Using Quantile Regression to Detect Relationships between Large-scale Predictors and Local Precipitation over Northern China

Quantile regression（QR） is proposed to examine the relationships between large-scale atmospheric variables and all parts of the distribution of daily precipitation amount at Beijing Station from 1960 to 2008. QR is also applied to evaluate the relationship between large-scale predictors and extreme precipitation（90th quantile） at 238 stations in northern China.Finally, QR is used to fit observed daily precipitation amounts for wet days at four sample stations. Results show that meridional wind and specific humidity at both 850 h Pa and 500 h Pa（V850, SH850, V500, and SH500） strongly affect all parts of the Beijing precipitation distribution during the wet season（April–September）. Meridional wind, zonal wind, and specific humidity at only 850 h Pa（V850, U850, SH850） are significantly related to the precipitation distribution in the dry season（October–March）. Impacts of these large-scale predictors on the daily precipitation amount with higher quantile become stronger, whereas their impact on light precipitation is negligible. In addition, SH850 has a strong relationship with wet-season extreme precipitation across the entire region, whereas the impacts of V850, V500, and SH500 are mainly in semi-arid and semi-humid areas. For the dry season, both SH850 and V850 are the major predictors of extreme precipitation in the entire region. Moreover, QR can satisfactorily simulate the daily precipitation amount at each station and for each season, if an optimum distribution family is selected. Therefore, QR is valuable for detecting the relationship between the large-scale predictors and the daily precipitation amount.

Explanatory System of Support Vector Regression and Its Application in QSPR of Surfactants

In order to solve the problem of poor interpretability of support vector re- gression （SVR） applied in quantitative structure-property relationship （QSPR）, a com- plete set of explanatory system for SVR was established based on F-test, The nov- el explanatory system includes significance tests of model and single-descriptor im- portance, single-descriptor effect and sensitivity analysis, and significance tests of interaction between two descriptors, etc. The results of example indicated that the explanatory results of the new system were consistent well with those of stepwise linear regression model and quadratic polynomial stepwise regression model. The explanatory SVR model will play an important role in regression analysis such as QSPR.

Predicting the Acute Toxicity of Aromatic Amines by Linear and Nonlinear Regression Methods

In current paper, a quantitative structure-activity relationship （QSAR） study was performed for the prediction of acute toxicity of aromatic amines. A set of 56 compounds was randomly divided into a training set of 46 compounds and a test set of 10 compounds. The electronic and topological descriptors computed by the Scigress package and Dragon software were used as predictor variables. Multiple linear regression （MLR） and support vector machine （SVM） were utilized to build the linear and nonlinear QSAR models, respectively. The obtained models with five descriptors show strong predictive ability. The linear model fits the training set with R2 = 0.71, with higher SVM values of R2 = 0.77. The validation results obtained from the test set indicate that the SVM model is comparable or superior to that obtained by MLR, both in terms of prediction ability and robustness.

Flexible Model Selection Criterion for Multiple Regression

Predictors of a multiple linear regression equation selected by GCV (Generalized Cross Validation) may contain undesirable predictors with no linear functional relationship with the target variable, but are chosen only by accident. This is because GCV estimates prediction error, but does not control the probability of selecting irrelevant predictors of the target variable. To take this possibility into account, a new statistics “GCVf” (“f”stands for “flexible”) is suggested. The rigidness in accepting predictors by GCVf is adjustable;GCVf is a natural generalization of GCV. For example, GCVf is designed so that the possibility of erroneous identification of linear relationships is 5 percent when all predictors have no linear relationships with the target variable. Predictors of the multiple linear regression equation by this method are highly likely to have linear relationships with the target variable.

