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Momentum Spectroscopy for Multiple Ionization of Cold Rubidium in the Elliptically Polarized Laser Field

Chinese Physics Letters 2020年第5期37卷 33-37页

作者： junyang yuan Yixuan Ma Renyuan Li Huanyu Ma Yizhu Zhang Difa Ye Zhenjie Shen Tianmin Yan Xincheng Wang Matthias Weidemüller Yuhai Jiang Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210 University of Chinese Academy of Sciences Beijing 100049 School of Physical Science and Technology ShanghaiTech UniversityShanghai 201210 Center for Terahertz Waves and College of Precision Instrument and Optoelectronics Engineering Key Laboratory of the Ministry of EducationTianjin UniversityTianjin 300072 Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijing 100088 Hefei National Laboratory for Physical Sciences at the Microscale and Shanghai Branch University of Science and Technology of ChinaShanghai 201315 CAS Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of ChinaShanghai 201315 Physikalisches Institut Universitat HeidelbergIm Neuenheimer Feld 22669120 HeidelbergGermany

Employing recently developed magneto-optical trap recoil ion momentum spectroscopy(MOTRIMS)combined with cold atoms,strong laser pulse,and ultrafast technologies,we study momentum distributions of the multiply ionized cold rubidium(Rb)induced by the elliptically polarized laser pulses(35 fs,1.3×10^15 W/cm^2).The complete vector momenta of Rb^n+ions up to charge state n=4 are recorded with extremely high resolution(0.12 *** Rb^+).Variations of characteristic multi-bands are displayed in momentum distributions because the ellipticity varies from the linear to circular polarization,are interpreted qualitatively with the classical overbarrier ionization *** momentum spectroscopy of cold heavy alkali atoms presents novel strong-field phenomena beyond the noble gases.

关键词： polarization momentum alkali

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如何开展城镇土地权属调查

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科技传播 2011年第2期3卷 133-134页

作者：张晓强袁俊阳陈余才黄河勘测规划设计有限公司测绘院河南郑州450045

随着社会经济的快速发展,现有土地调查成果已难以满足新形势下经济宏观调控和国土资源管理的客观需要。如何开展城镇土地权属调查工作,本文从地籍调查的分类、方法、初始地籍调查的步骤以及内容等方面进行阐述。

关键词：地籍调查土地权属调查地籍测量

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冷原子成像谱仪中靶表征及光电离研究

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中国激光 2020年第6期47卷 98-107页

作者：马祎璇李任远袁俊阳孟秋香马欢玉阮舒舒张逸竹阎天民沈镇捷王新成江玉海中国科学院上海高等研究院上海201210 上海科技大学物质科学与技术学院上海201210 中国科学院大学北京100049 天津大学精密仪器与光电子工程学院太赫兹研究中心天津300072

将冷原子、超快和强激光技术结合,本课题组最近成功研发了一种新型磁光阱反冲离子动量谱仪(MOTRIMS)装置。介绍了MOTRIMS工作原理,使用吸收成像法和光电离法对靶的密度、温度和速度进行分析标定,得到三维MOT靶的温度为(130±30)μK,速度... 详细信息

将冷原子、超快和强激光技术结合,本课题组最近成功研发了一种新型磁光阱反冲离子动量谱仪(MOTRIMS)装置。介绍了MOTRIMS工作原理,使用吸收成像法和光电离法对靶的密度、温度和速度进行分析标定,得到三维MOT靶的温度为(130±30)μK,速度为(0.1±0.1)m/s,密度约为10^9 atom/cm^3,二维MOT和molasses靶的密度分别约为10^7 atom/cm^3和10^8 atom/cm^3。对比热蒸气靶,在离子飞行方向上冷原子靶的动量分辨率提高了约14倍,飞行时间谱的质量分辨率高达3000。利用800 nm飞秒激光探测Rb^+的三维动量分布,在低激光强度10^11 W/cm^2下观测到明显的阈上电离现象,表明铷原子有很大的阈上电离截面。MOTRIMS具有高分辨离子动量分布全空间成像能力,是研究金属原子强场量子微观动力学的新工具。

