Connectivity correlations in three topological spaces of urban bus-transport networks in China

In this paper, an empirical investigation is presented, which focuses on unveiling the universality of connectivity correlations in three spaces （the route space, the stop geographical space and bus-transferring space） of urban bustransport networks （BTNs） in four major cities of China. The underlying features of the connectivity correlations are shown in two statistical ways. One is the correlation between the （weighted） average degree of all the nearest neighbouring vertices with degree k, （Knn^w,（k）） Knn（k）, and k, and the other is the correlations between the assortativity coefficient r and, respectively, the network size N, the network diameter D, the averaged clustering coefficient C, and the averaged distance （l）. The obtained results show qualitatively the same connectivity correlations of all the considered cities under all the three spaces.

QQuantitative Correlation between Group Connectivity Index and Property of Flotation Reagent

Topological method is applied firstly to calculate the group connectivity indexes of some flotation reagents for sulfide minerals and oxide minerals. The study reveals that some properties of flotation reagents, such as group electronegativity, energy criterion, solubility product of chemicals and maximum wavelength of ultraviolet absorbency, have linear correlation with the first order group connectivity index (GCI) of polar group, and the related coefficients are all larger than 0.900. The GCI can be used to characterize the structure of groups, and is a sort of new effective structural parameter to study the quantitative structure activity relationship of flotation reagents.

Reliability Distribution of Numerical Control Lathe Based on Correlation Analysis

Combined Reliability distribution with correlation analysis,a new method has been proposed to make Reliability distribution where considering the elements about structure correlation and failure correlation of subsystems.Firstly,we make a sequence for subsystems by means of TOPSIS which comprehends the considerations of Reliability allocation,and introducing a Copula connecting function to set up a distribution model based on structure correlation,failure correlation and target correlation,and then acquiring reliability target area of all subsystems by Matlab.In this method,not only the traditional distribution considerations are concerned,but also correlation influences are involved,to achieve supplementing information and optimizing distribution.

Spatial correlation-based characterization of acoustic emission signal-cloud in a granite sample by a cube clustering approach

To extract more in-depth information of acoustic emission(AE)signal-cloud in rock failure under triaxial compression,the spatial correlation of scattering AE events in a granite sample is effectively described by the cube-cluster model.First,the complete connection of the fracture network is regarded as a critical state.Then,according to the Hoshen-Kopelman(HK)algorithm,the real-time estimation of fracture con-nection is effectively made and a dichotomy between cube size and pore fraction is suggested to solve such a challenge of the one-to-one match between complete connection and cluster size.After,the 3D cube clusters are decomposed into orthogonal layer clusters,which are then transformed into the ellip-soid models.Correspondingly,the anisotropy evolution of fracture network could be visualized by three orthogonal ellipsoids and quantitatively described by aspect ratio.Besides,the other three quantities of centroid axis length,porosity,and fracture angle are analyzed to evaluate the evolution of cube cluster.The result shows the sample dilatancy is strongly correlated to four quantities of aspect ratio,centroid axis length,and porosity as well as fracture angle.Besides,the cube cluster model shows a potential pos-sibility to predict the evolution of fracture angle.So,the cube cluster model provides an in-depth view of spatial correlation to describe the AE signal-cloud.

Investigating connectional characteristics of Motor Cortex network

To understand the connectivity of cerebral cor-tex, especially the spatial and temporal pattern of movement, functional magnetic resonance imaging (fMRI) during subjects performing finger key presses was used to extract functional networks and then investigated their character-istics. Motor cortex networks were constructed with activation areas obtained with statistical analysis as vertexes and correlation coefficients of fMRI time series as linking strength. The equivalent non-motor cortex networks were constructed with certain distance rules. The graphic and dynamical measures of motor cor-tex networks and non-motor cortex networks were calculated, which shows the motor cortex networks are more compact, having higher sta-tistical independence and integration than the non-motor cortex networks. It indicates the motor cortex networks are more appropriate for information diffusion.

