Correlation Analysis of Fiscal Revenue and Housing Sales Price Based on Multiple Linear Regression Model

This paper selects seven indicators of financial revenue and housing sales price in recent 19 years in China,and uses SPSS and Excel to carry out descriptive statistics,independent sample t-test,correlation analysis and regression analysis to comprehensively study the correlation between financial revenue and housing sales price in China,and establishes the relationship between financial revenue and housing sales price When the average selling price of commercial housing increases by one unit,the fiscal revenue will increase by 27.855 points.

Rising trends of global precipitable water vapor and its correlation with flood frequency

Using 4 global reanalysis data sets, significant upward trends of precipitable water vapor(PWV) were found in the 3 time periods of 1958-2020, 1979-2020, and 2000-2020. During 1958-2020, the global PWV trends obtained using the ERA5 and JRA55 data sets are 0.19 ± 0.01 mm per decade(1.15 ± 0.31%)and 0.23 ± 0.01 mm per decade(1.45 ± 0.32%), respectively. The PWV trends obtained using the ERA5,JRA55, NCEP-NCAR, and NCEP-DOE data sets are 0.22 ± 0.01 mm per decade(1.18 ± 0.54%),0.21 ± 0.00 mm per decade(1.76 ± 0.56%), 0.27 ± 0.01 mm per decade(2.20 ± 0.70%) and 0.28 ± 0.01 mm per decade(2.19 ± 0.70%) for the period 1979-2020. During 2000-2020, the PWV trends obtained using ERA5, JRA55, NCEP-DOE, and NCEP-NCAR data sets are 0.40 ± 0.25 mm per decade(2.66 ± 1.51%),0.37 ± 0.24 mm per decade(2.19 ± 1.54%), 0.40 ± 0.26 mm per decade(1.96 ± 1.53%) and 0.36 ± 0.25 mm per decade(2.47 ± 1.72%), respectively. Rising PWV has a positive impact on changes in precipitation,increasing the probability of extreme precipitation and then changing the frequency of flood disasters.Therefore, exploring the relationship between PWV(derived from ERA5 and JRA55) change and flood disaster frequency from 1958 to 2020 revealed a significant positive correlation between them, with correlation coefficients of 0.68 and 0.79, respectively, which explains the effect of climate change on the increase in flood disaster frequency to a certain extent. The study can provide a reference for assessing the evolution of flood disasters and predicting their frequency trends.

A review on the coordinative structure of human walking and the application of principal component analysis

Walking is a complex task which includes hundreds of muscles, bones and joints working together to deliver smooth movements. With the complexity, walking has been widely investigated in order to identify the pattern of multi-segment movement and reveal the control mechanism. The degree of freedom and dimensional properties provide a view of the coordinative structure during walking, which has been extensively studied by using dimension reduction technique. In this paper, the studies related to the coordinative structure, dimensions detection and pattern reorganization during walking have been reviewed. Principal component analysis, as a popular technique, is widely used in the processing of human movement data. Both the principle and the outcomes of principal component analysis were introduced in this paper. This technique has been reported to successfully reduce the redundancy within the original data, identify the physical meaning represented by the extracted principal components and discriminate the different patterns. The coordinative structure during walking assessed by this technique could provide further information of the body control mechanism and correlate walking pattern with injury.

Similarity/dissimilarity analysis of protein sequences using the spatial median as a descriptor

A novel 3-D graphical representation of protein sequence has been introduced. A right cone of a unit base and unit height has been selected to represent protein sequences on its surface. The twenty amino acids have been represented by 20 circles and all protein's residues have been represented by n lines on the cone's surface. All the spots which represent the protein's residues have been shown in the cone's top view. The spatial median of all the spots is used as a new descriptor of any protein sequence. This approach was applied on two short segments of protein of yeast Saccharomyces cerevisiae. The examination of the similarities/dissimilarities for the eight ND5 proteins and the six β-globin proteins illustrate the utility of our approach. A linear correlation and significance analysis have been provided to compare our results and the percentage sequence alignment identity.

