Predicting
Lesson-51-Predicting-the-future-预测未来
§ Lesson 51 Predicting the future 预测未来【New words and expressions】生词和短语notoriously adv. 臭名昭著的, 声名狼藉的(尤指因坏事)众所周知地notorious [nəu’tɔ:riəs]adj.臭名昭著的, 声名狼藉的He is a notorious rake.他是个声名狼藉的浪子。
a notorious gambler有名的赌棍be notorious for以...出名It is notorious that ... ...是众所周知的(事实)。
mainframe [‘meinfreim]n.(大型电脑的)主机, 中央处理机full-time adj.全部时间的,专任的technician [tek’niʃən]n.技术人员, 专家; 技巧好的人The technician is busy repairing the machine.技师正忙于修理那台机器。
software [‘sɔftwɛə]n.软件IBM n.美国国际商用机器公司(International Business Machines)1911年创立于美国,IBM是专业的信息技术和业务解决方案公司,IBM业务遍及160多个国家和地区。
IBM硬件产品包括IBM服务器,IBM存储设备,IBM打印机,IBM POS机,IBM网络设备,还有IBM技术服务,IBM软件产品等主营业务。
DOS 磁盘操作系统(Disk Operating System)Microsoft n.美国微软公司user-friendly adj.用户界面友好的,用户容易掌握使用的multimedia [,mʌlti’mi:djə]adj.多种手段的, 多种方式的alternative [ɔ:l’tə:nətiv]adj.两者择一的, 供替代的We returned by the alternative road.我们从另一条路回来的。
2021届高考英语词句仿写训练+篇章续写(十三)
2021高考英语词句仿写训练+篇章续写(十三)1.Supported by his academic research, Professor Salovey suggests that when predicting someone’s future success, their character, as measured by EQ tests, might actually matter more than their IQ.【句式翻译】萨洛维教授在他学术研究的基础上提出, 在预测一个人未来的成功时, 他的性格,通过情商测验来衡量, 也许比他的智商更为重要。
【句式分析】本句是复合句,that引导宾语从句,从句中包含有一个when引导的省略的状语从句,supported 和predicting在句中作状语。
【词语点拨】1)support vt. 支撑;支持;供养;为…提供资金;n. 支持;支撑物;赡养他们支持我们争取增加工资。
They supported us in the struggle for an increase in pay.这项研究由政府出资。
The research was supported by the government.我靠朋友给我感情支持。
I depend on my friends for emotional support.她家全靠她的工作维持生计。
Her job is the family’s only means of support.2)suggest vt.建议;表明。
当“建议”讲时,后面可以接动名词,接从句时,从句用虚拟语气;当“表明”时,后面的从句常用陈述语气。
他苍白的脸色表明他病得很厉害。
His pale face suggested that he was badly ill.我建议结束会议。
I suggest bringing the meeting to an end.=I suggest that we should bring the meeting to an end.2:He also dismissd concern in the media that parliament was slipping into the Thames, while the commission’s spokesman denied that the walls around the palace were suffering from a particularly bad sinking problem causing Big Ben to lean.【句式翻译】他还摒除了媒体对议会会滑落到泰晤士河的顾虑,而委员会的发言人则否认是宫殿周围的墙受到严重的下沉,使得大本钟倾斜。
2012-Predicting and explaining use intention and purchasing intention in online group shopping
25th Bled eConferenceeDependability:Reliable and Trustworthy eStructures, eProcesses, eOperationsand eServices for the FutureJune 17, 2012 – June 20, 2012; Bled, SloveniaPredicting and explaining use intention and purchasing intention in online g ro up s hoppingHongxiu LiDepartment of Surveying, School of Engineering, Aalto UniversityFinland hongxiu.li@aalto.fiYong LiuIAMSR, Åbo Akademi UniversityFinland yong.liu@abo.fiAbstractOnline group shopping has emerged as a new e-commerce model and received attention in both academics and practice. Prior research focuses on investigating online group shopping ma i n ly f rom the marketing discipline, and little research has tried to examine individual consumers’ acceptance of online group shopping from the technology acceptance perspectives. This paper aims at investigating the drivers determining individual consumers’ intention to use online group shopping as well as the relationships between the intention to use online group shopping and the intention to purchase via online group shopping websites. The research model was developed based on technology acceptance theories and extended to purchasing behaviour. The research model is empirically tested with data collected from 318 Chinese college students and analyzed using PLS. The results show that all the individuals’intention to use online group shopping are motivated by relative advantages, perceived complexity and perceived enjoyment, and their use intention is significantly related to their purchasing intention via online group shopping websites. Finally, the limitations of the current research are discussed, and the directions for further research are suggested.Keywords: Online group shopping, use intention, purchasing intention,innovative services.1 IntroductionWith the penetration of Internet into business and people’s life, e-commerce has increasingly become popular. Recently, online group shopping, as a new e-commerce model, has attracted the attention in both academics and practice. As Kauffman et al. (2010) argued online group shopping is an online retailing concept, which seeks to provide cheap services or products through leveraging the buying power of individual consumers as a group. Internet makes it possible for customers to receive accurate, timely, and inexpensive information. As a result, customers can also make comparison of product/service prices and choose the supplier with the lowest price (Gounaris et al. 2010). Online group shopping is popular among individual customers mainly because of its lower prices compared to thatHongxiu Li, Yong Liu offered b y alternative product/service suppliers. In online group shopping, consumers can leverage the collective bargaining power to lower the prices of the product or services they are interested in, whereas suppliers can diminish their cost of recruiting customers. The goal of online group shopping is to create a win-win mechanism between consumers and suppliers in e-commerce by making each party better off (Kauffman et al. 2010).The most popular online group shopping websites should be Groupon in USA. It has been successful in its online group shopping business, and its success has inspired many merchants in the world to develop online group shopping. In China, the emergency of online group shopping has attracted the interest of venture capitalist, the market and consumers. In 2010, the first online group shopping website, , was initiated. After that, online group shopping websites boomed in China. According to the released report of (2011), by September in 2011, there were about 5682 different online group shopping websites in China. Some firms involved in online group shopping section are popular in China, such as Meituan (), Teambuy (), Lashou (), and Taobao (). Online group shopping has evolved as a new e-commerce model for e-commerce companies in China. According to the report released by iResearch Consulting Group, there were about 50 million online group shopping users in China in 2010, which accounts for 12 percent of all Chinese Internet users at that time (iResearch 2011a), and one in every four Chinese Internet users (130 million) is expected to use online group shopping in 2011 (iResearch 2011b). Obviously, online group shopping has already achieved its acceptance and success in the Chinese online business market. It seems that online group shopping, as a new e-commerce model, appears to have tremendous capacity for creating e-commerce business opportunities in China (Chen et al. 2009).The emergence of online group shopping has attracted the attention of researchers around the world. Kauffman and Wang (2001, 2002) have conducted some early work on online group shopping to explore consumer transaction behaviour in online group shopping. Following their research, Anand and Aron (2003) explored the outcomes of online group shopping and identified how to understand online group shopping performance compared to the traditional fixed-price selling mechanism. Later Kauffman et al. (2010) examine the relationships of incentive mechanisms, the fairness and consumers’ participation in online group shopping. Chen et al. (2009) examined how the cooperation among bidders in online group shopping mechanism can be effectively enhanced to produce higher benefits for participants.Prior research attempted to explain online group shopping from the marketing perspective, such as the transaction process, price mechanism, and benefits. Little research has been done in the information systems (IS) research field to explore the phenomenon, such as why consumers would like to accept online group shopping. In addition, few studies have investigated the relationship between consumers’acceptance of online group shopping and their purchasing behaviour. In this concern, this study aims at exploring the predictors determining both individual consumers’ intention to use online group shopping and intention to purchase via online group shopping websites, which provides some fresh insights into both technology acceptance and online group shopping research. The research will also offer some practical guidance for online group shopping websites on their strategies in attracting consumers to use their services.Predicting and explaining use intention and purchasing intention in online group shoppingThe remainder of this paper is organized as follows. Following this introduction, the research model on individual consumers’ intention to use online group shopping and intention to purchase via online group shopping websites are put forward together with the research hypotheses. Then, the methodology of this study and the date collection are presented, followed by measurement validity and results from the data analysis. Finally, the findings as well as the limitations are discussed, and suggestions for further research are presented.2 Research model and hypotheses2.1 Relative advantagesAs defined in Diffusion of Innovation Theory (DOI) (Rogers 1995), relative advantage refers to “the degree to which an innovation is perceived as better than the idea it supersedes”(Rogers 1995, p. 212). Relative advantages means that a new technology is superior to existing substitute (Bennett & Bennett 2003), and it also indicates both the cost and the benefits resulting from a new technology adoption (Rogers 1995). Prior research has found that relative advantage is the most essential factor influencing individuals’ willingness to adopt new technology with empirical validation from different research contexts, such as e-learning, virtual community, mobile service and so on (Mehrtens et al. 2001; Kim et al. 2009). As regards to online group shopping, relative advantages means that individual consumers believe that using online group shopping is better than the other conventional online shopping models, such as B2C or C2C. Online group shopping provides both products and services to individual customers with heavy discounts, such as more than 50% price off. Online group shopping makes it possible for individual customers to get offer of products/services with the lowest prices and to reduce purchase cost to a great extent. It increases the benefits to individual customers in their using of online group shopping. Thus, it is expected that relative advantages will be positively associated with individuals’intention to use online group shopping. When they are aware of the relative advantage of online group shopping over conventional online shopping approaches, they will be more intended to use online group shopping. Hence, the following hypothesis is proposed:H1: Relative advantage is associated with individuals’ intention to use online group shopping.2.2 Perceived complexityAs defined in DOI (Rogers 1995), perceived complexity refers to “the degree to which an innovation is perceived as relatively difficult to understand and use”(Rogers 1995, pp. 242). The construct complexity is similar to the construct perceived ease of use in the Technology Acceptance Model (TAM) (Davis 1989). Prior research has validated that complexity or perceived ease of use affect individuals’adoption of a new technology (i.e. Liu & Li 2010). If an innovative technology is easy but not complex to use, individuals are more likely to use the innovation. Thus, it is expected that a relationship between complexity and adoption intention may exists in the research context of online group shopping, and the following hypothesis is posited:H2: Perceived complexity is associated with individuals’intention to use online group shopping.Hongxiu Li, Yong Liu 2.3 Perceived enjoymentPerceived enjoyment refers to ‘‘the extent to which the activity of using a specific system is perceived to be enjoyable in its own right, aside from any performance consequences resulting from system use” (Venkatesh 2000, p. 348). Perceived enjoyment has been defined as an intrinsic motivation for individual to use a new technology. It has been found to be a significant predictor determining individuals’use of new technologies, especially for hedonic systems, such as mobile gaming and online gaming (Liu & Li 2011). When individual users feel that using a new technology is with fun and pleasure, they are more likely to use the new technology. Nowadays, more and more new technologies are designed with both hedonic and utilitarian purposes. Thus, individual users can experience enjoyment in their using of utilitarian systems. Ha & Stoel (2009) stated that individual s’ beliefs about shopping enjoyment play a significant role in their acceptance of online shopping. Though prior research on investigating online shopping motives suggested that the primary advantages of online shopping are related to utilitarian perspectives of online shopping, some hedonic aspects of online shopping are also found to affect individual s’usage of online shopping, such as perceived enjoyment, such as perceived enjoyment, social experiences (Ha & Stoel 2009; Joines et al. 2003; Parsons 2002). Thus, it is expected that individual users will experience enjoyment in using online group shopping, and the perceived enjoyment in their use of online group shopping may influence their intention to use online group shopping. Hence, the following hypothesis is suggested:H3: Perceived enjoyment is associated with individuals’intention to use online group shopping.2.4 Use intention and purchasing intentionIn IS research, one of the main themes is users’ intention to use a new technology. However, little research has investigated the relationship between their use intention and their purchasing intention in the online environment. There is no evidence from the prior research to explain whether individual users are more likely to shopping online if they are more likely to use online shopping. The online shop managers are not provided with explanation from research that their strategies in pushing individuals’use of their online shops can help increase online sales. In this research, we assumed that individual users are more likely to purchasing via online group shopping websites if they have higher tendencies of using online group shopping. Thus, the following hypothesis is proposed:H4: Individuals’ intention to use online group shopping is associated with their intention to purchase via online group shopping websites.The research model is presented in Figure 1.Predicting and explaining use intention and purchasing intention in online group shoppingFigure 1: Research Model3 Research Method and Results3.1 Data collectionSurvey is employed in this study to collect empirical data. The sample of the research is some students in the campus of Zhejiang Normal University. The questionnaires were distributed at the campus of Zhejiang Normal University in September 2011. The distribution of questionnaire last for two days in the campus. The respondents were provided with candies in order to please the respondents and to seduce their patience in answering the questionnaire. It costs about 3 RMB (approximately 0.5 USA) to please each respondent. The respondents were asked to response on their use of online group shopping. They were also required to recall their prior online group shopping experience and to indicate the motives for their use of online group shopping and their intention to purchase via online group shopping websites.The constructs included in the research model were developed mainly based on the adaptation from the prior research on technology acceptance. Some modification and rewording has been conducted in order to make it fit to the research context of online group shopping. A seven-point Likert-scale ranging from strongly disagree (1) to strongly agree (7) was used to measure the proposed research items.Totally, 389 questionnaires had been distributed, and 369 were collected. Among the 369 copies of questionnaire, 318 were acceptable and used as the basis of the research. The final sample consists of 129 males (40.6%) and 189 females (59.4%).3.2 Measurement ValidityPartial Least Squares, in practice SmartPLS software, was employed in the current research to obtain estimates for the measurement and structural parameters in our structural equation model.Convergent validity indicates the extent to which the measures of a construct that are theoretical related are also related in reality. Convergent validity can be evaluated by inspecting the factor loadings of the measures on their respective constructs (Chin 1998; Hulland 1999; Tenenhaus et al. 2005), the reliability of the measures can be assessed usingHongxiu Li, Yong Liu composite reliability (CR) and average variance extracted (AVE). In this study the factor loading are all satisfactory with the cut-off value above 0.7. The composite reliability (CR) satisfies the threshold value of 0.7 and average extracted variance (AVE) meet the threshold value of 0.5 respectively (See Table 1). The results in this study demonstrate good internal consistency, and the convergent validity and reliability of the measures in this study are supported (Fornell & Larcker 1981).According to Fornell and Larcker (1981), discriminant validity can be verified with the square root of the average variance extracted for each construct (Fornell & Larcker 1981). As shown in Table 2, each construct shares a greater variance with its own measures than with any other construct. The results reveal that each construct in the proposed research model is more closely related to its own measures than to those of other constructs. It meets the criteria suggested by Fornell and Larcker (1981).correlation estimates.Table 2: Correlations between ConstructsPredicting and explaining use intention and purchasing intention in online group shopping 3.3 ResultsIn this study a bootstrapping procedure was adopted to test the effects and the statistical significance of the parameters in the structural model. Figure 2 provides a graphic presentation of the research results. As shown in Figure 2, perceived complexity (ȕ=0.235, p<0.001), relative advantages (ȕ=0.304, p<0.001), and perceived enjoyment (ȕ=0.234, p<0.001) are found to be significantly associated with individual users’intention to use online group shopping. In addition, the intention to use online group shopping is found to be positively associated with their intention to purchase via online group shopping websites. All the proposed hypotheses are supported in this study.The proposed research model explains 32.4% of individuals’use intention and 45.6% of their purchase intention in the online group shopping context.Note: ***: p-v al u e<0.001Figure 2: Structural Analysis of the Research