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arXiv 2025年

作者： Zhao, Zi-Qiang Nie, Zhang-Yu Zhang, Jing-Fei Zhang, Xin College of Sciences Northeastern University Shenyang110819 China Center for Gravitation and Astrophysics Kunming University of Science and Technology Kunming650500 China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China

We study the properties of supercritical Reissner-Nordström Anti-de Sitter (RN-AdS) black holes in the extended phase space with the pressure defines as the cosmological constant. Supercritical black holes exist in the region where both temperature and pressure exceed the critical point, known as the supercritical region. The conventional view states that black holes in this regime are indistinguishable between large and small phases. However, recent research reveals that the supercritical regime exhibits universal gas-like and liquid-like phase separation, which shed light on the study on the supercritical region of RN-AdS black holes in the extended phase space. In this work, we calculate the thermodynamic potential and quasinormal modes (QNMs) of RN-AdS black holes, and identify transition curves between two different states in supercritical region using thermodynamic and dynamic methods. On one hand, we find the thermodynamic crossover curve (Widom line) by defining the scaled variance Ω (a higher-order derivative of Gibbs free energy). On the other hand, we identify the dynamic crossover curve (Frenkel line) by analyzing transitions between distinct QNM decay modes. Copyright © 2025, The Authors. All rights reserved.

关键词： Gibbs free energy

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Revisiting holographic dark energy after DESI 2024

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The European Physical Journal C 2025年第6期85卷 608-608页

作者： Tian-Nuo Li Yun-He Li Guo-Hong Du Peng-Ju Wu Lu Feng Jing-Fei Zhang Xin Zhang Liaoning Key Laboratory of Cosmology and Astrophysics College of Sciences Northeastern University 110819 Shenyang China School of Physics Ningxia University 750021 Yinchuan China College of Physical Science and Technology Shenyang Normal University 110034 Shenyang China MOE Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University 110819 Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University 110819 Shenyang China

New insights from the Dark Energy Spectroscopic Instrument (DESI) 2024 baryon acoustic oscillations (BAO) data, in conjunction with cosmic microwave background (CMB) and Type Ia supernova (SN) data, suggest that dark energy may not be a cosmological constant. In this work, we investigate the cosmological implications of holographic dark energy (HDE) and interacting holographic dark energy (IHDE) models, utilizing CMB, DESI BAO, and SN data. By considering the combined DESI BAO and SN data, we determine that in the IHDE model, the parameter c>1 and the dark-energy equation of state w does not cross -1 at the 1σ confidence level, whereas in the HDE model, it marginally falls below this threshold. Upon incorporating CMB data, we observe that in the HDE model, the parameter c<1 and w crosses -1 at a level beyond 10σ. Conversely, for the IHDE model, the likelihood of w crossing -1 is considerably diminished, implying that the introduction of interaction within the HDE model could potentially resolve or mitigate the cosmic big rip conundrum. Furthermore, our analysis reveals that the HDE and IHDE models are statistically as viable as the ΛCDM model when assessing Bayesian evidence with DESI BAO data combined with SN data. However, when CMB data are added, the HDE and IHDE models are significantly less favored than the ΛCDM model. Our findings advocate for further exploration of the HDE and IHDE models using forthcoming, more precise late-universe observations.

关键词： Dark energy

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Relieving the H0 tension with a new interacting dark energy model

arXiv

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arXiv 2021年

作者： Gao, Li-Yang Zhao, Ze-Wei Xue, She-Sheng Zhang, Xin Department of Physics College of Sciences MOE Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang110819 China ICRANet Piazzale della Repubblica Pescara10-65122 Italy Physics Department Sapienza University of Rome P.le A. Moro 5 Rome00185 Italy

We investigate an extended cosmological model motivated by the asymptotic safety of gravitational field theory, in which the matter and radiation densities and the cosmological constant receive a correction parametrized by the parameters δG and δΛ, leading to that both the evolutions of the matter and radiation densities and the cosmological constant slightly deviate from the standard forms. Here we explain this model as a scenario of vacuum energy interacting with matter and radiation. We consider two cases of the model: (i) ΛCDM ∼ with one additional free parameter δG, with δG and δΛ related by a low-redshift limit relation and (ii) eΛCDM ∼ with two additional free parameters δG and δΛ that are independent of each other. We use two data combinations, CMB+BAO+SN (CBS) and CMB+BAO+SN+H0 (CBSH), to constrain the models. We find that, in the case of using the CBS data, neither ΛCDM ∼ nor eΛCDM ∼ can effectively alleviate the H0 tension. However, it is found that using the CBSH data the H0 tension can be greatly relieved by the models. In particular, in the case of eΛCDM, ∼ the H0 tension can be resolved to 0.71σ. We conclude that as an interacting dark energy model, ΛCDM ∼ is much better than Λ(t)CDM in the sense of both relieving the H0 tension and fitting to the current observational data. © 2021, CC BY.

