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Reliable calculations of nuclear binding energies by the Gaussian process of machine learning

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Nuclear Science and Techniques 2024年第6期35卷 130-144页

作者： Zi-yi Yuan Dong Bai Zhen wang Zhong-Zhou ren School of Physics Science and Engineering Tongji UniversityShanghai 200092China College of Science Hohai UniversityNanjing 211100China Key Laboratory of Advanced Micro-Structure Materials Ministry of EducationShanghai 200092China

Reliable calculations of nuclear binding energies are crucial for advancing the research of nuclear physics. Machine learning provides an innovative approach to exploring complex physical problems. In this study, the nuclear binding energies are modeled directly using a machine-learning method called the Gaussian process. First, the binding energies for 2238 nuclei with Z > 20 and N > 20 are calculated using the Gaussian process in a physically motivated feature space, yielding an average deviation of 0.046 MeV and a standard deviation of 0.066 MeV. The results show the good learning ability of the Gaussian process in the studies of binding energies. Then, the predictive power of the Gaussian process is studied by calculating the binding energies for 108 nuclei newly included in AME2020. The theoretical results are in good agreement with the experimental data, reflecting the good predictive power of the Gaussian process. Moreover, the α-decay energies for 1169 nuclei with 50 ≤ Z ≤ 110 are derived from the theoretical binding energies calculated using the Gaussian process. The average deviation and the standard deviation are, respectively, 0.047 MeV and 0.070 MeV. Noticeably, the calculated α-decay energies for the two new isotopes ^ (204 )Ac(Huang et al. Phys Lett B 834, 137484(2022)) and ^ (207) Th(Yang et al. Phys Rev C 105, L051302(2022)) agree well with the latest experimental data. These results demonstrate that the Gaussian process is reliable for the calculations of nuclear binding energies. Finally, the α-decay properties of some unknown actinide nuclei are predicted using the Gaussian process. The predicted results can be useful guides for future research on binding energies and α-decay properties.

关键词： Nuclear binding energies decay Machine learning Gaussian process

Visible Light Indoor Positioning System Based on Pisarenko Harmonic Decomposition and Neural Network

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Chinese Journal of Electronics 2024年第1期33卷 195-203页

作者： Li ZHAO yi ren Qi wang Lange DENG Feng ZHANG School of Electronic Information Engineering Xi’an Technological University

Visible-light indoor positioning is a new generation of positioning technology that can be integrated into smart lighting and optical communications. The current received signal strength(RSS)-based visible-light positioning systems struggle to overcome the interferences of background and indoor-reflected noise. Meanwhile, when ensuring the lighting, it is impossible to use the superposition of each light source to accurately distinguish light source information; furthermore, it is difficult to achieve accurate positioning in complex indoor environments. This study proposes an indoor positioning method based on a combination of power spectral density detection and a neural network. The system integrates the mechanism for visible-light radiation detection with RSS theory, to build a back propagation neural network model fitting for multiple reflection channels. Different frequency signals are loaded to different light sources at the beacon end, and the characteristic frequency and power vectors are obtained at the location end using the Pisarenko harmonic decomposition method. Then, a complete fingerprint database is established to train the neural network model and conduct location tests. Finally, the location effectiveness of the proposed algorithm is verified via actual positioning experiments. The simulation results show that, when four groups of sinusoidal waves with different frequencies are superimposed with white noise, the maximum frequency error is 0.104 Hz and the maximum power error is 0.0362 W. For the measured positioning stage, a 0.8 m × 0.8 m × 0.8 m solid wood stereoscopic positioning model is constructed, and the average error is 4.28 cm. This study provides an effective method for separating multi-source signal energies, overcoming background noise, and improving indoor visible-light positioning accuracies.

