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Chinese Journal of Polymer Science 2024年第6期42卷 751-765页

作者： Jun-hao Zhou Li-{1. Tang Key Laboratory of Advanced Materials of Ministry of Education of China Department of Chemical EngineeringTsinghua UniversityBeijing100084China

In this study,a series of hindered urea bond(HUB)containing polyurethane-urea methacrylate prepolymers and a none HUB containing polyurethane methacrylate prepolymer were prepared using isobornyl methacrylate as the reactive diluent via one-pot *** prepolymers were characterized fully by various ***,their thermosets were fabricated via UV curing in presence of a photo initiator,and their mechanical property and thermal behavior were investigated and *** from the none HUB containing thermoset,the HUB containing thermosets(defined as PUT)could be recycled and reprocessed by hot press under relatively mild conditions with high recovery ratio of mechanical ***,zinc oxide(ZnO)nanoparticles were modified with 3-(trimethoxysilyl)propyl methacrylate and the modified ZnO(defined as ZnO-TPM)was dispersed and polymerized into PUT matrix to prepare their *** influence of ZnO-TPM on the mechanical performance of the composites was evaluated,which indicated that the Young’s modulus and tensile strength increased gradually to the maximum values at ZnO-TPM content of 1.wt%and then *** composites also displayed good reprocessability with improved recovery ratio compared to the pure PUT *** addition,the composite materials exhibited strong UV absorption capacity,implying their potential application in the circumstance where UV-shielding was required.

关键词： Polyurethane-urea methacrylate Hindered urea bond ZnO nanoparticles Reprocessing UV curing

Variational data encoding and correlations in quantum-enhanced machine learning

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Chinese Physics B 2024年第9期33卷 298-306页

作者： ming-{1. Wang Hua L¨u School of Physics Hubei UniversityWuhan 430062China School of Science Hubei University of TechnologyWuhan 430068China

Leveraging the extraordinary phenomena of quantum superposition and quantum correlation,quantum computing offers unprecedented potential for addressing challenges beyond the reach of classical *** paper tackles two pivotal challenges in the realm of quantum computing:firstly,the development of an effective encoding protocol for translating classical data into quantum states,a critical step for any quantum *** encoding strategies can significantly influence quantum computer ***,we address the need to counteract the inevitable noise that can hinder quantum *** primary contribution is the introduction of a novel variational data encoding method,grounded in quantum regression algorithm *** adapting the learning concept from machine learning,we render data encoding a learnable *** allowed us to study the role of quantum correlation in data *** numerical simulations of various regression tasks,we demonstrate the efficacy of our variational data encoding,particularly post-learning from instructional ***,we delve into the role of quantum correlation in enhancing task performance,especially in noisy *** findings underscore the critical role of quantum correlation in not only bolstering performance but also in mitigating noise interference,thus advancing the frontier of quantum computing.

关键词： quantum machine learning variational data encoding quantum correlation

Planning of DC Electric Spring with Particle Swarm Optimization and Elitist Non-dominated Sorting Genetic Algorithm

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 574-583页

作者： Qingsong Wang Siwei Li hao Ding {1. Cheng Giuseppe Buja School of Electrical Engineering and Jiangsu Provincial Key Laboratory of Smart Grid Technology and Equipment Southeast UniversityNanjing 210096China School of Software Engineering Southeast universitySuzhou 215123China

This paper addresses the planning problem of parallel DC electric springs (DCESs). DCES, a demand-side management method, realizes automatic matching of power consumption and power generation by adjusting non-critical load (NCL) and internal storage. It can offer higher power quality to critical load (CL), reduce power imbalance and relieve pressure on energy storage systems (RESs). In this paper, a planning method for parallel DCESs is proposed to maximize stability gain, economic benefits, and penetration of RESs. The planning model is a master optimization with sub-optimization to highlight the priority of objectives. Master optimization is used to improve stability of the network, and sub-optimization aims to improve economic benefit and allowable penetration of RESs. This issue is a multivariable nonlinear mixed integer problem, requiring huge calculations by using common solvers. Therefore, particle Swarm optimization (PSO) and Elitist non-dominated sorting genetic algorithm (NSGA-II) were used to solve this model. Considering uncertainty of RESs, this paper verifies effectiveness of the proposed planning method on IEEE 33-bus system based on deterministic scenarios obtained by scenario analysis.

