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Spatial Prediction of Soil Salinity in a Semiarid Oasis: Environmental Sensitive Variable Selection and Model Comparison 预览
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作者 LI Zhen LI Yong +4 位作者 XING An ZHUO Zhiqing ZHANG Shiwen ZHANG Yuanpei HUANG Yuanfang 《中国地理科学:英文版》 SCIE CSCD 2019年第5期784-797,共14页
Timely monitoring and early warning of soil salinity are crucial for saline soil management. Environmental variables are commonly used to build soil salinity prediction model. However, few researches have been done to... Timely monitoring and early warning of soil salinity are crucial for saline soil management. Environmental variables are commonly used to build soil salinity prediction model. However, few researches have been done to summarize the environmental sensitive variables for soil electrical conductivity(EC) estimation systematically. Additionally, the performance of Multiple Linear Regression(MLR), Geographically Weighted Regression(GWR), and Random Forest regression(RFR) model, the representative of current main methods for soil EC prediction, has not been explored. Taking the north of Yinchuan plain irrigation oasis as the study area, the feasibility and potential of 64 environmental variables, extracted from the Landsat 8 remote sensed images in dry season and wet season, the digital elevation model, and other data, were assessed through the correlation analysis and the performance of MLR, GWR, and RFR model on soil salinity estimation was compared. The results showed that: 1) 10 of 15 imagery texture and spectral band reflectivity environmental variables extracted from Landsat 8 image in dry season were significantly correlated with soil EC, while only 3 of these indices extracted from Landsat 8 image in wet season have significant correlation with soil EC. Channel network base level, one of the terrain attributes, had the largest absolute correlation coefficient of 0.47 and all spatial location factors had significant correlation with soil EC. 2) Prediction accuracy of RFR model was slightly higher than that of the GWR model, while MLR model produced the largest error. 3) In general, the soil salinization level in the study area gradually increased from south to north. In conclusion, the remote sensed imagery scanned in dry season was more suitable for soil EC estimation, and topographic factors and spatial location also play a key role. This study can contribute to the research on model construction and variables selection for soil salinity estimation in arid and semiarid regions. 展开更多
关键词 soil SALINITY ENVIRONMENTAL variable random forest regression GEOGRAPHIC weighted regression Yinchuan Plain IRRIGATION OASIS
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Combining Environmental Factors and Lab VNIR Spectral Data to Predict SOM by Geospatial Techniques 预览
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作者 GUO Long ZHANG Haitao +1 位作者 CHEN Yiyun QIAN Jing 《中国地理科学:英文版》 SCIE CSCD 2019年第2期258-269,共12页
Soil organic matter(SOM) is an important parameter related to soil nutrient and miscellaneous ecosystem services. This paper attempts to improve the performance of traditional partial least square regression(PLSR) mod... Soil organic matter(SOM) is an important parameter related to soil nutrient and miscellaneous ecosystem services. This paper attempts to improve the performance of traditional partial least square regression(PLSR) model by considering the spatial autocorrelation and soil forming factors. Surface soil samples(n = 180) were collected from Honghu City located in the middle of Jianghan Plain, China. The visible and near infrared(VNIR) spectra and six environmental factors(elevation, land use types, roughness, relief amplitude, enhanced vegetation index, and land surface water index) were used as the auxiliary variables to construct the multiple linear regression(MLR), PLSR and geographically weighted regression(GWR) models. Results showed that: 1) the VNIR spectra can increase about 39.62% prediction accuracy than the environmental factors in predicting SOM;2) the comprehensive variables of VNIR spectra and the environmental factors can improve about 5.78% and 44.90% relative to soil spectral models and soil environmental models, respectively;3) the spatial model(GWR) can improve about 3.28% accuracy than MLR and PLSR. Our results suggest that the combination of spectral reflectance and the environmental variables can be used as the suitable auxiliary variables in predicting SOM, and GWR is a promising model for predicting soil properties. 展开更多
