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十大自然科技突破人物 -回复

十大自然科技突破人物-回复题目：十大自然科技突破人物——对人类进步的巨大贡献引言：自然科技突破是指在自然界中发现并应用新的科技方法和手段，从而推动人类社会的进步和发展。

在人类历史的长河中，有许多伟大的科学家和工程师投身于自然科技的研究，他们通过不懈的探索、勇于创新和顽强的毅力，为人类带来了革命性的突破。

下面将介绍十位具有重大影响力的自然科技突破人物。

一、研究细胞的微生物学家Robert Hooke作为细胞学的奠基人之一，Robert Hooke于17世纪成功地设计和使用了第一台显微镜，并用它观察到了植物细胞。

他的研究为细胞学的探索打下了坚实的基础，为后来的生物学研究提供了重要的参考。

二、发现重力定律的物理学家Isaac NewtonIsaac Newton在17世纪发现了重力定律，揭示了天体运动的基本规律，极大地推动了物理学的发展。

他的研究成果包括《自然哲学的数学原理》等重要著作，为后来的科学家打开了探索宇宙奥秘的大门。

三、发明电磁感应的科学家Michael FaradayMichael Faraday在19世纪发明了电磁感应，为电动机、发电机等电力装置的发展奠定了基础。

他的研究成果促进了电磁学的发展，开辟了电力工业的新纪元。

四、揭示遗传规律的遗传学家Gregor MendelGregor Mendel通过一系列对豌豆的杂交实验，揭示了遗传规律的规律性，并提出了遗传的基本原理。

他的研究为遗传学的建立奠定了基础，为后来基因工程的发展提供了理论支持。

五、创造周期表的化学家Dmitri MendeleevDmitri Mendeleev在19世纪提出了周期表的概念，并根据元素的性质和周期性将元素排列起来。

他的创造为化学研究提供了系统和全面的分类方法，对于新元素的发现和研究起到了重要的指导作用。

六、发现DNA结构的科学家James Watson和Francis CrickJames Watson和Francis Crick于20世纪50年代发现了DNA的双螺旋结构，这一重大突破奠定了现代生物学的基石。

Lecture 3 (文艺复兴时期及莎士比亚 (1))

Spain, the Netherlands, and England. Renaissance is considered as the great flowering of art, architecture, politics, and the study of literature, and is also usually seen as the end of the Middle Ages and the beginning of the modern world. The emphasis of the Renaissance is humanism.
Greatness of man under the principle of human reason
Of all God’s works, which do this world adorn, There is no one more fair and excellent, Than is man’s body both for power and form, Whiles it is kept in sober government; But none than it, more fowl and indecent, Distempered through misrule and passions base:
1：忠贞、神圣。 2：人双重属性或者两难处境（天使与兽类、精神与物质等）；背叛、不忠贞（三心二意；两边倒）。 3：开头、中间、结尾三部分（完整）；奇数＋偶数（生殖繁衍）
4：四种体液、人生四季、四种基本美德（谨慎，坚毅，克制，正义）、四种能力（理性，知识，意见，感觉）、物质性（四种宇宙基本元素）
人的两个眼睛：太阳和月亮 (two eyes—the sun and the moon) 骨头和指甲的硬度：石头的硬度 (hardness of bone and nail— hardness of stone) 人体的血管：树枝 (arteries—branches of tree)

creativity and innovation

Assignment 3 Innovation and CreativityName:Student number:Email:1. Explain the difference/s or similarities between creativity and innovation.Star with the definition, creativity is the capability of conceiving originally or unusually, while innovation is the implementation of the new idea. That is to say, if you are creative, you can come up with thousands of new ideas through brainstorming or just dreaming up, and the activity of doing so is the process of displaying creativity. However, not until you try the new ideas and take the risk to turn them into implementations, you are innovating something new. So we can roughly classify the innovation into two categories: one is the invention, a real product or method that has never existed before, and the other is the new usage of something already existed. For example, if one company first uses the internet as a major way of selling products, this practice can be seen as an innovation even though internet has already existed for a long time. Similar examples can be a new process, method, or even a new business model.To be more specific, the creativity is subjective and cannot be measured, it is just the idea within one person’s mind. However, the innovation, on the other hand, is completely measurable. It is the action of making changes, and getting things done. To put this two words into the equation separately, we can get:Creativity = ideas but, Innovation = ideas + action2. Name a creativity tool or method and explain its background/history?Brain storming is probably one of the most popular creativities tools that adopted in today’s word, and almost every one of us has participated in a brainstorming meeting. The history of brain mapping can be traced back to the 1940s in the United States.In 1939, Alex Osborn was the founder and the CEO of the BBDO (Batten, Barton, Durstine & Osborn), a worldwide marketing agency network company. During the daily work, Osborn found that his employees were gradually lack the creative ideas for advertising. This phenomenon really frustrated Osborn since it is fatal to advertising company if there is no enough new idea. To break this atmosphere, Osborn started to organizing small group-thinking meetings including both novices and experts. As he described in his book, the rule of the creative meeting is to “apply imagination”. Therefore, participants in the meeting are simply encouraged to provide wild and unexpected answers that might lead to the solution of the issue, with no criticism or judgment from other participants. Soon after the application of this method, Osborn found a significant improvement in the quality and quantity of advertising ideas.Later in the 1948, Osborn introduced his successful creativity methods in his book 'Your Creative Power'. In chapter 33, “How to Organize a Squad to Create Ideas, he described the method as “using the brain to storm a creative problem”, form where the term “brainstorming” first came into being. Osborn’s brainstorming fast taken by the world as numerous organizations using this method to create new ideas. What’s more, the world leading innovation consulting company IDEO even take brainstorming as the culture of the company, and writing the rules of it directly onto the meeting walls.3. Name a creative tool or method and explain what are the features and benefits? Give an example to demonstrate the application of creativity linked to innovation. Brain-mapping is a useful tool for creativity thinking and is newly becoming popular these days especially in consulting firms. Brain-mapping is the combination of two creativity tools—the brain writing (group doodling for no-verbal stimulation) and the mind-mapping (hierarchical breakdown and exploration). Brain-mapping is applied in the following ways:1st, Form the problem solving team: Gather a group of people working on different parts of the project or a creative workshop.2nd, write the problem: write the problem in the center of a chart flip using a single clear statement, such as a short phrase or a single word.3rd, add a stimulus: Each person now draws one branch to add a single stimulus from the circle of the problem. The stimulus should be helpful for further creative ideas to solve the problem.4th, complete the stimuli: add sub-elements to the stimulus, branching off the stimulus you wrote in the third step or the sub-stimulus.5th, develop the ideas: as the process continues and repeats, you can now complete the thought of solving the problem.The feature and the benefit of brain-mapping is that it combines the benefits of the brain writing and the mind-mapping. Like brain writing, it keeps people re-triggering more and detailed ideas. And like mind-mapping, it provides a visual structure through which ideas can be linked and breaking down into pieces.Above is an example of the application of creativity linked to innovation using brain-mapping. How to sell ice-cream to Eskimos? As can be seen, if the salespersons implicate each method at the end of the mapping branch, a new business model can be created.4. Discuss why/how creativity techniques/methodologies are best used in a group context or individually?As far as I am concerned, the creativity techniques are best used in a group context rather than by individual. The reasons are as follows:To begin with, the outcome of group thinking has potential divergence, making the group-workshop more attractive. It is common to assume that a group of people can brainstorm more ideas than a person alone, since people can get inspire by others’opinion.Second, shared information is also one of the advantages of using creativity methodologies with a group of people. This does not only mean to get access to more information but also means to have more precise or high quality information. It is easy to infer that a more complete information landscape will lead to a higher quality decision making and creativity thinking. What’s more, information biases are more likely to be happened when making a decision by oneself than by groups.To sum up, group can apply creative methods better than individuals for the two major reasons: synergy and the sharing of the information. Of course, there are also disadvantages of group thinking of creative ideas. Such as the social influence or homogeneity of the decision, as one or two opinion leader may influence the creative thinking of other group members. However, the advantages can overweigh the disadvantages since the bad effects can be reduced with the variation and development of various methods of creativity and by properly choosing the participants in the creative workshop group.5. Can an entrepreneur’s or an organization’s capacity for creativity and innovation be enhanced through the implementation of creativity methodologies? Give an example.ReferencesAmabile, T. M. (1996). Creativity and innovation in organizations (Vol. 5). Boston: Harvard Business School.Diehl, M., & Stroebe, W. (1987). Productivity loss in brainstorming groups: Toward the solution of a riddle. Journal of personality and social psychology, 53(3), 497.Glynn, M. A. (1996). Innovative genius: A framework for relating individual and organizational intelligences to innovation. Academy of management review, 21(4), 1081-1111.OECD. 1982. Innovation in Small and Medium Firms. Paris: Organisation for Economic Cooperation and DevelopmentRawlinson, J. G. (1981). Creative thinking and brainstorming. Farnborough, Hants: Gower.Roffe, I. (1999). Innovation and creativity in organisations: a review of the implications for training and development. Journal of European Industrial Training, 23(4/5), 224-241. Sutton, R. I., & Hargadon, A. (1996). Brainstorming groups in context: Effectiveness in a product design firm. Administrative Science Quarterly, 685-718.Taylor, D. W., Berry, P. C., & Block, C. H. (1958). Does group participation when using brainstorming facilitate or inhibit creative thinking?. Administrative Science Quarterly, 23-47.。

Adaptive mixtures of local experts

2
2
Figure 1: A system of expert and gating networks. Each expert is a feedforward network and all experts receive the same input and have the same number of outputs. The gating network is also feedforward and typically receives the same input as the expert networks. It has normalized outputs pj = exp(xj )/ i exp(xi ), where xj is the total weighted input received by output unit j of the gating network. The selector acts like a multiple input, single output stochastic switch; the probability that the switch will select the output from expert j is pj .
i c pc i oi 2

(1)
where oic is the output vector of expert i on case c, pc i is the proportional contribution of c expert i to the combined output vector, and d is the desired output vector in case c. This error measure compares the desired output with a blend of the outputs of the local experts, so, to minimize the error, each local expert must make its output cancel the residual error that is left by the combined eﬀects of all the other experts. When the weights in one expert change, the residual error changes, and so the error derivatives for all the other local experts change. 2 This strong coupling between the experts causes them to cooperate nicely, but tends to lead to solutions in which many experts are used for each case. It is possible to encourage competition by adding penalty terms to the objective function to encourage solutions in which only one expert is active (Jacobs, Jordan, and Barto, 1990), but a simpler remedy is to redeﬁne the error function so that the local experts are encouraged to compete rather than cooperate. Instead of linearly combining the outputs of the separate experts, we imagine that the gating network makes a stochastic decision about which single expert to use on each occasion (see ﬁgure 1). The error is then the expected value of the squared diﬀerence between the desired and actual output vectors

2023北京高三一模英语

1.【此处可播放相关音频，请去附件查看】
What will the speakers do next?
A.Visit a friend.B.Pick up Billy.C.Buy some beans.
【答案】C
【解析】
【原文】M: Billy needs some beans for his science project at school. Maybe we can stop by a store on our way home.
W: That would be terrible.
听下面一段较长对话，回答以下小题。【此处可播放相关音频，请去附件查看】
14.Whose speech did the woman listen to this morning?
A.John Miller's.B.David Thompson's.C.Allan Brown's.
C.They pay all her expenses.
12.What does Ethan suggest Becky do regarding her mother?
A.Have patience.
B.Provide company.
C.Express gratitude.
13.Why is Ethan concerned about his parents living on their own?
M: Do you pay a contribution to the house?
W: Of course, I do. But it’s still much less than I would pay to live in my own flat. Right?

