Google map 无疑凝聚了许多当代最先进的科学和技术。前一阵子听说有人还用它做出了科学上的重要发现 — 从 googlemap 上他看到有一块绿色的区域没有任何标识，于是他按图索骥找到那个地方，发现了一处不为人知的热带雨林。 我们虽然不期待做出重大发现，但是透过精美的 google map 图片，能够看到其中蕴含的一些物理规律还是非常有趣的。这篇博文要讲的就是 google 的卫星拍下来的船在水面留下的波纹。 如图一所示，在一些港口，河流附近，很容易发现一些非常清晰的行船照片。图一来自上海黄浦江。我们注意到船的后面留下了一个精致的波纹图案。这个被称作兴波（ wake ）的波纹与许多人熟悉的“马赫角”有些关系。 图一：黄浦江上的船波。 当飞机，子弹等以超音速在空气中飞行时，它们所激发起来的声波被落在了身后，如图二所示。子弹在 A 点处激发起来的各种频率的声波向外扩张（图中以 A 为圆心的圆代表了声波的波前），由于各种频率的波具有相同的速度，所以它们齐头并进。当 t 时刻，子弹到达 B 点时，声波的波前到达了 O 点。 BO 两点的连线与 AB 的夹角满足 , 即声波速度与子弹速度之比。此角为马赫角。容易证明， AB 连线上其他点所发出的声波的波前在 t 时刻也到达了 BO 连线上。所以所有声波的能量都被局限在了马赫角内。通过测量马赫角的大小，以及对声波速度的了解（大约三百多米每秒），我们就能推断出子弹的速度来。图三上图显示了子弹的马赫角，而图三下图则显示了飞机的速度刚刚超过声速时，突破所谓“音障”，此时马赫角 度（图片来自网站 http://www.acs.psu.edu/drussell/Demos/doppler/doppler.html ）。 图二：马赫角。 图三：马赫角的实例。 那么我们能否通过测量船身后的波纹所形成的夹角来推算出船的速度呢？一百多年前，伟大的开尔文勋爵告诉我们这是不可能的。他的计算显示，不论船的速度大小，其波纹形成的夹角始终为 39 度多一点。因此船的波纹被称为 Kelvinwave 。 那么船在水面的波纹与子弹在空气中的“波纹”为什么会有如此大的差别呢？这完全来自于空气和水的不同色散关系。空气中所有声波，不论波长的长短（在一定的波长范围内），都有相同的声速，这被称为没有色散的介质；而水面上的波纹，波长越长的速度越大（适用于深水区），这被称为有色散的介质。 图四显示了这两种介质的不同。图四上图是空气中的情形 ，下图是水面的情形。空气中，由于各种声波的速度都一样，在某一时刻， O 点处接收到的声波都是从 A 点发出的，从其他点发出的声波无法达到 O 点（如图中的红色圆圈代表从 C 点发出的声波），所以 BO 连线上的点总是能接收到单一波源发出的很强的声波，不会由于不同波源之间的干涉而减弱。而水面的情况就不一样了。因为任何一点发出的水波的波长有长有短，波速也有快有慢，所以从 A 点发出的某一种波长的水波和从 C 点发出的更长波长的水波可以在 O 点相遇形成干涉。实际上， AB 连线上的各点发出的波都有可能在 O 点相遇，通常情况下由于这些波的相位，波长和波速千差万别，它们会在那里干涉相消，从而看不到波纹。 图四：无色散与有色散的介质。 可以计算表明（见参考资料 1 ， 2 ），水面上从 A 点发出的各种波长的波，与其他点发出的波干涉之后，只在一个有限的区域内不会干涉相消，这个区域如图五所示，它是一个圆， A 是圆周上的一点，圆的直径 AC 等于船行驶距离 AB 的一半。这样，所有的水波都被局限在了一个夹角内，其角度 满足 ，即 约为 19 度，也就是说 BO 和 BO’ 之间的角度为 38 度左右。图一中黄浦江上的船后面形成的波纹夹角就为 35 度左右，考虑到一些测量和照片的误差，已经非常接近理论预期了。 图五：计算船波夹角。 用某个物理学家的话说，无论是一只鸭子还是一艘万吨巨轮，其身后留下的波纹形状都是类似的。物理之美也许就体现在这里了吧！ 参考资料： 1：Scientific American 1988 年的相关文章 http://jesseenterprises.net/amsci/1988/02/1988-02-fs.html 2：H. D. Keith, Simplified Theory of Ship Waves, American Journal of Physics, 25, 466(1957)
The Obama administration is planning a decade-long scientific effort to examine the workings of the human brain and build a comprehensive map of its activity, seeking to do for the brain what the Human Genome Project did for genetics . The project, which the administration has been looking to unveil as early as March, will include federal agencies, private foundations and teams of neuroscientists and nanoscientists in a concerted effort to advance the knowledge of the brain’s billions of neurons and gain greater insights into perception, actions and, ultimately, consciousness. Scientists with the highest hopes for the project also see it as a way to develop the technology essential to understanding diseases like Alzheimer’s and Parkinson’s , as well as to find new therapies for a variety of mental illnesses. Moreover, the project holds the potential of paving the way for advances in artificial intelligence. The project, which could ultimately cost billions of dollars, is expected to be part of the president’s budget proposal next month. And, four scientists and representatives of research institutions said they had participated in planning for what is being called the Brain Activity Map project. The details are not final, and it is not clear how much federal money would be proposed or approved for the project in a time of fiscal constraint or how far the research would be able to get without significant federal financing. In his State of the Union address , President Obama cited brain research as an example of how the government should “invest in the best ideas.” “Every dollar we invested to map the human genome returned $140 to our economy — every dollar,” he said. “Today our scientists are mapping the human brain to unlock the answers to Alzheimer’s. They’re developing drugs to regenerate damaged organs, devising new materials to make batteries 10 times more powerful. Now is not the time to gut these job-creating investments in science and innovation.” Story C. Landis, the director of the National Institute of Neurological Disorders and Stroke, said that when she heard Mr. Obama’s speech, she thought he was referring to an existing National Institutes of Health project to map the static human brain. “But he wasn’t,” she said. “He was referring to a new project to map the active human brain that the N.I.H. hopes to fund next year.” Indeed, after the speech, Francis S. Collins, the director of the National Institutes of Health, may have inadvertently confirmed the plan when he wrote in a Twitter message : “Obama mentions the #NIH Brain Activity Map in #SOTU.” A spokesman for the White House Office of Science and Technology Policy declined to comment about the project. The initiative, if successful, could provide a lift for the economy. “The Human Genome Project was on the order of about $300 million a year for a decade,” said George M. Church , a Harvard University molecular biologist who helped create that project and said he was helping to plan the Brain Activity Map project. “If you look at the