关键词 VISIBLE near infrared spectral reflectance environmental factors spatial characteristics partial least SQUARES regression geographically weighted regression
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Evaluation and Correction of Ground-Based Microwave Radiometer Observations Based on NCEP-FNL Data 预览
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作者 Qing Li Ming Wei +2 位作者 Zhenhui Wang Yanli Chu Lina Ma 《大气和气候科学(英文)》 2019年第2期229-242,共14页
Consistency between the brightness temperatures observed with a ground-based microwave radiometer and the brightness temperatures computed by forward modeling is important in many different data applications. Using th... Consistency between the brightness temperatures observed with a ground-based microwave radiometer and the brightness temperatures computed by forward modeling is important in many different data applications. Using the National Centers for Environmental Prediction-Final Operational Global Analysis (NCEP-FNL) dataset as a reference, the brightness temperature was obtained through the radiation transfer model for forward calculation. The problem of segmented features in long time of observational data from ground-based microwave radiometers (the so-called “jumping problem”) was identified. By analyzing the deviation and correlation between the observational bright temperature data and the forward calculated data under clear sky conditions, a revised scheme is proposed for the bright temperature observational data. Data obtained with a ground-based microwave radiometer in Beijing from January 1, 2010 to December 31, 2011 around the date of liquid nitrogen calibration show that the correlation between the observed brightness temperatures and the forward computed brightness temperatures is better after correction, especially at 28 and 30 GHz. The “jumping” problem in the observational data for the brightness temperature is eliminated after correction and the time continuity of the observational data and its consistency with the forward calculated data based on the NCEP-FNL dataset are improved. The proposed correction scheme can be used both for real-time data quality control and to improve the accuracy of historical datasets obtained with poorly calibrated microwave radiometers or radiometers working in polluted environments. 展开更多
关键词 ATMOSPHERIC REMOTE Sensing DATA CORRECTION FORWARD Model Regression
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Unnecessity of lymph node regression evaluation for predicting gastric adenocarcinoma outcome after neoadjuvant chemotherapy 预览
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作者 Yue-Lu Zhu Yong-Kun Sun +3 位作者 Xue-Min Xue Jiang-Ying Yue Lin Yang Li-Yan Xue 《世界胃肠肿瘤学杂志:英文版(电子版)》 CAS 2019年第1期48-58,共11页
BACKGROUND Neoadjuvant chemotherapy has been applied worldwide to improve the survival of patients with gastric adenocarcinoma (GAC). The evaluation of histological regression in primary tumors is valuable for predict... BACKGROUND Neoadjuvant chemotherapy has been applied worldwide to improve the survival of patients with gastric adenocarcinoma (GAC). The evaluation of histological regression in primary tumors is valuable for predicting prognosis. However, the prognostic effect of regression change in lymph nodes (LNs) remains unclear. AIM To confirm whether the evaluation of regression change in LNs could predict the prognosis of GAC patients who received neoadjuvant chemotherapy followed by surgery. METHODS In this study, we evaluated the histological regression of resected LNs from 192 GAC patients (including those with esophagogastric junction adenocarcinoma) treated with neoadjuvant chemotherapy. We classified regression change and residual tumor in LNs into four groups:(A) true negative LNs with no evidence of a preoperative therapy effect,(B) no residual metastasis but the presence of regression change in LNs,(C) residual metastasis with regression change in LNs, and (D) metastasis with minimal or no regression change in LNs. Correlations between regression change and residual tumor groups in LNs and regression change in the primary tumor, as well as correlations between regression change in LNs and clinicopathological characteristics, were analyzed. The prognostic effect of regression