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Plant Phenomics 2022年第1期4卷 110-126页

作者： Meiyan Shu shuaipeng fei Bingyu Zhang Xiaohong Yang Yan Guo Baoguo Li Yuntao Ma College of Land Science and Technology China Agricultural UniversityBeijing 100091China State Key Laboratory of Plant Physiology and Biochemistry National Maize Improvement Center of ChinaChina Agricultural UniversityBeijing 100091China

High-throughput estimation of phenotypic traits from UAV(unmanned aerial vehicle)images is helpful to improve the screening efficiency of breeding *** estimating phenotyping traits of breeding maize at plot scale helps to promote gene mining for specific traits and provides a guarantee for accelerating the breeding of superior *** an efficient and accurate estimation model is the key to the application of UAV-based multiple sensors data.

关键词： breeding estimation traits

基于高光谱遥感和集成学习方法的冬小麦产量估测研究

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中国农业科学 2021年第16期54卷 3417-3427页

作者：费帅鹏禹小龙兰铭李雷夏先春何中虎肖永贵河南理工大学测绘与国土信息工程学院中国农业科学院作物科学研究所 3CIMMYT中国办事处

【目的】利用2种灌溉处理下不同发育阶段的冬小麦冠层高光谱信息,通过机器学习方法对小麦籽粒产量进行估测精度研究,明确产量最佳估测模型,对于育种工作有着重要应用价值。【方法】以黄淮麦区207个主栽小麦品种为材料,于2018—2019和201... 详细信息

【目的】利用2种灌溉处理下不同发育阶段的冬小麦冠层高光谱信息,通过机器学习方法对小麦籽粒产量进行估测精度研究,明确产量最佳估测模型,对于育种工作有着重要应用价值。【方法】以黄淮麦区207个主栽小麦品种为材料,于2018—2019和2019—2020年度连续2个生长季在河南省新乡基地的正常灌溉和节水处理下种植,并调查开花期、灌浆前期和灌浆中期的冠层高光谱数据,分别以6种机器学习方法和集成方法建立光谱指数产量估测模型。【结果】2种灌溉处理下,3个生育期各光谱指数均与产量呈极显著相关(P<0.0001),且表现出较高的遗传力(0.61-0.85),主要受遗传因素控制。在正常灌溉处理下,与传统机器学习方法表现最佳的模型相比,集成学习方法在3个生育期的平均决定系数(R~2)分别由0.610、0.611和0.640提高至0.649、0.612和0.675,平均均方根误差(RMSE)分别降低至0.607、0.612和0.593 t·hm;节水处理下,3个生育期的平均R2分别由0.461、0.408和0.452提高至0.467、0.433和0.498,平均RMSE分别降低至0.519、0.559和0.504 t·hm。【结论】利用集成方法将不同模型估测结果进行结合,能够有效地提高产量估测精度,2种灌溉处理下均在灌浆中期估测精度最佳,可为冬小麦育种工作中产量估测提供参考。

关键词：冬小麦产量高光谱集成方法机器学习

Precise gradient separation of nanoparticles using reduced graphene oxide-polyoxomolybdate membranes

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Science China Chemistry 2025年第1期68卷 209-216页

作者： Yixin Yang Wanlei Zhao Ruoxuan Zheng Hongqiang Li Wei Chen Yu-fei Song State Key Laboratory of Chemical Resource Engineering Beijing University of Chemical Technology Quzhou Institute for Innovation in Resource Chemical Engineering

The purification and size sieving of nanoparticles （NPs） represents a significant scientific ***,the r GO-polyoxometalates (POMs=PMo12,Mo132and Mo368) membranes were fabricated successfully via a simple and scalable POMsassisted in-situ photoreduction *** the mass ratio of r GO to PMo12was 1:30,the resultant r GO-PMo12membranes with highly ordered and uniform nanochannels of 1.0 nm can be obtained,which showed an accurate carbon quantum dots（CQDs） separation with a cut-off size approximately 1.0 *** increase of the size of the interlayered POMs from PMo12（1.0 nm）,{Mo132}（2.9 nm） to{Mo368}（3.8 nm）,the resultant r GO-PMo12,r GO-Mo132and r GO-Mo368exhibited precise gradient separation of CQDs and Au nanoparticles by size exclusion *** results pave a promising way to construct new twodimensional （2D） membranes for highly efficient size-selective separation.

