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Amadeus5中文软件使用手册范本

AMADEUS 5 门禁和报警管理软件完善的综合保安网络系统用户手册© DDS, Jan. 2003Publication 10UE400 rev C.目录1. 前言 (6)1.1.关于A MADEUS 5 (6)1.2.监控工具 (6)1.2.1. 门禁控制l (6)1.2.2. 报警管理 (6)1.2.3. 电梯管理 (7)1.2.4. 停车场管理 (7)1.2.5. 考勤管理 (7)1.3.安装类型 (8)1.4.程序模块 (8)1.4.1. 数据库 (8)1.4.2. 通讯 (8)1.4.3. 操作 (8)1.5.基本配置 (9)1.5.1. 操作系统和计算机r (9)1.5.2. 控制器 (9)1.5.3. 读卡器 (9)1.5.4. 其他外围设备 (9)1.6.AMADEUS5的使用概述 (10)1.6.1. 安装 (10)1.6.2. 设置 (10)1.6.3. 退出系统 (10)1.6.4. 新数据登记项 (11)1.6.5. 修改数据登记项 (11)1.6.6. 演示版本和加密狗 (11)2. 界面概述 (12)2.1.主界面 (12)2.2.工具栏 (13)2.3.下拉菜单 (13)2.4.工具条 (14)2.5.个性化工具条 (14)3.菜单:参数 (15)3.1.控制器网络 (15)3.1.1. 控制器网络–常规 (15)3.1.2. 控制器网络–定义 (16)3.2.控制器 (16)3.2.1. 控制器–常规 (18)3.2.2. 控制器–读卡器 (20)3.2.3. 读卡器 (21)3.2.3.1. 控制器-读卡器-常规 (22)3.2.3.2. 控制器- 读卡器–门控制 (23)3.2.3.3. 双门互锁 (24)3.2.3.4. 控制器- 读卡器- 通行模式 (26)3.2.3.5. 控制器–读卡器–卡格式 (27)3.2.3.6. 控制器–读卡器- 其他 (32)3.2.4.控制器 -输入 (29)3.2.5. 输入 (34)3.2.6控制器 -输出 (31)3.2.7. 输出 (36)3.2.8. 控制器–本地联动 (37)3.2.9. 本地联动 (38)3.3.时间区 (39)3.3.1. 基本概念 (39)3.3.2.日编程 (36)3.3.3. 周编程 (41)3.3.4. 节假日 (42)3.4.通行级别 (43)3.5.部门 (44)3.6.卡 (41)3.6.1. 卡搜索 (46)3.6.2卡的设置 (43)3.7.持卡人 (48)3.7.1. 持卡人–基本概念 (48)3.7.2. 持卡人–概述 (48)3.7.3. 持卡人–个人信息 (50)3.7.4. 持卡人–位置 (51)3.7.5. 持卡人–自定义 (52)3.8.访客 (52)3.9.授权等级 (52)3.10.使用者 (54)3.11.自定义标签 (55)3.12.防跟随 (55)3.12.1. 基本概念 (55)3.12.2. 本地防跟随 (55)3.12.3. 时间防跟随 (56)3.12.4. 全局防跟随 (56)3.12.5.防跟随级别 (57)3.13.退出应用 (57)4.2.地图 (59)4.3.定位 (61)4.4.输入组 (62)4.5.输出组 (63)4.6.动作 (64)4.7.处理步骤 (66)4.8.计数器 (67)4.9.全局联动 (69)4.9.1. 全局联动–基本概念 (69)4.9.2. 全局联动- 概述 (69)4.9.3. 全局联动–属性 (70)4.10.事件处理编程 (74)4.10.1. 事件处理编程–基本概念 (74)4.10.2. 事件处理编程- 概述 (74)4.10.3. 事件处理编程- 报警 (75)4.10.4. 报警属性 (76)4.10.5. 事件处理编程–全局联动 (77)4.11.启动报警 (78)4.11.1. 启动报警界面 (78)4.11.2. 继电器控制 (82)4.11.3. 输入状况 (83)5. 菜单：模块 (88)5.1.停车场 (88)5.1.1. 停车场–基本概念 (88)5.1.2. 停车场 (89)5.1.2.1. 停车场-概述 (89)5.1.2.2. 停车场–在场车辆明细 (91)5.1.3. 停车场用户组 (91)5.1.3.1. 停车场用户组- 概述 (92)5.1.3.2. 停车场用户组–在场用户明细 (92)5.1.4. 停车区域 (93)5.1.4.1. 停车区域–常规 (93)5.1.4.2. 停车区域- 进入 (95)5.1.4.3. 停车区域- 在场升级 (95)5.1.5.重置停车区域 (96)5.2.电梯编程 (97)5.2.1电梯编程- 常规 (98)5.2.2. 电梯编程-持卡人 (99)5.3.考勤管理 (99)5.4.保安员 (101)5.5.巡更 (101)6. 菜单：通讯 (102)6.1.停止/恢复轮询 (102)6.2.查看记录显示 (102)6.3.显示照片 (103)6.4.诊断 (103)7.2.继电器操作 (108)7.3.执行操作 (108)8. 菜单：工具 (109)8.1.自定义报表 (109)8.1.1. 基本概念 (109)8.1.2. 开始界面: 选择报表 (109)8.1.3. 第二界面: 选择数据 (110)8.1.4. 第三界面: 数据过滤 (112)8.1.5. 第四界面：数据结构 (113)8.1.6. “报表预览“界面 (115)8.1.7. 修改界面 (116)8.1.8. “浏览数据“界面 (116)8.1.9. 日志查询 (117)8.2.产生新的数据库 (118)8.3.存储数据库 (118)8.4.恢复数据库 (120)8.5.产生新的日志 (121)8.6.保留日志 (121)8.7.恢复日志 (122)8.8.创建卡组 (123)8.9.选项 (124)8.9.1. 文档定位 (124)8.8.2. 语言 (124)8.9.3. 通讯 (125)8.9.4. 日志/ 记录界面 (127)8.9.5. 概述 (128)9. 菜单: 帮助 (129)9.1.A MADEUS 帮助内容 (129)9.2.A MADEUS 帮助索引 (129)9.3.A MADEUS 帮助搜索 (129)9.4.A MADEUS在 WEB (130)9.5.关于A MADEUS (130)1. 前言1.1. 关于 Amadeus 5Amadeus 5, 用户界面友好的高级门禁和报警管理软件, 满足各种客户对安全的需求。

纳芯威NS4150B超低EMI无需滤波器单声道D类音频功放用户手册说明书

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revman5

revman5.0以观察性研究为例教程以观察性研究为例，本文将介绍用 RevMan 5.0 来编制系统性文献综述（SR）的详细步骤。

RevMan 5.0 是一个软件，专为创建和维护Cochrane 证明性医学准则而设计。

RevMan 5.0 提供一个良好的结构来帮助独立调查者对数据进行分析和编制SR。

1.首先，使用 RevMan 5.0 下载启动档，然后双击该文件，即可安装 RevMan 5.0 软件。

2. 然后，要创建 SR 报告，需要首先在 RevMan 5.0 创建一个新文档， istart.exe 是一个可供使用进行 SR 的向导。

如果要开始新文档，则需要使用 Wizard 工具，该工具可提示文档中所有必要的内容。

3.系统性文献综述（SR ）的标签信息的输入可以使用 Wizard 功能完成。

这些标签信息包括：作者的姓名、日期、文献类型、调查发布日期、报告标题以及摘要等信息。

4.然后，需要输入主要的议题，包括检索标准、范围及随机试验组别。

在这种情况下，在 RevMan 5.0 进行观察性研究，主要议题是题主检索所有可用的定性资料 | 包括原始调查和研究。

5.然后，数据输入可以从数据库检索或者从文献摘录表进行。

在这种情况下，研究者必须检索所有相关的数据，然后输入到RevMan 5.0，为了允许对文献的自动摘要和标记，研究者可以使用数据库中的文献。

RevMan 5.0 支持多种数据库检索，包括 MEDLINE 、EMBASE 、PEDro 、Cochrane Library等。

6. 最后，SR 的分析可以进行，RevMan 5.0 允许研究者使用文章摘要及图表和图形计算分析。

在这种情况下，SR 的分析可以使用Graphpad Prism 6 来实现，Graphpad Prism 6 是一个用于统计分析和图形展示的高级计算机应用软件。

系统评价与meta分析之-Review manager-Revman软件使用-水天之间 - DXY

含三个选项：Low risk，unclear risk，high risk，默认为unclear risk）； 3. 右侧栏为选择该选项的依据（Support for judgement）
水天之间
RevMan实战—纳入研究风险偏倚及图形
• 表格（Risk of bias table）更改条目：
水天之间
为结局指标添加纳入研究的对话框
水天之间
7
水天之间
RevMan实战—二分类变量meta
2014/1/3 Friday
RevMan实战—二分类变量meta
水天之间
翻转结局标注
水天之间
RevMan实战—二分类变量meta
RevMan实战—二分类变量meta
保存森林图操作
水天之间
二分类数据分析状态下，计算器的计算界面及结果
RevMan实战—“比较”和“结局指标”
RevMan实战—“比较”和“结局指标”
新加结局指标数据类型选择框
水天之间
6
新加结局指标名称及组标签
水天之间
RevMan实战—“比较”和“结局指标”
2014/1/3 Friday
RevMan实战—“比较”和“结局指标”
分析方法选择框
水天之间
分析细节对话框
水天之间
• “Data and analyses”部分为Revman软件 meta分析功能的核心也是重点，在数据分析下，有三个水平：
1. Comparison； 2. Outcome； 3. Subgroup。
水天之间
RevMan实战—“比较”和“结局指标”
• 增加Comparison同样有两种方法： • 1.鼠标左键点击大纲面板的“Data and

