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Maximal contractions in Boolean algebras

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Science china(Information Sciences) 2012年第9期55卷 2044-2055页

作者： shi huixian & {2. guojun institute of mathematics,shaanxi Normal university,xi’an 710062.china 1. institute of mathematics Shaanxi Normal University Xi’an 710062 China

In the present paper,the concepts of deductive element and maximal contraction are introduced in Boolean algebras,and corresponding theories of consistency and maximal contractions are *** algorithm principle is proposed to compute all maximal contractions for a consistent set with respect to its refutation in Boolean *** is pointed out that the quotient algebra of the first-order language with respect to its provable equivalence relation constitutes a Boolean algebra,and hence the computation of R-contractions for closed formulas in first-order languages can be converted into the one in Boolean algebras proposed in this ***,the concept of basic element is introduced in Boolean algebras,which contributes to the definitions of clause and Horn clause transplanted from logic to a special type of Boolean algebras generated by basic *** is also pointed out that the computation of R-contractions for clauses in the classical propositional logic can be converted into the one in Boolean algebras generated by basic elements proposed in this paper.

关键词： deductive element consistency maximal contraction minimal subtraction basic element clause

Central nervous system efficacy of aumolertinib versus gefitinib in patients with untreated,EGFR-mutated,advanced non-small cell lung cancer:data froma randomized phase III trial(AENEAS)

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Cancer Communications 2024年第9期44卷 1005-1017页

作者： Shun Lu xiaorong Dong Hong Jian Jianhua Chen Gongyan Chen Yuping Sun Yinghua Ji Zipingwang Jianhua shi Junguo Lu Shaoshui Chen Dongqing Lv {2. Zhang Chunling Liu Juan Li xinmin Yu Zhong Lin Zhuang Yu Zhehaiwang Jiuwei Cui xingxiang Xu Jian Fang Jifeng Feng Zhi Xu RuiMa Jie Hu Nong Yang xiangdong Zhou xiaohongWu Chengping Hu Zhihong Zhang You Lu Yanping Hu Liyan Jiang Qiming wang Renhua Guo Jianying Zhou Baolan Li Chunhong Hu Wancheng Tong Helong Zhang LinMa Yuan Chen Zhijun Jie Yu Yao Longzhen Zhang JieWeng Weidong Li Jianping xiong xianwei Ye Jianchun Duan Haihua Yang Meili Sun HongyingWei JiaweiWei Zheyu Zhang QiongWu Department of Medical Oncology Shanghai Chest Hospital Shanghai JiaoTong UniversityShanghaiP.R.China Cancer Center Union Hospital Tongji Medical College Huazhong University of Science and TechnologyWuhanHubeiP.R.China Department of Medical Oncology-Chest Hunan Cancer Hospital&The Affiliated Cancer Hospital of Central South University Xiangya School of MedicineChangshaHunanP.R.China Thoracic OncologyMedicine HarbinMedical University Cancer HospitalHarbinHeilongjiangP.R.China Department of Oncology Jinan Central HospitalJinanShandongP.R.China Department of Oncology First Affiliated Hospital of Xinxiang Medical UniversityXinxiangHenanP.R.China Department of Chest Medicine Beijing Cancer HospitalBeijingP.R.China Department of Internal Medicine Linyi Cancer HospitalLinyiShandongP.R.China Department of Respiratory Medicine Nantong Tumor HospitalNantongJiangsuP.R.China Department of Oncology Binzhou Medical University HospitalBinzhouShandongP.R.China Breath Internal Medicine Taizhou Hospital of Zhejiang ProvinceLinhaiZhejiangP.R.China Department of Respiration The First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanP.R.China Department of Lung Internal Medicine Affiliated Tumor Hospital of Xinjiang Medical UniversityUrumqiXinjiangP.R.China Department of Thoracic Oncology Sichuan Cancer HospitalChengduSichuanP.R.China Thoracic Medical Oncology Cancer Hospital of the University of Chinese Academy of Sciences(Zhejiang Cancer Hospital)HangzhouZhejiangP.R.China Department of Thoracic Oncology The Fifth Subsidiary Sun Yat-sen University HospitalZhuhaiGuangdongP.R.China Department of Oncology The Affiliated Hospital of QingdaoUniversityQingdaoShandongP.R.China Department of Oncology Shandong Cancer HospitalJinanShandongP.R.China Department of Oncology The First BethuneHospital of Jilin UniversityChangchunJilinP.R.China Department of Respiration Northern Jiangsu People’s Hospital Affiliated to Yangzhou UniversityYangzhouJiangsuP.R.China Department of Internal