关键词： reduced graphene oxide polyoxometalates membranes gradient separation nanoparticles

Design and Application of Turbine Cascade Partitioned Endwall Profiling

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Journal of Thermal Science 2025年第1期34卷 110-128页

作者： ZENG fei JIANG Ruiqi XUE Xingxu DU Wei LUO Lei ZHOU Xun AECC Hunan Aviation Powerplant Research Institute School of Energy Science and Technology Harbin Institute of Technology Science and Technology on Diesel Engine Turbocharging Laboratory China North Engine Research Institute

The influence of partitioned profiling design based on a large-pitch highly loaded cascade is studied by numerical *** partitioned profile is mainly composed of a pressure-side convex structure near the leading edge and a suction-side convex structure at the midstream and downstream sides of the *** influence of the change in the vertex axial position and peak value of the B-line on the secondary flow control is *** this paper,air(ideal gas) is selected as the flow *** average static pressure at the outlet and the average total temperature at the inlet are kept *** γ-θ is used as the turbulence *** results show that the pressure-side convex structure suppresses the spanwise and pitchwise migration of the inlet flow by adjusting the static pressure distribution of the flow field,so the development of the pressure-side leg of the horseshoe vortex is effectively *** suction-side convex structure adjusts the static pressure distribution of the flow field and increases the included angle between the cross-flow and suction surface,so the accumulation of low-momentum fluid,the development of a corner vortex and the flow separation at the trailing edge of the suction-side surface are all suppressed near the endwall-suction ***,the energy loss coefficient of the large-pitch highly loaded cascade is decreased from 0.0564 to 0.0485,representing a 25% reduction in secondary flow losses.

关键词： Vortex flow

An efficient quantum proactive incremental learning algorithm

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 49-57页

作者： Lingxiao Li Jing Li Yanqi Song Sujuan Qin Qiaoyan Wen fei Gao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications School of Cyberspace Security Beijing University of Posts and Telecommunications

In scenarios where a large amount of data needs to be learned, incremental learning can make full use of old knowledge, significantly reduce the computational cost of the overall learning process, and maintain high performance. In this paper, taking the MaxCut problem as our example, we introduce the idea of incremental learning into quantum computing, and propose a Quantum Proactive Incremental Learning algorithm(QPIL). Instead of a one-off training of quantum circuit, QPIL contains a multi-phase training on gradually-increased subgraphs of all vertices, proactively reducing large-scale problems to smaller ones to solve in steps, providing an efficient solution for MaxCut. Specifically, some vertices and corresponding edges are randomly selected for training to obtain optimized parameters of the quantum circuit at first. Then, in each incremental phase, the remaining vertices and corresponding edges are gradually added and the parameters obtained from the previous phase are reused in the parameter initialization of the current phase. We perform experiments on 120 different small-scale graphs, and it shows that QPIL performs superior to prevalent quantum and classical baselines in terms of approximation ratio(AR), time cost, anti-forgetting, and solving stability. In particular, QPIL's AR surpasses 20% of mainstream quantum baselines, and the time cost is less than 1/5 of them. The idea of QPIL is expected to inspire efficient and high-quality solutions in large-scale Max Cut and other combinatorial optimization problems.

关键词： variational quantum algorithm incremental learning multi-phase training MaxCut quantum computing

多光谱与热红外数据融合在冬小麦产量估测中的应用

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麦类作物学报 2021年第12期41卷 1564-1572页

作者：兰铭费帅鹏禹小龙李雷夏先春肖永贵孟亚雄甘肃农业大学农学院甘肃兰州730070 中国农业科学院作物科学研究所北京100081

为了解多光谱与热红外数据融合对冬小麦产量估测精度的影响,以30个黄淮麦区冬小麦品种为材料,利用三种灌溉处理(处理1、处理2和处理3灌水量分别为240、190和145 mm)下冬小麦拔节期、挑旗期、抽穗期与灌浆期的无人机多光谱和热红外动态数... 详细信息

为了解多光谱与热红外数据融合对冬小麦产量估测精度的影响,以30个黄淮麦区冬小麦品种为材料,利用三种灌溉处理(处理1、处理2和处理3灌水量分别为240、190和145 mm)下冬小麦拔节期、挑旗期、抽穗期与灌浆期的无人机多光谱和热红外动态数据,构造了多个光谱指数,以支持向量机构建冬小麦产量估测模型,并验证其精度。结果表明,植被指数与籽粒产量的相关性受溉水量影响,处理1下植被指数与籽粒产量均呈正相关,处理2下植被指数除土壤调整植被指数(SAVI)和转化叶绿素吸收反射指数(TCARI)外均与籽粒产量呈正相关,处理3下植被指数与籽粒产量均呈负相关。通过多光谱和热红外数据融合构建的冬小麦产量估测模型的预测精度比仅使用多光谱数据构建的模型提高8%。不同灌溉条件下,通过多光谱与热红外数据融合构建的模型的预测精度存在差异,在处理1、处理2和处理3下拔节期、挑旗期、抽穗期和灌浆期验证决定系数(r^(2))最高值分别为0.63、0.68和0.56,均方根误差(RMSE)最低值分别为0.60、0.24和0.41 t·hm^(-2),且在三种灌溉条件下灌浆期预测效果均最佳。因此,利用无人机光谱对小麦品种产量估测时应将多光谱与热红外数据融合,用支持向量机(SVM)算法构建产量估测模型,且模型在灌浆期具有较高预测精度。

