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Uncertainty and sensitivity analysis of in-vessel phenomena under severe accident mitigation strategy based on ISAA-SAUP program

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Nuclear Science and Techniques 2024年第1期35卷 108-123页

作者： Hao Yang Ji-Shen Li zhi-Ran zhang Bin zhang Jian-Qiang Shan School of Nuclear Science and Technology Xi’an Jiaotong UniversityXi’an 710049China State Key Laboratory of Multiphase Flow in Power Engineering Xi’an Jiaotong UniversityXi’an 710049China

The phenomenology involved in severe accidents in nuclear reactors is highly complex.Currently,integrated analysis programs used for severe accident analysis heavily rely on custom empirical parameters,which introduce considerable uncertainty.Therefore,in recent years,the field of severe accidents has shifted its focus toward applying uncertainty analysis methods to quantify uncertainty in safety assessment programs,known as“best estimate plus uncertainty(BEPU).”This approach aids in enhancing our comprehension of these programs and their further development and improvement.This study concentrates on a third-generation pressurized water reactor equipped with advanced active and passive mitigation strategies.Through an Integrated Severe Accident Analysis Program(ISAA),numerical modeling and uncertainty analysis were conducted on severe accidents resulting from large break loss of coolant accidents.Seventeen uncertainty parameters of the ISAA program were meticulously screened.Using Wilks'formula,the developed uncertainty program code,SAUP,was employed to carry out Latin hypercube sampling,while ISAA was employed to execute batch calculations.Statistical analysis was then conducted on two figures of merit,namely hydrogen generation and the release of fission products within the pressure vessel.Uncertainty calculations revealed that hydrogen production and the fraction of fission product released exhibited a normal distribution,ranging from 182.784 to 330.664 kg and from 15.6 to 84.3%,respectively.The ratio of hydrogen production to reactor thermal power fell within the range of 0.0578–0.105.A sensitivity analysis was performed for uncertain input parameters,revealing significant correlations between the failure temperature of the cladding oxide layer,maximum melt flow rate,size of the particulate debris,and porosity of the debris with both hydrogen generation and the release of fission products.

关键词： Gen-III PWR Severe accident mitigation Wilks’formula Hydrogen Fission products Uncertainty and sensitivity analysis

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Community structure and species diversity dynamics of a subtropical evergreen broad-leaved forest in China:2005 to 2020

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Plant Diversity 2024年第1期46卷 70-77页

作者： shi-Guang Wei Lin Li Kun-Dong Bai zhi-Feng Wen Jing-Gang Zhou Qin Lin Key Laboratory of Ecology of Rare and Endangered Species and Environmental Protection(Guangxi Normal University) Ministry of EducationGuilin 541004GuangxiChina School of Life&Environmental Sciences Guilin University of Electronic TechnologyGuilin 541004GuangxiChina Guangxi Key Laboratory of Landscape Resources Conservation and Sustainable Utilization in Lijiang River Basin Guilin 541004GuangxiChina Key Laboratory of Vegetation Restoration and Management of South China Botanical Garden of Degraded Ecosystems Chinese Academy of SciencesGuangzhou 510650GuangdongChina Center of Plant Ecology Core Botanical GardensChinese Academy of SciencesGuangzhou 510650GuangdongChina

Here,we characterize the temporal and spatial dynamics of forest community structure and species diversity in a subtropical evergreen broad-leaved forest in China.We found that community structure in this forest changed over a 15-year period.Specifically,renewal and death of common species was large,with the renewal of individuals mainly concentrated within a few populations,especially those of Aidia canthioides and Cryptocarya concinna.The numbers of individual deaths for common species were concentrated in the small and mid-diameter level.The spatial distribution of community species diversity fluctuated in each monitoring period,showing a more dispersed diversity after the 15-year study period,and the coefficient of variation on quadrats increased.In 2010,the death and renewal of the community and the spatial variation of species diversity were different compared to other survey years.Extreme weather may have affected species regeneration and community stability in our subtropical monsoon evergreen broad-leaved forests.Our findings suggest that strengthening the monitoring and management of the forest community will help better understand the long-and short-term causes of dynamic fluctuations of community structure and species diversity,and reveal the factors that drive changes in community structure.

