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The qqsQ(q=u,d;Q=c,b)tetraquark system in a chiral quark model

Chinese Physics C 2024年第7期48卷 84-105页

作者：杨刚平加伦 Jorge Segovia Department of Physics Zhejiang Normal UniversityJinhua 321004China Department of Physics and Jiangsu Key Laboratory for Numerical Simulation of Large Scale Complex Systems Nanjing Normal UniversityNanjing 210023China Departamento de Sistemas Físicos Químicos y NaturalesUniversidad Pablo de OlavideE-41013 SevillaSpain

The S-wave qqsQ(q=u,d;Q=c,b)tetraquarks,with spin-paritiesJ^(P)=0^(+),1^(+),and 2^(+),in both isoscalar and isovector sectors,are systematically studied using a chiral quark *** meson-meson,diquark-antidiquark,and K-type arrangements of quarks and all possible color wave functions are comprehensively *** four-body system is solved using the Gaussian expansion method,a highly efficient computational ***,a complex-scaling formulation of the problem is established to disentangle bound,resonance,and scattering *** theoretical framework has already been successfully applied in various tetra-and penta-quark *** the complete coupled channel and within the complex-range formulation,several narrow resonances of qqsC and qqsb systems are obtained,in each allowed I(J^(P))-channel,within the energy regions of 2.4-3.4 GeV and 5.7-6.7GeV,*** predicted exotic states,which indicate a richer color structure when going towards multiquark systems beyond mesons and baryons,are expected to be confirmed in future high-energy particle and nuclear experiments.

关键词： quantum chromodynamics quark models

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Reliability Assessment of LCC-HVDC links Using Protection Devices 1

Reliability Assessment of LCC-HVDC links Using Protection De...

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1st International Conference on Renewable Solutions for Ecosystems: Towards a Sustainable Energy Transition, ICRSEtoSET 2023

作者： Hameurlaine, Abdelhadi Boukezzi, Larbi Karef, Salah Sayah, Haouari University of Djelfa Laboratory of Applied Automation & Industrial Diagnostics Djelfa Algeria University of Djelfa Materials Science and Informatics Laboratory Msil Djelfa Algeria University of Djelfa Com Laboratory of Modeling Optimization and Simulation of Real Complex Systems Djelfa Algeria University of SidiBel-Abbes Intelligent Control and Electrical Power System Laboratory SidiBel-Abbes Algeria

ISBN: (纸本)9798350346336

the DC/AC interaction and the presence of the high nonlinearity in the HVDC links, these problems needs to examine the behavior of the system in steady-state and transient conditions in order to define the great role of protection functions and control parameters. Furthermore, Control and protection systems are a vital part of an HVDC system to guarantee the safety, reliability, and stability of its operation. This research aim is to provide the solutions for control and protection problems that can occur in HVDC link. In this paper, we study how we mitigate this serious malfunction of commutation failure process in the operation of HVDC converters. Indeed some protection functions like the commutation failure prevention. Furthermore, HVDC transmission systems are vulnerable to DC faults and its protection becomes ever more important with the fast growth in the number of installations. Finally, the dynamics of the studied power system including the HVDC link is thoroughly investigated through various simulation scenarios. © 2023 IEEE.

关键词： HVDC power transmission

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A Client Selection Method Based on Loss Function Optimization for Federated Learning

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Computer modeling in Engineering & Sciences 2023年第10期137卷 1047-1064页

作者： Yan Zeng Siyuan Teng Tian Xiang Jilin Zhang Yuankai Mu Yongjian Ren Jian Wan School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Key Laboratory of Complex System Modeling and Simulation Ministry of EducationHangzhou310018China Zhejiang Engineering Research Center of Data Security Governance Hangzhou310018China Intelligent Robotics Research Center Zhejiang LabHangzhou311100China HDU-ITMO Joint Institute Hangzhou Dianzi UniversityHangzhou310018China

Federated learning is a distributedmachine learningmethod that can solve the increasingly serious problemof data islands and user data privacy,as it allows training data to be kept locally and not shared with other *** trains a globalmodel by aggregating locally-computedmodels of clients rather than their ***,the divergence of local models caused by data heterogeneity of different clients may lead to slow convergence of the global *** this problem,we focus on the client selection with federated learning,which can affect the convergence performance of the global model with the selected local *** propose FedChoice,a client selection method based on loss function optimization,to select appropriate local models to improve the convergence of the global *** firstly sets selected probability for clients with the value of loss function,and the client with high loss will be set higher selected probability,which can make them more likely to participate in ***,it introduces a local control vector and a global control vector to predict the local gradient direction and global gradient direction,respectively,and calculates the gradient correction vector to correct the gradient direction to reduce the cumulative deviationof the local gradient causedby *** experiments to verify the validity of FedChoice on CIFAR-10,CINIC-10,MNIST,EMNITS,and FEMNIST datasets,and the results show that the convergence of FedChoice is significantly improved,compared with FedAvg,FedProx,and FedNova.