关键词：无人机多光谱热红外支持向量机估产冬小麦

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Multi-Stage Voltage Control Optimization Strategy for Distribution Networks Considering Active-Reactive Co-Regulation of Electric Vehicles

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Energy Engineering 2025年第1期122卷 221-242页

作者： Shukang Lyu fei Zeng Huachun Han HuiyuMiao Yi Pan Xiaodong Yuan Electric Power Research Institute State Grid Jiangsu Electric Power Co.Ltd.Nanjing211103China

The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the existing research has not deeply explored the EV active-reactive synergistic regulating characteristics,and failed to realize themulti-timescale synergistic control with other regulatingmeans,For this reason,this paper proposes amultilevel linkage coordinated optimization strategy to reduce the voltage deviation of the distribution ***,a capacitor bank reactive power compensation voltage control model and a distributed photovoltaic(PV)activereactive power regulationmodel are ***,an external characteristicmodel of EVactive-reactive power regulation is developed considering the four-quadrant operational characteristics of the *** optimization model of the distribution network is then constructed considering the time-series coupling constraints of multiple types of voltage regulators.A multi-timescale control strategy is proposed by considering the impact of voltage regulators on active-reactive EV energy consumption and PV energy ***,a four-stage voltage control optimization strategy is proposed for various types of voltage regulators with multiple time ***-objective optimization is solved with the improvedDrosophila algorithmto realize the power fluctuation control of the distribution network and themulti-stage voltage control *** results validate that the proposed voltage control optimization strategy achieves the coordinated control of decentralized voltage control resources in the distribution *** effectively reduces the voltage deviation of the distribution network while ensuring the energy demand of EV users and enhancing the stability and economic efficiency of the distribution network.

关键词： Electric vehicle(EV) distribution network multi-stage optimization active-reactive power regulation voltage control

Interaction of major facilitator superfamily domain containing 2A with the blood-brain barrier

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Neural Regeneration Research 2025年第8期20卷 2133-2152页

作者： Yilun Ma Taiwei Dong fei Luan Juanjuan Yang Feng Miao Peifeng Wei College of Pharmacy and First Clinical Medical College Shaanxi University of Chinese MedicineXianyangShaanxi ProvinceChina National Drug Clinical Trial Agency The Second Affiliated Hospital of Shaanxi University of Chinese Medicine/Xixian New District Central HospitalXi′anShaanxi ProvinceChina

The functional and structural integrity of the blood-brain barrier is crucial in maintaining homeostasis in the brain microenvironment;however,the molecular mechanisms underlying the formation and function of the blood-brain barrier remain poorly *** major facilitator superfamily domain containing 2A has been identified as a key regulator of blood-brain barrier *** plays a critical role in promoting and maintaining the formation and functional stability of the blood-brain barrier,in addition to the transport of lipids,such as docosahexaenoic acid,across the blood-brain ***,an increasing number of studies have suggested that major facilitator superfamily domain containing 2A is involved in the molecular mechanisms of blood-brain barrier dysfunction in a variety of neurological diseases;however,little is known regarding the mechanisms by which major facilitator superfamily domain containing 2A affects the blood-brain *** paper provides a comprehensive and systematic review of the close relationship between major facilitator superfamily domain containing 2A proteins and the blood-brain barrier,including their basic structures and functions,cross-linking between major facilitator superfamily domain containing 2A and the blood-brain barrier,and the in-depth studies on lipid transport and the regulation of blood-brain barrier *** comprehensive systematic review contributes to an in-depth understanding of the important role of major facilitator superfamily domain containing 2A proteins in maintaining the structure and function of the blood-brain barrier and the research progress to *** will not only help to elucidate the pathogenesis of neurological diseases,improve the accuracy of laboratory diagnosis,and optimize clinical treatment strategies,but it may also play an important role in prognostic *** addition,the effects of major facilitator superfamily domain containing 2A on blood-brain barrier lea

关键词： blood-brain barrier(BBB) caveolin-1 central nervous system docosahexaenoic acid endothelial cells lysophosphatidylcholine major facilitator superfamily domain containing 2A(MFSD2A) transcytosis