Understanding individual human mobility patterns

a r X i v :0806.1256v 1 [p h y s i c s .s o c -p h ] 7 J u n 2008Understanding individual human mobility patternsMarta C.Gonz´a lez,1,2C´e sar A.Hidalgo,1and Albert-L´a szl´o Barab´a si 1,2,31Center for Complex Network Research and Department of Physics and Computer Science,University of Notre Dame,Notre Dame IN 46556.2Center for Complex Network Research and Department of Physics,Biology and Computer Science,Northeastern University,Boston MA 02115.3Center for Cancer Systems Biology,Dana Farber Cancer Institute,Boston,MA 02115.(Dated:June 7,2008)Despite their importance for urban planning [1],trafﬁc forecasting [2],and the spread of biological [3,4,5]and mobile viruses [6],our understanding of the basic laws govern-ing human motion remains limited thanks to the lack of tools to monitor the time resolved location of individuals.Here we study the trajectory of 100,000anonymized mobile phone users whose position is tracked for a six month period.We ﬁnd that in contrast with the random trajectories predicted by the prevailing L´e vy ﬂight and random walk models [7],human trajectories show a high degree of temporal and spatial regularity,each individual being characterized by a time independent characteristic length scale and a signiﬁcant prob-ability to return to a few highly frequented locations.After correcting for differences in travel distances and the inherent anisotropy of each trajectory,the individual travel patterns collapse into a single spatial probability distribution,indicating that despite the diversity of their travel history,humans follow simple reproducible patterns.This inherent similarity in travel patterns could impact all phenomena driven by human mobility,from epidemic prevention to emergency response,urban planning and agent based modeling.Given the many unknown factors that inﬂuence a population’s mobility patterns,ranging from means of transportation to job and family imposed restrictions and priorities,human trajectories are often approximated with various random walk or diffusion models [7,8].Indeed,early mea-surements on albatrosses,bumblebees,deer and monkeys [9,10]and more recent ones on marine predators [11]suggested that animal trajectory is approximated by a L´e vy ﬂight [12,13],a random walk whose step size ∆r follows a power-law distribution P (∆r )∼∆r −(1+β)with β<2.While the L´e vy statistics for some animals require further study [14],Brockmann et al.[7]generalized this ﬁnding to humans,documenting that the distribution of distances between consecutive sight-ings of nearly half-million bank notes is fat tailed.Given that money is carried by individuals, bank note dispersal is a proxy for human movement,suggesting that human trajectories are best modeled as a continuous time random walk with fat tailed displacements and waiting time dis-tributions[7].A particle following a L´e vyﬂight has a signiﬁcant probability to travel very long distances in a single step[12,13],which appears to be consistent with human travel patterns:most of the time we travel only over short distances,between home and work,while occasionally we take longer trips.Each consecutive sightings of a bank note reﬂects the composite motion of two or more indi-viduals,who owned the bill between two reported sightings.Thus it is not clear if the observed distribution reﬂects the motion of individual users,or some hitero unknown convolution between population based heterogeneities and individual human trajectories.Contrary to bank notes,mo-bile phones are carried by the same individual during his/her daily routine,offering the best proxy to capture individual human trajectories[15,16,17,18,19].We used two data sets to explore the mobility pattern of individuals.Theﬁrst(D1)consists of the mobility patterns recorded over a six month period for100,000individuals selected randomly from a sample of over6million anonymized mobile phone users.Each time a user initiates or receives a call or SMS,the location of the tower routing the communication is recorded,allowing us to reconstruct the user’s time resolved trajectory(Figs.1a and b).The time between consecutive calls follows a bursty pattern[20](see Fig.S1in the SM),indicating that while most consecutive calls are placed soon after a previous call,occasionally there are long periods without any call activity.To make sure that the obtained results are not affected by the irregular call pattern,we also study a data set(D2)that captures the location of206mobile phone users,recorded every two hours for an entire week.In both datasets the spatial resolution is determined by the local density of the more than104mobile towers,registering movement only when the user moves between areas serviced by different towers.The average service area of each tower is approximately3km2 and over30%of the towers cover an area of1km2or less.To explore the statistical properties of the population’s mobility patterns we measured the dis-tance between user’s positions at consecutive calls,capturing16,264,308displacements for the D1and10,407displacements for the D2datasets.Weﬁnd that the distribution of displacements over all users is well approximated by a truncated power-lawP(∆r)=(∆r+∆r0)−βexp(−∆r/κ),(1)withβ=1.75±0.15,∆r0=1.5km and cutoff valuesκ|D1=400km,andκ|D2=80km(Fig.1c,see the SM for statistical validation).Note that the observed scaling exponent is not far fromβB=1.59observed in Ref.[7]for bank note dispersal,suggesting that the two distributions may capture the same fundamental mechanism driving human mobility patterns.Equation(1)suggests that human motion follows a truncated L´e vyﬂight[7].Yet,the observed shape of P(∆r)could be explained by three distinct hypotheses:A.Each individual follows a L´e vy trajectory with jump size distribution given by(1).B.The observed distribution captures a population based heterogeneity,corresponding to the inherent differences between individuals.C.A population based heterogeneity coexists with individual L´e vy trajectories,hence(1)represents a convolution of hypothesis A and B.To distinguish between hypotheses A,B and C we calculated the radius of gyration for each user(see Methods),interpreted as the typical distance traveled by user a when observed up to time t(Fig.1b).Next,we determined the radius of gyration distribution P(r g)by calculating r g for all users in samples D1and D2,ﬁnding that they also can be approximated with a truncated power-lawP(r g)=(r g+r0g)−βr exp(−r g/κ),(2) with r0g=5.8km,βr=1.65±0.15andκ=350km(Fig.1d,see SM for statistical validation). L´e vyﬂights are characterized by a high degree of intrinsic heterogeneity,raising the possibility that(2)could emerge from an ensemble of identical agents,each following a L´e vy trajectory. Therefore,we determined P(r g)for an ensemble of agents following a Random Walk(RW), L´e vy-Flight(LF)or Truncated L´e vy-Flight(T LF)(Figure1d)[8,12,13].Weﬁnd that an en-semble of L´e vy agents display a signiﬁcant degree of heterogeneity in r g,yet is not sufﬁcient to explain the truncated power law distribution P(r g)exhibited by the mobile phone users.Taken together,Figs.1c and d suggest that the difference in the range of typical mobility patterns of indi-viduals(r g)has a strong impact on the truncated L´e vy behavior seen in(1),ruling out hypothesis A.If individual trajectories are described by a LF or T LF,then the radius of gyration should increase in time as r g(t)∼t3/(2+β)[21,22]while for a RW r g(t)∼t1/2.That is,the longer we observe a user,the higher the chances that she/he will travel to areas not visited before.To check the validity of these predictions we measured the time dependence of the radius of gyration for users whose gyration radius would be considered small(r g(T)≤3km),medium(20<r g(T)≤30km)or large(r g(T)>100km)at the end of our observation period(T=6months).Theresults indicate that the time dependence of the average radius of gyration of mobile phone users is better approximated by a logarithmic increase,not only a manifestly slower dependence than the one predicted by a power law,but one that may appear similar to a saturation process(Fig.2a and Fig.S4).In Fig.2b,we have chosen users with similar asymptotic r g(T)after T=6months,and measured the jump size distribution P(∆r|r g)for each group.As the inset of Fig.2b shows,users with small r g travel mostly over small distances,whereas those with large r g tend to display a combination of many small and a few larger jump sizes.Once we rescale the distributions with r g(Fig.2b),weﬁnd that the data collapses into a single curve,suggesting that a single jump size distribution characterizes all users,independent of their r g.This indicates that P(∆r|r g)∼r−αg F(∆r/r g),whereα≈1.2±0.1and F(x)is an r g independent function with asymptotic behavior F(x<1)∼x−αand rapidly decreasing for x≫1.Therefore the travel patterns of individual users may be approximated by a L´e vyﬂight up to a distance characterized by r g. Most important,however,is the fact that the individual trajectories are bounded beyond r g,thus large displacements which are the source of the distinct and anomalous nature of L´e vyﬂights, are statistically absent.To understand the relationship between the different exponents,we note that the measured probability distributions are related by P(∆r)= ∞0P(∆r|r g)P(r g)dr g,whichsuggests(see SM)that up to the leading order we haveβ=βr+α−1,consistent,within error bars, with the measured exponents.This indicates that the observed jump size distribution P(∆r)is in fact the convolution between the statistics of individual trajectories P(∆r g|r g)and the population heterogeneity P(r g),consistent with hypothesis C.To uncover the mechanism stabilizing r g we measured the return probability for each indi-vidual F pt(t)[22],deﬁned as the probability that a user returns to the position where it was ﬁrst observed after t hours(Fig.2c).For a two dimensional random walk F pt(t)should follow ∼1/(t ln(t)2)[22].In contrast,weﬁnd that the return probability is characterized by several peaks at24h,48h,and72h,capturing a strong tendency of humans to return to locations they visited before,describing the recurrence and temporal periodicity inherent to human mobility[23,24].To explore if individuals return to the same location over and over,we ranked each location based on the number of times an individual was recorded in its vicinity,such that a location with L=3represents the third most visited location for the selected individual.Weﬁnd that the probability ofﬁnding a user at a location with a given rank L is well approximated by P(L)∼1/L, independent of the number of locations visited by the user(Fig.2d).Therefore people devote mostof their time to a few locations,while spending their remaining time in5to50places,visited with diminished regularity.Therefore,the observed logarithmic saturation of r g(t)is rooted in the high degree of regularity in their daily travel patterns,captured by the high return probabilities(Fig.2b) to a few highly frequented locations(Fig.2d).An important quantity for modeling human mobility patterns is the probabilityΦa(x,y)toﬁnd an individual a in a given position(x,y).As it is evident from Fig.1b,individuals live and travel in different regions,yet each user can be assigned to a well deﬁned area,deﬁned by home and workplace,where she or he can be found most of the time.We can compare the trajectories of different users by diagonalizing each trajectory’s inertia tensor,providing the probability ofﬁnding a user in a given position(see Fig.3a)in the user’s intrinsic reference frame(see SM for the details).A striking feature ofΦ(x,y)is its prominent spatial anisotropy in this intrinsic reference frame(note the different scales in Fig3a),and weﬁnd that the larger an individual’s r g the more pronounced is this anisotropy.To quantify this effect we deﬁned the anisotropy ratio S≡σy/σx, whereσx andσy represent the standard deviation of the trajectory measured in the user’s intrinsic reference frame(see SM).Weﬁnd that S decreases monotonically with r g(Fig.3c),being well approximated with S∼r−ηg,forη≈0.12.Given the small value of the scaling exponent,other functional forms may offer an equally goodﬁt,thus mechanistic models are required to identify if this represents a true scaling law,or only a reasonable approximation to the data.To compare the trajectories of different users we remove the individual anisotropies,rescal-ing each user trajectory with its respectiveσx andσy.The rescaled˜Φ(x/σx,y/σy)distribution (Fig.3b)is similar for groups of users with considerably different r g,i.e.,after the anisotropy and the r g dependence is removed all individuals appear to follow the same universal˜Φ(˜x,˜y)prob-ability distribution.This is particularly evident in Fig.3d,where we show the cross section of ˜Φ(x/σ,0)for the three groups of users,ﬁnding that apart from the noise in the data the curves xare indistinguishable.Taken together,our results suggest that the L´e vy statistics observed in bank note measurements capture a convolution of the population heterogeneity(2)and the motion of individual users.Indi-viduals display signiﬁcant regularity,as they return to a few highly frequented locations,like home or work.This regularity does not apply to the bank notes:a bill always follows the trajectory of its current owner,i.e.dollar bills diffuse,but humans do not.The fact that individual trajectories are characterized by the same r g-independent two dimen-sional probability distribution˜Φ(x/σx,y/σy)suggests that key statistical characteristics of indi-vidual trajectories are largely indistinguishable after rescaling.Therefore,our results establish the basic ingredients of realistic agent based models,requiring us to place users in number propor-tional with the population density of a given region and assign each user an r g taken from the observed P(r g)ing the predicted anisotropic rescaling,combined with the density function˜Φ(x,y),whose shape is provided as Table1in the SM,we can obtain the likelihood ofﬁnding a user in any location.Given the known correlations between spatial proximity and social links,our results could help quantify the role of space in network development and evolu-tion[25,26,27,28,29]and improve our understanding of diffusion processes[8,30].We thank D.Brockmann,T.Geisel,J.Park,S.Redner,Z.Toroczkai and P.Wang for discus-sions and comments on the manuscript.This work was supported by the James S.McDonnell Foundation21st Century Initiative in Studying Complex Systems,the National Science Founda-tion within the DDDAS(CNS-0540348),ITR(DMR-0426737)and IIS-0513650programs,and the U.S.Ofﬁce of Naval Research Award N00014-07-C.Data analysis was performed on the Notre Dame Biocomplexity Cluster supported in part by NSF MRI Grant No.DBI-0420980.C.A.Hi-dalgo acknowledges support from the Kellogg Institute at Notre Dame.Supplementary Information is linked to the online version of the paper at /nature.Author Information Correspondence and requests for materials should be addressed to A.-L.B.(e-mail:alb@)[1]Horner,M.W.&O’Kelly,M.E.S Embedding economies of scale concepts for hub networks design.Journal of Transportation Geography9,255-265(2001).[2]Kitamura,R.,Chen,C.,Pendyala,R.M.&Narayaran,R.Micro-simulation of daily activity-travelpatterns for travel demand forecasting.Transportation27,25-51(2000).[3]Colizza,V.,Barrat,A.,Barth´e l´e my,M.,Valleron,A.-J.&Vespignani,A.Modeling the WorldwideSpread of Pandemic Inﬂuenza:Baseline Case and Containment Interventions.PLoS Medicine4,095-0110(2007).[4]Eubank,S.,Guclu,H.,Kumar,V.S.A.,Marathe,M.V.,Srinivasan,A.,Toroczkai,Z.&Wang,N.Controlling Epidemics in Realistic Urban Social Networks.Nature429,180(2004).[5]Hufnagel,L.,Brockmann,D.&Geisel,T.Forecast and control of epidemics in a globalized world.Proceedings of the National Academy of Sciences of the United States of America101,15124-15129 (2004).[6]Kleinberg,J.The wireless epidemic.Nature449,287-288(2007).[7] D.Brockmann,D.,Hufnagel,L.&Geisel,T.The scaling laws of human travel.Nature439,462-465(2006).[8]Havlin,S.&ben-Avraham,D.Diffusion in Disordered Media.Advances in Physics51,187-292(2002).[9]Viswanathan,G.M.,Afanasyev,V.,Buldyrev,S.V.,Murphy,E.J.,Prince,P.A.&Stanley,H.E.L´e vyFlight Search Patterns of Wandering Albatrosses.Nature381,413-415(1996).[10]Ramos-Fernandez,G.,Mateos,J.L.,Miramontes,O.,Cocho,G.,Larralde,H.&Ayala-Orozco,B.,L´e vy walk patterns in the foraging movements of spider monkeys(Ateles geoffroyi).Behavioral ecol-ogy and Sociobiology55,223-230(2004).[11]Sims D.W.et al.Scaling laws of marine predator search behaviour.Nature451,1098-1102(2008).[12]Klafter,J.,Shlesinger,M.F.&Zumofen,G.Beyond Brownian Motion.Physics Today49,33-39(1996).[13]Mantegna,R.N.&Stanley,H.E.Stochastic Process with Ultraslow Convergence to a Gaussian:TheTruncated L´e vy Flight.Physical Review Letters73,2946-2949(1994).[14]Edwards,A.M.,Phillips,R.A.,Watkins,N.W.,Freeman,M.P.,Murphy,E.J.,Afanasyev,V.,Buldyrev,S.V.,da Luz,M.G.E.,Raposo,E.P.,Stanley,H.E.&Viswanathan,G.M.Revisiting L´e vyﬂightsearch patterns of wandering albatrosses,bumblebees and deer.Nature449,1044-1049(2007). [15]Sohn,T.,Varshavsky,A.,LaMarca,A.,Chen,M.Y.,Choudhury,T.,Smith,I.,Consolvo,S.,High-tower,J.,Griswold,W.G.&de Lara,E.Lecture Notes in Computer Sciences:Proc.8th International Conference UbiComp2006.(Springer,Berlin,2006).[16]Onnela,J.-P.,Saram¨a ki,J.,Hyv¨o nen,J.,Szab´o,G.,Lazer,D.,Kaski,K.,Kert´e sz,K.&Barab´a si A.L.Structure and tie strengths in mobile communication networks.Proceedings of the National Academy of Sciences of the United States of America104,7332-7336(2007).[17]Gonz´a lez,M.C.&Barab´a si,plex networks:From data to models.Nature Physics3,224-225(2007).[18]Palla,G.,Barab´a si,A.-L.&Vicsek,T.Quantifying social group evolution.Nature446,664-667(2007).[19]Hidalgo C.A.&Rodriguez-Sickert C.The dynamics of a mobile phone network.Physica A387,3017-30224.[20]Barab´a si,A.-L.The origin of bursts and heavy tails in human dynamics.Nature435,207-211(2005).[21]Hughes,B.D.Random Walks and Random Environments.(Oxford University Press,USA,1995).[22]Redner,S.A Guide to First-Passage Processes.(Cambridge University Press,UK,2001).[23]Schlich,R.&Axhausen,K.W.Habitual travel behaviour:Evidence from a six-week travel diary.Transportation30,13-36(2003).[24]Eagle,N.&Pentland,A.Eigenbehaviours:Identifying Structure in Routine.submitted to BehavioralEcology and Sociobiology(2007).[25]Yook,S.-H.,Jeong,H.&Barab´a si A.L.Modeling the Internet’s large-scale topology.Proceedings ofthe Nat’l Academy of Sciences99,13382-13386(2002).[26]Caldarelli,G.Scale-Free Networks:Complex Webs in Nature and Technology.(Oxford UniversityPress,USA,2007).[27]Dorogovtsev,S.N.&Mendes,J.F.F.Evolution of Networks:From Biological Nets to the Internet andWWW.(Oxford University Press,USA,2003).[28]Song C.M.,Havlin S.&Makse H.A.Self-similarity of complex networks.Nature433,392-395(2005).[29]Gonz´a lez,M.C.,Lind,P.G.&Herrmann,H.J.A system of mobile agents to model social networks.Physical Review Letters96,088702(2006).[30]Cecconi,F.,Marsili,M.,Banavar,J.R.&Maritan,A.Diffusion,peer pressure,and tailed distributions.Physical Review Letters89,088102(2002).FIG.1:Basic human mobility patterns.a,Week-long trajectory of40mobile phone users indicate that most individuals travel only over short distances,but a few regularly move over hundreds of kilometers. Panel b,displays the detailed trajectory of a single user.The different phone towers are shown as green dots,and the V oronoi lattice in grey marks the approximate reception area of each tower.The dataset studied by us records only the identity of the closest tower to a mobile user,thus we can not identify the position of a user within a V oronoi cell.The trajectory of the user shown in b is constructed from186 two hourly reports,during which the user visited a total of12different locations(tower vicinities).Among these,the user is found96and67occasions in the two most preferred locations,the frequency of visits for each location being shown as a vertical bar.The circle represents the radius of gyration centered in the trajectory’s center of mass.c,Probability density function P(∆r)of travel distances obtained for the two studied datasets D1and D2.The solid line indicates a truncated power law whose parameters are provided in the text(see Eq.1).d,The distribution P(r g)of the radius of gyration measured for the users, where r g(T)was measured after T=6months of observation.The solid line represent a similar truncated power lawﬁt(see Eq.2).The dotted,dashed and dot-dashed curves show P(r g)obtained from the standard null models(RW,LF and T LF),where for the T LF we used the same step size distribution as the onemeasured for the mobile phone users.FIG.2:The bounded nature of human trajectories.a,Radius of gyration, r g(t) vs time for mobile phone users separated in three groups according to theirﬁnal r g(T),where T=6months.The black curves correspond to the analytical predictions for the random walk models,increasing in time as r g(t) |LF,T LF∼t3/2+β(solid),and r g(t) |RW∼t0.5(dotted).The dashed curves corresponding to a logarithmicﬁt of the form A+B ln(t),where A and B depend on r g.b,Probability density function of individual travel distances P(∆r|r g)for users with r g=4,10,40,100and200km.As the inset shows,each group displays a quite different P(∆r|r g)distribution.After rescaling the distance and the distribution with r g(main panel),the different curves collapse.The solid line(power law)is shown as a guide to the eye.c,Return probability distribution,F pt(t).The prominent peaks capture the tendency of humans to regularly return to the locations they visited before,in contrast with the smooth asymptotic behavior∼1/(t ln(t)2)(solid line)predicted for random walks.d,A Zipf plot showing the frequency of visiting different locations.The symbols correspond to users that have been observed to visit n L=5,10,30,and50different locations.Denoting with(L)the rank of the location listed in the order of the visit frequency,the data is well approximated by R(L)∼L−1. The inset is the same plot in linear scale,illustrating that40%of the time individuals are found at theirﬁrsttwo preferred locations.FIG.3:The shape of human trajectories.a,The probability density functionΦ(x,y)ofﬁnding a mobile phone user in a location(x,y)in the user’s intrinsic reference frame(see SM for details).The three plots, from left to right,were generated for10,000users with:r g≤3,20<r g≤30and r g>100km.The trajectories become more anisotropic as r g increases.b,After scaling each position withσx andσy theresulting˜Φ(x/σx,y/σy)has approximately the same shape for each group.c,The change in the shape of Φ(x,y)can be quantiﬁed calculating the isotropy ratio S≡σy/σx as a function of r g,which decreases as S∼r−0.12(solid line).Error bars represent the standard error.d,˜Φ(x/σx,0)representing the x-axis cross gsection of the rescaled distribution˜Φ(x/σx,y/σy)shown in b.。