Using Multiple Linear Regression and Artificial Neural Network Techniques for Predicting CCR5 Binding Affinity of Substituted 1-(3, 3-Diphenylpropyl)-Piperidinyl Amides and Ureas

Quantitative structure–activity relationship (QSAR) models were developed to predict for CCR5 binding affinity of substituted 1-(3, 3-diphenylpropyl)-piperidinyl amides and ureas using multiple linear regression (MLR) and artificial neural network (ANN) techniques. A model with four descriptors, including Hydrogen-bonding donors HBD(R7), the partition coefficient between n-octanol and water logP and logP(R1) and Molecular weight MW(R7), showed good statistics both in the regression and artificial neural network with a configuration of (4-3-1) by using Bayesian and Leven-berg-Marquardt Methods. Comparison of the descriptor’s contribution obtained in MLR and ANN analysis shows that the contribution of some of the descriptors to activity may be non-linear.

Spectrum-effect relationship between components and antitumor activity of Lonicerae Japonicae Flos based on orthogonal partial least squares regression

Lonicerae Japonicae Flos is a significant food and traditional Chinese medicine,known as plant antibiotics.It has rich chemical constituents and significant pharmacological effects.The antitumor activity of Lonicerae Japonicae Flos has been clarified,but the study on its spectrum-effect relationship has not been reported.The compounds responsible for its antitumor activity are still unknown.In this study,processed products of Lonicerae Japonicae Flos at different temperatures were taken as experimental materials,and SMMC-7721,A549,andMGC80-3 cells were tested.The orthogonal partial least squares regressionmethod was used to analyze the common compounds in different processed products and the antitumor activity.The results show that processed products have a stronger inhibitory effect on A549 cells and MGC80-3 cells than SMMC-7721 cells.Compounds such as secologanic acid,isochlorogenic acid A,serotonin,and chlorogenic acid play an important role in their antitumor effects.

Comparison of Long-Term Forecasting of June-August Rainfall over Changjiang-Huaihe Valley

In terms of an Artificial Neural Network (ANN) established is a long-term prediction model for June-August flood/drought in the Changjiang-Huaihe Basins and a regression forecasting expression is formulated with the aid of the same factors and sample size for comparison. Results show that the ANN is superior in predictions and fittings due to its higher self-adaptive learning recognition and nonlinear mapping especially in the years of severe flood and drought. This shows great promise in using ANN in the research of flood/drought prediction on a long-range basis.

Quantitative Structure-activity Relationship(QSAR) Study of Toxicity of Substituted Aromatic Compounds to Photobacterium Phosphoreum

With the artificial neural network（ANN） method combined with the multiple linear regression（MLR）,based on a series of quantum chemical descriptors and molecular connectivity indexes,quantitative structure-activity relationship（QSAR） models to predict the acute toxicity（-lgEC50） of substituted aromatic compounds to Photobacterium phosphoreum were established.Four molecular descriptors that appear in the MLR model,namely,the second order valence molecular connectivity index（2XV）,the energy of the highest occupied molecular orbital（EHOMO）,the logarithm of n-octyl alcohol/water partition coefficient（logKow） and the Connolly molecular area（MA）,were inputs of the ANN model.The root-mean-square error（RMSE） of the training and validation sets of the ANN model are 0.1359 and 0.2523,and the correlation coefficient（R） is 0.9810 and 0.8681,respectively.The leave-one-out（LOO） cross validated correlation coefficient（Q L2OO） of the MLR and ANN models is 0.6954 and 0.6708,respectively.The result showed that the two methods are complementary in the calculations.The regression method gave support to the neural network with physical explanation,and the neural network method gave a more accurate model for QSAR.In addition,some insights into the structural factors affecting the acute toxicity and toxicity mechanism of substituted aromatic compounds were discussed.

Spatial-temporal Evolution of the Urban-rural Coordination Relationship in Northeast China in 1990-2018