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不同施氮处理下无人机光谱感知冬小麦产量

灌溉排水学报 2023年第1期42卷 24-30页

作者：丁凡陈震李长春程千费帅鹏李景勃徐洪刚李宗鹏河南理工大学河南焦作454003 中国农业科学院农田灌溉研究所河南新乡453002 河南农业大学郑州450002

【目的】快速、准确地预测冬小麦产量,构建最佳产量预测模型,对精准农业农田管理有重要应用价值。【方法】以抽穗期、开花期和灌浆期3个不同时期的冬小麦为研究对象,通过无人机搭载的多光谱传感器采集冠层光谱信息并提取植被指数。使用... 详细信息

【目的】快速、准确地预测冬小麦产量,构建最佳产量预测模型,对精准农业农田管理有重要应用价值。【方法】以抽穗期、开花期和灌浆期3个不同时期的冬小麦为研究对象,通过无人机搭载的多光谱传感器采集冠层光谱信息并提取植被指数。使用逐步回归与随机森林2种方法,筛选最优特征并建立产量预测模型。【结果】在抽穗期,绿(Green,G)、蓝(Blue,B)、修改型土壤调查植被指数(Modified Soil-adjusted Vegetation Index 2,MSAVI2)和土壤调节植被指数(Soil-adjusted Vegetation Index,SAVI)组合使用随机森林算法对产量的预测效果最好同时AIC(Akaike information criterion,赤池信息准则)较低,R^(2)为0.65。在开花期,过绿指数(Excess Green,ExG)、近红外光(Near Infrared,NIR)、归一化差异植被指数(Normalized Difference Index,NDI)、蓝(Blue,B)和修改型土壤调查植被指数2(Modified Soil-adjusted Vegetation Index 2,MSAVI2)特征变量组合使用随机森林算法对产量的预测效果最好,同时AIC较低,R^(2)为0.71。在灌浆期,以全部植被特征变量构建的随机森林回归模型对冬小麦产量预测的精度最高,R^(2)达到0.76,绿(Green,G)、蓝(Blue,B)、过绿指数(Excess Green,ExG)、过红指数(Excess Red,ExR)、归一化植被指数(Normalized Difference Vegetation Index,NDVI)、比值植被指数(Ratio Vegetation Index,RVI)和归一化差分植被指数(Normalized Difference Vegetation Index,TNDVI)特征变量组合的随机森林回归模型R^(2)较高同时AIC较低,R^(2)达到0.73。同时使用冬小麦3个生育期,以抽穗期蓝(Blue,B)、开花期过绿指数(Excess Green,ExG)、灌浆期近红外光(Near Infrared,NIR)、灌浆期归一化差异植被指数(Normalized Difference Index,NDI)和灌浆期过绿减过红指数(Excess Green-Excess Red,ExG-ExR)为特征变量组合构建的随机森林回归模型R^(2)较高,同时AIC较低,R^(2)达到0.76。【结论】通过逐步回归筛选出特征变量间共线性最小的特征变量组合并利用随机森林构建回归模型具有可行性,能够准确地预测冬小麦产量。

关键词：多光谱植被指数逐步回归随机森林产量预测

Co-production of hydrogen, oxygen, and electricity via an integrated solar-driven system with decoupled water electrolyzer and Na-Zn ion battery

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Journal of Energy Chemistry 2025年第1期100卷 621-627页

作者： fei Lv Longjie Liu Jiazhe Wu Pengfei Wang Lixia Pan Dengwei Jing Yubin Chen International Research Center for Renewable Energy State Key Laboratory of Multiphase Flow in Power Engineering Xi'an Jiaotong University

Combining water electrolysis and rechargeable battery technologies into a single system holds great promise for the co-production of hydrogen （H2） and ***,the design and development of such systems is still in its ***,an integrated hydrogen-oxygen （O2）-electricity co-production system featuring a bipolar membrane-assisted decoupled electrolyzer and a Na-Zn ion battery was established with sodium nickelhexacyanoferrate （NaNiHCF） and Zn2+/Zn as dual redox *** decoupled electrolyzer enables to produce H2and O2in different time and space with almost 100%Faradaic efficiency at 100 mA ***,the charged NaNiHCF and Zn electrodes after the electrolysis processes formed a Na-Zn ion battery,which can generate electricity with an average cell voltage of 1.75 V at 10 m A *** connecting Si photovoltaics with the modular electrochemical device,a well-matched solar driven system was built to convert the intermittent solar energy into hydrogen and electric energy with a solar to hydrogen-electricity efficiency of 16.7%,demonstrating the flexible storage and conversion of renewables.

关键词： Hydrogen Electricity Decoupled water electrolyzer Na-Zn ion battery Solar energy