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Based on the Beijing Flickr Meetup online survery, there is the analysis
Many thanks to Lei's help, and if you are interested in this, please fill in the form for a reference.
我的excel强迫症又来了, 我总是要很纠结地把什么事情都必须excel量化, 再研究数据, 做chart...否则我就很不舒服。其实真没必要。不过也借此学习了Google Docs &Spreadsheet的使用, 很开心。
Analysis of employment prospects - shows numbers of Hungarian refugees staying in England and emigrating to the USA and Canada.
GB127.Council Proceedings
PESTEL analysis template. CC by-SA 2.0 (free for personal use as long as you cite "Designed by Greg Emmerich" somewhere in the description). Contact me for the free Adobe Illustrator file. Font: j.mp/ZnkxAi
Daily weather analysis at the airfield in St. John’s Canada were essential for planning the day’s flight operations. The airborne side of the NAAMES project travels with two full-time meteorologists.
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The North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) is a five year investigation to resolve key processes controlling ocean system function, their influences on atmospheric aerosols and clouds and their implications for climate.
Michael Starobin joined the NAAMES field campaign on behalf of Earth Expeditions and NASA Goddard Space Flight Center’s Office of Communications. He presented stories about the important, multi-disciplinary research being conducted by the NAAMES team, with an eye towards future missions on the NASA drawing board. This is a NAAMES photo essay put together by Starobin, a collection of 49 photographs and captions.
Photo and Caption Credit: Michael Starobin
NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission.
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PhD student Mario Toubes-Rodrigo is using X-ray fluorescence to determine the mineral composition of a glacier. These results will support understanding of the glacier microbial ecology being investigated by a range of culture-based and DNA-based techniques. The glacier is svínafellsjökull in Iceland.
Col busy analyzing the thousands of DNA sequences generated by the Barcode Wales project
The Barcode Wales Paper: dx.plos.org/10.1371/journal.pone.0037945
WSDOT has marked a major milestone in its effort to replace the aging and vulnerable State Route 520 floating bridge. After 13 years of thorough analysis and input from thousands of people, the state has announced a preferred alternative for the I-5 to Medina: Bridge Replacement and HOV Project.
Major safety, transit and environmental improvements are in store for the SR 520 corridor from I-5 in Seattle across Lake Washington to Medina. The SR 520 preferred alternative takes key steps to get ready for future light rail, help manage traffic in the Arboretum and transform the future highway with a landscaped lid and median for a parkway experience.
The new floating bridge and highway will have six lanes, including two general-purpose lanes and a new transit/HOV lane in each direction. Adding transit/HOV lanes makes travel in the corridor faster and more reliable for buses and carpools and supports regional plans for completing the HOV system to reduce the number of single-occupancy vehicles.
Details are on our website, including images of what a new, larger landscaped lid at Montlake Boulevard would look like. The preferred SR 520 alternative directly responds to input we received from the public, the City of Seattle, the University of Washington and environmental regulatory agencies. Work continues on design refinements for the Montlake area with those groups as well as transit agencies.
Highlights include:
Room for future light rail: The bridge deck will accommodate future light rail trains and the west end of the floating bridge will have room for trains to leave the corridor and head to the University of Washington area. Pontoons could be added to the floating bridge in the future to carry the weight of the trains.
Less traffic in the Arboretum: The project removes the ramps that currently carry traffic directly to Lake Washington Boulevard and the Washington Park Arboretum. Westbound off-ramps instead will carry buses and general purpose traffic to 24th Avenue E. and continue on to Montlake Boulevard.
Buses and a lid at Montlake: New direct-access ramps will carry buses to a new landscaped park lid at the Montlake Boulevard interchange. The open space will extend from Montlake Boulevard into the Arboretum.
Parkway on Portage Bay: A slimmed-down Portage Bay Bridge will be built as a 45-mph landscaped parkway with a 6-foot-wide planted median. The 105-foot-wide bridge is narrower than the 154 feet previously planned in the 2006 draft environmental impact statement.