计量关联性与市场关联性——近代四川米谷市场量制研究

近代四川米谷市场是一个较为封闭的区域性市场,省内五大米谷流通区域内除了大量基层市场和规模较小的地区性集散点外,还有20余个较大规模的市场,分为消费市场、集散市场、集散兼消费市场三类。近代四川米谷市场所用米斗纷繁复杂,单位量值参差。各类米谷市场计量关联性表现不一:广泛分布于各产米区、主要吸纳周边较小范围内米谷的基层市场包括中心镇集、规模较小的地区性集散点与其上位市场的计量关联性显著;消费市场、集散市场和集散兼消费市场与其上位市场间的计量关联性表现复杂,或强或弱,或有或无。计量关联性的强弱程度受市场关联性影响。当市场关联性增强时,上位市场往往会逐步采用与中心市场单位量值相同或比之更大的计量器具。

主播-品牌关联度对消费者购买意愿的影响

主播作为品牌直播间的“前沿”代言人,在品牌建设中扮演着重要角色,但主播的代言效果并不总是尽如人意,这取决于主播与品牌之间的关联度。目前对于主播-品牌关联度是否会对消费者购买意愿产生影响尚不明晰。文章基于Hovland说服模型,通过问卷调查法探讨主播-品牌关联度对消费者购买意愿的影响以及这一影响发生的内在机制和边界条件。研究结果表明:主播-品牌关联度对消费者购买意愿具有显著正向影响;在此过程中,消费者自我-品牌联结发挥着部分中介作用。此外,由品牌知识的调节效应分析发现,其能够正向调节主播-品牌关联度与消费者自我-品牌联结的关系。这将丰富直播营销对消费者购买意愿影响的相关研究,为商家有针对性地选择主播,提升品牌代言效果提供重要参考。

基于典型相关分析的脑网络研究方法综述

脑网络分析在研究大脑的认知活动、探究大脑的信息处理模式和辅助精神类疾病的诊断等方面都起着重要作用。近年来,基于多变量数据集的脑网络研究方法得到了普遍关注。典型相关分析(CCA)作为一种基于数据驱动的多元统计方法,能够有效捕捉多变量数据间的隐含关系,被广泛地应用于脑网络研究。综述CCA在脑网络研究中的作用、具体应用模式、存在的优势和局限性。首先,对传统的CCA其及常见变体的算法原理进行归纳总结;然后,阐述基于CCA分析方法在脑网络构建、脑网络分析、脑网络标记物识别方面的研究现状;最后,对基于CCA的脑网络研究方法进行总结并探讨未来研究的方向。

基于脑电信号的上肢运动想象与运动执行脑网络的动态功能连接研究

目的:本研究通过记录脑卒中偏瘫患者及健康人进行指定的运动想象(motor imagery,MI)及运动执行时的脑电信号,评估不同任务态初级运动区(motor area,M1)及运动前区(premotor area,PMA)相关节点的功能连接的差异,并探索其与共同运动、分离运动控制之间的关系。方法:共纳入10例脑卒中偏瘫患者及19例健康人,所有受试者根据视觉指令完成运动想象与运动执行任务,采集执行任务时的脑电信号,选取包括M1和PMA上的12个信道数据进行分析。对8-30Hz频带脑电信号进行滤波处理,采用基于改良S变换提取特征,并进行归一化处理避免个体差异,分别对左右手运动想象以及两种运动执行时12个节点的数据进行相关性分析(Pearson相关系数)以明确脑功能连接,并对节点度进行χ^(2)检验观察差异。结果:在脑卒中患者中,各节点关联度在运动想象及运动执行中的分布趋势与健康人类似;与运动执行相比,运动想象时各节点功能连接强度和密度都有升高趋势,与健康人结果类似。健康人在进行左手运动想象时,M1区的C4节点关联度明显高于对侧C3(P<0.05),当右手运动想象时,在左侧PMA区FC3和FC1的功能连接总和明显高于对侧FC4和FC2总和(P<0.05);当进行右上肢分离运动时,C3节点度明显降低(P<0.05)。结论:右手运动想象的功能连接的核心区域在左侧PMA区,且各信道节点关联度高于运动执行,而左手运动想象功能连接分布较平均。共同运动与分离运动模式所激活的主要脑区存在差异。