total spending in neuroscience and nanoscience that might be relative to this today, we are already spending more than that. We probably won’t spend less money, but we will probably get a lot more bang for the buck.” Scientists involved in the planning said they hoped that federal financing for the project would be more than $300 million a year, which if approved by Congress would amount to at least $3 billion over the 10 years. The Human Genome Project cost $3.8 billion. It was begun in 1990 and its goal, the mapping of the complete human genome, or all the genes in human DNA, was achieved ahead of schedule, in April 2003. A federal government study of the impact of the project indicated that it returned $800 billion by 2010. The advent of new technology that allows scientists to identify firing neurons in the brain has led to numerous brain research projects around the world. Yet the brain remains one of the greatest scientific mysteries. Composed of roughly 100 billion neurons that each electrically “spike” in response to outside stimuli, as well as in vast ensembles based on conscious and unconscious activity, the human brain is so complex that scientists have not yet found a way to record the activity of more than a small number of neurons at once, and in most cases that is done invasively with physical probes. But a group of nanotechnologists and neuroscientists say they believe that technologies are at hand to make it possible to observe and gain a more complete understanding of the brain, and to do it less intrusively. In June in the journal Neuron, six leading scientists proposed pursuing a number of new approaches for mapping the brain. One possibility is to build a complete model map of brain activity by creating fleets of molecule-size machines to noninvasively act as sensors to measure and store brain activity at the cellular level. The proposal envisions using synthetic DNA as a storage mechanism for brain activity. “Not least, we might expect novel understanding and therapies for diseases such as schizophrenia and autism ,” wrote the scientists, who include Dr. Church; Ralph J. Greenspan, the associate director of the Kavli Institute for Brain and Mind at the University of California, San Diego; A. Paul Alivisatos, the director of the Lawrence Berkeley National Laboratory; Miyoung Chun, a molecular geneticist who is the vice president for science programs at the Kavli Foundation; Michael L. Roukes, a physicist at the California Institute of Technology; and Rafael Yuste, a neuroscientist at Columbia University. The Obama initiative is markedly different from a recently announced European project that will invest 1 billion euros in a Swiss-led effort to build a silicon-based “brain.” The project seeks to construct a supercomputer simulation using the best research about the inner workings of the brain. Critics, however, say the simulation will be built on knowledge that is still theoretical, incomplete or inaccurate. The Obama proposal seems to have evolved in a manner similar to the Human Genome Project, scientists said. “The genome project arguably began in 1984, where there were a dozen of us who were kind of independently moving in that direction but didn’t really realize there were other people who were as weird as we were,” Dr. Church said. However, a number of scientists said that mapping and understanding the human brain presented a drastically more significant challenge than mapping the genome. “It’s different in that the nature of the question is a much more intricate question,” said Dr. Greenspan, who said he is involved in the brain project. “It was very easy to define what the genome project’s goal was. In this case, we have a more difficult and fascinating question of what are brainwide activity patterns and ultimately how do they make things happen?” The initiative will be organized by the Office of Science and Technology Policy, according to scientists who have participated in planning meetings. The National Institutes of Health, the Defense Advanced Research Projects Agency and the National Science Foundation will also participate in the project, the scientists said, as will private foundations like the Howard Hughes Medical Institute in Chevy Chase, Md., and the Allen Institute for Brain Science in Seattle. A meeting held on Jan. 17 at the California Institute of Technology was attended by the three government agencies, as well as neuroscientists, nanoscientists and representatives from Google, Microsoft and Qualcomm. According to a summary of the meeting, it was held to determine whether computing facilities existed to capture and analyze the vast amounts of data that would come from the project. The scientists and technologists concluded that they did. They also said that a series of national brain “observatories” should be created as part of the project, like astronomical observatories.