关键词： DC distribution network DC electric spring non-dominated sorting genetic algorithm particle swarm optimization renewable energy source

Strategic regulation of nitrogen-containing intermediates for enhanced nitrate reduction over Co_(3)O_(4)/SiC catalyst having multiple active centers

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Journal of Energy Chemistry 2024年第9期96卷 291-299页

作者： ming-{1. Guan {1.-Nan Xu Jin Liu Tao Wu An-Hui Lu State Key Laboratory of Fine Chemicals Liaoning Key Laboratory for Catalytic Conversion of Carbon ResourcesSchool of Chemical EngineeringDalian University of TechnologyDalian 116024LiaoningChina

Regulating the intermediates involved in the electrocatalytic nitrate reduction reaction(NO_(3)RR)is crucial for the enhancement of reaction ***,it remains a great challenge to regulate the reaction intermediates through active site manipulation on the surface of the ***,a family of n%-Co_(3)O_(4)/SiC(n=5,8,1.,20)catalysts with a delicate percentage of Co^(2+)and Co^(3+)were prepared for NO_(3)*** found that Co^(3+)primarily acts as the active site for NO_(3)^(−)reduction to NO_(2)^(−),while Co^(2+)is responsible for the conversion of NO_(2)^(−)to NH_(3).Moreover,the conversion of these intermediates over the active sites is autonomous and separately *** processes synergistically accomplish the reduction of nitrate ions to synthesize *** the experimental studies and density functional theory(DFT)calculations,it is discovered the pathway(^(*)NHO→^(*)NHOH→^(*)NH_(2)OH→^(*)NH_(2)→^(*)NH_(3))is more favorable due to the lowerΔG value(0.25 eV)for the rate-limiting step(^(*)NO→^(*)NHO).The NH_(3)yield rate of 8%-Co_(3)O_(4)/SiC reached 1.08 mmol/(cm^(2)h)with a Faradaic efficiency of 96.4%at−0.89 V versus the reversible hydrogen electrode(RHE),surpassing those of most reported non-noble NO_(3)RR *** strategy not only provides an efficient catalyst for NO_(3)RR but also serves as an illustrative model for the regulation of multi-step reaction intermediates through the design of distinct active sites,thereby presenting a new approach to enhance the efficiency of intricate reactions.

关键词： Electrocatalysis Nitratereduction Co_(3)O_(4)/SiC catalysts Intermediates control

Integrating machine learning and CALPHAD method for exploring low-modulus near-β-Ti alloys

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Rare Metals 2024年第1期43卷 309-323页

作者： hao Zou Yue-Yan Tian Li-Gang Zhang Ren-hao Xue Zi-Xuan Deng ming-ming Lu Jian-Xin Wang Li-Bin Liu School of Computer Science and Engineering Central South UniversityChangsha 410083China School of Materials Science and Engineering Central South UniversityChangsha 410083China State Key Laboratory of Powder Metallurgy Central South UniversityChangsha 410083China

Traditional theoretical and empirical calculation methods can guide the design of β-and metastable β-alloys for bio-titanium. However, it is still difficult to obtain novel near-β-Ti alloys with low modulus. This study developed a method that combines machine learning with calculation of phase diagrams(CALPHAD) to facilitate the design of near-β-Ti alloys. An elastic modulus database of Ti–Nb–Zr–Mo–Ta–Sn system was constructed first, and then three features(the electron to atom ratio, mean absolute deviation of atom mass, and mean electronegativity) were selected as the key factors of modulus by performing a three-step feature selection. With these features, a highly accurate model was built for predicting the modulus of near-β-Ti alloys. To further ensure the accuracy of modulus prediction, machine learning with the elastic constants calculated was leveraged by CALPHAD database. The root mean square error of the well-trained model can be as low as 6.75 GPa. Guided by the prediction of machine learning and CALPHAD, three novel near-β-Ti alloys with elastic modulus below 50 GPa were successfully designed in this study. The best candidate alloy(Ti–26Nb–4Zr–4Sn–1.o–Ta) exhibits an ultra-low modulus(36.6 GPa) after cold rolling with a thickness reduction of 20%. Our method can greatly save time and resources in the development of novel Ti alloys, and experimental verifications have demonstrated the reliability of this method.