基于tr-PCBAMSiam的小目标跟踪算法

无人机在目标跟踪过程中,存在分辨率低、运动模糊、目标遮挡、目标密集、相似目标干扰等问题,导致算法跟踪精度下降。针对这一问题,在SiamRPN的基础上提出tr-PCBAMSiam,即一种基于混合注意力的聚合残差连接、transformer互相关运算以及基于无锚框的区域回归网络的目标跟踪算法。将该算法与其他目标跟踪算法在OTB100数据集上进行对比,在精度和成功率方面,与SiamRPN算法相比分别有6.9%和8%的提升;在LaTOT数据集上与SiamRPN相比,精度和成功率分别有13.1%和8.5%的提升。

一种改进的残余力向量法在结构损伤识别中的应用

残余力向量法是结构损伤识别中常用的方法,复杂结构中单元数量较多而损伤位置较少,容易造成无关变量增多,进而导致计算量过大的问题。鉴于此,本文提出一种改进的残余力向量法用于结构的损伤识别。该方法利用刚度联系向量与残余力向量之间线性相关的特性,以向量投影值作为损伤定位的影响系数,初步筛选出结构可能出现损伤的单元范围。在此基础上,构建出残余力向量对应的线性方程组,根据顺序主子式不为零的条件,对线性方程组进行行初等变换,再根据单元的数量保留前n维线性方程,通过求解该方程组的代数解可得到该结构单元的刚度损伤参数。以简支梁为例的数值模拟表明,本文方法可减少无关单元变量的计算,降低残余力向量的维度并且具有较好的抗噪能力。

茂兰保护区华南五针松群落乔木层主要物种的种间亲和性

基于茂兰保护区华南五针松群落调查数据,采用重要值、物种联结指数、Spearman秩相关系数、生态位宽度和生态位重叠等指标,探明了15个主要树种的种间联结性、相互关系及生态位。结果表明:(1)群落总体呈不显著正联结,物种间的相互作用竞争不强烈,形成的种间搭配关系对资源的利用较好;(2)主要物种重要值和生态位宽度值排前5的分别为:华南五针松、化香、乌冈栎、荔波鹅耳枥和青冈栎,在群落中个体数量较多,具有相似的生态习性和资源需求,既是乔木层的优势种和重要种,也是群落演替的主导树种,优势种间具有较高的生态位重叠,存在着激烈的竞争;(3)Spearman秩相关系数显示的105种对中,显示正相关的有66对,负相关有39对,比值为1.692,种间相关性较为松散,各种群趋向于独立分布,有较大的随机性。