Model4 Discussion and ConclusionsThe results in this study show that individual users’ intention to use online group shopping is mainly motivated by perceived complexity, relative advantages and perceived enjoyment, and their use intention will influence their purchasing intention via online group shopping websites directly. Also there are indirect effects of perceive complexity, relative advantages and perceived enjoyment on purchasing intention.Of the three predictor of individuals’ intention to use online group shopping, relative advantages has the strongest influence on use intention, followed by perceived complexity and perceived enjoyment. The results indicate that individuals use online group shopping mainly because of its relative advantages over the conventional online shopping approaches. Perceived enjoyment is found to be a significant motivator of use intention. It suggests that the fun and pleasure perceived in using online group shopping are also important factors influencing use intention. The findings on the relationship between perceived complexity and use intention is consistent with prior findings and suggested that if individual users feel it is easy to use online group shopping, they are more likely to use online group shopping. The study results suggest that in order to increase customer base online group shopping websites should not only consider about their strategies in increasing its relative advantages compared to conventional e-commerce model, they should also take consideration of making online group shopping as easy as possible in use and enhancing the fun and pleasure in using it as well.In addition, in this research, individuals’ intention to use online group shopping is found to affect purchasing intention significantly. It indicates that motivating individuals’intentionHongxiu Li, Yong Liu to use online group shopping can also enhancing their purchasing intention via online group shopping. It implies that the strategies aiming at attracting online group shopping users may also help increasing their potential online group shopping sales as well. Note that an intention to use the technology doesn’t guarantee the behaviour to use the online shopping sites, while the intention to use the online shopping sites doesn’t necessarily lead to the actual purchase on the site (Pavlou & Fygenson 2006). For instance, a work of Pavlou and Fygenson (2006) investigated the relationship between intention/behavior to get product information in online shopping sites and intention/behaviour to actual purchase the product, and found very low impacts among these variables (all path ȕ 0.3). Some consumers may adopt online group shopping for information seeking purpose, i.e. locating a restaurant nearby, but do not necessarily make a purchase in the sites. Even if our model shows a relatively high path coefficient between technology use intention and purchasing intention, the model only explains 45.6 percent of purchasing intention. Hence, to the extent that current online shopping studies on online shopping almost exclusively focus on technology adoption intention, we call for more research attentions on purchasing intentions, considering their significant differences between two concepts.5 Limitations and Future ResearchThis study has offered some valuable insight into online group shopping studies. However, this study involves a number of limitations that need to be acknowledged. First, the empirical study was conducted only in China. It is recommended to replicate the study in different nations to get international sample. In addition, the current study was conducted in the context of online group shopping. Thus, the results of this study can be generalized in the online group shopping context, but it is hard to generalize the research results to other sectors. Finally, in this study, other factors, such as perceived risks, social influence, have not included in the current research. Further research uncovering these aspects would substantially increase the understanding of user adoption of online group shopping and their purchasing behaviour via online group shopping as well. Our future research will include more variables in order to different drivers of technology acceptance intention and purchasing intention in online group shopping contexts.AcknowledgementThe authors wish to acknowledge the MIDE Institute of Aalto University for its support to this research. ReferencesAnand, K., and Aron, R. (2003) Group-buying on the web: a comparison of price discovery mechanisms. Management Science. 49(11), pp. 1546-1562.Bennett, J. and Bennett, L. (2003) A review of factors that influence the diffusion of innovation when structuring a faculty training program. The Internet and Higher Education. 6(1), pp. 53-63.Chen, J., Chen, X-L, Kauffman, R. J. and Song, X-P. (2009) Should we collude? Analyzing the benefits of bidder cooperation in online group-buying auctions. Electronic Commerce Research and Applications. 8(4), pp.191-202.Chin, W. (1998). The Partial Least Squares Approach to Structural Equation Modeling. In Modern Business Research Methods, Marcoulides, G.A. (Ed.), Lawrence Erlbaum Associates, Mahwah, NJ, 1998, pp. 295-336.Predicting and explaining use intention and purchasing intention in online group shopping Davis, F. D. (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly. 13(3), pp. 319-340.Fornell, C. and Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, pp. 39-50.Gounaris, S., Dimitriadis, S. and Stathakopoulos, V. (2010) An examination of the effects of service quality and satisfaction on customers’ behavioural intentions in e- shopping. Journal of Services Marketing. 24(2), pp. 142-156Goutuan. Net. (2011) Big Tuangou events in one week: number of Tuangou websites up to 5682, available from /bbs/article-14691-1.html, 2011, (InChinese), accessed on April 22.2012.Ha, S. and Stoel, L. (2009) Consumer e-shopping acceptance: Antecedents in a technology acceptance model. Journal of Business Research. 62(5), pp. 565-571Hulland, J. (1999) Use of partial least squares (PLS) in strategic management research: a review of four recent studies. Strategic Management Journal. 20(2), pp. 195-204. iResearch, (2011a), iResearch China Online Group Shopping Research Report 2011, available from: /need/midea/images/midea/top2011.pdf (In Chinese), accessed on April 24, 2012.iResearch, (2011b), iResearch Releases May 2011 China Group Buying Websites Ranking, available from: /view.aspx?id=9218 (in Chinese), accessed on April 24, 2012.Joines J. L., Scherer C. W. and Scheufele D. A. (2003) Exploring motivations for consumer web use and their implications for e-commerce. Journal of Consumer Market. 20(2), pp. 90-108.Kauffman, R. J., Lai, H. and Ho. C-H. (2010) Incentive mechanisms, fairness and participation in online group-buying auctions. Electronic Commerce Research and Applications. 9(3), pp. 249-262.Kauffman, R. J. and Wang, B. (2001) New buyers’arrival under dynamic pricing market microstructure: The case of group-buying discounts on the Internet. Journal of Management Information Systems. 18(2), pp. 157-188.Kauffman, R. J. and Wang, B. (2002) Bid together, buy together: On the efficacy of the group-buying business model in Internet-based selling. In P. B. Lowry, J. O.Cherrington, and R. R. Watson (eds.), Handbook of Electronic Commerce in Business and Society, CRC Press, Baca Raton, FL, 2002. pp. 99-137.Kim, G., Shin, B. and Lee, H. G. (2009) Understanding dynamics between initial trust and usage intentions of mobile banking. Information Systems Journal. 19(3), pp. 283-311. Liu, Y. and Li, H-X. (2011) Exploring the impact of use context on mobile hedonic services adoption: An empirical study on mobile gaming in China. Computers in Human Behaviour. 27(2), pp. 890-898.Liu, Y. and Li, H-X. (2010) Mobile internet diffusion in China: an empirical study.Industrial Management & Data Systems. 110(3), pp. 309-324.Mehrtens, J., Cragg, P. B. and Mills, A. M. (2001) A model of internet adoption by SMEs.Information Management. 39(3), pp. 165-176.Parsons A. G. (2002) Non-functional motives for online shoppers: why we click. Journal of Consumer Market. 19(5), pp. 380-392.Hongxiu Li, Yong Liu Pavlou, P. A. and Fygenson, M. (2006). Understanding and Prediction Electronic Commerce Adoption: An Extension of the Theory of Planned Behavior. MIS Quarterly. 30(1), pp. 115-143.Rogers, E. (1995) Diffusion of Innovations (4th edition), The Free Press, New York. Tenenhaus, M., Vinzi, V.E., Chatelin, Y.-M. and Lauro, C. (2005). PLS path modeling.Computational Statistics & Data Analysis. 48(1), pp. 159-205.Venkatesh, V. (2000) Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research. 11(4), pp. 342-365.。
英语阅读课课件 predicting
You are a monument, without a tomb And are alive still, while your book does live And we have wits to read, and praise to give
In order to predict, you must …
How to Predict?
1.
Байду номын сангаас
Before Reading:
Using the skill of “Previewing”, predict what information you may find in the text, before you begin to read it in detail. For example, after looking at the title, you can ask yourself what you know and do not know about the subject before you read the text. Or you can formulate questions that you would like to have answered by reading the text. These exercises will help you focus more effectively on the ideas in a text when you actually start reading.