Background:The initial randomized,double-blinded,actively controlled,phase III ANEAS study(NCT03849768)demonstrated that aumolertinib showed superior efficacy relative to gefitinib as first-line therapy in epidermal growth factor receptor(EGFR)-mutated advanced non-small cell lung cancer(NSCLC).Metastatic disease in the central nervous system(CNS)remains a challenge in the management of *** study aimed to compare the efficacy of aumolertinib versus gefitinib among patients with baseline CNS metastases in the ANEAS ***:Eligible patients were enrolled and randomly assigned in a 1:1 ratio to orally receive either aumolertinib or gefitinib in a double-blinded *** with asymptomatic,stable CNS metastases were ***-up imaging of the same modality as the initial CNS imaging was performed every 6 weeks for 15 months,then every *** responsewas assessed by a neuroradiological blinded,independent central review(neuroradiological-BICR).The primary endpoint for this subgroup analysis was CNS progression-free survival(PFS).Results:Of the 42. patients enrolled and randomized in the ANEAS study,106 patients were found to have CNS metastases(CNS Full Analysis Set,cFAS)at baseline by neuroradiological-BICR,and 60 of them had CNS target lesions(CNS Evaluable for Response,cEFR).Treatment with aumolertinib significantly prolonged median CNS PFS compared with gefitinib in both cFAS(2..0 vs.8.3 months;hazard ratio[HR]=0.31;95%confidence interval[CI],0.17-0.56;P1)and cEFR(2..0 vs.8.3 months;HR=0.2.;95%CI,0.11-0.57;P1).The confirmed CNS overall response rate in cEFRwas 85.7%and 75.0%in patients treated with aumolertinib and gefitinib,*** risk analysis showed that the estimated probability of CNS progression without prior non-CNS progression or death was consistently lower with aumolertinib than with gefitinib in patients with and without CNSmetastases at *** new safety findings were ***:The

关键词： aumolertinib brain metastasis non-small cell lung cancer third-generation EGFR-TKI

Boosting electrocatalytic selectivity in carbon dioxide reduction:The fundamental role of dispersing gold nanoparticles on silicon nanowires

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Chinese Chemical Letters 2022年第9期33卷 4380-4384页

作者： Fan Liao xing Fan huixian shi Qing Li Mengjie Ma Wenxiang Zhu Haiping Lin Youyong Li Ming{2. Shao institute of Functional Nano&Soft Materials(FUNSOM) Jiangsu Key Laboratory for Carbon-Based Functional Materials&DevicesSoochow UniversitySuzhou 215123China School of Physics and Information Technology Shaanxi Normal UniversityXi'an 710062China

Carbon dioxide electrochemical reduction(CO_(2.RR)has been recognized as an efficient way to mitigate CO_(2.emissions and alleviate the pressure on global warming and associated environmental ***(Au)is reported as stable and active electrocatalysts to convert CO_(2.to CO at low overpotential due to its moderate adsorption strength of^(*)COOH and^(*)*** request for improved catalytic performance,however,is motivated by current unsatisfied catalytic selectivity because of the side hydrogen evolution *** this context,the design of Au based binary catalysts that can boost CO selectivity is of great *** the present work,we report that Au nanoparticles can be feasibly dispersed and anchored on silicon nanowires to form Au-Si binary *** Au-Si may stably drive CO_(2.RR with a CO Faraday efficiency of 95.6%at−0.6 V *** in 0.5 mol/L KHCO_(3)*** selectivity outperforms Au particles by up to 61%.Controlled experiments illustrate that such catalytic enhancement can chiefly be ascribed to electronic effects of binary *** calculations reveal that spontaneously produced silicon oxide may not only inhibit hydrogen evolution reaction,but also stabilize the key intermediate^(*)COOH in CO formation.

关键词： Gold nanoparticles Silicon nanowires Electrocatalysis Carbon dioxide reduction Noble metals

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基于RNN-FCNN的多尺度油水层识别方法

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高技术通讯 2020年第3期30卷 305-313页

作者：胡家琦孙连山石敏朱登明李国军陕西科技大学电子信息与人工智能学院西安710021 华北电力大学控制与计算机工程学院北京102206 中国科学院计算技术研究所北京100190 中国石油集团测井有限公司西安710065

对油藏开采领域而言,快速、准确地识别油水层有利于节省大量的人力物力,提高不可再生资源的开采率。现有的层位识别方法未能考虑测井数据的序列关系,并且对所有层位进行统一识别,导致物性相近的层位识别易混淆,识别效果存在局限性。针... 详细信息

对油藏开采领域而言,快速、准确地识别油水层有利于节省大量的人力物力,提高不可再生资源的开采率。现有的层位识别方法未能考虑测井数据的序列关系,并且对所有层位进行统一识别,导致物性相近的层位识别易混淆,识别效果存在局限性。针对测井数据的特点,本文提出基于循环神经网络(RNN)和全连接神经网络(FCNN)的多尺度油水层识别方法。该方法首先基于RNN建立粗粒度的识别模型,再通过串联FCNN的方式实现更细粒度的层位识别,不仅考虑了测井数据在空间上的关联性,同时以多尺度方法识别易混淆的层位。解决了测井数据特征提取困难、层位识别率低的问题。本文在真实测井数据上进行了实验验证,实验结果表明本文方法油水层识别效果良好,有较强的实用性。

关键词：深度学习测井数据空间关联性多尺度油水层识别

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