关键词：原子与分子物理学磁光阱反冲离子动量谱仪离子高分辨动量分布铷原子激光冷却强场电离

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Predicting microbial extracellular electron transfer activity in paddy soils with soil physicochemical properties using machine learning

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Science China(Technological Sciences) 2024年第1期67卷 259-270页

作者： OU JiaJun LUO XiaoShan LIU junyang HUANG LinYan ZHOU LiHua yuan Yong School of Automation Guangdong University of TechnologyGuangzhou 510006China Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control Guangzhou Key Laboratory Environmental Catalysis and Pollution ControlSchool of Environmental Science and EngineeringInstitute of Environmental Health and Pollution ControlGuangdong University of TechnologyGuangzhou 510006China School of Biomedical and Pharmaceutical Sciences Guangdong University of TechnologyGuangzhou 510006China

Soil extracellular electron transfer(EET)is a pivotal biological process within the realm of ***,EET suffers from a lack of predictive ***,an intricately crafted machine learning model has been developed for the purpose of predicting soil EET by using the physicochemical properties of soil as independent input variables and the EET capabilities in terms of current density(j_(max))and Coulombic charge(C_(out))as dependent output *** autoencoder ensemble stacking(AES)model was developed to address the aforementioned issue by integrating support vector machine,multilayer perceptron,extreme gradient boosting,and light gradient boosting machine algorithms as the stacking *** 10-fold crossvalidation,the AES model exhibited notable improvements in predicting j_(max)and C_(out),with average test R^(2)values of 0.83 and 0.84,respectively,surpassing those of single machine learning(ML)models and the basic ensemble *** utilizing partial correlation plots(PDPs),Shapley Additive explanations(SHAP)values,and SHAP decision plots,we quantitatively explained the impact and contribution of the input molecules on the AES model’s predictions of j_(max)and C_(out).In the context of the SHAP method for the AES model,total carbon(TC)was identified as the most correlated descriptor for j_(max),while total organic carbon(TOC)stood out as the most relevant descriptor for C_(out).In the prediction tasks of j_(max)and C_(out)within the AES model,employing a multitask ML approach allowed the model to benefit from the shared information of input variables,thereby enhancing its overall *** study provides a feasible tool for the prediction of soil EET from soil physiochemical properties and an advanced understanding of the relationship between soil physiochemical properties and EET capability.

关键词： extracellular electron transfer paddy soil machine learning prediction autoencoder ensemble stacking model

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A Novel Anti-Collision Algorithm for Large Scale of UHF RFID Tags Access Systems

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Computers, Materials & Continua 2024年第7期80卷 897-912页

作者： Xu Zhang Yi He Haiwen Yi Yulu Zhang yuan Li Shuai Ma Gui Li Zhiyuan Zhao Yue Liu junyang Liu Guangjun Wen Jian Li School of Information and Communication Engineering/Yibin Institute University of Electronic Science and Technology of ChinaChengdu611731China Department of IoT Technology Research China Mobile Research InstituteBeijing100053China The 10th Research Institute of China Electronics Technology Group Corporation Chengdu611731China College of Information and Communication National University of Defense TechnologyWuhan430019China

When the radio frequency identification(RFID)system inventories multiple tags,the recognition rate will be seriously affected due to *** on the existing dynamic frame slotted Aloha(DFSA)algorithm,a sub-frame observation and cyclic redundancy check(CRC)grouping combined dynamic framed slotted Aloha(SUBF-CGDFSA)algorithm is *** algorithm combines the precise estimation method of the quantity of large-scale tags,the large-scale tags grouping mechanism based on CRC pseudo-randomcharacteristics,and the Aloha anti-collision optimization mechanism based on sub-frame *** grouping tags and sequentially identifying themwithin subframes,it accurately estimates the number of remaining tags and optimizes frame length accordingly to improve efficiency in large-scale RFID *** outcomes demonstrate that this proposed algorithmcan effectively break through the system throughput bottleneck of 36.8%,which is up to 30%higher than the existing DFSA standard scheme,and has more significant advantages,which is suitable for application in largescale RFID tags scenarios.