Identifying a preferred design keeps us on track for opening a new bridge to traffic in 2014.
www.wsdot.wa.gov/Communications/ExpressLane/2010/05_07.htm
www.wsdot.wa.gov/Projects/SR520Bridge/I5ToMedina/Default....
SR 520 - I-5 to Medina: Bridge Replacement and HOV Project
Status
February 2011
ESSB 6392 reports now available
We've sent two final reports to the governor and state legislators (High Capacity Transit Planning and Financing and the Washington Park Arboretum Mitigation Plan). This completes the requirements of Senate Bill 6392.
Floating bridge construction
Three teams have until spring to submit their bids and proposals for the new SR 520 floating bridge. Construction starts in 2012 and the bridge opens in 2014.
Overview
The I-5 to Medina: Bridge Replacement and HOV Project will replace the interchanges and roadway between I-5 in Seattle and the eastern end of the floating bridge.
Why is WSDOT pursuing this project?
About 115,000 vehicles and more than 190,000 people cross Lake Washington every day on the SR 520 floating bridge. It’s a key regional route for commuters and freight.
After floating for nearly 50 years, the four-lane bridge is often clogged by traffic and is showing its age.
The floating bridge pontoons are vulnerable to windstorms, and bridge support columns are vulnerable to earthquakes.
The End Result
The I-5 to Medina Bridge Replacement and HOV Project includes a new floating bridge and highway with six lanes, including two general-purpose lanes and one new transit/HOV lane in each direction.
The project also takes key steps to get ready for future light rail, help manage traffic in the Arboretum and transform the future corridor from Montlake to I-5 into a city parkway with landscaped lids and medians.
Project Benefits
The new SR 520 corridor through Seattle will:
Provide transit connections and priority.
Create a pedestrian-friendly urban interchange at Montlake Boulevard.
Restore park area and connections next to the Washington Park Arboretum.
Reduce noise levels from the Portage Bay Bridge.
Be ready for light rail if the region chooses to fund it in the future.
What is the project timeline?
Spring 2011: Publish final environmental impact statement
Mid-2011: Select contractor team for new SR 520 floating bridge
2012: Begin construction of floating bridge
2014: Open new floating bridge to drivers
The schedule for constructing the segments of the corridor west of Lake Washington is pending additional funding.
Financial Information
We are moving forward with construction on a new SR 520 floating bridge, which is fully funded by a variety of state and federal sources, including SR 520 tolling that is set to begin in spring 2011.
We are continuining to work with the Legislature to fund the elements of the project from I-5 to the floating bridge.
Visit the SR 520 Costs, Funding and Tolling page for additional information.
How can I get more information?
Contact:
E-mail: SR520bridge@wsdot.wa.gov
Phone: 206-770-3500
Infoline: 1-888-520-NEWS (6397)
Mail: I-5 to Medina: Bridge Replacement and HOV Project
SR 520 Bridge Replacement and HOV Program
600 Stewart Street, Suite 520
Seattle, WA 98101
The third illsutration for Chrysallis Counselling and Psychotherapy, this image depicts the idea of stepping out of behavioural patterns initiated in childhood.
Rangeland Resources and Wildland Soils students conduct vegetation analysis on the South Spit of Humboldt Bay.
If you can, please spare the time to visit my current exhibition of drawings here on Flickr. Simply follow the link below. Thank you very much. Julian.
www.flickr.com/groups/globalworldawards/discuss/721576241...
Calcrete paleosol capping Pleistocene limestone at Green Cay, offshore-northwestern San Salvador Island, eastern Bahamas.
The dominant paleosol type on San Salvador Island (& other Bahamian islands) consists of hard, reddish-brown to orangish-brown colored, irregularly-sculpted crusts. These are referred to as calcretes or caliches or terra rosas. Calcrete paleosols cap all of the Pleistocene-aged stratigraphic units, except where removed by erosion. The Holocene-aged units (Hanna Bay Member & North Point Member of the Rice Bay Formation) haven’t been around long enough to develop calcrete paleosols atop their outcrops.
