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Search for exotic interactions of solar neutrinos in the CDEX-10 experiment

Physical Review D 2023年第11期107卷 112002-112002页

作者： X. P. Geng L. T. Yang Q. Yue K. J. Kang Y. J. Li H. P. An Greeshma C. J. P. Chang Y. H. Chen J. P. Cheng W. H. Dai Z. Deng C. H. Fang H. Gong Q. J. Guo X. Y. Guo L. He S. M. He J. W. Hu H. X. Huang T. C. Huang H. T. Jia X. Jiang S. Karmakar H. B. Li J. M. Li J. Li Q. Y. Li R. M. J. Li X. Q. Li Y. L. Li Y. F. Liang B. Liao F. K. Lin S. T. Lin J. X. Liu S. K. Liu Y. D. Liu Y. Liu Y. Y. Liu Z. Z. Liu H. Ma Y. C. Mao Q. Y. Nie J. H. Ning H. Pan N. C. Qi J. Ren X. C. Ruan Z. She M. K. Singh T. X. Sun C. J. Tang W. Y. Tang Y. Tian G. F. Wang L. Wang Q. Wang Y. F. Wang Y. X. Wang H. T. Wong S. Y. Wu Y. C. Wu H. Y. Xing R. Xu Y. Xu T. Xue Y. L. Yan N. Yi C. X. Yu H. J. Yu J. F. Yue M. Zeng Z. Zeng B. T. Zhang F. S. Zhang L. Zhang Z. H. Zhang Z. Y. Zhang K. K. Zhao M. G. Zhao J. F. Zhou Z. Y. Zhou J. J. Zhu Key Laboratory of Particle and Radiation Imaging (Ministry of Education) and Department of Engineering Physics Tsinghua University Beijing 100084 Department of Physics Tsinghua University Beijing 100084 Institute of Physics Academia Sinica Taipei 11529 NUCTECH Company Beijing 100084 YaLong River Hydropower Development Company Chengdu 610051 College of Nuclear Science and Technology Beijing Normal University Beijing 100875 College of Physics Sichuan University Chengdu 610065 School of Physics Peking University Beijing 100871 Department of Nuclear Physics China Institute of Atomic Energy Beijing 102413 Sino-French Institute of Nuclear and Technology Sun Yat-sen University Zhuhai 519082 School of Physics Nankai University Tianjin 300071 Department of Physics Banaras Hindu University Varanasi 221005 Department of Physics Beijing Normal University Beijing 100875 Department of Physics and Institute of Theoretical Physics Nanjing Normal University Nanjing 210023

We investigate exotic neutrino interactions using the 205.4−kg·day dataset from the CDEX-10 experiment at the China Jinping Underground Laboratory. New constraints on the mass and couplings of new gauge bosons are presented. Two nonstandard neutrino interactions are considered: a U(1)B–L gauge-boson-induced interaction between an active neutrino and electron/nucleus, and a dark-photon-induced interaction between a sterile neutrino and electron/nucleus via kinetic mixing with a photon. This work probes an unexplored parameter space involving sterile neutrino coupling with a dark photon. New laboratory limits are derived on dark photon masses below 1 eV/c2 at some benchmark values of Δm412 and g′2sin22θ14.

关键词： Extensions of gauge sector Particle astrophysics Particle interactions Particle mixing & oscillations Solar neutrinos Hypothetical gauge bosons Neutrinos Sterile neutrinos Dark matter detectors Neutrino detectors Solid-state detectors

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Optimal Energy Allocation Against Denial-of-Service Attack in Cache-enabled Wireless Networks 91

Optimal Energy Allocation Against Denial-of-Service Attack i...

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91st IEEE Vehicular technology Conference, VTC Spring 2020

作者： Gan, Ruimeng Xiao, Yue Shao, Jinliang Zhou, Xiaotian Xiang, Wei University of Electronic Science and Technology of China National Key Laboratory of Science and Technology on Communications Chengdu611731 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China Science and Technology on Communication Networks Laboratory Shijiazhuang050000 China James Cook University College of Science Engineering Cairns4870 Australia

ISBN: (纸本)9781728152073

In this paper, the security issue of cache-enabled wireless networks is considered, where data transmission from the small base station (SBS) to the user is interfered by denial-of-service (DoS) attacks. To deal with this type of DoS attacks, we firstly investigate the energy dispatch problem from the perspective of the SBS. We further establish an optimization model by using the average number of subfiles requested from the macro base station (MBS) to measure the system performance, and then present the corresponding algorithm to derive the optimal scheduler. Additionally, numerical simulations are carried out to validate the effectiveness of the proposed algorithm. © 2020 IEEE.