Supporting myself at an early age was the best training for life I could possibly have received. I still consider myself a trouper and have yet to miss a day of work. I take more pride in the fact that I can always be counted up than I do in how much I earn or how wellknow I am. What is the meaning of “trouper” and “have yet to miss a day of work”? What is the theme of the story?
predict是什么意思
predict是什么意思predict是我们在英语中常用的动词,那么你predict做动词都表达哪些意思吗?下面店铺为大家带来predict的英语意思解释和英语例句,希望对你有所帮助!predict作动词的意思:预言,预测;预示,预告predict的英语音标:英 [priˈdikt] 美 [prɪˈdɪkt]predict的时态:现在分词: predicting过去式: predicted过去分词: predictedpredict的英语例句:1. The test can accurately predict what a bigger explosion would do.该试验能准确地预测一次更大爆炸的威力。
2. There's no way to predict the future health of the banking industry.银行业未来是否会兴旺发达根本无法预测。
3. Many predict 1991 will rival the great vintage of 1965.许多人预测1991年酿制的葡萄酒可以与1965年生产的佳酿相媲美。
4. The most recent polls predict a tight three-way race.最近的调查显示,这将是一场三方激烈角逐的比赛。
5. Weather forecasts predict more hot weather, gusty winds and lightning strikes.天气预报预测高温、大风和雷电天气将继续。
6. I predict it will run and run.我估计这会一直延续下去。
7. We can't predict the outcome. There are too many imponderables.我们无法预测结果。
难以逆料的情况太多了。
PREDICTING VENTRICULAR FIBRILLATION
专利名称:PREDICTING VENTRICULAR FIBRILLATION 发明人:YANIV, Yael,KEIDAR, Noam,EIDELSZTEIN, Gal,BRONSTEIN, Alex申请号:IL2019/050999申请日:20190905公开号:WO2020/049571A1公开日:20200312专利内容由知识产权出版社提供专利附图:摘要:A method comprising: at a training stage, training a machine learning algorithm on a training set comprising: (i) Heart Rate Variability (HRV) parameters extracted from temporal beat activity samples, wherein at least some of said samples include arepresentation of a Ventricular Fibrillation (VF) event, (ii) labels associated with one of: a first period of time immediately preceding a VF event in a temporal beat activity sample, a second period of time immediately preceding the first period of time in a temporal beat activity sample, and all other periods of time in a temporal beat activity sample; at an inference stage, receiving, as input, a target HRV parameters representing temporal beat activity in a subject; and applying said machine learning algorithm to said target HRV parameters, to predict an onset time of a VF event in said subject.申请人:TECHNION RESEARCH & DEVELOPMENT FOUNDATION LIMITED地址:Senate House, Technion City 3200004 Haifa IL国籍:IL代理人:KESTEN, Dov et al.更多信息请下载全文后查看。
Predicting RUSLE (Revised Universal Soil Loss Equation)
The Environmentalist, 25, 63–70, 2005 C2005 Springer Science + Business Media, Inc. Manufactured in The Netherlands.Predicting RUSLE (Revised Universal Soil Loss Equation) Monthly Erosivity Index from Readily Available Rainfall Datain Mediterranean AreaNAZZARENO DIODATO*Monte Pino Research Observatory on Climate and Landscape (82100 Benevento, Italy) GTOS/ TEMS Network – Terrestrial Ecosystem Monitoring Sites (FAO–United Nations)Summary. Seasonal rainerosivity is important in the structure and dynamics of Mediterranean ecosystems.The present paper contributes to the quantitative assessment of RUSLE’s monthly erosion index in a data-scarce Mediterranean region. Therefore, a regionalized relationship for estimating monthly erosion index (EI30-month) from only three rainfall parameters has been obtained. Knowledge of the seasonal and annual distribution of erosivity index, permit soil and water conservationists to make improved designs for erosion control, water harvesting or small hydraulic structures. Although a few long data sets were used in the analysis, validation with established monthly erosivity index values from other Italian locations, suggest that the model presented (r2=0.973) is robust. It is recommended to monthly erosivity estimates when experimental data-scarce rainfall become available. Keywords: Erosivity index, rainfall, Revised Universal Soil Loss Equation, RUSLE, Mediterranean.1.IntroductionErosional soil degradation by stormwater is perceived as one of the main problems worldwide since it has large environmental and economic impacts, especially in agricultural areas (Cooke and Doornkamp, 1990; Ram´ırez and Finnerty, 1996; Lal, 1997; Steer, 1998; Arshad and Martin, 2002). This is particularly so in Mediterranean Europe, subjected to strong spatial-temporal variability of weather (Crisci et al., 2002; Renschler and Harbor, 2002), especially in rainfall erosivity, which is extremely important for risk as-sessment for soil erosion (Aronica and Ferro, 1997; D’Odorico et al., 2001; Le Bissonnais et al., 2002). Erosivity is the term used in the latest RUSLE version of the well known model of Universal Soil Loss Equa-tion (Revised USLE, Renard el al., 1997; Foster, 2004) to described the potential for soil to be washed off into waterways during storms.*Corresponding author: E-mail: nazdiod@tin.itRecent applications of the RUSLE to evaluate the risks associated with soil erosion spatial variability in Europe (Renschler et al., 1999; Abel et al., 2000; van der Knijff, 2000; Deumlich et al., 2000; Bartolini et al., 2003; Svorin, 2003) and elsewhere in the world(Toy and Osterkamp, 1995; Yoder and Lown, 1995; Mitasova et al., 1996; Turnage et al., 1997; Cox and Madramootoo, 1998; Mitra et al., 1998; Andrew and Millward Janet, 1999; Mati et al., 2000; Boggs et al. 2001; Wang et al., 2002; Sonneveld and Nearing, 2003) stimulated new interest for the estimation of the rain-fall erosivity. However, to obtain an average measure of long-term rainfall erosivity according to the RUSLE methodology, requires high-resolution of rainfall mea-surements and an accurate computation of each storm erosivity index: a very onerous procedure. Historical information on high-resolution measurements of rain-fall in Italy is documented from 1994 for only a few, tipping-bucket, electronic stations of the network RAN (Rete Agrometeorologica Nazionale – Ministero per le Politiche Agricole e Forestali). In such situations,64Diodatoalternative procedures to fill this gap for estimation rainfall erosivity index are suggested in RUSLE hand-book (Renard et al., 1997). Attempts were therefore made to estimate the erosivity index by using a few rainfall data such as storm amount and duration (Istok et al., 1986; Bagarello and D’Asaro, 1994; Mannaerts and Gabriels, 2000). More readily available types of data on precipitation, such as mean monthly or annual rainfall also have been utilised. In Hawaii, for example, Lo et al. (1985) found a correlation between mean an-nual precipitation and the long-term average of annual rainfall erosivity (R). In USA, Reinard and Freimund (1994), instead, used both mean annual precipitation and the Modified Fournier Index (Arnoldus, 1977) to estimate R. Other authors proposed similar approaches for Belgium (Bollinne et al., 1979), Bavaria (Rogler and Schwertmann, 1981), Spain (Bergsma et al., 1996) and southeastern Australia (Yu and Rosewell, 1996). Recently two procedures to estimate erosivity index on monthly scale have been presented: the first was based on daily rainfall amount and frequency for south-ern Portugal (de Santos Loureiro and de Azevedo Coutinho, 2001), the second on daily rainfall for penin-sular Malaysia (Yu et al., 2001). Finally, one issue on RUSLE climate inputs was presented by Yu (2001) to generate monthly distribution of rain erosivity using a stochastic weather generator.The objective of my paper is to report a new proce-dure to estimate the RUSLE’s monthly erosivity index (EI30-month) from readily available rainfall data in Italy. This variable time scale allows to fit forecasting to the real soil conditions and to discriminate stations with equal annual rainfall erosivity, but characterized by different seasonal distribution of erosivity.2.Environmental setting and available dataItaly is located at the centre of the Mediterranean sea-basin. Because the Mediterranean area has geograph-ical features (of the Alps, Apennines, and sea), and their effects on the mesoscale circulation, precipita-tion varies greatly (Meneguzzo et al., 1996; Paolucci et al., 1999), with both climate and weather at all spatio-temporal scales, from the micro-scale to the synoptic scale. In the cold season, the rain is princi-pally caused by fronts associated with Mediterranean cyclone. Advective activity is particularly important on the surrounding Alps and Apennines, where ris-ing air masses cause frequent orographic precipitation. Even though extreme rainfall events are not rare in the cold season, stormy events with the highest hourly and half-hourly intensity occur from May to Septem-ber (Diodato, 2005). In this period, rainfall extreme events contribute in a major way to erosion and sedi-ment transport in Mediterranean Europe (Wainwright, 1996; Bazzoffi et al., 1997; Hooke and Mant, 2000; Coppus and Imeson, 2002; Mart´ınez-Casasnovas et al., 2002; Conedera et al., 2003).2.1.High resolution tipping-bucketraingauge networkItaly uses several gauges and methods to measure pre-cipitation depending on its type (i.e., rain or snow) and the type of observation (i.e., amount, intensity, etc.). The primary interest for my study is the precipitation intensity. For rainfall intensity, the official rain gauges currently in use are the tipping-bucket electronic rain-gauge stations (Esposito and Beltrano, 1996; Perini and Beltrano, 2003) of the network RAN (Rete Agrom-eteorologica Nazionale–UCEA). The RAN gauge is 36 cm high, has an orifice area of 400 cm2 and is mounted with the orifice 140 cm above the ground surface. The gauge has a collecting funnel and outer container made of high strength plastic which mini-mizes adhesion of rainwater to the gauge surface, and a clear plastic inner container, graduated for direct read-ing to the nearest 0.2 mm. The data for this study were obtained from RAN rain gauge stations for the years from 1996 to 2000. The data set includes fifteen sta-tions and a total of 408 months after the elimination of all the years incomplete or unreliable; eight addi-tional stations, for a total of 212 monthly of data, were also considered for validation of the results (Fig. 1). Although some data were eliminated as unreliable, the data set used may be considered representative of the variability that occurs annually and among seasons be-cause some absolute outliers were included in both interpolation and validation