A New Method for Constructing Travelling Wave Solutions to the modified Benjamin--Bona--Mah

合集下载

The Challenge of Learning a New Language

The challenge of learning a new language is one that many people face, and it can be both exciting and daunting at the same time. Whether you are learning a new language for travel, work, or personal enrichment, the process of acquiring a new language can be a rewarding and fulfilling experience.One of the biggest challenges of learning a new language is simply getting started. It can be overwhelming to think about all the vocabulary, grammar rules, and pronunciation that you need to learn in order to become proficient in a new language. However, taking small steps and setting achievable goals can help to make the process more manageable.Another challenge of learning a new language is finding the time and motivation to practice regularly. Like any new skill, language learning requires consistent practice in order to make progress. This can be especially difficult for those who have busy schedules or limited access to language learning resources. However, there are many tools and resources available, such as language learning apps, online tutorials, and language exchange programs, that can help to make language learning more accessible.Additionally, learning a new language can be challenging because it requires a willingness to step out of your comfort zone. Speaking a new language can be intimidating, especially if you are worried about making mistakes or not being understood. However, embracing the discomfort and being open to making mistakes is an essential part of the language learning process. By practicing speaking and listening in a new language, you can buildconfidence and improve your communication skills over time.Furthermore, learning a new language requires cultural awareness and sensitivity. Language and culture are closely intertwined, and understanding the cultural context of a language can be just as important as learning the vocabulary and grammar. This requires a willingness to explore and appreciate new cultural perspectives, as well as a commitment to using language in a respectful and inclusive manner.Despite the challenges, learning a new language can also be an incredibly rewarding experience. Being able to communicate in a new language can open up new opportunities for travel, work, and personal connections. It can also lead to a deeper understanding of other cultures and perspectives, and can help to build empathy and compassion for others.In conclusion, the challenge of learning a new language is a significant undertaking, but one that can be incredibly rewarding. By setting achievable goals, practicing regularly, embracing discomfort, being culturally sensitive, and staying motivated, anyone can overcome the challenges of language learning and become proficient in a new language. So, if you are considering learning a new language, take the first step and embrace the challenge – you may find that it leads to a world of new possibilities and opportunities.。

方法引用英文范文

方法引用英文范文Method ReferenceMethod reference is a feature in Java that allows you to refer to a method without invoking it. It provides a shorthand notation for lambda expressions when the lambda body is a direct invocation of a single method. This feature was introduced in Java 8 and greatly simplifies the code by eliminating the needto define lambda expressions in certain cases.In Java, a method reference is represented by :: (double colon) operator. It consists of two parts: the class or object name and the method name, separated by the :: operator. Theclass or object name can be a class name, an instance name, or a constructor name. There are four types of method references in Java:1. Reference to a static method: A static method from aclass can be referenced using the class name followed by :: operator and the method name. For example, Math::max refers to the static max method in the Math class.2. Reference to an instance method of an object: An instance method of an object can be referenced using the object name followed by :: operator and the method name. For example,str::length refers to the length method of the string object str.3. Reference to an instance method of a class: An instance method of a class can be referenced using the class name followed by :: operator and the method name. For example, String::toLowerCase refers to the toLowerCase method of the String class.4. Reference to a constructor: A constructor can be referenced using the class name followed by :: operator and the keyword new. For example, ArrayList::new refers to the constructor of the ArrayList class.Method reference can be used in various contexts such as functional interfaces, streams, and lambda expressions. It provides a concise and readable way to define behavior without the need to explicitly define lambda expressions. It also helps in promoting code reusability by allowing methods to be referenced and reused in different parts of the code.One of the main advantages of method reference is code brevity. It reduces the boilerplate code by providing a simple and direct way to reference methods. This improves code readability and makes it easier to understand the logic of the program. It also eliminates the need to write lambda expressions for simple method invocations, making the code more concise and efficient.Another advantage of method reference is improved performance. Since method reference directly invokes a method,it avoids the overhead of creating an anonymous class and executing the lambda expression. This can lead to faster execution and improved performance, especially in performance-critical applications.Method reference is also useful in cases where you need to pass a method as a parameter. Instead of creating a lambda expression for the method, you can directly reference it using method reference. This simplifies the code and makes it easierto understand the intention of passing the method as a parameter.In conclusion, method reference is a powerful feature in Java that simplifies the code, improves readability, and enhances performance. It provides a concise and efficient way to reference methods without the need for lambda expressions. By understanding and utilizing method reference, you can write cleaner and more efficient code in Java.。

关于旅游垃圾的英语作文

Tourism has become an integral part of modern life,offering people a chance to relax and explore new places.However,the growth of tourism has also brought about a significant issue:the accumulation of touristgenerated waste.This English essay will discuss the causes of tourism waste,its impacts on the environment and society,and potential solutions to mitigate this problem.IntroductionThe popularity of travel has soared in recent years,with millions of people visiting different destinations annually.While tourism boosts local economies and fosters cultural exchange,it also leads to the generation of waste that,if not managed properly,can harm the environment and detract from the beauty of tourist sites.Causes of Tourism Wasteck of Awareness:Many tourists are unaware of the environmental impact of their actions.They may not realize that disposing of waste improperly can harm the environment.2.Inadequate Waste Management Infrastructure:In some tourist destinations,the waste management system may not be equipped to handle the volume of waste generated by visitors.3.Cultural Differences:Tourists from different cultures may have varying attitudes towards waste disposal,leading to improper waste management practices.4.Overcrowding:Popular tourist spots often experience overcrowding,which can overwhelm local waste management systems and lead to littering.Impacts of Tourism Waste1.Environmental Degradation:Littering can lead to the pollution of natural habitats, affecting flora and fauna.Plastic waste,in particular,poses a threat to wildlife.2.Health Risks:Improper waste disposal can contaminate water sources and spread diseases.3.Economic Losses:The presence of waste can deter tourists,leading to a decline in tourism revenue for local economies.4.Cultural Erosion:Litter and pollution can deface historical and cultural sites, diminishing their value and appeal.Solutions to Tourism Wastecation and Awareness:Educating tourists about the importance of proper wastedisposal can encourage responsible behavior.2.Improved Waste Management Systems:Investing in better waste management infrastructure,such as more bins and recycling facilities,can help reduce waste accumulation.3.Regulations and Enforcement:Implementing and enforcing strict waste disposal regulations can deter littering and improper waste management.4.Sustainable Tourism Practices:Encouraging ecofriendly practices among tourists,such as using reusable bags and water bottles,can reduce waste generation.munity Involvement:Engaging local communities in waste management efforts can ensure that solutions are culturally sensitive and effective.ConclusionTourism waste is a pressing issue that requires immediate attention.By understanding its causes and impacts,and by implementing effective solutions,we can ensure that the tourism industry continues to thrive without compromising the environment or local communities.It is the collective responsibility of tourists,travel agencies,governments, and local communities to work together to minimize the environmental footprint of tourism and preserve the natural and cultural heritage for future generations.。

旅游与交通的英语作文

Travel and transportation are integral parts of our lives,and they have evolved significantly over the years.In this essay,we will explore the various aspects of tourism and transportation,their importance,and how they have transformed over time.IntroductionTravel has always been a means of exploration,education,and relaxation.It allows individuals to experience different cultures,landscapes,and cuisines.Transportation,on the other hand,is the backbone of travel,providing the necessary infrastructure and services to facilitate movement from one place to another.The Evolution of Travel and Transportation1.Early Forms of Travel:In the past,travel was limited to walking,riding animals,or using simple boats.These methods were slow and often dangerous,but they laid the foundation for future advancements.2.The Industrial Revolution:The introduction of steam engines revolutionized transportation.Trains and steamships became the primary means of longdistance travel, making it faster and more accessible.3.The Age of Aviation:The20th century saw the birth of commercial aviation,which drastically reduced travel times and opened up new destinations.Air travel has since become the most popular mode of longdistance travel.4.Modern Transportation:Today,we have a variety of transportation options,including highspeed trains,buses,cars,and bicycles.Each mode has its advantages and caters to different travel preferences and needs.The Impact of Travel and Transportation on Society1.Economic Growth:Tourism is a significant contributor to the global economy.It creates jobs,boosts local businesses,and generates revenue through taxes and fees.2.Cultural Exchange:Travel promotes cultural understanding and appreciation.It allows people to learn about different traditions,languages,and customs,fostering tolerance and respect among diverse communities.3.Environmental Concerns:The growth of tourism and transportation has also led to environmental challenges,such as pollution,habitat destruction,and resource depletion.Sustainable tourism and ecofriendly transportation options are becoming increasingly important.Challenges and Opportunities1.Accessibility:Ensuring that transportation is accessible to all,including those with disabilities,is a challenge that many countries are working to address.2.Technological Advancements:Innovations like selfdriving cars and electric vehicles are changing the landscape of transportation,offering new opportunities for efficiency and sustainability.3.Safety:As travel becomes more widespread,ensuring the safety of travelers and the reliability of transportation systems remains a priority.ConclusionTravel and transportation are essential components of our modern world,connecting people and places in ways that were once unimaginable.As we continue to innovate and adapt,it is crucial to consider the social,economic,and environmental implications of our choices,striving for a future where travel is both enjoyable and sustainable.。

湖北省黄冈市部分普通高中2023-2024学年高一上学期期中英语试题

2023年秋季黄冈市部分普通高中高一年级阶段教学质量监测英语黄冈市教育科学研究院命制本试卷共10页,满分150分。

考试用时120分钟。

★祝考试顺利★注意事项:1.答题前,先将自己的姓名,准考证号,考场亏,座位亏项与在试卷和答题卡上并认真核准准考证号条形码上的以上信息,将条形码粘贴在答题卡上的指定位置。