CAR、SII在结缔组织病合并肺间质病变中的临床价值的探讨

目的探究C-反应蛋白/白蛋白比值(C-reactive protein/albumin,CAR)、系统免疫炎症指数(systemic immune-inflammation Index,SII)在结缔组织病合并肺间质病变(connective tissue disease-Interstitial lung disease,CTD-ILD)的中的临床价值。方法选择2021年1月—2023年5月本院收治的120例结缔组织病患者作为研究对象,根据是否发生结缔组织病相关性间质性肺疾病进行分组,将63例结缔组织病相关性间质性肺疾病患者作为CTD-ILD组,57例结缔组织病未合并间质性肺疾病的患者作为CTD组。记录两组人口学特征资料、诊断、血液学相关指标、Müller评分、CAR、SII及相关结果,采用t检验、Mann-Whitney U检验、χ^(2)检验、Spearman相关性分析、单因素及多因素Logistics回归分析,绘制受试者工作特征曲线(ROC)探讨CAR、SII对结缔组织病相关性间质性肺疾病的临床价值。结果CTD-ILD组患者的白细胞计数、纤维蛋白原分解产物(FDP)、乳酸脱氢酶(LDH)、铁蛋白(SF)、尿酸(UA)、CAR、SII水平明显高于CTD组,差异均有统计学意(P<0.05或P<0.001),Spearman相关分析显示,CAR与Müller评分呈正相关(r=0.738,P<0.001),SII与Müller评分呈正相关(r=0.832,P<0.001),单因素和多因素Logistics回归分析结果显示,CAR、SII、纤维蛋白原分解产物为CTD-ILD的独立危险因素(OR=1.026,95%CI,1.001~1.052,P=0.043;OR=1.002,95%CI,1.000~1.004,P=0.030;OR=1.220,95%CI,1.008~1.478,P=0.041),ROC分析显示CAR、SII、联合因子对结缔组织病相关性间质性肺疾病的曲线下面积分别为0.803(95%CI 0.722~0.883,P<0.001)、0.738(95%CI 0.650~0.827,P<0.001)、0.802(95%CI 0.722~0.882,P<0.001)。结论CAR、SII与Müller评分有相关性,CAR、SII及联合因子对CTD-ILD具有较高的诊断价值,或许可用于预测CTD-ILD疾病的建议参考指标。

基于FEEMD-GRU-FC模型的滑坡位移预测

水库库区滑坡同时受到降雨和水库水位周期性变化的影响,这会干扰滑坡位移预测模型在该类区域的预测性能。为此,提出了一种针对环境周期性变化特点的FEEMD-GRU-FC(Fast Ensemble Empirical Mode Decomposition-Gated Recurrent Unit-Fully Connected Layer)滑坡位移预测模型。该模型充分考虑位移数据不同频率之间的噪声影响,将位移分解为不同频率的子序列分别预测,采用PCC(Pearson Correlation Coefficient)方法选择影响较大的外部环境因素,添加全连接层完善GRU模型提取环境数据中周期性变化特征的能力,模型结合三峡库区八字门滑坡的实际数据进行模拟仿真,使用不同预测模型和评价指标进行多角度对比验证。实验结果表明:所提模型在八字门滑坡的预测效果要优于其他3种模型,其拟合度达到了98.6%,3种不同误差指标均为最低,证明了该模型可以准确预测库区滑坡位移。研究成果可为防灾减灾工作提供指导。

CT肺容积参数评估CTD-ILD肺功能、病情严重程度的价值分析

目的 分析CT肺容积参数评估结缔组织病相关间质性肺疾病(CTD-ILD)患者肺功能、病情严重程度的价值。方法 选取2020年4月—2022年12月收治的126例CTD-ILD,根据肺间质病变临床严重程度分级分为Ⅰ级组(43例)、Ⅱ级组(45例)、Ⅲ级组(38例),均接受肺功能检查,采用高分辨率CT检查获取CT肺容积参数[正常肺组织容积(NL)、间质纤维化肺容积(ILDV)、全肺总容积(WL)、正常肺组织容积百分比(NL%)、间质纤维化肺容积百分比(ILDV%)],比较各组肺功能指标[肺总量(TLC)、第1秒用力呼气容积(FEV1)、用力肺活量(FVC)、残气量(RV)、第1秒用力呼气容积占预计值百分比(FEV1%)]、GAP等级评分、图像纤维化评分及CT肺容积参数,分析CT肺容积参数与肺功能指标、GAP等级评分、图像纤维化评分的关系,以CT肺容积参数对CTD-ILDⅢ级进行诊断,并分析其与肺功能指标联合检测对CTD-ILDⅢ级的诊断效能。结果 FVC、FEV1、TLC、RV、FEV1%Ⅰ级组>Ⅱ级组>Ⅲ级组,GAP等级评分、国际纤维化评分Ⅰ级组<Ⅱ级组<Ⅲ级组(P<0.05);NL、WL、NL%Ⅰ级组>Ⅱ级组>Ⅲ级组,ILDV、ILDV%Ⅰ级组<Ⅱ级组<Ⅲ级组(P<0.05);NL、WL、NL%与FVC、FEV1、TLC、RV、FEV1%呈正相关,与GAP等级评分、图像纤维化评分呈负相关(P<0.05);ILDV、ILDV%与GAP等级评分、图像纤维化评分呈正相关,与FVC、FEV1、TLC、RV、FEV1%呈负相关(P<0.05);NL、WL、ILDV、NL%、ILDV%诊断CTD-ILDⅢ级的受试者工作特征曲线下面积(AUC)分别为0.765、0.715、0.754、0.789、0.768,NL、WL、ILDV、NL%、ILDV%联合诊断的AUC最大,为0.944。结论 CTD-ILD患者NL、WL、ILDV、NL%、ILDV%与肺功能联系密切,且联合检测对患者病情严重程度有较高的诊断效能。