普林斯顿高等研究院

现任研究人员
数学学院自然科学学院
历史研究学院社会科学学院
恩里科·蓬皮埃利（菲尔兹奖得主）辛康·布尔甘（菲尔兹奖得主）皮埃尔·德利涅（菲尔兹奖得主）菲利普·格里菲斯罗伯特·郎兰兹（沃尔夫奖得主） Robert MacPherson Atel Selberg（菲尔兹奖和沃尔夫奖得主） Thomas Spencer Vladimir Voevodsky（菲尔兹奖得主） Avi Wigderson（奈望林纳奖得主）
研究院虽然和大学没有互属关系，但是有很深的渊源。研究院最早是借用普林斯顿数学系的办公室，主要人员如约翰·冯·诺依曼、范布伦也来自数学系。研究院的许多教授也同时兼职普林斯顿教授。
数学家丘成桐是第一位受聘为美国高等研究院终身教授的华人学者。杨振宁教授也曾在高等研究院度过十几年学术生涯的黄金岁月。
罗伯特·奥本海默在二战后曾长期任研究院的院长。
普林斯顿高等研究院
世界著名理论研究机构
01 历史发展
03 组织结构 05 研究成果
目录
02 现任院长 04 itute for Advanced Study），简称IAS，1930年成立于美国新泽西州普林斯顿市，是世界著名理论研究机构，但并不是普林斯顿大学的一部分。
2022年9月23日，普林斯顿高等研究院詹姆斯·梅纳德（James Maynard）对解析数论的多项贡献，特别是对素数分布的贡献，荣获“ 2023年数学新视野奖”。
历史名人
阿尔伯特·爱因斯坦冯·诺依曼哥德尔奥本海默阿蒂亚外尔陈省身杨振宁李政道华罗庚
感谢观看
普林斯顿高等研究院是各个领域的最一流学者做最纯粹的尖端研究，而不受任何教学任务、科研资金或者赞助商压力的研究机构。二十世纪上半叶，阿尔伯特·爱因斯坦、约翰·冯·诺依曼、库尔特·哥德尔、罗伯特·奥本海默等学者迁往普林斯顿高等研究院之后，使之成为世界著名的学术圣地，不少著名学者在这里做出了一生中最重要的成果。在其它地方也有效仿普林斯顿高等研究院而建立的高等研究院，例如清华大学设立的高等研究院或香港科技大学的高等研究院。

高考英语阅读理解态度题单选题30题

高考英语阅读理解态度题单选题30题1. The author's attitude towards the new law can be described as _____.A. supportiveB. indifferentC. criticalD. ambiguous答案：C。

本题考查作者对新法律的态度。

选项A“supportive”意为支持的，若选此选项则表明作者对新法律持积极肯定态度，但文中作者列举了新法律的诸多弊端，并非支持。

选项B“indifferent”意为漠不关心的，而文中作者有明确的观点和评价，并非漠不关心。

选项C“critical”意为批评的，符合文中作者通过列举问题对新法律进行批判的态度。

选项D“ambiguous”意为模糊不清的，文中作者态度明确，并非模糊不清。

2. What is the attitude of the writer towards the proposed solution?A. OptimisticB. PessimisticC. DoubtfulD. Confident答案：C。

此题考查作者对所提出的解决方案的态度。

选项A“Optimistic”表示乐观的，若选此选项意味着作者认为该解决方案可行且效果良好，但文中作者对其可行性提出了质疑。

选项B“Pessimistic”表示悲观的，然而文中作者并非完全否定该方案，只是存在怀疑。

选项C“Doubtful”意为怀疑的，符合文中作者对方案的态度，作者在文中指出了方案可能存在的问题和不确定性。

选项D“Confident”表示自信的，与文中作者的态度不符。

3. The tone of the passage when referring to the recent development is _____.A. excitedB. cautiousC. enthusiasticD. worried答案：B。

Geographic_Information_Systems_and_Science_Today_and_Tomorrow

A technology of dynamics
• Real-time, continuous monitoring • The state of the world at all times
– the state of the transportation network – the state of human health – the state of the environment
• Theories of representation
– discrete objects and continuous fields – object fields, metamaps – unification
• Models of uncertainty
– error propagation – downscaling
– goods from production to retail display to sale – construction materials – pets, livestock, children, parolees
NYC Office of Emergency Management and NY Office of Cyber Security and Critical Infrastructure Coordination
• The science behind the systems • The fundamental issues raised by the technologies • The principles implemented in the technologies
Major discoveries in GIScience

The Techsat-21 Autonomous Space Science Agent

Figure 1 - Techsat-21 Configuration
Keywords
Space exploration agent, science agent, planning and scheduling, robust execution, mode identification
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Onboard science algorithm that will analyze the image data to detect trigger conditions such as science events, “interesting” features, and changes relative to previous observations Model-based mode identification and execution (MZ-R) that uses component-based hardware models to analyze anomalous situations and to generate novel command sequences and repairs. Robust execution management software using the Spacecraft Command Language (SCL) [7] package to enable event-driven processing and low-level autonomy The Continuous Activity Planning, Scheduling, and Replanning (CASPER) [4] planner that will replan activities, including downlink, based on science observations in the previous orbit cycles Observation Planning (OP) software will enable the cluster to predict overflights of targets to facilitate onboard retasking.

Geometric Modeling

Geometric ModelingGeometric modeling is a fundamental concept in the field of computer graphics and design. It involves the creation and manipulation of digital representations of objects and environments using geometric shapes and mathematical equations. This process is essential for various applications, including animation, virtual reality, architectural design, and manufacturing. Geometric modeling plays a crucial role in bringing creative ideas to life and enabling the visualization of complex concepts. In this article, we will explore the significance of geometric modeling from multiple perspectives, including its technical aspects, creative potential, and real-world applications. From a technical standpoint, geometric modeling relies on mathematical principles to define and represent shapes, surfaces, and volumes in a digital environment. This involves the use of algorithms to generate and manipulate geometric data, enabling the creation of intricate and realistic 3D models. The precision and accuracy of geometric modeling are essential for engineering, scientific simulations, and industrial design. Engineers and designers utilize geometric modeling software to develop prototypes, analyze structural integrity, and simulate real-world scenarios. The ability to accurately model physical objects and phenomena in a virtual space is invaluable for testing and refining concepts before they are realized in the physical world. Beyond its technical applications, geometric modeling also offers immense creative potential. Artists and animators use geometric modeling tools to sculpt, texture, and animate characters and environments for films, video games, and virtual experiences. The ability to manipulate geometric primitives and sculpt organic forms empowers creatives to bring their imaginations to life in stunning detail. Geometric modeling software provides a canvas for artistic expression, enabling artists to explore new dimensions of creativity and visual storytelling. Whether it's crafting fantastical creatures or architecting futuristic cityscapes, geometric modeling serves as a medium for boundless creativity and artistic innovation. In the realm of real-world applications, geometric modeling has a profound impact on various industries and disciplines. In architecture and urban planning, geometric modeling software is used to design and visualize buildings, landscapes, and urban developments. This enables architects and urban designers toconceptualize and communicate their ideas effectively, leading to the creation of functional and aesthetically pleasing spaces. Furthermore, geometric modelingplays a critical role in medical imaging and scientific visualization, allowing researchers and practitioners to study complex anatomical structures and visualize scientific data in meaningful ways. The ability to create accurate and detailed representations of biological and physical phenomena contributes to advancementsin healthcare, research, and education. Moreover, geometric modeling is integral to the manufacturing process, where it is used for product design, prototyping,and production. By creating digital models of components and assemblies, engineers can assess the functionality and manufacturability of their designs, leading tothe development of high-quality and efficient products. Geometric modeling also facilitates the implementation of additive manufacturing technologies, such as 3D printing, by providing the digital blueprints for creating physical objects layer by layer. This convergence of digital modeling and manufacturing technologies is revolutionizing the production landscape and enabling rapid innovation across various industries. In conclusion, geometric modeling is a multifaceteddiscipline that intersects technology, creativity, and practicality. Its technical foundations in mathematics and algorithms underpin its applications in engineering, design, and scientific research. Simultaneously, it serves as a creative platform for artists and animators to realize their visions in virtual spaces. Moreover,its real-world applications extend to diverse fields such as architecture, medicine, and manufacturing, where it contributes to innovation and progress. The significance of geometric modeling lies in its ability to bridge the digital and physical worlds, facilitating the exploration, creation, and realization of ideas and concepts. As technology continues to advance, geometric modeling will undoubtedly play an increasingly pivotal role in shaping the future of design, visualization, and manufacturing.。