2.请按题号顺序在答题卡上各题目的答题区域内作答,写在试卷、草稿纸和答题卡上的非答题区域均无效。

3.选择题用2B铅笔在答题卡上把所选答案的标号涂黑;非选择题用黑色签字笔在答题卡上作答;字体工整,笔迹清楚。

4.考试结束后,请将试卷和答题卡一并上交.第一部分听力(共两节，满分30分)第一节听下面5段对话。

每段对话后有一个小题，从题中所给的A、B、C三个选项中选出最佳选项，并标在试卷的相应位置。

听完每段对话后，你都有10秒钟的时间来回答有关小题和阅读下一小题。

每段对话仅读一遍。

1. What exam will the man have tomorrow?A. English.B. Physics.C. Math.2. How old is the man’s brother?A. 16 years old.B. 21 years old.C. 26 years old.3. Where does the conversation most probably take place?A. In the hospital.B. In the school.C. In the pany.4. What are the speakers mainly talking about?A. Their favorite books.B. Their weekend plans.C. The man’s reading habits.5. What will the woman do next month?A. Meet a foreign client.B. Fly to Britain.C. Hold a wedding party.第二节听下面5段对话或独白。

Travelling+Around+Project 高中英语人教版(2019)必修第一册+

Pingyao Ancient City study the decoration technique of Chang’s manor
the Terracotta Army admire a collection of terracotta sculptures
Day 7
Greater Wild Goose learn ancient architecture Tower
Steps
Checklist
Step 4: Presentation Have you used maps, lists, diagrams and charts to show facts
clearly?
Have you used drawings, cartoons, photos, ect. to make your
Activity 2 Research, plan and design a tour Brainstorm different types of tours and add them to the list.
History
Education
Types of tours
Adventure
Cuisine
Hi! My name is Anna, and I’m from Russia. I’ve just started my last year of high school, and I want to visit a few universities or colleges in China, since I hope to study there next year. I want to experience some Chinese culture and see the natural beauty there, but my main goal is to find out which university is the best for me to attend. universities and colleges in China

211218188_Exploring_the_Impact_of_Serious_Reading_

Cultural and Religious Studies, April 2023, Vol. 11, No. 4, 195-198doi: 10.17265/2328-2177/2023.04.008 Exploring the Impact of Serious Reading of ClassicalMetatextuals on University Students ’ Learningof Traditional CultureZHANG Pei, LI JiaxianBeijing Institute of Graphic Communication, Beijing, ChinaAn important carrier of excellent Chinese traditional culture is the classic book, and university students carry theimportant responsibility of promoting Chinese excellent traditional culture and propagating cultural confidence. Theserious reading of classical texts is an important way for university students to learn traditional culture, which has theadvantages of professionalism and systematization. At the present stage, university students lack the awareness andaction to read the classics, and the time for serious reading is very limited. The genre, subject matter, and extent ofIP development are important factors that affect the in-depth reading of the classic metatext.Keywords: traditional culture, Chinese traditional culture, university students, reading abilityPrefaceThis paper adopts a questionnaire, data analysis, and control variables research method, after screening a total of 200 valid questionnaires, of which questions 11 and 12 are the data source and research basis for this paper. The 11th question asked “Have you ever learned about ‘sutras, histories and sons ’?” and the 12th question asked “Among the following books, which one do you like to read most in its original form?” The answers included the Analects of Confucius , Zhou Yi , The Records of the Grand Historian , Xu Xia Ke ’s Travels, Zhuang Zi , Shan Hai Jing , Chu Shu , and Wen Xin Diao Long , which are multiple choice questions. After the analysis of the two questions, two points were made. Firstly, at this stage, university students lack the awareness and action to read the classic metatext, and their reading behaviour is influenced by their personal reading efforts and the strength of the dissemination of the classic metatext. Secondly, the genre of the classics and the extent of IP development are positively and strongly correlated with students ’ interest and action in reading. Therefore, university students should study traditional culture independently and spontaneously, and increase the time spent on serious reading. * This paper is the periodic research result of the research project: Beijing University Student Innovation Training Program - A Study on the Practical Logic of Traditional Culture Acquisition for College Students (北京大学生创新训练项目-大学生传统文化习得中的实践逻辑研究, Serial Number S200210015037); Key Project of Teaching Reform and Innovation at the College Level of Beijing Institute of Printing and Technology - National First Class Major - Research on the Construction of Editing and Publishing Majors (北京印刷学院校级教改创新重点项目-国家级一流专业——编辑出版学专业建设研究, Serial Number22150223075). ZHANG Pei, Ph.D., post-doctoral of law, Associate Professor, master instructor, Department of editing and publishing, School of Publishing, Beijing Institute of Graphic Communication, Beijing, China.Li Jiaxian, undergraduate, Tao Fen Experimental Class, Grade 2021, School of Publishing, Beijing Institute of Graphic Communication, Beijing, China.DA VID PUBLISHINGDEXPLORING THE IMPACT OF SERIOUS READING OF CLASSICAL METATEXTUALS196Do You Automatically Know About “Scripture, History, and Songs”?Firstly, the scope of traditional culture is defined, and in this section the scope of traditional culture is narrowed down to the various types of books representing traditional culture, with a clear point of reference.The Sui Shu—Scriptures and Records is the first historical catalogue in China’s history to be named after the four sections of the scriptures, history, sons, and collections, and it is also the earliest extant tetrad catalogue. Scholars generally agree that the New Book of the Middle Classics was the beginning of the classification of the four divisions.The Siku classification is an important way of classifying ancient Chinese texts and is authoritative and universal.In question 11, on the question “Have you ever automatically learned about ‘sutras, histories, sons and collections’?”, the largest proportion of respondents (36.5%) said “No”, followed by “actively studied but not in depth” (30%), followed by “asked by teacher to study” and “actively studied and studied in depth” (26% and 7.5% respectively). The survey results show that all the subjects were asked to learn about the subject by their teachers but not in depth. The results of the survey show that the largest number of respondents did not know about the subject, the smallest number knew about it and did not learn about it, and the largest number did not know about it, the teacher asked them to learn about it, and they took the initiative to learn about it but did not learn about it.When the subjects were selected in the “Science” and “Engineering” samples, the results were different from the first two, with the highest percentage of subjects having no knowledge and the lowest number of subjects being asked by teachers to learn more.From these results it is clear that at this stage university students lack the awareness and action to read the classical metatext. Compared to the study of university courses and recreational extra-curricular learning, university students do not take enough initiative, and even among students majoring in real literature, the results of carrying out the study are not satisfactory and differ greatly from the ideal state. The reasons for this are twofold.The first is reading ability. The reading of classical texts is difficult and boring, and requires a high level of reading ability and knowledge accumulation, so the interest and enthusiasm in reading will quickly disappear if the reading ability does not meet the requirements. At the same time, university students who are accustomed to the one-answer exams of secondary education are not as comfortable with independent learning and exploration as they would be if they were able to read, but they are afraid to delve deeper and speak up because they do not have standard answers or guidance from their teachers. Similarly, in the process of reading, problems are encountered, problems are solved, knowledge is accumulated, and positive feedback is given to promote reading enthusiasm and reading ability, so that autonomy and reading ability become bigger and bigger like a snowball, with better and better results. Therefore, university students must take the initiative to enhance their self-learning ability and gradually improve their reading ability in the serious reading of classic metatextuals. No one is born with the ability to read everything, and knowledge and ability are gained in the process of learning.The second is the strength of dissemination. In a contemporary age of highly developed media technology, more and more people are looking to dissemination as an important measure of the quality of a publication’s content, however, the truth is that publications with a high level of dissemination do not necessarily have a high impact, and publications with a high impact have a high level of dissemination versus not necessarily a high impact. The classics are generally not as widely distributed as bestsellers or popular online videos because of theEXPLORING THE IMPACT OF SERIOUS READING OF CLASSICAL METATEXTUALS197reading skills they require, but the impact of serious reading on a person is profound and far greater than that of reading and watching other types of publications. Therefore, the question of how to increase the dissemination of classic metatextuals within an appropriate range and stimulate university students’ interest in reading them is an important one for the publishing industry and editors to consider in the future.Another conclusion that can be drawn from combining the results of the three different analyses is the importance and effectiveness of the teacher’s role in guiding university students in the seriousness of the classic metatext. The teacher’s guidance and requirements are important guides to enable university students to bre ak out of their information cocoons and join the seriousness reading stage. It can be concluded from random interviews that students in editing and publishing have carried out readings about the Historical Records under the guidance of their teachers in the Classics Reading course, and will form a reading report based on their reading experience, which will also be exchanged and presented in the class during the course, giving an important impetus to the effectiveness of the acquisition of the Historical Records. The teacher has an important role to play in the learning of students, and also has an important role to play in guiding and promoting the attitude of students in reading the classical canon. In the editing and publishing course, the teacher strongly recommends, encourages, and requires the students of the course to read the canon and to write reading reports. The students are guided by their teachers to engage in serious reading, mobilise their interest in reading, and gain a sense of satisfaction and feedback in the process of sharing, which in turn leads to further reading action and improved reading skills.Of the Following Books, Which Would You Most Like to Read in Their Original Form?The next question is the twelfth, “Which of the following books would you most like to read in their original form?” The purpose of this question was to specifically analyse the reading of the more widely recognized meta-texts in the different categories of scripture, history, and scripture, with the aim of identifying the key factors influencing university students’ interest in reading meta-texts.The top three were the Shanhaijing with 68%, the Shiji with 46%, and the Xu Xiake’s Travels with 41%. Because a large proportion of the subjects were from the editing and publishing program, and the fact that the teacher of the major course required the reading of The Chronicle of History influenced the findings, the top three were chosen to exclude The Chronicle of History from the analysis, and the top three after that were The Scriptures of the Mountain and the Sea, The Travels of Xu Xia Ke, and The Analects of Confucius. The reasons for the share of each book are analysed in the following:The high willingness to read the Shanhaijing reflects the influence of IP development and film and television productions. The Shanhaijing is a marvelous book among China’s classical masterpieces, containing a large amount of information on geography, local objects, ethnic folklore, and mythological stories in its 30,000 words, and the rich cultural resources it contains have great potential for exploitation. From The Legend of Nezha to the light of national comics, Big Fish Begonia has incorporated a large number of these mythological stories and elements, forming an important cultural feature of domestic animation. As a result, the dissemination and recognition of the original has been made more widespread through mass communication and IP development, and because of the penetration of various cultural creations and film productions in everyday life, the original Shan Hai Jing has become closer to people and more familiar. The higher degree of familiarity and dissemination in turn reduces the difficulty of reading to a certain extent, which, together with popular interest, makes university students highly willing to read the Shanhaijing metatext. However, after the extraction of elements and theEXPLORING THE IMPACT OF SERIOUS READING OF CLASSICAL METATEXTUALS198shaping of values, IP development and cultural creativity have presented products that have deviated from the content written in the Canon itself. Therefore, the adapted works should not be used as a way to study the classic Canon, but to truly read the Canon in depth and look at the adapted works objectively.The high willingness to read Xu Xiakai’s Travels reflects the influence of the style of the text and the low reading ability required. Xu Xiake’s Travels is a prose travelogue written by the Ming dynasty geographer Xu Xiake over a period of more than 30 years. The author takes the process of travelling as the backbone of the entire work, describing many natural images of aesthetic interest; crossed with the author’s emotional ups and downs and spiritual direction, achieving the effect of the subject’s spiritual experience against the aesthetics of objective scenery. The book is mainly about landscape scenery and contains a wealth of geographical knowledge and accounts of natural phenomena, making it extremely appealing to geography enthusiasts. At the same time, Xu Xiakai adopts a diary style of writing, which requires low reading skills and is more interesting to the reader, hence the high willingness of university students to read it.The higher willingness to read The Analects reflects the influence of exposure to learning and cultural stratigraphy piled up from childhood and the lower reading ability required for the discursive form. The Analects, an important Confucian classic, has permeated every aspect of students’ studies from childhood to adulthood, and university students are expected to have a high level of knowledge of the Analects and its content. At the same time, the format of the discourse style is reader-friendly and requires less reading skills. The two reasons combine to make university students more willing to read the Analects of Confucius.The high willingness to read the Histories reflects the role of teacher guidance on students’ reading of the metatext. Editorial and publishing students are influenced and motivated by their teachers to read the Shiji metatext and to write reading reports that incorporate their personal understandings, receiving timely feedback and good acquisition results.After the above analysis, two conclusions are drawn. The first is that university students are more enthusiastic and take action in learning traditional culture through extra-curricular entertaining means than through serious reading of the classical metatext and university courses. This is closely related to the degree of dissemination and the reading ability of university students. Secondly, we should return to reading the classical canon and encourage and guide university students to read in depth. We should take an objective view of film and television works and online videos.ReferencesFeng, P. (2023). Study on the strategy of mass culture work to help revitalize rural culture. Farmers’ Counselor, 10, 206-208. Jia, W. H. (2021). The four overseas scriptures of the Shanhaijing. Sichuan Library Journal, 43(4), 87-91.Luo, Y. M. (2004). Reading the classics and teaching English literature. Studies in Foreign Literature, 27(2), 141-146+176. Wang, L. (2021). The interplay of literature and geographical culture from The Travels of Xu Xiake. Journal of Modern and Ancient Literature, 2(7), 66-67.Wang, L. N., & Ding, W. (2019). The recreation of the image of the gods and monsters in the Shanhaijing in animation. Southeast Communication, 16(11), 54-56.Wang, Y. P. (2021). A new exploration of the four titles of the scriptures, history, sons and collections. Chinese Classics and Culture, 30(2), 130-136.Wei, B. B. (2023). An introduction to the role of mass culture in the comprehensive promotion of rural revitalization.Farmers’ Counselor, 10, 203-205.。