The calcrete horizon shown above has been dated to 9.2 ka (early Holocene). It caps a Pleistocene limestone unit that is probably the Owl's Hole Formation, according to John Mylroie.
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The surface bedrock geology of San Salvador consists entirely of Pleistocene and Holocene limestones. Thick and relatively unforgiving vegetation covers most of the island’s interior (apart from inland lakes). Because of this, the most easily-accessible rock outcrops are along the island’s shorelines.
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Stratigraphic Succession in the Bahamas:
Rice Bay Formation (Holocene, <10 ka), subdivided into two members (Hanna Bay Member over North Point Member)
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Grotto Beach Formation (lower Upper Pleistocene, 119-131 ka), subdivided into two members (Cockburn Town Member over French Bay Member)
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Owl's Hole Formation (Middle Pleistocene, ~215-220 ka & ~327-333 ka & ~398-410 ka & older)
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San Salvador’s surface bedrock can be divided into two broad lithologic categories:
1) LIMESTONES
2) PALEOSOLS
The limestones were deposited during sea level highstands (actually, only during the highest of the highstands). During such highstands (for example, right now), the San Salvador carbonate platform is partly flooded by ocean water. At such times, the “carbonate factory” is on, and abundant carbonate sediment grains are generated by shallow-water organisms living on the platform. The abundance of carbonate sediment means there will be abundant carbonate sedimentary rock formed after burial and cementation (diagenesis). These sea level highstands correspond with the climatically warm interglacials during the Pleistocene Ice Age.
Based on geochronologic dating on various Bahamas islands, and based on a modern understanding of the history of Pleistocene-Holocene global sea level changes, surficial limestones in the Bahamas are known to have been deposited at the following times (expressed in terms of marine isotope stages, “MIS” - these are the glacial-interglacial climatic cycles determined from δ18O analysis):
1) MIS 1 - the Holocene, <10 k.y. This is the current sea level highstand.
2) MIS 5e - during the Sangamonian Interglacial, in the early Late Pleistocene, from 119 to 131 k.y. (sea level peaked at ~125 k.y.)
3) MIS 7 - ~215 to 220 k.y. - late Middle Pleistocene
4) MIS 9 - ~327-333 k.y. - late Middle Pleistocene
5) MIS 11 - ~398-410 k.y. - late Middle Pleistocene
Bahamian limestones deposited during MIS 1 are called the Rice Bay Formation. Limestones deposited during MIS 5e are called the Grotto Beach Formation. Limestones deposited during MIS 7, 9, 11, and perhaps as old as MIS 13 and 15, are called the Owl’s Hole Formation. These stratigraphic units were first established on San Salvador Island (the type sections are there), but geologic work elsewhere has shown that the same stratigraphic succession also applies to the rest of the Bahamas.
During times of lowstands (= times of climatically cold glacial intervals of the Pleistocene Ice Age), weathering and pedogenesis results in the development of soils. With burial and diagenesis, these soils become paleosols. The most common paleosol type in the Bahamas is calcrete (a.k.a. caliche; a.k.a. terra rosa). Calcrete horizons cap all Pleistocene-aged stratigraphic units in the Bahamas, except where erosion has removed them. Calcretes separate all major stratigraphic units. Sometimes, calcrete-looking horizons are encountered in the field that are not true paleosols.
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Subsurface Stratigraphy of San Salvador Island:
The island’s stratigraphy below the Owl’s Hole Formation was revealed by a core drilled down ~168 meters (~550-feet) below the surface (for details, see Supko, 1977). The well site was at 3 meters above sea level near Graham’s Harbour beach, between Line Hole Settlement and Singer Bar Point (northern margin of San Salvador Island). The first 37 meters were limestones. Below that, dolostones dominate, alternating with some mixed dolostone-limestone intervals. Reddish-brown calcretes separate major units. Supko (1977) infers that the lowest rocks in the core are Upper Miocene to Lower Pliocene, based on known Bahamas Platform subsidence rates.