masterbroschuere_uni_mannheim_englisch

I NFORMATION ON MASTER’S PROGRAMSAT THE U NIVERSITY OF M ANNHEIMFALL SEMESTER 2017In this guide, you will get a first overview of the master’s programs, of responsibilities and application deadlines, which have to be considered if you want to start a master’s program at the University of Mannheim in the fall semester 2017.Please note: All information is current as at 30th January 2017 and subject to changes. Changes are possible both with respect to application process and Selection Statutes. Therefore, please check our website www.bewerbung.uni-mannheim.de regularly.You have to apply for all of the master’s programs and almost each of them is selective. In this guide, you can find information on the application process and on application requirements that need to be considered for an application at the University of Mannheim. Please read this guide carefully. Certainly, many of your questions will be answered afterwards.Please refer to the German brochure “Infos für das Masterstudium” if you are intereste d in a German master’s p rogram as this guide focuses on master’s p rograms taught in English.The University of Mannheim offers a two step application procedure. Firstly, you need to apply online; secondly, you need to send your application form for admission together with all supporting documents by post to the Admissions Office.Application is obligatory for all master’s programs.For further information on the application process, please refer to point 3 in this guide.Contact details if you need further information:Admissions Office:Visitor address: L 1, 1, (entrance B), first floorPostal address: Universität MannheimZulassungsstellePostfach 10346268131 MannheimHead of Admissions Office:Ms. KloppenburgCo-Director of Admissions Office: Mr. BraunContact: Mrs. Dörr / Mr. Braun (room 157)Ms. Kendzia (room 158) International applicantsMrs. Sinn (room 158) Applicants with refugee background,International applicantsInternet: http://www.uni-mannheim.de/applicationPhone consultation: until 31.03.2017 Tue 10:00am – 12:00pm01.04. to 15.07. Mo 12:00pm – 01:00pmWed 01:00pm – 02:00pm Phone Admissions Office:…German applicants:0621/181-1199 or -1279…foreign applicants0621/181-3517 or -1259Fax number: 0621/181-1229Opening hours: Monday 9:00am – 12:00pmWednesday 2:00pm – 5:00pmOpening hours are subject to change. Changes will be published on our website.E-mail addresses for more information…… concerning application/ admission:application@uni-mannheim.de… concerning academic studies:studium@verwaltung.uni-mannheim.deHotline:0621/181-2222 (Mo-Fr 9:00am – 04:00pm)Bank Account:Universität MannheimBaden-Württembergische BankAccount No: 1379273Bank Code: 60050101IBAN: DE23 6005 0101 0001 3792 73BIC: SOLA DE ST 6001. GENERAL INFORMATION ON THE ADMISSIONS PROCESS (5)1.1A DMISSION REQUIREMENTS (5)1.2S ELECTIVE ADMISSION (5)1.3A PPLICATION FORM FOR ADMISSION (5)1.4M ASTER’S PROGRAMS AND DEADLINES FOR APPLICATION (5)1.5C ONTACTS (6)2. ADMISSION REQUIREMENTS AND SELECTION CRITERIA FOR EACH MASTE R’S PROGRAM (7)2.1G ENERAL INFORMATION ON ADMISSION REQUIREMENTS AND SELECTION CRITERIA (7)2.2B ACHELOR’S DEGREE NOT YET COMPLETED (7)2.3I NFORMATION ON THE INDIVIDUAL MASTER’S PROGRAMS (7)2.3.1 Master of Arts – History (7)2.3.2 Master of Arts – Intercultural German Studies (8)2.3.3 Master of Arts – Culture and Economy: English and American Studies (8)2.3.4 Master of Arts – Culture and Economy: French Studies (9)2.3.5 Master of Arts – Culture and Economy: German Studies (9)2.3.6 Master of Arts – Culture and Economy: Hispanic Studies (9)2.3.7 Master of Arts – Culture and Economy: History (10)2.3.8 Master of Arts – Culture and Economy: Italian Studies (10)2.3.9 Master of Arts – Culture and Economy: Philosophy (10)2.3.10 Master of Arts – Culture and Economy: Media and Communication Studies (11)2.3.11 Master of Arts – Language and Communication (11)2.3.12 Master of Arts – Literature, Media and Culture in the Modern Era (11)2.3.13 Master of Arts – Media and Communication Studies: Digital Communication (12)2.3.14 Master of Arts – Political Science (12)2.3.15 Master of Arts – Sociology (12)2.3.16 Master of Science – Business Informatics (13)2.3.17 Mannheim Master in Data Science (13)2.3.18 Master of Science – Mathematics in Business and Economics (13)2.3.19 Master of Science – Economics (14)2.3.20 Master of Science – Economic and Business Education (14)2.3.21 Master of Science – Mannheim Master in Business Research (14)2.3.22 Master of Science – Mannheim Master in Management (15)2.3.23 Master of Science – Psychology: Cognitive and Clinical Psychology (15)2.3.24 Master of Science – Psychology: Work, Economy and Society (16)2.3.25 Comparative Business Law (M.C.B.L.) Mannheim/ Adelaide (16)2.3.26 Master of Laws (LL.M) (17)2.3.27 Master of Competition Law and Regulation (LL.M) (17)3. THE APPLICATION PROCESS OF THE UNIVERSITY OF MANNHEIM (17)3.1D OCUMENTS TO SUBMIT (17)3.2I MPORTANT INFORMATION ON THE A PPLICATION F ORM FOR A DMISSION (18)3.2.1 Confirmation of Receipt (18)3.2.2 Delayed submission of documents / extended deadlines (19)3.2.3 Proof of foreign language proficiency (19)3.2.4 Proof of German language proficiency (21)3.2.5 GMAT (22)3.2.6 Proof of extracurricular activities (22)3.2.7 Dispatch of Notifications (22)3.2.8 Exclusion from the admissions process (22)3.2.9 Admission by lottery draw (22)3.2.10 Hardship exemptions (23)4. FURTHER INFORMATION (23)4.1S EMESTER FEES (23)4.2C OURSE CATALOG (23)4.3R EPRESENTATIVE FOR STUDENTS WITH SPECIAL NEEDS (23)4.4S TUDIERENDENWERK (RESPONSIBLE FOR HOUSING, CATERING, COUNSELLING) (24)4.5C OURSE GUIDANCE (24)5. IMPORTANT DATES: (24)6. CONTACTS FOR MORE INFORMATION: (25)1. General Information on the Admissions Process1.1 Admission requirementsThe University of Mannheim operates its own selection processes for graduate programs. In principle, master’s programs are consecutive, which means they require a corresponding bachelor’s degree as defined by the Statutes of Selection. The b achelor’s degree program could be passed either at a university in Germany or at a university abroad as well as at a “Berufsakademie” (public university of cooperative education) or a university of applied sciences.1.2 Selective admissionAll master’s programs at the University of Mannheim are selective, except the following master’s programs:Master in Comparative Business Law, Master in Culture and Economy: History, Master in Culture and Economy: French Studies, Master in Culture and Economy: Hispanic Studies, Master in Culture and Economy: Italian Studies, Master in Culture and Economy: Philosophy. However, the online application is obligatory for all programs.1.3 Application form for admissionIn the application process for the master’s p rograms, you are allowed to submit up to three applications. All applications are treated equally. Please take note of the different admission requirements and selection criteria of the master’s p rograms.1.4 Master’s programs and deadli nes for applicationIn the following, you will get a first overview of the master’s programs offered. In case you are or were enrolled in a similar master’s program, please apply for an advanced semester.The deadlines for the fall semester 2017 are listed in the table below. Please note: These are definitive deadlines, which means the application form for admission together with all supporting documents must have arrived at the Admissions Office by the specific deadline.1.5 ContactsThe Admissions Office is the central office for all applicants (German as well as foreign applicants). Therefore, you need to apply to the Admissions Office if you∙are a German citizen∙have got a German university entrance qualification∙have got a b achelor’s degree acquired in Germany (even if you do not get your degree until the beginning of the fall semester)and∙if you are a foreign applicant without a German university entrance qualification or ab achelor’s degree acquired in GermanyIf you have any questions, please contact us via e-mail at application@uni-mannheim.deThe International Office also provides a variety of information for international degree-seeking students on its website. Please visit the website http://www.uni-mannheim.de/aaa. Some information that you need may not be included in this brochure.Applicants from Chinese, Mongolian and Vietnamese universities additionally require the original Certificate of the Academic Test Centre (APS-Zertifikat). Contacts: German Embassy in Beijing () or the German Embassy in Ulan Bator (www.ulan-bator.diplo.de) or the German Embassy in Hanoi (www.hanoi.diplo.de).Many questions are very subject-specific. In this case, please contact the departmental advisory service:2. Admission Requirements and Selection Criteria for each Master’s Program2.1 General information on admission requirements and selection criteriaAdmission requirements of each master’s pr ogram can be found in the Selection Statutes which can be downloaded on the following website:http://bewerbung.uni-mannheim.de/english/study_programmes_selection_criteria/index.html For all of the m aster’s programs y ou need to apply in due time and form.You will find a checklist stating all required documents attached to your application form for admission. Please read this checklist carefully, since incomplete applications have to be excluded from the selection process.2.2 Bachelor’s degree not yet completedIf you have not yet completed your b achelor’s degree within the application period, you may still apply for a master’s p rogram. However, you need to have successfully completed a certain amount of ECTS credits as stated in the selection statutes. To prove the number of ECTS, please send in a current Transcript of Records. This document (original, bearing the official stamp and a signature of the university) has to arrive at the Admissions Office before the end of the application period.Please note: Transcripts of Records containing a verification link or code can only be accepted, if this document can be downloaded completely.Please note: If the b achelor’s degree has not been completed by the end of the application period, admission to a master’s p rogram is preliminary as you have to submit your b achelor’s degree certificate to the University of Mannheim before the first registration for an exam. If you do not provide this document in due time, your admission to the master’s program will be rescinded.2.3 Information on the individual master’s p rogramsIn the following, you will get an overview of the admission requirements and the selection criteria which may improve your chances in getting a place of study. Please note: Many of the master’s p rograms require proof of good German language proficiency. These programs are highlighted in yellow. The University of Mannheim offers several courses in English within the master’s p rograms in order to prepare the graduates for an internationally oriented career. Programs that are completely taught in English are the following: Master of Comparative Business Law, M.A. Political Science, M.A. Sociology, M.Sc. in Business Informatics, M.Sc. Economics, Mannheim Master in Data Science (M.Sc.) as well as the Mannheim Master in Business Research (M.Sc.). The Mannheim Master in Management (M.Sc.) also offers an all-English track.2.3.1 Master of Arts – History1For more information on proof of German language proficiency, please see 3.2.42Compulsory internships cannot be considered. However, if the internships last longer than required, this time may be considered.2.3.2 Master of Arts – Intercultural German StudiesThe joint master’s program in Intercultural German Studies is offered by two universities –the University of Mannheim as well as the University of Waterloo (Canada). Two semesters are to be spent at the corresponding partner university.‘Deutsch als Fremdsprache’) including at least five courses in German literature and/or German linguistics equivalent to one basic and one advanced module.3The “Common European Framework of Reference” was devel oped by the European Council to standardize the levels of language skills in Europe. Level A corresponds to an elementary level, level B corresponds to a sufficient level and level C corresponds to a competent level. Each level is divided into different sub-levels. Level C1 corresponds to an advanced level, level C2 corresponds to almost mother-tongue level.4If qualifications in the field of cultural studies are missing, you may still apply for a place of study. In this case, you need to submit a declaration stating that youwill acquire the qualificat ions within the master’s program (in addition to the required exams) until the end of the second semester. You need to submit this Declaration of Commitment together with your application documents.5If qualifications in the field of history of up to 20 ECTS are missing, you may still apply for a spot. In this case, you need to send in a declaration stating that you will acquire the qualificat ions within the master’s program (in addition to the required exams) until the end of the second semester. You need to send in this Declaration of Commitment together with your application documents.6If qualifications in the field of philosophy are missing, you may still apply for a study place. In this case, you need to send in a declaration stating that you will acquire the qualifications within the Master Program (in addition to the required exams) until the end of the second semester. You need to send in this Declaration of Commitment together with your application documents.7For further Information about good English language skills, see point 3.2.3applicant needs to send in a Declaration of Commitment stating that he /she will make up the missing qualifications within the first and second semester of the master’s program.Accounting, Finance, Information Systems, Management, Marketing, Operations, TaxationThe MMBR distinguishes itself through a strong and above average focus on research. The program offers an ideal preparation for a structured PhD program as offered by the Center for Doctoral Studies in Business (CDSB). MMBR graduates can abbreviate the PhD program offered by the CDSB by one year and will use their Master thesis as a research proposal.8For further information concerning the letters of recommendation, please have a look on our website.The Selection Statute of the Mannheim Master in Management provides two tracks: a German-English track as well as an all- English track. Applicants for the German-English track need to prove German language skills (see point 3.2.4). You can only apply for one track. Applications for both tracks are not possible. Furthermore, it is not possible to change the track within the study program.This master’s program lasts one year. The tuition fee amounts to 8,500 Euro. The master’s program in Comparative Business Law offers two tracks: the ‘Mannheim/ Adelaide track’as well as the ‘Mannheim track’:‘Mannheim/ Adelaide track’: The program starts at the University of Mannheim and will be continued at the University in Adelaide (Australia).‘Mannheim track’: Both semesters are to be spent at the University of Mannheim.Please note: Applicants for this track need to have obtained their bachelor’s d egree at a university outside of Germany.A change of tracks during the program is not possible!3. The Application Process of the University of MannheimThe University of Mannheim offers its applicants a two-step application process. Firstly, you have to apply online; secondly, you need to send your application form for admission together with all supporting documents by post to the Admissions Office.In order to apply, please use the online application portal of the University of Mannheim:http://www.uni-mannheim.de/applicationOn this website, you also find information about all programs at the University of Mannheim as well as the curriculum of various programs and the respective requirements.Furthermore, our website has information on everything regarding application and enrollment. Most questions should be covered. However, if questions arise, please do not hesitate to contact us:application@uni-mannheim.de3.1 Documents to submitThe online application is obligatory for each master’s p rogram. After entering all data online, please print the application form for admission. Please make sure not to change any data by hand on the printed form without contacting the Admissions Office. Please submit amongst others the following documents by post to the Admissions Office (postal addresse see point 3.2)1) Printed and signed Application Form for Admission2) University entrance qualification (e.g. High School Diploma, School Leaving Certificate, Attestat,etc.). Please note: A degree obtained at a German University also qualifies as a university entrance qualification3) Proof of the fulfilled admission requirements for your chosen master’s p rogram (e.g. notarizedtranscript of records, proof of required language proficiency, etc.)4) Certificate of enrollment listing all semesters at German universities5) Curriculum VitaeAttached to the application form for admission, you will find a checklist that will help you to compile all documents. Please read this list carefully since incomplete applications have to be excluded from the admissions process.All documents proving admission requirements (see point 2.3) have to be provided as notarized copies.Please note: Foreign documents can only be accepted in either German or English. If your documents are in another language, a translation into German or English is necessary. All records and transcripts of records need to be translated by a sworn translator. The module catalog, plan of studies and the examination regulations (especially relevant for the Mannheim Master in Management and Master in Business Informatics) can be translated by any other person, if your university testifies the correctness by an official stamp and signature.Please note: Transcripts of records bearing a verification link or code may only be accepted if the document can be downloaded completely.3.2 Important information on the Application Form for AdmissionAfter you have completed the online questionnaire, please print the application form for admission. Sign it and send the form together with the requested records and proofs to the Admissions Office. Attached to your application form for admission, you will find a checklist that will help you to check whether your documents are complete.Only applications that have been filled in completely, which are signed and contain all the relevant documents may be considered for the admissions process. Furthermore, the application needs to have arrived at the Admissions Office by the respective application deadline.Unfortunately, we are unable to accept applications outside of the regular application period. Moreover, in your application, you cannot refer to documents you sent in during an earlier application process or to documents that are in files of the University Mannheim. Any documents in support of an application become property of the University Mannheim and will not be returned. We appreciate your understanding in this matter.Please do not submit: Original documents, photographs, an insurance certificate, proof that you have paid the semester contribution, a self-addressed stamped envelope.Avoid loose-leaf binders and paper clips. We kindly ask you to use a filing fastener like the one below:Please punch holes in your documents and fix them with a filing fastener on the left hand side of the file. Then send the application toUniversität Mannheim Universität MannheimBewerbungs- und Zulassungsstelle or Bewerbungs- und ZulassungsstellePostfach 103462 L1,168131 Mannheim 68161 Mannheim3.2.1 Confirmation of ReceiptYou have a personal account on the application portal. Once you have logged in, you can check the status of your application. In order to do so, please go to “Application Management”and click on“s tatus” next to the application number.3.2.2 Delayed submission of documents / extended deadlinesIdeally, you submit all requested documents at once and do not have to send in further documents lateron. However, if you have already sent your documents in support of an application and want to hand i n further documents (e.g. your bachelor’s d egree), you can use the form for delayed submission of documents that can be downloaded in the application portal. Please use this form also if we request for any further documents. You will find the form after you have finished your online application under “Application Management”. Please note: Delayed documents have to arrive at the Admissions Office by post before the application deadline, too.Extended deadlinesIn general, delayed documents have to arrive before the deadline of the application period. However, some Selection Statutes offer extended deadlines for some documents. These are:∙Master Political Science, Master Sociology, Master of Comparative Business Law, Master of Laws, Master in Business Informatics, Mannheim Master in Data Science and Master in Economics: o Proof of English language skills may be submitted until August, 15th(for the fall semester).3.2.3 Proof of foreign language proficiencyFor some of the master’s programs, applicants have to prove a minimum of English language skills.If you intend to pass a language test, you have to provide the result either as an original or as notarized copy within the application period. Furthermore, you have to gain the minimum grades. If you want to pass a TOEFL iBT and want the institution to send the result to the University of Mannheim, please use the Institution Code "0254".Please bear in mind that it may take several weeks until the result will be sent. If you are not sure whether the result will arrive in due time, please send in the following documents:∙Print of your online TOEFL result: please confirm that the Admissions Office is allowed to check your result online with your signature∙Log in data (user name and password)Some of the master’s programs offer an extended deadline for language test results. Please check point 3.2.2 whether the master’s program you wish to apply for offers this possibi lity.The language tests TOEFL and IELTS could be passed at the University of Mannheim, Service and Marketing GmbH. For questions, please contact Ms. Maria Collado, Office hours: Monday 09:00am-12:00pm; Tuesday - Thursday 09:00am-12:00am, 2:00pm-5:00pm; phone: 0621/181-1164; e-mail: info@studiumgenerale.uni-mannheim.de.3.2.3.1 Business Informatics or Mannheim Master in Data Science▪completed first degree in English.▪university entrance qualification at a school with English as language of instruction.If this is not the case, students must present one of the following test results as proof of language proficiency:▪Test of English as a Foreign Language:▪Internet-Based Test (TOEFL iBT) with a score of at least 79 points▪TOEFL Computer-Based Test (CBT) with a score of at least 213 points▪TOEFL Paper-Based Test (PBT) with a score of at least 550 points ▪Certificate of Proficiency in English (CPE) to at least level C.▪Certificate in Advanced English (CAE) to at least level C.▪International English Language Testing System - Academic Test (IELTS) to at least score6.0.▪General Management Admission Test (GMAT) with a score of at least 500 points▪Graduate Record Examination (GRE), general test at least 60% in Verbal Reasoning and at least 80% in Quantitative Reasoning▪Language test offered by the Universität Mannheim Service und Marketing GmbH with at least level B2 in the fields Listening Comprehension, Written Language, Spoken Languageand Reading Comprehension.Please note: The result of the language test will not be accepted, if it is older than 5 years.The admissions committee may decide on exceptions.3.2.3.2 Economicsa. A university entrance qualification acquired after at least 2 years at a school with English aslanguage of instruction.b. A university program with English as language of instruction of at least 1 year.If a or b does not apply, you must present one of the following test results:∙Test of English as a Foreign Language:o Internet-Based Test (TOEFL iBT) with a score of at least 79 points.o TOEFL Computer-Based Test (CBT) with a score of at least 213 points.o TOEFL Paper-Based Test (PBT) with a score of at least 550 points.∙International English Language Testing System – Academic Test (IELTS) to at least score 6.5 ∙Verbal score of the GRE General Test or GRE revised General Testo Verbal score of at least 320 in the GRE General Testo Verbal score of at least 140 in the GRE revised General TestPlease note: Test results will not be accepted, if they are older than 2 years.The admissions committee may decide on exceptions.3.2.3.3 Master of Laws, Master of Comparative Business Law (Mannheim track), Master of Competition Law and Regulationa. All-English university entrance qualification (all subjects in English; classes lasting aminimum of one year)b. Participation in an English study program for at least one yearc. If a or b does not apply, students must present one of the following test results as proof oflanguage proficiency:▪Test of English as a Foreign Language:▪Internet-Based Test (TOEFL iBT) with a score of at least 90 points▪IELTS of at least 6.5▪Or an equivalent test result3.2.3.4 Master of Comparative Business Law (Mannheim/Adelaide track):a. Participation in an English study program for at least one yearb. If a does not apply, students must present one of the following test results as proof of languageproficiency:▪Test of English as a Foreign Language:▪Internet-Based Test (TOEFL iBT) with a score of at least 94 points (27 points in “Writing”, 23 points in “Speaking”, 20 points in “Reading and Listening”)▪IELTS of at least 7.0 (7.0 in “Writing and Speaking”; 6.5 in “Reading and Listening”)3.2.3.5 Culture and Economy:o proof of language skills according to the chosen major on level C1.3.2.3.6 Intercultural German Studies, Media and Communication Studies, Language and Communication, Literature, Media and Culture in the Modern Era:o proof of English language skills on level B2. For more information, please refer to the corresponding Selection Statute as well as on point 2.3.If you are not sure, whether your proof will be considered, please contact the program manager Mr. Hempen (master@phil.uni-mannheim.de).3.2.3.7 Political Science, Sociology:a. Proof of constant participation in English classes during the last two years in secondary-schoolwith a minimum of grades recorded on the record of the university entrance qualification (see the relevant Selection Statutes)b. All-English university entrance qualification (all subjects in English)c. Completion of a first degree in English.d. If a, b or c does not apply, students must present one of the following test results as proof oflanguage proficiency:▪Test of English as a Foreign Language:▪Internet-Based Test (TOEFL iBT) with a score of at least 79 points.▪TOEFL Computer-Based Test (CBT) with a score of at least 213 points.▪TOEFL Paper-Based Test (PBT) with a score of at least 550 points.▪Certificate of Proficiency in English (CPE) to at least level C.▪Certificate in Advanced English (CAE) to at least level C.▪International English Language Testing System - Academic Test (IELTS) to at least score 6.0.3.2.4 Proof of German language proficiencyFor some of the master’s p rograms, applicants have to prove a minimum of German language skills. The following proofs can be accepted:a. All-German university entrance qualificationb. Completed degree in a study program taught completely in GermanIf a. or b. does not apply, applicants need to pass one of the following tests:▪Test DaF, with at least 4 points in each part▪…Deutsche Sprachprüfung zum Hochschulzugang (DSH)“ pass ed with at least grade 2 (DSH 2)▪German …Sprachdiplom“ of the …Kultusministerkonferenz“ (DSD II)▪passed …Feststellungsprüfung“ at a …Studienkolleg“ of a German University or the University of Applied Sciences Konstanz▪telc Deutsch C1 Hochschule▪Goethe-Zertifikat C1 or better▪Report of the …Zentrale Oberstufenprüfung (ZOP)“ of the Goethe-Institute passed at an Goethe-Institute in Germany or abroad before January, 1st 2012▪…Kleines Deutsches Sprachdiplom“ or …Großes Deutsches Sprachdiplom“ awarded by the …Goethe-Institute“ by order of the “Ludwig-Maximilians-University München passedbefore January, 1st 2012▪Austrian language certificate (ÖSD) C1 or betterApplicants who can prove one of the following qualifications by sending in the respective certificates do not have to pass one of the above-mentioned tests in addition:▪deutschsprachige Hochschulzugangsberechtigung, die in einem Staat oder einer Region mit offizieller Amtssprache Deutsch absolviert wurde und der Deutsch als Unterrichtssprachezugrunde lag.▪deutschsprachiger Hochschulabschluss, der in einem Staat oder einer Region mit offizieller Amtssprache Deutsch absolviert wurde und dem Deutsch als Unterrichtssprache zugrundelag.▪Hochschulreifeprüfung nach der Ordnung der Prüfung zur Erlangung eines Zeugnisses der deutschen Hochschulreife an deutschen Schulen im Ausland, die zum Sekundarabschlussnach den Landesbestimmungen führen▪Der Deutschnachweis im französischen Diplome du Baccalaureat, das nach dem Besuch eines zweisprachigen deutsch-französischen Zweigs einer Sekundarschule erworbenwurde.▪US-Advanced Placement-Prüfung (AP-Prüfung) im Fach Deutsch.▪Abschlusszeugnis der Oberstufe des Sekundarunterrichts aus der Deutschsprachigen Gemeinschaft des Königreichs Belgien▪Sekundarschulabschlusszeugnisse aus dem Großherzogtum Luxemburg▪Reifediplome der Schulen mit Deutsch als Unterrichtssprache aus der Autonomen Provinz Bozen-Südtirol (Italien)▪Abschlusszeugnis der internationalen Sektion deutscher Sprache am Liceo Gimnasiale …Luigi Galvani“ in Bologna▪Abschlusszeugnis eines deutsch-irischen zweisprachigen Sekundarschulabschlusses (bilingual Leaving Certificate) an der Deutschen Schule Dublin, St. Kilian’s▪Abschlusszeugnis der bilingualen Abteilung am Liceo Gimnasio Statale …Romagnosi“ in Parma und am Liceo Classico Statale Socrate in Bari。