高中英语竞赛试题及答案

高中英语竞赛试题及答案一、听力理解（共20分）1. What does the man mean by saying "It's raining cats and dogs"?A. It's raining heavily.B. It's raining with thunder and lightning.C. It's raining with a lot of wind.D. It's raining with a lot of snow.答案：A2. How many people are there in the woman's family?A. Three.B. Four.C. Five.D. Six.答案：B二、阅读理解（共30分）阅读下面的短文，然后回答3-5题。

A New Way to TravelWith the development of technology, a new way of travel has been introduced to the public. This method allows people to travel at high speeds without the need for traditional fuel. It is expected to revolutionize the transportation industry.3. What is the main topic of the passage?A. The impact of technology on travel.B. A new way of travel.C. The future of the transportation industry.D. The need for traditional fuel in travel.答案：B4. What is the advantage of the new travel method mentioned in the passage?A. It's fast.B. It's cheap.C. It's environmentally friendly.D. It uses traditional fuel.答案：A5. What does the author think about the new travel method?A. It will change the transportation industry.B. It will replace traditional travel methods.C. It will be popular among travelers.D. It will face many challenges.答案：A三、完形填空（共20分）阅读下面的短文，从每题所给的四个选项中，选出最佳选项填入空白处。

英语技术写作试题及答案

英语技术写作试题及答案一、选择题（每题2分，共20分）1. The term "API" stands for:A. Application Programming InterfaceB. Artificially Programmed IntelligenceC. Advanced Programming InterfaceD. Automated Programming Interface答案：A2. Which of the following is not a common data type in programming?A. IntegerB. StringC. BooleanD. Vector答案：D3. In technical writing, what is the purpose of using the term "shall"?A. To indicate a requirement or obligationB. To suggest a recommendationC. To express a possibilityD. To denote a future action答案：A4. What does the acronym "GUI" refer to in the context of computing?A. Graphical User InterfaceB. Global User InterfaceC. Generalized User InterfaceD. Graphical Unified Interface答案：A5. Which of the following is a correct statement regarding version control in software development?A. It is used to track changes in software over time.B. It is a type of software testing.C. It is a method for encrypting code.D. It is a way to compile code.答案：A6. What is the primary function of a compiler in programming?A. To debug codeB. To execute codeC. To translate code from one language to anotherD. To optimize code for performance答案：C7. In technical documentation, what does "RTFM" commonly stand for?A. Read The Frequently Asked QuestionsB. Read The Full ManualC. Read The File ManuallyD. Read The Final Message答案：B8. Which of the following is a common method for organizing code in a modular fashion?A. LoopingB. RecursionC. EncapsulationD. Inheritance答案：C9. What is the purpose of a "pseudocode" in programming?A. To provide a detailed step-by-step guide for executing codeB. To serve as a preliminary version of code before actual codingC. To act as an encryption for the codeD. To be used as a substitute for actual code in production答案：B10. What does "DRY" stand for in software development?A. Don't Repeat YourselfB. Data Retrieval YieldC. Database Record YieldD. Dynamic Resource Yield答案：A二、填空题（每空2分，共20分）1. The process of converting a high-level code into machine code is known as _______.答案：compilation2. In programming, a _______ is a sequence of characters that is treated as a single unit.答案：string3. The _______ pattern in object-oriented programming is a way to allow a class to be used as a blueprint for creating objects.答案：prototype4. A _______ is a type of software development methodology that emphasizes iterative development.答案：agile5. The _______ is a set of rules that defines how data is formatted, transmitted, and received between software applications.答案：protocol6. In technical writing, the term "should" is used toindicate a _______.答案：recommendation7. The _______ is a type of software that is designed to prevent, detect, and remove malicious software.答案：antivirus8. A _______ is a variable that is declared outside the function and hence belongs to the global scope.答案：global variable9. The _______ is a programming construct that allows you to execute a block of code repeatedly.答案：loop10. In software development, the term "branch" in version control refers to a _______.答案：separate line of development三、简答题（每题10分，共40分）1. Explain the difference between a "bug" and a "feature" in software development.答案：A "bug" is an unintended behavior or error in a software program that causes it to behave incorrectly or crash. A "feature," on the other hand, is a planned and intentional part of the software that provides some functionality or capability to the user.2. What is the significance of documentation in technical writing?答案：Documentation in technical writing is significant as it serves to provide detailed information about a product or system, making it easier for users, developers, and other stakeholders to understand its workings, usage, and maintenance. It is crucial for training, troubleshooting, and future development.3. Describe the role of a software architect in a software development project。

新人教 BOOK2 Unit 3 The Internet-语言点

(3)Reading _b_e_n_e_f_i_tsus.=We benefit _f_r_o_m_ reading.=Reading is b__e_n_e_f_ic_i_a_l to
us. 阅读使我们受益。
(4)（2018浙江高考）Acting as a volunteer of English Association will not only enrich my life, but also be of great benefit to me in the long run. 英语协会的志愿者工作不仅会丰富我的生活，而且从长远来看对我有很大的好处。
2. learn the method of constructing knowledge trees through selfstudy &cooperative exploration.
3. enjoy the fun of expressing yourself using English and participate in class with passion.
(1)We’ll _u_p__d_a_t_e_y_o_u_ on the day’s top news stories.
我们将为您提供当天的重要新闻。
(2)He was back in the office, updating the work schedule on the computer. 他已回到办公室，正在电脑上更新工作日程。
Self-study time：15 minutes
convenient, update, network, company, benefit, inspire, access, plus, now that…

Unit+4+Using+language 高中英语人教版(2019)选择性必修第二册

“Hello, my name is Jean-Phher, "he said, “and I was wondering where you are from.” “你们好，我叫让-菲利普，是一名摄影师。”他说道，“请问你们从哪里来?”
The girls told him they were from China and were on a train trip across Canada. When they told him they had only one day in Montreal, he said, “That's too bad. You owe it to yourselves to stay longer. Overall, Montreal is a city with wonderful sights and sounds. Most of us speak both English and French, and the city has unique Quebec culture and traditions. There are fantastic restaurants and clubs around, too. Here, we love good coffee, toast, and cheese. And good music, of c姑她总ou娘们的rs们只来e!”告能说oo我ww诉，在ee应他蒙蒙istt该，特特hto.要她利利too努n们尔尔seb力s来是逗.e/ls学f自一留tht习o.中座一把d。国声天o某s，色时事th并俱，.归自且佳他功己正的说于应在城道某该进市:人“做那行。/某某太横我事事可跨们惜加大了拿多，大数你的人们火既应车说该之英多旅语待。，几得也天知说。法和乐语俱 !” 。乐这部HI他座。eo把w城这owe他市里eitd的拥的tho成有人ims 就独酷aycs归h特爱elife功的美vtoe于魁味mst努北的eund力克咖tys 工th文啡oa作化、rhdis。和吐. h传司ar统和d w。奶o此酪rk外。. ，当这然里，还还有有很动棒听的的餐音馆

