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arXiv 2019年

作者： Ma, Jingying Du, Jinming Zheng, Yuanshi School of Mathematics and Statistics Ningxia University Yinchuan750021 China Institute of Industrial and Systems Engineering College of Information Science and Engineering Northeastern University Shenyang110819 China Liaoning Engineering Laboratory of Operations Analytics and Optimization for Smart Industry Northeastern University Shenyang110819 China Ministry of Education Shenyang110819 China Center for Complex Systems School of Mechano-electronic Engineering Xidian University Xi'an710071 China

Coalescence, as a kind of ubiquitous group behavior in the nature and society, means that agents, companies or other substances keep consensus in states and act as a whole. This paper considers coalescence for n rational agents with distinct initial states. Considering the rationality and intellectuality of the population, the coalescing process is described by a bimatrix game which has the unique mixed strategy Nash equilibrium solution. Since the process is not an independent stochastic process, it is difficult to analyze the coalescing process. By using the first Borel-Cantelli Lemma, we prove that all agents will coalesce into one group with probability one. Moreover, the expected coalescence time is also evaluated. For the scenario where payoff functions are power functions, we obtain the distribution and expected value of coalescence time. Finally, simulation examples are provided to validate the effectiveness of the theoretical results. Copyright © 2019, The Authors. All rights reserved.

关键词： Coalescence

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Prospects for Observing High-redshift Radio-loud Quasars in the SKA Era: Paving the Way for 21-cm Forest Observations

arXiv

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arXiv 2024年

作者： Niu, Qi Li, Yichao Xu, Yidong Guo, Hong Zhang, Xin Liaoning Key Laboratory of Cosmology and Astrophysics College of Sciences Northeastern University Shenyang110819 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Key Laboratory of Radio Astronomy and Technology Chinese Academy of Sciences Beijing100101 China Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China MOE Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang110819 China

The 21-cm forest is a sensitive probe for the early heating process and small-scale structures during the epoch of reionization (EoR), to be realized with the upcoming Square Kilometre Array (SKA). Its detection relies on the availability of radio-bright background sources, among which the radio-loud quasars are very promising, but their abundance during the EoR is still poorly constrained due to limited observations. In this work, we use a physics-driven model to forecast future radio-loud quasar observations. We fit the parameters of the model using observational data of high-redshift quasars. Assuming Eddington accretion, the model yields an average lifetime of tq ∼ 105.5yr for quasars at z ∼ 6, consistent with recent results obtained from quasar proximity zone observation. We show that if the radio-loud fraction of quasars evolves with redshift, it will significantly reduce the abundance of observable radio-loud quasars in the SKA era, making 21-cm forest studies challenging. With a constant radio-loud fraction, our model suggests that a one-year sky survey conducted with SKA-LOW has the capability to detect approximately 20 radio-loud quasars at z ∼ 9, with sufficient sensitivity to resolve individual 21-cm forest lines. Copyright © 2024, The Authors. All rights reserved.

关键词： Radio astronomy

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How to Perform Energy-Balanced Underwater data Collection in AUV-Aided UASNs: A Social Welfare-Based Node Clustering Approach

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IEEE Transactions on Computational Social Systems 2024年

作者： Lin, Chuan Han, Guangjie Lu, Chang Hussain Shah, Syed Bilal Zhang, Yu Wang, Feng Northeastern University Software College Shenyang110819 China Hohai University Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Nanjing210013 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education Nanjing210013 China Dalian University of Technology School of Software Dalian116024 China Dar Al-Hekma University School of Engineering Computing and Informatics Jeddah21589 Saudi Arabia Suzhou Vocational University School of Engineering Suzhou215004 China

The rapid evolution of the Internet of Underwater Things (IoUT) has led to the widespread adoption of autonomous underwater vehicle (AUV)-assisted underwater acoustic sensor networks (UASNs) for various applications such as marine environment monitoring and resource exploration. This article introduces an energy-balanced data collection scheme tailored for AUV-supported UASNs. The proposed scheme combines a node clustering method based on a social welfare function and an intelligent path planning strategy for the AUV. The node clustering approach integrates canopy and K-means algorithms for initial node clustering, followed by reclustering using an enhanced hierarchical clustering algorithm. To balance energy distribution, Atkinson's social welfare function is employed to select and rotate cluster heads (CHs) within each cluster. To address limited CH memory constraints, a lossless compression technique is introduced to reduce data storage requirements at the CHs. Moreover, the article introduces the use of the deep Q-network (DQN) technique for AUV path planning, considering multiple pertinent factors simultaneously. Simulation results demonstrate that the proposed data collection scheme effectively reduces energy consumption, prolongs network lifespan, and enhances data collection efficiency when compared to recent research endeavors. © 2014 IEEE.