change and residual tumor groups in LNs was also analyzed. RESULTS We found that regression change and residual tumor groups in LNs were significantly correlated with regression change in the primary tumor, tumor differentiation, ypT stage, ypN stage, ypTNM stage, lymph-vascular invasion, perineural invasion and R0 resection status. Regression change and residual tumor groups in LNs were statistically significant using univariate Cox proportional hazards analysis, but were not independent predictors. For patients who had no residual tumor in LNs, the 5-year overall survival (OS) rates were 67.5% in Group A and 67.4% in Group B. For the patients who had residual tumors in LNs, the 5-year OS rates were 28.2% in Group C and 39.5% in Group D. T 展开更多
关键词 Gastric cancer NEOADJUVANT CHEMOTHERAPY LYMPH NODES Regression RESIDUAL tumor Regression change
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安络化纤丸联合恩替卡韦治疗可显著提高慢性乙型肝炎病毒感染者肝纤维化的改善率 被引量:3
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作者 苗社 杨婉娜 +26 位作者 董晓琴 张占卿 谢仕斌 张大志 张绪清 成军 张国 赵巍峰 谢青 刘映霞 马安林 李军 尚佳 白浪 曹立华 邹志强 李家斌 吕福东 刘晖 王志津 张明香 陈黎明 梁伟锋 高慧 庄辉 赵鸿 王贵强 《中华肝脏病杂志》 CAS CSCD 北大核心 2019年第7期521-526,共6页
目的探索慢性乙型肝炎病毒(HBV)感染者接受恩替卡韦单独或联合安络化纤丸治疗78周对肝纤维化的改善作用。方法慢性HBV感染者随机接受恩替卡韦单独或联合安络化纤丸治疗78周,肝穿刺标本采用Ishakscore盲态判读。比较患者治疗前后的肝纤... 目的探索慢性乙型肝炎病毒(HBV)感染者接受恩替卡韦单独或联合安络化纤丸治疗78周对肝纤维化的改善作用。方法慢性HBV感染者随机接受恩替卡韦单独或联合安络化纤丸治疗78周,肝穿刺标本采用Ishakscore盲态判读。比较患者治疗前后的肝纤维化改善情况。对计量资料采用Student'st检验、非参数检验(Mann-WhitneyU-Test及Kruskal-Wallis检验)方法分析;计数资料采用Chi-squared检验方法分析;Spearman分级检验法分析双变量相关性。结果治疗78周后肝纤维化改善率为36.53%(80/219)、进展率为23.29%(51/219)。肝纤维化改善与基线纤维化程度和治疗方法相关(P<0.05)。在安络化纤丸联合恩替卡韦治疗且基线肝纤维化评分(F)≥3的患者中,肝纤维改善率(54.74%,52/95)显著高于仅接受恩替卡韦治疗者(33.33%,16/48),P=0.016;联合治疗组肝纤维化进展比例(13.68%,13/95)在数值上低于单独治疗组(18.75%,9/48),P=0.466。在基线F<3的患者中,联合治疗组肝纤维化改善和稳定的患者比例(68.08%,32/47)高于单独治疗组(51.72%,15/29)。结论安络化纤丸联合恩替卡韦治疗可显著提高慢性HBV感染者肝纤维化的改善率,并有提高肝纤维化稳定率和降低进展率的趋势。 展开更多
关键词 肝炎病毒 乙型 治疗 肝纤维化 改善
Comparative Analysis of Fractional Vegetation Cover Estimation Based on Multi-sensor Data in a Semi-arid Sandy Area 预览
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作者 LIU Qiuyu ZHANG Tinglong +3 位作者 LI Yizhe LI Ying BU Chongfeng ZHANG Qingfeng 《中国地理科学:英文版》 SCIE CSCD 2019年第1期166-180,共15页
The estimation of fractional vegetation cover(FVC) is important for identifying and monitoring desertification, especially in arid and semiarid regions. By using regression and pixel dichotomy models, we present the c... The estimation of fractional vegetation cover(FVC) is important for identifying and monitoring desertification, especially in arid and semiarid regions. By using regression and pixel dichotomy models, we present the comparison of Sentinel-2A(S2) multispectral instrument(MSI) and Landsat 8(L8) operational land imager(OLI) data regarding the retrieval of FVC in a semi-arid sandy area(Mu Us Sandland, China, in August 2016). A combination of unmanned aerial vehicle(UAV) high-spatial-resolution images and field plots were used to produce verified data. Based on a normalized difference vegetation index(NDVI) regression model, the results showed that, compared with that of L8, the coefficient of determination(R2) of S2 increased by 26.0%, and the root mean square error(RMSE) and the sum of absolute error(SAE) decreased by 3.0% and 11.4%, respectively. For the ratio vegetation index(RVI) regression model, compared with that of L8, the R2 of S2 increased by 26.0%, and the RMSE and SAE decreased by 8.0% and 20.0%, respectively. When the pixel dichotomy model was used, compared with that of L8, the RMSE of S2 decreased by 21.3%, and the SAE decreased by 26.9%. Overall, S2 performed better than L8 in terms of FVC inversion. Additionally, in this paper, we develop a verified scheme based on UAV data in combination with the object-based classification method. This scheme is feasible and sufficiently robust for building relationships between field data and inversion results from satellite data. Further, the synergy of multi-source sensors(especially UAVs and satellites) is a potential effective way to estimate and evaluate regional ecological environmental parameters(FVC). 展开更多
关键词 fractional vegetation cover (FVC) Sentinel-2A (S2) unmanned AERIAL vehicle (UAV)image PIXEL DICHOTOMY MODEL regression MODEL
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Spatial modelling of deforestation in Romanian Carpathian Mountains using GIS and Logistic Regression