The partition technique for overlays of envelopes

∗ This work was supported by a grant from the Israel Science Fund (for a Center of Excellence in Geometric Computing), and is part of the ﬁrst author’s Ph.D. dissertation, prepared under the supervision of the second author in Tel Aviv University. Work by Micha Sharir was also supported by NSF Grants CCR-97-32101 and CCR-00-98246, by a grant from the U.S.-Israel Binational Science Foundation, and by the Hermann Minkowski–MINERVA Center for Geometry at Tel Aviv University. A preliminary version of this paper appeared in the Proceedings of the 43nd IEEE Symposium on Foundations of Computer Science, 2002. † Computer Science Division, University of California, Berkeley, CA 94720-1776, USA. vladlen@ ‡ School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel and Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA. sharir@cs.tau.ac.il

How to build a tunnel

专利名称：How to build a tunnel发明人：川内大輔,星智久,伊東俊彦,柳本速雄,佐藤啓介,生野康之申请号：JP2019054467申请日：20190322公开号：JP2020153175A公开日：20200924专利内容由知识产权出版社提供专利附图：摘要：PROBLEM TO BE SOLVED: To provide a method for constructing a tunnelcapable of prolonging the construction period and suppressing an increase inconstruction cost. SOLUTION: This is a method of constructing a tunnel 1 providedunderground from a wellhead 4a to a wellhead 4b, in which the tunnel 1 is excavated at the front end of an excavator and propelled to the rear part of an already assembled tunnel skeleton. At the process of constructing a part 1b of tunnel 1 with a downward slope from the wellhead 4a and at the front end of the excavator using the propulsion method of connecting pipes and propelling the tunnel frame forward to construct an underground pipeline. Using the shield method of excavating the tunnel 1 and connecting the segment pipe to the front part of the already assembled tunnel frame to construct the underground pipeline, at least the deepest part is included, and the other side from the part 1a of the tunnel 1. A step of constructing the portions 1a and 1c of the tunnel 1 on the wellhead 5a side of the above is provided. [Selection diagram] Fig. 1申请人：株式会社奥村組,日鉄パイプライン＆エンジニアリング株式会社地址：大阪府大阪市阿倍野区松崎町２丁目２番２号,東京都品川区大崎一丁目５番１号国籍：JP,JP代理人：特許業務法人創成国際特許事務所更多信息请下载全文后查看。

高级英语构造函数

高级英语构造函数Constructors in Advanced English (Graduate Entrance Exam Orientation)In object-oriented programming, constructors are special methods within a class that are responsible for initializing the object's state. They are called automatically when an object is created and are used to set the initial values of the object's member variables.Constructors are essential for creating objects and are particularly crucial in the context of graduate entrance exam preparation for advanced English. This article will explain the concept of constructors, their importance, and provide examples to illustrate their usage.The primary purpose of constructors is to ensure that an object is created with valid initial values for its member variables. This is achieved by providing default values or by accepting parameters that initialize the object's state. Constructors can have parameters or be parameterless, depending on the requirements of the class and the specific object to be created.When an object is instantiated using the new keyword, the constructor is called automatically and executed before any other methods in the class. The constructor allocates memory forthe object, initializes its member variables, and performs any necessary setup.Constructors have the same name as the class they belong to and do not have a return type, not even void. They are defined using the public access modifier to ensure they can be accessed from outside the class. This allows the object to beinstantiated and initialized by external code.Let's illustrate the concept of constructors with an example. Consider a class called Student, which represents a student's information. We can define a constructor for this class to initialize the student's name and age variables.```javapublic class Studentprivate String name;private int age;public Student(String name, int age) = name;this.age = age;}```In the example above, the Student class has a constructor that takes two parameters: name and age. The constructor assigns the values of these parameters to the corresponding member variables using the this keyword to refer to the object being created.To create a new instance of the Student class and initialize its state, we can use the following code:```javaStudent student = new Student("John Doe", 20);```In this case, the constructor is called with the arguments "John Doe" and 20, and the created object has its name and age variables set accordingly.Let's extend our Student class with an overloaded constructor that only takes the name parameter:```javapublic class Studentprivate String name;private int age;public Student(String name, int age) = name;this.age = age;}public Student(String name) = name;this.age = 0; // Default age set to 0}```In this example, the second constructor provides a default age of 0 when only the name is provided.In summary, constructors are crucial for initializing object states in object-oriented programming. They are automatically called when an object is created and are used to set the initial values of member variables. By understanding and utilizing constructors effectively, candidates preparing for graduate entrance exams can demonstrate their knowledge and skills in advanced English programming concepts.。

英语作文介绍旅游城市海报

When crafting an English essay to introduce a travel city poster,it is essential to highlight the key attractions,cultural significance,and unique experiences that the city offers to potential visitors.Here is a detailed approach to writing such an essay:Title:Discover the Charm of City Name:A Journey Through Our Travel City PosterIntroduction:Begin by capturing the readers attention with a vivid description of the citys allure. Mention the essence of the city that makes it a mustvisit destination.Paragraph1:Historical SignificanceIntroduce the citys rich history and how it has shaped its presentday attractions. Describe historical landmarks featured in the poster,such as ancient monuments, museums,or architectural wonders.Paragraph2:Cultural ExperiencesDiscuss the citys cultural diversity and the various traditions that visitors can immerse themselves in.Mention cultural festivals,local cuisine,and traditional crafts that are depicted in the poster.Paragraph3:Natural BeautyHighlight the citys natural landscapes,such as mountains,beaches,or parks,that are showcased in the poster.Describe how these natural features contribute to the citys overall appeal and offer recreational activities for visitors.Paragraph4:Urban AttractionsShift focus to the citys urban attractions,including modern landmarks,shopping districts, and entertainment venues.Explain how these attractions provide a contrast to the citys historical and natural aspects, offering a wellrounded experience.Paragraph5:Accessibility and InfrastructureTouch upon the citys transportation options,accommodation facilities,and tourist services that make it easy for visitors to explore.Mention any special offers or packages that are featured in the poster to entice potential travelers.Paragraph6:Testimonials and ReviewsInclude quotes or reviews from satisfied visitors to the city,as depicted in the poster,to add credibility and encourage reader interest.Highlight the unique experiences and memories that visitors have had,which they wouldnt want to miss.Conclusion:Summarize the main points of the essay and reiterate the citys appeal as a travel destination.End with a call to action,inviting readers to explore the city for themselves and experience the charm captured in the travel city poster.Postscript:Provide additional information on how to access the poster,whether its available online or at specific locations.Include contact details for tourism offices or travel agencies for further inquiries. Remember to use descriptive language and vivid imagery to bring the city to life on the page.This will help readers visualize the experiences they could have and inspire them to plan a visit.。