http://cimewww.epfl.ch/people/stadelmann/jemsWebSite/HREMImageMap.html 6. HREM image map The simulation of High Resolution Electron microscope image maps is possible by either the multislice or the Blochwave methods. 6.1 Multislice method After loading or creating a crystal file, activate the "Multislicer" window (menu Image, item Multisclice or Alt+I, Ctrl+M), this will open the multislicer window as shown on figure 6.1 : Figure 6.1 (Multislicer) The multislicer consists in 6 different programs that perform the following calculation for a specific crystal and a specific zone axis direction. Fresnel propagator generates and shows the complex Fresnel propagator Phase object function generates and shows the complex phase object function Projected potential generates and shows the complex projected potential Atom position generates and shows the projected position of the atoms HREM map calculates High Resolution Electron Microscope image maps Super-cell image calculates High Resolution Electron Microscope images Direct access to the microscope selection dialog , the specimen settings dialog and the transfer function window . Further more the generated images can be saved using the save dialog . The real part of the Fresnel propagator for ZnO in the direction is calculated by clicking on the start button. It is shown on figure 6.2: Figure 6.2 (ZnO Fresnel propagator) Clicking on the imaginary "tab" would display its imaginary part. The complex phase object image is obtained by selecting the "Phase object function" tab and clicking the start button as for the Fresnel propagator. Its imaginary part (the weak phase object) is shown on figure 6.3 where Ce atoms are located at the largest and brightest spots position (Ce and O have very different atomic form factors that result in a much larger projected potential for Ce than for O). A mouse click on the bright dots identifies the atom column. Figure 6.3 (Phase object function) The positions of the cerium atoms are given on figure 6.4: Figure 6.4 (Ce positions) The positions of the oxygen atoms or of both the Cerium and oxygen would be obtained by selecting the "O" or "All" tab. These images can be saved as .gif images by activating the icon. The image of the O atoms is: Figure 6.5 (O position image) Finally, in order to calculate HREM map it is necessary to define several parameters for the multislice iteration, the imaging parameters and the illumination. The HREM map dialog is shown on figure 6.6. At its top left corner one finds icons that allows to save the HREM map or beam plot, , to print the HREM map or beam plot, , to adjust the microscope settings, , to change the specimen orientation, , and to work out the transfer conditions, . Figure 6.6 (HREM dialogue) The illumination conditions, imaging parameters and iteration parameters are organized in three panels. The illumination panel contains two sliders that controls the coherence of the illumination: spatial coherence : half-convergence of the illumination (mrad), temporal coherence : defocus spread (nm), plot of the transfer function with the reflections transmitted in the selected zone axis (indexing is done by positioning the mouse on a line and clicking). The imaging panel contains six controls: Defocus min / nm is set to 94 : the first image is calculated for a defocus of 94 nm (in jems as the "z" axis is pointing up, i.e. towards the electron source, positive defocus is under focus). Defocus step / nm is set to 2 : the defocus between to images of a map's row will be 4 nm. Defocus number is set to 8 : each map row will contains 16 images. Image dup-x is set to 2 : individual images are repeated 2 times in the horizontal direction. Image dup-y is set to 2 : individual images are repeated 2 times in the vertical direction. Noise % is set to 20 % : random noise of value 0.20 is added to the calculated images The iteration panel contains three controls: Start after is set to 2 : the first HREM image is calculated for a crystal 2 unit cells thick in the selected direction. Number is set to 8 : the image map will contain 16 rows of images each row for a different thickness. Increment is set to 2 : between two rows the thickness increases by 2 unit cells. and an option box that at present allows to make image maps with the projected potential, wave function and HREM images placed side-by-side. The beam plot box controls the type (if any) of the beam plot: conventional amplitude versus thickness, Argand or diffraction. Furthermore, Transmission Cross-Coefficients are used to produce the images, a Plot of the beams' amplitude and phase is required. Clicking on the start button fires the calculation. After about 7 seconds (5 reflections transmitted by the objective aperture, Pentium III at 450 MHz), the image map is put into the map window and into a dedicated window. The calculation time is very much dependent on the number of beams that participate to the image formation. This number is selected using the microscope dialog . The HREM map dialog is now shown on figure 6.7. Using the scroll bars located below and on the right of the map, all the map can be inspected. Figure 6.7 HREM dialogue after calculation (Au 300 kV, the wave function at 2.4 nm is highlighted) Selecting the Plot "tab" would show either a amplitude/phase versus thickness plot, an Argand plot (complex plot) or the dynamical diffraction patterns for 2, 4, 6, .. unit cells thick Al crystals. Figure 6.8 shows the thickness plot, 6.9 the Argand plot and 6.10 the diffraction pattern. The slider on the left of the plots changes the beam that is shown (thickness/Argand plot) or the thickness of the diffraction pattern. The slider on the right changes the gamma correction used for the gray lookup table. Figure 6.8 Intensity versus thickness plot (Au 300kV) Figure 6.9 Argand plot (Au 300 kV) Figure 6.10 Dynamical diffraction plot (Au 300 kV, only the beams passing through the "objective aperture" are shown) The image of the dedicated HREM montage map window is shown on figure 6.11: Figure 6.11 HREM map image with identified atomic columns The icons at its top left are used to print , save or to overlay thickness/defocus information on a HREM map as show below on figure 6.12: Figure 6.12 (Annotated HREM map image) Moving the mouse on the window will also show the defocus/thickness values of the individual images. A click of the mouse on the atomic column position identifies the column, Figure 6.13 shows a very large map of Si at 200 kV, direction that contains image features (extra "lattice" planes) due to interference effects between the reflections (non-linear imaging effects). Figure 6.13 (HREM map image Si 200 kV, The effect of the objective lens aberrations like astigmatism (2- and 3-fold), coma, image shift and specimen vibration and drift can be imaged interactively (see paragraph 6.4 ). Images of crystalline defects are calculated when the super-cell image panel is selected (see paragraph 6.5 ). 