关键词： Neural networks Measurement uncertainty Position measurement Harmonic analysis Light emitting diodes Frequency estimation Indoor positioning systems

Accelerated thermal ageing on performance of polypropylene-based semiconducting screen for high voltage direct current cable applications-Effect of antioxidants

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High Voltage 2024年第2期9卷 410-418页

作者： Haitian wang yi Luo Mingyu Zhou Xintong ren George Chen GEIRI BerlinGermany The Tony Davies High Voltage Laboratory University of SouthamptonSouthamptonUK

The potential of using eco-friendly thermoplastic polypropylene(PP)-based insulation for high voltage direct current(HVDC)cable has been widely investigated but much less work on the PP-based semiconducting screen(SC).Considering a long service life(>30 years)under high temperature and high electrical stress is required for typical HVDC cables,and investigations on the effect of antioxidant(AO)concentration and thermal oxidative sta-bility,mechanical,and electrical properties of PP-based SCs have been *** has been demonstrated that an appropriate combination and amounts of AOs are critical for achieving high thermal stability and maintaining the mechanical properties of SC after ageing in a harsh environment(150°C,with Cu,in air,7 days).Although higher amounts of space charges have been observed in SC/PP/SC samples with higher AO concentrations,the impact on space charge behaviours is less after ageing,suggesting that ageing(or operating at high temperature)leads to microstructure evolution in SC and can potentially mitigate space accumulation in PP-based insulating materials.

关键词： microstructure thermal high

An anti-icing scaling method for wind tunnel tests of aircraft thermal ice protection system

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Chinese Journal of Aeronautics 2024年第6期37卷 1-6页

作者： Yu LIU Qiang wang Xian yi Ningli CHEN Jinghao ren Weihao LI Key Laboratory of Icing and Anti/de-icing China Aerodynamics Research and Development CenterMianyang 621000China State Key Laboratory of Aerodynamics Mianyang 621000China

The efficiency of the aircraft Ice Protection Systems(IPSs)needs to be verified through icing wind tunnel ***,the scaling method for testing the IPSs has not been systematically established yet,and further research is *** the present study,a scaling method specifically designed for thermal IPSs was derived from the governing equation of thin water *** scaling parameters were adopted to address the heat and mass transfer involved in the thermal anti-icing *** method validation,icing wind tunnel tests were conducted using a jet engine nacelle model equipped with a bleed air *** non-dimensional surface temperature and runback ice closely matched for both the reference and scaled *** validation confirms that the scaling method is capable of achieving the similarity of surface temperature and the runback ice *** anti-icing scaling method can serve as an important supplement to the existing icing similarity theory.

关键词： Scaling method Ice protection system Water film Anti-icing test Icing wind tunnel

A multi-mechanism numerical simulation model for CO_(2)-EOR and storage in fractured shale oil reservoirs

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Petroleum Science 2024年第3期21卷 1814-1828页

作者： Yuan-Zheng wang ren-yi Cao Zhi-Hao Jia Bin-Yu wang Ming Ma Lin-Song Cheng College of Petroleum Engineering China University of Petroleum(Beijing)Beijing102249China John&Willie Leone Department of Energy&Mineral Engineering The Pennsylvania State UniversityUniversity Park16802PA.USA

Under the policy background and advocacy of carbon capture,utilization,and storage(CCUS),CO_(2)-EOR has become a promising direction in the shale oil reservoir *** multi-scale pore structure distribution and fracture structure lead to complex multiphase flow,comprehensively considering multiple mechanisms is crucial for development and CO_(2) storage in fractured shale *** this paper,a multi-mechanism coupled model is developed by *** to the traditional Eclipse300 and MATLAB Reservoir Simulation Toolbox(MRST),this model considers the impact of pore structure on fluid phase behavior by the modified Peng—Robinson equation of state(PR-EOS),and the effect simultaneously radiate to Maxwell—Stefan(M—S)diffusion,stress sensitivity,the nano-confinement(NC)***,a modified embedded discrete fracture model(EDFM)is used to model the complex fractures,which optimizes connection types and half-transmissibility calculation approaches between non-neighboring connections(NNCs).The full implicit equation adopts the finite volume method(FVM)and Newton—Raphson iteration for discretization and *** model verification with the Eclipse300 and MRST is *** results show that the interaction between the mechanisms significantly affects the production performance and storage *** effect of molecular diffusion may be overestimated in oil-dominated(liquid-dominated)shale *** well spacing and injection gas rate are the most crucial factors affecting the production by sensitivity ***,the potential gas invasion risk is *** model provides a reliable theoretical basis for CO_(2)-EOR and sequestration in shale oil reservoirs.