data. In Table 1 the fol-lowing characteristics are summarized for each station: code number, latitude, longitude, altitude and length of record.3.MethodsThe rainfall erosivity index is a numerical descriptor of the capacity of rainfall to erode soil (Wischmeier, 1959). For a given location, the monthly values arePredicting RUSLE (Revised Universal Soil Loss Equation)65Figure 1. Location of the study area and positions of the Italian raingauge stations using for calibration (squares) and for validation (triangles).given by the sum of all the single-storm EI 30 values for the month j; where EI 30 is the product of storm kinetic energy (E ) and the maximum 30 min intensity (I 30). The EI calculation involves the analysis of hyetographs for every rain event excluding rains with less than 13 mm depth, and at least 6 hours distant from the previous or the next events, but including the showers of at least 6.35 mm in 15-min (Wischmeier and Smith, 1978). To compute E , Foster (2004) recommend to use the following equation:E =ν0.29 ·P k 1 − 0.72e −0.082Pkt(1)where (EI 30-a )α = EI 30-a for storm α; s = number of storm in an j month period.4. Data and resultsUsing 408 months of data from the RAN network, re-gression between monthly erosivity index values (Eqn. 2) and different rainfall parameters were computed, ob-taining the following multiplicative power equation:EI 30-month = 0.1174 · √· d 0.53 · h 1.18(3)m k =1where EI 30-month (in MJ mm ha −1 h −1) is the monthlyerosivity index; m is the monthly precipitation amountwhere the kinetic energy E is computed for any event(mm), d is the monthly maximum daily precipitation subdivided in ν time steps of even length t ; P k is(mm), and h is the monthly maximum hourly precipi- the precipitation (mm) in the time step t . Then, thetation (mm). The results make sense, because d and monthly erosion index may be simply computed as: h are descriptors of the extreme rainfall occurring, respectively, in storms and heavy showers (Diodato, s2004). The parameter m , is, instead, representativeof the weakly-erosive precipitation. Because monthly E I30−a j =(E I 30)α(2)erosivity is much less variable in comparison to eventα=166 DiodatoTable 1. Characteristics of the Italian raingauge stations used to derive the simplified relationship for estimating EI (calibration) and of the additional stations used for testing (validation).Length ofRaingauge stations Latitude northingLongitude eastingElevation (m)record (months)CalibrationVigalzano 46◦ 04 09◦ 27 539 24 Verzuolo 44◦ 36 07◦ 29 420 24 Zanzarina45◦ 13 10◦ 32 40 23 S. Piero a Grado 43◦ 40 10◦ 21 3 12 Monsampolo 42◦ 53 13◦ 48 43 24 Santa Fista 43◦ 31 12◦ 08 311 24 Caprarola42◦ 20 12◦ 11 650 24 Castel di Sangro 41◦ 45 14◦ 06 810 24 Borgo S. Michele 41◦ 27 12◦ 54 12 17 Piano Cappelle 41◦ 07 14◦ 50 240 24 Pontecagnano 40◦ 37 14◦ 52 29 31 Palo del Colle 41◦ 03 16◦ 38 191 12 Sibari39◦ 44 16◦ 27 10 12 SantaLucia 39◦59 08◦37 14 24 Pietranera 37◦ 3013◦31 15821ValidationFiume Veneto 45◦55 12◦ 43 19 20 Carpeneto 44◦ 41 08◦ 37 230 24 S. Polo D Enza 44◦37? 10◦ 26 192 12 S. Casciano 43◦ 40 11◦ 09 230 24 Marsciano 43◦ 00 12◦ 18 229 24 Campochiaro 41◦ 28 14◦ 32 502 24 Aliano 40◦ 17 16◦ 09 250 24 S. Pietro37◦0714◦ 3231312EI 30 equation (3) contains more relevant variables. The multiplicative term (d · h ) was introduced to take into account the possibility of having different storm types in different seasons. This term allows erosivity for a given amount of rain to vary seasonally. The four con-stants (0.1174, √(.), 0.53 and 1.18) were estimated by minimizing the sum squared errors (SSE):M2SSE =EI30-aj −E I30-month , j(4)j =1monthly rainfall and erosivity ratios is similar from December to April, reverse for summer period and November and especially May, July and August when extreme erosivities occur during rainstorms. A simi-lar trend is verifiable also at Settefonti Meteorological Station, North Italy (Calzolari et al ., 2001), where the relative proportion in summer time is very different from that in other months and indicate events with a high erosivity (Fig. 3b).where EI 30-a j is the actual rainfall erosivity for the month j ; M is the number of months when some plu-viograph data are available.Equation (3) was computed from readily available rainfall data from the Monte Pino Research Obser-vatory on Climate and Landscape in the central Italy (Fig. 2), for an adequate period of time (20 years). Figure 3a presents the results, indicating a seasonal variability in the ratio of monthly to annual precipita-tion and erosivity. The relative proportion between the4.1. Validation of the EI 30-month with a new data setIn order to examine whether equation (3), calibrated by using Italian point data, can be applied in a larger area, eight additional stations, with 212 months of data, was considered. A further test of the EI 30-month model was carried out using data from research stations. In partic-ular, data from Carpeneto, Fiume Veneto and S. Polo D ’Enza located in North Italy, S. Casciano, Marsciano, Campochiaro located in Central Italy, Aliano located in peninsular South Italy and S. Pietro located inPredicting RUSLE (Revised Universal Soil Loss Equation) 67Figure 2. Based-station of Benevento –Monte Pino Research Ob-servatory on Climate and Landscape.Sicilia island, were used. Validation with established monthly erosivity index values from 212 months of data, suggest robustly of the model presented herein (r 2= 0.973 significant at p = 0.01). The reduced major-axis regression lines for erosivity index show1:1 relationships between measured and estimated val-ues (Fig. 4).The difference between the estimated and the mea-sured actual monthly erosivity index (EI 30-a j ) is the experimental error (ε):εi , j =E I30-month , j −E I30-aj,(5) from which can be computed:Mean Absolute Errors:1n(6)MAE =i =1 εi , jn and Root Mean Square Errors:1nRMSE =εi 2, j(7)ni =1where n is the number of locations subjected to vali-dation.Statistics of the experimental errors are reported in Table 2, where very little errors are reproduced. There is clearly a significant performance when tak-ing into account descriptor of the extreme rainfall (d and h variables of the Equation (3)): the MAE spread13–46 MJmm ha −1 h −1, in March and in October re-spectively; the RMSE spread 16–96 MJmm ha −1h −1, in March and in October, respectively. These tests indi-cate that the new procedure can give reasonably accu-rate results for the Mediterranean climate of southern Europe.Figure 3. Ratio of average monthly precipitation (Pmon) and erosion index (EImon) to average annual values for Benevento –Monte Pino Research Observatory, Middle Italy (a), and Settefonti Meteorological Station, North Italy (b).。
英文作文题目预测
英文作文题目预测## Predicting English Composition Topics: A Multifaceted Exploration Predicting essay topics can feel like gazing into a crystal ball, hoping for a glimpse of the future. While we can't see the exact prompts that will grace your exam paper, we can certainly analyze trends, educational focuses, and recurring themes to equip ourselves with the best possible preparation. One reliable approach is to **examine current events and societal issues**. Essay prompts often seek to gauge your understanding of the world around you and your ability to articulate your thoughts on complex matters. Topics like climate change and its impact on various communities, the ethical implications of artificial intelligence, or the evolving landscape of social media and mental health are all potentialareas of exploration. Consider the multifaceted nature of these issues, exploring economic, social, and political dimensions. Furthermore, **delving into literary works and their enduring themes** is another avenue worth exploring. Classicnovels and plays often grapple with universal human experiences like love, loss, coming-of-age, and the struggle for justice. Essay prompts may ask you to analyze character development, symbolism, or the author's use of language to convey a specific message. Be prepared to discuss literary devices, narrative structure,and the historical and cultural context of the work. **Examining historicalevents and figures** also provides fertile ground for essay prompts. Consider pivotal moments like the Civil Rights Movement, the World Wars, or the rise andfall of empires. Essays might require you to analyze the causes and consequences of these events, the motivations of key figures, or the impact on society and culture. Be prepared to discuss different historical interpretations and perspectives, demonstrating your ability to think critically and engage in nuanced analysis. **Science and technology** offer a wealth of potential essay topics. Explore advancements in fields like genetic engineering, space exploration, or renewable energy. Prompts might ask you to consider the ethical implications of these developments, their potential benefits and drawbacks, or their impact on society and the environment. Be prepared to discuss the scientific principles behind these innovations and to analyze their potential consequences. Remember, **personal reflection and introspection** can also be valuable sources of essayinspiration. Prompts might ask you to explore your own experiences, values, and beliefs, or to reflect on a significant event that shaped your perspective. These essays allow you to showcase your ability to connect personal anecdotes with broader themes and insights, demonstrating self-awareness and maturity. Finally, don't underestimate the power of **practicing with past essay prompts and sample essays**. This will familiarize you with the format, style, and expectations of essay writing. Analyze how successful essays structure their arguments, use evidence, and develop their ideas. Pay attention to the language and tone used, and strive to emulate the clarity and sophistication of well-written essays. By considering these diverse avenues and engaging in consistent practice, you'll be well-equipped to tackle any essay prompt that comes your way. Remember, the key is to approach the task with a curious mind, a willingness to explore different perspectives, and a commitment to expressing your ideas with clarity and conviction.。
王蔷 英语教学法教程 第二版 Unit11
