaluate the proposed framework using ground truth social network datasets.

关键词： social network graph embedding privacy preservation adversarial learning

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