关键词： CO_(2)-EOR CO_(2)storage Shale oil reservoir Complex fracture model Multiple mechanisms

Assessment on gas-polyethylene terephthalate solid interface partial discharge properties of C4F7N/CO_(2)gas mixture for eco-friendly gas insulating transformer

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High Voltage 2024年第1期9卷 35-45页

作者： Song Xiao yifan wang Chenhua ren Haoran Xia Yue Zhao Jingzi Qin Xiaoxing Zhang yi Luo yi Li State Key Laboratory of Power Grid Environmental Protection School of Electrical Engineering and AutomationWuhan UniversityWuhanHubeiChina Beijing International S&T Cooperation Base for Plasma Science and Energy Conversion Institute of Electrical EngineeringChinese Academy of SciencesBeijingChina State Grid Anhui Electric Power Company Anhui Electric Power Research Institute HefeiAnhuiChina Hubei Engineering Research Center for Safety Monitoring of New Energy and Power Grid Equipment School of Electrical and Electronic EngineeringHubei University of TechnologyWuhanHubeiChina

The eco-friendly insulating gas perfluoroisobutyronitrile(C_(4)F_(7)N)is potentially used in gas-insulated transformers(GIT)to replace sulphur hexafluoride(SF_(6)).However,evaluation of the long-term insulation reliability and gas–solid interface discharge decomposition characteristics of the gas–solid film insulation structure in GIT is *** authors simulated the gas–solid film insulation structure in GIT and explored the interface partial discharge(PD)characteristics of C4F7N/CO_(2)gas mixture with polyethylene terephthalate(PET).The effect of gas pressure,mixing ratio on gas–solid interface gas decomposition,PET degradation was investigated,and the interaction mechanism was *** is found that the interface PD generated three degradation regions on a PET *** gas–solid interface reaction in the electrode contact region and the discharge development trace was significantly higher than that of halation *** content of gas decomposition products decreases with the increase of gas pressure and the PD intensity of SF6-PET is inferior to that of C_(4)F_(7)N/CO_(2)under the same *** results provide reference for the development and application of C_(4)F_(7)N/CO_(2)based GIT.

关键词： interface solid insulation

Discovering the ultralow thermal conductive A_(2)B_(2)O_(7)-type high-entropy oxides through the hybrid knowle dge-assiste d data-driven machine learning

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Journal of Materials Science & Technology 2024年第1期168卷 131-142页

作者： ying Zhang Ke ren William yi wang Xingyu Gao Ruihao Yuan Jun wang yiguang wang Haifeng Song Xiubing Liang Jinshan Li State Key Laboratory of Solidification Processing Northwestern Polytechnical UniversityXi’an 710072China Innovation Center NPU ChongqingChongqing 401135China Institute of Advanced Structure Technology Beijing Institute of TechnologyHaidian DistrictBeijing 100081China Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijing 100088China Defense Innovation Institute Academy of Military Sciences of the PLA of ChinaBeijing 100071China

Lattice engineering and distortion have been considered one kind of effective strategies for discovering advanced *** instinct chemical flexibility of high-entropy oxides(HEOs)motivates/accelerates to tailor the target properties through phase transformations and lattice ***,a hybrid knowledge-assisted data-driven machine learning(ML)strategy is utilized to discover the A_(2)B_(2)O_(7)-type HEOs with low thermal conductivity(κ)through 17 rare-earth(RE=Sc,Y,La-Lu)solutes optimized A-site.A designing routine integrating the ML and high throughput first principles has been proposed to predict the key physical parameter(KPPs)correlated to the targetedκof advanced *** the smart-designed 6188(5RE_(0.2))_(2)Zr_(2)O_(7)HEOs,the best candidates are addressed and validated by the princi-ples of severe lattice distortion and local phase transformation,which effectively reduceκby the strong multi-phonon scattering and weak interatomic ***,(Sc_(0.2)Y_(0.2)La_(0.2)Ce_(0.2)Pr_(0.2))_(2)Zr_(2)O_(7)with predictedκbelow 1.59 Wm^(−1)K^(−1)is selected to be verified,which matches well with the ex-perimentalκ=1.69 Wm^(−1)K^(−1)at 300 K and could be further decreased to 0.14 Wm^(−1)K^(−1)at 1473 ***,the coupling effects of lattice vibrations and charges on heat transfer are revealed by the cross-validations of various models,indicating that the weak bonds with low electronegativity and few bond-ing charge density and the lattice distortion(r∗)identified by cation radius ratio(r A/r B)should be the KPPs to decreaseκ*** work supports an intelligent designing strategy with limited atomic and electronic KPPs to accelerate the development of advanced multi-component HEOs with proper-ties/performance at multi-scales.

