Deep Web技术使得大量隐藏在接口背后的有用信息更容易被用户查找到.然而,随着数据源的增多,如何从众多的数据源中快速地找到合适的结果这一问题变得越来越重要.通过传统的链接分析方法和相关性评估方法来对数据源进行排序,已经不能满...Deep Web技术使得大量隐藏在接口背后的有用信息更容易被用户查找到.然而,随着数据源的增多,如何从众多的数据源中快速地找到合适的结果这一问题变得越来越重要.通过传统的链接分析方法和相关性评估方法来对数据源进行排序,已经不能满足高精度的要求.提出一种通过抽样方法和数据质量评估来判断数据源的优劣性的算法.本文提出的抽样方法,改进了分层抽样和雪球抽样,使得在较少的样本点时,能够准确的反映整体特征.定义了能基本反映数据源的优劣程度的6个主要质量标准,并给出计算方法;通过质量标准,结合权重向量来量化数据源的质量.实验通过对数据源进行抽样分析,求解数据源得分的期望值,并根据该期望值对数据源进行了整体排序.结果表明,利用抽样对数据源的数据质量进行估计和评分,具有很好的准确性和可操作性.展开更多
Indoor air quality becomes increasingly important,partly because the COVID-19 pandemic increases the time people spend indoors.Research into the prediction of indoor volatile organic compounds(VOCs)is traditionally co...Indoor air quality becomes increasingly important,partly because the COVID-19 pandemic increases the time people spend indoors.Research into the prediction of indoor volatile organic compounds(VOCs)is traditionally confined to building materials and furniture.Relatively little research focuses on estimation of human-related VOCs,which have been shown to contribute significantly to indoor air quality,especially in densely-occupied environments.This study applies a machine learning approach to accurately estimate the human-related VOC emissions in a university classroom.The time-resolved concentrations of two typical human-related(ozone-related)VOCs in the classroom over a five-day period were analyzed,i.e.,6-methyl-5-hepten-2-one(6-MHO),4-oxopentanal(4-OPA).By comparing the results for 6-MHO concentration predicted via five machine learning approaches including the random forest regression(RFR),adaptive boosting(Adaboost),gradient boosting regression tree(GBRT),extreme gradient boosting(XGboost),and least squares support vector machine(LSSVM),we find that the LSSVM approach achieves the best performance,by using multi-feature parameters(number of occupants,ozone concentration,temperature,relative humidity)as the input.The LSSVM approach is then used to predict the 4-OPA concentration,with mean absolute percentage error(MAPE)less than 5%,indicating high accuracy.By combining the LSSVM with a kernel density estimation(KDE)method,we further establish an interval prediction model,which can provide uncertainty information and viable option for decision-makers.The machine learning approach in this study can easily incorporate the impact of various factors on VOC emission behaviors,making it especially suitable for concentration prediction and exposure assessment in realistic indoor settings.展开更多
背景流行病学调查发现，15％～29％的白内障患者术前存在≥1．5D的角膜散光，角膜散光矫正已成为白内障屈光手术的重要部分。Toric人工晶状体（IOL）植入术是矫正角膜散光的一种新方法。Acry Sof Toric IOL矫正具有角膜散光的白内障患...背景流行病学调查发现，15％～29％的白内障患者术前存在≥1．5D的角膜散光，角膜散光矫正已成为白内障屈光手术的重要部分。Toric人工晶状体（IOL）植入术是矫正角膜散光的一种新方法。Acry Sof Toric IOL矫正具有角膜散光的白内障患者的早期临床研究显示了其良好的稳定性，但是其远期临床疗效评价文献较少见。目的观察术前合并角膜散光的白内障患者植入Toric IOL术后的长期效果。方法采用前瞻性系列病例观察临床试验设计。选择行超声乳化白内障摘出联合IOL植入的患者78例120眼，术前角膜规则散光度为≥1．0 D°植入Toric IOL Acry Sof SN60TT，术后随访2年。评价术后1d，1、3、6个月，1年、2年的裸眼视力和最佳矫正远视力（BCDVA）、残余散光度、散光矢量、散光矫正准确性及散光IOL旋转度。结果共67例患者101眼完成2年的随访，患者19例19眼术后1个月因合并其他疾病行动不便而失访。患者植入Acry Sof Toric IOL术后2年裸眼视力（10gMAR）为0．16（0．20），BCDVA为0（0．1），残余散光-0．75（0．5）D，IOL轴位旋转2．87°±1．78°，眼内散光矫正的矢量大小为（1．2±0．6）D，散光矫正指数（cI）为0．90±0．41，术后2年术眼的实际矫正矢量（SIA）和目标矫正矢量（TIA）呈正相关（r=0．740，P=0．000）。与术后1、3、6个月，1年的检查结果比较，术后2年时患者的视力、残余散光和IOL的旋转度有轻度增大的趋势，cI减小，但差异均无统计学意义（P〉0．05）。结论Acry Sof Toric IOL植入术后2年仍具有良好的旋转稳定性，这种IOL通过眼内散光弥补了角膜的散光，具有良好的矫正准确性，患者获得较好的裸眼远视力。AcrySof Toric IOL植入术具有长期的准确性和稳定性。展开更多
基金supported by the National Natural Science Foundation of China (No.52178062)the Alfred P.Sloan Foundation (No.G-2016-7050)the Opening Fund of State Key Laboratory of Green Building in Western China (LSKF202311).