罗克韦尔运动坐标系统1756-HYD02 1756-M02AE 说明书.

坐标系统属性 (Coordinate System Properties) 对话框 - 几何结构 (Geometry) 选项卡参数 .............................................................. 20 “坐标系统属性”对话框 -“单位”选项卡 ...............................................21 “坐标系统属性”对话框 -“单位”选项卡参数.................................21 “坐标系统属性”对话框 -“偏移量”选项卡 .......................................... 22 “坐标系统属性”对话框 -“偏移量”选项卡参数 ............................23 “坐标系统属性”对话框 -“关节”选项卡 .............................................. 24 “坐标系统属性”对话框 -“关节”选项卡参数................................ 24 坐标系统属性 (Coordinate System Properties) 对话框 - 动力学 (Dynamics) 选项卡 .................................................................................. 25 “坐标系统属性”对话框 -“动态”选项卡参数................................ 25 “手动调整”对话框 -“动态”选项卡 ................................................ 27 “坐标系统属性”对话框 -“运动规划器”选项卡.................................. 28 “坐标系统属性”对话框 -“运动规划器”选项卡参数 ................... 28 “坐标系统属性”对话框 -“Tag”选项卡................................................. 29 “坐标系统属性”对话框 -“Tag”选项卡参数 .................................. 29 确定坐标系统类型 ................................................................................... 30 更新受控应用程序的应用程序数据 .......................................................34

RevMan5.2.5应用实习指南

阿斯匹林（aspirin）预防心肌梗死的研究资料* 阿斯匹林安慰剂 K 个研究死亡数(n) 治疗总数(N) 死亡数(n) 治疗总数(N) MRC-1 1974 49 615 67 624 CDP 1976 44 758 64 771 MRC-2 1979 102 832 126 850 GASP 1979 32 317 38 309 PARIS 1980 85 810 52 406 AMIS 1980 246 2267 219 2257 ISIS-2 1988 1570 8587 1720 8600 *取自 Fleiss JL 的资料
图 Zhinan-7 “New Outcome Wizard”对话框（II）
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图 Zhinan-8 “New Outcome Wizard”对话框（III）（4）定义亚组分析(见下一部分) 用鼠标单击用户在上一步定义的分析结果名称，如单击“1.1 Mortality”后，点击右键，此时系统出现“Add Subgroup”选项，若用户不用亚组分析，可选择 “Add Study Data” ，进入下一步；四．数据的分析与表达（(相当于统计分析软件的部分(2)) 以干预性研究的两分类数据为例（1）添加研究数据当上一步点击“Add Study Data”后，将出现“New Study Data Wizard”对话框，如图 Zhinan-9 所示，用户可每次选定一个需要分析的研究名称，然后重复上一步，逐一将纳入分析的各研究名称添加到数据表中，也可全部选定 s1－s9，再进行数据录入，最后，将出现数据输入对话框，如图 Zhinan-10 所示。
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2. 输入系统评价信息和报告结构本文为方便叙述，引入 Fleiss JL 的阿斯匹林（aspirin）预防心肌梗死的研究资料为例，使用 Review Manager 软件对该资料进行 Meta 分析。在“Title ( New review Wizard)”对话框中，按以下步骤逐步输入： ①在“Title”信息框中输入研究的名称，如本例输入“aspirin for myocardial infarction” ； ②在 “Stage” 三个圆钮中选择，一般情况可选择 “Protocol” 或 “Full review” ； ③点击“Finish”完成项目建立，出现如图 Zhinan-2 界面；

Cochrane系统评价软件RevMan简介

四、未来发展与展望
随着医学和健康科学的不断发展，Cochrane快速系统评价将会更加完善和普及。未来，可以通过改进和完善Cochrane快速系统评价的方法和标准，提高其准确性和可靠性。可以进一步拓展Cochrane快速系统评价的应用领域，将其应用于不同国家和地区的研究中，为全球的医学和健康科学领域提供更加全面和准确的决策依据。
4、实时更新：Cochrane快速系统评价会随着新研究的出现而不断更新，确保其结论的时效性和准确性。
二、方法学解读
1、问题定义与目标：首先需要明确评价的目标和问题，确定所要研究的疾病或干预措施，以及评价的目标人群、结局指标等。
2、文献检索：通过全面的文献检索，收集所有相关的研究。Cochrane快速系统评价通常采用计算机辅助检索和手工检索相结合的方式，以确保检出所有相关研究。
也需要注意合理使用图形和遵循伦理规范等问题。通过正确使用RevMan和其他相关工具和方法，我们可以更好地总结和传播临床医学和其他领域的研究证据，从而为提高医疗质量和促进科学发展做出贡献。
参考内容
关键词：Meta分析，RevMan软件，学术研究，统计分析
在当今学术研究领域，Meta分析被广泛认为是有效的统计方法之一，可用于汇总和评估先前的研究结果。这种分析方法通过合并多个独立研究的结果，以提供更全面、更可靠的总体估计。与此同时，Meta分析的流程和方法也经历了不断的发展和优化。本次演示将介绍Meta分析的基本概念、优点及其在学术研究中的应用，并探讨使用RevMan软件进行Meta分析的实践方法。
3、高效可靠：CDEGS软件采用了先进的计算方法和算法，确保计算结果准确可靠。
4、兼容性好：CDEGS软件可与多种主流的工程软件进行无缝对接，如 AutoCAD、ANSYS等。

revman操作详细步骤

Revman5.0 meta分析详细操作步骤123点击N 4N5输入研究名称：中药 for 变应性鼻炎如果只有英文版，则输入英文：TCM for AR 6N7选full，点击finish 8下拉编辑区，出现下图：9点击左侧目录树的background，在右册相应条目下对应填写整个研究的背景、目标等。