To comprehensively understand the law of urban-rural relationship and propose scientific measures of urban-rural coordinated development in Northeast China,this study uses the coupling coordination degree model and geographically and temporally weighted regression(GTWR)model to analyze the spatial-temporal patterns and the corresponding driving mechanisms of its urban-rural coordination since 1990.The results are as follows.First,the urban-rural coupling coordination degree in Northeast China was very low and improved slowly,but its stages of evolution is a good interpretation of the strategic arrangements of China's urbanization.Second,the urban-rural coupling coordination degree in Northeast China had spatial differences and was characterized by central polarization,converging on urban agglomeration,which was high in the south and low in the north.Moreover,the gap between the north and south weakened.Third,the spatial-temporal evolution of the urban-rural coordination relationship in Northeast China was influenced by pulling from the central cities,pushing from rural transformation,and government regulations.The influence intensity of the three mechanisms was weak,but the pulling from the central cities was stronger than that of the other two mechanisms.Furthermore,the spatial difference between the three mechanisms determines the spatial pattern and its evolution of the urban-rural coordination relationship in Northeast China.Fourth,to promote the development of urban-rural coordination in Northeast China,it is essential to advance urban-rural economic correlation,enhance the government^role in regulating and guiding,and adopt different policies for each region in Northeast China.

Quantitative Structure-biodegradability Relationship Study about the Aerobic Biodegradation of Some Aromatic Compounds

10 quantum chemical descriptors of 21 aromatic compounds have been calculated by the semi-empirical quantum chemical method AM1. The Quantitative Structure-Biodegradability Relationships （QSBR） studies were performed by the multiple linear regression （MLR）, principal component regression （PCR） and back propagation artificial neural network （BP-ANN）, respectively. The root mean square error （RMSE） of the training and validation sets of the BP-ANN model are 0.1363 and 0.0244, the mean absolute percentage errors （MAPE） are 0.1638 and 0.0326, the squared correlation coefficients （R^2） are 0.9853 and 0.9996, respectively. The results show that the BP-ANN model achieved a better prediction result than those of MLR and PCR. In addition, some insights into the structural factors affecting the aerobic biodegradation mechanism were discussed in detail.

Studies on a Novel Characteristic Atom-pair Holographic Code Applied to Quantitative Structure-chromatographic Retention Relationship of Organic Compounds

6 Atomic fragment types of organic compound have been defined, and the multilevel atom-pair frequency matrix has been constructed according to the occurrence number in pairs of atomic fragments with different bond lengths in the molecule. On the basis of them, a novel molecular coding technique： characteristic atom-pair holographic code （CAHC）, is obtained. To some extent, this method exhibits a large number of benefits at the same time. For example, it can calculate 2D molecular topological descriptor easily, operate without difficulty and possess definite physicochemical meaning of 3D molecular structural characterization methods, and may fetch the complicated information of molecule, etc. Therefore, it is appropriate for the study on quantitative structure-property/activity relationship （QSPR/QSAR） of medicines and biological molecules. We attempt in this paper to utilize the method of CAHC to the quantitative prediction of reversed-phase liquid chromatogram （RPLC） retention data of 33 purine derivatives and 24 steroids. The fitting multiple correlation coefficient R2, cross-validated multiple correlation coefficient Q2 and predicted ability Q^2 pred over test set＇s samples of obtained partial least-square （PLS） regression model are respectively 0.990, 0.893 and 0.977, 0.897, 0.941.

Dew amount and its long-term variation in the Kunes River Valley,Northwest China

Dew is an essential water resource for the survival and reproduction of organisms in arid and semi-arid regions.Yet estimating the dew amount and quantifying its long-term variation are challenging.In this study,we elucidate the dew amount and its long-term variation in the Kunes River Valley,Northwest China,based on the measured daily dew amount and reconstructed values(using meteorological data from 1980 to 2021),respectively.Four key results were found:(1)the daily mean dew amount was 0.05 mm during the observation period(4 July-12 August and 13 September-7 October of 2021).In 35 d of the observation period(i.e.,73%of the observation period),the daily dew amount exceeded the threshold(>0.03 mm/d)for microorganisms;(2)air temperature,relative humidity,and wind speed had significant impacts on the daily dew amount based on the relationships between the measured dew amount and meteorological variables;(3)for estimating the daily dew amount,random forest(RF)model outperformed multiple linear regression(MLR)model given its larger R^(2) and lower MAE and RMSE;and(4)the dew amount during June-October and in each month did not vary significantly from 1980 to the beginning of the 21^(st) century.It then significantly decreased for about a decade,after it increased slightly from 2013 to 2021.For the whole meteorological period of 1980-2021,the dew amount decreased significantly during June-October and in July and September,and there was no significant variation in June,August,and October.Variation in the dew amount in the Kunes River Valley was mainly driven by relative humidity.This study illustrates that RF model can be used to reconstruct long-term variation in the dew amount,which provides valuable information for us to better understand the dew amount and its relationship with climate change.