Logistic model trees

Logistic Model Trees†Niels LandwehrInstitute for Computer Science,University of Freiburg,Freiburg,Germany. landwehr@informatik.uni-freiburg.deMark Hall and Eibe FrankDepartment of Computer Science,University of Waikato,Hamilton,New Zealand. {mhall,eibe}@June10,2004Abstract.Tree induction methods and linear models are popular techniques for supervised learning tasks,both for the prediction of nominal classes and numeric values.For predicting numeric quantities,there has been work on combining these two schemes into‘model trees’,i.e.trees that contain linear regression functions at the leaves.In this paper,we present an algorithm that adapts this idea for classiﬁcation problems,using logistic regression instead of linear regression.We use a stagewiseﬁtting process to construct the logistic regression models that can select relevant attributes in the data in a natural way,and show how this approach can be used to build the logistic regression models at the leaves by incrementally reﬁning those constructed at higher levels in the tree.We compare the performance of our algorithm to several other state-of-the-art learning schemes on36benchmark UCI datasets,and show that it produces accurate and compact classiﬁers. Keywords:Model tree,logistic regression,classiﬁcation2Landwehr,Hall and Frank1.IntroductionTwo popular methods for classiﬁcation are linear logistic regression and tree induction,which have somewhat complementary advantages and disadvantages.The formerﬁts a simple(linear)model to the data,and the process of modelﬁtting is quite stable,resulting in low variance but potentially high bias.The latter,on the other hand,exhibits low bias but often high variance:it searches a less restricted space of models, allowing it to capture nonlinear patterns in the data,but making it less stable and prone to overﬁtting.So it is not surprising that neither of the two methods is superior in general—earlier studies(Perlich et al., 2003)have shown that their relative performance depends on the size and the characteristics of the dataset(e.g.,the signal-to-noise ratio).It is a natural idea to try and combine these two methods into learners that rely on simple regression models if only little and/or noisy data is available and add a more complex tree structure if there is enough data to warrant such structure.For the case of predicting a numeric variable,this has lead to‘model trees’,which are decision trees with linear regression models at the leaves.These have been shown to produce good results(Quinlan,1992).Although it is possible to use model trees for classiﬁcation tasks by transforming the classiﬁcation problem into a regression task by binarizing the class(Frank et al., 1998),this approach produces several trees(one per class)and thus makes theﬁnal model harder to interpret.A more natural way to deal with classiﬁcation tasks is to use a combination of a tree structure and logistic regression models resulting in a single tree.Another advantage of using logistic regression is that explicit class probability estimates are produced rather than just a classiﬁcation.In this paper,we present a method,called LMT(Logis-tic Model Trees),that follows this idea.We discuss a new scheme for selecting the attributes to be included in the logistic regression models, and introduce a way of building the logistic models at the leaves by reﬁning logistic models that have been trained at higher levels in the tree,i.e.on larger subsets of the training data.We evaluate the performance of LMT on36datasets taken from the UCI repository(Blake and Merz,1998).Included in the experiments are the standard decision tree learners C4.5(Quinlan,1993)and CART (Breiman et al.,1984),linear logistic regression,and other tree-based classiﬁers,such as boosted C4.5,model treesﬁt to the class indicator variables(Frank et al.,1998),functional trees(Gama,2004),naive Bayes trees(Kohavi,1996),and a diﬀerent algorithm for building lo-gistic model trees:Lotus(Chan and Loh,2004).The experiments show that LMT produces more accurate classiﬁers than C4.5,CART,logisticLogistic Model Trees3 regression,model trees,functional trees,naive Bayes trees and Lotus. It is competitive with boosted decision trees,which are considered to be one of the best‘oﬀthe shelf’classiﬁcation systems,while producing models that are easier to interpret.We also present empirical evidence that LMT smoothly adapts the tree size to the complexity of the data set.The rest of the paper is organized as follows.In Section2we brieﬂy discuss the two learning methods that LMT is based on:tree induction and logistic regression.Section3discusses related work on tree-based learning.In Section4we present the LMT algorithm for learning logis-tic model trees.Section5describes our experimental study,followed bya discussion of results.Finally,we draw some conclusions in Section6.2.Tree Induction and Logistic RegressionThis section discusses the two basic approaches to learning that our method is based upon:tree induction and logistic regression.We brieﬂy introduce the process of tree induction,discuss the application of re-gression to classiﬁcation tasks,and then describe our implementation of logistic regression.2.1.Tree InductionThe goal of supervised learning is toﬁnd a subdivision of the instance space into regions labeled with one of the target classes.Top-down tree inductionﬁnds this subdivision by recursively splitting the instance space,stopping when the regions of the subdivision are reasonably ‘pure’in the sense that they contain examples with mostly identical class labels.The regions are labeled with the majority class of the examples in that region.Important advantages of tree models(with axis-parallel splits)are that they can be constructed eﬃciently and are easy to interpret.A path in a decision tree basically corresponds to a conjunction of boolean expression of the form‘attribute=value’(for nominal attributes)or ‘attribute≤value’(for numeric attributes),so a tree can be seen as a set of rules that say how to classify instances.The goal of tree induction is toﬁnd a subdivision that isﬁne enough to capture the structure in the underlying domain but does notﬁt random patterns in the training data.As an example,Figure1shows a sample of500instances from an artiﬁcial domain,namely the sign-boundary of the functionf(x1,x2)=x21+x1+x2+e,4Landwehr,Hall and FrankFigure 1.The artiﬁcial‘polynomial-noise’dataset and the uncorrupted class boundary.Figure2.Subdivisions of increasing complexity for the‘polynomial-noise’dataset, generated by(from left to right)a decision stump learner,C4.5with the‘minimum instances’parameter set to20,and C4.5with standard options.Colors(from light to dark)indicate class probability estimates in the diﬀerent regions.a polynomial of the two attributes x1,x2that is corrupted by Gaussian noise e.The function was uniformly sampled in[−1,1]2.The origi-nal decision boundary of the polynomial(without noise)is also given (black/white region).We refer to this dataset as the‘polynomial-noise’dataset,it will be used again later.Figure2shows three subdivision of the R2instance space for the ‘polynomial-noise’dataset,generated by a decision stump learner(i.e.a one-level decision tree),C4.5(Quinlan,1993)with the‘minimum in-stances’parameter set to20,and C4.5with standard options.They are increasingly more complex;in this case,the center one would probably be adequate,while the rightmost one clearly overﬁts the examples.The usual approach to the problem ofﬁnding the best number of splits is toﬁrst perform many splits(build a large tree)and afterwards use a‘pruning’scheme to undo some of these splits.Diﬀerent pruning schemes have been proposed.For example,C4.5uses a statistically motivated estimate for the true error given the error on the training data,while the CART(Breiman et al.,1984)method cross-validates a ‘cost-complexity’parameter that assigns a penalty to large trees.Logistic Model Trees5 2.2.Classification via RegressionThe term‘regression’sometimes refers to a particular kind of paramet-ric model for estimating a numeric target variable,and sometimes to the process of estimating a numeric target variable in general(as opposed to a discrete one).For the moment,we take the latter meaning—we explain how to solve a classiﬁcation problem with a learner that can only produce estimates for a numeric target variable.Assume we have a class variable G that takes on values1,...,J. The idea is to transform this class variable into J numeric‘indicator’variables G1,...,G J to which the regression learner can beﬁt.The indicator variable G j for class j takes on value1whenever class j is present and value0everywhere else.A separate model is thenﬁt to every indicator variable G j using the regression learner.When classi-fying an unseen instance,predictions u1,...,u J are obtained from the numeric estimatorsﬁt to the class indicator variables,and the predicted class isj∗=argmaxju j.We will use this transformation process several times,for example when using model trees for classiﬁcation.Transforming a classiﬁcation task into a regression problem in this fashion,we can use standard linear regression model for classiﬁcation. Linear regressionﬁts a parameter vectorβto a numeric target variable to form a modelf(x)=βT xwhere x is the vector of attribute values for the instance(we assume a constant component in the input vector to accommodate the intercept). The model isﬁt to minimize the squared error:β∗=argminβni=1(f(x i)−y i)2,where we have n training instances x i that have target values y i.How-ever,this approach has some ually,the predictions given by the regression functionsﬁt to the class indicator variables are not conﬁned to[0,1]and can even become negative.Besides,the approach is known to suﬀer from masking problems in the multiclass case:even if the class regions of the instance space are linearly sepa-rable,two classes can‘mask’a third one such that the learned model cannot separate it from the other two—see for example(Hastie et al., 2001).6Landwehr,Hall and Frank2.3.Logistic RegressionA better way to use regression for classiﬁcation tasks is to use a logistic regression model that models the posterior class probabilities P r(G= j|X=x)for the J classes.Given estimates for the class probabilities, we can classify unseen instances byP r(G=j|X=x).j∗=argmaxjLogistic regression models these probabilities using linear functions in x while at the same time ensuring they sum to one and remain in[0,1]. The model is speciﬁed in terms of J−1log-odds that separate each class from the‘base class’J:P r(G=j|X=x)log1+ J−1l=1eβT l x,j=1,...,J−11P r(G=J|X=x)=Logistic Model Trees7LogitBoost(J classes)1.Start with weights w ij=1/n,i=1,...,n,j=1,...,J,F j(x)=0and p j(x)=1/J∀j2.Repeat for m=1,...,M:(a)Repeat for j=1,...,J:pute working responses and weights in the j th classy∗ij−p j(x i)z ij=(f mj(x)−1JJ k=1e F k(x)F j(x)3.Output the classiﬁer argmaxjFigure3.LogitBoost algorithm(Friedman et al.,2000).likelihood(Friedman et al.,2000).These models are a generalization of the(linear)logistic regression models described above.Generally,they have the forme F j(x)P r(G=j|X=x)=8Landwehr,Hall and FrankLogitBoost performs forward stagewiseﬁtting:in every iteration,it computes‘response variables’z ij that encode the error of the currently ﬁt model on the training examples(in terms of probability estimates), and then tries to improve the model by adding a function f mj to the committee F j,ﬁt to the response by least-squared error.As shown in (Friedman et al.,2000),this amounts to performing a quasi-Newton step in every iteration,where the Hessian matrix is approximated by its diagonal.Any class of functions f mj can be used as the‘weak learner’in the al-gorithm,as long as they areﬁt by a(weighted)least-squares regression. Depending on the class of functions,we get a more expressive or more restricted overall model.In the special case that the f mj(x)and so the F j(x)are linear functions of the input variables,the additive logistic regression model is equivalent to the linear logistic model introduced above.Assuming that F j(x)=αT j x,the equivalence of the two models is established by settingαj=βj−βJ for j=1...J−1andαJ=βJ. Note that the condition J k=1F k(x)=0is for stability only,adding a constant to all F k(x)does not change the model.This means we can use the LogitBoost algorithm to learn linear logistic regression models,byﬁtting a standard least-squares regres-sion function as the f mj in step2(a)ii.of the algorithm.In fact,in the two-class case this algorithm is equivalent to the standard‘iter-ative reweighted least squares’method used forﬁtting linear logistic regression models(Hastie et al.,2001).2.3.1.Attribute SelectionTypical real-world data includes various attributes,only a few of which are actually relevant to the true target concept.If non-relevant at-tributes are included in,for example,a logistic regression model,they will usually allow the training data to beﬁtted with a smaller error, because there is by chance some correlation between the class labels and the values of these attributes for the training data.They will not,how-ever,increase predictive power over unseen cases,and can sometimes even signiﬁcantly reduce accuracy.Furthermore,including attributes that are not relevant will make it harder to understand the structure of the domain by looking at theﬁnal model,because it is‘distorted’by the inﬂuence of these attributes.Therefore,it is important toﬁnd some way to select the most relevant attributes to include in the logistic regression models.When we say that weﬁt a linear regression function f mj by least squares regression in a LogitBoost iteration,we may consider a multiple linear regression that makes use of all the attributes.However,it is also possible to use even simpler functions for the f mj:simple regressionLogistic Model Trees9 functions,that perform a regression on only one attribute present in the training data.Fitting simple regression by least-squared error means ﬁtting a simple regression function to each attribute in the data using least-squares as the error criterion,and then selecting the attribute that gives the smallest squared error.Because every multiple linear regression can be expressed as a sum of simple linear regression functions,the general model does not change if we use simple instead of multiple regression for the f mj.Furthermore, theﬁnal model found by LogitBoost will be the same because quasi-Newton stepping is guaranteed to actuallyﬁnd the maximum