From Data Mining to Knowledge Discovery in Databases

s Data mining and knowledge discovery in databases have been attracting a signiﬁcant amount of research, industry, and media atten-tion of late. What is all the excitement about?This article provides an overview of this emerging ﬁeld, clarifying how data mining and knowledge discovery in databases are related both to each other and to related ﬁelds, such as machine learning, statistics, and databases. The article mentions particular real-world applications, speciﬁc data-mining techniques, challenges in-volved in real-world applications of knowledge discovery, and current and future research direc-tions in the ﬁeld.A cross a wide variety of ﬁelds, data arebeing collected and accumulated at adramatic pace. There is an urgent need for a new generation of computational theo-ries and tools to assist humans in extracting useful information (knowledge) from the rapidly growing volumes of digital data. These theories and tools are the subject of the emerging ﬁeld of knowledge discovery in databases (KDD).At an abstract level, the KDD ﬁeld is con-cerned with the development of methods and techniques for making sense of data. The basic problem addressed by the KDD process is one of mapping low-level data (which are typically too voluminous to understand and digest easi-ly) into other forms that might be more com-pact (for example, a short report), more ab-stract (for example, a descriptive approximation or model of the process that generated the data), or more useful (for exam-ple, a predictive model for estimating the val-ue of future cases). At the core of the process is the application of speciﬁc data-mining meth-ods for pattern discovery and extraction.1This article begins by discussing the histori-cal context of KDD and data mining and theirintersection with other related ﬁelds. A briefsummary of recent KDD real-world applica-tions is provided. Deﬁnitions of KDD and da-ta mining are provided, and the general mul-tistep KDD process is outlined. This multistepprocess has the application of data-mining al-gorithms as one particular step in the process.The data-mining step is discussed in more de-tail in the context of speciﬁc data-mining al-gorithms and their application. Real-worldpractical application issues are also outlined.Finally, the article enumerates challenges forfuture research and development and in par-ticular discusses potential opportunities for AItechnology in KDD systems.Why Do We Need KDD?The traditional method of turning data intoknowledge relies on manual analysis and in-terpretation. For example, in the health-careindustry, it is common for specialists to peri-odically analyze current trends and changesin health-care data, say, on a quarterly basis.The specialists then provide a report detailingthe analysis to the sponsoring health-care or-ganization; this report becomes the basis forfuture decision making and planning forhealth-care management. In a totally differ-ent type of application, planetary geologistssift through remotely sensed images of plan-ets and asteroids, carefully locating and cata-loging such geologic objects of interest as im-pact craters. Be it science, marketing, ﬁnance,health care, retail, or any other ﬁeld, the clas-sical approach to data analysis relies funda-mentally on one or more analysts becomingArticlesFALL 1996 37From Data Mining to Knowledge Discovery inDatabasesUsama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth Copyright © 1996, American Association for Artificial Intelligence. All rights reserved. 0738-4602-1996 / $2.00areas is astronomy. Here, a notable success was achieved by SKICAT ,a system used by as-tronomers to perform image analysis,classiﬁcation, and cataloging of sky objects from sky-survey images (Fayyad, Djorgovski,and Weir 1996). In its ﬁrst application, the system was used to process the 3 terabytes (1012bytes) of image data resulting from the Second Palomar Observatory Sky Survey,where it is estimated that on the order of 109sky objects are detectable. SKICAT can outper-form humans and traditional computational techniques in classifying faint sky objects. See Fayyad, Haussler, and Stolorz (1996) for a sur-vey of scientiﬁc applications.In business, main KDD application areas includes marketing, ﬁnance (especially in-vestment), fraud detection, manufacturing,telecommunications, and Internet agents.Marketing:In marketing, the primary ap-plication is database marketing systems,which analyze customer databases to identify different customer groups and forecast their behavior. Business Week (Berry 1994) estimat-ed that over half of all retailers are using or planning to use database marketing, and those who do use it have good results; for ex-ample, American Express reports a 10- to 15-percent increase in credit-card use. Another notable marketing application is market-bas-ket analysis (Agrawal et al. 1996) systems,which ﬁnd patterns such as, “If customer bought X, he/she is also likely to buy Y and Z.” Such patterns are valuable to retailers.Investment: Numerous companies use da-ta mining for investment, but most do not describe their systems. One exception is LBS Capital Management. Its system uses expert systems, neural nets, and genetic algorithms to manage portfolios totaling $600 million;since its start in 1993, the system has outper-formed the broad stock market (Hall, Mani,and Barr 1996).Fraud detection: HNC Falcon and Nestor PRISM systems are used for monitoring credit-card fraud, watching over millions of ac-counts. The FAIS system (Senator et al. 1995),from the U.S. Treasury Financial Crimes En-forcement Network, is used to identify ﬁnan-cial transactions that might indicate money-laundering activity.Manufacturing: The CASSIOPEE trou-bleshooting system, developed as part of a joint venture between General Electric and SNECMA, was applied by three major Euro-pean airlines to diagnose and predict prob-lems for the Boeing 737. To derive families of faults, clustering methods are used. CASSIOPEE received the European ﬁrst prize for innova-intimately familiar with the data and serving as an interface between the data and the users and products.For these (and many other) applications,this form of manual probing of a data set is slow, expensive, and highly subjective. In fact, as data volumes grow dramatically, this type of manual data analysis is becoming completely impractical in many domains.Databases are increasing in size in two ways:(1) the number N of records or objects in the database and (2) the number d of ﬁelds or at-tributes to an object. Databases containing on the order of N = 109objects are becoming in-creasingly common, for example, in the as-tronomical sciences. Similarly, the number of ﬁelds d can easily be on the order of 102or even 103, for example, in medical diagnostic applications. Who could be expected to di-gest millions of records, each having tens or hundreds of ﬁelds? We believe that this job is certainly not one for humans; hence, analysis work needs to be automated, at least partially.The need to scale up human analysis capa-bilities to handling the large number of bytes that we can collect is both economic and sci-entiﬁc. Businesses use data to gain competi-tive advantage, increase efﬁciency, and pro-vide more valuable services to customers.Data we capture about our environment are the basic evidence we use to build theories and models of the universe we live in. Be-cause computers have enabled humans to gather more data than we can digest, it is on-ly natural to turn to computational tech-niques to help us unearth meaningful pat-terns and structures from the massive volumes of data. Hence, KDD is an attempt to address a problem that the digital informa-tion era made a fact of life for all of us: data overload.Data Mining and Knowledge Discovery in the Real WorldA large degree of the current interest in KDD is the result of the media interest surrounding successful KDD applications, for example, the focus articles within the last two years in Business Week , Newsweek , Byte , PC Week , and other large-circulation periodicals. Unfortu-nately, it is not always easy to separate fact from media hype. Nonetheless, several well-documented examples of successful systems can rightly be referred to as KDD applications and have been deployed in operational use on large-scale real-world problems in science and in business.In science, one of the primary applicationThere is an urgent need for a new generation of computation-al theories and tools toassist humans in extractinguseful information (knowledge)from the rapidly growing volumes ofdigital data.Articles38AI MAGAZINEtive applications (Manago and Auriol 1996).Telecommunications: The telecommuni-cations alarm-sequence analyzer (TASA) wasbuilt in cooperation with a manufacturer oftelecommunications equipment and threetelephone networks (Mannila, Toivonen, andVerkamo 1995). The system uses a novelframework for locating frequently occurringalarm episodes from the alarm stream andpresenting them as rules. Large sets of discov-ered rules can be explored with ﬂexible infor-mation-retrieval tools supporting interactivityand iteration. In this way, TASA offers pruning,grouping, and ordering tools to reﬁne the re-sults of a basic brute-force search for rules.Data cleaning: The MERGE-PURGE systemwas applied to the identiﬁcation of duplicatewelfare claims (Hernandez and Stolfo 1995).It was used successfully on data from the Wel-fare Department of the State of Washington.In other areas, a well-publicized system isIBM’s ADVANCED SCOUT,a specialized data-min-ing system that helps National Basketball As-sociation (NBA) coaches organize and inter-pret data from NBA games (U.S. News 1995). ADVANCED SCOUT was used by several of the NBA teams in 1996, including the Seattle Su-personics, which reached the NBA ﬁnals.Finally, a novel and increasingly importanttype of discovery is one based on the use of in-telligent agents to navigate through an infor-mation-rich environment. Although the ideaof active triggers has long been analyzed in thedatabase ﬁeld, really successful applications ofthis idea appeared only with the advent of theInternet. These systems ask the user to specifya proﬁle of interest and search for related in-formation among a wide variety of public-do-main and proprietary sources. For example, FIREFLY is a personal music-recommendation agent: It asks a user his/her opinion of several music pieces and then suggests other music that the user might like (<http:// www.fﬂ/>). CRAYON(/>) allows users to create their own free newspaper (supported by ads); NEWSHOUND(<http://www. /hound/>) from the San Jose Mercury News and FARCAST(</> automatically search information from a wide variety of sources, including newspapers and wire services, and e-mail rele-vant documents directly to the user.These are just a few of the numerous suchsystems that use KDD techniques to automat-ically produce useful information from largemasses of raw data. See Piatetsky-Shapiro etal. (1996) for an overview of issues in devel-oping industrial KDD applications.Data Mining and KDDHistorically, the notion of ﬁnding useful pat-terns in data has been given a variety ofnames, including data mining, knowledge ex-traction, information discovery, informationharvesting, data archaeology, and data patternprocessing. The term data mining has mostlybeen used by statisticians, data analysts, andthe management information systems (MIS)communities. It has also gained popularity inthe database ﬁeld. The phrase knowledge dis-covery in databases was coined at the ﬁrst KDDworkshop in 1989 (Piatetsky-Shapiro 1991) toemphasize that knowledge is the end productof a data-driven discovery. It has been popular-ized in the AI and machine-learning ﬁelds.In our view, KDD refers to the overall pro-cess of discovering useful knowledge from da-ta, and data mining refers to a particular stepin this process. Data mining is the applicationof speciﬁc algorithms for extracting patternsfrom data. The distinction between the KDDprocess and the data-mining step (within theprocess) is a central point of this article. Theadditional steps in the KDD process, such asdata preparation, data selection, data cleaning,incorporation of appropriate prior knowledge,and proper interpretation of the results ofmining, are essential to ensure that usefulknowledge is derived from the data. Blind ap-plication of data-mining methods (rightly crit-icized as data dredging in the statistical litera-ture) can be a dangerous activity, easilyleading to the discovery of meaningless andinvalid patterns.The Interdisciplinary Nature of KDDKDD has evolved, and continues to evolve,from the intersection of research ﬁelds such asmachine learning, pattern recognition,databases, statistics, AI, knowledge acquisitionfor expert systems, data visualization, andhigh-performance computing. The unifyinggoal is extracting high-level knowledge fromlow-level data in the context of large data sets.The data-mining component of KDD cur-rently relies heavily on known techniquesfrom machine learning, pattern recognition,and statistics to ﬁnd patterns from data in thedata-mining step of the KDD process. A natu-ral question is, How is KDD different from pat-tern recognition or machine learning (and re-lated ﬁelds)? The answer is that these ﬁeldsprovide some of the data-mining methodsthat are used in the data-mining step of theKDD process. KDD focuses on the overall pro-cess of knowledge discovery from data, includ-ing how the data are stored and accessed, howalgorithms can be scaled to massive data setsThe basicproblemaddressed bythe KDDprocess isone ofmappinglow-leveldata intoother formsthat might bemorecompact,moreabstract,or moreuseful.ArticlesFALL 1996 39A driving force behind KDD is the database ﬁeld (the second D in KDD). Indeed, the problem of effective data manipulation when data cannot ﬁt in the main memory is of fun-damental importance to KDD. Database tech-niques for gaining efﬁcient data access,grouping and ordering operations when ac-cessing data, and optimizing queries consti-tute the basics for scaling algorithms to larger data sets. Most data-mining algorithms from statistics, pattern recognition, and machine learning assume data are in the main memo-ry and pay no attention to how the algorithm breaks down if only limited views of the data are possible.A related ﬁeld evolving from databases is data warehousing,which refers to the popular business trend of collecting and cleaning transactional data to make them available for online analysis and decision support. Data warehousing helps set the stage for KDD in two important ways: (1) data cleaning and (2)data access.Data cleaning: As organizations are forced to think about a uniﬁed logical view of the wide variety of data and databases they pos-sess, they have to address the issues of map-ping data to a single naming convention,uniformly representing and handling missing data, and handling noise and errors when possible.Data access: Uniform and well-deﬁned methods must be created for accessing the da-ta and providing access paths to data that were historically difﬁcult to get to (for exam-ple, stored ofﬂine).Once organizations and individuals have solved the problem of how to store and ac-cess their data, the natural next step is the question, What else do we do with all the da-ta? This is where opportunities for KDD natu-rally arise.A popular approach for analysis of data warehouses is called online analytical processing (OLAP), named for a set of principles pro-posed by Codd (1993). OLAP tools focus on providing multidimensional data analysis,which is superior to SQL in computing sum-maries and breakdowns along many dimen-sions. OLAP tools are targeted toward simpli-fying and supporting interactive data analysis,but the goal of KDD tools is to automate as much of the process as possible. Thus, KDD is a step beyond what is currently supported by most standard database systems.Basic DeﬁnitionsKDD is the nontrivial process of identifying valid, novel, potentially useful, and ultimate-and still run efﬁciently, how results can be in-terpreted and visualized, and how the overall man-machine interaction can usefully be modeled and supported. The KDD process can be viewed as a multidisciplinary activity that encompasses techniques beyond the scope of any one particular discipline such as machine learning. In this context, there are clear opportunities for other ﬁelds of AI (be-sides machine learning) to contribute to KDD. KDD places a special emphasis on ﬁnd-ing understandable patterns that can be inter-preted as useful or interesting knowledge.Thus, for example, neural networks, although a powerful modeling tool, are relatively difﬁcult to understand compared to decision trees. KDD also emphasizes scaling and ro-bustness properties of modeling algorithms for large noisy data sets.Related AI research ﬁelds include machine discovery, which targets the discovery of em-pirical laws from observation and experimen-tation (Shrager and Langley 1990) (see Kloes-gen and Zytkow [1996] for a glossary of terms common to KDD and machine discovery),and causal modeling for the inference of causal models from data (Spirtes, Glymour,and Scheines 1993). Statistics in particular has much in common with KDD (see Elder and Pregibon [1996] and Glymour et al.[1996] for a more detailed discussion of this synergy). Knowledge discovery from data is fundamentally a statistical endeavor. Statistics provides a language and framework for quan-tifying the uncertainty that results when one tries to infer general patterns from a particu-lar sample of an overall population. As men-tioned earlier, the term data mining has had negative connotations in statistics since the 1960s when computer-based data analysis techniques were ﬁrst introduced. The concern arose because if one searches long enough in any data set (even randomly generated data),one can ﬁnd patterns that appear to be statis-tically signiﬁcant but, in fact, are not. Clearly,this issue is of fundamental importance to KDD. Substantial progress has been made in recent years in understanding such issues in statistics. Much of this work is of direct rele-vance to KDD. Thus, data mining is a legiti-mate activity as long as one understands how to do it correctly; data mining carried out poorly (without regard to the statistical as-pects of the problem) is to be avoided. KDD can also be viewed as encompassing a broader view of modeling than statistics. KDD aims to provide tools to automate (to the degree pos-sible) the entire process of data analysis and the statistician’s “art” of hypothesis selection.Data mining is a step in the KDD process that consists of ap-plying data analysis and discovery al-gorithms that produce a par-ticular enu-meration ofpatterns (or models)over the data.Articles40AI MAGAZINEly understandable patterns in data (Fayyad, Piatetsky-Shapiro, and Smyth 1996).Here, data are a set of facts (for example, cases in a database), and pattern is an expres-sion in some language describing a subset of the data or a model applicable to the subset. Hence, in our usage here, extracting a pattern also designates ﬁtting a model to data; ﬁnd-ing structure from data; or, in general, mak-ing any high-level description of a set of data. The term process implies that KDD comprises many steps, which involve data preparation, search for patterns, knowledge evaluation, and reﬁnement, all repeated in multiple itera-tions. By nontrivial, we mean that some search or inference is involved; that is, it is not a straightforward computation of predeﬁned quantities like computing the av-erage value of a set of numbers.The discovered patterns should be valid on new data with some degree of certainty. We also want patterns to be novel (at least to the system and preferably to the user) and poten-tially useful, that is, lead to some beneﬁt to the user or task. Finally, the patterns should be understandable, if not immediately then after some postprocessing.The previous discussion implies that we can deﬁne quantitative measures for evaluating extracted patterns. In many cases, it is possi-ble to deﬁne measures of certainty (for exam-ple, estimated prediction accuracy on new data) or utility (for example, gain, perhaps indollars saved because of better predictions orspeedup in response time of a system). No-tions such as novelty and understandabilityare much more subjective. In certain contexts,understandability can be estimated by sim-plicity (for example, the number of bits to de-scribe a pattern). An important notion, calledinterestingness(for example, see Silberschatzand Tuzhilin [1995] and Piatetsky-Shapiro andMatheus [1994]), is usually taken as an overallmeasure of pattern value, combining validity,novelty, usefulness, and simplicity. Interest-ingness functions can be deﬁned explicitly orcan be manifested implicitly through an or-dering placed by the KDD system on the dis-covered patterns or models.Given these notions, we can consider apattern to be knowledge if it exceeds some in-terestingness threshold, which is by nomeans an attempt to deﬁne knowledge in thephilosophical or even the popular view. As amatter of fact, knowledge in this deﬁnition ispurely user oriented and domain speciﬁc andis determined by whatever functions andthresholds the user chooses.Data mining is a step in the KDD processthat consists of applying data analysis anddiscovery algorithms that, under acceptablecomputational efﬁciency limitations, pro-duce a particular enumeration of patterns (ormodels) over the data. Note that the space ofArticlesFALL 1996 41Figure 1. An Overview of the Steps That Compose the KDD Process.methods, the effective number of variables under consideration can be reduced, or in-variant representations for the data can be found.Fifth is matching the goals of the KDD pro-cess (step 1) to a particular data-mining method. For example, summarization, clas-siﬁcation, regression, clustering, and so on,are described later as well as in Fayyad, Piatet-sky-Shapiro, and Smyth (1996).Sixth is exploratory analysis and model and hypothesis selection: choosing the data-mining algorithm(s) and selecting method(s)to be used for searching for data patterns.This process includes deciding which models and parameters might be appropriate (for ex-ample, models of categorical data are differ-ent than models of vectors over the reals) and matching a particular data-mining method with the overall criteria of the KDD process (for example, the end user might be more in-terested in understanding the model than its predictive capabilities).Seventh is data mining: searching for pat-terns of interest in a particular representa-tional form or a set of such representations,including classiﬁcation rules or trees, regres-sion, and clustering. The user can signiﬁcant-ly aid the data-mining method by correctly performing the preceding steps.Eighth is interpreting mined patterns, pos-sibly returning to any of steps 1 through 7 for further iteration. This step can also involve visualization of the extracted patterns and models or visualization of the data given the extracted models.Ninth is acting on the discovered knowl-edge: using the knowledge directly, incorpo-rating the knowledge into another system for further action, or simply documenting it and reporting it to interested parties. This process also includes checking for and resolving po-tential conﬂicts with previously believed (or extracted) knowledge.The KDD process can involve signiﬁcant iteration and can contain loops between any two steps. The basic ﬂow of steps (al-though not the potential multitude of itera-tions and loops) is illustrated in ﬁgure 1.Most previous work on KDD has focused on step 7, the data mining. However, the other steps are as important (and probably more so) for the successful application of KDD in practice. Having deﬁned the basic notions and introduced the KDD process, we now focus on the data-mining component,which has, by far, received the most atten-tion in the literature.patterns is often inﬁnite, and the enumera-tion of patterns involves some form of search in this space. Practical computational constraints place severe limits on the sub-space that can be explored by a data-mining algorithm.The KDD process involves using the database along with any required selection,preprocessing, subsampling, and transforma-tions of it; applying data-mining methods (algorithms) to enumerate patterns from it;and evaluating the products of data mining to identify the subset of the enumerated pat-terns deemed knowledge. The data-mining component of the KDD process is concerned with the algorithmic means by which pat-terns are extracted and enumerated from da-ta. The overall KDD process (ﬁgure 1) in-cludes the evaluation and possible interpretation of the mined patterns to de-termine which patterns can be considered new knowledge. The KDD process also in-cludes all the additional steps described in the next section.The notion of an overall user-driven pro-cess is not unique to KDD: analogous propos-als have been put forward both in statistics (Hand 1994) and in machine learning (Brod-ley and Smyth 1996).The KDD ProcessThe KDD process is interactive and iterative,involving numerous steps with many deci-sions made by the user. Brachman and Anand (1996) give a practical view of the KDD pro-cess, emphasizing the interactive nature of the process. Here, we broadly outline some of its basic steps:First is developing an understanding of the application domain and the relevant prior knowledge and identifying the goal of the KDD process from the customer’s viewpoint.Second is creating a target data set: select-ing a data set, or focusing on a subset of vari-ables or data samples, on which discovery is to be performed.Third is data cleaning and preprocessing.Basic operations include removing noise if appropriate, collecting the necessary informa-tion to model or account for noise, deciding on strategies for handling missing data ﬁelds,and accounting for time-sequence informa-tion and known changes.Fourth is data reduction and projection:ﬁnding useful features to represent the data depending on the goal of the task. With di-mensionality reduction or transformationArticles42AI MAGAZINEThe Data-Mining Stepof the KDD ProcessThe data-mining component of the KDD pro-cess often involves repeated iterative applica-tion of particular data-mining methods. This section presents an overview of the primary goals of data mining, a description of the methods used to address these goals, and a brief description of the data-mining algo-rithms that incorporate these methods.The knowledge discovery goals are deﬁned by the intended use of the system. We can distinguish two types of goals: (1) veriﬁcation and (2) discovery. With veriﬁcation,the sys-tem is limited to verifying the user’s hypothe-sis. With discovery,the system autonomously ﬁnds new patterns. We further subdivide the discovery goal into prediction,where the sys-tem ﬁnds patterns for predicting the future behavior of some entities, and description, where the system ﬁnds patterns for presenta-tion to a user in a human-understandableform. In this article, we are primarily con-cerned with discovery-oriented data mining.Data mining involves ﬁtting models to, or determining patterns from, observed data. The ﬁtted models play the role of inferred knowledge: Whether the models reﬂect useful or interesting knowledge is part of the over-all, interactive KDD process where subjective human judgment is typically required. Two primary mathematical formalisms are used in model ﬁtting: (1) statistical and (2) logical. The statistical approach allows for nondeter-ministic effects in the model, whereas a logi-cal model is purely deterministic. We focus primarily on the statistical approach to data mining, which tends to be the most widely used basis for practical data-mining applica-tions given the typical presence of uncertain-ty in real-world data-generating processes.Most data-mining methods are based on tried and tested techniques from machine learning, pattern recognition, and statistics: classiﬁcation, clustering, regression, and so on. The array of different algorithms under each of these headings can often be bewilder-ing to both the novice and the experienced data analyst. It should be emphasized that of the many data-mining methods advertised in the literature, there are really only a few fun-damental techniques. The actual underlying model representation being used by a particu-lar method typically comes from a composi-tion of a small number of well-known op-tions: polynomials, splines, kernel and basis functions, threshold-Boolean functions, and so on. Thus, algorithms tend to differ primar-ily in the goodness-of-ﬁt criterion used toevaluate model ﬁt or in the search methodused to ﬁnd a good ﬁt.In our brief overview of data-mining meth-ods, we try in particular to convey the notionthat most (if not all) methods can be viewedas extensions or hybrids of a few basic tech-niques and principles. We ﬁrst discuss the pri-mary methods of data mining and then showthat the data- mining methods can be viewedas consisting of three primary algorithmiccomponents: (1) model representation, (2)model evaluation, and (3) search. In the dis-cussion of KDD and data-mining methods,we use a simple example to make some of thenotions more concrete. Figure 2 shows a sim-ple two-dimensional artiﬁcial data set consist-ing of 23 cases. Each point on the graph rep-resents a person who has been given a loanby a particular bank at some time in the past.The horizontal axis represents the income ofthe person; the vertical axis represents the to-tal personal debt of the person (mortgage, carpayments, and so on). The data have beenclassiﬁed into two classes: (1) the x’s repre-sent persons who have defaulted on theirloans and (2) the o’s represent persons whoseloans are in good status with the bank. Thus,this simple artiﬁcial data set could represent ahistorical data set that can contain usefulknowledge from the point of view of thebank making the loans. Note that in actualKDD applications, there are typically manymore dimensions (as many as several hun-dreds) and many more data points (manythousands or even millions).ArticlesFALL 1996 43Figure 2. A Simple Data Set with Two Classes Used for Illustrative Purposes.。

