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Effects of drip and flood irrigation on carbon dioxide exchange and crop growth in the maize ecosystem in the Hetao Irrigation District,China

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Journal of Arid Land 2024年第2期16卷 282-297页

作者： li chaoqun HAN Wenting PENG Manman College of Mechanical and Electrical Engineering Heze UniversityHeze 274015China College of Mechanical and Electronic Engineering Northwest A&F UniversityYangling 712100China Institute of Soil and Water Conservation Northwest A&F UniversityYangling 712100China

Drip irrigation and flood irrigation are major irrigation methods for maize crops in the Hetao Irrigation District,Inner Mongolia Autonomous Region,*** research delves into the effects of these irrigation methods on carbon dioxide(CO_(2))exchange and crop growth in this *** experimental site was divided into drip and flood irrigation *** irrigation schedules of this study aligned with the local commonly used irrigation *** employed a developed chamber system to measure the diurnal CO_(2)exchange of maize plants during various growth stages under both drip and flood irrigation *** May to September in 2020 and 2021,two sets of repeated experiments were *** each experiment,a total of nine measurements of CO_(2)exchange were performed to obtain carbon exchange data at different growth stages of maize *** each CO_(2)exchange measurement event,CO_(2)flux data were collected every two hours over a day-long period to capture the diurnal variations in CO_(2)*** each CO_(2)exchange measurement event,the biological parameters(aboveground biomass and crop growth rate)of maize and environmental parameters(including air humidity,air temperature,precipitation,soil water content,and photosynthetically active radiation)were *** results indicated a V-shaped trend in net ecosystem CO_(2)exchange in daytime,reducing slowly at night,while the net assimilation rate(net primary productivity)exhibited a contrasting ***,compared with flood irrigation,drip irrigation demonstrated significantly higher average daily soil CO_(2)emission and greater average daily CO_(2)absorption by maize ***,within the maize ecosystem,drip irrigation appeared more conducive to absorbing atmospheric CO_(2).Furthermore,drip irrigation demonstrated a faster crop growth rate and increased aboveground biomass compared with flood irrigation.A strong linear relationship existed between leaf area index and light utiliza

关键词： carbon dioxide exchange maize growth drip irrigation harvest index net primary productivity Hetao Irrigation District

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FNNWV: farthest-nearest neighbor-based weighted voting for class-imbalanced crowdsourcing

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Science China(Information Sciences) 2024年第10期 172-188页

作者： Wenjun ZHANG liangxiao JIANG Ziqi CHEN chaoqun li School of Computer Science China University of Geosciences Key Laboratory of Artificial Intelligence Ministry of Education School of Mathematics and Physics China University of Geosciences

In crowdsourcing scenarios, we can hire crowd workers to label crowdsourced tasks and then use label integration algorithms to infer the integrated label for each instance in the tasks. As more and more label integration algorithms are proposed, the performance of inference based only on the information of the inferred instance gradually converges. Recent algorithms attempt to exploit the information of the inferred instance's nearest neighbors to infer and achieve good performance. However, when crowdsourced tasks are class-imbalanced, negative instances are more easily to occur in the nearest neighbors because negative instances are the majority, and thus recent algorithms are more easily biased toward the negative class. To this end, in this paper, we propose a novel label integration algorithm called farthest-nearest neighbor-based weighted voting(FNNWV) for class-imbalanced crowdsourcing. Specifically, FNNWV considers the nearest neighbors to be more similar to the inferred instance and thus uses them to vote ayes in weighted voting. Yet at the same time, FNNWV considers the farthest neighbors to be more different from the inferred instance and thus uses them to vote nays in weighted voting. Since negative instances are easier to occur in both the nearest neighbors and the farthest neighbors, FNNWV weakens the effect of negative instances by voting ayes and nays. The experimental results on 22 simulated and one real-world crowdsourced datasets show that FNNWV significantly outperforms all the other state-of-the-art competitors.