6.2 Blochwave method The Blochwave method proceeds in the same spirit, but could be much faster for high zone axis. Furthermore High Order Laue Zone reflections are included in the calculation of the exit wave function. The HRTEM dialogue is part of the larger Blochwaver dialogue. Figure 6.14 shows the panel as it appears after selection: Figure 6.14 (HRTEM map - Blochwaver dialogue) It is organized in two sub-panels, the left sub-panel controls the calculation, the right sub-panel shows the calculation results. The "Beam plot" box controls the plot of the amplitude/phase of the diffracted beam (either amplitude/phase versus thickness, complex or diffraction). On the sides of the diffraction sub-sub-panel, the left slider controls the camera length, the right slider the deviation parameter (i.e. the number of reflections considered). The green cross is positioned at the Laue Circle Center. Selecting it, and moving it under mouse control allows to change the tilt of the illumination. The orientation of the specimen, as well as the number of Laue zones considered or the foil normal are controlled by the specimen dialogue, . The microscope dialogue, , allows to change the accelerating voltage, the size of the objective aperture and the position of the optical axis. On the left of the HREM map panel, the "Illumination" panel allows to control the image formation under coherent, partially coherent (envelope functions) or partially coherent using Transmission Cross Coefficients. Two sliders allow to modify the incident beam convergence (spatial coherence) and the defocus spread (temporal coherence). The reflections (up to 0.1 nm) are shown on the transfer function plot. They are indexed using the mouse. The imaging panel is shown on figure 6.15. The 6 sliders are used to calculate series of defocused images, the first image is calculated at "Defocus min", the subsequent at a defocus increased by "Defocus step". The series size is "Defocus number", each image is duplicated horizontally by "Image dup-x" and vertically by "Image dup-y". Some random noise can be added to the image. On figure 6.15, the center of the Laue circle (crystal tilt) has been moved to (1,1,0). Reflections (2,0,0), (0,2,0) and (2,2,0) are at Bragg condition. Clicking on a disk will display the reflection indices distance to (0,0,0) and deviation. Figure 6.15 (Imaging panel of the HRTEM map panel) Having adjusted the imaging conditions, the dynamical calculation parameters are selected using the "Iteration" sub-panel shown on figure 6.16. The iteration conditions selected are: a crystal of initial thickness 1 nm, 20 wave functions will be calculated at thickness increasing by 8 increments of 0.25 nm (i.e. 2 nm), and the number of reflections that will be considered strong is 25. The check boxes labeled "Bethe", "All" and "Save" tells the program to use Bethe potential to include weak reflections, to include all reflections (selected with the deviation slider on the right of the Diffraction panel) and to save the beams amplitude/phase as a function of crystal thickness. In this figure, the convergence of the incident beam is quite large. Figure 6.16 (Iteration sub-panel) Figures 6.17 a and b show the microscope dialogue that allows to set the aperture diameter and the optical axis position. Figure 6.17a and b (Objective aperture size and position, optical axis position) With these settings pushing the start button makes the program calculates the following HREM map (Figure 6.18), where the incident beam convergence has been decreased (better spatial coherence) from 4.8 (a) to 0.8 nm -1 (b), and the defocus spread from 20 (b) to 6 (c) nm. The effect of the crystal tilt (Laue circle center), beam tilt (optical axis position) and objective aperture centering are obvious. Figure 6.18a,b,c (HREM map, Al 300 kV, CLC at (1,1,0), CM-300 UT) Adjusting the crystal tilt, i.e. setting the Center of the Laue Circle (CLC) at (0,0,0) produces image a) of 6.19, centering the objective aperture image b) and adjusting the optical axis image c). Figure 6.19 a, b, c (Improving imaging parameters) The specimen orientation and other related parameters are set using the " Specimen dialogue ". The Bloch wave approach is easy to apply to zone axis directions of high . Figure 6.20 shows a montage of Al in the direction. Figure 6.20 (HREM montage Al in ) The dynamical diffraction pattern corresponding to the wave function at 7.5 nm is shown on figure 6.21. Figure 6.21 (Dynamical diffraction pattern) 6.3 Exporting .ems images 6.3.1 Saving or loading images from or into jems jems HREM and CBED images can be saved in the *.ems format described below and loaded back without loosing the image map information or CBED information. HREM image in .ems format are automatically generated when the HREM image maps are saved as *.gif files. Figure 6.22 (How to load .ems images) 6.3.2 Loading .ems images into Mathematica The following lines show the Mathematica code used to read into Mathematica .ems images, . To set the directory where the image files are: SetDirectory Off To show all files that have a name with Si114: FileNames To open and read the image: in = OpenReadBinary col = ReadBinary ; row = ReadBinary ; data = ReadBinary , {row}]]; Close Show , AspectRatio - Automatic]]; To plot line scans: ListPlot ], PlotJoined - True, PlotRange - All]; ListPlot ], {150, 250}], PlotJoined - True, PlotRange - All]; The .ems image format is quite simple to read as it starts with two 32 bit integers that gives the number of rows and columns of the image. It is followed by the image data. The data points are Real 4 Bytes ( single precision floating point). Figures 6.23 and 6.24 a, b show a HREM montage map as loaded into Mathematica and line scans across it. Figure 6.23 ( HREM montage as displayed by Mathematica bitmap). Figure 6.24 a, b (line scans across Mathematica image) Several simple Mathematica notebooks are given in jemsData/MathematicaCode. 