高二英语科学家名称单选题20题

高二英语科学家名称单选题20题1.Who is known for his theory of relativity?A.NewtonB.EinsteinC.DarwinD.Galileo答案：B。

爱因斯坦以相对论闻名于世。

牛顿提出万有引力定律等；达尔文提出进化论；伽利略在天文学和物理学方面有重要贡献。

2.Which scientist is famous for his discovery of penicillin?A.FlemingB.PasteurC.CurieD.Bohr答案：A。

弗莱明因发现青霉素而闻名。

巴斯德在微生物学方面有重大贡献；居里夫人发现镭等放射性元素；玻尔在量子力学方面有重要成就。

3.Who is the scientist associated with the law of universal gravitation?A.EinsteinB.NewtonC.DarwinD.Hawking答案：B。

牛顿与万有引力定律相关。

爱因斯坦以相对论闻名；达尔文提出进化论；霍金在黑洞等领域有重要研究。

4.Which scientist is renowned for his work on evolution?A.NewtonB.DarwinC.EinsteinD.Fleming答案：B。

达尔文因在进化方面的工作而闻名。

牛顿提出万有引力定律等；爱因斯坦以相对论闻名；弗莱明发现青霉素。

5.Who is the scientist known for his research on black holes?A.HawkingB.EinsteinC.NewtonD.Darwin答案：A。

霍金以对黑洞的研究而闻名。

爱因斯坦以相对论闻名；牛顿提出万有引力定律等；达尔文提出进化论。

6.Who is known for making significant contributions to the field of agriculture in Asia?A.Yuan LongpingB.Albert EinsteinC.Thomas EdisonD.Isaac Newton答案：A。

科技创新英语试题及答案

科技创新英语试题及答案一、选择题（每题2分，共10分）1. Which of the following is NOT a characteristic of technological innovation?A. Incremental improvementsB. Radical changesC. Predictable outcomesD. High riskAnswer: C2. What is the primary goal of innovation in the field of technology?A. To increase profitsB. To solve problemsC. To reduce costsD. To entertainAnswer: B3. In the context of technology, what does the acronym "AI" stand for?A. Artificial IntelligenceB. Advanced InnovationC. Artificial ImitationD. Advanced IntegrationAnswer: A4. Which of the following is an example of disruptive technology?A. Incremental improvements to existing productsB. New products that replace existing onesC. Enhancements to existing servicesD. Minor modifications to existing processesAnswer: B5. What is the term used to describe the process of creating new ideas and products through the combination of existing technologies?A. Technological convergenceB. Technological divergenceC. Technological innovationD. Technological adaptationAnswer: A二、填空题（每空1分，共10分）6. The process of developing new products or services through the application of scientific knowledge is known as ________. Answer: innovation7. In the technology sector, ________ refers to the abilityto quickly adapt to changes and improve existing technologies. Answer: agility8. One of the key drivers of technological advancement is the need to ________.Answer: solve problems9. The use of technology to improve social and environmental conditions is known as ________.Answer: sustainable technology10. The term ________ is used to describe the integration of digital technology into everyday life.Answer: digitalization三、阅读理解题（每题2分，共20分）阅读下面的短文，回答11-15题。

James lovelock

First formulated by Lovelock during the 1960s as a result of work for NASA concerned with detecting life on Mars, the Gaia hypothesis proposes that living and non-living parts of the Earth form a complex interacting system that can be thought of as a single organism. Named after the Greek goddess Gaia at the suggestion of novelist William Golding, the hypothesis postulates that the biosphere has a regulatory effect on the Earth's environment that acts to sustain life.
尽管盖亚假说已顺利地被许多环境学家所接受，但它在科学界还没有得到广泛认可。进化生物学家理查德-道金斯、福特-杜丽特尔和史蒂芬-杰-古德是当时对盖亚学说较有名的批评家。由于对其他科学问题中给出了三重学说的多样性的看法，他们名噪一时。这些（还有其他的）批评家质疑自然选择作用于个体生物的现象如何会导致行星规模内稳态的进化。Lovelock已经用Daisyword这样的模型对这些批评作出回应，这种模型解释了个体水平的效应怎样在适合的环境中转化成整个行星的内部平衡。
About gaia
在19世纪60年代，洛夫洛克第一次提出了盖亚假说，鉴于当时他参与了美国太空总署关于探测火星生命痕迹的课题。该假说认为，地球上生命体和非生命体形成一个可相互作用的复杂系统，这个系统可以被认为是一个有机生命体。在小说家威廉·戈尔丁的建议下，这一假说以希腊女神盖亚的名字命名，它认为生物圈对地球环境有一种调节效应，正是这一效应维系着生命。

International-Human-Resource-Management国际人力资源管理 (1

International Human Resources and Multinational
Corporations
• HRM typically refers to those activities undertaken by an organisation to utilize human resources effectively,
Boxhall and Purcell give a broader definition as ‘human resources management includes the firm’s work system and its employment practices. It embraces both individual and collective aspects of people management’
• HR planning • Staffing (recruitment, selection, placement) • Performance management • Training and development • Compensation (remuneration) and benefits • Industrial relations
International Organisational structure
Chief executive/Headquarters
Production
Marketing
Finance
Personnel
International division
International geographic structure
What changes when HRM goes international?