第11章Teaching Reading一、The way of reading:Reading aloud and silent reading: are two types of reading practice commonly found in classrooms. Differences between reading aloud and silent reading:二、What do effective readers do:①have a clear purpose in reading;②read silently;③read phrase by phrase, rather than word by word;④concentrate on the important bits, skim the rest, and skip the insignificant parts;⑤use different speeds and strategies for different reading tasks;⑥perceive the information in the target language rather than mentally translate;⑦guess the meaning of new words from the context, or ignore them;⑧have and use background information to help understand the text.三、The content of readingESL/EFL reading textbooks should have a great variety of authentic materials.Teachers should ensure not only there is a greater variety but also we can help prepare students to meet their future needs.Besides authentic texts, ESL textbooks also employ a lot of non-authentic texts, i.e. simulated texts.四、Strategies involved in reading comprehension:1. reading and reading comprehensionReading: According to Day and Bamford, reading is the construction of meaning from a printed or written message.Reading comprehension: involves extracting the relevant information from the text as efficiently as possible, connecting the information from the written message with one’s own knowledge to arrive at an understanding.A characteristic: Reading is a silent and individual activity since the writer’s intention was for the text to be read rather than heard.2. Two broad levels in reading①a recognition task of perceiving visual signals from the printed page through the eyes.②a cognitive task of interpreting the visual information, relating the received information with the reader’s own general knowledge, and reconstructing the meaning that the writer had meant to convey.3. The skills involved in reading: reading strategies五、The role of vocabulary in readingA large majority of students believe that vocabulary is the main obstacle in learning to read and this has already been pointed out by Grabe.The lack of such vocabulary may be the greatest single impediment of fluent reading.Fluent reading depends on an adequate sight vocabulary, a general knowledge about the target language, some knowledge about the topic, wide knowledge about the world and enough knowledge about text types. According to Day and Bamford, efficient reading begins with a lightening-like automatic recognition of words. This initial process of accurate, rapid and automatic recognition of vocabulary frees one’s mind to use other resources.Less the 3% of new words in a reading text will enable smooth, meaningful and enjoyable reading. Therefore, helping students to develop the ability of automatic word recognition is the basis for developing their reading skills.Sight vocabulary: words that one is able to immediately recognize with both sounds and meanings without special effort from the brainThe best and easiest way to develop sight vocabulary is to read extensively.Through intensive and extensive reading;Keeping a vocabulary notebook;Using a dictionary;However, the materials chosen must be at the right level and a degree of monitoring should be available to keep the motivation high so that students can feel a sense of achievement by sharing their reading experiences with others.六、Principles and models for teaching reading1. Principles for teaching reading:1) The selected texts and attached tasks should be accessible to the students.2) Tasks should be clearly given in advance.3) Tasks should be designed to encourage selective and intelligent reading for the main meaning rather than test the students’ understanding of trivial details.4) Tasks should help develop student’s reading skills rather than test reading comprehension.5) Develop student’s reading strategies and reading ability in general.6) Provide enough guidance at the beginning and help them become independent reader eventually.2. Models of teaching reading(1)Bottom-up modelThe way one teaches reading always reflects the way one understands reading and the reading process. Some teachers teach reading by introducing new vocabulary and new structures first and then going over the text sentence by sentence. This is followed by some questions and answers and reading aloud practice.Reading comprehension is based on the understanding and mastery of all the new words, new phrases, and new structures as well as a lot of reading aloud practice.In reading, information is transmitted along a linear process: letters—words—phrases—clauses—sentences—paragraphs—whole discourse.(2)Top-down model -- Schema theoryBottom-up model believes that one’s background knowledge plays a more important role than new words and new structures in reading comprehension.Teaching process: the teacher should teach the background knowledge first so that students equipped with such knowledge will be able to guess meaning from the printed page.Reading process: a psycholinguistic guessing game(Goodman, 1970).(3)Interactive modelThe current theory views reading as an interactive process.Reading comprehension is based on the interactive process between visual information obtained from the reading materials and the readers’ prior knowledge.Reading process: brain receives visual information and at the same time, interprets or reconstructs the meaning the writer had in mind when he wrote the text. This process does not only involve the printed page but also the reader’s knowledge of the language in general, of the world, and of the text types.七、Reading activitiesThe three stages are pre-reading, while-reading, and post-reading.1. Pre-reading activities(1) Definition of pre-reading activitiesPre-reading activities refer to tasks/activities that students do before they read the text in detail.(2) Purpose:To stimulate students’ interests, to facilitate while-reading activities. By:①pooling existing knowledge about the topic;②predicting the contents of the text;③skimming or scanning the text or parts of the text for certain purposes;④learning key words and structures.To sum up, the purpose is to prepare the students linguistically, thematically and affectively for the tasks in while-reading activities.(3) Pre-reading activities in details:1) Predicting—confirm or reject prediction in readingPredicting will get the mind close to the theme of the text to be read, making reading more intriguing and purposeful and resulting in better comprehension compared with the situation where the learner starts reading with a blank mind.Predictions can be done in many different ways①Predicting based on the title;②Predicting based on vocabulary;③Predicting based on the T/F questions.2) Setting the scene1) Aim: get the students familiarized with the cultural and social background knowledge relevant to the reading text.2) Ways of setting the scene:①Discussing culture-bound aspects of the text;②Relating what students know to what they want to know, and then ask the students to read the text to see if they can find what they want to know;③Using visual aids to set the scene.3) SkimmingIt means to read quickly to get the gist.4) ScanningReading for specific information, and should ignore the irrelevant parts when reading.2. While-reading activitiesThere are two ways of exploiting texts:1) Focusing on the results of reading:Multiple-choice questions; T/F; open questions, paraphrasing, translation.2) Focusing on the process of understanding:①Information transfer activities:When information in text form is transferred to another form , it can be more effectively processed and retained. Information transfer activities: The way to transfer information from one form to another is called a transition device.Purposes of transition deviceWhen using transition devices, we need to ensure that it is an appropriate form to encapsulate the main information contained in the text. We need to bear in mind the purposes of transition devices.①Focus attention on the main meaning of the text②Be able to simplify sophisticated input so that it becomes the basis for output;③Allow students to perform tasks while they are reading;④Highlight the main structural organization of a text/part of a text, and show how the structure relates to meaning;⑤Involve all the students in clearly defined reading tasks;⑥Precede one step at a time and students should do easier tasks before doing more complicated ones;⑦When a TD is completed, use it as a basis for further oral or written language practice.②Reading comprehension questionsNuttall’s classification of reading questions①Questions of literal comprehension;②Questions involving reorganization or reinterpretation;③Questions for inferences;④Questions for evaluation or appreciation;⑤Questions for personal response.③Understanding referencesAll natural language, spoken or written, uses referential words such as pronouns to refer to people or things already mentioned previously in the context. Understanding what these words refer to is crucial forcomprehension.④Making inferencesIt requires the reader to use background knowledge in order to infer the implied meaning of the author. Making inferences is actually the process of relating the given information to what we have known about the world. 3. Post-reading Activities(1) Objectives①To check the fulfillment of reading tasks;②To evaluate the application of reading strategies;③To apply what has been learned;④To integrate reading with other skills.(2) RequirementsPost-reading tasks should provide the students with opportunities to relate what they have read to what they already know or what they feel.Post-reading tasks should enable students to produce language based on what they learned.(3) Types of post-reading activitiesRole play, Gap-filling, Discussion, Retelling and Writing.。
how to predict in English reading如何在英语阅读中预测
-ship:
a. b. c. d. (1) state, nature, condition: friendship rank, status, or office: leadership art, skill, or craft: craftsmanship a collective body: membership If your Lordship will give me time, I will produce the evidence. (2) Marriage is based on comradeship. (3) The Daily Echo has a readership of over ten million. (4)horsemanship, swordsmanship, penmanship
You are a monument, without a tomb坟墓 And are alive still, while your book does live And we have wits to read, and praise to give
In order to predict, you must …
1. 2. 3. 4. 5.