如果只是做数据分析可以不填写。

10点击左侧methods，在右侧填写对应资料如下图。

如果只是做数据分析也可以忽略。

1112点击左侧目录树study and referance，在右侧点击ADD study，或者点击左侧study and referance下级目录referances study出现下图左侧的included studys右键included study后出现下图13点击左侧ADD study，出现下图14在study ID 栏目输入纳入文献的标记，一般采用第一作者名字与文章发表年份，如下图15这幅图表示金慧鸣作者2010年发表的文献如下图。

点击N后出现下一幅图16点击右侧下拉菜单，出现下图：本图显示纳入的金慧鸣2010发表的文献属于已经发表。

此处根据study ID 输入具体某文献实际情况分为：已经发表、在研等。

一般都是已发表的文献，所以选择第一项，出现下图：点击N出现下图点击N出现下图此处点击ADD identifier后可以添加前述文献金慧鸣2010的其他标签（如合作研究等自由词）。

一般可以不填写。

点击N出现下图选择add a ref.for the new study，点击finish出现下图此处可以不处理。

点击右上角关闭符号，关闭此页，出现下图此图左侧下方可见金慧鸣2010年文献已经纳入。

点击左下侧，点击右键，出现：下图点击左下侧add study，出现下图。

回到上述第14步骤如下图：依次输入第二篇符合纳入标准的文献：提桂香2013。

见下图表示提桂香2013年发表的如下文献：依次重复前述步骤到下图点击finish，出现下图可见，左下侧已经有金慧鸣2010、提桂香2013共2篇文献。

Gamma5

1 Introduction
The measurement of the inclusive neutral current deep inelastic scattering (DIS) cross section (ep ! eX ) has been one of the prime tasks at HERA. Its precise determination is required in the accessible kinematic range for the understanding of proton substructure and precision tests of QCD. A new, preliminary, result is presented here based on data taken with the H1 detector in 1997 comprising two samples: For momentum transfers squared Q2 > 10 GeV2 the full data taken in the year 1997 has been analyzed using a luminosity of 15 pb?1 . The acceptance of the backward apparatus with the calorimeter SPACAL and the planar drift chamber BDC extends to Q2 120 GeV2 . Very large values of the inelasticity variable y = Q2 =sx are reached in the acceptance region of the central jet drift chamber (CJC) which permits subtraction of the photoproduction background using the measured charge of the scattered positron candidate. Here x is the Bjorken scale variable and s is the centre of mass energy s = 4EeEp determined by the positron and proton beam energies Ee and Ep. For 2 Q2 < 10 GeV2 a data sample with a luminosity of 2 pb?1 was collected in a dedicated run using minimum bias triggers at the end of the 1997 running period. The measurement accuracy was improved and its range extended towards low values of y in the acceptance region of the backward silicon tracker (BST) which has been used for the rst time in the inclusive cross section measurement for track and vertex reconstruction and for photoproduction background rejection. This paper describes the analysis of the new data which leads to a number of results presented here: The deep inelastic scattering cross section is measured with a statistical precision of about 1%. The systematic uncertainty has reached a new level of accuracy with values between about 3% and 4% in the bulk region of this data which are compared with the previous H1 measurements 1{4]. The improved accuracy permits a new analysis of the proton structure function F2 (x; Q2 ) and of the DIS cross section using NLO QCD and including data of the muon-nucleon xed target experiments and also the preliminary high Q2 200 GeV2 cross section data recently released by H1 5]. The data has been used to measure at xed Q2 the derivative of the reduced cross section r , @ =@ log y, which is determined accurately due to the large data statistics and cancellation of systematic e ects. Using this cross section derivative data a novel method is introduced to access at large y the longitudinal structure function FL (x; Q2 ) extending its determination to the region of low Q2 . The paper is organized as follows. Section 2 describes the cross section measurement, i.e. the DIS kinematics, the H1 detector, the event selection and simulation, and the resulting cross section data. In Section 3 the QCD analysis is presented. Section 4 discusses the high y cross section measurement and the results on the extraction of the longitudinal structure function. A short summary is given in Section 5. 1

1-2007_-_Y_F_Han_-_PreparationofnanosizedMn3O4SBA15catalystforcomplet[retrieved-2016-11-15]