Instructional_design

Instructional designFrom Wikipedia, the free encyclopediaInstructional Design(also called Instructional Systems Design (ISD)) is the practice of maximizing the effectiveness, efficiency and appeal of instruction and other learning experiences. The process consists broadly of determining the current state and needs of the learner, defining the end goal of instruction, and creating some "intervention" to assist in the transition. Ideally the process is informed by pedagogically(process of teaching) and andragogically(adult learning) tested theories of learning and may take place in student-only, teacher-led or community-based settings. The outcome of this instruction may be directly observable and scientifically measured or completely hidden and assumed. There are many instructional design models but many are based on the ADDIE model with the five phases: 1) analysis, 2) design, 3) development, 4) implementation, and 5) evaluation. As a field, instructional design is historically and traditionally rooted in cognitive and behavioral psychology.HistoryMuch of the foundations of the field of instructional design was laid in World War II, when the U.S. military faced the need to rapidly train large numbers of people to perform complex technical tasks, fromfield-stripping a carbine to navigating across the ocean to building a bomber—see "Training Within Industry(TWI)". Drawing on the research and theories of B.F. Skinner on operant conditioning, training programs focused on observable behaviors. Tasks were broken down into subtasks, and each subtask treated as a separate learning goal. Training was designed to reward correct performance and remediate incorrect performance. Mastery was assumed to be possible for every learner, given enough repetition and feedback. After the war, the success of the wartime training model was replicated in business and industrial training, and to a lesser extent in the primary and secondary classroom. The approach is still common in the U.S. military.[1]In 1956, a committee led by Benjamin Bloom published an influential taxonomy of what he termed the three domains of learning: Cognitive(what one knows or thinks), Psychomotor (what one does, physically) and Affective (what one feels, or what attitudes one has). These taxonomies still influence the design of instruction.[2]During the latter half of the 20th century, learning theories began to be influenced by the growth of digital computers.In the 1970s, many instructional design theorists began to adopt an information-processing-based approach to the design of instruction. David Merrill for instance developed Component Display Theory (CDT), which concentrates on the means of presenting instructional materials (presentation techniques).[3]Later in the 1980s and throughout the 1990s cognitive load theory began to find empirical support for a variety of presentation techniques.[4]Cognitive load theory and the design of instructionCognitive load theory developed out of several empirical studies of learners, as they interacted with instructional materials.[5]Sweller and his associates began to measure the effects of working memory load, and found that the format of instructional materials has a direct effect on the performance of the learners using those materials.[6][7][8]While the media debates of the 1990s focused on the influences of media on learning, cognitive load effects were being documented in several journals. Rather than attempting to substantiate the use of media, these cognitive load learning effects provided an empirical basis for the use of instructional strategies. Mayer asked the instructional design community to reassess the media debate, to refocus their attention on what was most important: learning.[9]By the mid- to late-1990s, Sweller and his associates had discovered several learning effects related to cognitive load and the design of instruction (e.g. the split attention effect, redundancy effect, and the worked-example effect). Later, other researchers like Richard Mayer began to attribute learning effects to cognitive load.[9] Mayer and his associates soon developed a Cognitive Theory of MultimediaLearning.[10][11][12]In the past decade, cognitive load theory has begun to be internationally accepted[13]and begun to revolutionize how practitioners of instructional design view instruction. Recently, human performance experts have even taken notice of cognitive load theory, and have begun to promote this theory base as the science of instruction, with instructional designers as the practitioners of this field.[14]Finally Clark, Nguyen and Sweller[15]published a textbook describing how Instructional Designers can promote efficient learning using evidence-based guidelines of cognitive load theory.Instructional Designers use various instructional strategies to reduce cognitive load. For example, they think that the onscreen text should not be more than 150 words or the text should be presented in small meaningful chunks.[citation needed] The designers also use auditory and visual methods to communicate information to the learner.Learning designThe concept of learning design arrived in the literature of technology for education in the late nineties and early 2000s [16] with the idea that "designers and instructors need to choose for themselves the best mixture of behaviourist and constructivist learning experiences for their online courses" [17]. But the concept of learning design is probably as old as the concept of teaching. Learning design might be defined as "the description of the teaching-learning process that takes place in a unit of learning (eg, a course, a lesson or any other designed learning event)" [18].As summarized by Britain[19], learning design may be associated with:∙The concept of learning design∙The implementation of the concept made by learning design specifications like PALO, IMS Learning Design[20], LDL, SLD 2.0, etc... ∙The technical realisations around the implementation of the concept like TELOS, RELOAD LD-Author, etc...Instructional design modelsADDIE processPerhaps the most common model used for creating instructional materials is the ADDIE Process. This acronym stands for the 5 phases contained in the model:∙Analyze– analyze learner characteristics, task to be learned, etc.Identify Instructional Goals, Conduct Instructional Analysis, Analyze Learners and Contexts∙Design– develop learning objectives, choose an instructional approachWrite Performance Objectives, Develop Assessment Instruments, Develop Instructional Strategy∙Develop– create instructional or training materialsDesign and selection of materials appropriate for learning activity, Design and Conduct Formative Evaluation∙Implement– deliver or distribute the instructional materials ∙Evaluate– make sure the materials achieved the desired goals Design and Conduct Summative EvaluationMost of the current instructional design models are variations of the ADDIE process.[21] Dick,W.O,.Carey, L.,&Carey, J.O.(2004)Systematic Design of Instruction. Boston,MA:Allyn&Bacon.Rapid prototypingA sometimes utilized adaptation to the ADDIE model is in a practice known as rapid prototyping.Proponents suggest that through an iterative process the verification of the design documents saves time and money by catching problems while they are still easy to fix. This approach is not novel to the design of instruction, but appears in many design-related domains including software design, architecture, transportation planning, product development, message design, user experience design, etc.[21][22][23]In fact, some proponents of design prototyping assert that a sophisticated understanding of a problem is incomplete without creating and evaluating some type of prototype, regardless of the analysis rigor that may have been applied up front.[24] In other words, up-front analysis is rarely sufficient to allow one to confidently select an instructional model. For this reason many traditional methods of instructional design are beginning to be seen as incomplete, naive, and even counter-productive.[25]However, some consider rapid prototyping to be a somewhat simplistic type of model. As this argument goes, at the heart of Instructional Design is the analysis phase. After you thoroughly conduct the analysis—you can then choose a model based on your findings. That is the area where mostpeople get snagged—they simply do not do a thorough-enough analysis. (Part of Article By Chris Bressi on LinkedIn)Dick and CareyAnother well-known instructional design model is The Dick and Carey Systems Approach Model.[26] The model was originally published in 1978 by Walter Dick and Lou Carey in their book entitled The Systematic Design of Instruction.Dick and Carey made a significant contribution to the instructional design field by championing a systems view of instruction as opposed to viewing instruction as a sum of isolated parts. The model addresses instruction as an entire system, focusing on the interrelationship between context, content, learning and instruction. According to Dick and Carey, "Components such as the instructor, learners, materials, instructional activities, delivery system, and learning and performance environments interact with each other and work together to bring about the desired student learning outcomes".[26] The components of the Systems Approach Model, also known as the Dick and Carey Model, are as follows:∙Identify Instructional Goal(s): goal statement describes a skill, knowledge or attitude(SKA) that a learner will be expected to acquire ∙Conduct Instructional Analysis: Identify what a learner must recall and identify what learner must be able to do to perform particular task ∙Analyze Learners and Contexts: General characteristic of the target audience, Characteristic directly related to the skill to be taught, Analysis of Performance Setting, Analysis of Learning Setting∙Write Performance Objectives: Objectives consists of a description of the behavior, the condition and criteria. The component of anobjective that describes the criteria that will be used to judge the learner's performance.∙Develop Assessment Instruments: Purpose of entry behavior testing, purpose of pretesting, purpose of posttesting, purpose of practive items/practive problems∙Develop Instructional Strategy: Pre-instructional activities, content presentation, Learner participation, assessment∙Develop and Select Instructional Materials∙Design and Conduct Formative Evaluation of Instruction: Designer try to identify areas of the instructional materials that are in need to improvement.∙Revise Instruction: To identify poor test items and to identify poor instruction∙Design and Conduct Summative EvaluationWith this model, components are executed iteratively and in parallel rather than linearly.[26]/akteacher/dick-cary-instructional-design-mo delInstructional Development Learning System (IDLS)Another instructional design model is the Instructional Development Learning System (IDLS).[27] The model was originally published in 1970 by Peter J. Esseff, PhD and Mary Sullivan Esseff, PhD in their book entitled IDLS—Pro Trainer 1: How to Design, Develop, and Validate Instructional Materials.[28]Peter (1968) & Mary (1972) Esseff both received their doctorates in Educational Technology from the Catholic University of America under the mentorship of Dr. Gabriel Ofiesh, a Founding Father of the Military Model mentioned above. Esseff and Esseff contributed synthesized existing theories to develop their approach to systematic design, "Instructional Development Learning System" (IDLS).The components of the IDLS Model are:∙Design a Task Analysis∙Develop Criterion Tests and Performance Measures∙Develop Interactive Instructional Materials∙Validate the Interactive Instructional MaterialsOther modelsSome other useful models of instructional design include: the Smith/Ragan Model, the Morrison/Ross/Kemp Model and the OAR model , as well as, Wiggins theory of backward design .Learning theories also play an important role in the design ofinstructional materials. Theories such as behaviorism , constructivism , social learning and cognitivism help shape and define the outcome of instructional materials.Influential researchers and theoristsThe lists in this article may contain items that are not notable , not encyclopedic , or not helpful . Please help out by removing such elements and incorporating appropriate items into the main body of the article. (December 2010)Alphabetic by last name∙ Bloom, Benjamin – Taxonomies of the cognitive, affective, and psychomotor domains – 1955 ∙Bonk, Curtis – Blended learning – 2000s ∙ Bransford, John D. – How People Learn: Bridging Research and Practice – 1999 ∙ Bruner, Jerome – Constructivism ∙Carr-Chellman, Alison – Instructional Design for Teachers ID4T -2010 ∙Carey, L. – "The Systematic Design of Instruction" ∙Clark, Richard – Clark-Kosma "Media vs Methods debate", "Guidance" debate . ∙Clark, Ruth – Efficiency in Learning: Evidence-Based Guidelines to Manage Cognitive Load / Guided Instruction / Cognitive Load Theory ∙Dick, W. – "The Systematic Design of Instruction" ∙ Gagné, Robert M. – Nine Events of Instruction (Gagné and Merrill Video Seminar) ∙Heinich, Robert – Instructional Media and the new technologies of instruction 3rd ed. – Educational Technology – 1989 ∙Jonassen, David – problem-solving strategies – 1990s ∙Langdon, Danny G - The Instructional Designs Library: 40 Instructional Designs, Educational Tech. Publications ∙Mager, Robert F. – ABCD model for instructional objectives – 1962 ∙Merrill, M. David - Component Display Theory / Knowledge Objects ∙ Papert, Seymour – Constructionism, LOGO – 1970s ∙ Piaget, Jean – Cognitive development – 1960s∙Piskurich, George – Rapid Instructional Design – 2006∙Simonson, Michael –Instructional Systems and Design via Distance Education – 1980s∙Schank, Roger– Constructivist simulations – 1990s∙Sweller, John - Cognitive load, Worked-example effect, Split-attention effect∙Roberts, Clifton Lee - From Analysis to Design, Practical Applications of ADDIE within the Enterprise - 2011∙Reigeluth, Charles –Elaboration Theory, "Green Books" I, II, and III - 1999-2010∙Skinner, B.F.– Radical Behaviorism, Programed Instruction∙Vygotsky, Lev– Learning as a social activity – 1930s∙Wiley, David– Learning Objects, Open Learning – 2000sSee alsoSince instructional design deals with creating useful instruction and instructional materials, there are many other areas that are related to the field of instructional design.∙educational assessment∙confidence-based learning∙educational animation∙educational psychology∙educational technology∙e-learning∙electronic portfolio∙evaluation∙human–computer interaction∙instructional design context∙instructional technology∙instructional theory∙interaction design∙learning object∙learning science∙m-learning∙multimedia learning∙online education∙instructional design coordinator∙storyboarding∙training∙interdisciplinary teaching∙rapid prototyping∙lesson study∙Understanding by DesignReferences1.^MIL-HDBK-29612/2A Instructional Systems Development/SystemsApproach to Training and Education2.^Bloom's Taxonomy3.^TIP: Theories4.^Lawrence Erlbaum Associates, Inc. - Educational Psychologist -38(1):1 - Citation5.^ Sweller, J. (1988). "Cognitive load during problem solving:Effects on learning". Cognitive Science12 (1): 257–285.doi:10.1016/0364-0213(88)90023-7.6.^ Chandler, P. & Sweller, J. (1991). "Cognitive Load Theory andthe Format of Instruction". Cognition and Instruction8 (4): 293–332.doi:10.1207/s1532690xci0804_2.7.^ Sweller, J., & Cooper, G.A. (1985). "The use of worked examplesas a substitute for problem solving in learning algebra". Cognition and Instruction2 (1): 59–89. doi:10.1207/s1532690xci0201_3.8.^Cooper, G., & Sweller, J. (1987). "Effects of schema acquisitionand rule automation on mathematical problem-solving transfer". Journal of Educational Psychology79 (4): 347–362.doi:10.1037/0022-0663.79.4.347.9.^ a b Mayer, R.E. (1997). "Multimedia Learning: Are We Asking theRight Questions?". Educational Psychologist32 (41): 1–19.doi:10.1207/s1*******ep3201_1.10.^ Mayer, R.E. (2001). Multimedia Learning. Cambridge: CambridgeUniversity Press. ISBN0-521-78239-2.11.^Mayer, R.E., Bove, W. Bryman, A. Mars, R. & Tapangco, L. (1996)."When Less Is More: Meaningful Learning From Visual and Verbal Summaries of Science Textbook Lessons". Journal of Educational Psychology88 (1): 64–73. doi:10.1037/0022-0663.88.1.64.12.^ Mayer, R.E., Steinhoff, K., Bower, G. and Mars, R. (1995). "Agenerative theory of textbook design: Using annotated illustrations to foster meaningful learning of science text". Educational TechnologyResearch and Development43 (1): 31–41. doi:10.1007/BF02300480.13.^Paas, F., Renkl, A. & Sweller, J. (2004). "Cognitive Load Theory:Instructional Implications of the Interaction between InformationStructures and Cognitive Architecture". Instructional Science32: 1–8.doi:10.1023/B:TRUC.0000021806.17516.d0.14.^ Clark, R.C., Mayer, R.E. (2002). e-Learning and the Science ofInstruction: Proven Guidelines for Consumers and Designers of Multimedia Learning. San Francisco: Pfeiffer. ISBN0-7879-6051-9.15.^ Clark, R.C., Nguyen, F., and Sweller, J. (2006). Efficiency inLearning: Evidence-Based Guidelines to Manage Cognitive Load. SanFrancisco: Pfeiffer. ISBN0-7879-7728-4.16.^Conole G., and Fill K., “A learning design toolkit to createpedagogically effective learning activities”. Journal of Interactive Media in Education, 2005 (08).17.^Carr-Chellman A. and Duchastel P., “The ideal online course,”British Journal of Educational Technology, 31(3), 229-241, July 2000.18.^Koper R., “Current Research in Learning Design,” EducationalTechnology & Society, 9 (1), 13-22, 2006.19.^Britain S., “A Review of Learning Design: Concept,Specifications and Tools” A report for the JISC E-learning Pedagogy Programme, May 2004.20.^IMS Learning Design webpage21.^ a b Piskurich, G.M. (2006). Rapid Instructional Design: LearningID fast and right.22.^ Saettler, P. (1990). The evolution of American educationaltechnology.23.^ Stolovitch, H.D., & Keeps, E. (1999). Handbook of humanperformance technology.24.^ Kelley, T., & Littman, J. (2005). The ten faces of innovation:IDEO's strategies for beating the devil's advocate & driving creativity throughout your organization. New York: Doubleday.25.^ Hokanson, B., & Miller, C. (2009). Role-based design: Acontemporary framework for innovation and creativity in instructional design. Educational Technology, 49(2), 21–28.26.^ a b c Dick, Walter, Lou Carey, and James O. Carey (2005) [1978].The Systematic Design of Instruction(6th ed.). Allyn & Bacon. pp. 1–12.ISBN020*******./?id=sYQCAAAACAAJ&dq=the+systematic+design+of+instruction.27.^ Esseff, Peter J. and Esseff, Mary Sullivan (1998) [1970].Instructional Development Learning System (IDLS) (8th ed.). ESF Press.pp. 1–12. ISBN1582830371. /Materials.html.28.^/Materials.htmlExternal links∙Instructional Design - An overview of Instructional Design∙ISD Handbook∙Edutech wiki: Instructional design model [1]∙Debby Kalk, Real World Instructional Design InterviewRetrieved from "/wiki/Instructional_design" Categories: Educational technology | Educational psychology | Learning | Pedagogy | Communication design | Curricula。