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河南省年季降水集中度研究

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华北水利水电大学学报（自然科学版） 2024年第2期45卷 73-80页

作者：吴奕李飞李超群河南省水文水资源测报中心河南郑州450003 南水北调中线实业发展有限公司北京100038 黄河勘测规划设计研究院有限公司河南郑州450003

河南省地处中原,跨海河、黄河、淮河、长江四大流域,研究其降水时间分布均匀特征对全省水资源动态管理和水旱灾害防御具有重要意义。以河南省为研究对象,基于522个雨量站点的1956-2021年长系列降水量数据,采用降水集中指数PCI、降水集中... 详细信息

河南省地处中原,跨海河、黄河、淮河、长江四大流域,研究其降水时间分布均匀特征对全省水资源动态管理和水旱灾害防御具有重要意义。以河南省为研究对象,基于522个雨量站点的1956-2021年长系列降水量数据,采用降水集中指数PCI、降水集中度Q等指标,辅以Kriging空间插值等方法,分析了全省降水时间均匀特征及变化特点。结果表明:河南省降水时间均匀特征具有地区差异性,北部降水时间分布较为集中,南部降水时间分布较为均匀,集中程度总体上呈现由北向南递减;降水时间均匀特征的多年变化趋势较小,除河南省东北部及省辖淮河流域西北部分区域外,其他区域略呈集中趋势;降水时间均匀特征与降水量、气象干旱程度有一定的正向关联但相关性极弱,因此研究旱涝事件时应将降水时间均匀特征作为一个独立因素加以考虑。

关键词：降水量时间均匀特征降水集中指数降水集中度变化趋势

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长三角碳排放空间网络结构及协同减排效应

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环境科学与技术 2024年第2期47卷 183-194页

作者：宋青李超群陈骏宇苏州科技大学商学院江苏苏州215009 天津大学管理与经济学部天津300072

该研究基于修正的引力模型构建长三角地区碳排放空间关联网络矩阵,运用社会网络分析方法考察碳排放空间网络结构形态,并从整体、个体和空间聚类3个维度深入探究碳排放空间关联网络特征,同时对长三角地区的协同减排效应进行测算和分析。... 详细信息

该研究基于修正的引力模型构建长三角地区碳排放空间关联网络矩阵,运用社会网络分析方法考察碳排放空间网络结构形态,并从整体、个体和空间聚类3个维度深入探究碳排放空间关联网络特征,同时对长三角地区的协同减排效应进行测算和分析。研究结果表明:(1)长三角地区的碳排放空间关联网络结构趋于复杂和稳定;(2)各节点城市在长三角地区碳排放空间关联网络中所处的地位和角色具有明显的非均衡性,苏州、无锡、常州和上海是碳排放空间关联网络中的核心节点;(3)碳排放空间关联网络划分为4个板块且板块间存在显著的溢出效应;(4)长三角地区的整体碳减排协同水平有很大提升空间,各个城市与上海的碳减排协同度差异较为显著。

关键词：碳排放空间关联网络社会网络分析协同减排

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不同种源红砂的花粉形态特征研究

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西北农林科技大学学报（自然科学版） 2024年第5期52卷 69-79页

作者：陈君婵李毅张咏梅李超群李蒙甘肃农业大学林学院甘肃农业大学甘肃兰州730070 干旱生境作物学国家重点实验室甘肃兰州730070

【目的】研究红砂花粉形态特征及其种内花粉变异程度,为红砂孢粉学研究与种质资源育种提供理论基础。【方法】以来自甘肃、宁夏、内蒙古并在试验地种植适应生长10余年的15个红砂种源花粉为材料,运用扫描电镜观测花粉性状指标;用Kolmogor... 详细信息