In light of the successful island-to-island correlations of Middle Pleistocene, Upper Pleistocene, and Holocene units throughout the Bahamas (see the Bahamas geologic literature list below), it seems reasonable to conclude that San Salvador’s subsurface dolostones may correlate well with sub-Pleistocene dolostone units exposed in the far-southeastern portions of the Bahamas Platform.
Recent field work on Mayaguana Island has resulted in the identification of Miocene, Pliocene, and Lower Pleistocene surface outcrops (see: www2.newark.ohio-state.edu/facultystaff/personal/jstjohn/...). On Mayaguana, the worked-out stratigraphy is:
- Rice Bay Formation (Holocene)
- Grotto Beach Formation (Upper Pleistocene)
- Owl’s Hole Formation (Middle Pleistocene)
- Misery Point Formation (Lower Pleistocene)
- Timber Bay Formation (Pliocene)
- Little Bay Formation (Upper Miocene)
- Mayaguana Formation (Lower Miocene)
The Timber Bay Fm. and Little Bay Fm. are completely dolomitized. The Mayaguana Fm. is ~5% dolomitized. The Misery Point Fm. is nondolomitized, but the original aragonite mineralogy is absent.
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The stratigraphic information presented here is synthesized from the Bahamian geologic literature.
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Supko, P.R. 1977. Subsurface dolomites, San Salvador, Bahamas. Journal of Sedimentary Petrology 47: 1063-1077.
Bowman, P.A. & J.W. Teeter. 1982. The distribution of living and fossil Foraminifera and their use in the interpretation of the post-Pleistocene history of Little Lake, San Salvador, Bahamas. San Salvador Field Station Occasional Papers 1982(2). 21 pp.
Sanger, D.B. & J.W. Teeter. 1982. The distribution of living and fossil Ostracoda and their use in the interpretation of the post-Pleistocene history of Little Lake, San Salvador Island, Bahamas. San Salvador Field Station Occasional Papers 1982(1). 26 pp.
Gerace, D.T., R.W. Adams, J.E. Mylroie, R. Titus, E.E. Hinman, H.A. Curran & J.L. Carew. 1983. Field Guide to the Geology of San Salvador (Third Edition). 172 pp.
Curran, H.A. 1984. Ichnology of Pleistocene carbonates on San Salvador, Bahamas. Journal of Paleontology 58: 312-321.
Anderson, C.B. & M.R. Boardman. 1987. Sedimentary gradients in a high-energy carbonate lagoon, Snow Bay, San Salvador, Bahamas. CCFL Bahamian Field Station Occasional Paper 1987(2). (31) pp.
1988. Bahamas Project. pp. 21-48 in First Keck Research Symposium in Geology (Abstracts Volume), Beloit College, Beloit, Wisconsin, 14-17 April 1988.
1989. Proceedings of the Fourth Symposium on the Geology of the Bahamas, June 17-22, 1988. 381 pp.
1989. Pleistocene and Holocene carbonate systems, Bahamas. pp. 18-51 in Second Keck Research Symposium in Geology (Abstracts Volume), Colorado College, Colorado Springs, Colorado, 14-16 April 1989.
Curran, H.A., J.L. Carew, J.E. Mylroie, B. White, R.J. Bain & J.W. Teeter. 1989. Pleistocene and Holocene carbonate environments on San Salvador Island, Bahamas. 28th International Geological Congress Field Trip Guidebook T175. 46 pp.