likelihood solution if the likelihood function is convex,which it is for linear logistic ing simple regression functions instead of multiple ones will basically slow down the learning process,building gradually more complex models that include more and more attributes.However,all this only holds provided the algorithm is run until convergence(i.e., until the likelihood does not change anymore between two successive it-erations).If it is stopped before it converges to the maximum likelihood solution,using simple regression will result in automatic attribute selec-tion,because the model will only include the most relevant attributes present in the data.The stopping criterion can be based on cross-validation:only perform more iterations(and include more attributes) if this actually improves predictive accuracy over unseen instances.On the other hand,slowing down the modelﬁtting process can lead to higher computational costs.Althoughﬁtting a simple regression is computationally more eﬃcient thanﬁtting a multiple regression model, it could be necessary to consider the same attribute multiple times if the overall model has changed because other attributes have been included.This means many iterations have to be performed before the algorithm converges to a reasonable model.The computational complexity of a simple linear regression on one attribute is O(n),so one iteration of LogitBoost would take O(n·a)because we have to build a simple regression model on all attributes in order toﬁnd out which one is the best(where n denotes the number of training examples and a the number of attributes present in the data).The computational complexity for performing a multiple regression is O(n·a2+a3).1The relative speed of the two methods depends on how many LogitBoost iterations are required when using simple regression functions,but it is reasonable to expect that using multiple regression does converge faster.10Landwehr,Hall and FrankWe decided to use simple regression functions in our implementation because that approach improved predictive accuracy and signiﬁcantly reduced the number of attributes included in theﬁnal model for some datasets(see Section5.3for an empirical comparison of this method to building a‘full’logistic model on all attributes).Note that we used simple regression in both the logistic model tree algorithm LMT that builds logistic regression functions at the nodes of a decision tree(see Section4)and the standalone logistic regression learner we use as a benchmark in our experimental evaluation.We determine the optimum number of LogitBoost iterations by aﬁve fold cross-validation:we split the dataﬁve times into training and test sets,run LogitBoost on every training set up to a maximum number of iterations(500)and log the classiﬁcation error on the respective test set.Afterwards,we run LogitBoost again on all data using the number of iterations that gave the smallest error on the test set averaged over theﬁve folds.We will refer to this implementation as SimpleLogistic.2.3.2.Handling Nominal Attributes and Missing ValuesIn real-world domains important information is often carried by nomi-nal attributes whose values are not necessarily ordered in any way and thus cannot be treated as numeric(for example,the make of a car in the‘autos’dataset from the UCI repository).However,the regression functions used in the LogitBoost algorithm can only beﬁt to numeric attributes,so we have to convert those attributes to numeric ones.We followed the standard approach for doing this:a nominal attribute with k values is converted into k numeric indicator attributes,where the l-th indicator attribute takes on value1whenever the original attribute takes on its l-th value and value0everywhere else.Note that a dis-advantage of this approach is that it can lead to a high number of attributes presented to the logistic regression if the original attributes each have a high number of distinct values.It is well-known that a high dimensionality of the input data(in relation to the number of training examples)increases the danger of overﬁtting.On such datasets,at-tribute selection techniques like the one implemented in SimpleLogistic will be particularly important.Another problem with real-world datasets is that they often contain missing values,i.e.instances for which not all attribute values are ob-served.For example,an instance could describe a patient and attributes correspond to results of medical tests.For a particular patient results might only be available for a subset of all tests.Missing values can occur both during training and when predicting the class of an unseen instance.The regression functions that have to beﬁt in an iteration ofLogitBoost cannot directly handle missing values,so one has toﬁll in the missing values for such instances.We used a simple global scheme for this:at training time,we cal-culate the mean(for numeric attributes)or the mode(for nominal attributes)of the values for each attribute and use these to replace missing values in the training data.When classifying unseen instances with missing values,the same mean/mode is used toﬁll in the missing value.3.Related Tree-Based Learning SchemesStarting from simple decision trees,several advanced tree-based learn-ing schemes have been developed.In this section we will describe some of the methods related to logistic model trees,to show what our work builds on and where we improve on previous solutions.Some of the related methods will also be used as benchmarks in our experimental study,described in Section5.3.1.Model TreesThis section describes the‘model tree’algorithm developed by Quin-lan,which combines regression and tree induction for tasks where the target variable to be predicted is numeric(Quinlan,1992).The logistic model trees developed in this paper are an analogue to model trees for categorical target variables,so a description of model trees is a good starting point for understanding our method.Model trees,like ordinary regression trees,predict a numeric value for an instance that is deﬁned over aﬁxed set of numeric or nominal attributes.Unlike ordinary regression trees,model trees construct a piecewise linear(instead of a piecewise constant)approximation to the target function.Theﬁnal model tree consists of a tree with linear re-gression functions at the leaves(Frank et al.,1998),and the prediction for an instance is obtained by sorting it down to a leaf and using the prediction of the linear model associated with that leaf.The M5’model tree algorithm(Wang and Witten,1997),which is a‘rational reconstruction’of Quinlan’s M5algorithm(Quinlan,1992), constructs trees as follows.First,after all nominal attributes have been replaced by binary ones,an unpruned regression tree is grown,using variance reduction as the splitting criterion.Then,linear regression models are placed at every node of the tree,where the attributes considered in the regression are restricted to those that occur in the subtree rooted at the corresponding node.Further attribute selection inthe linear models is performed by greedily dropping terms to minimize an error estimate that introduces a penalty for every parameter used in the model.Once all linear models are in place,subtrees are considered for replacement based on theﬁnal error estimate for each linear model.At prediction time,the algorithm generates a’smoothed’output by averaging the prediction of the linear model at a leaf node with the predictions obtained from the models on the path from that leaf to the root.The smoothing heuristic eﬀectively performs a linear combi-nation of linear models,which can be written as a linear model itself. Hence it is possible to achieve the same eﬀect by replacing the original unsmoothed model at each leaf node with a smoothed version(Frank et al.,1998).Model trees have been shown to produce good results for numeric prediction problems(Wang and Witten,1997).They have also been successfully applied to classiﬁcation problems using the transformation described in Section2.2(Frank et al.,1998).In our experimental sec-tion,we will give results for this‘M5’for classiﬁcation’algorithm and compare it to our method.3.2.Stepwise Model Tree InductionIn this section,we will brieﬂy discuss a diﬀerent algorithm for induc-ing(numeric)model trees called‘Stepwise Model Tree Induction’or SMOTI(Malerba et al.,2002)that builds on an earlier system called TSIR(Lubinsky,1994).Although we are more concerned with clas-siﬁcation problems,SMOTI uses a scheme for constructing the linear regression functions associated with the leaves of the model tree that is related to the way our method builds the logistic regression functions at the leaves of a logistic model tree.The idea is to construct theﬁnal multiple regression function at a leaf from simple regression functions that areﬁt at diﬀerent levels in the tree,from the root down to that particular leaf.This means that theﬁnal regression function takes into account‘global’eﬀects of some of the variables—eﬀects that were not inferred from the examples at that leaf but from some superset of examples found on the path to the root of the tree.An advantage of this technique is that only simple linear regressions have to beﬁtted at the nodes of the tree,which is faster thanﬁtting a multiple regres-sion every time(that has to estimate the global inﬂuences again and again at the diﬀerent nodes).The global eﬀects should also smooth the predictions because there will be less extreme discontinuities between the linear functions at adjacent leaves if some of their coeﬃcients have been estimated from the same(super)set of examples.To implement these ideas,SMOTI trees consist of two types of nodes:split nodes and regression nodes.Split nodes partition the sam-ple space in the usual way,while regression nodes perform simple linear regression on one attribute.A regression nodeﬁts a simple regression to the examples passed down to it from the parent node,and passes on a modiﬁed version of the examples to its only child node,removing the linear eﬀect of the attribute used in the simple regression.This means the model at a leaf of the tree is constructed incrementally, adding more and more variables to it at the diﬀerent regression nodes on the path to the leaf while the tree is grown.Our method uses a similar scheme for constructing the logistic regression models at the leaves:the simple regression functions produced in the iterations of the LogitBoost algorithm areﬁt on the nested sequence of sets of examples associated with the nodes on the path from the leaf to the root of the tree.Note,however,that our method is not restricted to a single simple linear model at each node.We will give a detailed description of this in Section4.3.3.Logistic Regression Trees with Unbiased Selection Lotus(Chan and Loh,2004)is a logistic regression tree learner for two class problems that has come from the statistics community.The algorithm constructs(binary)logistic regression trees in a top-down fashion,emphasizes the importance of unbiased split variable selection through the use of a modiﬁed chi-square test,and uses only numeric attributes for constructing logistic models.Lotus canﬁt either multiple or simple logistic regressions at the nodes.After the initial tree is grown,it is pruned back using a pruning method similar to the one employed in the CART algorithm(Breiman et al.,1984).The idea is to use a‘cost-complexity measure’that com-bines the error of the tree on the training data with a penalty term for the model complexity,as measured by the number of terminal nodes. The cost-complexity-measure in CART is based on the misclassiﬁcation error of a(sub)tree,whereas in Lotus it is based on the deviance.The deviance of a set of instances M is deﬁned asdeviance=−2·logP(M|T)where P(M|T)denotes the probability of the data M as a function of the current model T(which is the tree being constructed).3.4.Functional TreesThe LTree algorithm embodies a general framework for learning func-tional trees(Gama,2004)—that is,multivariate classiﬁcation or regres-sion trees that can use combinations of attributes at decision nodes,leaf nodes,or both.The algorithm uses a standard top-down recursive partitioning strat-egy to construct a decision tree.Splitting at each node is univariate,but considers both the original attributes in the data and new attributes constructed using an attribute constructor function:multiple linear regression in the regression setting and linear discriminants or multiple logistic regression in the classiﬁcation setting.The value of each new attribute is the prediction of the constructor function for each example that reaches the node.In the classiﬁcation case,one new attribute is created for each class and the values are predicted probabilities.In the regression case,a single new attribute is created.In this way the algo-rithm considers oblique splits based on combinations of attributes in addition to standard axis-parallel splits based on the original attributes. For split point selection,information gain is used in the classiﬁcation case and variance reduction in the regression case.Once a tree has been grown,it is pruned back using a bottom-up procedure.At each non-leaf node three possibilities are considered: performing no pruning(i.e,leaving the subtree rooted at the node in place),replacing the node with a leaf that predicts a constant, or replacing it with a leaf that predicts the value of the constructor function that was learned at the node during tree construction.C4.5’s error-based criterion(Quinlan,1993)is used to make the decision.Predicting a test instance using a functional tree is accomplished by traversing the tree from the root to a leaf.At each decision node the local constructor function is used to extend the set of attributes and the decision test determines the path that the instance will follow.Once a leaf is reached,the instance is classiﬁer using either the constant or the constructor function at that leaf(depending on what was put in place during the pruning procedure).3.5.Naive Bayes TreesNBTree is an algorithm that constructs decision trees with naive Bayes models at the leaves(Kohavi,1996).A tree is grown in a top-down fashion with univariate splits chosen according to information gain.A pre-pruning strategy is employed during the construction of the tree that considers,at each node,whether the data at that node should be split,or a leaf created that contains a local naive Bayes model trained on the data at that node.The pruning decision at each node is made。