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作者 Gheorghe KUCSICSA Cristina DUMITRIC? 《山地科学学报:英文版》 SCIE CSCD 2019年第5期1005-1022,共18页
Deforestation process represents a wide concern mainly in the mountain environments due to its role in global warming, biodiversity loss, land degradation and natural hazards occurrence. Thus, the present study is foc... Deforestation process represents a wide concern mainly in the mountain environments due to its role in global warming, biodiversity loss, land degradation and natural hazards occurrence. Thus, the present study is focused on the largest afforested landform unit of Romania and, consequently, the most affected area by forest losses: Carpathian Mountains. The main goal of the paper is to examine and analyze the various explanatory variables associated with deforestation process and to model the probability of deforestation using GIS spatial analysis and logistic regression. The forest cover for 1990 and 2012, derived from CORINE Land Cover(CLC) database, were used to quantify historical forest cover change included in the modelling. To explain the biophysical and anthropogenic effects, this study considered several explanatory factors related to local topography, forest cover pattern, accessibility, urban growth and population density. Using ROC(Receiver Operating Characteristic) and 500 controlling sampling points, the statistical and spatial validations were assessed in order to evaluate the performance of the resulted data. The analysis showed that the area experienced a continuous forest cover change, leading to the loss of over 250,000 ha of forested area during the period 1990–2012. The most significant influence of the explanatory factors of deforestation were noticed in case of distance to forest edge(β=–4.215), forest fragmentation(β=2.231), slope declivity(β=–1.901), elevation(β=1.734) and distance to roads(β=–1.713). The statistical and spatial validation indicates a good accuracy of the model with reasonably AUC(0.736) and Kappa(0.739) values. The model’s results suggest an intensification of the deforestation process in the area, designing numerous new clusters with high probability in the Apuseni Mountains, northern and central part of the Eastern Carpathians, western part of the Southern Carpathians and northern part of the Banat Mountains. The study could represent a useful outcome to 展开更多
关键词 DEFORESTATION PROBABILITY Romanian CARPATHIANS LOGISTIC Regression
Driving factors of urban land growth in Guangzhou and its implications for sustainable development
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作者 Xuezhu CUI Shaoying LI +1 位作者 Xuetong WANG Xiaolong XUE 《地球科学前沿:英文版》 CSCD 2019年第3期464-477,共14页
Since 2000, China’s urban land has expanded at a dramatic speed because of the country’s rapid urbanization. The country has been experiencing unbalanced development between rural and urban areas, causing serious ch... Since 2000, China’s urban land has expanded at a dramatic speed because of the country’s rapid urbanization. The country has been experiencing unbalanced development between rural and urban areas, causing serious challenges such as agricultural security and land resources waste. Effectively evaluating the driving factors of urban land growth is essential for improving efficient land use management and sustainable urban development. This study established a principal component regression model based on eight indicators to identify their influences on urban land growth in Guangzhou. The results provided a grouping analysis of the driving factors, and found that economic growth, urban population, and transportation development are the driving forces of urban land growth of Guangzhou, while the tertiary industry has an opposite effect. The findings led to further suggestions and recommendations for urban sustainable development. Hence, local governments should design relevant policies for achieving the rational development of urban land use and strategic planning on urban sustainable development. 展开更多
关键词 driving factors URBAN LAND growth principal component regression LAND management POLICY SUSTAINABLE development GUANGZHOU
回归与矩阵分解的业务过程时间预测算法
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作者 李帅标 赵海燕 +1 位作者 陈庆奎 曹健 《小型微型计算机系统》 CSCD 北大核心 2019年第10期2189-2194,共6页
随着信息技术的不断发展,企业的竞争也越来与越激烈,为了提高业务过程效率,业务过程时间预测在近几年得到了越来越多的关注.然而,现有的业务过程时间预测方法没有考虑隐因子对业务过程活动时间预测的影响.为此,本文提出一种结合回归与... 随着信息技术的不断发展,企业的竞争也越来与越激烈,为了提高业务过程效率,业务过程时间预测在近几年得到了越来越多的关注.然而,现有的业务过程时间预测方法没有考虑隐因子对业务过程活动时间预测的影响.为此,本文提出一种结合回归与联合概率矩阵分解的时间预测方法.该方法结合上下文环境(可观测特征与不可观测特征)对活动执行时间进行预测,并将该方法应用于两种不同业务过程类型的真实数据集中.实验结果表明,我们的方法与相关的业务过程时间预测方法相比具有更高的预测精度. 展开更多