关键词： Near-β-Ti alloy Machine learning Calculation of phase diagram Low-modulus alloy Feature selection

Weakly Approximate Diagonalization of Homomorphisms into Finite von Neumann Algebras

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Acta Mathematica Sinica,English Series 2024年第9期40卷 2187-2194页

作者： Wen Hua QIAN Jun hao SHEN Wen {1. WU School of Mathematical Sciences Chongqing Normal UniversityChongqing 401331P.R.China Department of Mathematics and Statistics University of New HampshireDurhamNH 03824USA

Let A be a unital C^(∗)-algebra and B a unital C^(∗)-algebra with a faithful traceτ.Let n be a positive *** give the definition of weakly approximate diagonalization(with respect toτ)of a unital homomorphismφ:A→Mn(B).We give an equivalent characterization of McDuff Ⅱ_(1. *** show that,if A is a unital nuclear C^(∗)-algebra and B is a type Ⅱ_(1. factor with faithful traceτ,then every unital^(∗)-homomorphism φ:A→M_(n)(B)is weakly approximately *** B is a unital simple infinite dimensional separable nuclear C^(∗)-algebra,then any finitely many elements in Mn(B)can be simultaneously weakly approximately diagonalized while the elements in the diagonals can be required to be the same.

关键词： Weakly approximately diagonalizable McDuff factors nuclear hyperfinite

Al-containing Ge-rich IWV:rapid synthesis via interzeolite transformation and application in Baeyer-Villiger oxidation

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Science China Chemistry 2024年第7期67卷 2282-2291页

作者： Meichen Jiao Chunmin Jia hao Xu Peng Wu Xian-{1. Zhang School of Chemistry&Material Science Shanxi Normal UniversityTaiyuan 030006China Shanghai Key Laboratory of Green Chemistry and Chemical Processes School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghai 200062China School of Chemistry Taiyuan University of TechnologyTaiyuan 030024China

Ge-containing Lewis acidic zeolites are typically synthesized using a conventional hydrothermal strategy from amorphous silica sources,through a long time-and energy-consuming approach limiting their wide *** study reports a rapid synthesis strategy for a series of IWV aluminogermanosilicate zeolites(T-x-Al)with a wide range of Si/Ge molar ratios(5–1.)by interzeolite transformation of dealuminated FAU(DA-FAU).Notably,Ge-rich IWV could be obtained with crystallization time as short as one day by the equimolecular Si/Ge ratio *** effect of synthetic parameters was investigated and the obtained T-x-Al samples were characterized by XRD,SEM,EDS,ICP,^(29)Si and^(27)Al MAS NMR,FTIR,and N_(2)physisorption *** with a previously reported IWV germanosilicate(H-3),T-x-Al displayed a better catalytic activity in Baeyer-Villiger(BV)oxidation,attributed to the presence of the active framework Ge sites with the assistance of the extraframework Al *** cyclic catalysis tests revealed that the presence of framework Al species contributed to the stability of the ***,T-x-Al,with 1.-membered ring(MR)supercages,outperformed the typical 1.-MR Sn-beta catalyst in the BV oxidation of large ketone-like 2-admantanone,indicating a promising potential application in the macromolecular catalytic process.

关键词： Ge-rich IWV interzeolite transformation Al assistance Baeyer-Villiger oxidation

Collective Movement Simulation:Methods and Applications

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Machine Intelligence Research 2024年第3期21卷 452-480页

作者： Hua Wang Xing-Yu Guo hao Tao {1.-Liang Xu School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou450001China Engineering Research Center of Intelligent Swarm Systems Ministry of EducationZhengzhou450001China National Supercomputing Center in Zhengzhou Zhengzhou450001China China Ship Development and Design Center Wuhan430060China

Collective movement simulations are challenging and important in many areas,including life science,mathematics,physics,information science and public *** this survey,we provide a comprehensive review of the state-of-the-art techniques for collective movement *** start with a discussion on certain concepts to help beginners understand it more ***,we analyze the intelligence among different collective objects and the emphasis in different ***,we classify existing collective movement simulation methods into four categories according to their effects,namely versatility,accuracy,dynamic adaptability,and assessment feedback ***,we introduce five applications of layout optimization,emergency control,dispatching,unmanned systems,and other derivative ***,we summarize possible future research directions.