关键词： Cosmology

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Models of vacuum energy interacting with cold dark matter:Constraints and comparison

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Science China(Physics,Mechanics & Astronomy) 2019年第12期62卷 19-27页

作者： Hai-Li Li Lu Feng Jing-Fei Zhang Xin Zhang Department of Physics College of SciencesNortheastern UniversityShenyang 110819China College of Physical Science and Technology Shenyang Normal UniversityShenyang 110034China Ministry of Education Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern UniversityShenyang 110819China Center for High Energy Physics Peking UniversityBeijing 100080China

In this paper, we investigate the observational constraints on the scenario of vacuum energy interacting with cold dark *** consider eight typical interaction forms in such an interacting vacuum energy scenario. The observational data used in this work to constrain these models include the JLA sample of type Ia supernovae observation, the Planck 2015 distance priors data of cosmic microwave background anisotropies observation, the baryon acoustic oscillations data, and the Hubble constant direct measurement. We find that the current observational data almost equally favor these interacting vacuum energy models. We also find that for all these models of vacuum energy interacting with cold dark matter the case of no interaction is actually well consistent with the current observational data within 1σ range.

关键词： interacting dark energy interacting vacuum energy models cosmological observations cosmological constraints modelcomparison

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TEGDNet: Texture Enhancement Guided Detection Network for spiral welded pipeline defect detection

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Measurement 2025年

作者： Yu Zhang Kechen Song Wenqi Cui Yunhui Yan Guotong Lv Yanning Zhang School of Mechanical Engineering & Automation Northeastern University Shenyang 110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang 110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education China Lingang Beipiao Steel Pipe Company Ltd. Chaoyang China School of Software Shenyang University of Technology Shenyang China

Spiral welded pipelines play a crucial role in the transportation industry. However, defects in the pipeline welds pose significant safety hazards, and their detection is challenging. Current detection methods are highly subjective, labor-intensive, and prone to missed or overlooked defects without sufficient texture information. Therefore, we propose a solution to address these challenges. Firstly, we construct a dataset of spiral welded pipeline weld defects (NEU-WELD-2000). Furthermore, we propose a texture enhancement guided detection network (TEGDNet) to detect defects with insufficient texture information, such as weak textures, strong interference, scale variation, and tiny targets. TEGDNet includes an encoder structure for feature extraction and enhancement and a decoder structure based on super-resolution reconstruction. Experiments have demonstrated that our method achieves excellent detection results with fewer than 4M parameters, while mAP 50 improves by 3.2%, and mAP 75 , which is typically harder to improve, increases by 1.2% compared to the baseline. Additionally, we validate the effectiveness of each module through ablation experiments. Finally, the proposed detection method shows great potential in actual pipeline weld detection, significantly saving time and costs for operators through visualization software. The source code and the dataset are publicly available at https://***/acb-123-xxx/TEGDNet .

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Dynamic Topic Analysis in Academic Journals using Convex Non-negative Matrix Factorization Method

arXiv

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arXiv 2025年

作者： Yang, Yang Zhang, Tong Wu, Jian Su, Lijie National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China China Criminal Police University Shenyang China Center for Advanced Process Decision-making Carnegie Mellon University PittsburghPA United States Beijing Institute for General Artificial Intelligence Beijing China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Northeastern University ChinaShenyang Shenyang110819 China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang China

With the rapid advancement of large language models, academic topic identification and topic evolution analysis are crucial for enhancing AI’s understanding capabilities. Dynamic topic analysis provides a powerful approach to capturing and understanding the temporal evolution of topics in large-scale datasets. This paper presents a two-stage dynamic topic analysis framework that incorporates convex optimization to improve topic consistency, sparsity, and interpretability. In Stage 1, a two-layer non-negative matrix factorization (NMF) model is employed to extract annual topics and identify key terms. In Stage 2, a convex optimization algorithm refines the dynamic topic structure using the convex NMF (cNMF) model, further enhancing topic integration and stability. Applying the proposed method to IEEE journal abstracts from 2004 to 2022 effectively identifies and quantifies emerging research topics, such as COVID-19 and digital twins. By optimizing sparsity differences in the clustering feature space between traditional and emerging research topics, the framework provides deeper insights into topic evolution and ranking analysis. Moreover, the NMF-cNMF model demonstrates superior stability in topic consistency. At sparsity levels of 0.4, 0.6, and 0.9, the proposed approach improves topic ranking stability by 24.51%, 56.60%, and 36.93%, respectively. The source code is available at https://***/meetyangyang/CDNMF. © 2025, CC BY.