【目的】研究红砂花粉形态特征及其种内花粉变异程度,为红砂孢粉学研究与种质资源育种提供理论基础。【方法】以来自甘肃、宁夏、内蒙古并在试验地种植适应生长10余年的15个红砂种源花粉为材料,运用扫描电镜观测花粉性状指标;用Kolmogorov-Smirnow方法对各指标数据进行正态分布检验,对不同指标数据处理后进行种源间比较,计算其变异系数,采用组间连接法进行系统聚类分析。【结果】红砂花粉均以单粒形式存在,极轴长/赤道轴长为2.14(1.54~2.46),形状呈超长球形或长球形;极面观为三裂圆形具三沟,沟长长达两极,但不形成合沟;不同红砂种源的花粉形状和萌发沟数目相似。15个红砂种源的花粉大小为26.38(15.32~30.97)μm×12.37(10.06~14.50)μm,多为中型花粉粒;花粉萌发沟长为22.76(13.78~26.02)μm,萌发沟宽为0.39(0.14~1.66)μm,各种源萌发沟脊面形状宽窄不一,差异明显;萌发孔直径为0.21(0.16~0.27)μm,花粉外壁纹饰为细网孔纹饰,存在向极细网孔纹饰进化的趋势;不同红砂种源的花粉特征存在差异。各花粉特征指标的变异系数为4.39%~102.12%,种源整体变异程度较大,种源间存在遗传多样性。基于不同指标的聚类结果表明,各种源聚类距离远近与其花粉特征有关,其中花粉大小的影响较大;且甘肃酒泉瓜州(G-J-GZ)种源始终聚为一类,其花粉大小显著小于其他种源,属于较为原始的种质。【结论】不同种源红砂的花粉形状与萌发沟数具有遗传保守性,花粉特征具有遗传多样性,其中花粉大小可在一定程度上反映红砂种源间的差异性。

关键词：红砂种源花粉特征扫描电镜

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Naive Bayes for value difference metric

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Frontiers of Computer Science 2014年第2期8卷 255-264页

作者： chaoqun li liangxiao JIANG Hongwei li Department of Mathematics China University of Geosciences Wuhan 430074 China Department of Computer Science China University of Geosciences Wuhan 430074 China

The value difference metric （VDM） is one of the best-known and widely used distance functions for nominal attributes. This work applies the instance weighting technique to improve VDM. An instance weighted value difference met- ric （IWVDM） is proposed here. Different from prior work, IWVDM uses naive Bayes （NB） to find weights for train- ing instances. Because early work has shown that there is a close relationship between VDM and NB, some work on NB can be applied to VDM. The weight of a training instance x, that belongs to the class c, is assigned according to the dif- ference between the estimated conditional probability P（c／x） by NB and the true conditional probability P（c／x）, and the weight is adjusted iteratively. Compared with previous work, IWVDM has the advantage of reducing the time complex- ity of the process of finding weights, and simultaneously im- proving the performance of VDM. Experimental results on 36 UCI datasets validate the effectiveness of IWVDM.

关键词： value difference metric instance weighting,naive Bayes distance-based learning algorithms

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汽车双叉臂前悬架仿真分析及优化设计研究

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农业装备与车辆工程 2024年第9期62卷 67-72页

作者：李超群张军大连交通大学机车车辆工程学院辽宁大连116000

针对某车型驾驶性能不佳及轮胎磨损严重等问题,利用ADAMS对样车的双叉臂前悬架进行运动学特性分析,建立双叉臂前悬架组合的虚拟样机模型,并对仿真模型进行双轮同向激振试验。研究样车悬架各关键点的坐标位置对悬架运动学特性的影响,分... 详细信息

针对某车型驾驶性能不佳及轮胎磨损严重等问题,利用ADAMS对样车的双叉臂前悬架进行运动学特性分析,建立双叉臂前悬架组合的虚拟样机模型,并对仿真模型进行双轮同向激振试验。研究样车悬架各关键点的坐标位置对悬架运动学特性的影响,分析行驶时车辆性能不佳及轮胎磨损加剧等问题出现的原因。通过ADAMS/Insight对双叉臂前悬架坐标位置进行优化,提出双叉臂前悬架坐标位置的改进建议,使存在的问题得到有效改善。

关键词： ADAMS 双叉臂前悬架运动学特性

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Attribute augmentation-based label integration for crowdsourcing

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Frontiers of Computer Science 2023年第5期17卷 41-51页