关键词： Federated learning model aggregation Non-IID

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Investigating structural features of dimeric SARS-CoV-2 Mpro catalytic site with bound covalent ligands at physiological temperature 47

Investigating structural features of dimeric SARS-CoV-2 Mpro...

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47th Vietnam Conference on Theoretical Physics, VCTP 2022

作者： Lai, Hien T. T. Nguyen, Toan T. Key Laboratory for Multiscale Simulation of Complex Systems Department of Theoretical Physics Faculty of Physics University of Science Vietnam National University - Hanoi 334 Nguyen Trai street Thanh Xuan district Hanoi11400 Viet Nam

The SARS-CoV-2 main protease (Mpro) plays an important role in the viral transcription and replication of the SARS-CoV-2 virus that is causing the Covid-19 pandemic worldwide. Therefore, it represents a very attractive target for drug development for treatment of this disease. It is a cysteine protease because it has in the active site the catalytic dyad composed of cysteine (C145) and histidine (H41). The catalytic site represents the binding site for inhibitors, many of them bind to Mpro with a covalent bond. In this research, structural and physiochemical characteristics of the Mpro binding site are investigated when the ligand 11a is covalently and non-covalently bound. All-atom molecular dynamics (MD) simulations were run for 500 ns at physiological temperature (310 K). It is found that conformations of both the Mpro protein and the ligand are stable during the simulation with covalently bound complex showing stronger stability. When the ligand is covalently bound (its final state), residues that stably interact with the ligand are H41, C145, H163, H164 and E166. The optimal conformation of these residues is stabilized also via the Hbond interactions with the catalytic water present in the Mpro binding site. In the case of the non-covalently bound ligand (state before the covalent bond is formed), the binding site residues retain their conformations similar to the covalent binding site, and they still form Hbonds with the catalytic water, except H41. This residue, instead, adopts a different conformation and looses the Hbond with the catalytic water, leaving more freedom to move to the ligand. We hypothesize that H41 could play a role in guiding the ligand to the optimal position for final covalent bonding. Further analyses are in process to check this hypothesis. These results represent an important basis for studying drug candidates against SARS-CoV-2 by means of computer aided drug design. © Published under licence by IOP Publishing Ltd.

关键词： Ligands

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FedAG:A Federated Learning Method Based on Data Importance Weighted Aggregation

FedAG:A Federated Learning Method Based on Data Importance W...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Xu, Mengchu Zeng, Yan Xue, Meiting Zhang, Ji-Lin Wan, Jian Zhou, Mingyao Wen, Yilin Shi, Yukun Hangzhou Dianzi University School of Computer Science and Technology Hangzhou China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou China Zhejiang Engineering Research Center of Data Security Governance Hangzhou China Hangzhou Dianzi University School of Cyberspace Hangzhou China Huawei Enterprise Communication Technology Co Hangzhou China

ISBN: (纸本)9798350345384

Federated learning is a distributed machine learning approach that trains models with multiple clients and data locally. However, the existing methods ignore the differences between local models caused by the data heterogeneity, and give a high aggregation weight for the low-quality local model, which leads to low accuracy of the global model. For this problem, we propose a federated learning method based on data importance weighted aggregation, FedAG. It constructs an evaluation model of data importance based on the data distribution skewness, data volume, and data quality of clients, and aggregates local models with different weights computed by the evaluation model of data importance into a global model. We make experiments on five datasets including EMNIST, MNIST, and CIFAR-10. The results show that the accuracy of FedAG is respectively improved by 6.5%, 1.8%, 1.3% and 2.1%, compared with FedAvg, FedProx, FedVar, and Scaffold algorithms. © 2023 IEEE.

关键词： Scaffolds

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HRSTORY: Historical News Review Based Online Story Discovery 25

HRSTORY: Historical News Review Based Online Story Discovery

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Renjie Zhou Haoran Ye Jian Wan Yong Liao Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education Hangzhou Dianzi University Hangzhou Zhejiang China Zhejiang University of Science and Technology Hangzhou Zhejiang China University of Science and Technology of China Hefei Anhui China

ISBN: (纸本)9798400712456

Story discovery on news streams can help people quickly find story from vast amounts of news, improving the efficiency of information acquisition. Recent online story discovery methods encode text topics and then cluster articles into stories based on similarity. However, the results obtained by these methods are one-time, and clustered news cannot adaptively update in a continuous news stream. Additionally, the inadequate quality of article encoding and the presence of noise data deteriorate the performance of story discovery. To this end, we propose HRSTORY for online story discovery on news streams, which employs a historical news review method to enable news to continuously adapt to the latest environment in the stream data and make corrections and updates. Furthermore, HRSTORY captures better article embeddings through modeling multi-layer relational dependencies within the text. By using sentence-level noise masking, HRSTORY improves the relevance of news article representation to core topics and reduces the interference of noise data. Experiments on real news datasets show that HRSTORY outperforms the state-of-the-art algorithms in unsupervised online story discovery performance.