关键词： High-entropy oxides Thermal conductivity Pyrochlore Key physical parameter First-Principles

Severe/critical COVID-19 early warning system based on machine learning algorithms using novel imaging scores

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World Journal of Clinical Cases 2023年第12期11卷 2716-2728页

作者： Qiu-Yu Li Zhuo-Yu An Zi-Han Pan Zi-Zhen wang yi-ren wang Xi-Gong Zhang Ning Shen Department of Respiratory and Critical Care Medicine Peking University Third HospitalBeijing 100191China Department of Education Peking University People’s HospitalBeijing 100044China Department of Education China-Japan Friendship HospitalBeijing 100029China Department of Education Beijing Jishuitan HospitalBeijing 100096China

BACKGROUND Early identification of severe/critical coronavirus disease 2019(COVID-19)is crucial for timely treatment and *** computed tomography(CT)score has been shown to be a significant factor in the diagnosis and treatment of pneumonia,however,there is currently a lack of effective early warning systems for severe/critical COVID-19 based on dynamic CT *** To develop a severe/critical COVID-19 prediction model using a combination of imaging scores,clinical features,and biomarker *** This study used an improved scoring system to extract and describe the chest CT characteristics of COVID-19 *** study also took into consideration the general clinical indicators such as dyspnea,oxygen saturation,alternative lengthening of telomeres(ALT),and androgen suppression treatment(AST),which are commonly associated with severe/critical COVID-19 *** study employed lasso regression to evaluate and rank the significance of different disease *** The results showed that blood oxygen saturation,ALT,IL-6/IL-10,combined score,ground glass opacity score,age,crazy paving mode score,qsofa,AST,and overall lung involvement score were key factors in predicting severe/critical COVID-19 *** study established a COVID-19 severe/critical early warning system using various machine learning algorithms,including XGBClassifier,Logistic Regression,MLPClassifier,RandomForestClassifier,and AdaBoost *** study concluded that the prediction model based on the improved CT score and machine learning algorithms is a feasible method for early detection of severe/critical COVID-19 *** The findings of this study suggest that a prediction model based on improved CT scores and machine learning algorithms is effective in detecting the early warning signals of severe/critical COVID-19.

关键词： COVID-19 Clinical prediction model Electron computed tomography Machine learning

Enhanced Cu/graphene adhesion by doping with Cr and Ti:A first principles prediction

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Transactions of Nonferrous Metals Society of China 2019年第8期29卷 1721-1727页

作者： Yang LIU Gang wang yi-ren wang Yong JIANG Dan-qing yi Key Laboratory of Nonferrous Metal Materials Science and Engineering Ministry of Education School of Materials Science and Engineering Central South University Changsha 410083 China State Key Laboratory of Powder Metallurgy Central South University Changsha 410083 China

We presented a density functional theory study on doping effects of transition metals(Cr and Ti)on the Cu/graphene interface *** undoped Cu/graphene interface structures were constructed using both the sandwich and the surface *** calculations showed that the interface binding strength only weakly depends on interface *** interface models predicted the top-fcc coordination type as the most energy-favored,with a low binding energy *** Cr prefers to substituting for Cu, while Ti occupies a hollow site at the *** the segregation tendencies are both very weak,once present on the interface,both dopants can greatly increase the interface binding energy and improve the adhesion.

关键词： Cu Cr Ti grapheme doping interface first principles