Preparation of nanosized Mn 3O 4/SBA-15catalyst for complete oxidation of low concentration EtOH in aqueous solution with H 2O 2Yi-Fan Han *,Fengxi Chen,Kanaparthi Ramesh,Ziyi Zhong,Effendi Widjaja,Luwei ChenInstitute of Chemical and Engineering Sciences,1Pesek Road,Jurong Island 627833,Singapore Received 11May 2006;received in revised form 18December 2006;accepted 29May 2007Available online 2June 2007AbstractA new heterogeneous Fenton-like system consisting of nano-composite Mn 3O 4/SBA-15catalyst has been developed for the complete oxidation of low concentration ethanol (100ppm)by H 2O 2in aqueous solution.A novel preparation method has been developed to synthesize nanoparticles of Mn 3O 4by thermolysis of manganese (II)acetylacetonate on SBA-15.Mn 3O 4/SBA-15was characterized by various techniques like TEM,XRD,Raman spectroscopy and N 2adsorption isotherms.TEM images demonstrate that Mn 3O 4nanocrystals located mainly inside the SBA-15pores.The reaction rate for ethanol oxidation can be strongly affected by several factors,including reaction temperature,pH value,catalyst/solution ratio and concentration of ethanol.A plausible reaction mechanism has been proposed in order to explain the kinetic data.The rate for the reaction is supposed to associate with the concentration of intermediates (radicals: OH,O 2Àand HO 2)that are derived from the decomposition of H 2O 2during reaction.The complete oxidation of ethanol can be remarkably improved only under the circumstances:(i)the intermediates are stabilized,such as stronger acidic conditions and high temperature or (ii)scavenging those radicals is reduced,such as less amount of catalyst and high concentration of reactant.Nevertheless,the reactivity of the presented catalytic system is still lower comparing to the conventional homogenous Fenton process,Fe 2+/H 2O 2.A possible reason is that the concentration of intermediates in the latter is relatively high.#2007Elsevier B.V .All rights reserved.Keywords:Hydrogen peroxide;Fenton catalyst;Complete oxidation of ethanol;Mn 3O 4/SBA-151.IntroductionRemediation of wastewater containing organic constitutes is of great importance because organic substances,such as benzene,phenol and other alcohols may impose toxic effects on human and animal anic efﬂuents from pharmaceu-tical,chemical and petrochemical industry usually contaminate water system by dissolving into groundwater.Up to date,several processes have been developed for treating wastewater that contains toxic organic compounds,such as wet oxidation with or without solid catalysts [1–4],biological oxidation,supercritical oxidation and adsorption [5,6],etc.Among them,catalytic oxidation is a promising alternative,since it avoids the problem of the adsorbent regeneration in the adsorption process,decreases signiﬁcantly the temperature and pressure in non-catalytic oxidation techniques [7].Generally,the disposalof wastewater containing low concentration organic pollutants (e.g.<100ppm)can be more costly through all aforementioned processes.Thus,catalytic oxidation found to be the most economical way for this purpose with considering its low cost and high efﬁciency.Currently,a Fenton reagent that consists of homogenous iron ions (Fe 2+)and hydrogen peroxide (H 2O 2)is an effective oxidant and widely applied for treating industrial efﬂuents,especially at low concentrations in the range of 10À2to 10À3M organic compounds [8].However,several problems raised by the homogenous Fenton system are still unsolved,e.g.disposing the iron-containing waste sludge,limiting the pH range (2.0–5.0)of the aqueous solution,and importantly irreversible loss of activity of the reagent.To overcome these drawbacks raised from the homogenous Fenton system,since 1995,a heterogeneous Fenton reagent using metal ions exchanged zeolites,i.e.Fe/ZSM-5has proved to be an interesting alternative catalytic system for treating wastewater,and showed a comparable activity with the homogenous Fenton system [9].However,most reported heterogeneous Fenton reagents still need UV radiation during/locate/apcatbApplied Catalysis B:Environmental 76(2007)227–234*Corresponding author.Tel.:+6567963806.E-mail address:han_yi_fan@.sg (Y .-F.Han).0926-3373/$–see front matter #2007Elsevier B.V .All rights reserved.doi:10.1016/j.apcatb.2007.05.031oxidation of organic compounds.This might limit the application of homogeneous Fenton system.Exploring other heterogeneous catalytic system considering the above disadvantages,is still desirable for this purpose.Here,we present an alternative catalytic system for the complete oxidation of organic com-pounds in aqueous solution using supported manganese oxide as catalyst under mild conditions,which has rarely been addressed.Mn-containing oxide catalysts have been found to be very active for the catalytic wet oxidation of organic efﬂuents (CWO)[10–14],which is operated at high air pressures(1–22MPa)and at high temperatures(423–643K)[15].On the other hand,manganese oxide,e.g.MnO2[16],is well known to be active for the decomposition of H2O2in aqueous solution to produce hydroxyl radical( OH),which is considered to be the most robust oxidant so far.The organic constitutes can be deeply oxidized by those radicals rapidly[17].The only by-product is H2O from decomposing H2O2.Therefore,H2O2is a suitable oxidant for treating the wastewater containing organic compounds.Due to the recent progress in the synthesis of H2O2 directly from H2and O2[18,19],H2O2is believed to be produced through more economical process in the coming future.So,the heterogeneous Fenton system is economically acceptable.In this study,nano-crystalline Mn3O4highly dispersed inside the mesoporous silica,SBA-15,has been prepared by thermolysis of organic manganese(II)acetylacetonate in air. We expect the unique mesoporous structure may provide add-itional function(conﬁnement effect)to the catalytic reaction, i.e.occluding/entrapping large organic molecules inside pores. The catalyst as prepared has been examined for the complete oxidation of ethanol in aqueous solution with H2O2,or to say, wet peroxide oxidation.Ethanol was selected as a model organic compound because(i)it is one of the simplest organic compounds and can be easily analyzed,(ii)it has high solu-bility in water due to its strong hydrogen bond with water molecule and(iii)the structure of ethanol is quite stable and only changed through catalytic reaction.Presently,for theﬁrst time by using the Mn3O4/SBA-15catalyst,we investigated the peroxide ethanol oxidation affected by factors such as temperature,pH value,ratio of catalyst(g)and volume of solution(L),and concentration of ethanol in aqueous solution. In addition,plausible reaction mechanisms are established to explain the peroxidation of ethanol determined by the H2O2 decomposition.2.Experimental2.1.Preparation and characterization of Mn3O4/SBA-15 catalystSynthesis of SBA-15is similar to the previous reported method[20]by using Pluronic P123(BASF)surfactant as template and tetraethyl orthosilicate(TEOS,98%)as silica source.Manganese(II)acetylacetonate([CH3COCH C(O)CH3]2Mn,Aldrich)by a ratio of2.5mmol/gram(SBA-15)wereﬁrst dissolved in acetone(C.P.)at room temperature, corresponding to ca.13wt.%of Mn3O4with respect to SBA-15.The preparation method in detail can be seen in our recent publications[21,22].X-ray diffraction proﬁles were obtained with a Bruker D8 diffractometer using Cu K a radiation(l=1.540589A˚).The diffraction pattern was taken in the Bragg angle(2u)range at low angles from0.68to58and at high angles from308to608at room temperature.The XRD patterns were obtained by scanning overnight with a step size:0.028per step,8s per step.The dispersive Raman microscope employed in this study was a JY Horiba LabRAM HR equipped with three laser sources(UV,visible and NIR),a confocal microscope,and a liquid nitrogen cooled charge-coupled device(CCD)multi-channel detector(256pixelsÂ1024pixels).The visible 514.5nm argon ion laser was selected to excite the Raman scattering.The laser power from the source is around20MW, but when it reached the samples,the laser output was reduced to around6–7MW after passing throughﬁltering optics and microscope objective.A100Âobjective lens was used and the acquisition time for each Raman spectrum was approximately 60–120s depending on the sample.The Raman shift range acquired was in the range of50–1200cmÀ1with spectral resolution1.7–2cmÀ1.Adsorption and desorption isotherms were collected on Autosorb-6at77K.Prior to the measurement,all samples were degassed at573K until a stable vacuum of ca.5m Torr was reached.The pore size distribution curves were calculated from the adsorption branch using Barrett–Joyner–Halenda(BJH) method.The speciﬁc surface area was assessed using the BET method from adsorption data in a relative pressure range from 0.06to0.10.The total pore volume,V t,was assessed from the adsorbed amount of nitrogen at a relative pressure of0.99by converting it to the corresponding volume of liquid adsorbate. The conversion factor between the volume of gas and liquid adsorbate is0.0,015,468for N2at77K when they are expressed in cm3/g and cm3STP/g,respectively.The measurements of transmission electron microscopy (TEM)were performed at Tecnai TF20S-twin with Lorentz Lens.The samples were ultrasonically dispersed in ethanol solvent,and then dried over a carbon grid.2.2.Kinetic measurement and analysisThe experiment for the wet peroxide oxidation of ethanol was carried out in a glass batch reactor connected to a condenser with continuous stirring(400rpm).Typically,20ml of aqueous ethanol solution(initial concentration of ethanol: 100ppm)wasﬁrst taken in the round bottomﬂask(reactor) together with5mg of catalyst,corresponding to ca.1(g Mn)/30 (L)ratio of catalyst/solution.Then,1ml of30%H2O2solution was introduced into the reactor at different time intervals (0.5ml at$0min,0.25ml at32min and0.25ml at62min). The total molar ratio of H2O2/ethanol is about400/1. Hydrochloric acid(HCl,0.01M)was used to acidify the solution if necessary.NH4OH(0.1M)solution was used to adjust pH to9.0when investigating the effect of pH.The pH for the deionized water is ca.7.0(Oakton pH meter)and decreased to 6.7after adding ethanol.All the measurements wereY.-F.Han et al./Applied Catalysis B:Environmental76(2007)227–234 228performed under the similar conditions described above if without any special mention.For comparison,the reaction was also carried out with a typical homogenous Fenton reagent[17], FeSO4(5ppm)–H2O2,under the similar reaction conditions.The conversion of ethanol during reaction was detected using gas chromatography(GC:Agilent Technologies,6890N), equipped with HP-5capillary column connecting to a thermal conductive detector(TCD).There is no other species but ethanol determined in the reaction system as evidenced by the GC–MS. Ethanol is supposed to be completely oxidized into CO2and H2O.The variation of H2O2concentration during reaction was analyzed colorimetrically using a UV–vis spectrophotometer (Epp2000,StellarNet Inc.)after complexation with a TiOSO4/ H2SO4reagent[18].Note that there was almost no measurable leaching of Mn ion during reaction analyzed by ICP(Vista-Mpx, Varian).3.Results and discussion3.1.Characterization of Mn3O4/SBA-15catalystThe structure of as-synthesized Mn3O4inside SBA-15has beenﬁrst investigated with powder XRD(PXRD),and the proﬁles are shown in Fig.1.The proﬁle at low angles(Fig.1a) suggests that SBA-15still has a high degree of hexagonal mesoscopic organization even after forming Mn3O4nanocrys-tals[23].Several peaks at high angles of XRD(Fig.1b)indicate the formation of a well-crystallized Mn3O4.All the major diffraction peaks can be assigned to hausmannite Mn3O4 structure(JCPDS80-0382).By N2adsorption measurements shown in Fig.2,the pore volume and speciﬁc surface areas(S BET)decrease from 1.27cm3/g and937m2/g for bare SBA-15to0.49cm3/g and 299m2/g for the Mn3O4/SBA-15,respectively.About7.7nm of mesoporous diameter for SBA-15decreases to ca.6.3nm for Mn3O4/SBA-15.The decrease of the mesopore dimension suggests the uniform coating of Mn3O4on the inner walls of SBA-15.This nano-composite was further characterized by TEM. Obviously,the SBA-15employed has typical p6mm hex-agonal morphology with the well-ordered1D array(Fig.3a). The average pore size of SBA-15is ca.8.0nm,which is very close to the value(ca.7.7nm)determined by N2adsorption. Along[001]orientation,Fig.3b shows that the some pores areﬁlled with Mn3O4nanocrystals.From the pore A to D marked in Fig.3b correspond to the pores from empty to partially and fullyﬁlled;while the features for the SBA-15 nanostructure remains even after forming Mn3O4nanocrys-tals.Nevertheless,further evidences for the location of Mn3O4inside the SBA-15channels are still undergoing in our group.Raman spectra obtained for Mn3O4/SBA-15is presented in Fig.4a.For comparison the Raman spectrum was also recorded for the bulk Mn3O4(97.0%,Aldrich)under the similar conditions(Fig.4b).For the bulk Mn3O4,the bands at310,365, 472and655cmÀ1correspond to the bending modes of Mn3O4, asymmetric stretch of Mn–O–Mn,symmetric stretch of Mn3O4Fig.1.XRD patterns of the bare SBA-15and the Mn3O4/SBA-15nano-composite catalyst.(a)At low angles:(A)Mn3O4/SBA-15,(B)SBA-15;and (b)at high angles of Mn3O4/SBA-15.Fig.2.N2adsorption–desorption isotherms:(!)SBA-15,(~)Mn3O4/SBA-15.Y.