关键词： Non-negative matrix factorization

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Publisher Correction: Two-stage salient object detection based on prior distribution learning and saliency consistency optimization

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The Visual Computer 2022年第12期39卷 6771-6771页

作者： Wu, Yunhe Chang, Xingya Chen, Dongyue Chen, Lei Jia, Tong College of Information Science and Engineering Northeastern University Shenyang China Midea Group Shanghai China Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education Shenyang China

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Event-Triggered and Intermittent Emulation of Safety-Critical Controllers for Nonlinear Systems

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IEEE Transactions on Automatic Control 2025年

作者： Long, Lijun Zhang, Zhongkui Chen, Zhiyong Northeastern University College of Information Science and Engineering Shenyang110819 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang110016 China The University of Newcastle School of Engineering CallaghanNSW2308 Australia

This paper explores the emulation of safety-critical controllers for nonlinear systems within the constraints of limited computational resources and system data. We introduce innovative dynamic event-triggered and intermittent control strategies to digitally implement any given safety-critical controller constructed using a high-order control barrier function and its resultant Lyapunov function. These strategies simultaneously ensure forward invariance, asymptotic convergence, and prevent the Zeno phenomenon while providing an explicit characterization of the minimal dwell time. Our approach offers significant advantages in terms of resource efficiency compared to continuous-time safety-critical controllers. Additionally, we develop an extended framework to address the same control problem in the presence of unknown dynamics, enabling the application of a learning-based approach. We illustrate the effectiveness of the proposed design strategies through both an academic example and a practical problem in adaptive cruise control. © 1963-2012 IEEE.

关键词： Nonlinear control systems

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ABC: An Auction-Based Blockchain Consensus-Incentive Mechanism

ABC: An Auction-Based Blockchain Consensus-Incentive Mechani...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Zhengpeng Ai Yuan Liu Xingwei Wang Software College Northeastern University Shenyang China Ministry of Education Key Laboratory of Data Analytics and Optimization for Smart Industry (NEU) Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

The rapid development of blockchain technology and its various applications have attracted huge attention in the last five years. The consensus mechanism and incentive mechanism are the backbone of a blockchain network. The consensus mechanism plays a crucial role in sustaining the network security, integrity, and efficiency. The incentive mechanism motivates the distributed nodes to “mine” so as to participate the consensus mechanism. The existing mechanisms bear the fairness and justice issues. In this paper, from the perspective of mechanism design, we propose a consensus-incentive mechanism through applying continuous double auction theory, which is abbreviated as ABC mechanism. Our mechanism consists of four stages, including initiation stage, auction stage, completion stage, and confirmation stage. The auction model in use is the continuous double auction to ensure the transactions are stored in a real-time manner. Through extensive experimental evaluations, our mechanism is proven to improve the fairness and justice of the blockchain network.

关键词： Conferences Blockchain Real-time systems Security Communication networks

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Multiobjective multitasking optimization based on decomposition with dual neighborhoods

arXiv

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arXiv 2021年

作者： Wang, Xianpeng Dong, Zhiming Tang, Lixin Zhang, Qingfu Ministry of Education Shenyang110819 China Liaoning Engineering Laboratory of operation Analytics and Optimization for Smart Industry Liaoning Key Laboratory of Manufacturing System and Logistics Shenyang110819 China Institute of Industrial and Systems Engineering Northeastern University Shenyang110819 China City University of Hong Kong Shenzhen Research Institute Shenzhen518057 China

This paper proposes a multiobjective multitasking optimization evolutionary algorithm based on decomposition with dual neighborhood. In our proposed algorithm, each subproblem not only maintains a neighborhood based on the Euclidean distance among weight vectors within its own task, but also keeps a neighborhood with subproblems of other tasks. Gray relation analysis is used to define neighborhood among subproblems of different tasks. In such a way, relationship among different subproblems can be effectively exploited to guide the search. Experimental results show that our proposed algorithm outperforms four state-of-the-art multiobjective multitasking evolutionary algorithms and a traditional decomposition-based multiobjective evolutionary algorithm on a set of test problems. Copyright © 2021, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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