关键词： Wireless networks

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Search for Double Beta Decay of 136Xe to the 0+1 Excited State of 136Ba with PandaX-4T

arXiv

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arXiv 2025年

作者： Luo, Lingyin Bo, Zihao Chen, Wei Chen, Xun Chen, Yunhua Cheng, Zhaokan Cui, Xiangyi Fan, Yingjie Fang, Deqing Gao, Zhixing Geng, Lisheng Giboni, Karl Guo, Xunan Guo, Xuyuan Guo, Zichao Han, Chencheng Han, Ke He, Changda He, Jinrong Huang, Di Huang, Houqi Huang, Junting Hou, Ruquan Hou, Yu Ji, Xiangdong Ji, Xiangpan Ju, Yonglin Li, Chenxiang Li, Tao Li, Jiafu Li, Mingchuan Li, Shuaijie Li, Zhiyuan Lin, Qing Liu, Jianglai Lu, Congcong Lu, Xiaoying Luo, Yunyang Ma, Wenbo Ma, Yugang Mao, Yajun Meng, Yue Ning, Xuyang Pang, Binyu Qi, Ningchun Qian, Zhicheng Ren, Xiangxiang Shan, Dong Shang, Xiaofeng Shao, Xiyuan Shen, Guofang Shen, Manbin Sun, Wenliang Tao, Yi Wang, Anqing Wang, Guanbo Wang, Hao Wang, Jiamin Wang, Lei Wang, Meng Wang, Qiuhong Wang, Shaobo Wang, Siguang Wang, Wei Wang, Xiuli Wang, Xu Wang, Zhou Wei, Yuehuan Wu, Weihao Wu, Yuan Xiao, Mengjiao Xiao, Xiang Xiong, Kaizhi Xu, Yifan Yao, Shunyu Yan, Binbin Yan, Xiyu Yang, Yong Ye, Peihua Yu, Chunxu Yuan, Ying Yuan, Zhe Yun, Youhui Zeng, Xinning Zhang, Minzhen Zhang, Peng Zhang, Shibo Zhang, Shu Zhang, Tao Zhang, Wei Zhang, Yang Zhang, Yingxin Zhang, Yuanyuan Zhao, Li Zhou, Jifang Zhou, Jiaxu Zhou, Ning Zhou, Xiaopeng Zhou, Yubo Zhou, Zhizhen New Cornerstone Science Laboratory Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai201210 China State Key Laboratory of Nuclear Physics and Technology School of Physics Peking University Beijing100871 China Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai200240 China Jinping Deep Underground Frontier Science and Dark Matter Key Laboratory of Sichuan Province China Yalong River Hydropower Development Company Ltd. 288 Shuanglin Road Chengdu610051 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-Sen University Zhuhai519082 China Department of Physics Yantai University Yantai264005 China Institute of Modern Physics Fudan University Shanghai200433 China School of Physics Beihang University Beijing102206 China Peng Huanwu Collaborative Center for Research and Education Beihang University Beijing100191 China International Research Center for Nuclei and Particles in the Cosmos Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing100191 China Institute of Modern Physics Chinese Academy of Sciences Huizhou516000 China SJTU Paris Elite Institute of Technology Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiao Tong University Sichuan Research Institute Chengdu610213 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Physics University of Maryland College ParkMD20742 United States School of Physics Nankai University Tianjin300071 China School of Physics Sun Yat-Sen University Guangzhou510275 China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei230026 China Department of Modern Physics University of Science and Technology of China Hefei230026 China Research Center for Particle Science and Technology Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China Key Laboratory o

We perform a search of double beta decay of 136Xe to the excited state, 0+1 , of 136Ba (2νββ-0+1 ), using the dual-phase xenon detector of PandaX-4T with the first 94.9-day commissioning data. The multi-site events are reconstructed up to the MeV energy scale, which helps to improve the background model significantly. The background contribution from the stainless steel platform outside PandaX-4T cryostat is evaluated for the first time. No significant evidence for 2νββ-0+1 is observed, resulting in a lower limit on half-life of 7.5×1022 yr at the 90% confidence level. This is the first experimental limit on such a rare decay in a natural xenon-based detector. © 2025, CC BY.