关键词： Collective movement simulation multiple objects multiple discipline simulation effect collective intelligence

Sensing Matrix Optimization for Random Stepped-Frequency Signal Based on Two-Dimensional Ambiguity Function

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Chinese Journal of Electronics 2024年第1期33卷 161-174页

作者： mingjiu LYU {1. CHEN Jun YANG Xia WU ming ZHOU Xiaoyan MA Radar NCO School Air Force Early Warning Academy Department of Early Warning Technology Air Force Early Warning Academy

Compressive sensing technique has been widely applied to achieve range-Doppler reconstruction of high frequency radar by utilizing sparse random stepped-frequency(SRSF) signal, which can suppress the complex electromagnetic interference and greatly reduce the coherent processing interval. An important way to improve the performance of sparse signal reconstruction is to optimize the sensing matrix(SM). However, the existing research on the SM optimization needs to design a measurement matrix with superior performance, which needs a large amount of computation and does not consider the influence of the waveform parameters design. In order to improve the superior reconstruction performance, a novel SM optimization approach for SRSF signal is proposed by using two-dimensional ambiguity function(TDAF) in this paper. Firstly, based on the two-dimensional sparse reconstruction model of the SRSFs, the internal relationship between the waveform parameters and the SM was derived. Secondly, the SM optimization problem was directly transformed into the waveform design of SRSFs. Furthermore, on the basis of analyzing the relationship between the mutual coherence matrix of SM and the TDAF matrix of SRSFs, the purpose of optimizing the SM can be achieved by designing the TDAF of the SRSFs. Based on this analysis, a sparse waveform optimization method with joint constraints of maximum and mean sidelobes of the TDAF by using the genetic algorithm was derived. Compared with the traditional SM optimization method, our method not only avoids generating a new measurement matrix, but also further reduces the complexity of the waveform optimization. Simulation experiments verified the effectiveness of the proposed method.

关键词： Correlation Computational modeling Electromagnetic interference Optimization methods Signal reconstruction High frequency radar Sensors

Research on simulation of gun muzzle flow field empowered by artificial intelligence

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Defence Technology（防务技术） 2024年第2期32卷 196-208页

作者： Mengdi Zhou Linfang Qian Congyong Cao Guangsong Chen Jin Kong ming-{1. Tong School of Mechanical Engineering Nanjing University of Science and TechnologyNanjing210094China Northwest Institute of Mechanical and Electrical Engineering Xianyang712099ShaanxiChina School of Automation Nanjing University of Science and TechnologyNanjing210094China

Artificial intelligence technology is introduced into the simulation of muzzle flow field to improve its simulation efficiency in this paper.A data-physical fusion driven framework is ***,the known flow field data is used to initialize the model parameters,so that the parameters to be trained are close to the optimal *** physical prior knowledge is introduced into the training process so that the prediction results not only meet the known flow field information but also meet the physical conservation *** two examples,it is proved that the model under the fusion driven framework can solve the strongly nonlinear flow field problems,and has stronger generalization and *** proposed model is used to solve a muzzle flow field,and the safety clearance behind the barrel side is *** is pointed out that the shape of the safety clearance under different launch speeds is roughly the same,and the pressure disturbance in the area within 9.2 m behind the muzzle section exceeds the safety threshold,which is a dangerous *** with the CFD results shows that the calculation efficiency of the proposed model is greatly improved under the condition of the same calculation *** proposed model can quickly and accurately simulate the muzzle flow field under various launch conditions.

关键词： Muzzle flow field Artificial intelligence Deep learning Data-physical fusion driven Shock wave