6.3.3 Loading ems images into DigitalMicrograph It is equally easy to read these images into DigitalMicrograph . The following procedure loads the .ems images into DM3.4: start DigitalMicrograph and select the import Data item of the File menu, select the .ems image, the DM import image opens, specify the width (number of columns) and height (number of rows) of the image, select the Real 4 Byte data type, select Binary and an offset of 8 bytes (the two 32 bit integers that specify the image dimension), select Swap Data Bytes. Figures 6.25 a, b show some image filtering on noisy HREM montage. Figure 6.25 a, b (DigitalMicrograph processing of HREM montage map) 6.4 Objective lens aberrations and specimen vibration and drift When one selects the "Plot" panel and the "Image" radio button of the "Beam plot" box, the HREM image at a given thickness and defocus is displayed. The thickness of the crystal is selected using the slider positioned on the left, the right slider changes the gamma correction. Figure 6.26 shows the panel controlling 2-fold, 3-fold astigmatism and axial coma. The sliders change the amplitude of these aberrations and the compasses the angle. The image contrast depends on the illumination condition (coherent, envelope or TCC). The transmission cross-coefficients take fully into account 2-fold, 3-fold, axial coma and image shift. Figure 6.26 2-fold, 3-fold and axial coma controls The controls of the illumination, imaging, objective lens, shift and vibrations panel will modify interactively the high resolution image allowing to see their various effects in real time. A click on the image will draw the normal to two basis planes (figure 6.27). Figure 6.27 Normal to the planes (reciprocal basis, Au ) 6.5 Super-cell images A panel similar to the HREM map panel controls the simulation of HREM images of crystal defects. When the defect has a 3-D structure one prepares several slices. The wavefunction propagates through the slices from top (first slice) to bottom (last slice). Figure 6.28 shows a Sigma 5 310 interface in Au. It is distorded due to 3-fold astigmatism. A detail of the distortion is given on figure 6.29. Figure 6.28 Image simulation of crystal defects When 3-fold astigmatism is present it is easily demonstrated that , the orientation of the interface is of upper-most importance for obtaining an image without distortion. Figure 6.29 Detail of right panel of figure 6.28
1 Beware mind mapping essays can make them too complicated, a list of key points is an alternative. 2 Write your title down . If you have a choice of titles decide NOW and write it down. " wikiHow should not be editable". How far do you agree? 3 Decide on your paragraphs and these come off your title . You must have an introduction and a conclusion. You need about three other paragraphs. Vandalism, Range of Subjects, Rules and regulating 4 Start researching . Find points to put in your middle paragraphs. Some paragraphs might only have one point but others may have three, four or five! This is one of the hardest steps and is the core to your essay. It only needs to be written in short hand. Keep a record of websites, books (including page numbers) and articles you use to help you More People More knowledge, people work in different fields. 5 Back up your points . For each point you write you need to find evidence to prove it. This could be a quote or an example. If it's a quote be sure you write it down exactly how it is so you can copy it from your mind map. How to Teach Piano would require a piano teacher to write it and How to Read and Speak Like a TV News Reporter would need the experience of a reporter. 6 Write your introduction . Just a few key points for the plan. Make sure you don't try to put too much in the introduction. At the moment wikiHow is editable by everyone. This is its unique feature and makes it an ever expanding compilation of "how to's". However, it can lead to some problems. 7 Write the conclusion points . Make sure you tie up everything nicely and that you've answered the question. You should also say your opinion here if appropriate. Overall I think that wikiHow should be editable by all because this is what makes it so useful. 8 Write links between the paragraphs . The best essays will link between the paragraphs so it all flows smoothly. Often this is quite difficult and sometimes you might just have to move abruptly to a new topic but avoid this as much as possible. There are so many subjects (paragraph 2) but there have to be ways in which the public can decide when topics are irrelevant (1) 9 Consider all the help that you've got from teachers such as the mark scheme . Make sure you've included everything they've suggested. It might be useful to make a checklist. For some things you'll have to wait until you've written it. Check this again after you've finished it too. 10 Write your essay . Don't feel you need to stick to your plan but have it nearby because you'll find it helpful.
I never had to worry about finding my way in Beijing before. It's not because I knew Beijing well, but because I had a local guide. This time, I am on my own (though I can email and call for help). So, how can I NOT get lost? I had no sense of directions until I had to drive. A friend majored in geology taught me this trick: Put yourself on the map, so you always know which direction you are going. It worked like a charm when I was in South Florida, in D.C., and in Seattle. After we have moved to Honolulu, I had to learn one more thing: toward the ocean or toward the mountain. Yes, when you are in Hawaii, that's how the locals will give you the direction. ps. LH, this Blog is for you.