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Numerical Simulation of Flow-Thermal Coupling Field Inside Upper Plenum and Hot Legs of Pressurized Water Reactor (Pwr)

SSRN

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SSRN 2022年

作者： Sun, Ziyun Zhou, Xinzhi He, Zhengxi Xu, Tao Zhu, Jialiang Dong, Chenlong College of Electronics And Information Engineering Sichuan University No. 24 South Section 1 1st Ring Road Sichuan Chengdu China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China No. 328 Section 1 Changshun Avenue Sichuan Chengdu China

The temperature and flow rate of coolant in the hot legs of pressurized water reactor (PWR) systems directly reflect the nuclear power and the heat transfer state of the reactor core, and are key parameters for reactor power control and safety protection. Most of existing studies have been conducted to analyze the steady-state thermal hydraulic features in the upper plenum and hot legs from a spatial scale, but less from a temporal scale to describe and analyze the transient features. In order to meet the requirements of key parameters measurement and safe operation, this paper established a reasonably simplified 3D geometrical model of the upper plenum and hot legs of the HPR1000 reactor based on a multi-physics field simulation platform and investigated the steady-state and transient features of the flow-thermal coupling field of coolant under numerical simulation, focusing on the coupling analysis of the flow-thermal coupling field on the spatial and temporal scales. The analysis of the steady-state features shows that the coolant flowing into the upper plenum from the upper core plate is characterized by a high middle temperature and a low peripheral temperature. As a result, there is an uneven coolant temperature distribution at the inlet of the hot legs that the temperature differences between the cold lower part and hot upper part are relatively large. However, with the axial flow of coolant, the temperature distribution in hot legs tends to be more uniform and the temperature differences decrease. The simulation results show that the flow of the low temperature coolant in hot legs dominates the change of coolant temperature distribution. The analysis of the transient features shows that a small fluctuation of flow rate at the core outlet causes changes in the turbulent kinetic energy of coolant in hot legs, but the effect on coolant temperature distribution is relatively weak. These such variations on the temporal scale can change the coupling relations of c

关键词： Multiphysics

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Lattice dynamics and elastic properties of α-U at high-temperature and high-pressure by machine learning potential simulations

arXiv

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arXiv 2022年

作者： Wang, Hao Pan, Xiao-Long Wang, Yu-Feng Chen, Xiang-Rong Wang, Yi-Xian Geng, Hua-Yun College of Physics Sichuan University Chengdu610065 China National Key Laboratory of Shock Wave and Detonation Physics Institute of Fluid Physics CAEP Mianyang621900 China College of Science Xi'an University of Science and Technology Xi'An710054 China HEDPS Center for Applied Physics and Technology College of Engineering Peking University Beijing100871 China

Studying the physical properties of materials under high pressure and temperature through experiments is difficult. Theoretical simulations can compensate for this deficiency. Currently, large-scale simulations using machine learning force fields are gaining popularity. As an important nuclear energy material, the evolution of the physical properties of uranium under extreme conditions is still unclear. Herein, we trained an accurate machine learning force field on α-U and predicted the lattice dynamics and elastic properties at high pressures and temperatures. The force field agrees well with the ab initio molecular dynamics (AIMD) and experimental results and it exhibits higher accuracy than classical potentials. Based on the high-temperature lattice dynamics study, we first present the temperature-pressure range in which the Kohn anomalous behavior of the Σ4 optical mode exists. Phonon spectral function analysis showed that the phonon anharmonicity of α-U is very weak. We predict that the single-crystal elastic constants C44, C55, C66, polycrystalline modulus (E, G), and polycrystalline sound velocity (CL, CS) have strong heating-induced softening. All the elastic moduli exhibited compression-induced hardening behavior. The Poisson's ratio shows that it is difficult to compress α-U at high pressures and temperatures. Moreover, we observed that the material becomes substantially more anisotropic at high pressures and temperatures. The accurate predictions of α-U demonstrate the reliability of the method. This versatile method facilitates the study of other complex metallic materials. © 2022, CC BY.