关键词： Community structure Death and renewal dynamics Species diversity dynamics South subtropical forest

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Quantitative Green's function estimates for lattice quasi-periodic Schrodinger operators

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Science China Mathematics 2024年第5期67卷 1011-1058页

作者： Hongyi Cao Yunfeng shi zhifei zhang School of Mathematical Sciences Peking UniversityBeijing 100871China School of Mathematics Sichuan UniversityChengdu 610064China

In this paper,we establish quantitative Green’s function estimates for some higher-dimensional lattice quasi-periodic(QP)Schrodinger operators.The resonances in the estimates can be described via a pair of symmetric zeros of certain functions,and the estimates apply to the sub-exponential-type non-resonance conditions.As the application of quantitative Green’s function estimates,we prove both the arithmetic version of Anderson localization and the finite volume version of(1/2-)-Holder continuity of the integrated density of states(IDS)for such QP Schrodinger operators.This gives an affirmative answer to Bourgain’s problem in Bourgain(2000).

关键词： Holder continuity of the IDS quantitative Green's function estimates quasi-periodic Schrodinger operators arithmetic Anderson localization multi-scale analysis

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Simultaneously quantifying hundreds of acylcarnitines in multiple biological matrices within ten minutes using ultrahigh-performance liquid-chromatography and tandem mass spectrometry

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Journal of Pharmaceutical Analysis 2024年第1期14卷 140-148页

作者： Jingxian zhang Qinsheng Chen Lianglong zhang Biru shi Men Yu Qingxia Huang Huiru Tang State Key Laboratory of Genetic Engineering School of Life SciencesHuman Phenome InstituteZhangjiang Fudan International Innovation CenterMetabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular PhenomicsZhongshan HospitalFudan UniversityShanghai200032China Wuhan Laboratory for Shanghai Metabolome Institute(SMI)Ltd. Wuhan430000China

Acylcarnitines are metabolic intermediates of fatty acids and branched-chain amino acids having vital biofunctions and pathophysiological significances. Here, we developed a high-throughput method for quantifying hundreds of acylcarnitines in one run using ultrahigh performance liquid chromatography and tandem mass spectrometry (UPLC-MS/MS). This enabled simultaneous quantification of 1136 acylcarnitines (C0–C26) within 10-min with good sensitivity (limit of detection 0.992), accuracy (relative error ship (goodness of fit > 0.998) for predicting retention time (tR) of acylcarnitines with no standards and built a database of their multiple reaction monitoring parameters (tR, ion-pairs, and collision energy). Furthermore, we quantified 514 acylcarnitines in human plasma and urine, mouse kidney, liver, heart, lung, and muscle. This provides a rapid method for quantifying acylcarnitines in multiple biological matrices.

关键词： Acylcarnitine UPLC-MS/MS Quantitative structure-retention relationship Molecular phenotype

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Learning topological defects formation with neural networks in a quantum phase transition

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Communications in Theoretical Physics 2024年第5期76卷 68-76页

作者： Han-Qing shi Hai-Qing zhang Center for Gravitational Physics Department of Space ScienceBeihang UniversityBeijing 100191China Peng Huanwu Collaborative Center for Research and Education Beihang UniversityBeijing 100191China

Neural networks possess formidable representational power,rendering them invaluable in solving complex quantum many-body systems.While they excel at analyzing static solutions,nonequilibrium processes,including critical dynamics during a quantum phase transition,pose a greater challenge for neural networks.To address this,we utilize neural networks and machine learning algorithms to investigate time evolutions,universal statistics,and correlations of topological defects in a one-dimensional transverse-field quantum Ising model.Specifically,our analysis involves computing the energy of the system during a quantum phase transition following a linear quench of the transverse magnetic field strength.The excitation energies satisfy a power-law relation to the quench rate,indicating a proportional relationship between the excitation energy and the kink numbers.Moreover,we establish a universal power-law relationship between the first three cumulants of the kink numbers and the quench rate,indicating a binomial distribution of the kinks.Finally,the normalized kink-kink correlations are also investigated and it is found that the numerical values are consistent with the analytic formula.