-F.Han et al./Applied Catalysis B:Environmental76(2007)227–234229groups,respectively [24–26].However,a downward shift ($D n 7cm À1)of the peaks accompanying with a broadening of the bands was observed for Mn 3O 4/SBA-15.For instance,the distinct feature at 655cm À1for the bulk Mn 3O 4shifted to 648cm À1for the nanocrystals.The Raman bands broadened and shifted were observed for the nanocrystals due to the effect of phonon conﬁnement as suggested previously in the literature [27,28].Furthermore,a weak band at 940cm À1,which should associate with the stretch of terminal Mn O,is an indicative of the existence of the isolated Mn 3O 4group [26].The assignment of this unique band has been discussed in our previous publication [22].3.2.Kinetic study3.2.1.Blank testsUnder a typical reaction conditions,that is,20ml of 100ppm ethanol aqueous solution (pH 6.7)mixed with 1ml of 30%H 2O 2,at 343K,there is no conversion of ethanol was observed after running for 120min in the absence of catalyst or in the presence of bare SBA-15(5mg).Also,under the similar conditions in H 2O 2-free solution,ethanol was not converted for all blank tests even with Mn 3O 4/SBA-15catalyst (5mg)in the reactor.It suggests that a trace amount of oxygen dissolved in water or potential dissociation of adsorbed ethanol does not have any contribution to the conversion of ethanol under reaction conditions.To study the effect of low temperature evaporation of ethanol during reaction,we further examined the concentration of ethanol (100ppm)versus time at different temperatures in the absence of catalyst and H 2O 2.Loss of ca.5%ethanol was observed only at 363K after running for 120min.Hence,to avoid the loss of ethanol through evaporation at high temperatures,which may lead to a higher conversion of ethanol than the real value,the kinetic experiments in this study were performed at or below 343K.The results from blank tests conﬁrm clearly that ethanol can be transformed only by catalytic oxidation during reaction.3.2.2.Effect of amount of catalystThe effect of amount of catalyst on ethanol oxidation is presented in Fig.5.Different amounts of catalyst ranging from 2to 10mg were taken for the same concentration of ethanol (100ppm)in aqueous solution under the standard conditions.It can be observed that the conversion of ethanol increases monotonically within 120min,reaching 15,20and 12%for 2,5and 10mg catalysts,respectively.On the other hand,Fig.5shows that the relative reaction rates (30min)decreased from 0.7to ca 0.1mmol/g Mn min with the rise of catalyst amount from 2to 10mg.Apparently,more catalyst in the system may decrease the rate for ethanol peroxidation,and a proper ratio of catalyst (g)/solution (L)is required for acquiring a balance between the overall conversion of ethanol and reaction rate.In order to investigate the effects from other factors,5mg (catalyst)/20ml (solution),corresponding to 1(g Mn )/30(L)ratio of catalyst/solution,has been selected for the followedexperiments.Fig.4.Raman spectroscopy of the Mn 3O 4/SBA-15(a)and bulk Mn 3O 4(b).Fig.3.TEM images recorded along the [001]of SBA-15(a),Mn 3O 4/SBA-15(b):pore A unﬁlled with hexagonal structure,pores B and C partially ﬁlled and pore D completely ﬁlled.Y.-F .Han et al./Applied Catalysis B:Environmental 76(2007)227–2342303.2.3.Effect of temperatureAs shown in Fig.6,the reaction rate increases with increasing the reaction temperature.After 120min,the conversion of ethanol increases from 12.5to 20%when varying the temp-erature from 298to 343K.Further increasing the temperature was not performed in order to avoid the loss of ethanol by evaporation.Interestingly,the relative reaction rate increased with time within initial 60min at 298and 313K,but upward tendency was observed above 333K.3.2.4.Effect of pHIn the pH range from 2.0to 9.0,as illustrated in Fig.7,the reaction rate drops down with the rise of pH.It indicates that acidic environment,or to say,proton concentration ([H +])in the solution is essential for this reaction.With considering our target for this study:purifying water,pH approaching to 7.0in the reaction system is preferred.Because acidifying the solution with organic/inorganic acids may potentially causea second time pollution and result in surplus cost.Actually,there is almost no effect on ethanol conversion with changing pH from 5.5to 6.7in this system.It is really a merit comparing with the conventional homogenous Fenton system,by which the catalyst works only in the pH range of 2.0–5.0.3.2.5.Effect of ethanol concentrationThe investigation of the effect of ethanol concentration on the reaction rate was carried out in the ethanol ranging from 50to 500ppm.The results in Fig.8show that the relative reaction rate increased from 0.07to 2.37mmol/g Mn min after 120min with increasing the concentration of ethanol from 50to 500ppm.It is worth to note that the pH value of the solution slightly decreased from 6.7to 6.5when raising the ethanol concentration from 100to 500ppm.paring to a typical homogenous Fenton reagent For comparison,under the similar reaction conditions ethanol oxidation was performed using aconventionalFig.5.The ethanol oxidation as a function of time with different amount of catalyst.Conversion of ethanol vs.time (solid line)on 2mg (&),5mg (*)and 10mg (~)Mn 3O 4/SBA-15catalyst,the relative reaction rate vs.time (dash line)on 2mg (&),5mg (*)and 10mg (~)Mn 3O 4/SBA-15catalyst.Rest conditions:20ml of ethanol (100ppm),1ml of 30%H 2O 2,708C and pH of6.7.Fig.6.The ethanol oxidation as a function of temperature.Conversion of ethanol vs.time (solid line)at 258C (&),408C (*),608C (~)and 708C (!),the relative reaction rate vs.time (dash line)at 258C (&),408C (*),608C (~)and 708C (5).Rest conditions:20ml of ethanol (100ppm),1ml of 30%H 2O 2,pH of 6.7,5mg ofcatalyst.Fig.7.The ethanol oxidation as a function of pH value.Conversion of ethanol vs.time (solid line)at pH value of 2.0(&),3.5(*),4.5(~),5.5(!),6.7(^)and 9.0("),the relative reaction rate vs.time (dash line)at pH value of 2.0(&),3.5(*),4.5(~),5.5(5),6.7(^)and 9.0(").Rest conditions:20ml of ethanol (100ppm),1ml of 30%H 2O 2,708C,5mg ofcatalyst.Fig.8.The ethanol oxidation as a function of ethanol concentration.Conver-sion of ethanol vs.time (solid line)for ethanol concentration (ppm)of 50(&),100(*),300(~),500(!),the relative reaction rate vs.time (dash line)for ethanol concentration (ppm)of 50(&),100(*),300(~),500(5).Condi-tions:20ml of ethanol,pH of 6.7,1ml of 30%H 2O 2,708C,5mg of catalyst.Y.-F .Han et al./Applied Catalysis B:Environmental 76(2007)227–234231homogenous reagent,Fe 2+(5ppm)–H 2O 2(1ml)at pH of 5.0.It has been reported to be an optimum condition for this system [17].As shown in Fig.9,the reaction in both catalytic systems exhibits a similar behavior,that is,the conversion of ethanol increases with extending the reaction time.Varying reaction temperature from 298to 343K seems not to impact the conversion of ethanol when using the homogenous Fenton reagent.Furthermore,the conversion of ethanol (deﬁning at 120min)in the system of Mn 3O 4/SBA-15–H 2O 2is about 60%of that obtained from the conventional Fenton reagent.There are no other organic compounds observed in the reaction mixture other than ethanol suggesting that ethanol directly decomposing to CO 2and H 2O.3.2.7.Decomposition of H 2O 2In the aqueous solution,the capability of metal ions such as Fe 2+and Mn 2+has long been evidenced to be effective on the decomposition of H 2O 2to produce the hydroxyl radical ( OH),which is oxidant for the complete oxidation/degrading of organic compounds [9,17].Therefore,ethanol oxidation is supposed to be associated with H 2O 2decomposition.The investigation of H 2O 2decomposition has been performed under the reaction conditions (in an ethanol-free solution)with different amounts of catalyst.H 2O 2was introduced into the reaction system by three steps,initially 0.5ml followed by twice 0.25ml at 32and 62min,the pH of 6.7is set for all experiments except pH of 5.0for Fe 2+.As shown in Fig.10,H 2O 2was not converted in the absence of catalyst or presence of bare SBA-15(5mg);in contrast,by using the Mn 3O 4/SBA-15catalyst we observed that ca.Ninety percent of total H 2O 2was decomposed in the whole experiment.It can be concluded that that dissociation of H 2O 2is mainly caused by Mn 3O paratively,the rate of H 2O 2decomposition is relatively low with the homogenous Fenton reagent,total conversion of H 2O 2,was ca.50%after runningfor 120min.Considering the fact that H 2O 2decomposition can be signiﬁcantly enhanced with the rise of Fe 2+concentration,however,it seems not to have the inﬂuence on the reaction rate for ethanol oxidation simultaneously.The similar behavior of H 2O 2decomposition was also observed during ethanol oxidation.The rate for ethanol oxidation is lower for Mn 3O 4/SBA-15comparing to the conventional Fenton reagent.The possible reasons will be discussed in the proceeding section.3.3.Plausible reaction mechanism for ethanol oxidation with H 2O 2In general,the wet peroxide oxidation of organic constitutes has been suggested to proceed via four steps [15]:activation of H 2O 2to produce OH,oxidation of organic compounds withOH,recombination of OH to form O 2and wet oxidation of organic compounds with O 2.It can be further described by Eqs.(1)–(4):H 2O 2À!Catalyst =temperture 2OH(1)OH þorganic compoundsÀ!Temperatureproduct(2)2 OHÀ!Temperature 12O 2þH 2O(3)O 2þorganic compoundsÀ!Temperature =pressureproduct(4)The reactive intermediates produced from step 1(Eq.(1))participate in the oxidation through step 2(Eq.(2)).In fact,several kinds of radical including OH,perhydroxyl radicals ( HO 2)and superoxide anions (O 2À)may be created during reaction.Previous studies [29–33]suggested that the process for producing radicals could be expressed by Eqs.(5)–(7)when H 2O 2was catalytically decomposed by metal ions,such asFeparison of ethanol oxidation in systems of typical homogenous Fenton catalyst (5ppm of Fe 2+,20ml of ethanol (100ppm),1ml of 30%H 2O 2,pH of 5.0acidiﬁed with HCl)at room temperature (~)and 708C (!),and Mn 3O 4/SBA-15catalyst (&)under conditions of 20ml of ethanol (100ppm),pH of 6.7,1ml of 30%H 2O 2,708C,5mg ofcatalyst.Fig.10.An investigation of H 2O 2decomposition under different conditions.One milliliter of 30%H 2O 2was dropped into the 20ml deionized water by three intervals,initial 0.5ml followed by twice 0.25ml at 32and 62min.H 2O 2concentration vs.time:by calculation (&),without catalyst (*),SBA-15(~),5ppm of Fe 2+(!)and Mn 3O 4/SBA-15(^).Rest conditions:5mg of solid catalyst,pH of 7.0(5.0for Fe 2+),708C.Y.-F .Han et al./Applied Catalysis B:Environmental 76(2007)227–234232and Mn,S þH 2O 2!S þþOH Àþ OH (5)S þþH 2O 2!S þ HO 2þH þ(6)H 2O $H þþO 2À(7)where S and S +represent reduced and oxidized metal ions,both the HO 2and O 2Àare not stable and react further with H 2O 2to form OH through Eqs.(8)and (9):HO 2þH 2O 2! OH þH 2O þO 2(8)O 2ÀþH 2O 2! OH þOH ÀþO 2(9)Presently, OH radical has been suggested to be the main intermediate responsible for oxidation/degradation of organic compounds.Therefore,the rate for ethanol oxidation in the studied system is supposed to be dependent on the concentra-tion of OH.Note that the oxidation may proceed via step four (Eq.(4))in the presence of high pressure O 2,which is so-called ‘‘wet oxidation’’and usually occurs at air pressures (1–22MPa)and at high temperatures (423–643K)[15].However,it is unlikely to happen in the present reaction conditions.According to Wolfenden’s study [34],we envisaged that the complete oxidation of ethanol may proceed through a route like Eq.(10):C 2H 5OH þ OH À!ÀH 2OC 2H 4O À! OHCO 2þH 2O(10)Whereby,it is believed that organic radicals containing hydroxy-groups a and b to carbon radicals centre can eliminate water to form oxidizing species.With the degrading of organic intermediates step by step as the way described in Eq.(10),the ﬁnal products should be CO 2and H 2O.However,no other species but ethanol was detected by GC and GC–MS in the present study possibly due to the rapid of the reaction that leads to unstable intermediate.Fig.5indicates that a proper ratio of catalyst/solution is a necessary factor to attain the high conversion of ethanol.It can be understood that over exposure of H 2O 2to catalyst will increase the rate of H 2O 2decomposition;but on the other hand,more OH radical produced may be scavenged by catalyst with increasing the amount of catalyst and transformed into O 2and H 2O as expressed in Eq.(3),instead of participating the oxidation reaction.In terms of Eq.(10),stoichiometric ethanol/H 2O 2should be 1/6for the complete oxidation of ethanol;however,in the present system the total molar ratio is 1/400.In other words,most intermediates were extinguished through scavenging during reaction.This may explain well that the decrease of reaction rate with the rise of ratio of catalyst/solution in the system.The same reason may also explain the decrease of reaction rate with prolonging the time.Actually,H 2O 2decomposition (ca.90%)may be completed within a few minutes over the Mn 3O 4/SBA-15catalyst as illustrated in Fig.10,irrespective of amount of catalyst (not shown for the sake of brevity);in contrast,the rate for H 2O 2decomposition became dawdling for Fe 2+catalyst.As a result,presumably,the homogenous system has relatively high concentration ofradicals.It may explain the superior reactivity of the conventional Fenton reagent to the presented system as depicted in Fig.9.Therefore,how to reduce scavenging,especially in the heterogeneous Fenton system [29],is crucial for enhancing the reaction rate.C 2H 5OH þ6H 2O 2!2CO 2þ9H 2O(11)On the other hand,as illustrated by Eqs.(1)–(4),all steps in the oxidation process are affected by the reaction temperature.Fig.6demonstrates that increasing temperature remarkably boosts the reactivity of ethanol oxidation in the system of Mn 3O 4/SBA-15–H 2O 2possibly,due to the improvement of the reactions in Eqs.(2)and (4)at elevated temperatures.In terms of Eqs.(6)and (7),acidic conditions may delay the H 2O 2decomposition but enhance the formation of OH (Eqs.(5),(8)and (9)).This ‘‘delay’’is supposed to reduce the chance of the scavenging of radicals and improve the efﬁciency of H 2O 2in the reaction.The protons are believed to have capability for stabilizing H 2O 2,which has been elucidated well previously [18,19].Consequently,it is understandable that the reaction is favored in the strong acidic environment.Fig.7shows a maximum reactivity at pH of 2.0and the lowest at pH of 9.0.As depicted in Fig.8,the reaction rate for ethanol oxidation is proportional to the concentration of ethanol in the range of 50–500ppm.It suggests that at low concentration of ethanol (100ppm)most of the radicals might not take part in the reaction before scavenged by catalyst.With increasing the ethanol concentration,the possibility of the collision between ethanol and radicals can be increased signiﬁcantly.As a result,the rate of scavenging radicals is reduced relatively.Thus,it is reasonable for the faster rate observed at higher concentration of ethanol.Finally,it is noteworthy that as compared to the bulk Mn 3O 4(Aldrich,98.0%of purity),the reactivity of the nano-crystalline Mn 3O 4on SBA-15is increased by factor of 20under the same typical reaction conditions.Obviously,Mn 3O 4nanocrystal is an effective alternative for this catalytic system.The present study has evidenced that the unique structure of SBA-15can act as a special ‘‘nanoreactor’’for synthesizing Mn 3O 4nanocrystals.Interestingly,a latest study has revealed that iron oxide nanoparticles could be immobilized on alumina coated SBA-15,which also showed excellent performance as a Fenton catalyst [35].However,the role of the pore structure of SBA-15in this reaction is still unclear.We do expect that during reaction SBA-15may have additional function to trap larger organic molecules by adsorption.Thus,it may broaden its application in this ﬁeld.So,relevant study on the structure of nano-composites of various MnO x and its role in the Fenton-like reaction for remediation of organic compounds in aqueous solution is undergoing in our group.4.ConclusionsIn the present study,we have addressed a new catalytic system suitable for remediation of trivial organic compound from contaminated water through a Fenton-like reaction withY.-F .Han et al./Applied Catalysis B:Environmental 76(2007)227–234233。