Sutjiadi_et_al-2014-The_Structural_Design_of_Tall_and_Special_Buildings

Structural design and analysis of vertical double-layer space structures in super-tall buildingsHendry Yahya Sutjiadi *,†and Andrew W.CharlesonSchool of Architecture,Victoria University of Wellington,New ZealandSUMMARYThis paper investigates the potential of double-layer space structures to be applied vertically as a new structural system in super-tall buildings.The investigation using case studies covers four stages:structural designs of 100-storey buildings in order to obtain internal force distributions and determine appropriate structural member sizes,analyses of the impacts of wind and seismic loads on the structures,sensitivity of structural weight ratios and lateral de ﬂection constraints to changing structural geometry,and comparison of the lateral de ﬂected shapes and structural weights per unit area with those of other current tall structural systems.The results show that changing the angles of diagonal members to make them span two storeys rather than one storey reduces structural weight and has little impact on lateral de ﬂpared with other current tall structures,vertical double-layer space structures are relatively ef ﬁcient structurally.The study concludes that double-layer space structures can be applied vertically as a structural system of super-tall buildings.Copyright ©2012John Wiley &Sons,Ltd.Received 27January 2012;Revised 4July 2012;Accepted 29September 2012KEY WORDS :structural design;space structures;tall buildings;structural systems;steel structures;vertical structures1.INTRODUCTIONDouble-layer space structures,also known as space frames,have developed from the triangle concept as the most rigid geometry (Ambrose,1994).The applications of this concept are commonly found in trusses as two-dimensional structures and in space structures as three-dimensional structures.Stevens (1975)categorizes space structures into single-layer and double-layer space structures.The three-dimensional action of single-layer space structures relies on their curved geometries,whereas the connection of the dual layers of space structures by diagonal members also create two-way action.Double-layer space structures have been commonly used for long-span horizontal structures such as domes,roofs and canopies because of their structural advantages.In these structures,gravity loads travel in two directions in the plan through all members to columns and then the foundations.This makes double-layer space structures more effective compared with planar trusses,which distribute loads in one way only (Chilton,2000)(Figure 1).As three-dimensional structures,double-layer space structures also have the potential for vertical applications in super-tall buildings.Structural systems of tall and super-tall buildings have developed from two-dimensional to three-dimensional structures to ef ﬁciently resist lateral loads.Fazlur Khan argued that a structure can be designed to be more ef ﬁcient as a three-dimensional unit (Ali,2001).Rigid frames for example,which are normally analysed as two-dimensional structures,are not ef ﬁcient for taller buildings.Three-dimensional structures,such as framed-tubes,bundled-tubes and braced-tubes,have been used in several super-tall buildings,such as World Trade Center in New York,John Hancock Center and Willis Tower,both in Chicago.*Correspondence to:Hendry Yahya Sutjiadi,School of Architecture,Victoria University of Wellington,New Zealand.†E-mail:hendryyahya@THE STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS Struct.Design Tall Spec.Build.23,512–525(2014)Published online 7November 2012in Wiley Online Library (/journal/tal).DOI:10.1002/tal.1057Structural engineers have yet to use double-layer space structures for tall and super-tall buildings.However,some building designers have considered their potential for high-rise structural systems.For example,in 1956Kahn and Tyng proposed a project for a 184.8-m-high building using space structures (Ayad,1997).The tower,named the City Tower,was a triangulated precast and pre-stressed concrete frame.This project was not executed for structural and economic reasons (Komendant,1975).In 1971,Swenson proposed a 150-storey building using a large-scale super-frame or double-layer space structure as the structural system (Swenson,1971).Unfortunately,this project also has yet to be realized.We report on an investigation of the potential application of double-layer space structures in super-tall buildings.This application leads to several issues including structural,systems integration and construction aspects.Initial research was conducted using a structural design of a 100-storey double-layer space structure building that has 48m Â48m footprint (Sutjiadi and Charleson,2010).On the basis of these results,we explored how to integrate the structure with architectural and services systems (Sutjiadi and Charleson,2011a)and the construction aspects of vertical double-layer space structures (Sutjiadi and Charleson,2011b,2011c).This paper reports on structural issues of the application of double-layer space structures in super-tall buildings,such as appropriate member sizes,the impact of wind and seismic loads to the structures and their structural ef ﬁciency when compared with other current structural systems that were not covered in the initial research.To address these issues,we conducted research in several stages.The ﬁrst stage comprises of a case study of two 100-storey buildings using double-layer space structures.The aim is to analyse force dis-tribution in the structure and determine appropriate structural member sizes.The second stage investi-gates and compares the impacts of wind and seismic loads on the structures.The next stage evaluates the sensitivity of changing structural geometry on structural weight and lateral de ﬂection.The last stage compares double-layer space structures with other current tall structural systems by considering lateral-de ﬂected shapes and structural weights per unit area.The main purpose of this research is to investigate vertical double-layer space structures in super-tall buildings from a structural perspective.2.CASE STUDY OF 100-STOREY BUILDINGSAs a case study,we conducted design and analysis of two 100-storey buildings with typical ﬂoor plan dimensions of 48m Â48m and 60m Â60m.The double-layer space structures carry a combination of lateral and gravity loads.We designed vertical double-layer space structures positioned on the building perimeters in order to maximize their capacity to resist lateral loads (Figure 2).The buildings also have horizontal double-layer space structures vertically subdividing the building into four sections (Figure 3).The horizontal double-layer space structures transfer gravity loads from suspended columns underneath them to the perimeter vertical double-layer space structures.The aim is to optimize the capacity of vertical double-layer space structures to resist the combination of gravity and lateral loads.The horizontal double-layer space structures are two ﬂoors deep.Floor-to-ﬂoor height is 4m.In the 48m Â48m building,the structural module is 8m and the distance between internal and external layers of the verticalspace Figure 1.De ﬂection of planar trusses and a space structure under concentrated loads (Chilton,2000,p.13).STRUCTURAL DESIGN AND ANALYSIS OF VERTICAL DOUBLE-LAYER SPACE STRUCTURES 513structure is 4m on all sides.The 60m Â60m building has a 10-m module and a 5-m cavity within the vertical double-layer space structure.We used ASCE7-05,Minimum Design Loads for Buildings and Other Structures (ASCE,2005),to determine building loads and load combinations consisting of dead and live gravity loads and wind lateral loads.The buildings ’location is assumed to be in Los Angeles with a wind speed of 85mph.The exposure type is B for a location in an urban area.The importance factor is 1.15basedon Figure 2.Position of a vertical perimeter double-layer spacestructure.Figure 3.Structural model.514H.Y.SUTJIADI AND A.W.CHARLESONoccupancy category III and the topographical factor is1for aﬂat area.We do not include seismic load at this stage but discuss it in a later section.The material for the double-layer space structures is steel ASTM500with50ksi or345MPa yield strength.We used AISC360-05,Speciﬁcation for Structural Steel Buildings(AISC,2005),for the steel design.For structural member proﬁles and sizes,we input a variety of rectangular hollow sections into the structural analysis program,ETABS(ETABS version9,2005).The structure has all joints pinned except the connection between adjacent gravity beams which were designed as continuous beams to minimize vertical deﬂections.Because of an expectation of minimum construction tolerances and a high accuracy of construction,geometrical irregularities were neglected.Theﬂoor slab system comprised a conventional gravity system comprising of steel I-beams and concrete over corrugated steel decks.We speciﬁed H/500as the lateral deﬂection limit.Although lateral deﬂection limits of tall buildings vary between H/200and H/800(Khan,1970),the limit H/400–H/500is generally sufﬁcient to minimize damage to cladding and non-structural internal walls in tall buildings(Taranath,2005).ETABS then analysed and designed the structures iteratively to optimize all the structural members.3.INTERNAL FORCES AND STRUCTURAL MEMBER SIZES3.1.Internal force distributionThe results from the design by ETABS show the behaviour of double-layer space structures in resisting lateral and gravity loads:1.Wind force distribution-Wind forces within the structure travel as compression and tension forces mainly through the vertical and diagonal members of the double-layer space structures to the foundation.-Wind loads acting on the structures cause overturning moments.External vertical members of the double-layer space structure make a large contribution in resisting them in compression and tension.-The double-layer space structures act as two different systems:vertical cantilevered beams and moment resisting frames(Figure4(a)).If acting upon a vertically cantilevered beam,the overturning moment generates compression and tension at the two ends of the vertical structure that are perpen-dicular to the wind load.Frame action occurs from the interaction of the horizontal andverticalFigure4.(a)Beam and moment frame action and(b)axial forces in the vertical members under windload only.STRUCTURAL DESIGN AND ANALYSIS OF VERTICAL DOUBLE-LAYER SPACE STRUCTURES515double-layer space structures.The frame resists some wind load in bending causing compression and tension at the external and internal vertical layers.The accumulation of compression at the external vertical layer on the leeward side causes larger compression on the bottom ﬂoors.On the other hand,tension from frame action reduces the compression at the internal vertical layer on the leeward side.As a result,the external vertical members sustain larger axial forces on the lower ﬂoors,but the axial forces in the internal vertical members decrease on the lower ﬂoors (Figure 4(b)).-On the ground ﬂoor,axial wind forces travel uniformly to the external columns that are perpendicular to the wind direction (Figure 5).The internal columns take a small proportion of the axial forces.Axial loads at the outer internal columns are larger than those at the middle internal columns because of the shear lag effect.-Diagonal members in the two sides of the structure parallel to the wind direction resist shear forces caused by wind load.Section A –A (Figure 6)shows the axial forces in the diagonal members along the sides parallel to the wind pression and tension increase from the top to the bottom members.Section B –B shows the difference between forces in the diagonal members in the side perpendicular to the wind direction (left side of the structure)and parallel to the wind direction (right side of the structure).2.Gravity force distribution-Gravity loads travel from steel ﬂoor beams through the suspended gravity columns to the horizon-tal double-layer space structures and then down through the perimeter structures to the foundations (Figure 7).The horizontal double-layer space structures act as deep beams/slabs,where their diagonal members resist shear force from the gravityloads.Figure 5.(a)Wind force distribution in tension (light colour)and compression (dark colour)in theground ﬂoor columns and (b)wind force distribution in the ground ﬂoor internalcolumns.Figure 6.(a)Two sides of the perimeter structure parallel to the wind load resist shear forces caused bythe wind load and (b)axial forces under wind load in the diagonal members.516H.Y.SUTJIADI AND A.W.CHARLESON3.2.Structural member sizesThe structural members have various dimensions from the bottom to the top ﬂoor.Table 1shows the structural pro ﬁles as designed by ETABS,ful ﬁlling lateral de ﬂection criteria rather than the strength limit.In many structural members,the strength demands are much less than the strength capacities.The average demand to capacity (D/C)ratio of all structural members is 0.52.This occurs because the lateral displacement target H/500is more critical than the strengthlimit.Figure 7.(a)Gravity loads travel to the foundation and (b)axial gravity forces in the structure.Table 1.Structural member dimensions of the double-layer space structures (all dimensions in millimetres).60m Â60m building48m Â48m building Vertical double-layer space structure1External layerTop ﬂoorBox 350.350.35Box 300.300.30Bottom ﬂoorBox 1100.1100.110Box 950.950.952Internal layerTop ﬂoorBox 400.400.40Box 350.350.35Bottom ﬂoorBox 900.900.90Box 900.900.903Diagonal membersTop ﬂoorBox 450.450.45Box 450.450.45Bottom ﬂoorBox 550.550.55Box 750.750.754Horizontal membersTop ﬂoorBox 250.250.25Box 250.250.25Bottom ﬂoorBox 300.300.30Box 300.300.30Horizontal double-layer space structureTop layerBox 500.500.50Box 300.300.30Bottom layerBox 550.550.55Box 400.400.40Diagonal membersBox 700.700.70Box 500.500.50Suspended columnsTop ﬂoor in a zoneTube 1100.110Tube 800.80Bottom ﬂoor in a zoneTube 200.20Tube 200.20STRUCTURAL DESIGN AND ANALYSIS OF VERTICAL DOUBLE-LAYER SPACE STRUCTURES 517TERAL LOAD ANALYSESSince lateral loads dominate the structural design of super-tall buildings,it is necessary to elaborate upon the design of vertical double-layer space structures for wind and seismic loads and compare the base shears.4.1.Wind loadThe design of 100-storey double-layer space structures for wind load follows the second of three methods from Minimum Design Loads for Buildings /ASCE 7-05(ASCE,2005,pp.21–30).The method is for regular-shape buildings.On the basis of the assumptions explained above,the design parameters are as follows:•Basic wind speed,V =85mph •Gust effect factor,G f =1.61•External pressure coef ﬁcient,C p =0.8(windward wall)and C p =À0.5(leeward wall)The wind load has a vertical curved distribution on the windward side and is linear on the leeward side.The design adopts two calculation methods,using a spreadsheet and by ETABS.From the spreadsheet,the total wind loads for the 48m Â48m building are V =61831kN consisting of 34314kN for windward and 27517kN for ing the same steps above,the total wind load on the 60m Â60m building is V =77289kN.These results are essentially identical to those from ETABS.4.2.Seismic loadThis section discusses the static and dynamic seismic analyses of the vertical double-layer space struc-tures for the two 100-storey buildings based upon Minimum Design Loads for Buildings /ASCE 7-05(ASCE,2005),combined with the International Building Code (IBC,2006).The design parameters are as follows:•Location:Los Angeles City,California;•Site Class D for unknown soil properties;•Occupancy Category III;•Site spectral accelerations,the National Seismic Hazard Maps (USGS,2008),S S =1.20g and S 1=0.40g ,S DS =0.82g and S D1=0.43g (Figure 8).For the static analysis,the calculation for the seismic base shear is from V =C s ÂW .The value of C s follows S DS /(R /I ),but needs not exceed S D1/(T (R /I )).The T value or natural period is obtained from the ETABS calculation,the I value is 1.25for occupancy category III (ASCE,2005,p.116).However,ASCE (2005,p.120)does not provide an R value for vertical double-layer space structures.We determined R =3.25,on the basis of the assumption that an ordinary steel concentrically braced frame is the closest system to a vertical double-layer spacestructure.Figure 8.Response spectrum.518H.Y.SUTJIADI AND A.W.CHARLESONSTRUCTURAL DESIGN AND ANALYSIS OF VERTICAL DOUBLE-LAYER SPACE STRUCTURES519 The dynamic analysis uses complete quadratic combination(CQC)as the modal combination method and the square root of the sum of the squares(SRSS)as the directional combination method (ASCE,2005,p.132).A damping ratio of0.01is appropriate for the steel buildings(Irvine,2004; Tamura,2010).Table2summarizes the results from the static and dynamic analysis using ETABS. The periods of vibration in one direction(the building is symmetrical)for theﬁrst four modes were 6.1s,1.7s,0.88s and0.58s,respectively,with92%of mass participation.In order to check if the results were reasonable,we reviewed literature on the seismic analysis of existing tall buildings for comparison.The C value of the structure is4.5%(Table2).According to Taranath(2005),the base shear of a60-storey steel moment frame building in California is about 4%of the building mass.Theﬁrst mode building periods from ETABS are about6s,which are similar to the5.7s period of the421-m Jin Mao building,Shanghai(Taranath,2005)and6.21s period of the Taipei101,508-m high(Fan et al.,2009).The base shear from the static analysis is greater than that of the dynamic analysis,which provides more accurate results by including numerous modal and directional combinations,which represent the dynamic behaviour more closely.4.3.The comparison of wind and seismic loadsThis section compares wind and seismic loads in the48mÂ48m and the60mÂ60m vertical double-layer space structure buildings.Table3shows the base shears from the wind and seismic dynamic ana-lysis as explained previously.In the60mÂ60m building,the seismic base shear is lower than that of the48mÂ48m building because the building period is longer even though the large building is heavier.The comparison also shows that the wind base shears are slightly greater than the seismic base shears,which is often the case in the structural designs of tall buildings(Willford et al.,2008).In this case study,the assumed building location is Los Angeles City,which is in an area of high earthquake intensity.Although these designs were driven by the lateral deﬂection limit,it is recommended that further research be conducted on the structural performance for the more severe performance states using non-linear dynamic analysis and assessment.As mentioned previously,the R value or the ductility of vertical double-layer space structures is not covered by the building code(ASCE,2005).The seismic base shears in this calculation follow the assumption of a structural ductility of R=3.25.Further research needs to be conducted to determine if this ductility is appropriate for vertical double-layer space structures.parison of the static and dynamic analyses.Item Period(s)C BaseNotesshear(kN)Static analysisT is based on ASCE 6.110.04577920I and R factors are included in the base shear Dynamic analysisCQC modal combination+SRSS6.1059926I and R factors are included in the base shear directional combinationTable3.Base shears from wind and seismic dynamic analyses.No.Item Period(s)Base shear(kN) Building48mÂ48m1Seismic load 6.37599262Wind load61831 Building60mÂ60m1Seismic load7.05547812Wind load77289Vertical double-layer space structures have several similar characteristics to braced tubes.Both these structural systems have diagonal structural members that transfer the building shear,high levels of rigidity and the same lateral de ﬂection patterns illustrating cantilever action.According to Fazlur Khan,a braced tube is a rigid system and insuf ﬁciently ductile in high-seismic zones (Khan,2004).On the basis of this opinion,the authors do not recommend vertical double-layer space structures used for super-tall buildings in high-seismic areas until further research on their structural ductility is conducted.5.SENSITIVITY ANALYSISThis section analyses the structural sensitivity to lateral loads by changing several member sizes and then comparing the different structural weights and lateral de ﬂections with the original condition.The aim is to investigate structural modi ﬁcations that signi ﬁcantly impact on structural de ﬂection and weight in order to provide an optimum design.As a case study,we designed another 100-storey double-layer space structure as a benchmark.The design followed the same procedure as discussed previously but the geometry of the structure was slightly different to the previous model (Figure 9):•The ﬂoor plan is 64m Â64m.The structure consists of eight bays at 8m each bay.•The lateral system is a vertical double-layer space structure,and the gravity system comprises mo-ment frames connected by pinned joints to the lateral system.Moment frames reduce beam de ﬂec-tions caused by gravity loads compared with a post-and-beam structure.•We did not use horizontal double-layer space structures and suspended columns in this design.The aim is to analyse the behaviour of the vertical double-layer space structure to wind load without any contribution from the horizontal double-layer space structures.We also designed ﬁve other models with different structural member sizes and diagonal member slope.The six models have varied structural con ﬁgurations as follows (Table 4):•Model 1is used as the benchmark.The external and internal vertical,horizontal and diagonal members have the same cross-sections for every storey up thebuilding.Figure 9.Idealization of the six structural models.520H.Y.SUTJIADI AND A.W.CHARLESON•Models 2and 3have smaller internal vertical members,but the diagonal members have the same size and slope.•Models 4and 5use smaller cross-sectional areas for the diagonal members,but the same size of vertical members and slope of diagonal members.•Model 6is identical to Model 1,except the diagonal members span two storeys rather than one.ETABS analysed the six models.The purpose was to investigate the effect of changing structural member sizes and diagonal members ’slopes on reduction of structural weight and additional lateral de ﬂection.Each structural member ful ﬁlled the strength limitation,but the de ﬂection limit of H/500was waived.Table 5shows the comparison of the lateral de ﬂections and structural weights of the six models.The different rooftop de ﬂections between the last ﬁve models and the ﬁrst model are in millimetres (column c)and percentages (column d).The table shows the different structural weights in kilonewton (column f)and percentages (column g).The ﬁnal column compares the de ﬂections and structural weight percentage differences between the last ﬁve models and the ﬁrst model using the d W /d d ratio (column h).High d W /d d ratios are desirable because they show that high structural weight reduction does not signi ﬁcantly increase lateral de ﬂection.The results show that Model 6,with a changed diagonal member angle,has the largest d W /d d ratio.This means a high structural weight reduction,but a low additional lateral de ﬂection.This is the most desirable of the structural changes.Models 4and 5,in which diagonal member sizes are varied,have the smallest d W /d d ratio;structural weight reduction signi ﬁcantly increases lateral de ﬂection.This is an undesirable structural change that should be avoided.This sensitivity analysis shows that changing the angle of the diagonal members has a high sensitivity on structural weight and a low sensitivity on lateral de ﬂection.Further research on varying the con ﬁgura-tions of double-layer space structures is recommended to investigate their most effective geometries.Table 4.Varied structural components of the six models.ModelExternal vertical members Internal vertical members Horizontal members Diagonal members 1st model:the benchmarkBox 0.90/0.090Box 0.90/0.090Box 0.30/0.030Box 0.40/0.040A =0.29m 2A =0.29m 2A =0.032m 2A =0.058m 22nd model:smaller internalvertical membersBox 0.90/0.090Box 0.80/0.080Box 0.30/0.030Box 0.40/0.040A =0.29m 2A =0.023m 2A =0.023m 2A =0.058m 23rd model:smaller internalvertical membersBox 0.90/0.090Box 0.70/0.070Box 0.30/0.030Box 0.40/0.040A =0.29m 2A =0.18m 2A =0.032m 2A =0.058m 24th model:smallerdiagonal membersBox 0.90/0.090Box 0.90/0.090Box 0.30/0.030Box 0.35/0.035A =0.29m 2A =0.29m 2A =0.032m 2A =0.044m 25th model:smallerdiagonal membersBox 0.90/0.090Box 0.90/0.090Box 0.30/0.030Box 0.30/0.030A =0.29m 2A =0.29m 2A =0.032m+A =0.032m 26th model The diagonal members have a different angle and span two storeysTable 5.Different lateral de ﬂections and structural weights of the six models.Model De ﬂectionStructural weight d W /d d ratio mm d d (mm)d d (%)kN d W (kN)d W (%)ab c d e f g h =g/d 1st model:the bench mark7562780002nd model:smaller internal vertical members78125 3.3120900068325 3.140.953rd model:smaller internal vertical members815597.80263000114917 5.280.684th model:smaller diagonal members8448811.6429300084688 3.890.335th model:smaller diagonal members99624031.752230001551457.120.226th model:the diagonal members have adifferent angle and span two storeys77317 2.2529200085604 3.93 1.75STRUCTURAL DESIGN AND ANALYSIS OF VERTICAL DOUBLE-LAYER SPACE STRUCTURES 521PARISON WITH OTHER STRUCTURAL SYSTEMSIn order to investigate the structural efﬁciency of vertical double-layer space structures,we compared these structures with three other structural systems:a bundled tube,a braced tube and a diagrid(Figure10). In all of these structural systems,including vertical double-layer space structures,the majority of the lateral load-resisting structural components are at the building perimeter(Ali and Moon,2007).The three structural systems have different and unique characteristics:1.Bundled tube:the lateral structural rigidity is from the stiffness of the deep beams and columnsacting as a perforated tube(Iyengar,1972).2.Braced tube:the vertical truss action of the corner columns and perimeter diagonal braces providesthe lateral structural rigidity(Khan,2004).3.Diagrid:this structural system does not have exterior columns.The triangulated pattern formed bythe horizontal and diagonal members carries both gravity and lateral loads(Moon et al.,2007). The comparison between the double-layer space structures and the three other structural systems includes lateral deﬂection proﬁles,steel weight per area and structural member sizes.The aim is to determine the efﬁciency of vertical double-layer space structures compared with other structural systems. Bundled and braced tubes have been used as structures of existing super-tall buildings,whereas the diagrid is a relatively new structural system for tall buildings.The design of each structure consists of twoﬂoor areas,48mÂ48m and60mÂ60m.The structural designs by ETABS follow the same procedures as the designs of the vertical double-layer space structures that cover the combination of gravity and wind loads.teral-deﬂected shapesThe four structural systems have different lateral-deﬂected shapes as follows(Figure11):1.The deﬂected shape of the double-layer space structure forms a curve,showing that this structureresponds more in bending than in shear.2.In the bundled tube,the lateral deﬂection pattern is linear.This structure deforms predominantlyin shear.Figure10.Structural idealization of the bundled tube,braced tube and diagrid structures.。