关键词 联合概率矩阵分解 活动时间预测 上下文环境 隐因子 回归
一种针对异常点的自适应回归特征选择方法 预览
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作者 郭亚庆 王文剑 苏美红 《计算机研究与发展》 EI CSCD 北大核心 2019年第8期1695-1707,共13页
数据集中含有不相关特征和冗余特征会使学习任务难度提高,特征选择可以有效解决该问题,从而提高学习效率和学习器性能.现有的特征选择方法大多针对分类问题,面向回归问题的较少,特别是当数据集含异常点时,现有方法对异常点敏感.虽然某... 数据集中含有不相关特征和冗余特征会使学习任务难度提高,特征选择可以有效解决该问题,从而提高学习效率和学习器性能.现有的特征选择方法大多针对分类问题,面向回归问题的较少,特别是当数据集含异常点时,现有方法对异常点敏感.虽然某些方法可以通过给样本损失函数加权来提高其稳健性,但是其权值一般都已预先设定好,且在特征选择和学习器训练过程中固定不变,因此方法的自适应性不强.针对上述问题,提出了一种针对异常点的回归特征选择方法(adaptive weight LASSO, AWLASSO),它首先根据回归系数更新样本误差,并通过自适应正则项将误差大于当前阈值的样本的损失函数赋予较小权重,误差小于阈值的样本的损失函数赋予较大权重,再在更新权重后的加权损失函数下重新估计回归系数,不断迭代上述过程.AWLASSO算法采用阈值来控制样本是否参与回归系数的估计,在阈值作用下,误差较小的样本才可参与估计,所以迭代完成后会获得较优的回归系数估计.另外,AWLASSO算法的阈值不是固定不变的,而是不断增大的(为使初始回归系数估计值较准确,其初始值较小),这样误判为异常点的样本可以重新进入训练集,并保证训练集含有足够的样本.对于误差大于最大阈值的样本点,由于其学习代价较大,算法将其识别为异常点,令其损失函数权重为0,从而有效降低了异常点的影响.在构造数据和标准数据上的实验结果表明:对于含有异常点的数据集,提出的方法比经典方法具有更好的稳健性和稀疏性. 展开更多
关键词 特征选择 回归 异常点 稳健 自适应正则项
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Monte Carlo sampling for error propagation in linear regression and applications in isochron geochronology
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作者 Yang Li Shuang Zhang +4 位作者 Richard Hobbs Camila Caiado Adam D.Sproson David Selby Alan D.Rooney 《科学通报:英文版》 SCIE EI CSCD 2019年第3期189-197,共9页
Geochronology is essential for understanding Earth’s history. The availability of precise and accurate isotopic data is increasing;hence it is crucial to develop transparent and accessible data reduction techniques a... Geochronology is essential for understanding Earth’s history. The availability of precise and accurate isotopic data is increasing;hence it is crucial to develop transparent and accessible data reduction techniques and tools to transform raw mass spectrometry data into robust chronological data. Here we present a Monte Carlo sampling approach to fully propagate uncertainties from linear regressions for isochron dating. Our new approach makes no prior assumption about the causes of variability in the derived chronological results and propagates uncertainties from both experimental measurements(analytical uncertainties) and underlying assumptions(model uncertainties) into the final age determination.Using synthetic examples, we find that although the estimates of the slope and y-intercept(hence age and initial isotopic ratios) are comparable between the Monte Carlo method and the benchmark‘‘Isoplot' algorithm, uncertainties from the later could be underestimated by up to 60%, which are likely due to an incomplete propagation of model uncertainties. An additional advantage of the new method is its ability to integrate with geological information to yield refined chronological constraints. The new method presented here is specifically designed to fully propagate errors in geochronological applications involves linear regressions such as Rb-Sr, Sm-Nd, Re-Os, Pt-Os, Lu-Hf, U-Pb(with discordant points),Pb-Pb and Ar-Ar. 展开更多
关键词 LINEAR regression ISOCHRON GEOCHRONOLOGY UNCERTAINTY PROPAGATION MONTE Carlo Isoplot
Advanced reliability analysis of slopes in spatially variable soils using multivariate adaptive regression splines 预览
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作者 Leilei Liu Shaohe Zhang +1 位作者 Yung-Ming Cheng Li Liang 《地学前缘:英文版》 CAS CSCD 2019年第2期671-682,共12页
This study aims to extend the multivariate adaptive regression splines(MARS)-Monte Carlo simulation(MCS) method for reliability analysis of slopes in spatially variable soils. This approach is used to explore the infl... This study aims to extend the multivariate adaptive regression splines(MARS)-Monte Carlo simulation(MCS) method for reliability analysis of slopes in spatially variable soils. This approach is used to explore the influences of the multiscale spatial variability of soil properties on the probability of failure(P_f) of the slopes. In the proposed approach, the relationship between the factor of safety and the soil strength parameters characterized with spatial variability is approximated by the MARS, with the aid of Karhunen-Loeve expansion. MCS is subsequently performed on the established MARS model to evaluate Pf.Finally, a nominally homogeneous cohesive-frictional slope and a heterogeneous cohesive slope, which are both characterized with different spatial