关键词： Marine applications

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Dark energy and matter interacting scenario to relieve H_(0) and S_(8) tensions

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Chinese Physics C 2024年第5期48卷 1-7页

作者：高立场薛社生张鑫 Key Laboratory of Cosmology and Astrophysics(Liaoning Province)&Department of Physics College of SciencesNortheastern UniversityShenyang 110819China Kapteyn Astronomical Institute University of GroningenP.O.Box 8009700 AV Groningenthe Netherlands ICRANet Piazzale della Repubblica10-65122PescaraItaly ICTP-AP University of Chinese Academy of SciencesBeijing 100049China Physics Department Sapienza University of RomeP.le A.Moro 500185RomeItaly INFN Sezione di PerugiaVia A.PascoliI-06123PerugiaItaly Key Laboratory of Data Analytics and Optimization for Smart Industry(Ministry of Education) Northeastern UniversityShenyang 110819China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China

We consider a new cosmological model(calledΛCDM),in which the vacuum energy interacts with matter and radiation,and test this model using the current cosmological *** the CMB+BAO+SN(CBS)dataset to constrain the model,we find that H_(0) and S_(8) tensions are relieved to 2.87σand 2.77σ,***,in this case,theΛCDM model is not favored by the data,compared withΛ*** find that when the H_(0) and S_(8) data are added to the data combination,the situation is significantly *** the CBS+H_(0) case,the model relieves the H_(0) tension to 0.47σ,and the model is favored overΛ*** the CBS+H_(0)+S_(8) case,we obtain a synthetically best situation,in which the H_(0) and S_(8) tensions are relieved to 0.72σand 2.11σ,*** this case,the model is most favored by the ***,this cosmological model can greatly relieve the H_(0) tension and simultaneously effectively alleviate the S_(8) tension.

关键词： dark energy interacting dark energy model the Hubble constant Hubble tension cosmological constraint

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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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optimization of the raw material field layout for iron-steel enterprise

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IOP Conference Series: Materials Science and engineering 2019年第2期631卷

作者： Wenbo Liu Yuehong Kong Department of Logistics Management Liaoning Provincial College of Communications Shenyang Liaoning 110122 China. Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Shenyang Liaoning 110819 China.

This paper studies a short-term layout optimization problem with the raw material field for iron-steel enterprises. The raw material field is a centralized storage place and distribution unit of various raw materials such as iron ore, which plays a significant role in the iron front logistics for iron and steel enterprises. Whether the stacking of raw materials is reasonable or not directly affects the logistics efficiency and logistics cost of raw materials for iron-steel enterprises. Based on the status and basic information of the raw materials, storage space of the field shall be made full use of to allocate the optimal position for each type of raw material. To achieve this goal, a mixed integer programming (MIP) model is developed, which is purposed to minimize the transportation cost and the loss of mixing. The solution compares favorably with the current practice in the iron-steel enterprises.

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Decomposition is All You Need: Single-Objective to Multi-Objective optimization Towards Responsible Artificial General Intelligence

SSRN

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SSRN 2024年

作者： Wendi, Xu Yuemao, Zhao Ming, Zhang College of Energy and Mining Engineering Shandong University of Science and Technology Qingdao266590 China National Frontier Sciences Center for Industrial Intelligence and Systems Optimization Ministry of Education China College of Information Science and Engineering Northeastern University United States Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education China Xinjiang Astronomical Observatory Chinese Academy of Sciences China Key Laboratory for Radio Astronomy Chinese Academy of Sciences Nanjing China University of Chinese Academy of Sciences China Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Shenyang110819 China