关键词： Elasticity

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Effect of thermal-hydraulic parameters and pH on the corrosion product deposition in steam generator based on CFD

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Annals of nuclear Energy 2025年 223卷

作者： Yunchen Lai Haiyan Xie Shiyu Tan Ke Wang Dongyun Li Xuesong Li Yang Gao Hongguo Hou Nan Chao Caishan Jiao Yu Zhou Fundamental Science on Nuclear Safety and Simulation Technology Laboratory College of Nuclear Science and Technology Harbin Engineering University Harbin 150001 Heilongjiang Province China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu 610041 Sichuan Province China

The deposition of corrosion products in the primary circuit of a pressurized water reactors is known to have an adverse impact on reactor system’s performance, which can cause localized corrosion. They can also impede the flow of coolant, or in severe cases block the tube channels. Besides, the deposits have a lower thermal conductivity than the structural materials, which may increase the thermal resistance, thus reducing heat transfer efficiency. Despite the presence of a considerable amount of corrosion products in steam generators, research on the deposition of corrosion products within is still insufficient. A comprehensive 3-D model of thermal–hydraulic and chemical processes was used to investigate the factors affecting the deposition of corrosion products in a steam generator. For the purpose of determine the deposition distribution along the heat transfer tube, the tube was separated into 11 main nodes based on the equal temperature differences according to the thermal–hydraulic data calculated. The simulation results indicate that the deposition thickness is sensitive to coolant temperature, flow velocity and pH value, and it was shown that higher temperature(from 560 K to 600 K decreasing 9.31 %), pH value (from 6.5 to 8.0 decreasing 9.92 %) and lower flow velocity (from 5 m/s to 20 m/s increasing 8.13 %)was effective for reducing the corrosion product deposition due to the physicochemical properties of the coolant and the turbulent flow, while working pressure has less influence on it(from 6 MPa to 15 MPa increasing only 1.03 %).

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A Multimodal Deep Learning Approach for Very Short-Term Solar Forecasts using Sky Images and Historical Numerical Data

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Renewable Energy 2025年

作者： Anto Leoba Jonathan Olusola Bamisile Dongsheng Cai Chukwuebuka Joseph Ejiyi Joseph Junior Nkou Nkou Kombou Victor Chiagoziem C. Ukwuoma Liu Wei Qi Huang College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan P.R. 610059 China Sichuan Engineering Technology Research Center for Industrial Internet Intelligent Monitoring and Application Chengdu University of Technology Sichuan P.R. 610059 China Energy Environment and Society Division University of Dundee United Kingdom School of Computer Science and Engineering (School of Cyber Security) Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China (UESTC) Chengdu 611731 China

The increased solar energy integration into the power system introduces various issues due to its intermittent nature. As solar power penetration increases, grid management becomes increasingly complex, highlighting the importance of precise solar power forecasts. This paper addresses a critical challenge in solar power integration, highlighting the significance of very short-term solar forecasting (VSTSF) for grid operators. We propose a novel MULSKIN hybrid framework that combines a custom ResNet and a novel targeted feature attention mechanism (TFAM), which dynamically focuses on the most relevant regions in sky images, improving the model’s ability to capture essential cloud dynamics and spatiotemporal patterns. Unlike existing methods in literature that either use image (sky images) or numerical (meteorological) data, the proposed MULSKIN approach integrates both data types, allowing for a more holistic understanding of atmospheric conditions. This paper contributes to advancing the field of solar forecasting by presenting a new, effective, and scalable solution suitable for operational deployment in modern energy systems. With six years of high-resolution data, the model outperforms traditional machine learning (ML), deep learning (DL) models, and baseline reference models, achieving a forecast skill score of 32.84% and a root mean square error (RMSE) of 54.36W/m 2 . Our Results indicate that incorporating sky images alongside numerical meteorological data significantly enhances forecasting accuracy, with the MULSKIN model demonstrating its superiority in capturing dynamic cloud movements and weather patterns over short time horizons. The findings underscore the potential of combining multimodal data sources to address the challenges of VSTSF, offering promising improvements in solar irradiance prediction for real-time grid operations.