variabilities, are utilized to illustrate the proposed approach.Results showed that the proposed approach can estimate the P_f of the slopes efficiently in spatially variable soils with sufficient accuracy. Moreover, the approach is relatively robust to the influence of different statistics of soil properties, thereby making it an effective and practical tool for addressing slope reliability problems concerning time-consuming deterministic stability models with low levels of P_f.Furthermore, disregarding the multiscale spatial variability of soil properties can overestimate or underestimate the P_f. Although the difference is small in general, the multiscale spatial variability of the soil properties must still be considered in the reliability analysis of heterogeneous slopes, especially for those highly related to cost effective and accurate designs. 展开更多
关键词 SLOPE stability Efficient reliability analysis Spatial VARIABILITY Random field MULTIVARIATE adaptive regression splines Monte Carlo simulation
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上海市社区2型糖尿病视网膜病变患者一年随访调查
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作者 秦飞 施榕 +10 位作者 贾丽丽 江华 冯易 张胜冰 宋道平 蒋玉立 顾文娟 彭云 程慧琴 娄洁琼 龙雯 《中华全科医师杂志》 2019年第6期529-534,共6页
目的探索上海市社区2型糖尿病视网膜病变(DR)患者不同转归结局及其影响因素。方法2015年10月至2016年4月,用目标抽样与整群随机抽样结合的方法,抽取上海市花木、金杨、三林、殷行、四平、大桥6家社区卫生服务中心管理的533例2型糖尿病D... 目的探索上海市社区2型糖尿病视网膜病变(DR)患者不同转归结局及其影响因素。方法2015年10月至2016年4月,用目标抽样与整群随机抽样结合的方法,抽取上海市花木、金杨、三林、殷行、四平、大桥6家社区卫生服务中心管理的533例2型糖尿病DR患者进行随访,收集患者的人口学信息、体格检查、实验室检测及眼底检查结果,进行DR诊断分级,分析患者的DR不同转归结局,采用有序logistic回归模型探索DR的影响因素。结果1年后,478例患者完成随访,女性占58.6%(280/478),男性占41.4%(198/478)。患者年龄(64±7)岁,病程(8.85±4.20)年。35例病情减轻,好转率为7.3%(35/478);29例病情加重,进展率为6.1%(29/478)。有序logistic回归分析发现,年龄(OR=0.197,95%CI:0.056~0.699)、BMI(OR=0.383,95%CI:0.171~0.856)、糖化血红蛋白(HbA1c)(OR=0.287,95%CI:0.102~0.803)、TG(OR=0.541,95%CI:0.295~0.991)、尿微量清蛋白与尿肌酐比值(ACR)(OR=0.218,95%CI:0.066~0.720)是患者DR病情发生不同转归结局的影响因素(均P<0.05)。结论DR患者的转归结局与年龄、BMI、血糖、血脂以及肾脏功能密切相关,降低BMI、控制血糖、血脂以及维持正常肾脏功能对防止DR病情加重以及促进DR病情好转具有重要意义。 展开更多
关键词 糖尿病 2型 糖尿病视网膜病变 随访研究 转归 影响因素
Influencing factors on the accuracy of local geoid model
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作者 Shazad Jamal Jalal Tajul Ariffin Musa +3 位作者 Ami Hassan Md Din Wan Anom Wan Aris WenBin Shen Muhammad Faiz Pa’suya 《大地测量与地球动力学:英文版》 2019年第6期439-445,共7页
Different modification methods and software programs were developed to obtain accurate local geoid models in the past two decades.The quantitative effect of the main factors on the accuracy of local geoid modeling is ... Different modification methods and software programs were developed to obtain accurate local geoid models in the past two decades.The quantitative effect of the main factors on the accuracy of local geoid modeling is still ambiguous and has not been clearly diagnosed yet.This study presents efforts to find the most influential factors on the accuracy of the local geoid model,as well as the amount of each factor’s effect quantitatively.The methodology covers extracting the quantitative characteristics of 16 articles regarding local geoid models of different countries.The Statistical Package of Social Sciences(SPSS)software formulated a strong multiple regression model of correlation coefficient r = 0.999 with a high significance coefficient of determination R~2 = 0.997 and adjusted R~2 = 0,98 for the required effective factors.Then,factor analysis is utilized to extract the dominant factors which include:accuracy of gravity data(40%),the density of gravity data(25%)(total gravity factors is 65%),the Digital Elevation Model(DEM)resolution(16%),the accuracy of GPS/leveling points(10%)and the area of the terrain of the country/state under the study(9%).These results of this study will assist in developing more accurate local geoid models. 展开更多
关键词 ACCURACY of LOCAL GEOID MODEL MULTIPLE regression MODEL Influence FACTORS
Modified Independent Component Regression Method Without Prewhitening 预览
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作者 Rong Guo Jimin Ye 《哈尔滨工业大学学报:英文版》 EI CAS 2019年第4期50-57,共8页