Responsible artificial general intelligence (AGI) is deeply connected with both explainable artificial intelligence and interpretable artificial intelligence. In frontier science of evolutionary transfer optimization (ETO) towards responsible AGI, transfer core (TC) is both explainable and responsible for single-objective to multi-objective optimization (SMO) that can harness comprehensive intelligent models for complex task-solving. From both theoretical and practical sides, we get the work of "HMeets" (this paper) for SMO on TC in both production and logistic systems. In HMeets, TC is further confirmed its role or responsibility for selection pressure, which performs general-purpose problem-solving abilities. We tend to believe that TC or SMO maybe serves as a fundamental concept and common tool like what happens in ResNets, Transformers and WARSHIP (our visual AGI) for wildly applications beyond manufacturing here in the future. © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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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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Microstructure and mechanical properties of solid state recycled 4Cr5MoSiV (H11) steel prepared by powder metallurgy

Results in Materials

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Results in Materials 2021年 10卷

作者： Bhatta, Gyanendra De Los Santos Valladares, Luis Liu, Xinggang Ma, Zhaojun Bustamante Domínguez, A.G. Moreno, N.O. Espinoza Suarez, S.M. Barnes, C.H.W. Zhang, Deliang School of Materials Science and Engineering Northeastern University Liaoning Shenyang 110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry (Ministry of Education) Northeastern University Shenyang 110819 China Laboratorio de Ceramicos y Nanomaterailes Facultad de Ciencias Físicas Universidad Nacional Mayor de San Marcos Ap. Postal 14-0149 Lima Peru Cavendish Laboratory Department of Physics University of Cambridge UK Cambridge CB3 0HE United Kingdom Departamento de Física Universidade Federal de Sergipe SE São Cristóvão 49100- 000 Brazil

4Cr5MoSiV (H11) steel machining chips were crushed to a powder and then consolidated into discs by hot pressing under various consolidation parameters. The discs were subjected to modified spheroidizing annealing treatment, and their evolved microstructure, tensile mechanical properties and fracture behavior were studied. The microstructure consisted of α−Fe as a matrix, together with heterogeneously distributed non metallic inclusions from the chip-crushing step and precipitated secondary phases. The yield strength, fracture strength and elongation to fracture of the consolidated discs were determined to be in the range of 215∼264 MPa, 344∼415 MPa and 1.2∼2.6%, respectively. The strength of the recycled H11 steel was comparable with that of the ingot metallurgy (IM) H11 steel, but the tensile ductility of the former was significantly lower than that of the latter, being mainly caused by the fracture of the non-metallic inclusions along with the hard secondary phase particles in the IM H11 steel which leads to premature fracture of the samples. The Vickers hardness of the samples were in the range of 144∼194 HV being comparable to that of the IM counterpart. Factographs of the fracture surface demonstrated that the tensile test specimens fractured in a brittle fracture mode. © 2021 The Author(s)

关键词： Brittle fracture H11 Steel Microstructure Modified spheroidizing Solid state recycling Tensile properties

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Intention-sensitive Preference Learning Network for Personalized Session-based Recommendation

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ACM Transactions on Recommender Systems 1000年

作者： Qingbo Zhang Xiaochun Yang Hao Chen Bin Wang Xiangmin Zhou Computer Science and Engineering Northeastern University Shenyang China Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education Shenyang China School of Computing Technologies RMIT University Melbourne Australia

Nowadays, research on session-based recommender systems (SRSs) is one of the hot spots in the recommendation domain. Existing methods make recommendations based on the user’s current intention (also called short-term preference) during a session, often overlooking the specific preferences associated with these intentions. In reality, users usually exhibit diverse preferences for different intentions, and even for the same intention, individual preferences can vary significantly between users. As users interact with items throughout a session, their intentions can shift accordingly. To enhance recommendation quality, it is crucial not only to consider the user’s intentions but also to dynamically learn their varying preferences as these intentions change. In this paper, we propose a novel Intention-sensitive Preference Learning Network (IPLN) including three main modules: intention recognizer, preference detector, and prediction layer. Specifically, the intention recognizer infers the user’s underlying intention within his/her current session by analyzing complex relationships among items. Based on the acquired intention, the preference detector learns the intention-specific preference by selectively integrating latent features from items in the user’s historical sessions. Besides, the user’s general preference is utilized to refine the obtained preference to reduce the potential noise carried from historical records. Ultimately, the fine-tuned preference and intention collaborate to instruct the next-item recommendation in the prediction layer. To prove the effectiveness of the proposed IPLN, we perform extensive experiments on two real-world datasets. The experiment results demonstrate the superiority of IPLN compared with other state-of-the-art models.

关键词： session-based recommender systems personalized next-item recommendation user intention intention-specific preference

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