1 IntroductionNowadays in China, there are more than six hundred million netizens [1]. On April 11, 2015, the nmnbet of simultaneous online users of the Chinese instant message application QQ reached two hundred milli...1 IntroductionNowadays in China, there are more than six hundred million netizens [1]. On April 11, 2015, the nmnbet of simultaneous online users of the Chinese instant message application QQ reached two hundred million [2]. The fast growth ol the lnternet pusnes me rapid development of information technology （IT） and communication technology （CT）. Many traditional IT service and CT equipment providers are facing the fusion of IT and CT in the age of digital transformation, and heading toward ICT enterprises. Large global ICT enterprises, such as Apple, Google, Microsoft, Amazon, Verizon, and AT＆T, have been contributing to the performance improvement of IT service and CT equipment.展开更多
In this work we present an analysis of a search for charged Higgs boson in the context of Two Doublet Higgs Model (2HDM) which is an extension of the Standard Model of particles physics where the 2HDM predicts by exis...In this work we present an analysis of a search for charged Higgs boson in the context of Two Doublet Higgs Model (2HDM) which is an extension of the Standard Model of particles physics where the 2HDM predicts by existence scalar sector with new five Higgs bosons;two of them are electrically charged and the other three Higgs bosons are neutral charged. Our analysis based on the Monte Carlo data produced from the simulation of 2HDM with proton antiproton collisions at the Tevatron = 1.96 TeV (Fermi Lab) and proton proton collisions at the LHC = 14 TeV (CERN) with final state includes electron, muon, multiple jets and missing transverse energy via the production and decay of the new Higgs in the hard process where the dominant background (electrons and muons) for this process comes from the Standard Model processes via the production and decay of top quark pair. We assumed that the branching ratio of charged Higgs boson to tau lepton and neutrino is 100%. We used the Artificial Neural Networks (ANNs) which are an efficient technique to discriminate the signal of charged Higgs boson from the SM background for charged Higgs boson masses between 80 GeV and 160 GeV. Also we calculated the production cross section at different energies, decay width, branching ration and different kinematics distribution for charged Higgs boson and for the final state particles.展开更多
The evolution of telecommunications has allowed the development of broadband services based mainly on fiber optic backbone networks. The operation and maintenance of these optical networks is made possible by using su...The evolution of telecommunications has allowed the development of broadband services based mainly on fiber optic backbone networks. The operation and maintenance of these optical networks is made possible by using supervision platforms that generate alarms that can be archived in the form of log files. But analyzing the alarms in the log files is a laborious and difficult task for the engineers who need a degree of expertise. Identifying failures and their root cause can be time consuming and impact the quality of service, network availability and service level agreements signed between the operator and its customers. Therefore, it is more than important to study the different possibilities of alarms classification and to use machine learning algorithms for alarms correlation in order to quickly determine the root causes of problems faster. We conducted a research case study on one of the operators in Cameroon who held an optical backbone based on SDH and WDM technologies with data collected from 2016-03-28 to “2022-09-01” with 7201 rows and 18. In this paper, we will classify alarms according to different criteria and use 02 unsupervised learning algorithms namely the K-Means algorithm and the DBSCAN to establish correlations between alarms in order to identify root causes of problems and reduce the time to troubleshoot. To achieve this objective, log files were exploited in order to obtain the root causes of the alarms, and then K-Means algorithm and the DBSCAN were used firstly to evaluate their performance and their capability to identify the root cause of alarms in optical network.展开更多
