目的探讨在阈下和阈上刺激条件下,癌症复发恐惧的乳腺癌患者对癌症相关刺激的注意偏向特点。方法2022年09月至2023年03月在陆军军医大学第一附属医院招募94名女性乳腺癌患者完成癌症复发恐惧量表简版(fear of cancer recurrence invento...目的探讨在阈下和阈上刺激条件下,癌症复发恐惧的乳腺癌患者对癌症相关刺激的注意偏向特点。方法2022年09月至2023年03月在陆军军医大学第一附属医院招募94名女性乳腺癌患者完成癌症复发恐惧量表简版(fear of cancer recurrence inventory-short form,FCRI-SF)及点探测任务。以FCRI-SF划界分(13分)将乳腺癌患者分为临床和非临床癌症复发恐惧组,每组招募患者47人。采用单样本t检验及重复测量方差分析对两组被试在注意偏向分数、注意定向分数及注意脱离困难分数上进行统计分析。结果临床癌症复发恐惧的乳腺癌患者对癌症相关负性词语有显著注意偏向(P<0.05)。在阈下刺激条件下,其主要成分为对癌症相关负性词语注意定向(P<0.05)。在阈上刺激条件下,其主要成分为对癌症相关负性词语所匹配的中性词语注意定向,且对癌症相关负性词语注意脱离困难以及对癌症相关正性词语注意回避(P<0.05)。结论临床癌症复发恐惧的乳腺癌患者存在对癌症相关负性刺激的注意偏向。减少对癌症相关负性刺激的关注可能是降低乳腺癌患者癌症复发恐惧的有效方法。展开更多
针对BIM(Building Information Modeling)软件在数字化设计阶段存在几何引擎开放程度低、架构不清晰、建模流程冗余及信息安全无法保证等问题,基于国产BIMBase图形引擎,使用C++语言对BIM平台进行二次开发,研发了一种铁路隧道轨下结构BI...针对BIM(Building Information Modeling)软件在数字化设计阶段存在几何引擎开放程度低、架构不清晰、建模流程冗余及信息安全无法保证等问题,基于国产BIMBase图形引擎,使用C++语言对BIM平台进行二次开发,研发了一种铁路隧道轨下结构BIM设计软件。在数据结构模块,以轨下结构几何语义分析和参数化建模算法研究为基础,结合国产图形引擎参数化组件管理机制,构建了继承自BP图形元素(BPGraphicElement)的轨下结构类;在参数化组件模块,通过重写抽象基类的重要虚函数,实现了组件的三维建模、属性管理、移动复制等功能;在交互式设计模块,解析本地XML文件,自动为组件横断面、扫掠路径属性赋值,完成BIM正向设计。该软件有效实现了国产化BIM设计,且为基于BIMBase/C++二次开发的BIM技术研究提供了基本思路和方法。展开更多
Timely identification and forecast of maize tasseling date(TD)are very important for agronomic management,yield prediction,and crop phenotype estimation.Remote sensing-based phenology monitoring has mostly relied on t...Timely identification and forecast of maize tasseling date(TD)are very important for agronomic management,yield prediction,and crop phenotype estimation.Remote sensing-based phenology monitoring has mostly relied on time series spectral index data of the complete growth season.A recent development in maize phenology detection research is to use canopy height(CH)data instead of spectral indices,but its robustness in multiple treatments and stages has not been confirmed.Meanwhile,because data of a complete growth season are needed,the need for timely in-season TD identification remains unmet.This study proposed an approach to timely identify and forecast the maize TD.We obtained RGB and light detection and ranging(Li DAR)data using the unmanned aerial vehicle platform over plots of different maize varieties under multiple treatments.After CH estimation,the feature points(inflection point)from the Logistic curve of the CH time series were extracted as TD.We examined the impact of various independent variables(day of year vs.accumulated growing degree days(AGDD)),sensors(RGB and Li DAR),time series denoise methods,different feature points,and temporal resolution on TD identification.Lastly,we used early CH time series data to predict height growth and further forecast TD.The results showed that using the 99th percentile of plot scale digital surface model and the minimum digital terrain model from Li DAR to estimate maize CH was the most stable across treatments and stages(R~2:0.928 to0.943).For TD identification,the best performance was achieved by using Li DAR data with AGDD as the independent variable,combined with the knee point method,resulting in RMSE of 2.95 d.The high accuracy was maintained at temporal resolutions as coarse as 14 d.TD forecast got more accurate as the CH time series extended.The optimal timing for forecasting TD was when the CH exceeded half of its maximum.Using only Li DAR CH data below 1.6 m and empirical growth rate estimates,the forecasted TD showed an RMSE of 3.90 d.In conclusion,this study exploited the growth characteristics of maize height to provide a practical approach for the timely identification and forecast of maize TD.展开更多
