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Evolution of cooperation under punishment

Physical Review E 2020年第6期101卷 062419-062419页

作者： Shiping Gao Jinming Du Jinling Liang School of Mathematics Southeast University Nanjing 210096 China Institute of Industrial and Systems Engineering College of Information Science and Engineering Northeastern University Shenyang 110891 China Liaoning Engineering Laboratory of Operations Analytics and Optimization for Smart Industry Northeastern University Shenyang 110891 China Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education Shenyang 110891 China

Punishment has been considered as an effective mechanism for promoting and sustaining cooperation. In most existing models, punishment always comes as a third strategy alongside cooperation and defection, and it is commonly assumed to be executed based on individual decision rules rather than collective decision rules. Differently from previous works, we employ a democratic procedure by which cooperators cast votes independently and simultaneously for whether to impose punishment on defectors, and we establish a relationship between the cooperators' willingness to punish defectors (WTPD) and whether the punishment is inflicted on defectors. The results illustrate that the population can evolve to full cooperation under consensual punishment. It is noteworthy that, compared with autonomous punishment, whether consensual punishment is more in favor of cooperation crucially depends on the minimum number of votes required for punishment execution as well as the cooperators' WTPD. Our findings highlight the importance of collective decision making in the evolution of cooperation and may provide a mathematical framework for explaining the prevalence of democracy in modern societies.

关键词： Evolutionary dynamics

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Theoretical Study on Coal Gasification Behavior in CO2 Atmosphere Driven by Slag Waste Heat

SSRN

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SSRN 2023年

作者： Duan, Wenjun Yang, Shuo Han, Jiachen Wu, Qinting Li, Peishi Cheng, Peiwen Lv, Xiaojun Wang, Zhimei Yu, Qingbo School of Metallurgy Northeastern University No 11 Lane 3 Wenhua Road Heping District Liaoning Shenyang110819 China College of Energy and Environment Shenyang Aerospace University No. 37 Daoyi South Street Shenbei New District Liaoning Shenyang110136 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University No. 11 Lane 3 Wenhua Road Heping District Liaoning Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education No. 11 Lane 3 Wenhua Road Heping District Liaoning Shenyang110819 China Northeastern University United States

Blast furnace slag, a main by-product of ironmaking process, contained high-quality sensible heat, accounting for almost 30% of total energy consumption in iron and steel industry. This paper proposed CO2 coal gasification method aimed at recovering waste heat from blast furnace slag. Based on Gibbs free energy minimization, influence of temperature, CO2/C, pressure and steam were investigated. Under optimal operating conditions at 1023~1198 K, CO2/C of 1.5~2.0 and atmospheric pressure, total amount of syngas was 2.78 kmol, with 67.1% CO and 8.79% H2. Addition of steam could adjust the compositions of syngas. H2/CO of syngas reached 1.0, 1.5 and 2.0 when H2O(g)/CO2 were 1.45/0.55, 1.73/0.27 and 1.95/0.05, which could be used as organic raw materials for chemical industry. Meantime, energy and exergy analysis were utilized to comprehend energy utilization effect of gasification reaction process. Energy and exergy efficiency of coal gasification were found to be 92.76% and 86.26%, respectively, under optimal operation conditions. For different syngas products, when H2O(g)/CO2 was 1.95/0.05, exergy loss was the lowest, with internal and external exergy loss amounting to 10.27% and 0.99% respectively. Utilizing coal gasification reaction was important for recovery of waste heat from blast furnace slag and reduction of CO2 emissions. © 2023, The Authors. All rights reserved.

关键词： Waste heat

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The universal crossover from thermodynamics and dynamics of supercritical RN-AdS black hole

arXiv

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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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Two-sided based Material Allocation for Intermediate Inventory of Process industry

Two-sided based Material Allocation for Intermediate Invento...

引用

Chinese Control Conference (CCC)

作者： Yiran Li Ying Meng Yuanqing Xia School of Automation Beijing Institute of Technology Beijing China Ministry of Education Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Shenyang China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

Two-sided matching is an effective decision methodology to solve resource allocation problem, aiming at satisfactions of two disjoint matching sides. Intermediate warehouse is designed to store semifinished products, which could ensure process industry logistics works continuously and flexibly. Allocating the surplus inventory materials to orders as a preprocess of production planning is a common method for made-to-order process manufacture. It relieves inventory pressure as well as improves material utilization. In this paper, a material-order two-sided matching model with consideration of both sides' preferences is formulated. Assessing both sides' satisfaction by complete and dynamic preference, the objective is to maximize the global matching preference and to get stable outcome. To solve the NP-hard problem efficiently, a tailored hybird algorithm PDADE is proposed. Firstly, a preference deferred acceptance based heuristic is designed to obtain high quality initial solution. Then, a series of improved differential evolution strategies considering real number encoding of sparse two-dimension matrix is designed to achieve near-optimal solutions in short time for practical large scale instances. According to experiments of actual production data instances, the proposed algorithm outperforms the commercial solver and standard DE both on run time and quality of solutions.