Research on the Model of Linear Data Fitting Method

By using the method of least square linear fitting to analyze data do not exist errors under certain conditions, in order to make the linear data fitting method that can more accurately solve the relationship expression between the volume and quantity in scientific experiments and engineering practice, this article analyzed data error by commonly linear data fitting method, and proposed improved process of the least distance squ^re method based on least squares method. Finally, the paper discussed the advantages and disadvantages through the example analysis of two kinds of linear data fitting method, and given reasonable control conditions for its application.

ON USING NON-LINEAR CANONICAL CORRELATION ANALYSIS FOR VOICE CONVERSION BASED ON GAUSSIAN MIXTURE MODEL

Voice conversion algorithm aims to provide high level of similarity to the target voice with an acceptable level of quality.The main object of this paper was to build a nonlinear relationship between the parameters for the acoustical features of source and target speaker using Non-Linear Canonical Correlation Analysis(NLCCA) based on jointed Gaussian mixture model.Speaker indi-viduality transformation was achieved mainly by altering vocal tract characteristics represented by Line Spectral Frequencies(LSF).To obtain the transformed speech which sounded more like the target voices,prosody modification is involved through residual prediction.Both objective and subjective evaluations were conducted.The experimental results demonstrated that our proposed algorithm was effective and outperformed the conventional conversion method utilized by the Minimum Mean Square Error(MMSE) estimation.

Statistical Analysis of Leaf Water Use Efficiency and Physiology Traits of Winter Wheat Under Drought Condition

Five statistical methods including simple correlation, multiple linear regression, stepwise regression, principal components, and path analysis were used to explore the relationship between leaf water use efficiency （WUE） and physiological traits （photosynthesis rate, stomatal conductance, transpiration rate, intercellular CO2 concentration, etc.） of 29 wheat cultivars. The results showed that photosynthesis rate, stomatal conductance, and transpiration rate were the most important leaf WUE parameters under drought condition. Based on the results of statistical analyses, principal component analysis could be the most suitable method to ascertain the relationship between leaf WUE and relative physiological traits. It is reasonable to assume that high leaf WUE wheat could be obtained by selecting breeding materials with high photosynthesis rate, low transpiration rate, and stomatal conductance under dry area.

Nonlinearly correlated failure analysis and autonomic prediction for distributed systems

In order to achieve failure prediction without manual intervention for distributed systems, a novel failure feature analysis and extraction approach to automate failure prediction is proposed. Compared with the traditional methods which focus on building heuristic rules or models, the autonomic prediction approach analyzes the nonlinear correlation of failure features by recognizing failure patterns. Failure data are sorted according to the nonlinear correlation and failure signature is proposed for autonomic prediction. In addition, the Manifold Learning algorithm named supervised locally linear embedding is applied to achieve feature extraction. Based on the runtime monitoring of failure metrics, the experimental results indicate that the proposed method has better performance in terms of both correlation recognition precision and feature extraction quality and thus it can be used to design efficient autonomic failure prediction for distributed systems.

The Influence of Chemical Element on Properties of Deformed Steel Bar

In this paper, the relationship between steel and chemical elements is explored. A production record of a steel mill is adopted for two years, which is used as the basic data to standardize the data. Then, according to the correlation coefficient method between the hot-rolled ribs No. 1 and No. 2 hot-rolled ribs, the correlation between the two sets of data is analyzed. The main influencing factors of the hot-rolled ribs properties are obtained qualitatively. Then, the logistic regression method is used. Finally, according to the national standard of Chinese steel, the linear optimization model of Cr and Mn and V elements was established, and the change range of Cr was obtained without affecting the performance of hot rolled ribs.

Night Vision Object Tracking System Using Correlation Aware LSTM-Based Modified Yolo Algorithm

Improved picture quality is critical to the effectiveness of object recog-nition and tracking.The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and different atmospheric conditions,such as mist,fog,dust etc.The pictures then shift in intensity,colour,polarity and consistency.A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient envir-onments.In recent years,target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of computer performance capabilities.However,the iden-tification of objects in the night world also poses further problems because of the distorted backdrop and dim light.The Correlation aware LSTM based YOLO(You Look Only Once)classifier method for exact object recognition and deter-mining its properties under night vision was a major inspiration for this work.In order to create virtual target sets similar to daily environments,we employ night images as inputs;and to obtain high enhanced image using histogram based enhancement and iterative wienerfilter for removing the noise in the image.The process of the feature extraction and feature selection was done for electing the potential features using the Adaptive internal linear embedding(AILE)and uplift linear discriminant analysis(ULDA).The region of interest mask can be segmen-ted using the Recurrent-Phase Level set Segmentation.Finally,we use deep con-volution feature fusion and region of interest pooling to integrate the presently extremely sophisticated quicker Long short term memory based(LSTM)with YOLO method for object tracking system.A range of experimentalfindings demonstrate that our technique achieves high average accuracy with a precision of 99.7%for object detection of SSAN datasets that is considerably more than that of the other standard object detection mechanism.Our approach may therefore satisfy the true demands of night scene target detection applications.We very much believe that our method will help future research.