Alteration of functional connectivity in patients with Alzheimer’s disease revealed by resting-state functional magnetic resonance imaging

The main symptom of patients with Alzheimer’s disease is cognitive dysfunction. Alzheimer’s disease is mainly diagnosed based on changes in brain structure. Functional connectivity reflects the synchrony of functional activities between non-adjacent brain regions, and changes in functional connectivity appear earlier than those in brain structure. In this study, we detected resting-state functional connectivity changes in patients with Alzheimer’s disease to provide reference evidence for disease prediction. Functional magnetic resonance imaging data from patients with Alzheimer’s disease were used to show whether particular white and gray matter areas had certain functional connectivity patterns and if these patterns changed with disease severity. In nine white and corresponding gray matter regions, correlations of normal cognition, early mild cognitive impairment, and late mild cognitive impairment with blood oxygen level-dependent signal time series were detected. Average correlation coefficient analysis indicated functional connectivity patterns between white and gray matter in the resting state of patients with Alzheimer’s disease. Functional connectivity pattern variation correlated with disease severity, with some regions having relatively strong or weak correlations. We found that the correlation coefficients of five regions were 0.3–0.5 in patients with normal cognition and 0–0.2 in those developing Alzheimer’s disease. Moreover, in the other four regions, the range increased to 0.45–0.7 with increasing cognitive impairment. In some white and gray matter areas, there were specific connectivity patterns. Changes in regional white and gray matter connectivity patterns may be used to predict Alzheimer’s disease;however, detailed information on specific connectivity patterns is needed. All study data were obtained from the Alzheimer’s Disease Neuroimaging Initiative Library of the Image and Data Archive Database.

Time-Series Data and Analysis Software of Connected Vehicles

In this study,we developed software for vehicle big data analysis to analyze the time-series data of connected vehicles.We designed two software modules:The rst to derive the Pearson correlation coefcients to analyze the collected data and the second to conduct exploratory data analysis of the collected vehicle data.In particular,we analyzed the dangerous driving patterns of motorists based on the safety standards of the Korea Transportation Safety Authority.We also analyzed seasonal fuel efciency(four seasons)and mileage of vehicles,and identied rapid acceleration,rapid deceleration,sudden stopping(harsh braking),quick starting,sudden left turn,sudden right turn and sudden U-turn driving patterns of vehicles.We implemented the density-based spatial clustering of applications with a noise algorithm for trajectory analysis based on GPS(Global Positioning System)data and designed a long shortterm memory algorithm and an auto-regressive integrated moving average model for time-series data analysis.In this paper,we mainly describe the development environment of the analysis software,the structure and data ow of the overall analysis platform,the conguration of the collected vehicle data,and the various algorithms used in the analysis.Finally,we present illustrative results of our analysis,such as dangerous driving patterns that were detected.