have a large vocabulary have a large information base know about cultural background know about writers and writing styles know about text types
When I was 17 I moved to Philadelphia费城 to study with the Pennsylvania Ballet. Three years later I went to New York City, and eventually pursued 从事an acting career. It seem the author became an actress. Was she successful? Who was she? In what films has she ever acted?
新概念英语第三册第51课-Predicting the future
新概念英语第三册第51课:Predicting the futureLesson 51 Predicting the future预测未来 Listen to the tape then answer the question below.听录音,然后回答以下问题。
What was the 'future' electronic development that Leon Bagrit wasn't able to foresee?Predicting the future is notoriously difficult. Who could have imagined, in the mid 1970s, for example, that by the end of the 20th century, computers would be as common in people's homes as TV sets? In the 1970s, computers were common enough, but only in big business, government departments, and large organizations. These were the so-called mainframe machines. Mainframe computers were very large indeed, often occupying whole air-conditioned rooms, employing full-time technicians and run on specially-written software. Though these large machines still exist, many of their functions have been taken over by small powerful personal computers, commonly known as PCs.In 1975, a primitive machine called the Altair, was launched in the USA. It can properly be described as the first 'home computer' and it pointed the way to the future. This was followed, at the end of the 1970s, by a machine called an Apple. In the early 1980s, the computer giant, IBM produced the world's first Personal Computer. This ran on an 'operating system' called DOS, produced by a then small company named Microsoft. The IBM Personal Computer was widely copied. From those humble beginnings, we have seen the development of the user-friendly home computers and multimedia machines which are in common use today.Considering how recent these developments are, it is even more remarkable that as long ago as the 1960s, an Englishman, Leon Bagrit, wasable to predict some of the uses of computers which we know today. Bagrit dismissed the idea that computers would learn to 'think' for themselves and would 'rule the world', which people liked to believe in those days. Bagrit foresaw a time when computers would be small enough to hold in the hand, when they would be capable of providing information about traffic jams and suggesting alternative routes, when they would be used in hospitals to help doctors to diagnose illnesses, when they would relieve office workers and accountants of dull, repetitive clerical work. All these computer uses have become commonplace. Of course, Leon Bagrit could not possibly have foreseen the development of the Internet, the worldwide system that enables us to communicate instantly with anyone in any part of the world by using computers linked to telephone networks. Nor could he have foreseen how we could use the Internet to obtain information on every known subject, so we can read it on a screen in our homes and even print it as well if we want to. Computers have become smaller and smaller, more and more powerful and cheaper and cheaper. This is what makes Leon Bagrit's predictions particularly remarkable. If he, or someone like him, were alive today, he might be able to tell us what to expect in the next fifty years.参考译文众所周知,预测未来是非常困难的。
predict的用法总结大全
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文档下载后可定制修改,请根据实际需要进行调整和使用,谢谢!并且,本店铺为大家提供各种类型的经典范文,如英语单词、英语语法、英语听力、英语知识点、语文知识点、文言文、数学公式、数学知识点、作文大全、其他资料等等,想了解不同范文格式和写法,敬请关注!Download tips: This document is carefully compiled by this editor.I hope that after you download it, it can help you solve practical problems. The document can be customized and modified after downloading, please adjust and use it according to actual needs, thank you!In addition, this shop provides various types of classic sample essays, such as English words, English grammar, English listening, English knowledge points, Chinese knowledge points, classical Chinese, mathematical formulas, mathematics knowledge points, composition books, other materials, etc. Learn about the different formats and writing styles of sample essays, so stay tuned!predict的用法总结大全predict的意思predict的简明意思v. 预言;预报;预知;预测英式发音 [prɪ'dɪkt] 美式发音 [prɪ'dɪkt]predict的词态变化为:形容词: predictable 副词: predictably 名词: predictability 过去式: predicted 过去分词: predicted 现在分词: predicting 第三人称单数: predicts predict的详细意思在英语中,predict不仅具有上述意思,还有更详尽的用法,predict作动词 v. 时具有预言,预料,预告,预知,预测,预报,预期,估计,揣摩,预见,预计等意思,predict的具体用法用作动词 v.predict的基本意思是“预言”“预测”“预示”,指根据事实或公认的自然法则推理而得,常指在预言时具有科学的准确性。
英语predict的中文是什么意思
英语predict的中文是什么意思英语predict的中文是什么意思predict的用法十分广泛,我们有必要知道它实际的中文意思。
以下是店铺为大家整理了英文单词predict具体的中文意思,一起来看看吧!predict的中文意思英 [prɪˈdɪkt] 美 [prɪˈdɪkt]第三人称单数:predicts现在分词:predicting过去分词:predicted过去式:predicted及物动词预言,预测; 预示,预告不及物动词预言,预示:预言某事,预言相关例句及物动词1. He predicted that an earthquake was imminent.他预言即将发生地震。
2. She predicted that he would marry a doctor.她预言他将娶一位医生。
3. He predicted a good harvest.他预言丰收。
predict的单语例句1. Scientists predict that people will increasingly be surrounded by machines that can not only see but also reason about what they are seeing.2. Although she had the foresight to predict the trend, even she has been caught by surprise by its rapid development.3. Farmers living within 10 square kilometres can predict the weather by observing the cliff.4. It is hard to predict whether Chinese calligraphy will see a rebirth or lose itself amid its flirtation with new trends in worldart.5. Many analysts predict Jean - who is very popular among Haitians, particularly the young - would easily win the presidential election if his candidacy were approved.6. Candy goes as far as to predict online shrines may soon consign cemeteries and graveyards to the past.7. New research findings suggest that the sound of a person's voice may predict his or her level of sexual activity.8. " I was in a unique position to predict the outcome of NBA games, " Donaghy told US District Court Judge Carol Amon in Brooklyn federal court.9. However, it is premature to predict the central bank's next step.10. Analysts still predict the country will get its first female chancellor in Angela Merkel, the leader of the conservative Christian Democrats.predict的双语例句1. However, stop codon susceptibility to gentamicin between different genes is difficult to predict.但是,不同基因的终止密码子对庆大霉素的易感性是难以预测的。
以预判为主题的作文
以预判为主题的作文英文回答:Predicting the future is a fascinating and complex endeavor. It requires a combination of analytical thinking, intuition, and a deep understanding of current trends and patterns. As humans, we are constantly trying to anticipate what will happen next, whether it's in our personal lives or in the world around us.In my opinion, one of the most important factors in successful prediction is having a solid foundation of knowledge and information. By staying informed about the latest developments in various fields, we can gain valuable insights that can help us make more accurate predictions. For example, if we are interested in the stock market, we need to closely follow economic indicators, company news, and market trends in order to make informed predictions about future stock prices.Another crucial aspect of prediction is the ability to recognize patterns and trends. By observing past events and their outcomes, we can identify recurring patterns and use them as a basis for predicting future outcomes. For instance, if we notice that every time it rains, thestreets get flooded, we can predict that the same will happen the next time it rains. This ability to recognize patterns is not only useful in predicting natural phenomena, but also in understanding human behavior and societal trends.Furthermore, intuition plays a significant role in prediction. Sometimes, our gut feelings or instincts can provide valuable insights that cannot be explained by logic or data. For instance, if we have a hunch that a certain investment opportunity is too good to be true, even though all the data suggests otherwise, it might be wise to trust our intuition and avoid making a risky decision.In conclusion, predicting the future is a complex process that requires a combination of knowledge, pattern recognition, and intuition. By staying informed,recognizing patterns, and trusting our instincts, we can improve our ability to make accurate predictions. However, it is important to remember that no prediction is foolproof and unexpected events can always occur.中文回答:预测未来是一项迷人且复杂的任务。
新东方新概念英语第三册Lesson 51Predicting the futurePPT课件
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❖In the early 1980s, the computer giant, IBM produced the world's first Personal Computer.