Analysis and Experiments on Two Linear Discriminant Analysis Methods

Foley-Sammon linear discriminant analysis (FSLDA) and uncorrelated linear discriminant analysis (ULDA) are two well-known kinds of linear discriminant analysis. Both ULDA and FSLDA search the kth discriminant vector in an n-k+1 dimensional subspace, while they are subject to their respective constraints. Evidenced by strict demonstration, it is clear that in essence ULDA vectors are the covariance-orthogonal vectors of the corresponding eigen-equation. So, the algorithms for the covariance-orthogonal vectors are equivalent to the original algorithm of ULDA, which is time-consuming. Also, it is first revealed that the Fisher criterion value of each FSLDA vector must be not less than that of the corresponding ULDA vector by theory analysis. For a discriminant vector, the larger its Fisher criterion value is, the more powerful in discriminability it is. So, for FSLDA vectors, corresponding to larger Fisher criterion values is an advantage. On the other hand, in general any two feature components extracted by FSLDA vectors are statistically correlated with each other, which may make the discriminant vectors set at a disadvantageous position. In contrast to FSLDA vectors, any two feature components extracted by ULDA vectors are statistically uncorrelated with each other. Two experiments on CENPARMI handwritten numeral database and ORL database are performed. The experimental results are consistent with the theory analysis on Fisher criterion values of ULDA vectors and FSLDA vectors. The experiments also show that the equivalent algorithm of ULDA, presented in this paper, is much more efficient than the original algorithm of ULDA, as the theory analysis expects. Moreover, it appears that if there is high statistical correlation between feature components extracted by FSLDA vectors, FSLDA will not perform well, in spite of larger Fisher criterion value owned by every FSLDA vector. However, when the average correlation coefficient of feature components extracted by FSLDA vectors is at a low level, the performance of FSLDA are comparable with ULDA.

基于外观性状和指标性成分划分枸杞子不同产地

目的 对不同产地枸杞子外观性状与其指标性成分进行分析,并探究两者是否存在关联性,为进一步研究枸杞子质量评价提供方法。方法 依据《中华人民共和国药典》2020版规定对枸杞子中甜菜碱、枸杞多糖的含量进行测定;对枸杞子的外观性状进行测量统计,采用逐步判别法、线性判别分析法(LDA)对枸杞子外观性状数据进行筛选并优化;并分析枸杞子外观性状与其指标性成分之间的关联性。结果 宁夏枸杞多糖含量高达3.43%,远高于其他产地。对枸杞子外观性状与内在品质进行关联性分析,发现果皮厚度与甜菜碱含量呈负相关性(r=-0.485,P<0.01);纵横比与枸杞多糖含量(r=0.420,P<0.05)、甜菜碱含量(r=0.815,P<0.01)呈正相关性。结论 不同产地的枸杞子在外观性状及内在指标上均有差异,该研究可为枸杞子的综合质量评价提供参考。