文摘Indoor air quality becomes increasingly important,partly because the COVID-19 pandemic increases the time people spend indoors.Research into the prediction of indoor volatile organic compounds(VOCs)is traditionally confined to building materials and furniture.Relatively little research focuses on estimation of human-related VOCs,which have been shown to contribute significantly to indoor air quality,especially in densely-occupied environments.This study applies a machine learning approach to accurately estimate the human-related VOC emissions in a university classroom.The time-resolved concentrations of two typical human-related(ozone-related)VOCs in the classroom over a five-day period were analyzed,i.e.,6-methyl-5-hepten-2-one(6-MHO),4-oxopentanal(4-OPA).By comparing the results for 6-MHO concentration predicted via five machine learning approaches including the random forest regression(RFR),adaptive boosting(Adaboost),gradient boosting regression tree(GBRT),extreme gradient boosting(XGboost),and least squares support vector machine(LSSVM),we find that the LSSVM approach achieves the best performance,by using multi-feature parameters(number of occupants,ozone concentration,temperature,relative humidity)as the input.The LSSVM approach is then used to predict the 4-OPA concentration,with mean absolute percentage error(MAPE)less than 5%,indicating high accuracy.By combining the LSSVM with a kernel density estimation(KDE)method,we further establish an interval prediction model,which can provide uncertainty information and viable option for decision-makers.The machine learning approach in this study can easily incorporate the impact of various factors on VOC emission behaviors,making it especially suitable for concentration prediction and exposure assessment in realistic indoor settings.
文摘背景流行病学调查发现，15％～29％的白内障患者术前存在≥1．5D的角膜散光，角膜散光矫正已成为白内障屈光手术的重要部分。Toric人工晶状体（IOL）植入术是矫正角膜散光的一种新方法。Acry Sof Toric IOL矫正具有角膜散光的白内障患者的早期临床研究显示了其良好的稳定性，但是其远期临床疗效评价文献较少见。目的观察术前合并角膜散光的白内障患者植入Toric IOL术后的长期效果。方法采用前瞻性系列病例观察临床试验设计。选择行超声乳化白内障摘出联合IOL植入的患者78例120眼，术前角膜规则散光度为≥1．0 D°植入Toric IOL Acry Sof SN60TT，术后随访2年。评价术后1d，1、3、6个月，1年、2年的裸眼视力和最佳矫正远视力（BCDVA）、残余散光度、散光矢量、散光矫正准确性及散光IOL旋转度。结果共67例患者101眼完成2年的随访，患者19例19眼术后1个月因合并其他疾病行动不便而失访。患者植入Acry Sof Toric IOL术后2年裸眼视力（10gMAR）为0．16（0．20），BCDVA为0（0．1），残余散光-0．75（0．5）D，IOL轴位旋转2．87°±1．78°，眼内散光矫正的矢量大小为（1．2±0．6）D，散光矫正指数（cI）为0．90±0．41，术后2年术眼的实际矫正矢量（SIA）和目标矫正矢量（TIA）呈正相关（r=0．740，P=0．000）。与术后1、3、6个月，1年的检查结果比较，术后2年时患者的视力、残余散光和IOL的旋转度有轻度增大的趋势，cI减小，但差异均无统计学意义（P〉0．05）。结论Acry Sof Toric IOL植入术后2年仍具有良好的旋转稳定性，这种IOL通过眼内散光弥补了角膜的散光，具有良好的矫正准确性，患者获得较好的裸眼远视力。AcrySof Toric IOL植入术具有长期的准确性和稳定性。