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Abstract

A model of grounded language acquisition:Sensorimotor features improve lexicaland grammatical learningSteve R.Howell *,Damian Jankowicz,Suzanna BeckerMcMaster University,Hamilton,Ont.,CanadaReceived 1December 2004;revision received 15March 2005Available online 25April 2005AbstractIt is generally accepted that children have sensorimotor mental representations for concepts even before they learn the words for those concepts.We argue that these prelinguistic and embodied concepts direct and ground word learn-ing,such that early concepts provide scaﬀolding by which later word learning,and even grammar learning,is enabled and facilitated.We gathered numerical ratings of the sensorimotor features of many early words (352nouns,90verbs)using adult human participants.We analyzed the ratings to demonstrate their ability to capture the embodied meaning of the underlying concepts.Then using a simulation experiment we demonstrated that with language corpora of suﬃ-cient complexity,neural network (SRN)models with sensorimotor features perform signiﬁcantly better than models without features,as evidenced by their ability to perform word prediction,an aspect of grammar.We also discuss the possibility of indirect acquisition of grounded meaning through ‘‘propagation of grounding’’for novel words in these networks.Ó2005Elsevier Inc.All rights reserved.Keywords:Language acquisition;Features;Semantics;SRN;Neural network;Sensorimotor;Conceptual learningConsiderable evidence suggests that by the time chil-dren ﬁrst begin to learn words around the age of 10–12months,they have already acquired a fair amount of sensorimotor (sensory/perceptual and motor/physical)knowledge about the environment (e.g.,Lakoﬀ,1987;Lakoﬀ&Johnson,1999;Bloom,2000;Langer,2001),especially about objects and their physical and percep-tual properties.By this age children are generally able to manipulate objects,navigate around their environ-ment,and attend to salient features of the world,includ-ing parental gaze and other cues important for word learning (Bloom,2000).Some have suggested that this pre-linguistic conceptual knowledge has a considerable eﬀect on the processes of language acquisition (Lakoﬀ,1987;Mandler,1992;Smith &Jones,1993)and even on later language processing (e.g.,Glenberg &Kaschak,2002;Barsalou,1999).We also argue that the evidence indicates that this early prelinguistic knowledge has great impact,directly and indirectly,throughout a num-ber of phases of language learning,and we attempt to begin to demonstrate this with a neural networkmodel.Journal of Memory and Language 53(2005)258–276Journal of Memory and Language0749-596X/$-see front matter Ó2005Elsevier Inc.All rights reserved.doi:10.1016/j.jml.2005.03.002*Corresponding author.Present address:Department of Psychology (WJ Brogden Hall),University of Wisconsin-Madison,1202West Johnson Street,Madison,WI 53703,USA.Fax:+16082624029.E-mail address:showell@ (S.R.Howell).To begin with,this prelinguistic conceptual informa-tion helps children to learn theirﬁrst words,which cor-respond to the most salient and imageable(Gillette, Gleitman,Gleitman,&Lederer,1999)objects and ac-tions in their environment,the ones they have the most experience with physically and perceptually.Generally speaking,the more‘‘concrete’’or‘‘imageable’’a word, the earlier it will be learned.This helps to explain the preponderance of nouns in childrenÕs early vocabularies (see Gentner,1982).The meanings of verbs are simply more diﬃcult to infer from context,as discussed by as demonstrated by Gillette et al.(1999).Only the most clearly observable or‘‘concrete’’verbs make it into chil-drenÕs early vocabularies.However,later verbs are ac-quired through the assistance of earlier-learned nouns. If a language learner hears a simple sentence describing a real-world situation,such as a dog chasing a cat,and already knows the words dog and cat,the only remain-ing word must be describing the event,especially if the learner already has built up a pre-linguistic concept of ‘‘dogs chasing cats’’at the purely observational level. As Bloom(2000)describes,the best evidence for ‘‘fast-mapping’’or one-shot learning of words in chil-dren comes from similar situations in which only one word in an utterance is unknown,and it has a clear, previously unknown,physical referent present.Of course,since the verb chase refers to an event rather than an object,the above example is not an exactﬁt to the fast-mapping phenomenon as it is usually de-scribed,but it is similar.These veryﬁrst words that children learn thus help constrain the under-determined associations between the words children hear and the objects and events in their environment,and help children to successfully map new words to their proper referents.This happens through the use of cognitive heuristics such as the idea that a given object has one and only one name(Mark-man&Wachtel,1988),or more basic object-concept primitives(Bloom,2000)such as object constancy.With a critical mass of some50words,children begin to learn how to learn new words,using heuristics such as the count-noun frame,or the adjective frame(Smith, 1999).These frames are consistent sentence formats of-ten used by care-givers that enable accurate inference on the part of the child as to the meaning of the framed word,e.g.,‘‘This is a___.’’These factors combine to produce a large increase in childrenÕs lexical learning at around20months.As they begin to reach another critical mass of words in their lexicon(approaching 300words),they start to put words together with other words—the beginnings of expressive grammar(Bates& Goodman,1999).Around28months of age children en-ter a‘‘grammar burst’’in which they rapidly acquire more knowledge of the syntax and grammar of their lan-guage,and continue to approach mature performance over the next few years.By this account of language acquisition,conceptual development has primacy;it sets the foundation for the language learning that will follow.Words are given meaning quite simply,by their associations to real-world,perceivable events.Words are directly grounded in embodied meaning,at least for the earliest words. Of course,it may not be just simple statistical associa-tions between concepts and words in the environment; the child is an active learner,and processes like joint attention or theory of mind may greatly facilitate the learning of word to meaning mappings(Bloom,2000).Of course,it seems clear that the incredible word-learning rates displayed by older children(Bloom, 2000)indicate that words are also acquired by linguistic context,through their relations to other words.Children simply are learning so many new words each day that it seems impossible that they are being exposed to the ref-erents of each new word directly.The meanings of these later words,and most of the more abstract,less image-able words we learn as adults,must clearly be acquired primarily by their relationships to other known words. It may in fact be true that these meanings can only be ac-quired indirectly,through relationships established to the meanings of other words.Evidence for the indirect acquisition of meaning is not limited to the speed with which children learn words. The work of Landauer and colleagues(e.g.,Landauer and Dumais,1997;Landauer,Laham,&Foltz,1998) provides perhaps the clearest demonstration that word ‘‘meanings’’can be learned solely from word-to-word relationships(although see Burgess&Lund,2000;for a diﬀerent method called HAL).LandauerÕs Latent Semantic Analysis(LSA)technique takes a large corpus of text,such as a book or encyclopedia,and creates a matrix of co-occurrence statistics for words in relation to the paragraphs in which they occur.Applying singu-lar-value decomposition to this matrix allows one to map the words into a high-dimensional space with dimensions ordered by signiﬁcance.This high-dimen-sional representation is then reduced to a more manage-able number of dimensions,usually300or so,by discarding the least signiﬁcant dimensions.The resulting compressed meaning vectors have been used by Landa-uer et.al.in many human language tasks,such as multi-ple choice vocabulary tests,domain knowledge tests,or grading of student exams.In all these cases,the LSA representations demonstrated human-level performance.While models based on these high-dimensional repre-sentations of meaning such as LSA and HAL perform well on real world tasks,using realistically sized vocab-ularies and natural human training corpora,they do have several drawbacks.First,they lack any consider-ation of syntax,since the words are treated as unordered collections(aÔbag of wordsÕ).Second,LSA and HAL meaning vectors lack any of the grounding in reality that comes naturally to a human language learner.Experi-S.R.Howell et al./Journal of Memory and Language53(2005)258–276259ments by Glenberg and Robertson(2000)have shown that the LSA method does poorly at the kinds of reason-ing in novel situations that are simple for human seman-tics to resolve,due largely to the embodied nature of human semantics.So it seems that there are two sources of meaning,di-rect embodied experience,and indirect relations to other words.However,there is an inﬁnite regress in the latter. If words are only ever deﬁned in relation to other words, we can never extract meaning from the system.We would have only a recursive system of self-deﬁned mean-ing,symbols chained to other symbols(similar to SearleÕs Chinese Room argument,Searle,1980).To avoid this dilemma,at least some of the words in our vocabularies must be deﬁned in terms of something external.In children,at least,the earliest words serve this role.They are deﬁned by their mappings to pre-lin-guistic sensory and motor experience,as discussed above.They do not require other words to deﬁne their meaning.The most imageable words are thus directly grounded,while the less imageable and more abstract words that are encountered during later learning are more and more indirectly grounded.At some point, we argue,the adult semantic system begins to look much like the LSA or HAL high-dimensional meaning space, with our many abstract words(e.g.,love,loyalty,etc.) deﬁned by relations among words themselves.However, the mature human semantic system is superior to the high-dimensional models,since it can trace its meaning representations back to grounded,embodied meaning, however indirectly for abstract words.Intuitively,this is something like trying to explain an abstract concept like‘‘love’’to a child by using concrete examples of scenes or situations that are associated with love.The abstract concept is never fully grounded in external reality,but it does inherit some meaning from the more concrete concepts to which it is related.Part of the concrete wordsÕembodied,grounded,meaning becomes attached to the abstract words which are often linked with it in usage.By our account,the grounded meaningÔpropagatesÕup through the syntactic links of the co-occurrence meaning network,from the simplest early words to the most abstract.Thus we have chosen to call this the‘‘propagation of grounding’’problem.We argue that this melding of direct,embod-ied,grounded meaning with high-dimensional,word co-occurrence meaning is a vital issue in understanding conceptual development,and hence language develop-ment.We believe it is essential to resolving the disputes between embodied meaning researchers and high-dimen-sional meaning researchers.In previous work(Howell&Becker,2000,2001;Ho-well,Becker,&Jankowicz,2001)we began developing what we consider to be a promising method for model-ing childrenÕs language acquisition processes using neu-ral networks.In this work,we continue this eﬀort,emphasizing the inclusion of pre-linguistic sensorimotor features that will ground in real-world meaning the words that the network will learn.This is a necessary precursor to addressing the‘‘propagation of grounding’’problem itself.Our overall goal is to capture with one model the es-sence of the process by which children learn theirﬁrst words and theirﬁrst syntax or grammar.As mentioned above,this is a period stretching from the earliest onset of theﬁrst true words(10–12months),through the‘‘lex-ical-development burst’’around20months up to the so-called‘‘grammar burst’’around28months.Developing a network that attempts to model the language acquisi-tion that is happening during this period in children is, of course,an ambitious undertaking,and our models are still relatively simple.However,given the discussion on propagation of grounding above,this sort of devel-opmental progression may actually be necessary not just for children learning language,but also for any abstract language learner such as a neural network or other com-putational model.That is,a multi-stage process of con-strained development may be necessary to simplify the problem and make it learnable,with eachÔstageÕprovid-ing the necessary foundation for the next,and ensuring that meaning continues to be incorporated in the pro-cess.As such,we seek to develop and extend a single model that can progress through theseÕstagesÕof lan-guage acquisition,from initial lexical learning,through rapid lexical expansion,to the learning of the earliest syntax of short utterances.Developing a model thatﬁts developmental behavioral data on child language acqui-sition is one way to ensure that this process is being fol-lowed.For the simulations reported here,we have adopted and extended the Simple-Recurrent Network architecture that has been shown many times to be capa-ble of learning simple aspects of grammar,namely basic syntax(e.g.,Elman,1990,1993;Howell&Becker,2001). Furthermore,SRNÕs have been shown to be able to pro-duce similar results to high-dimensional meaning mod-els.Burgess and Lund(2000)point out that their HAL method using their smallest text window produces simi-lar results in word meaning clustering to an Elman SRN. Also,they state that the SRN is somewhat more sensi-tive to grammatical nuances.SRNÕs may be able to model the acquisition of meaning and grammar,unlike the high-dimensional approaches.The present emphasis of our model is on the inclu-sion of sensorimotor knowledge of concepts or words (for clarity,in what follows we use the term‘‘concept’’to mean the mental representation of a thing or action, and the term‘‘word’’to mean merely the linguistic sym-bol that represents it).This pre-linguistic sensorimotor knowledge(following Lakoﬀ,1987)is represented by a set of features for each word presented to the network, features that attempt to capture perceptual and motor aspects of a concept,such as‘‘size,’’or‘‘hardness,’’or260S.R.Howell et al./Journal of Memory and Language53(2005)258–276‘‘has feathers’’.If a word that the network experiences is accompanied by a set of values or ratings on these fea-ture dimensions,then the network should be able to do more than just manipulate the abstract linguistic symbol of the concept(the word itself).Like a child learning theﬁrst words,it should then have some access to the meaning of the concept.The networkÕs under-standing would be grounded in embodied meaning,at least at the somewhat abstracted level available to a model without any actual sensory abilities of its own.Unlike most existing language models that employ semantic features(e.g.,Hinton&Shallice,1991;McRae, de Sa,&Seidenberg,1997)our sensorimotor feature set has been designed to be pre-linguistic in nature.That is, features that derive from associative knowledge about which words occur together or other language-related associations are excluded.Only features that a preverbal child could reasonably be expected to experience directly through his or her perceptual and motor interactions with the world are included.As discussed above,while childrenÕsﬁrst words are obviously learned without any knowledge of language-related word associations, children quickly begin to incorporate linguistic associa-tive information into the semantic meanings of concepts. Certainly,at some point words begin to acquire meaning not only from the sensory properties of the concept,but from the linguistic contexts in which the word has been experienced.We take the conservative stance herein of excluding any linguistic associative inﬂuences on senso-rimotor meaning;the sensorimotor feature representa-tions do not change with linguistic experience.This is primarily for practical issues of implementation.The network is capable of learning these associations,but they do not aﬀect the sensorimotor feature representa-tions directly.Whereas most language models employ binary fea-tures,our features are scalar-valued(range0–1),allow-ing a network to makeﬁner discriminations than merely the binary presence or absence of a feature. For example,two similar items(for example,two cats) may be perceived,but they are not identical;one is lar-ger.Our dimension of size would diﬀerentiate the two, with one receiving a rating of0.2,one of0.3.Binary fea-tures cannot easily make suchﬁne distinctions.Finally, inspired by the work of McRae et al.(1997)on hu-man-generated semantic features,the feature ratings that we use are all derived empirically from human participants.One of the advantages of the neural network model of child language development that we present below is the ability to measure word-learning performance using analogues of lexical comprehension tasks that have been used with children.Since the network learns to associate the sensorimotor features of each concept with a separate phonemic representation of the word, it is possible to examine the strength of the connection in either direction.Thus,given the phonemes of the word,we can measure the degree to which the network produces the appropriate sensorimotor meaning vector. This we refer to as theÔgroundingÕtask,analogous to when a child is asked questions about a concept and must answer with featural information,such as‘‘What kind of noise does a dog make?’’or‘‘Is the dog furry?’’Similarly,we can also ask if,when given the meaning vector alone,the network will produce the proper word. This is an analogue to theÔnamingÕtask in children, where a parent points to an object and asks‘‘What is that?’’In the network,if the completely correct answer is not produced,we can still measure how close the out-put was to the correct answer.For example,we can check whether the answer was a case of‘‘cat’’produced in place ofÔdogÕ,two concepts with a high degree of fea-tural overlap,or whether it was a complete miss.In this paper,we address the grounding task,but not the nam-ing task,although the model can account for both. However,the central aim of this paper is to investigate the contribution of the sensorimotor features to improv-ing the modelÕs lexical and grammatical learning.In Experiments1and2,we describe the empirical collection of feature ratings for nouns and verbs,respec-tively,and describe the results of several analyses per-formed to verify that they are capturing an abstract representation of the wordsÕmeanings.In Experiment 3we describe simulations of a neural network model using these features and trained with a large naturalistic corpus of child-directed speech.We examine the extent to which the inclusion of sensorimotor features improves lexical and grammatical learning over a control condi-tion,in an attempt to demonstrate the utility of feature grounding for language acquisition.However,it is important to note that in referring to‘‘grammatical learning’’we are in fact only considering the simplest as-pects of grammar,namely basic sequence learning. Experiment1—Generation of noun sensorimotor featuresDeveloping a set of sensorimotor dimensions that are plausible for8-to28-month-old infants was an importantﬁrst stage of this research eﬀort.In our pre-vious models of lexical grounding and acquisition of grammar(Howell&Becker,2001;Howell et al., 2001),we used a more simplistic semantic feature repre-sentation of words(Hinton&Shallice,1991)that was both artiﬁcial and confounded wordsÕconceptual semantics with‘‘associative semantics,’’the linguistic relationships between words.We needed a more child-appropriate set of semantic features.Of course, developing these semantic features actually involves two issues:one,collecting the ratings,and two,making sure that the results of the ratings actually reﬂect realis-tic early semantic relationships.If the collected ratingsS.R.Howell et al./Journal of Memory and Language53(2005)258–276261do not have plausible semantic cohesion,they cannot be very useful in further work.MethodTo avoid having artiﬁcial,experimenter-created semantic feature representations,we investigated the McRae et al.(1997)empirically generated feature set. However,of the thousands of features contained in that set,many were non-perceptual(e.g.,linguistically asso-ciative),and few were common across many concepts. To obtain an appropriate set of input features for a neu-ral network model of child language acquisition,we re-quired a more compact,concrete set of features that are perceptual and motor in nature,and could reasonably capture purely pre-linguistic knowledge.Thus,we nar-rowed down the McRae et al.feature list to some200 common and widely represented features.This list was further condensed by converting each set of polar oppo-sites and intermediate points to a single set of19polar-opposite dimensions.For example,‘‘small’’and‘‘large’’became a single continuous dimension of size,ranging from small(0)to large(10),and eliminating the need for‘‘tiny,’’‘‘medium,’’‘‘huge,’’etc.The remaining78 features which could not be unambiguously converted to a set of polar opposites were retained as a condensed list of scalar-valued dimensions,such as color(is_red)or texture(has_feathers),where the numeric value indi-cated the probability of possession of that feature by that concept.We use the termÔfeature dimensionÕorÔdi-mensionÕto refer to all97dimensions,however,since when considered as components of a meaning vector they each represent a spatial dimension in a97-dimen-sional space.This resulting list of features was then reviewed by an independent developmental psychologist,for accessibil-ity to children of the age range in question(8–28 months),and any features that were not considered developmentally appropriate were removed.For exam-ple,‘‘age’’is not reliably perceived by children beyond simply‘‘young’’or‘‘old’’(urel Trainor,private communication,2001)and so was removed.Theﬁnal list of97sensorimotor feature dimensions (see Appendix A)was small enough to be feasible as in-put for our neural network models,and broad enough to be applicable to many concepts.Given this set of feature dimensions,it was next necessary to obtain ratings of the early concepts along each feature dimension.We used a large sample of human raters to generate the featural ratings for our early words.Our raters were undergrad-uates at McMaster University who participated in this experiment for course credit in an introductory psychol-ogy course.Participants were presented with the concepts and the list of feature dimensions along which to rate them on a computer screen.The display was presented via a web browser,and responses were entered byﬁlling in re-sponse boxes on the display.Participants were given de-tailed instructions(see Appendix A)as to how to make judgments,and which anchoring points to use in assign-ing numerical values.For example,in rating the size of an object,the smallest item a child might know about might beÔpea,Õwhile for adults it might be something microscopic likeÔvirus.ÕThus,participants were speciﬁ-cally instructed to make judgments taking into account the limited frame of reference that a pre-school child would have,especially relevant for polar-opposite dimensions such as‘‘size.’’Participants entered their data as numbers between1and10,which were later scaled down to the0–1range for easier presentation to neural network models.The rating forms were administered over the Internet as web forms.The data was checked carefully for outli-ers.Three participantsÕdata were excluded due to obvi-ous response patterns(all0Õs,all10Õs,1-2-3Õs,etc.), indicating insuﬃcient attention given to the task.Rat-ings were collected for352noun concepts from the Mac-Arthur Communicative Development Inventory (MCDI,Fenson et al.,2000)in38separate phases with approximately10concepts each during winter,2002. Theﬁrst two phases had10participants each;the rest had5participants each,for a total of200participants. Participants received course credit for participation so long as the data were not obviously invalid as discussed above The resulting ratings were then averaged across participants yielding a single feature vector of size97 for each concept,352in all.Three forms of analysis were performed on these newly created feature representations,in order to dem-onstrate that they do capture important aspects of the meanings of the words represented:a hierarchical cluster analysis,a Kohonen self-organizing map,and a Euclid-ian-distance-based categorical membership analysis.ResultsWe analyzed the352averaged feature vectors in a hierarchical cluster analysis using SPSS version11.5, to see whether our features captured our intuitive sense of word similarity.The352concepts clearly clustered by meaning,with subcategories merging nicely into super-ordinate categories(see Appendix B).Animals are sepa-rated from people,people and animals are separated from vehicles and inanimate objects,etc.Thus,while the high degree of variability between participantsÕrat-ings was originally a concern,after averaging,the regu-larity inherent in the feature vectors is quite reassuring. To provide another view on the ratings,the ratings vec-tors were fed into a Self-Organizing Map(Kohonen, 1982,1995)neural network,which sought to group the concepts topographically onto a two-dimensional space based on their feature similarity.The resulting topo-262S.R.Howell et al./Journal of Memory and Language53(2005)258–276graphic organization respects the semantic similarities between concepts,showing intuitive groupings based only on the sensorimotor features of concepts(see Fig.1).Note,for example,the grouping of‘‘creatures that ﬂy’’in the top left corner,and the grouping of parts of the body in the middle-left.A more clearly deﬁned measure of success is provided by the categorical analysis.We formed category cent-roids for each of the pre-existing categories of nouns on the MCDI form from which the words were origi-nally drawn.This was done by taking all of the words that belonged to that category and averaging together their feature vector.Then each and every wordÕs feature vector was compared to the centroids of each of the11 categories represented,and the closest match indicated into which category the word should fall.This was done both with the target word included in the centroid gen-eration process,and with it excluded(a more conserva-tive approach).Results are very good,at92.8and88% accuracy,respectively(Chance performance would be 9.1%).See Table1for details.DiscussionWe believe all three analyses indicate the success of the experiment.The hierarchical clusteringanalysis,while vast and somewhat diﬃcult to interpret,shows many clear separations of concepts,and consistent local clusters of meaning.The SOM representation shows clear clustering by meaning,with bothﬁne-grained and broader similarity structures across the map.Finally, the categorical analysis provides a clear numerical mea-sure of the goodness ofﬁt of our features to the preex-isting categorizations of these nouns,with93% accuracy of word to category.The sensorimotor feature ratings thus capture much of the meaning of the con-cepts,deﬁnitely enough to be useful as inputs to our lan-guage learning model,and they certainly capture whatÕs important for categorical reasoning.Experiment2—Generation of verb sensorimotor features In this experiment we followed much the same meth-odology as for Experiment1,this time for verb features. However,given that verbs correspond to events in the world rather than to objects,the nature of verb features was expected to be diﬀerent from that for nouns.Also, there was no pre-existing taxonomy of verb features readily accessible in the literature,as there had been for nouns(what we mean by verb features is diﬀerent from verbÔclasses,Õthe way verbs are usually grouped). Therefore,our collection of verb features proceeded in two steps.First we conducted a pilot experiment in verb feature generation with human participants,and from that we created a set of verb feature dimensions to be rated in a web-based phase of the experiment exactly as in Experiment1.MethodThe pilot experiment was conducted with12under-graduate participants at McMaster University(see Appendix C for the instructions given to participants). Participants completed a feature generation form for some of the earliest(MCDI,Fenson et al.,2000),and most prototypical(Goldberg,1999)verbs,with the objective being not complete characterization of any gi-ven verb but rather the creative generation of a set of feature dimensions which might be common to many verbs.While fully half of the features generated were unus-able due to contamination by functional relationships with corresponding nouns,associational relationships, etc.,there were suﬃciently many perceptual and motor features identiﬁed to allow us to create an initial set of feature dimensions.From this beginning,we were able toﬁll in missing complements of existing dimensions. For example,several participants focused on limb movement to deﬁne verbs,which is in line with some existing models of verb deﬁnition in computer science (see for example Bailey,Feldman,Narayanan,&Lak-oﬀ,1997).From this and considerations of bodily motion and proprioceptive constraints in humans we were able to generate a large primary set of joint-mo-tion dimensions.We also included some other features that had been identiﬁed by pilot participants,which brought the total to84feature dimensions(see Appen-dix C for a list)A second group of45participants participated in the rating phase of the verb experiment(see Appendix C for the instructions given to participants).As in Experiment 1,they rated each verb on the list with a value between0 and10on the84feature dimensions.Each participant rated10of the concepts.We then converted these rat-ings to the0–1range,which became the feature repre-sentations for verbs used in the Experiment below.We analyzed the results of the experiment(the feature rat-ings)in the same three ways as in Experiment1:a hierarchical cluster analysis(see Appendix D),a self-Table1Noun category agreement results feature vectors compared to centroids of categories drawn from MCDICategory number Category name Inclusive accuracy Exclusive accuracy 1Animals0.82051280.82051282Vehicles0.91666670.753Toys0.91666670.83333334Food and drink115Clothing0.92857140.89285716Body parts10.8620697Small household items118Furniture and rooms0.84848480.78787889Outside things0.93333330.866666710Places to go0.86363640.636363611People0.84615380.8461538Overall0.92836680.8796562264S.R.Howell et al./Journal of Memory and Language53(2005)258–276。

