目的采用网状Meta分析系统评价冻融胚胎移植(FET)前不同内膜准备方案对子宫内膜异位症(EMs)患者妊娠结局的影响。方法检索中国知网(CNKI)、维普、万方、PubMed、Embase、Web of Science、Cochrane Library等数据库关于EMs患者行FET前采...目的采用网状Meta分析系统评价冻融胚胎移植(FET)前不同内膜准备方案对子宫内膜异位症(EMs)患者妊娠结局的影响。方法检索中国知网(CNKI)、维普、万方、PubMed、Embase、Web of Science、Cochrane Library等数据库关于EMs患者行FET前采用不同内膜准备方案与妊娠结局关联的研究,检索时间自建库至2025年2月。由2名评价员按Cochrane手册标准独立对文献资料进行提取,应用Jadad量表和纽卡斯尔-渥太华量表分别进行质量评价。采用Stata SE 16.0软件进行一致性检验与网络证据图绘制。结果最终纳入文献41篇,涉及到的内膜准备方案共14种。网状Meta分析结果显示,应用不同内膜准备方案EMs患者FET周期临床妊娠率的高低顺序依次为:提前降调节2个周期以上联合激素替代周期>卵泡期长效降调节联合诱导排卵周期>提前降调节2个周期联合激素替代周期>提前降调节2个周期以上>卵泡期长效降调节联合激素替代周期>提前降调节2个周期>黄体期长效降调节>诱导排卵周期>卵泡期长效降调节>激素替代周期>黄体期短效降调节>自然周期>促性腺激素释放激素拮抗剂>卵泡期短效降调节。结论从EMs患者FET周期的临床妊娠率来看,最优的子宫内膜准备方案是提前降调节2个周期以上联合激素替代周期,其次是卵泡期长效降调节联合诱导排卵周期,再次是提前降调节2个周期联合激素替代周期,而卵泡期短效降调节方案的效果相对较差。展开更多
Accurate prediction of manufacturing carbon emissions is of great significance for subsequent low-carbon optimization.To improve the accuracy of carbon emission prediction with insufficient hobbing data,combining the ...Accurate prediction of manufacturing carbon emissions is of great significance for subsequent low-carbon optimization.To improve the accuracy of carbon emission prediction with insufficient hobbing data,combining the advantages of improved algorithm and supplementary data,a method of carbon emission prediction of hobbing based on cross-process data fusion was proposed.Firstly,we analyzed the similarity of machining process and manufacturing characteristics and selected milling data as the fusion material for hobbing data.Then,the adversarial learning was used to reduce the difference between data from the two processes,so as to realize the data fusion at the characteristic level.After that,based on Meta-Transfer Learning method,the carbon emission prediction model of hobbing was established.The effectiveness and superiority of the proposed method were verified by case analysis and comparison.The prediction accuracy of the proposed method is better than other methods across different data sizes.展开更多
目的系统整合体外受精-胚胎移植(in vitro fertilization and embryo transfer,IVF-ET)治疗夫妻的心理体验与调适,为夫妻二元健康管理提供证据。方法计算机检索Cochrane Library、PubMed、Web of Science、CINAHL、Embase、Scopus、Oiv...目的系统整合体外受精-胚胎移植(in vitro fertilization and embryo transfer,IVF-ET)治疗夫妻的心理体验与调适,为夫妻二元健康管理提供证据。方法计算机检索Cochrane Library、PubMed、Web of Science、CINAHL、Embase、Scopus、Oivd、中国知网、万方和维普数据库中关于IVF-ET治疗夫妻心理体验与调适的质性研究,检索时限为建库至2023年12月31日,按照澳大利亚乔安娜布里格斯研究所(Joanna Briggs Institute,JBI)循证卫生保健中心质性研究质量评价标准进行文献方法学质量评价,采用汇集性整合的方法对结果进行Meta整合。结果纳入15篇文献,提炼出31个结果,归纳为9个类别,形成4个整合结果:获得的积极感受和社会支持、面临的身心压力和社会挑战、夫妻采取多种方式应对治疗压力、夫妻关系经过调适发生变化。结论加强以夫妻二元为中心的IVF-ET治疗全周期健康管理,重视夫妻治疗不同阶段的心理体验,提升夫妻积极应对与调适,完善社会文化和医疗支持体系,进而改善治疗结局和夫妻婚姻质量。展开更多