艺术体制理论的社会学解读

贝克尔的“艺术世界”[1] Becker, Art Worlds, 162.[2] 转引自Edward Skidelsky, “But is It Art? A New Look at the Institutional Theory of Art,” Philosophy 82, No.320 (Apr. 2007): 265.[3] Becker, Art Worlds, 163.[4] 丹托提出的是“artworld”，本文翻译为“艺术界”；而贝克尔提出的是“art world”，本文翻译为“艺术世界”作为区分。

[5] Victoria D. Alexander, Sociology of the Arts: Exploring Fine and Popular Forms (Maleden&Oxford: Blackwell, 2003), 69.[6] Becker, Art Worlds, 13.《艺术的法则：文学场的生成与结构》中译本将“artworld ”译成了术世界”，本文改成了“艺术界”，方便与贝克尔的“艺术世界”概念进行区分。

参见Pierre Bourdieu, The Rules of Art: Genesis and Structure of the Literary Field (CA: Stanford University Press. 1996), 287；[法]皮埃尔·布尔迪：《艺术的法则：文学场的生成与结构（新修订本）》，刘晖译，中央编译出版社2011年版，第271页。

皮埃尔·布尔迪厄著，《艺术的法则：文学场的生成与结构The Rules of Art: Genesis and Structure of the Literary Field ）。

Gaydar

SexualitVol. 8, No. 1, pp. 60-86.
60
Gaydar: Eye-Gaze as Identity Recognition
61
with straight peoplefamiliaP with gay/lesbian culture have an innate remote detector that picks up the behavior of individuals within a specified range. If the behavior experienced is consistent with the shared social meaning of identity associated with membership in the gay culture, Gaydar is triggered. The receiver of the stimuli is then of the opinion that the person whose behavior caused the "blip" in Gaydar is gay. 2 Studies that relate to Gaydar are important in the social sciences as such studies explore how humans define their social identity via communication rituals. These studies tell us that identity is, in Mead's (1934) terms, interpreted within the interaction. I treat Gaydar in this paper as a folk concept used by the cultural milieu around and within the gay, lesbian, and bisexual community to name the interactive process within which recognition emerges. In other words, Gaydar is simply used to label particular social meanings around an organization of behavior. Gaydar is created, named, and reified by the gay community within the interactive process. Communication scholars had long expounded on the importance of eye-gaze as a communicative force of identity, recognition, or objectification within the gay community (Darsey, 1981 ; Majors, 1992; Ringer, 1994), yet little or no work had been done to measure its significance as a social identity recognition tool. My intention here is to explore how gays and lesbians use eye-gaze as identity recognition. The purpose of my report is twofold. First, I investigate the existence of eye-gaze in relation to Gaydar in general and second, I discuss the results of an ethnography which concluded that eye-gaze is indeed associated with Gaydar use. I use the term "Gaydar Gaze" to reference eye-gaze related to identity recognition within the gay and lesbian culture. In the first section of the paper I present a brief overview of the development of gay and lesbian identity recognition studies within the communication field, followed by a discussion on the nature of Gaydar. A report on studies related to eye-gaze and how it may relate to Gaydar research is also supplied. I continue with a discussion of the findings of the ethnography on Gaydar operations in relation to eye-gaze. In this report, I include a discussion on gaze types such as the direct or broken stare, the levels of consciousness

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Multispectral satellite imagery
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Additional IDL applications are available from a variety of different independent, private and public sources
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VISualize 2012, Washington, DC
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