一文学会revman软件Meta简明教程（6）

⼀⽂学会revman软件Meta简明教程（6）Meta简明教程⽬录1. 认识⼀下meta⽅法！ | Meta简明教程（1）2. ⼀⽂初步学会Meta⽂献检索 | Meta简明教程（2）3. 如何搞定“⽂献筛选” | Meta简明教程（3）4.Meta分析⽂献质量评价 | Meta简明教程（4）5.Meta分析数据提取| Meta简明教程（5）Meta简明教程（6）⽬前⽤于meta分析的软件主要是Revman和Stata，⼤家可以到本公众号下载（重磅推荐：分类最全的统计分析相关软件，了解⼀下？请关注、收藏以备⽤），今天将介绍如何利⽤revman软件对提取的数据进⾏meta分析。

Meta分析数据提取| Meta简明教程（5）这⼀⽂中，介绍了四种结局效应的资料，⼆分类数据、连续型数据、诊断性试验数据、⽣存-时间数据，本⽂将结合这四种数据，进⼀步介绍⼀下Revman软件的操作步骤。

Revman软件是国际Cochrane系统评价的标准化专⽤软件，可⽤于数据的分析及meta论⽂的写作，今天介绍的版本是Review Manager5.2⼀、⼆分类数据分析数据形式例：研究阿司匹林（aspirin）预防⼼肌梗死（MI）7个临床随机对照试验，观察死亡率，数据提取如下：操作步骤1.建⽴项⽬1)启动revman 5.2 软件后，在⼯具栏中单击图标新建⼀个项⽬2）出现“new review wizard”对话框，单击“next”3）出现“type of review( new review wizard)”对话框，选择系统评价的类型“intervention review”，点“netx”4）在“title ( new review wizard)”对话框中，输⼊研究的名称，选择“Full review”5）完成项⽬的建⽴2. 添加研究1）展开左侧树型⽬录studies and references→references to studies→ included studies2）点击右键，选中“add study”按钮后，出现如图的“new study wizard”对话框3）在“new study wizard”对话框中，“study id”信息框中输⼊纳⼊分析的每⼀个研究名称及发表的年份（MRC-1 1968），然后按“finish”。

QS5 系列(A5)

1.2KW
6N·m
2000 r/min
6A
220 V Encode 2500P
S/N
M06047243
众为兴数控技术有限公司
生产编号额定旋转速
7
■ 型号的确认方法
ACH
09
075
QS5 系列驱动器
D
/
S
AC－交流伺服电机系列 ACF：F eries ACH：H Series ACK：K Series ACS：S Series
1.1 产品到货时的确认......................................... 7 1.2 产品各部分的名称......................................... 9 第二章 ....................................................... 11 安装 ........................................................ 12 2.1 伺服电机................................................ 12 2.2 伺服驱动器.............................................. 14 第三章 ....................................................... 17 配线 ......................................................... 17 3.1 主电路的配线............................................ 17 3.2 输入与输出信号.......................................... 20 3.3 与编码器的配线.......................................... 25 3.4 电机的配线.............................................. 27 3.5 标准连接实例............................................ 28 第四章 ....................................................... 31 参数设定及功能说明 ........................................... 31 4.1 根据机械所进行的设定.................................... 31 4.2 符合上位装置的设定...................................... 33 4.3 参数设置一览表...................................... - 48 4.4 高速定位................................................ 52

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RevMan5实例图解
目录
Meta分析简介...........................................................................................................................2 ReviewManager（RevMan）软件简介....................................................................................3 一、创建一个新的系统评价....................................................................................................3
Meta分析简介
随着科学技术的不断发展和互联网的普及，全世界的医学期刊每年大约刊登200万学术论文。临床医生和研究人员由于时间和资源有限不能全面及时掌握医学信息。因此，对原始文献的结果进行综合分析的需求应运而生。
Meta分析是对具有相同研究目的的多个独立研究结果进行系统分析、定量综合的一种研究方法。该方法源于Fisher1920年“合并P值”的思想，1976年，心理学家Glass进一步将之发展为“合并统计量”，并首次将这类分析命名为“Meta-analysis”，国内也称 “荟萃分析”。经过多年发展，Meta分析已经成为循证医学对文献资料进行系统综述的基本统计方法，广泛应用于医学研究的各个领域，包括病因研究、临床试验、诊断、治疗和预后研究等等。
RevMan目前可供下载有三个版本——RevMan4.2、RevMan4.3和RevMan5。 Cochrane协作网目前强制要求使用RevMan5，RevMa0年9月15日，版本号为5.0.25。
下载地址： RevMan5：/revman/download RevMan4.3：/download/revman/revman43.exe RevMan4.2：/download/revman/revman42.exe
四、森林图和漏斗图的绘制..................................................................................................21 1、在表中输入数据........................................................................................................22 2、绘制森林图................................................................................................................22 3、绘制漏斗图................................................................................................................23 4、调整图像.................................................................................................................... 23 5、森林图和漏斗图的解读............................................................................................26
ReviewManager（RevMan）软件简介
ReviewManager（RevMan）是Cochrane协作网出品的免费Meta分析软件，它和Cochrane 的Archie数据库一起组成Cochrane信息管理系统（CochraneInformationManagement System，IMS）。注册成为Cochrane评价小组（CochraneReviewGroup，CRG）的成员后，评价者就可利用RevMan进行Cochrane系统评价的准备和维护，如不注册，评价者还是可以利用RevMan进行Cochrane系统评价的准备和维护，但是完成后的系统评价不能进入Cochrane系统评价资料库（TheCochraneDatabaseofSystematicReviews，CDSR）。作为Cochrane协作网的系统综述写作软件，RevMan已内置Cochrane系统综述的模板，评价者只需按《Cochrane系统评价员手册》（CochraneHandbook）的要求逐一填写便可。
1、新建一个系统评价......................................................................................................3 2、选择系统评价类型......................................................................................................4 3、输入系统评价的标题..................................................................................................5 4、选择系统评价的类型..................................................................................................5 二、添加纳入研究....................................................................................................................7 1、展开面板......................................................................................................................7 2、添加纳入研究..............................................................................................................7 3、输入研究名称..............................................................................................................8 4、选择研究来源..............................................................................................................9 5、输入研究发表年份....................................................................................................10 6、添加研究识别码........................................................................................................11 7、添加下一个纳入研究................................................................................................12 8、添加全部纳入研究....................................................................................................13 9、完成纳入研究的添加................................................................................................13 10、展开面板........................................................................................ ............. ............. 14
三、添加比较和结局..............................................................................................................14 1、添加比较.......................................................................................... ............. ............. 14 2、输入比较名称............................................................................................................15 3、添加结局.......................................................................................... ............. ............. 16 4、选择数据类型............................................................................................................17 5、输入结局名称............................................................................................................17 6、选择分析方法............................................................................................................18 7、为结局添加相关研究................................................................................................19 8、选择纳入研究............................................................................................................20