目的基于网状Meta分析不同根充糊剂对根管治疗效果的影响。方法通过计算机检索中国知网、万方数据知识服务平台、维普、中国生物医学文献数据库、PubMed、Embase、Web of Science及Cochrane Library等数据库公开发表的关于根充糊剂的随...目的基于网状Meta分析不同根充糊剂对根管治疗效果的影响。方法通过计算机检索中国知网、万方数据知识服务平台、维普、中国生物医学文献数据库、PubMed、Embase、Web of Science及Cochrane Library等数据库公开发表的关于根充糊剂的随机对照研究。检索时限2011年至2024年,采用主题词加自由词的方式进行检索。由两名研究者独立进行文献筛选和数据提取,根据Cochrane评价手册,采用RevMan5.4软件对所纳入文献的质量进行偏倚风险评估,基于频率学框架采用Stata 17MP及其network、mvMeta程序包等进行统计分析。结果共纳入58篇随机对照试验(randomized controlled trial,RCT),包括6种干预措施(生物陶瓷类糊剂、环氧树脂类糊剂、氢氧化钙类糊剂、硅树脂类糊剂、氧化锌丁香油类糊剂、碘仿类糊剂),共8374例患者,试验组4074例,对照组4300例,涉及4个结局指标,其中有效率30篇RCT、成功率21篇RCT、恰填率9篇RCT、术后7 d视觉模拟量表(visual analogue scale,VAS)疼痛评分8篇RCT。网状Meta分析显示:有效率、成功率、恰填率及术后7 d VAS疼痛评分,累积排序概率图下面积中生物陶瓷类根充糊剂均排第一。其中主要结局指标有效率和成功率排名前三的均是:生物陶瓷类糊剂>环氧树脂类糊剂>氢氧化钙类糊剂。结论在提高临床有效率及成功率方面,生物陶瓷类糊剂根充糊剂有明显优势;在恰填率及镇痛方面,生物陶瓷类糊剂效果最好。受纳入文献数量和质量的限制,未来仍需开展更多高质量的临床试验给予验证。展开更多
基金supported in part by Ministry of Education/China Mobile joint research grant under Project No.5-10Nanjing University of Posts and Telecommunications under Grants No.NY214135 and NY215045
文摘1 IntroductionNowadays in China, there are more than six hundred million netizens [1]. On April 11, 2015, the nmnbet of simultaneous online users of the Chinese instant message application QQ reached two hundred million [2]. The fast growth ol the lnternet pusnes me rapid development of information technology （IT） and communication technology （CT）. Many traditional IT service and CT equipment providers are facing the fusion of IT and CT in the age of digital transformation, and heading toward ICT enterprises. Large global ICT enterprises, such as Apple, Google, Microsoft, Amazon, Verizon, and AT＆T, have been contributing to the performance improvement of IT service and CT equipment.
文摘In this work we present an analysis of a search for charged Higgs boson in the context of Two Doublet Higgs Model (2HDM) which is an extension of the Standard Model of particles physics where the 2HDM predicts by existence scalar sector with new five Higgs bosons;two of them are electrically charged and the other three Higgs bosons are neutral charged. Our analysis based on the Monte Carlo data produced from the simulation of 2HDM with proton antiproton collisions at the Tevatron = 1.96 TeV (Fermi Lab) and proton proton collisions at the LHC = 14 TeV (CERN) with final state includes electron, muon, multiple jets and missing transverse energy via the production and decay of the new Higgs in the hard process where the dominant background (electrons and muons) for this process comes from the Standard Model processes via the production and decay of top quark pair. We assumed that the branching ratio of charged Higgs boson to tau lepton and neutrino is 100%. We used the Artificial Neural Networks (ANNs) which are an efficient technique to discriminate the signal of charged Higgs boson from the SM background for charged Higgs boson masses between 80 GeV and 160 GeV. Also we calculated the production cross section at different energies, decay width, branching ration and different kinematics distribution for charged Higgs boson and for the final state particles.
文摘The evolution of telecommunications has allowed the development of broadband services based mainly on fiber optic backbone networks. The operation and maintenance of these optical networks is made possible by using supervision platforms that generate alarms that can be archived in the form of log files. But analyzing the alarms in the log files is a laborious and difficult task for the engineers who need a degree of expertise. Identifying failures and their root cause can be time consuming and impact the quality of service, network availability and service level agreements signed between the operator and its customers. Therefore, it is more than important to study the different possibilities of alarms classification and to use machine learning algorithms for alarms correlation in order to quickly determine the root causes of problems faster. We conducted a research case study on one of the operators in Cameroon who held an optical backbone based on SDH and WDM technologies with data collected from 2016-03-28 to “2022-09-01” with 7201 rows and 18. In this paper, we will classify alarms according to different criteria and use 02 unsupervised learning algorithms namely the K-Means algorithm and the DBSCAN to establish correlations between alarms in order to identify root causes of problems and reduce the time to troubleshoot. To achieve this objective, log files were exploited in order to obtain the root causes of the alarms, and then K-Means algorithm and the DBSCAN were used firstly to evaluate their performance and their capability to identify the root cause of alarms in optical network.