文摘目的探讨在阈下和阈上刺激条件下,癌症复发恐惧的乳腺癌患者对癌症相关刺激的注意偏向特点。方法2022年09月至2023年03月在陆军军医大学第一附属医院招募94名女性乳腺癌患者完成癌症复发恐惧量表简版(fear of cancer recurrence inventory-short form,FCRI-SF)及点探测任务。以FCRI-SF划界分(13分)将乳腺癌患者分为临床和非临床癌症复发恐惧组,每组招募患者47人。采用单样本t检验及重复测量方差分析对两组被试在注意偏向分数、注意定向分数及注意脱离困难分数上进行统计分析。结果临床癌症复发恐惧的乳腺癌患者对癌症相关负性词语有显著注意偏向(P<0.05)。在阈下刺激条件下,其主要成分为对癌症相关负性词语注意定向(P<0.05)。在阈上刺激条件下,其主要成分为对癌症相关负性词语所匹配的中性词语注意定向,且对癌症相关负性词语注意脱离困难以及对癌症相关正性词语注意回避(P<0.05)。结论临床癌症复发恐惧的乳腺癌患者存在对癌症相关负性刺激的注意偏向。减少对癌症相关负性刺激的关注可能是降低乳腺癌患者癌症复发恐惧的有效方法。
文摘针对BIM(Building Information Modeling)软件在数字化设计阶段存在几何引擎开放程度低、架构不清晰、建模流程冗余及信息安全无法保证等问题,基于国产BIMBase图形引擎,使用C++语言对BIM平台进行二次开发,研发了一种铁路隧道轨下结构BIM设计软件。在数据结构模块,以轨下结构几何语义分析和参数化建模算法研究为基础,结合国产图形引擎参数化组件管理机制,构建了继承自BP图形元素(BPGraphicElement)的轨下结构类;在参数化组件模块,通过重写抽象基类的重要虚函数,实现了组件的三维建模、属性管理、移动复制等功能;在交互式设计模块,解析本地XML文件,自动为组件横断面、扫掠路径属性赋值,完成BIM正向设计。该软件有效实现了国产化BIM设计,且为基于BIMBase/C++二次开发的BIM技术研究提供了基本思路和方法。
基金supported by National Science and Technology Major Project(2022ZD0115701)Nanfan Special Project,CAAS(YBXM2305,YBXM2401,YBXM2402,PTXM2402)+1 种基金National Natural Science Foundation of China(42071426,42301427)the Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences。
文摘Timely identification and forecast of maize tasseling date(TD)are very important for agronomic management,yield prediction,and crop phenotype estimation.Remote sensing-based phenology monitoring has mostly relied on time series spectral index data of the complete growth season.A recent development in maize phenology detection research is to use canopy height(CH)data instead of spectral indices,but its robustness in multiple treatments and stages has not been confirmed.Meanwhile,because data of a complete growth season are needed,the need for timely in-season TD identification remains unmet.This study proposed an approach to timely identify and forecast the maize TD.We obtained RGB and light detection and ranging(Li DAR)data using the unmanned aerial vehicle platform over plots of different maize varieties under multiple treatments.After CH estimation,the feature points(inflection point)from the Logistic curve of the CH time series were extracted as TD.We examined the impact of various independent variables(day of year vs.accumulated growing degree days(AGDD)),sensors(RGB and Li DAR),time series denoise methods,different feature points,and temporal resolution on TD identification.Lastly,we used early CH time series data to predict height growth and further forecast TD.The results showed that using the 99th percentile of plot scale digital surface model and the minimum digital terrain model from Li DAR to estimate maize CH was the most stable across treatments and stages(R~2:0.928 to0.943).For TD identification,the best performance was achieved by using Li DAR data with AGDD as the independent variable,combined with the knee point method,resulting in RMSE of 2.95 d.The high accuracy was maintained at temporal resolutions as coarse as 14 d.TD forecast got more accurate as the CH time series extended.The optimal timing for forecasting TD was when the CH exceeded half of its maximum.Using only Li DAR CH data below 1.6 m and empirical growth rate estimates,the forecasted TD showed an RMSE of 3.90 d.In conclusion,this study exploited the growth characteristics of maize height to provide a practical approach for the timely identification and forecast of maize TD.