关键词： neural networks machine learning transverse-field quantum Ising model kibble-zurek mechanism

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Biomarkers for neuromyelitis optica:a visual analysis of emerging research trends

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Neural Regeneration Research 2024年第12期19卷 2735-2749页

作者： Xiangjun Li Jiandong zhang Siqi zhang Shengling shi Yi’an Lu Ying Leng Chunyan Li Department of Ophthalmology Affiliated Hospital of Beihua UniversityJilinJilin ProvinceChina Department of Ophthalmology Changchun Bright Eye HospitalChangchunJilin ProvinceChina Departemt of Endocrinology Affiliated Hospital of Beihua UniversityJilinJilin ProvinceChina

Neuromyelitis optica is an inflammatory demyelinating disease of the central nervous system that differs from multiple sclerosis.Over the past 20 years,the search for biomarke rs for neuromyelitis optica has been ongoing.Here,we used a bibliometric approach to analyze the main research focus in the field of biomarkers for neuromyelitis optica.Research in this area is consistently increasing,with China and the United States leading the way on the number of studies conducted.The Mayo Clinic is a highly reputable institution in the United States,and was identified as the most authoritative institution in this field.Furthermore,Professor Wingerchuk from the Mayo Clinic was the most authoritative expe rt in this field.Keyword analysis revealed that the terms "neuro myelitis optica"(261 times), "multiple sclerosis"(220 times), "neuromyelitis optica spectrum disorder"(132 times), "aquaporin4"(99 times),and "optical neuritis"(87 times) were the most frequently used keywords in literature related to this field.Comprehensive analysis of the classical literature showed that the majority of publications provide conclusive research evidence supporting the use of aquaporin-4-IgG and neuromyelitis optica-IgG to effectively diagnose and differentiate neuromyelitis optica from multiple sclerosis.Furthermore,aquaporin-4-IgG has emerged as a highly specific diagnostic biomarker for neuromyelitis optica spectrum disorder.Myelin oligodendrocyte glycoprotein-IgG is a diagnostic biomarke r for myelin oligodendrocyte glycoprotein antibody-associated disease.Recent biomarkers for neuromyelitis optica in clude cerebrospinal fluid immunological biomarkers such as glial fibrillary acidic protein,serum astrocyte damage biomarkers like FAM19A5,serum albumin,and gammaaminobutyric acid.The latest prospective clinical trials are exploring the potential of these biomarkers.Preliminary results indicate that glial fibrillary acidic protein is emerging as a promising candidate biomarker for neuromyelitis optica spectrum disorder.The ultimate goal of

关键词： aquaporin-4 autoantibody multiple sclerosis myelin oligodendrocyte glycoprotein antibody-associated disease neuromyelitis optica neuromyelitis optica spectrum disorder optical coherence tomography

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The effects of cool materials,façade orientation,and morphological parameters on energy consumption at the residential neighborhood scale

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Building Simulation 2024年第4期17卷 525-542页

作者： Fusuo Xu Dongdong Tian zhi Gao Jianshun Jensen zhang Department of Mechanical and Aerospace Engineering Syracuse UniversitySyracuseNY13210USA School of Architecture and Urban Planning Nanjing University22 Hankou RoadNanjingJiangsu Province210093China

Building surface cool materials are novel materials that can reduce urban heat island intensity and decrease building energy consumption.This study investigated the impact of radiative properties of materials,façade orientation,and morphological parameters on energy consumption in six typical residential neighborhoods in Nanjing,China.The neighborhood energy consumption of 16 application schemes considering the façade orientation factor is compared to determine the best energy-saving scheme.Seasonal and annual energy-saving rates,savings in electricity costs,and the price ceiling for materials per unit area are analyzed.The results show that for low-rise buildings,using cool materials only on the roof can reduce the annual energy consumption by 1%.When cool or super cool materials are also used on the building façade,the annual energy saving rate can be up to 3.4%and 4.3%,respectively.Using cool materials on the south façade of buildings is not recommended due to significant heat loss in winter.Considering savings in electricity costs and the price ceiling for materials per unit area,the price of cool and super cool materials should be less than 3.0 and 3.7 RMB/m2,respectively,assuming a lifespan of eight years in Nanjing.

关键词： cool materials building energy saving neighborhood morphology façade orientation

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Construction and properties of venlafaxine hydrochloride sustained release system based on hollow mesoporous silica microspheres

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Particuology 2024年第3期86卷 205-210页

作者： Chenjing Liang Lingling shi Zhengguo Chen Lan Zhou Zhaoxia zhang Guoqing zhang School of Materials Science and Engineering Zhejiang Sci-tech UniversityHangzhouChina College of Textile Science and Engineering Zhejiang Sci-tech UniversityHangzhouChina Zhejiang Provincial Innovation Center of Advanced Textile Technology ShaoxingChina