英语外国景点作文

When it comes to writing an essay about foreign tourist attractions in English,there are several key elements to consider.Heres a detailed guide to help you craft an engaging and informative essay.Title:Exploring the Wonders of Foreign LandsIntroduction:Begin your essay by introducing the theme of foreign travel and its allure.Mention how exploring different countries can broaden ones perspective and enrich ones life experiences.Paragraph1:Choosing the DestinationDiscuss the process of selecting a foreign destination.Consider factors such as cultural interest,historical significance,natural beauty,and personal preferences.For example,if you are fascinated by ancient civilizations,you might choose to visit the pyramids of Egypt.Paragraph2:Preparing for the JourneyOutline the steps involved in preparing for a trip abroad.This includes obtaining necessary travel documents,researching local customs and etiquette,learning basic phrases in the local language,and planning the itinerary.Paragraph3:The Journey BeginsDescribe the excitement of the journeys commencement.Talk about the anticipation of discovering new places,meeting new people,and experiencing different cultures.You could mention the first impressions of the foreign country,such as the bustling airport or the welcoming atmosphere.Paragraph4:Exploring the AttractionsDelve into the specific attractions of the chosen destination.For instance,if you visited Paris,France,you might describe the grandeur of the Eiffel Tower,the artistic treasures of the Louvre Museum,and the charm of the ChampsÉlysées.Paragraph5:Cultural ExperiencesHighlight the cultural experiences that make the trip memorable.This could include attending a traditional dance performance in Japan,savoring authentic Italian cuisine in Rome,or participating in a local festival in Brazil.Paragraph6:Challenges and SolutionsDiscuss any challenges you faced during your travels,such as language barriers,navigating unfamiliar cities,or adjusting to different climates.Explain how you overcame these challenges and what you learned from the experience.Paragraph7:Reflections and Personal GrowthReflect on the personal growth and insights gained from your travels.Discuss how the experience has changed your perspective on life,your understanding of other cultures, and your appreciation for the diversity of the world.Conclusion:Conclude your essay by summarizing the key points and expressing your desire to continue exploring the world.Encourage readers to embark on their own journeys of discovery.Sample Sentences:The allure of foreign lands lies in their ability to challenge our preconceptions and offer a glimpse into the lives of others.As I stood at the base of the Great Wall of China,I was humbled by the sheer scale of human achievement.The aroma of freshly baked baguettes and the sound of laughter in a Parisian cafétransported me to a world of joie de vivre.Language barriers can be frustrating,but they also present opportunities for connection and understanding.Traveling has taught me that despite our differences,we share a common humanity that transcends borders.Remember to use descriptive language and vivid imagery to bring your experiences to life for the reader.Incorporate personal anecdotes and reflections to make your essay more engaging and relatable.。

Unit2TravellingAroundDiscoveringUsefulStructures教学

必修一Unit 2 Travelling Around Discovering Useful Structure1. Get students to have a good understanding of the basic usages of the present continuous tense used to express the future meaning.2. Strengthen students’ great interest in grammar learning.3. Instruct students to express their ideas with this grammar correctly.教学重难点How to enable students to use the grammar item: the present continuous tense used to express the future meaning flexibly both in their spoken English and written English.教学方法1. Cooperative Teaching Method,2. Taskbased language teaching教学资源教材，课件PPT，课时练教材分析：本单元学习的语法知识是掌握现在进行时表示将来计划的用法。

并运用这一表达与同伴讨论周末计划。

该板块的活动设计遵循“发现—归纳—练习—运用”的思路。

引导学生理解并在真实语境中恰当的运用现在进行时来表示计划安排好的事物或要采取的行动。

教学过程Step1 LeadinListen to a radio and figure out the new uses of “be doing structure” by filling in the blanks:Lin Tao: The vacation is ing soon. What are you doing during the holiday?Zhou Ting: I am writing an essay these days. After finishing it, I am travelling to enjoy the sunshine and beach. There I can visit “the End of The Earth”.Q1: What are the tense and structure？Q2: What is Zhou Ting doing recently?Q3: What will Zhou Ting do after finishing the essay?Q4: What do you think the tense in the sentences expresses?设计意图通过提问问题，引出今天上课的主题、学习的主要内容。

invoke getter method error, constructiondate

invoke getter method error, constructiondate The "invoke getter method error, constructiondate" errormessage typically indicates that there is an issue with invoking agetter method (`getConstructionDate`) on a particular object or class.Here are some possible causes and solutions:1. Method not defined: Make sure the class or object you are workingwith actually defines a getter method with the name`getConstructionDate`. Double-check the class definition or objectdocumentation to confirm its availability.2. Method visibility: Check if the getter method is properly visibility(public, protected, private) and ensure that you have access to it fromthe current context. If the method is private, it may not be accessiblefrom outside the class.3. Object instance: Ensure that you have an instance of theappropriate class on which to invoke the getter method. Make sureyou create an object or obtain an instance from a valid source beforecalling the method.4. Method signature: Verify that the parameter and return types of the getter method match the expected types in your code. If there is a type mismatch, it can result in an error.5. Compile-time errors: Check for any compile-time errors or warnings in your code. Sometimes, syntax errors or other issues can prevent the getter method from being recognized or invoked correctly.。