关键词： Radio frequency identification anti-collision tags grouping dynamic framed slotted Aloha

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Passive IoT Localization Technology Based on SD-PDOA in NLOS and Multi-Path Environments

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Computers, Materials & Continua 2024年第7期80卷 913-930页

作者： junyang Liu yuan Li Yulu Zhang Shuai Ma Gui Li Yi He Haiwen Yi Yue Liu Xiaotao Xu Xu Zhang Jinyao He Guangjun Wen Jian Li School of Information and Communication Engineering/Yibin Institute University of Electronic Science and Technology of ChinaChengdu611731China Department of IoT Technology Research China Mobile Research InstituteBeijing100053China The 10th Research Institute of China Electronics Technology Group Corporation Chengdu611731China College of Information and Communication National University of Defense TechnologyWuhan430019China

Addressing the challenges of passive Radio Frequency Identification(RFID)indoor localization technology in Non-Line-of-Sight(NLoS)and multipath environments,this paper presents an innovative approach by introducing a combined technology integrating an improved Kalman Filter with Space Domain Phase Difference of Arrival(SD-PDOA)and Received Signal Strength Indicator(RSSI).This methodology utilizes the distinct channel characteristics in multipath and NLoS contexts to effectively filter out interference and accurately extract localization information,thereby facilitating high precision and stability in passive RFID *** efficacy of this approach is demonstrated through detailed simulations and empirical tests conducted on a custom-built experimental platform consisting of passive RFID tags and an R420 *** findings are significant:in NLoS conditions,the four-antenna localization system achieved a notable localization accuracy of 0.25 m at a distance of 5 *** complex multipath environments,this system achieved a localization accuracy of approximately 0.5 m at a distance of 5 *** compared to conventional passive localization methods,our proposed solution exhibits a substantial improvement in indoor localization accuracy under NLoS and multipath *** research provides a robust and effective technical solution for high-precision passive indoor localization in the Internet of Things(IoT)system,marking a significant advancement in the field.

关键词： Indoor localization multipath and NLoS environments combined SD-PDOA and RSSI technology

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Data-Driven Discovery of Stochastic Differential Equations

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Engineering 2022年第10期17卷 244-252页

作者： Yasen Wang Huazhen Fang junyang Jin Guijun Ma Xin He Xing Dai Zuogong Yue Cheng Cheng Hai-Tao Zhang Donglin Pu Dongrui Wu Ye yuan Jorge Gonçalves Jürgen Kurths Han Ding School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Department of Mechanical Engineering University of KansasLawrenceKS 66045USA HUST-Wuxi Research Institute Wuxi 214174China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Department of Plant Sciences University of CambridgeCambridge CB23EAUK Luxembourg Centre for Systems Biomedicine University of LuxembourgBelvaux 4367Luxembourg Department of Physics Humboldt University of BerlinBerlin 12489Germany Department of Complexity Science Potsdam Institute for Climate Impact ResearchPotsdam 14473Germany

Stochastic differential equations(SDEs)are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different *** identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system’s *** practical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient data *** study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning(SBL)technique to search for a parsimonious,yet physically necessary representation from the space of candidate basis *** importantly,we use the analytical tractability of SBL to develop an efficient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series *** effectiveness of the proposed framework is demonstrated using real data on stock and oil prices,bearing variation,and wind speed,as well as simulated data on well-known stochastic dynamical systems,including the generalized Wiener process and Langevin *** framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences,economics,and engineering fields for analysis,prediction,and decision making.

关键词： Data-driven method System identification Sparse Bayesian learning Stochastic differential equations Random phenomena

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THE CONSTRUCTION AND PRELIMINARY APPRAISEMENT OF HSV-2 gD GENE DNA VACCINE

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Academic Journal of Xi'an Jiaotong University 2002年第2期14卷 163-165页

作者： Wang junyang(王军阳) Fan Guixiang(范桂香) Sheng li(胜利) yuan Yukang(袁育康) Department of Immunology Medical School of Xi''an Jiaotong University Xi''an 710061 China. Department of Immunology Medical School of Xi''an Jiaotong University Xi''an 710061 China. Department of Immunology Medical School of Xi''an Jiaotong University Xi''an 710061 China. Department of Immunology Medical School of Xi''an Jiaotong University Xi''an 710061 China.