The aim of this work is to develop a venlafaxine hydrochloride sustained release system based on hollow mesoporous silica microspheres(HMSMs).HMSMs were innovatively prepared with tetraethyl silicate(TEOS)as the precursor and volatile n-heptane as a soft template.The obtained HMSMs show a well-defined hollow structure with an average size of 967 nm and pore volume of 0.85 cm^(3)/g,implying it is a potential drug carrier.Subsequently,venlafaxine hydrochloride(VF)was absorbed in the HMSMs with a content of 37.67% or so.The sustained release effect is further measured by the dissolution in-strument at 37℃ and 50 rpm in ultrapure water.The results showed that the HMSMs/VF system shows good sustained release properties compared with sustained release tablets with hydroxypropyl meth-ylcellulose as the main component.This HMSMs sustained release system appears to be a promising candidate for a sustained drug release.

关键词： Hollowmesoporous silica microspheres(HMSMs) Venlafaxine hydrochloride Drug loading Sustained release

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Empowering smart city situational awareness via big mobile data

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Frontiers of Information Technology & Electronic Engineering 2024年第2期25卷 286-307页

作者： zhiguang SHAN Lei shi Bo LI Yanqiang zhang Xiatian zhang Wei CHEN State Information Center Beijing100045China School of Computer Science and Engineering Beihang UniversityBeijing100191China Zhongguancun Laboratory Beijing100094China Beijing Tendcloud Tianxia Technology Co. Ltd.Beijing100027China The State Key Lab of CAD&CG Zhejiang UniversityHangzhou310027China

Smart city situational awareness has recently emerged as a hot topic in research societies,industries,and governments because of its potential to integrate cutting-edge information technology and solve urgent challenges that modern cities face.For example,in the latest five-year plan,the Chinese government has highlighted the demand to empower smart city management with new technologies such as big data and Internet of Things,for which situational awareness is normally the crucial first step.While traditional static surveillance data on cities have been available for decades,this review reports a type of relatively new yet highly important urban data source,i.e.,the big mobile data collected by devices with various levels of mobility representing the movement and distribution of public and private agents in the city.We especially focus on smart city situational awareness enabled by synthesizing the localization of hundreds of thousands of mobile software Apps using the Global Positioning System(GPS).This technique enjoys advantages such as a large penetration rate(∼50%urban population covered),uniform spatiotemporal coverage,and high localization precision.We first discuss the pragmatic requirements for smart city situational awareness and the challenges faced.Then we introduce two suites of empowering technologies that help fulfill the requirements of(1)cybersecurity insurance for smart cities and(2)spatiotemporal modeling and visualization for situational awareness,both via big mobile data.The main contributions of this review lie in the description of a comprehensive technological framework for smart city situational awareness and the demonstration of its feasibility via real-world applications.

关键词： Smart city Mobile data Situational awareness

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ST-LaneNet: Lane Line Detection Method Based on Swin Transformer and LaneNet

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Chinese Journal of Mechanical Engineering 2024年第1期37卷 130-145页

作者： Yufeng Du Rongyun zhang Peicheng shi Linfeng Zhao Bin zhang Yaming Liu School of Mechanical Engineering Anhui Polytechnic UniversityWuhu 241000China Automotive New Technology Anhui Engineering and Technology Research Center Anhui Polytechnic UniversityWuhu 241000China School of Automotive and Transportation Engineering Hefei University of TechnologyHefei 230009China

The advancement of autonomous driving heavily relies on the ability to accurate lane lines detection.As deep learning and computer vision technologies evolve,a variety of deep learning-based methods for lane line detection have been proposed by researchers in the field.However,owing to the simple appearance of lane lines and the lack of distinctive features,it is easy for other objects with similar local appearances to interfere with the process of detecting lane lines.The precision of lane line detection is limited by the unpredictable quantity and diversity of lane lines.To address the aforementioned challenges,we propose a novel deep learning approach for lane line detection.This method leverages the Swin Transformer in conjunction with LaneNet(called ST-LaneNet).The experience results showed that the true positive detection rate can reach 97.53%for easy lanes and 96.83%for difficult lanes(such as scenes with severe occlusion and extreme lighting conditions),which can better accomplish the objective of detecting lane lines.In 1000 detection samples,the average detection accuracy can reach 97.83%,the average inference time per image can reach 17.8 ms,and the average number of frames per second can reach 64.8 Hz.The programming scripts and associated models for this project can be accessed openly at the following GitHub repository:https://***/Duane 711/Lane-line-detec tion-ST-LaneNet.

关键词： Autonomous driving Lane line detection Deep learning Swin transformer

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