Can't go anywhere without your sat-nav? You might be wiping out your memory We're losing our mental maps, scientists claim By Amy Oliver Last updated at 11:35 PM on 19th January 2012 Comments ( 67 ) Share They are supposed to make getting around easier. But over-reliance on sat-navs could leave us completely lost, a study has suggested. Scientists think our memory for places is like a mental map which we have learnt from looking at a real map of where we live. Lost your plot: Researchers are worried that the increasing reliance on GPS devices will eventually erase memorised maps from our brains Researchers from the Max Planck Institute for Biological Cybernetics in Germany tested 26 residents of a town, all of whom had lived there for at least two years. They were put into a virtual-reality headset which showed a 3D model of the town at locations familiar to them, but under fog. More... IBM creates world's smallest map... of the world Email in your eye? Next-generation video screen glasses could lay messages or GPS over your field of vision They had to point to a place they could not see, for example the fire station. Disorientating: Researchers put participants in a virtual 3D photo-realistic model of their hometown at familiar locations surrounded by fog Ever which way: Participants were oriented in 12 directions in each of the initial virtual locations They were then asked to draw a map of the town, including all the test locations. Though participants’ maps of Tubingen, Germany, differed, the results showed everyone performed most accurately when facing north. Pugh's take on our reliance on sat-navs Previous theories of how we find out where we are maintained that the further away an invisible location is, the longer it takes us to point in its direction. But given the accuracy of locating northwards, researchers concluded all participants had seen and remembered a map of the town at some point. Mr Meilinge thinks GPS devices will eventually erase these memories. He said: ‘If somebody doesn’t care to learn the environment, that’s fine. ‘But they shouldn’t complain if their mobile is not working and they are completely lost.’ The question is how should you avoid this? 'Look at maps before you start your trip, keep them at hand, but navigate yourself, and try to rely on your memory,' Dr Meilinger said. 'It will work better than you expect.' Read more: http://www.dailymail.co.uk/sciencetech/article-2088944/Lost-You-rely-sat-nav-say-scientists.html#ixzz1kEuytb2W
I will teach forests on Monday. I thought you may be interested in what a physical oceanographer has to say about world forests. Take a look at the three maps (= three thousand words?) in my lecture ppt. How do you feel as a Chinese? Forests_spr2011_4p.pdf
http://www.nature.com/nature/journal/v467/n7319/full/nature09534.html#/affil-auth A map of human genome variation from population-scale sequencing The 1000 Genomes Project Consortium Affiliations Contributions Corresponding author Journal name: Nature Volume: 467 , Pages: 10611073 Date published: (28 October 2010) DOI: doi:10.1038/nature09534 Received 20 July 2010 Accepted 30 September 2010 Published online 27 October 2010 Abstract Abstract Introduction Data generation, alignment and variant discovery Power to detect variants Genotype accuracy Putative functional variants Application to association studies Mutation, recombination and natural selection Discussion Methods References Acknowledgements Author information Supplementary information Comments Article tools 日本語要約 Print Email Download PDF Download citation Order reprints Rights and permissions Share/bookmark Connotea Cite U Like Facebook Twitter Delicious Digg The 1000 Genomes Project aims to provide a deep characterization of human genome sequence variation as a foundation for investigating the relationship between genotype and phenotype. Here we present results of the pilot phase of the project, designed to develop and compare different strategies for genome-wide sequencing with high-throughput platforms. We undertook three projects: low-coverage whole-genome sequencing of 179 individuals from four populations; high-coverage sequencing of two motherfatherchild trios; and exon-targeted sequencing of 697 individuals from seven populations. We describe the location, allele frequency and local haplotype structure of approximately 15 million single nucleotide polymorphisms, 1 million short insertions and deletions, and 20,000 structural variants, most of which were previously undescribed. We show that, because we have catalogued the vast majority of common variation, over 95% of the currently accessible variants found in any individual are present in this data set. On average, each person is found to carry approximately 250 to 300 loss-of-function variants in annotated genes and 50 to 100 variants previously implicated in inherited disorders. We demonstrate how these results can be used to inform association and functional studies. From the two trios, we directly estimate the rate of de novo germline base substitution mutations to be approximately 10 8 per base pair per generation. We explore the data with regard to signatures of natural selection, and identify a marked reduction of genetic variation in the neighbourhood of genes, due to selection at linked sites. These methods and public data will support the next phase of human genetic research. Subject terms: Genetics Genomics
科学论文引证关系图（Citation Map）可以揭示科学文献间的相互继承关系。利用Citation Map可以帮助您快速深入理解科学研究课题发展的来龙去脉，追踪历史，把握现状、预测未来。 科学论文引证关系图（Citation Map）实例见： 王振义院士等研究者1988年发表的这篇论文至今引用了1454次，可以从 论文引证关系图（Citation Map）看出研究者之间的关系。 请你见关系图 http://bloodjournal.hematologylibrary.org/cgi/citemap?id=bloodjournal;72/2/567 ME Huang, YC Ye, SR Chen, JR Chai, JX Lu, L Zhoa, LJ Gu, and ZY Wang Use of all-trans retinoic acid in the treatment of acute promyelocytic leukemia Blood, Aug 1988; 72: 567 - 572. ME Huang, YC Ye, SR Chen, JR Chai, - , 1988 - bloodjournal.hematologylibrary.org By Huang Meng-er, Ye Yu-chen, Chen Shu-rong. Chai Jin-ren, Lu Jia-Xiang, Zhoa Lin, Gu Long-jun. and Wang Zhen-yi ... Twenty-four patients with acute promyelocytic leukemia (API) were treated with all - trans retinoic acid (45 to 100 ... 1 6 cases were previously ... Cited by 1454 - Related articles - All 5 versions Citation Map (What's this?) What is it? Citation Map is a graphical representation of the articles citing or cited by your selected article. The map is based on the references found in the full text articles of the HighWire-hosted journals. The initial number of citations viewed in the map is 10, but you can change this number if you desire. What is it for? Develop reading lists to get up to speed on a new topic Generate bulk citation lists for import into literature-management programs Assist in refereeing or writing a review article What it does: Given a starting reference, Citation Map finds all articles related by citations either citing the article, or cited by the article. The result set is expanded outward from the starting article to make a collection of all the articles related by citation to the starting article. By noting the number of times each article in the collection is cited, the related papers with the greatest impact are graphed, along with the citing/cited-by relations among the articles in the collection. This shows you the most important papers related to a starting article, as well as temporal and line-of-cite relationships between these articles.