The scaling relationship of leaf area and total mass of sample plots across world trees

The scaling relationship between leaf area and total mass of plant has important implications for understanding resource allocations in the plant.The model of West,Brown and Enquist(WBE model)considers that a 3/4 scaling exponent of metabolic rate versus total mass to be optimal for each plant and has been confirmed numerous times.Although leaf area is a better proxy of the metabolic rate than leaf mass,few studies have focused on the scaling exponent of leaf area versus total mass and even fewer have discussed the diversification of this scaling exponent across different conditions.Here,I analyzed the scaling exponent of leaf area versus total mass of sample plots across world plants.I found that as the plant grows,it allocates fewer resources to photosynthetic tissues than expected by the WBE model.The results also empirically show that this scaling exponent varies significantly for different plant leaf habit,taxonomic class and geographic region.Therefore,leaf strategy in response to environmental pressure and constraint clearly plays a significant role.

Quantitative Structure-activity Relationship Models of Monomer Reactivity

The reactivity parameters,Q and e,in the Q-e scheme reflect the reactivities of a monomer(or a radical)in free-radical copolymerizations.By applying multiple linear regression(MLR)analysis,the optimal quantitative structure-activity relationship(QSAR)model for the reactivity parameter lnQ was developed based on five descriptors(NAF,NOF,EαLUMO,EβHOMO,and EβLUMO)and 69 monomers with the root mean square(rms)error of 0.61.The optimal MLR model of the parameter e obtained from five descriptors(TOcl,NpN,NSO,EαHOMO and DH)and 68 monomers produced rms error of 0.42.Compared with previous models,the two optimal MLR models in this paper show satisfactory statistical characteristics.The feasibility of combining 2D descriptors obtained from the monomers and 3D descriptors calculated from the radical structures(formed from monomers+H )to predict parameters Q and e has been demonstrated.

Long Run Relationships, Vertical Integration and International Competition： Can They Contribute to Explaining Regional Construction Cost Differences？

The existence of opportunistic behavior by contractors or sub-contractors in the bidding process encouraged by the governance structure of construction companies as well as the kind of relationship that exist between contractors and clients is thought to have some bearing on the rising construction cost observed in some regions of Sweden. Three hypotheses that are intended to test the impact that long run relationship between contractors and developers, vertically integrated firms, and the increase of international competition could have on the construction cost increase levels were tested on a predetermined number of projects from six cities in different regions. The semi-structured survey produces inconclusive results. Long run and collaborative relationship was prevalent in small region though respondents in this region did not draw strong connection between construction cost increase levels and the kind of observed relationship. In Stockholm region, short-term relationship was mostly prevalent. Vertical integration and foreign competition impacts on construction costs were not significant in either region.

1990-2020年长三角地区生境质量与夜间灯光的空间关系

基于1990-2020年长三角地区的土地利用数据和夜间灯光数据,运用InVEST生境质量评估模型、双变量空间自相关、相关系数对生境质量与夜间灯光的时空关系进行研究,并应用地理加权回归和归纳法分析两者的响应规律,结果表明:(1)浙江省总体上生境最优且城镇发展最快,安徽省在城镇快速发展的同时生境相对下降最少,生境质量和夜间灯光空间分布格局呈现出与区位、地形等因素的空间耦合性。(2)长三角地区生境质量与夜间灯光指数随着时间的发展空间上的依赖性越来越强,聚类模式主要为高-低、低-高及低-低3种。高-低聚类区域主要分布在浙江省和安徽省的南部、西部的山地丘陵,研究期内呈现缩小的趋势;低-高聚类区域在长三角东部平原区呈集聚分布、北部平原丘陵区呈零散分布,呈现扩张趋势。(3)不同年份各省份生境质量与夜间灯光指数呈显著负相关,且经济越发达的省份,生境质量与夜间灯光指数的负相关关系越强;生境质量与夜间灯光指数呈高度负相关的区县主要分布在长江下游沿岸和浙江省东部,相关性不显著的区县主要分布在安徽省、江苏省的北部,变化类型以负相关关系持续增强型占比最高。(4)生境质量与夜间灯光指数的相关关系随着城市发展呈U型变化,即随着城市发展,生境质量与夜间灯光指数的负相关关系先增强后下降。研究结果可为长三角城市可持续发展、生境调控提供前期基础。