关键词： Nickel

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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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An Improved MOEA/D Algorithm for the Carbon Black Production Line Static and Dynamic Multiobjective Scheduling Problem

An Improved MOEA/D Algorithm for the Carbon Black Production...

引用

Congress on Evolutionary Computation

作者： Yao Wang Zhiming Dong Tenghui Hu Xianpeng Wang Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education China Liaoning Engineering Laboratory of operation Analytics and Optimization for Smart Industry Northeastern University Shenyang China Liaoning Key Laboratory of Manufacturing System and Logistics Northeastern University Shenyang China Institute of Industrial & Systems Engineering Northeastern University Shenyang China

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

The make-to-order (MTO) manufacturers generally make production plans based on orders, which can help enterprises effectively avoid market risks, reduce market pressure and improve competitiveness. However, due to the characteristics of MTO production mode, the order static scheduling problem and rush order dynamic rescheduling problem have become more and more important for these MTO manufacturers. Therefore, in this paper, we take the packaging production line of a typical carbon black production enterprise as the research background to study the carbon black production line static and dynamic multiobjective scheduling problem. Firstly, multiobjective optimization models of both order static scheduling and rush order dynamic rescheduling are established. Then the improved MOEA/D algorithm combined the heuristic algorithm based on heuristic rules and discrete dynamic local search is developed to solve these two models. Based on the actual production data, eight instances of order static scheduling problems of different scales and four instances of rush order dynamic rescheduling problems of different scales are constructed respectively. Experimental results illustrate that the improved MOEA/D is effective and superior in solving these two problems.

关键词： Dynamic scheduling Job shop scheduling Mathematical model Heuristic algorithms Delays

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A Novel Speech Recognition Model with Bayesian optimization

A Novel Speech Recognition Model with Bayesian Optimization

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Chinese Automation Congress (CAC)

作者： Yunfei Zhang Meiling Xu Institute of Industrial & Systems Engineering Northeastern University Shenyang China Key Laboratory of Data Anlytics and Optimization for Smart Industry Northeastern University Minstry of Education Shenyang China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

In order to solve the problem of the speech recognition, this paper proposes a novel framework to process speech signals by using the deep fully convolutional neural network (DFCNN) as the acoustic model and the Transformer as the linguistic model. DFCNN can learn more about speech signals through a deep network structure, while Transformer's self-attention mechanism makes it an efficient linguistic model. In order to better extract the characteristic information of speech signal, we transform the speech signal into a two-dimensional language spectrum by frame division and Fourier transform. In this way, the language spectrum can be processed by using convolutional neural network, which has promising ability to process two-dimensional data. The output of the acoustic model is passed into the linguistic model, and a complete sentence is output. At the same time, considering that there are many hyper parameters in the model, in order to adjust these hyper parameters efficiently, we employ the Bayesian optimization algorithm to optimize the hyper parameters of the acoustic model and the linguistic model, so as to further improve the accuracy of speech recognition. Experimental results on speech recognition library THCHS30 show that compared with other models, the optimized model has higher recognition rate for audio and stronger generalization ability.

关键词： Speech recognition Acoustics Hidden Markov models Linguistics Speech processing optimization Modeling

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Piecewise Time Series Prediction Based on Stacked Long Short-Term Memory and Genetic Algorithm

Piecewise Time Series Prediction Based on Stacked Long Short...

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Chinese Automation Congress (CAC)

作者： Lu Chen Meiling Xu Institute of Industrial & Systems Engineering Northeastern University Shenyang China Key Laboratory of Data Anlytics and Optimization for Smart Industry (Northeastern University) Minstry of Education Shenyang China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

Time series prediction is an active area and attracts the attention of researchers from lots of fields. With the expansion of the application of time series forecasting in the real world, many traditional methods that predicted time series with a few data are no longer applicable. Motivated by the demand of accurate prediction, we come up with the piecewise time series prediction model combining stacked long short-term memory network with genetic algorithm. Stacked long short-term memory network (LSTM) is an advanced sequence learning technology. We divide the time series into several segments and then use the stacked LSTM trained on the previous segment to predict the next segment to realize online prediction. The parameters of the stacked LSTM for each segment prediction are optimized by improved genetic algorithm. For purpose of demonstrating the feasibility and effectivity of the proposed model, we carry out simulation experiments on basis of a hybrid benchmark chaotic time series and a real-world dataset of the hourly ozone concentration. Experimental results demonstrate that the proposed model can automatically select the proper structure according to the data, which avoids the structure being too redundant or too simple. Furthermore, it can improve the prediction accuracy of time series and has good practicability.

关键词： Time series analysis Logic gates Genetic algorithms Predictive models Mathematical model data models Recurrent neural networks

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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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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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