循环纤维细胞在坠积性肺炎中的检测及意义

目的:定量分析循环纤维细胞(CFs)在坠积性肺炎(HP)患者外周血中的含量,探究其与疾病严重程度的相关性及在发病风险预测中的应用价值。方法:选取2023年8月-2023年11月潍坊市人民医院重症医学科新发HP患者49例,另选取同期查体健康对照(CON)人员35名;采用流式细胞术检测外周血CD45+细胞中CFs(CD45+ColⅠ+细胞)比例,分析其与白细胞计数(WBC)、中性粒细胞百分比(NE%)、中性粒细胞绝对值(NE)、C反应蛋白(CRP)等感染指标相关性;构建ROC曲线评估CFs含量在HP辅助诊断中的价值。结果:HP患者中CFs比例(15.81±10.20)%显著高于健康人群(7.54±3.31)%(P<0.0001)。ROC曲线显示,CFs比例对HP具有辅助诊断价值(AUC为:0.746,cutoff值为:11.32%)。Spearman相关性分析显示,CFs比例与WBC(r=-0.3413,P=0.0164)、NE%(r=-0.2881,P=0.0495)、NE(r=-0.3352,P=0.0185)明显负相关。结论:HP患者体内CFs比例上调,具有较好的疾病鉴别价值,与临床感染指标负相关,提示CFs可能在HP临床诊疗中具有重要应用价值。

从线性校验子分析方法浅析曾肯成先生的密码分析思想

曾肯成先生于1986年洞察到了密码体制中的熵漏现象,后来在此基础上提出著名的线性校验子分析方法.本文全面剖析了线性校验子分析方法的发展历程,由此深刻揭示了曾先生的密码分析思想的精髓.首先,介绍曾先生通过观察Geffe序列生成器的熵漏现象,提出线性校验子分析方法的朴素思想的过程及其蕴含的思想方法;其次,介绍曾先生通过在Geffe序列生成器基础上凝练出的一般问题,提出解决这一问题的一般方法—线性校验子分析方法的过程及其蕴含的思想方法;再次,介绍曾先生通过分析线性校验子分析方法存在的缺陷,进一步完善和改进线性校验子分析方法的过程及其蕴含的思想方法;最后,通过分析从相关分析方法到线性校验子分析方法的进阶之路,阐述了线性校验子分析方法这把利剑的威力.

秦岭北麓乡村植物景观与物种丰富度的关系

选取陕西省秦岭北麓5个村落作为试验区,采用多元线性回归分析和熵值法等相结合,探讨了秦岭北麓乡村地区植物丰富度对乡村景观的影响。结果表明:远离城镇的乡村植物多样性单一,物质丰富度偏低,其乡村景观评价指标体系得分偏低。多元线性逐步回归分析结果表明,Pielou均匀度指数与社会效应、美感效果、生态质量之间存在显著正相关,Menhinick丰富度指数与美感效果、生态质量、文化价值之间存在显著正相关。综上,提出了在乡村景观建设过程中,应立足资源优势,选择适合乡村景观发展的优势特色植物,有效增加评价较低乡村近水生境、居民点周边、农田边缘、林地比例和物种丰富度等建议,以期在促进乡村可持续发展的同时维护农村的环境治理,为乡村景观建设中植物多样性研究提供参考。

湖北省温州蜜柑果实化渣性分析与评价

【目的】探究湖北省温州蜜柑果实的化渣性差异及其主要影响因素,为温州蜜柑高品质栽培提供理论基础。【方法】通过感官评价、质构仪检测对温州蜜柑化渣性进行综合评价,并通过相关性分析及多元线性回归分析初步解析温州蜜柑果实化渣性的主要影响因素。【结果】果实横径、纵径、单果质量与剪切力、木质素含量呈显著正相关;化渣度得分与可滴定酸(TA)含量、剪切力、穿刺力呈显著负相关,而剪切力与穿刺力呈显著正相关;木质素含量与果皮厚度和剪切力呈显著正相关,与固酸比(SAR)呈显著负相关;果胶含量与横径、TA、纤维素含量呈显著正相关。利用多元线性回归分析构建了包括可溶性固形物(TSS)含量、穿刺力、木质素含量和果胶含量4个指标且具有统计学意义的感官综合评价的预测模型:Y(化渣度得分)=5.875+0.108×X(TSS)-0.007×X(穿刺力)-0.007×X(木质素含量)+0.044×X(果胶含量)。模型综合口感预测得分与综合口感实际得分基本一致。【结论】基于回归分析建立的综合得分预测模型可实现温州蜜柑果实感官品质的综合评价,质构特征指标和理化成分指标作为客观方法可以较好地弥补感官分析的主观性缺陷,可应用于湖北省温州蜜柑的化渣性评价。