关键词： historical news

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Linear Array Synthesis Based on Artificial Neural Network Agent Model with Fast Fourier Transform

Linear Array Synthesis Based on Artificial Neural Network Ag...

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International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Aowei Li Jiahao Liu Xin Zhang Zhe Liu Foxiang Liu School of Information Engineering Nanchang University Nanchang China Key Laboratory of Numerical Simulation for Large Scale Complex Systems Ministry of Education

ISBN: (数字)9798350389968

ISBN: (纸本)9798350389975

This paper introduces a new array synthesis method based on back propagation (BP) neural network agent model with a training set quickly generated by fast Fourier transform. Moreover, it can be conveniently generalized to array models on different scenarios. A test example for synthesizing uniformly spaced linear array with non-uniform excitations is conducted. Synthesis results prove the effectiveness and superiority of the proposed method.

关键词： Training Microwave technology Microwave integrated circuits Fast Fourier transforms Millimeter wave technology Training data Prediction algorithms

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Architecture transformation research on joint border defense systems-of-systems 2021

Architecture transformation research on joint border defense...

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2021 5th International Conference on Management Engineering, Software Engineering and Service Sciences, ICMSS 2021

作者： Zhang, Mengmeng Yi, Shuanghui Lin, Menglong Chen, Honghui Science and Technology on Information Systems Engineering Laboratory China National Key Laboratory for Complex System Simulation China

ISBN: (纸本)9781450389709

Currently, with the deepening of development and open of China's border, issues of border joint defense become increasingly arduous. Traditional means of cooperation and coordination are far from being able to align requirements;information systems of different joint defense units are difficult to interconnect and interoperate;the previous results of joint defense informatization are difficult to inherit. The main reason is the lack of border joint defense information construction with a top-level design approach. Via the lens of top-level design, this paper establishes a multi-viewpoint architecture framework and transformation process, develops the as-is and to-be joint defense architecture, and finally analyzes transformation principles from the as-is architecture to to-be architecture. This paper mainly explores the architecture transformation mechanisms under the border joint defense scenario. © 2021 ACM.

关键词： Architecture

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NEW LIOUVILLE TYPE THEOREMS FOR 3D STEADY INCOMPRESSIBLE MHD EQUATIONS AND HALL-MHD EQUATIONS

arXiv

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arXiv 2025年

作者： Zhang, Zhibing School of Mathematics and Physics Key Laboratory of Modeling Simulation and Control of Complex Ecosystem in Dabie Mountains of Anhui Higher Education Institutes Anqing Normal University Anqing246133 China

In this paper, we study Liouville type results for the three-dimensional stationary incompressible MHD equations and Hall-MHD equations. By a new iteration argument, we establish Liouville type theorems if the velocity and magnetic field satisfy certain growth conditions of Lebesgue norms on the annulus. In our iteration procedure, the L2 norms of the gradients of the velocity and magnetic field and the L6 norms of the velocity and magnetic field are iterated together. The conditions imposed on the magnetic field are weaker than the velocity field in certain sense. As a consequence, we show that the velocity and magnetic field are trivial provided that they belong to some Lebesgue spaces or satisfy some decay conditions at infinity. Our results extend and improve the recent works of Chae-Lee (2024 Nonlinearity 37 095006) and Cho-Neustupa-Yang (2024 Nonlinearity 37 035007).MSC Codes 76W05, 76D03, 76D05 Copyright © 2025, The Authors. All rights reserved.

关键词： Magnetohydrodynamics

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Machine Learning Security Analysis Framework Based on Threat Matrix

Machine Learning Security Analysis Framework Based on Threat...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Hu Li Qiongmin Ma Ming Zhang Xiaohui Kuang National Key Laboratory of Science and Technology on Information System Security Beijing China National Key Laboratory for Complex Systems Simulation Beijing China

Machine Learning and Artificial Intelligence technology accelerates technological progress and promotes social development, but also brings many security problems. Machine learning models may be affected, deceived, controlled or destroyed at different stages, e.g. training and inference, which may lead to serious consequences. In view of the main stages of the life cycle of machine learning, we summarizes the related attack and defense technologies based on threat matrix, makes a comprehensive and systematic analysis on the security issues of machine learning, and puts forward several security protection suggestions for intelligent information systems.

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