轨道站网络中心性、客流与空间热力耦合分析

城市空间热力反映了人口聚集与街道活力。为探究城市轨道交通与空间热力分布的互动关系,从微观层面的轨道站点切入,采用百度热力图和轨道站点客流数据,以上海为例,对轨道站点的网络中心性、客流与站域空间热力进行耦合分析。首先采用Pearson双变量相关性研究两类轨道站点属性与空间热力的总体耦合关系,然后引入双变量空间自相关和地理加权回归分析方法分别挖掘网络中心性与站域热力、站域热力与站点客流的空间关联模式,并对比两类耦合性的空间差异。结果表明:轨道站点的网络中心性与空间热力的耦合性明显优于轨道客流与空间热力的耦合性,交通区位优势通常能够形成较高的空间热力,客流水平的影响因素则更为复杂;空间热力更适合量化核心区以外区域的轨道交通与城市空间互动关系,轨道交通网络化对空间热力提升具有乘数效应,而在开发密度低的区域提升空间热力更有助于刺激轨道客流;利用空间热力数据评估城市核心区以外区域的新建站点客流潜力具有可行性,但仅用热力预测客流具有局限性;轨道站点周边城市更新可参考不同空间区位站点的两类耦合性差异进行优化。该研究探索了结合城市空间热力分布完善轨道交通线网布局、针对不耦合因素优化轨道站点公共交通导向型开发(TOD)的分析框架,为微观层面衡量城市轨道交通“人-地”关系提供了新视角。

认罪认罚从宽制度中的控审关系

实然状态中异化了的控审关系从类型上可以划分为积极冲突、消极冲突和“沟通过剩”,原因涉及“检察主导”与“以审判为中心”的理念冲突、诉讼重心转移下权力与责任配置的错位,以及审方对于控辩合意的真实性与控方提出量刑建议能力的质疑。控审关系异化会造成被追诉人利益、制度公信力乃至司法权威性的巨大牺牲。应然状态下的控审关系应在实现理念“双向奔赴”的同时,建构与权力重构相匹配的法官卸责机制,健全旨在提升合意真实与量刑能力的配套制度。

城市路内违章停车时空特征及影响因素作用模型

为揭示城市路内违章停车的时空模式及影响机理,结合持续两个月共10396条电子警察违停抓拍数据,从土地利用、停车供给与道路设计这3个维度选取16个影响因素进行分析。利用标准椭圆方法、层次聚类法及皮尔逊相关性检验等方法,分析路内违停时空特征并进行影响因素筛选,进一步根据因变量特征构建贝叶斯分位数回归模型,解析早晚高峰违停数量与影响因素的关系。研究结果表明:从违停数量的时间特征来看,工作日违停数量明显高于周末违停数量,且主要集中在周中,工作日违停数量按小时聚类结果显示,违停数量分为3个时间模式,分别为早晚高峰时间段(8:00-10:00和17:00-19:00)、早晚高峰汇聚时间段(10:00-11:00和15:00-17:00)与其他时间段;从违停数量的空间特征来看,工作日违停数量的整体空间分布主要集中在商业和居住密度较高的区域,其在一天内的空间变化趋势呈现“扩撒-稳定-收缩”的态势;分位数回归结果显示,土地使用、停车供给及道路设计这3个维度因素对路内违停存在非线性影响与阈值效应,且早晚高峰违停产生机制具有差异,路内违停治理需要考虑时空特征进行差异化治理。