January 28, 2010 Science on the map Researchers have used a decade of citation data to map out changes in the landscape of scientific disciplines By Jef Akst Tricky T cells A new lymphocyte behind autoimmunity has created feverish excitementand raised as many questions as it answers By Katherine Bagley Scientist to Watch: Tara Kieffer From helix to hepatitis By Katherine Bagley Monty Python's take on science Ever had a colleague like this one? Tell us about it By The Scientist Community Will Obama's freeze chill science? Science advocates fear a steep budget drop-off with the US president's expected call for a three-year freeze on non-defense spending By Bob Grant Pluripotency not required In a striking demonstration of cellular flexibility, scientists have created functioning neurons directly from fibroblasts with no intermediate pluripotent stage By Jef Akst Radical journal's fate at risk A panel has recommended that Elsevier tame its most radical journal By Jef Akst Power couples Three highly productive couples give advice on how to balance life at home and in the lab By Victoria Stern Balancing oversight Should the NIH have a stronger hand in overseeing the projects it funds? By Jef Akst Baby bellies Dirty diapers help map the infant intestinal microbiota By Victoria Stern News in a nutshell European Research Council chief quits, a sharp rise in orphan drugs, and new rules for DNA forensics By Bob Grant Sonar links bats and whales In a striking example of evolutionary convergence, two very different organisms seem to have a lot in common By Jef Akst Bush stem cell line ok for approval One of the most heavily used embryonic stem cell lines was recommended for inclusion into the new stem cell registry By Jef Akst
Guide to creating maps with Stata The graphs and maps on this site are created with the Stata statistical package. This article describes how to make maps like those showing Millennium Development Goal regions and UNICEF regions in Stata from a shapefile. Shapefiles store geographic features and related information and were developed by ESRI for its ArcGIS line of software. The shapefile format is used by many other programs and maps in this format can be downloaded from various sites on the Internet. Another common map format is the MapInfo Interchange Format for use with the MapInfo software. Shapefile data is usually stored in a set of three files (.shp, .shx, .dbf), while MapInfo data is stored in two files (.mif, .mid). Some sources for shapefiles and other data are listed on the website of the U.S. Centers for Disease Control and Prevention (CDC) under Resources for Creating Public Health Maps . The CDC itself provides shapefiles for all countries with administrative boundaries down to the state level. Please note that these shapefiles are not in the public domain and are intended for use with the CDC's Epi Info software only. Other sources of shapefiles can be found with a Google search. This guide is divided into two parts. Read part 1 if you have Stata 9 or 10 and part 2 if you have Stata 8. The creation of maps is not supported in older versions of Stata. Part 1: Creating maps with Stata 9 or 10 To create a map with Stata 9 or 10 you need the following software. Stata version 9.2 or newer. spmap: Stata module for drawing thematic maps, by Maurizio Pisati. Install in Stata with the command ssc install spmap . shp2dta: Stata module for converting shapefiles to Stata format, by Kevin Crow. Install in Stata with the command ssc install shp2dta . Shapefile: For the example in this guide, download world_adm0.zip (646 KB), a shapefile that contains the boundaries of all countries of the world. Step 1: Convert shapefile to Stata format Unzip world_adm0.zip to a folder that is visible to Stata. The archive contains three files called world_adm0.dbf, world_adm0.shp, and world_adm0.shx. Start Stata and run this command: shp2dta using world_adm0, data(world-d) coor(world-c) genid(id) Two new files will be created: world-d.dta (with the country names and other information) and world-c.dta (with the coordinates of the country boundaries). If you plan to superimpose labels on a map, for example country names, you should run the following command instead, which will add centroid coordinates to the file world-d.dta: shp2dta using world_adm0, data(world-d) coor(world-c) genid(id) genc(c) Please refer to the spmap documentation to learn more about labels because they are not covered in this guide. The DBF, SHP, and SHX files can be deleted. Some shapefiles are not compatible with the shp2dta command and Stata will abort the conversion with an error message. If this is the case, you can use a combination of two other programs, shp2mif and mif2dta. These programs are explained in the instructions for Stata 8 (see Step 1 and Step 2 in part 2 of this guide). Step 2: Draw map in Stata Open world-d.dta in Stata. The file contains no country-specific data that could be used for this example so we will create a variable with the length of each country's name. The Stata command for this is: generate length = length(NAME) Draw a map that indicates the length of all country names with this command: spmap length using world-c.dta, id(id) Be patient because spmap is slow if a map contains many features. The default map is monochrome, it shows Antarctica, the legend is too small and the legend values are arranged from high to low. We can draw a second map without Antarctica, with a blue palette, and with a bigger legend with values arranged from low to high: spmap length using world-c.dta if NAME!