Independent component analysis(ICA) can reveal the essential underlying structure of data, and independent component regression(ICR) methods usually obtain better performance than other regression methods such as prin... Independent component analysis(ICA) can reveal the essential underlying structure of data, and independent component regression(ICR) methods usually obtain better performance than other regression methods such as principal component regression. However, when existing ICR methods separate or extract independent components using prewhitened data, the backward propagation of inevitable prewhitened errors deteriorates the final linear prediction accuracy. To overcome this weakness, first, we proposed using weighted orthogonal constraint condition to replace the prewhitening of the data in ICA. Next, the statistical independence of ICs and the close relationship between ICs and quality variables are considered at the same time. Then, by combining the merits of improved ICR and ensemble ICR algorithm which solved the problem of selecting an appropriate nonquadratic function in ICA iteration procedure, a modified independent component regression(MICR) method that directly used the measured process data was proposed. Finally, three experimental results were used to validate excellent performance of modified algorithm. 展开更多
关键词 INDEPENDENT COMPONENT analysis WEIGHTED ORTHOGONAL CONSTRAINT INDEPENDENT COMPONENT regression prewhitened data
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Effects of disparity distribution on visual comfort for multiple objects of stereoscopic images 预览
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作者 苏志斌 Li Dongrui +1 位作者 Zou Fangju Ren Hui 《高技术通讯:英文版》 CAS 2019年第1期42-47,共6页
With the development of stereoscopic technology, more attention is attracted on the stereoscopic three-dimensional (S3D) content and service, and researches on images and videos have emerged in large numbers. This pap... With the development of stereoscopic technology, more attention is attracted on the stereoscopic three-dimensional (S3D) content and service, and researches on images and videos have emerged in large numbers. This paper focuses mainly on visual comfort affected by characteristics of disparity for multiple objects. To find the relationship between disparity distribution and visual comfort perception, several subject evaluation experiments are done. The study contains two spatial distribution types of disparity: 1) only one of the foreground objects has zero disparity;2) one of the foreground objects has positive disparity, while the other one has negative disparity. The experimental results and relative regression analysis provide appropriate relationship between disparity distribution and visual comfort for both conditions, which is significant to meet the applicant field in S3D content acquisition, display adjustment and quality evaluation. 展开更多
关键词 DISPARITY DISTRIBUTION DISPARITY MAGNITUDE STEREOSCOPIC images visual COMFORT regression analyses
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大学英语语法教学回归的必要性及可行模式 预览 被引量:1
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作者 余鹏 《哈尔滨学院学报》 2019年第9期130-132,共3页
文章通过问卷和数据统计,论证了因主流教学观念和课程设置等因素造成的大学英语语法教学和科研的“边缘化”带来的影响:语法教学需求不能得到满足;教学效果下降,学生语言运用能力准确性大不如前。“语法教学微课”这一新型教学模式可以... 文章通过问卷和数据统计,论证了因主流教学观念和课程设置等因素造成的大学英语语法教学和科研的“边缘化”带来的影响:语法教学需求不能得到满足;教学效果下降,学生语言运用能力准确性大不如前。“语法教学微课”这一新型教学模式可以在不占用课堂教学的前提下,使语法得以回归大学英语教学,从而有效解决教学需求,提高大学英语教学效果。 展开更多
关键词 大学英语 语法教学 边缘化 回归 微课
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Groundwater Nitrate Contamination and Driving Forces from Intensive Cropland in the North China Plain 预览
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作者 Jing LIU Chengjun ZHANG +7 位作者 Peng LI Shunjiang LI Maoting MA Guoyin ZHANG Xianbiao GAO Changlin KOU Lihua JIANG Tongke ZHAO 《亚洲农业研究:英文版》 2019年第9期39-45,50共8页
High nitrate in groundwater is a serious problem especially in highly active agricultural areas.In this paper,the concentration and spatial distribution of groundwater nitrate in cropland area in the North China Plain... High nitrate in groundwater is a serious problem especially in highly active agricultural areas.In this paper,the concentration and spatial distribution of groundwater nitrate in cropland area in the North China Plain were assessed by statistical and geostatistical techniques.Nitrate concentration in groundwater reached a maximum of 526.58 mg/L,and 47.2%,21.33%and 11.13%of samples had levels in excess of nitrate safety threshold concentration(50 mg/L)in shallow,middle-deep and deep groundwater,respectively.And NO-3 content significantly decreased with groundwater depth.Groundwater nitrate concentrations under vegetable area are significantly higher than ones under grain and orchard.And there are great differences in spatial distribution of nitrate in the North China Plain and pollution hotspot areas are mainly in Shandong Province.Based on both multiple regressions combined with principal component analysis(PCA),significant variables for nitrate variation in three types of ground water were found:population per unit area,percentage of vegetable area,percentage of grain crop area,livestock per unit area,annual precipitation and annual mean temperature for shallow groundwater;population per unit area and percentage of vegetable area for middle-deep groundwater;percentage of vegetable area,percentage of grain crop area and livestock per unit area for deep groundwater. 展开更多