1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。

CHIN.PHYS.LETT.Vol.25,No.7(2008)2535 An Intelligent Segmented Burst Assembly Mechanism in Optical Burst SwitchingNetworks∗XIE Yi-Yuan(解宜原)∗∗,ZHANG Jian-Guo(张建国)State Key Laboratory of Transient Optics and Photonics,Xi’an Institute of Optics&Precision Mechanics,ChineseAcademy of Sciences,Xi’an710119(Received26February2008)We focus on the burst assembly mechanism and propose a new intelligent method in which the burst is assembled from several internet protocol(IP)packets in which the number of IP packets is changed according to the traﬃc load and the burst is segmented into several parts,called the ISOBS mechanism.The average burst assembly time of the ISOBS mechanism decreases as compared with theﬁxed-assembly-time andﬁxed-assembly-time-and-length mechanisms.The loss ratio decreases50%as compared with the general optical burst switching(OBS) mechanism.The last segment can carry high quality of service(QOS)information.We can achieve that the loss ratio of the last segment is almost zero when the traﬃc load is less than0.05.When the traﬃc load is0.9,the loss ratio of the last segment is0.0041.The ISOBS can support to transmit diﬀerent QOS data.PACS:42.81.Uv,42.79.SzOver the past decade,there has been a phenome-nal increase in internet traﬃc and the variety of inter-net applications.To move toward optical time divi-sion multiplexing(OTM),dense wavelength-division multiplexing(DWDM),optical code division multi-ple access(OCDMA)architectures and various opti-cal switching techniques[1−4]have been investigated to meet the exponential growth of internet traﬃc,de-manding a huge band-with at the backbone network. Internet protocol(IP)over wavelength-division mul-tiplexing(WDM),IP over time division multiplex-ing(TDM),IP over code division multiplexing(CDM) have been proposed.Three switching technologies,i.e. optical circuit switching(OCS),optical burst switch-ing(OBS)and optical packet switching(OPS),have been studied.OCS has its own limitations to sup-port dynamic traﬃcs.OCS is not bandwidth eﬃcient unless the duration of transmission is greater than the circuit establishment period.OCS has the largest transmission granularity among the three switching techniques.OPS uses a hop-by-hop store and forward scheme and needs buﬀering and processing at each in-termediate node.It isﬂexible,bandwidth eﬃcient and the least transmission granularity,but requires prac-tical cost-eﬀective scalable implementations of opti-cal buﬀering and optical header processing,for sev-eral years.OBS has emerged as a promising balanced approach between OCS and OPS.OBS that does not require optical buﬀer and intermediate transmission granularity is emerging as the new switching paradigm for the next generation optical networks.Figure1illustrates a typical OBS network archi-tecture.We consider an OBS network consisting of edge routers,which are responsible for burst assembly and disassembly and core routers,which are mainly responsible for header processing and burst routing. In OBS,several data packets are assembled together to form a burst.A control packet containing related control information,such as burst length and routing information is sent ahead of the burst on a control channel to set up a connection without waiting for ac-knowledgement for the connection establishment.A control packet goes through an O/E/O conversion at each core node for resource reservation.The data burst follows the header after an oﬀset time.Thus, no optical buﬀering is necessary at core nodes.Thus, OBS is eﬃciently suitable for traﬃcﬂows that are burst in nature.[5−7]A number of recent studies fo-cus on some major issues in OBS networks,[8−10]such as:burst assembly,transmission control protocol,con-tention resolution,QOS,and network conﬁguration. In an optical burst switching network,with higher load,contention increases,and hence the number of dropped bursts increases,leading to a serious loss of performance.Several methods can be used to lower the burst dropping probability,such as wavelength conversion and the use of buﬀers,but these solutions are still not used due to the high cost and the im-maturity of technology.Other simple solutions such as the delayed burst method and deﬂection routing have also been proposed.Burst assembly is one of the critical technologies,and signiﬁcantly aﬀects the per-formance of the networks such as QOS and loss burst probability.[11,12]There are several methods of burst assembly in an OBS edge node.First,Ge et al.proposed the ﬁxed-assembly-time(FAT)method based on assem-bly time,[13]but this method has limitations.FAT∗Supported by the Knowledge Innovation Project of Chinese Academy of Sciences(KGCX2-YW-108),and the International Co-operation Programme of Chinese Academy of Sciences.∗∗To whom correspondence should be addressed.Email:xieyiyuan1000@c 2008Chinese Physical Society and IOP Publishing Ltd2536XIE Yi-Yuan et al.Vol.25needs big buﬀer when the load is high,but at low load the delay time increases resulting ineﬃcient as-sembly.In the case of contention the whole burst is dropped.Second,Xiong et al.[14]proposed theﬁxed-assembly-time-and-length(FAT-L)method based on assembly time and burst length.This method has the same problem.In this Letter,we focus on the study of a burst assembly mechanism,and propose a new in-telligent method in which the burst is assembled from several IP packets where the number of IP packet is changed according to the traﬃc load and the burst is segmented into several parts.In the case of con-tention,only the parts that cause the conﬂicts will be discarded.We analyse the assembly time and the loss ratio.The segments of a burst can support diﬀer-ent classes of service because the dropping probability of each segment depends on its position in the burst. The segment at the end has the smallest probability of drop.By analysis and simulation we prove that the intelligent segmented burst assembly mechanism can improve the network performance and the scheme is more suitable for traﬃc with several class of service and reduces the loss ratio,enhances the data burst utilization and avoids the bandwidth contention.Fig.1.A typical OBS network architecture.BCP:burst control packet,BDP:burst data packet.In recent years,several burst assembly mecha-nisms have been proposed.However,these mecha-nisms have several limitations.First,when the load is low,the delay of the network will increase.Sec-ond,the contention of network will increase when the number of nodes increases.Third,in the case of con-tention,the whole burst will be discarded,so the drop-ping probability will be high.For solving the prob-lems,we propose a new intelligent segmented burst assembly mechanism(segmented burst and variable length)based on the segment optical burst switching (SOBS),[15]the new abbreviation is called the ISOBS mechanism.In ISOBS,the burst is divided into many segments with the same length.The edge node will group IP packets into a list of segments withﬁxed length.The length of segment is L s.The size of the burst is identi-ﬁed by the number of carried segments.The maximal length of the burst is BS max.The minimum length of the burst is BS min.The regulation step is de-ﬁned L step,in the ISOBS mechanism L step=L s.We deﬁne T th as the maximal assembly time and deﬁne Q th=(BS max+BS min)/2,it is a initialization of the burst size.We deﬁne N SB as the number of segments in burst.Theﬂow chart of the ISOBS mechanism is given in Fig.2.The ISOBS assembly mechanism includes four steps:(1)Initialize parameters.The threshold of the burst length is Q th.The regulation step is L step.The maximal assembly time is T th.The assembly time is T=0,the number of IP packets is N SB=0.(2) When theﬁrst IP packet arrives at the edge node,the arithmometer starts.If T≥T th or N SB×L step≥BS max,create a new data burst and a new control packet.(3)Compared N SB×L step to BS max and BS min,if BS min<N SB×L step<BS max,we change Q th to Q th=N SB×L step.If N SB×L step<BS min, Q th=BS min.If N SB×L step>BS max,Q th=BS max.(4)Initialize the calculagraph and the number of seg-ments of the arithmometer.Start new burst assembly.In OBS networks,burst loss due to contention is a major issue.The contention of bursts depends on the physical topology and resources available such as the number of wavelengths and the network connectivity, and also the burst length and the traﬃc load.The shorter the average burst length,the lower the drop-ping probability.However,if the average length of the burst is short,the whole switching time will increase, which leads to a waste of bandwidth and increase net-work delay.In general OBS networks,the edge node once sends a burst which will reach the destination or will be lost in the case of conﬂict in core nodes.In or-der to solve the problems we propose that the burst is divided into many segments with the same length and the burst threshold changed with the traﬃc payload. In the ISOBS mechanism,all the segments will be sent with the same burst control packet(BCP)and with a short time separation between two segments.In the case of contention,only the contending segments be-longing to the second burst will be discarded whereas the other segments can continue on their way as shown in Fig.2.In the ISOBS mechanism,the information carried by the BCP is necessary just to indicate the number of segments in a burst,and this information may change at intermediate nodes whenever some of the segments are dropped.Figure3shows contention between two ISOBS bursts.[16]ISOBS bursts adds moreﬂexibility to OBS and improves the throughput of the network since it change burst threshold with traﬃc payload and re-duces the dropping probability.We simulate ISOBS with Poisson data burst sources(no correlation between the bursts coming on diﬀerent inputs over each link with a rateλ)and no optical buﬀer.It is assumed that the behaviour of aNo.7XIE Yi-Yuan et al.2537singleswitch will reﬂect that of the mesh network.Fig.2.Flow chart of the ISOBS mechanism.Fig.3.Contention in ISOBS burst switching.We also assume several parameters:(1)In ISOBS assembly mechanism,the length of burst is variable between BS max and BS min .(2)A switch does not support wavelength conversion.(3)In this OBS net-work,there is no optical buﬀer.(4)Maximal assembly time is 8ms.(5)Maximal length of burst is 6Mbit.The minimum length of burst is 120kbit.(6)The regulation step L step is 120kbit.Figure 4plots the average burst assembly time ver-sus the traﬃc load.We can obtain the average burst assembly time of the FAT mechanism does not change with the traﬃc load.The FAT mechanism which is easily caused the continuous resource competition has the maximal time delay compare with the FAT-L and ISOBS mechanism.In the FAT mechanism,the length of burst has great change with the traﬃc load.Thus it needs big queue buﬀer at the edge OBS nodes.Un-der light traﬃc load FAT-L mechanism has the same problem compared with the FAT mechanism.Because the assembly time and the length of burst vary with the traﬃc load,the average assembly time of ISOBS less than the FAT and FAT-Lmechanism under the light traﬃc load.Fig.4.Average burst assembly time of FAT,FAT-L and ISOBS.Fig.5.Loss ratio versus the light traﬃc load.Figures 5and 6plot the loss ratio versus the traﬃc load.They show the performance measure in this sim-ulation is the dropping probability due to collisions.The loss is calculated as the ratio of the dropped bits and the total bits sent.As is expected,the dropping probability increases with the load.The loss ratio of the ISOBS will decrease by 50%as compared with the general OBS network.This can be explained by the following content.In ISOBS network,the average loss is given by the formula N s i −1P i ×i ×S s .Here P i is the probability to remove i segments which is equal to 1/N S .N S is the number of segments in a burst.S S is the average segment size.If S S =B S /N S where B S is the average burst size,then the average loss is B S /2.This means that the loss ratio will decrease.From Fig.2,we can see that the segment located at2538XIE Yi-Yuan et al.Vol.25the end ofthe burst has a greater chance of surviving contention because the dropped segments arethose at the beginning of the second burst.Thus the most important information can locate in the last segment.Figures 7and 8plot the loss ratio of the last seg-ment versus the traﬃc load.We can see that the loss ratio of the last segment is zero when the traﬃc load less than 0.05.Under the heavy traﬃc load,the loss ratio of the last segment is almost zero.When the traﬃc load is 0.9,the loss ratio of the last segment is 0.0041,while the loss ratio of the burst is 0.293.Thus we can ﬁnd that the ISOBS can support diﬀerent QOS data.The last segment has the least loss ratio,so it can be used to transmit the highest QOS data.Fig.6.Loss ratio versus the high traf-ﬁc load.Fig.7.Loss ratio of the last segment versus the light traﬃc load.Fig.8.Loss ratio of the last segment versus the high traﬃc load.In summary,the scheme of burst assembly at edge routers has been discussed for setting up more reason-able,less assembly time and lower loss ratio.Espe-cially,the concept that the number of segments varies with the traﬃc load is introduced to improve the OBS network performance.The simulation results show that:(1)Using the ISOBS mechanism,the average burst assembly time decreases compared with the FAT and FAT-L mechanism.(2)Using the ISOBS mecha-nism,the loss ratio decreases to 50%as compared with the general OBS mechanism.(3)Using the ISOBS mechanism,the last segment can carry high QOS in-formation.We can realize that the loss ratio of the last segment is almost zero when the traﬃc load is less than 0.05.When the traﬃc load is 0.9,the loss ratio of the last segment is 0.0041.(4)The ISOBS can support diﬀerent QOS data.References[1]Yoo M and Qio C 1999J.High Speed Networks 869[2]Yao S,Mukherjee B and Dixit S 2000IEEE Commun.Magn.3884[3]Huang A,Xie L,Li Z and Xu A 2003Photonic NetworkCommun.6169[4]Chen Y,Qiao C,and Yu X 2004IEEE Network 1816[5]Yoo M,Jeong M,and Qiao C 1997SPIE Proc.323079[6]Yoo M,Qiao C 1998SPIE Proc .3531396[7]Huang A and Xie L 2005Photonic Network Commun.10297[8]Vu H and Zukerman M 2002IEEE Commun.Lett.6214[9]Baldine I,Rouskas G,Perros H and Stevenson D 2002IEEECommun.Magn .4082[10]Gauger C M 2004Photo Network Commun.8139[11]Listanti M,Eramo V and Sabella R 2000IEEE Commun.Magn.3882[12]Wang X,Morikawa H and Aoyama T 2002SPIE OpticalNetworks Magazine 312[13]Ge A,Callegati F and Tamil L 2000IEEE Commun Lett.498[14]Xiong Y,Vandenhoute M and Cankaya H 2000IEEE J.Select.Areas Commun.181838[15]Maach A,Bochmann G V and Mouftah H 2004EleventhInternational Telecommunications Network Strategy and Planning Symposium 447(Vienna)[16]Maach A 2005PhD Thesis (University of Ottawa,Canada)。