大量未标注且杂乱的评论是当下情感分类任务面临的难题。文章提出了基于语义的特征迁移策略,对不同领域间的语句进行语义相关度和语义相似度度量,并量化排序,从而将源领域特征项的类别指示作用迁移至目标领域。此外,还提出了语句情感空...大量未标注且杂乱的评论是当下情感分类任务面临的难题。文章提出了基于语义的特征迁移策略,对不同领域间的语句进行语义相关度和语义相似度度量,并量化排序,从而将源领域特征项的类别指示作用迁移至目标领域。此外,还提出了语句情感空间向量元模型(Sentence Emotion Space Vector Meta Model,SESVMM),实验表明该方法具有可行性和优越性。展开更多
文摘目的采用网状Meta分析系统评价冻融胚胎移植(FET)前不同内膜准备方案对子宫内膜异位症(EMs)患者妊娠结局的影响。方法检索中国知网(CNKI)、维普、万方、PubMed、Embase、Web of Science、Cochrane Library等数据库关于EMs患者行FET前采用不同内膜准备方案与妊娠结局关联的研究,检索时间自建库至2025年2月。由2名评价员按Cochrane手册标准独立对文献资料进行提取,应用Jadad量表和纽卡斯尔-渥太华量表分别进行质量评价。采用Stata SE 16.0软件进行一致性检验与网络证据图绘制。结果最终纳入文献41篇,涉及到的内膜准备方案共14种。网状Meta分析结果显示,应用不同内膜准备方案EMs患者FET周期临床妊娠率的高低顺序依次为:提前降调节2个周期以上联合激素替代周期>卵泡期长效降调节联合诱导排卵周期>提前降调节2个周期联合激素替代周期>提前降调节2个周期以上>卵泡期长效降调节联合激素替代周期>提前降调节2个周期>黄体期长效降调节>诱导排卵周期>卵泡期长效降调节>激素替代周期>黄体期短效降调节>自然周期>促性腺激素释放激素拮抗剂>卵泡期短效降调节。结论从EMs患者FET周期的临床妊娠率来看,最优的子宫内膜准备方案是提前降调节2个周期以上联合激素替代周期,其次是卵泡期长效降调节联合诱导排卵周期,再次是提前降调节2个周期联合激素替代周期,而卵泡期短效降调节方案的效果相对较差。
基金Supported by National Natural Science Foundation of China(Grant No.52005062)Chongqing Municipal Natural Science Foundation of China(Grant No.CSTB2023NSCQ-MSX0390)。
文摘Accurate prediction of manufacturing carbon emissions is of great significance for subsequent low-carbon optimization.To improve the accuracy of carbon emission prediction with insufficient hobbing data,combining the advantages of improved algorithm and supplementary data,a method of carbon emission prediction of hobbing based on cross-process data fusion was proposed.Firstly,we analyzed the similarity of machining process and manufacturing characteristics and selected milling data as the fusion material for hobbing data.Then,the adversarial learning was used to reduce the difference between data from the two processes,so as to realize the data fusion at the characteristic level.After that,based on Meta-Transfer Learning method,the carbon emission prediction model of hobbing was established.The effectiveness and superiority of the proposed method were verified by case analysis and comparison.The prediction accuracy of the proposed method is better than other methods across different data sizes.
文摘大量未标注且杂乱的评论是当下情感分类任务面临的难题。文章提出了基于语义的特征迁移策略,对不同领域间的语句进行语义相关度和语义相似度度量,并量化排序,从而将源领域特征项的类别指示作用迁移至目标领域。此外,还提出了语句情感空间向量元模型(Sentence Emotion Space Vector Meta Model,SESVMM),实验表明该方法具有可行性和优越性。