基于机器学习的火驱注采井间连通性研究——以红浅1井区火驱先导试验区为例

判断火驱稠油油藏注采井间连通关系进而计算燃烧前缘位置是火驱开发调整的首要工作,而现有注采关联模型难以表征复杂的火驱注采井间连通情况。以红浅1井区火驱先导试验区为例,建立了适用于火驱的多元热采关联模型。首先对原始动态数据进行了数据预处理;然后利用决策树、随机森林和AdaBoost 3种机器学习算法进行了特征变量的重要性比较,选择计算精度最高的随机森林算法确定出影响火驱产量的3个主特征为含水率、注气量和油压;在此基础上,建立了火驱多元注采关联模型,对典型井组注采井间连通性进行了计算;最后利用生产特征和示踪剂测试对计算结果进行了验证。结果表明,井间连通性计算值符合生产动态分析对生产井状态的认识,示踪剂监测结果也进一步验证了注采连通性的准确性。本文方法准确简便,有助于清晰认识火驱井间状态,并为后续火驱注采调整和产量预测提供决策依据。

祁东煤矿构造控水特征和地下水运移规律

以祁东煤矿为例,通过研究矿井地下水运移规律及其所在区域的构造控水特征,以期为煤矿水害的超前精准治理和区域防治提供借鉴参考。利用构造控水理论结合祁东煤矿及其所在矿区、煤田的地质构造背景,对构造控水的逐级控制作用和构造控水作用方式进行研究,结果如下:祁东煤矿基岩地层形态整体受宿南向斜控制,局部受魏庙断层等构造控制,新生界地层形态亦受到构造的间接控制;在宿南向斜控制下,矿井内或矿井外南部风化后的二叠系煤系砂岩裂隙含水层、太灰、奥灰与四含角度不整合接触;在魏庙断层控制下,在矿井南部采区部分二叠系煤系砂岩裂隙含水层含水层再次露头,与四含角度不整合接触,不整合接触使得含水层间可产生水力联系。总结分析结果,认为:(1)地质构造通过对地层形态的控制,对地下含水层水起到控制作用;(2)地质构造通过控制含水层间的接触,对含水层间的水力联系起到控制作用。为进一步验证含水层间的水力联系,利用水位变化的对比分析和皮尔逊(Person)相关系数,对放水试验期间南部采区四含、太灰和正常水位观测期间四含、太灰、奥灰的钻孔水位观测结果进行分析,得出:(1)南部采区放水试验期间,四含(SQ10-14)与太灰(ST4)水位变化基本同步,相关性极强;(2)正常水位观测期间,四含(SQ10)、太灰(ST4)、奥灰(SO_(2))两两之间水位变化具有极强的相关性,同一含水层内不同观测孔间水位变化的相关性差异较大。证实:受构造控制,在矿井局部,四含与基岩含水层角度不整合接触区域,存在水力联系。利用地下水数值模拟软件Groundwater Model System(GMS)结合参数反演Parameter Estimation(PEST),对四含水位分布和径流规律进行研究,得出:四含水位在-7~-57 m,南部水位比北部水位高,在魏庙断层处,水力梯度较大;四含水径流集中在矿井西南部和中部,西南部整体向北径流,中部以东西向径流为主,四含径流有绕过魏庙断层。

Functional Connectivity Difference Detected in Bilateral Occipital Lobes Between the Abacus Experts and Control Groups: Using Nonlinear Processing Method

Objective:This paper is to investigate functional connectivity differences between the abacus experts and control groups by using nonlinear processing method spatiotemporal Lyapunov exponent.Methods:11 right-handed healthy control children and 12 abacus children were undergone functional magnetic resonance imaging(fMRI).After preprocessing fMRI data with SPM,linear and nonlinear methods for connectivity analysis were both employed.Results:Connectivity differences between the two groups were statistically P<0.05 by the correlation method,while the P value by the nonlinear method were P<0.01.Conclusion:There are significant differences between the two groups in functional connectivity of bilateral occipital lobes.The nonlinear method proposed here seems to be more specific compared with the common linear correlation method.