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Glaucoma progression prediction using multi-modal data and trusted multi-view learning

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Expert Systems with Applications 2025年 288卷

作者： Xiujuan Zheng Wo Wang Zhiqing Lv Ni Li Department of Automation College of Electrical Engineering Sichuan University Chengdu 610065 China Key Laboratory of Information and Automation Technology in Sichuan Province Chengdu 610065 China Department of Ophthalmology West China Hospital Sichuan University Chengdu 610041 China

Glaucoma is the leading cause of irreversible blindness worldwide, with its progression often being insidious and early symptoms easily overlooked. Therefore, predicting the progression of glaucoma is crucial for clinicians to timely intervene or adjust treatment plans. However, current research on glaucoma progression prediction based on multi-modal examination data often disregards the uncertainty in data quality, which may lead to inaccurate prediction results. To address this problem, we propose a novel glaucoma progression prediction model (GPPM) based on trusted multi-view learning. This model employs evidential deep learning theory to model the prediction uncertainty caused by fluctuations in the data quality from visual field testing (VFT) and color fundus photography (CFP). Then, it dynamically adjusts the contributions of multi-modal data with different quality through a specialized decision fusion approach and achieves accurate and reliable prediction results. We validated the proposed GPPM using the public dataset (GRAPE), comparing various models and progression criteria. The results show that under various progression criteria, the proposed model outperformed the baseline model, with a notably high prediction accuracy of 0.888, specificity of 0.956, and an AUC of 0.849 under the combined progression criteria. The model testing results indicate that the prediction uncertainty associated with the use of CFPs is higher than that associated with VFTs. The experimental results demonstrate that GPPM effectively enhances the accuracy of glaucoma progression prediction by utilizing two heterogeneous modal data types. Moreover, the model provides uncertainty estimates for the prediction results, making them more trustworthy. Therefore, the proposed model holds promise for assisting clinicians in diagnosing and evaluating the progression of glaucoma.

关键词： Glaucoma progression prediction Trusted multi-view learning Uncertainty estimation Visual field test Color fundus photograph

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Interaction of H2+ molecular beam with thin layer graphene foils

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Chinese Physics B 2019年第9期28卷 164-168页

作者： Min Li Guo-Feng Qu Yi-Zhou Wang Zhou-Sen Zhu Mian-Gong Shi Mao-Lei Zhou Dong Liu Zi-Xu Xu Ming-Jiang Song Jun Zhang Fan Bai Xiao-Dong Liao Ji-Feng Han Key Laboratory of Radiation Physics and Technology of the Ministry of Education Institute of Nuclear Science and TechnologySichuan UniversityChengdu 610064China College of Physics and Electronic Engineering Sichuan Normal UniversityChengdu 610066China

The interaction of MeV H2^+ molecular ions with thin layer graphene and graphite foils was studied by using a highresolution electrostatic analyzer.A large number of fragment protons were observed at zero degree(along the beam direction) when the H2^+ beam was passing through the monolayer graphene foil, which indicates that the electron of the H2^+ molecular ions can be stripped easily even by the monolayer graphene *** trailing than leading protons were found in the energy spectrum, which means significant wake effect was observed in the monolayer graphene *** ratio of the numbers of trailing protons over leading protons first increased with the thickness for the much thinner graphene foils, and then decreased with the thickness for the much thicker graphite foils, which indicates that the bending effect of the wake field on the trailing proton varied with the foil thickness.

关键词： H2^+ molecular ions graphene electron stripping wake effect

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Forecasting of Vessel Traffic Flow Using BPNN Based on Genetic Algorithm Optimization

Forecasting of Vessel Traffic Flow Using BPNN Based on Genet...

引用

International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Qihang Yi Yi Zuo Tieshan Li Yuhao Mao Yang Xiao Navigation College Dalian Maritime University Dalian China College of Automation Engineering University of Electronic Science and Technology of China Chengdu China The University of Alabama Tuscaloosa USA

Accurate prediction of vessel traffic flow plays a significant role in the field of modern intelligent transportation system. In order to enhance the prediction accuracy of vessel traffic flow, this paper combines genetic algorithm (GA) and Back Propagation neural network (BPNN) to build a prediction model. Based on the vessel traffic flow data of The Wuhan Yangtze River Bridge, the simulation experiments were carried out from 2013 to 2018. The average relative error of BPNN optimized by GA is 4.03%, which is better than the average relative error of direct BPNN prediction is 5.57%. The results show that accuracy of the prediction model using BPNN with GA optimization is higher than the traditional BPNN. The BPNN optimized by GA has achieved more ideal results in the forecast of vessel traffic flow. This paper provides the theoretical basis for the relevant decision-making of the water safety authorities so as to guarantee the water traffic safety.

关键词： Wireless communication Computational modeling Neural networks Predictive models Prediction algorithms Data models Safety

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