=Antarctica, id(id) fcolor(Blues) legend(symy(*2) symx(*2) size(*2)) legorder(lohi) You now have the map below. Darker colors indicate longer names, ranging from 4 letters (for example Cuba and Iraq) to 33 letters (Falkland Islands (Islas Malvinas)). To customize the map further, please read the Stata help file for spmap. Map created with spmap in Stata: length of country names The instructions above can be used to convert any shapefile to Stata format. If you have maps in MapInfo format you have to use another program called mif2dta that is described in part 2 of this guide. Part 2: Creating maps with Stata 8 To create a map with Stata 8 you need the following software. Stata version 8.2. tmap: Stata module for thematic mapping by Maurizio Pisati. Install in Stata with the command ssc install tmap . mif2dta: Stata module for converting files from MapInfo to Stata format, also by Maurizio Pisati. Install in Stata with the command ssc install mif2dta . SHP2MIF: DOS program for converting shapefiles to MapInfo format. Go to the the website of RouteWare and click on SHP2MIF (135 Kb) under the heading Converters to get ishp2mif.zip. Shapefile: For the example in this guide, download world_adm0.zip (646 KB), a shapefile that contains the boundaries of all countries of the world. Step 1: Convert shapefile to MapInfo format Unzip ishp2mif.zip. The archive contains three files, among them SHP2MIF.EXE. Unzip world_adm0.zip to the same folder as SHP2MIF.EXE. The archive contains three files called world_adm0.dbf, world_adm0.shp, and world_adm0.shx. Open a DOS command window: Windows Start menu - Run - command - OK. Change the path in the command window to the folder that contains SHP2MIF.EXE and the three map files. Use the DOS command cd to change the path. SHP2MIF works best with short file names in the 8.3 format (name up to 8 characters, extension up to 3 characters). Rename the map files with this DOS command: rename world_adm0.* world.* The map files are now called world.dbf, world.shp, and world.shx. Convert the maps to MapInfo format by typing shp2mif world in the DOS command window. This produces two new files: WORLD.MID and WORLD.MIF. Close the DOS command window. The DBF, SHP and SHX files can be deleted. Step 2: Convert MapInfo files to Stata format Move the MIF and MID files to a folder that is visible to Stata. Start Stata and run this command: mif2dta world, genid(id) Two new files will be created: world-Coordinates.dta (with the country boundaries) and world-Database.dta (with the country names and other information). If you plan to superimpose labels on a map, for example country names, you should run the following command instead, which will add centroid coordinates to the file world-Database.dta: mif2dta world, genid(id) genc(c) Please refer to the tmap documentation to learn more about labels because they are not covered in this guide. The MIF and MID files can be deleted. Step 3: Draw map in Stata Open world-Database.dta in Stata. The file contains no country-specific data that could be used for this example so we will create a variable with the length of each country's name. The Stata command for this is: generate length = length(name) Draw a map that indicates the length of all country names with this command: tmap choropleth length, map(world-Coordinates.dta) id(id) Be patient because tmap is slow if a map contains many features. The default map is monochrome, it shows Antarctica and the legend is too small. We can draw a second map without Antarctica, with a blue palette, and with a bigger legend: tmap choropleth length if name!=Antarctica, map(world-Coordinates.dta) id(id) palette(Blues) legsize(2) To reduce the margins, display the graph again and set the margins to zero: graph display, margins(zero) You now have the map below. Darker colors indicate longer names, ranging from 4 letters (for example Cuba and Iraq) to 33 letters (Falkland Islands (Islas Malvinas)). To customize the map further, please read the Stata help file for tmap and the tmap user's guide by Maurizio Pisati. The user's guide and additional tmap files can be downloaded in Stata with the commands ssc describe tmap and net get tmap . Map created with tmap in Stata: length of country names The instructions above can be used to convert any shapefile to Stata format. If you have maps in MapInfo format you can skip step 1 of the instructions and start with step 2. Related articles Guide to integrating Stata and external text editors Guide to creating PNG images with Stata Guide to reading Statalist with Gmail External links Stata FAQ: How do I graph data onto a map? Wikipedia article on shapefiles Wikipedia article on MapInfo Interchange Format Resources for Creating Public Health Maps from the Centers for Disease Control and Prevention (CDC) Friedrich Huebler, 6 November 2005 (edited 30 June 2009), Creative Commons License Permanent URL: http://huebler.blogspot.com/2005/11/creating-maps-with-stata.html http://huebler.blogspot.com/2005/11/creating-maps-with-stata.html
标签: map