The Everest of the TV industry 彩电业的珠穆郎玛
The Godfather of gambling industry
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❖In 1975, a primitive machine called the Altair, was launched in the USA. ▪ Introduce
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❖It can properly be described as the first 'home computer' and it pointed the way to the future. 写作模板---里程碑式的事物
写作模板---随着科技发展,某物达到普及
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❖In the 1970s, computers were common enough, but only in big business, government departments, and large organizations.
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Lesson 51 Predicting the Future
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LOGO
CONTENTS
写作模板---随着科技发展,某物达到普及 写作模板---里程碑式的事物 特点句的升级
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Words and Expressions
动名词 祈使句
怎样判断动词在句首 时是用原形还是v+ing?
同学们 ,要弄清这个问题我们可以从两个方面来理解和区分。 一. 从句子意思方面来理解。祈使句有命令和要求的意思, 而动名词短语只是说明一个道理或者一个看法. 例如: 1. Predicting the future is very difficult. 预测未来是很难的。(动词短语做主语) 2. Let us predict the future, Tom. Tom. 让我们来预测一下未来吧!(祈使句) 3. Watching TV makes me relaxed. 看电视使我感到很放松。 (动词短语做主语) 4. Watch TV less, Tom. 少看点电视吧,Tom. (祈使句)
二. 从句子成分方面来判断。祈使句中的动词是做谓语的,表示动 作; 而动名词短语中的动词是做主语的,表示一件事。 例如, 1. Read these words loudly,Please. 请大声朗读这些单词。 (祈使句,read 为谓语动作) 2. Reading loudly is important for learning English. 大声朗读 3. Close the window, Tom. 关上窗户吧,Tom。 (祈使句,close 为句子的谓语) 4. Protecting environment makes our world more beautiful. 保护环境使我们的世界更美丽。 (动词短语做主语, makes 为句子谓语)
练一练:用所给动词的正确形式填空 Collecting 1.________(collect)stamps is fun and very interesting. Clean 2. ______(clean ) the floor every day. 3. _______(catch) fish is great fun, Catching but eating them is even better. 4. Hurry (Hurry)up and you’ll ca the train. Making 5.______(make) small machines is my favorite hobby. Drink (drink) a cup of tea and 6. ____ have a good rest.
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article info
Article history: Received 10 March 2010 Received in revised form 2 July 2010 Accepted 14 July 2010
abstract
Erosion–corrosion (EC) is a serious degradation mechanism of piping, especially for nuclear power plants since it may result in the piping damage, plant shutdown, or personnel injury. The majority of this paper investigates the dependence of wall thinning on the hydrodynamic characteristics using the computational fluid dynamics (CFD) methodology. Four piping systems in a pressurized water reactor (PWR) power plant are selected in this investigation. Based on the plots showing the measured wall thinning with the calculated hydrodynamic parameters, the relationship between them is clearly revealed. Utilizing the characteristics of near-wall turbulence kinetic energy, an envelopment model is proposed herein to conservatively predict the amount of wall thinning distributed on the pipe wall. This estimation model can simply predict the possible distributions of severe EC wear sites and subsequently assist the plant staff to schedule the pipe wall monitoring program in the measured range of pipe wall for the fittings.
∗ Corresponding author. E-mail address: ymferng@.tw (Y.M. Ferng).
0029-5493/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.nucengdes.2010.07.031
The EC phenomenon had been investigated by previous simulation works or models which include CHEC code (Chexal et al., 1989), MIT model (Wu, 1989), CEACE code (FPI, 1991), EdF model (Remy and Bouchacourt, 1992), one-dimensional (1-D) flow models (Postlethwaite et al., 1986; Postlethwaite and Lotz, 1988; Blatt et al., 1989; Zeisel and Durst, 1990; Neisc and Postlethwaite, 1991a,b), and three-dimensional (3-D) two-phase computational fluid dynamics (CFD) model coupled with appropriate EC indica-
Nuclear Engineering and Design 240 (2010) 2836–2841
Contents lists available at ScienceDirect
Nuclear Engineering and Design
journal homepage: /locate/nucengdes
Predicting the wall thinning engendered by erosion–corrosion using CFD methodology
Yuh Ming Ferng ∗, Bin Hong Lin
Department of Engineering and System Science, Institute of Nuclear Engineering and Science, National Tsing Hua University, 101, Sec. 2. Kuang-Fu Rd., Hsingchu 30013, 325 Taiwan, ROC
C∞
concentration away from the wall surface (mol/mol)
hm
mass transfer coefficient (1/s)
k
turbulence kinetic energy (m2/s2)
p
pressure (N/m2)
R
total metal loss rate (1/s)
tors (Ferng et al., 1999a,b, 2000, 2008; Ferng, 2008), etc. Recently, a sophisticated model (Naitoh et al., 2009; Uehara et al., 2009; Uchida et al., 2009) was proposed to predict the EC occurrence and wall thinning rate, which combines a 1-D system code, a 3-D CFD code, a static electrochemical analysis and a dynamic double oxide layer analysis.
The majority of this paper is to investigate the relationship of wall thinning with the calculated hydrodynamic parameters for the piping in the NPP using the CFD methodology, which is an extended study of previous works (Ferng et al., 1999a,b, 2000). Four kinds of piping are selected in the present study and are belong to the feedwater piping system (AE-019), the main turbine piping system (AC-069) and two piping systems connecting the feedwater reheater to the reheater drain tank (AF-166 and 167), the MSR drain tank to the feedwater drain tank (AF-168 and 169), respectively. These four piping systems are located in the balance of plant (BOP) of a pressurized water reactor (PWR) that is at the southern tip of Taiwan. The local measured wall thinning on the wall surface of fittings in these piping systems are plotted versus the hydrodynamic parameters calculated by the CFD model in order to find their dependent trends. In addition, based on the curve describing these relationships, an envelopment model can be also derived to conservatively predict the amount of local wall thinning using the calculated hydrodynamic characteristics. This quick estimation model is useful to assist the plant staff in scheduling the pipe wall monitoring program.
Y.M. Ferng, B.H. Lin / Nuclear Engineering and Design 240 (2010) 2836–2841
2837
Nomenclature
C
concentration on the wall surface (mol/mol)
Ceq
equilibrium concentration (mol/mol)
© 2010 Elsevier B.V. All rights reserved.