6月龄鲁中肉羊生长性状多元统计分析

为探究鲁中肉羊体尺、体重性状间的关系,测定并收集1 094只(公羊325只,母羊769只)6月龄鲁中肉羊体尺和体重指标数据,对其进行相关性分析、主成分分析和多元线性回归分析。结果表明,公羊和母羊的各体尺指标均与体重呈极显著正相关,各体尺指标之间也均存在极显著的正相关关系。按照累计贡献率在85%以上的标准,公羊选取前3个主成分,分别反映6月龄鲁中肉羊公羊的整体体型结构状况、胸部发育状况和四肢发育状况;母羊选取前2个主成分,分别反映6月龄鲁中肉羊母羊的整体体型结构和四肢发育情况。6月龄鲁中肉羊公羊体重的最佳回归方程为:Y=0.268X_(2)+2.448X_(4)+1.319X_(5)-60.459(R^(2)=0.776,P<0.01),标准化后的最佳回归方程为:Y=0.328X_(2)+0.552X_(4)+0.156X_(5);母羊体重的最佳回归方程为:Y=0.070X_(1)+0.166X_(2)+0.819X_(3)+1.599X_(4)-2.547X_(5)-17.237(R^(2)=0.919,P<0.01),标准化后的最佳回归方程为:Y=0.176X_(1)+0.274X_(2)+0.237X_(3)+0.450X_(4)-0.235X_(5)。本研究结果可为提高鲁中肉羊早期选种选育效率提供理论依据。

北京典型汇水区域雨水径流温度特征及影响因素分析

城市化发展导致不透水地表面积率大幅攀升,由此带来的一系列问题逐渐受到人们关注,夏季城市汇水区域地表产生高温径流后汇入下游受纳水体所造成的雨水径流热污染,对水生态、水环境造成不良影响的风险尤为突出。选取北京市典型汇水区域,对2021—2022年多场降雨径流出流温度进行监测与分析,并对气象因素、下垫面温度及管道内径流热量等数据进行同步采集,运用皮尔逊相关系数法分析其影响因素。结果表明:研究区域夏季降雨常出现雨水径流温度升高现象,降水量小于12.5mm、降雨历时短于250 min的降雨场次更易于升温,升温幅度最高可达4.1℃;径流温度升高往往出现在径流过程初期,温度达峰平均时间为38 min;径流是否升温与降雨强度峰值位置之间没有明显关系;气温、不透水地表初始时刻温度、降雨历时及降水量是雨水径流温度的极显著影响因素(P<0.01);降雨期间气温、降雨历时、不透水地表初始时刻温度和管道内壁温度4个指标,可以基本解释研究区域96.7%的径流温度输出情况。

基于数据挖掘的课程教学成效分析与教学改进研究

随着现代信息技术的发展,教学数据采集已经覆盖线上线下教学的全流程,对教学数据能否进行深入挖掘分析将决定能否有效建立基于数据驱动的现代教学决策方式。该文从机械工程基础课程线上线下教与学的采集数据出发,运用相关系数分析、主成分分析及多元线性回归等多重数据处理和分析方法,对测试成绩的合理性、影响测试成绩的主成分要素的相关性及权重、学业成绩预测方程等进行深入研究,将信息化教学与大数据分析技术进行融合。该文初步建立基于教学数据挖掘的学习成效分析和学业诊断方法,为教学持续改进提供依据和思路,也为进一步建立数据驱动的教学反馈机制和形成个性化教学模式奠定基础。

基于水足迹的黄河流域九省(区)水资源可持续利用评价

为评估黄河流域九省(区)的水资源可持续利用水平,改善水资源状况,实现地区水资源可持续利用,基于水足迹模型提出适合黄河流域九省(区)的水资源可持续利用综合评价指标,并且使用Spearman相关分析法和多元线性回归分析法对黄河流域九省(区)水资源可持续利用水平的驱动因素进行了分析。研究结果表明:2010—2020年黄河流域九省(区)的水资源足迹呈现先增后减的小幅度变化,水环境足迹呈现减小的趋势,水资源足迹和水环境足迹空间分布极不均匀;黄河流域九省(区)整体的水资源可持续利用水平出现了不同程度的上升趋势,上游省(区)的水资源可持续利用水平较高,而中下游省份的水资源可持续利用水平较低。水资源可持续利用水平的主要驱动因素为人口密度和第二产业占比,其次为人均用水量、废水处理率和化肥施用强度。