关键词 Groundwater NITRATE CONTAMINATION Spatial variation Principal component ANALYSIS Regression ANALYSIS
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Using random-parameter and fixed-parameter ordered models to explore temporal stability in factors affecting drivers’ injury severity in single-vehicle collisions
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作者 Essam Dabbour Murtaza Haider Eman Diaa 《交通运输工程学报(英文版)》 CSCD 2019年第2期132-146,共15页
Understanding the temporal stability in the factors influencing drivers’ injury severity in single-vehicle collisions would help evaluating the effectiveness of implementing different safety treatments so that resear... Understanding the temporal stability in the factors influencing drivers’ injury severity in single-vehicle collisions would help evaluating the effectiveness of implementing different safety treatments so that researchers could understand whether any safety improvements,observed after applying a certain safety treatment, are attributed to the specific treatment or simply attributed to the temporal instability of the factors being addressed. This study investigates the temporal stability of the factors affecting drivers’ injury severity in singlevehicle collisions involving light-duty vehicles. The study is based on utilizing ordinal regression modeling to analyze the severity of drivers’ injuries in all police-reported lightduty single-vehicle collisions that occurred in North Carolina from January 1, 2007, to December 31, 2013. A separate regression model was estimated for each year so that statistical significance of each risk factor may be compared over the years. The study also estimated random-parameter(mixed) ordered logit models to explore the heterogeneity in data. The most significant factor that was found to increase the severity of drivers’ injuries in light-duty single-vehicle collisions is driving under the influence of alcohol or illicit drugs. Other significant factors, in decreasing order in terms of their significance, include driving on a highway curve, exceeding speed limit, lighting conditions, the age of the driver, and the age of the vehicle. In contrast, there were six factors that were found to be significant in only some years and not in all years. These six temporally unstable factors include the use of seatbelt, driver’s gender, rural highways, undivided highways, the type of the light-duty vehicle, and weather and road surface conditions. These same factors were found by other previous research studies to be significant and stable predictors of drivers’ injury severity in single-vehicle collisions. 展开更多
关键词 Drivers’ injury severity Single-vehicle collisions ORDINAL regression MODELS Mixed LOGIT MODELS Temporal stability
我国华南江南春季雷暴气候特征分析 预览
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作者 杨波 王园香 蔡雪薇 《热带气象学报》 CSCD 北大核心 2019年第4期470-479,共10页
基于我国华南江南地区274个基本地面气象观测站数据、全国闪电定位数据以及欧洲中心的全球大气再分析数据(ERA-Interim),对1981—2017年华南江南地区的春季雷暴日采用经验正交函数分解方法(EOF),并与气象要素场做回归分析。得出以下主... 基于我国华南江南地区274个基本地面气象观测站数据、全国闪电定位数据以及欧洲中心的全球大气再分析数据(ERA-Interim),对1981—2017年华南江南地区的春季雷暴日采用经验正交函数分解方法(EOF),并与气象要素场做回归分析。得出以下主要结论:(1)我国华南江南地区春季雷暴活动高发区主要在广西东部至广东西部;其高峰期在下午18:00和凌晨4:00左右,且大多数雷暴活动持续时间不超过3h;山区雷暴活动主要在傍晚至夜间;平原雷暴活动主要在白天,高峰在17:00及06:00前后;(2)华南江南地区的雷暴活动存在着3~5年的短周期和16年左右的长周期变化;(3)雷暴日距平EOF分析的前3个主成分累计方差贡献达到72.3%。按其向量场的方差贡献分型,Ⅰ型表现为华南江南雷暴活跃特征呈现较统一的变化规律。深厚西南低涡槽前、上干下湿的水汽层结、上冷下暖的温度层结为华南江南地区发生大范围雷暴天气提供良好的动力、水汽和位势不稳定条件,是华南江南雷暴活跃异常的主要模态;Ⅱ型表现为从华南南部到江西与浙江南部有一条西南-东北向、下宽上窄的雷暴活跃正距平异常区,而两侧为负距平异常区。其环流特征表现为温度整层偏冷,水汽整层偏湿,而西南槽前动力抬升有利于水汽抬升凝结触发对流形成雷暴;Ⅲ型表现为华南和江南地区雷暴活跃特征呈南北反位相异常,其分界线在26°N附近。其环流特征表现为较强的干冷空气南下与南方暖湿空气在南岭山区对峙形成异常的垂直环流圈。在其上升支,低层干冷空气被卷入中高层使得中高层暖湿空气凝结释放潜热形成对流,造成华南地区多雷暴发生,而江南地区处于垂直环流的下沉支,整层湿度偏干,造成江南地区雷暴相对偏少。 展开更多
关键词 春季雷暴 经验正交函数分解 回归分析 分型模态 环流特征
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