作者： Yao ZHANG liangxiao JIANG chaoqun li School of Computer Science China University of GeosciencesWuhan 430074China School of Mathematics and Physics China University of GeosciencesWuhan 430074China

Crowdsourcing provides an effective and low-cost way to collect labels from crowd *** to the lack of professional knowledge,the quality of crowdsourced labels is relatively low.A common approach to addressing this issue is to collect multiple labels for each instance from different crowd workers and then a label integration method is used to infer its true ***,to our knowledge,almost all existing label integration methods merely make use of the original attribute information and do not pay attention to the quality of the multiple noisy label set of each *** solve these issues,this paper proposes a novel three-stage label integration method called attribute augmentation-based label integration(AAli).In the first stage,we design an attribute augmentation method to enrich the original attribute *** the second stage,we develop a filter to single out reliable instances with high-quality multiple noisy label *** the third stage,we use majority voting to initialize integrated labels of reliable instances and then use cross-validation to build multiple component classifiers on reliable instances to predict all *** results on simulated and real-world crowdsourced datasets demonstrate that AAli outperforms all the other stateof-the-art competitors.

关键词： crowdsourcing label integration attribute aug-mentation instance filtering

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Attribute augmented and weighted naive Bayes

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Science China(Information Sciences) 2022年第12期65卷 113-126页

作者： Huan ZHANG liangxiao JIANG chaoqun li School of Computer Science China University of Geosciences Key Laboratory of Artifcial Intelligence Ministry of Education School of Mathematics and Physics China University of Geosciences

Numerous enhancements have been proposed to mitigate the attribute conditional independence assumption in naive Bayes(NB). However, almost all of them only focus on the original attribute space. Due to the complexity of real-world applications, we argue that the discriminative information provided by the original attribute space might be insufficient for classification. Thus, in this study, we expect to discover some latent attributes beyond the original attribute space and propose a novel two-stage model called attribute augmented and weighted naive Bayes(A2WNB). At the first stage, we build multiple random one-dependence estimators(RODEs). Then we use each built RODE to classify each training instance in turn and define the predicted class labels as its latent attributes. At last, we construct the augmented attributes by concatenating the latent attributes with the original attributes. At the second stage, to alleviate the attribute redundancy,we optimize the augmented attributes’ weights by maximizing the conditional log-likelihood(CLL) of the built model. Extensive experimental results show that A2WNB significantly outperforms NB and all the other existing state-of-the-art competitors.

关键词： naive Bayes attribute augmentation attribute weighting attribute conditional independence classification

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Learning from crowds with robust support vector machines

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Science China(Information Sciences) 2023年第3期66卷 133-149页

作者： Wenjun YANG chaoqun li liangxiao JIANG School of Mathematics and Physics China University of Geosciences School of Computer Science China University of Geosciences

Crowdsourcing system provides an easy way to obtain labeled training data. However, the labels provided by non-expert labelers often appear low quality. So in practice, each sample usually obtains a multiple label set from multiple different labelers. Learning-from-crowds(LFC) aims to design ground truth inference algorithms to infer the unknown true labels of data from multiple label sets. Despite their proper statistical foundations, the existing ground truth inference algorithms show limited performance when the number of labelers is small. However, more labelers mean higher costs. This paper tries to propose a novel ground truth inference algorithm which can maintain moderate performance and simultaneously reduce labeling costs. This paper addresses LFC from a point of view of robust classifiers and presents a new label noise robust support vector machine inference(RSVMI) algorithm. We prove that only one convex quadratic programming problem needs to be solved to build a robust support vector machine. Furthermore,in order to apply the robust support vector machine to crowdsourced data, two methods are proposed to estimate the noise level of integrated labels. By transforming the original LFC problem into a robust classifier learning problem, our algorithm shows good performance when the number of labelers is very small. In our experiments, the minimum number of labelers is set to 3. In terms of both label quality and model quality,the experimental results on benchmark data sets and real-world data sets show the effectiveness of RSVMI.

关键词： crowdsourcing learning ground truth inference integrated labels label noise robust support vector machines

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