Objective To prevent infection from herpes simplex virus type 2(HSV-2),and make the foundation for the construction of multi-valent DNA vaccine. Methods The complete DNA sequence,which encoded the amino acid sequence of the viral glycoprotein D(gD),was obtained from the HSV-2 genome by polymerase chain reaction(PCR).The fragment was inserted into the lower stream of Cytomegalovirus(CMV) promoter in the eukaryotic expression plasmid pcDNA3.1(+), then immunized mice by bilateral intramuscular injection into the rear legs with this recombinant plasmid and tested the specific antibodies against glycoprotein D by ELISA. Results Animal experiment have demonstrated that the recombinant plasmid(pcDNA-gD2)inoculated into mice could induce the production of specific antibodies against glycoprotein D. Conclusion The eukaryotic plasmid pcDNA-gD2 constructed by us could correctly express gD gene and induce the production of specific antibodies.

关键词： HSV-2 Glycoprotein D DNA vaccine PCR

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一种数据隐私保护下多方无损线性模型学习方法

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机械工程学报 2023年第12期59卷 17-27页

作者：华丰王亚森金骏阳袁烨华中科技大学机械科学与工程学院武汉430074 华中科技大学数字制造装备与技术国家重点实验室武汉430074 华中科技大学无锡研究院无锡214000 华中科技大学人工智能与自动化学院武汉430074

随着工业大数据技术的发展,制造企业通过收集和分析生产数据,获取在预测、诊断等方面的优化方法。然而制造企业受限于建模和算力等技术瓶颈,难以高效地实现数据分析,在联合其他参与方共同协作时即需要承担信息泄露的风险,又难以保证模... 详细信息

随着工业大数据技术的发展,制造企业通过收集和分析生产数据,获取在预测、诊断等方面的优化方法。然而制造企业受限于建模和算力等技术瓶颈,难以高效地实现数据分析,在联合其他参与方共同协作时即需要承担信息泄露的风险,又难以保证模型的性能无损。针对这些场景问题,提出基于数据隐私保护的多方无损线性模型学习方法。首先搭建多方协作计算框架,设计了数据单向加密算法来保护数据出场的隐私安全,各协作方分别基于加密数据进行线性模型的训练。随后,研究分析线性模型与数据集的关联特性,提出线性模型无损聚合算法。最后,在典型工业场景数据集上进行方法验证,试验结果表明提出的框架可以获得性能无损的全局模型,并实现数据持有方的隐私安全保护。

关键词：工业大数据隐私保护机器学习联邦学习数据分析

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维生素D及其受体在妇科癌症中的研究进展

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现代肿瘤医学 2022年第6期30卷 1129-1133页

作者：尹俊杨叶元桂林医学院附属医院妇科广西桂林541001

在过去的二十几年中,国内外大量的研究是关于维生素D及维生素D受体(vitamin D receptor,VDR)在癌症发生、发展中所起的作用。维生素D的活性代谢产物1,25(OH)_(2)D_(3)除了维持体内矿物质的稳态和骨骼的功能外,还具有抑制细胞增殖、诱导... 详细信息

在过去的二十几年中,国内外大量的研究是关于维生素D及维生素D受体(vitamin D receptor,VDR)在癌症发生、发展中所起的作用。维生素D的活性代谢产物1,25(OH)_(2)D_(3)除了维持体内矿物质的稳态和骨骼的功能外,还具有抑制细胞增殖、诱导细胞分化、凋亡及免疫调节的作用。近些年,越来越多的研究证据表明,维生素D及VDR在妇科癌症的发病机制中发挥着重要的作用,是研究妇科癌症治疗手段中新的切入点。现本文就维生素D及其受体在妇科恶性肿瘤中的研究现状进行综述。

关键词：维生素D VDR 卵巢癌宫颈癌子宫内膜癌外阴癌阴道癌

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