1990. The 5th Symposium on the Geology of the Bahamas, June 15-19, 1990, Abstracts and Programs. 29 pp.
1991. Proceedings of the Fifth Symposium on the Geology of the Bahamas. 247 pp.
1992. The 6th Symposium on the Geology of the Bahamas, June 11-15, 1992, Abstracts and Program. 26 pp.
1992. Proceedings of the 4th Symposium on the Natural History of the Bahamas, June 7-11, 1991. 123 pp.
Boardman, M.R., C. Carney, B. White, H.A. Curran & D.T. Gerace. 1992. The geology of Columbus' landfall: a field guide to the Holcoene geology of San Salvador, Bahamas, Field trip 3 for the annual meeting of the Geological Society of America, Cincinnati, Ohio, October 26-29, 1992. Ohio Division of Geological Survey Miscellaneous Report 2. 49 pp.
Carew, J.L., J.E. Mylroie, N.E. Sealey, M. Boardman, C. Carney, B. White, H.A. Curran & D.T. Gerace. 1992. The 6th Symposium on the Geology of the Bahamas, June 11-15, 1992, Field Trip Guidebook. 56 pp.
1993. Proceedings of the 6th Symposium on the Geology of the Bahamas, June 11-15, 1992. 222 pp.
Lawson, B.M. 1993. Shelling San Sal, an Illustrated Guide to Common Shells of San Salvador Island, Bahamas. San Salvador, Bahamas. Bahamian Field Station. 63 pp.
1994. The 7th Symposium on the Geology of the Bahamas, June 16-20, 1994, Abstracts and Program. 26 pp.
1994. Proceedings of the 5th Symposium on the Natural History of the Bahamas, June 11-14, 1993. 107 pp.
Carew, J.L. & J.E. Mylroie. 1994. Geology and Karst of San Salvador Island, Bahamas: a Field Trip Guidebook. 32 pp.
Godfrey, P.J., R.L. Davis, R.R. Smtih & J.A. Wells. 1994. Natural History of Northeastern San Salvador Island: a "New World" Where the New World Began, Bahamian Field Station Trail Guide. 28 pp.
Hinman, G. 1994. A Teacher's Guide to the Depositional Environments on San Salvador Island, Bahamas. 64 pp.
Mylroie, J.E. & J.L. Carew. 1994. A Field Trip Guide Book of Lighthouse Cave, San Salvador Island, Bahamas. 10 pp.
1995. Proceedings of the Seventh Symposium on the Geology of the Bahamas, June 16-20, 1994. 134 pp.
1995. Terrestrial and shallow marine geology of the Bahamas and Bermuda. Geological Society of America Special Paper 300.
1996. The 8th Symposium on the Geology of the Bahamas, May 30-June 3, 1996, Abstracts and Program. 21 pp.
1996. Proceedings of the 6th Symposium on the Natural History of the Bahamas, June 9-13, 1995. 165 pp.
1997. Proceedings of the 8th Symposium on the Geology of the Bahamas and Other Carbonate Regions, May 30-June 3, 1996. 213 pp.
Curran, H.A., B. White & M.A. Wilson. 1997. Guide to Bahamian Ichnology: Pleistocene, Holocene, and Modern Environments. San Salvador, Bahamas. Bahamian Field Station. 61 pp.
1998. The 9th Symposium on the Geology of the Bahamas and Other Carbonate Regions, June 4-June 8, 1998, Abstracts and Program. 25 pp.
Wilson, M.A., H.A. Curran & B. White. 1998. Paleontological evidence of a brief global sea-level event during the last interglacial. Lethaia 31: 241-250.
1999. Proceedings of the 9th Symposium on the Geology of the Bahamas and Other Carbonate Regions, June 4-8, 1998. 142 pp.
2000. The 10th Symposium on the Geology of the Bahamas and Other Carbonate Regions, June 8-June 12, 2000, Abstracts and Program. 29+(1) pp.
2001. Proceedings of the 10th Symposium on the Geology of the Bahamas and Other Carbonate Regions, June 8-12, 2000. 200 pp.
Bishop, D. & B.J. Greenstein. 2001. The effects of Hurricane Floyd on the fidelity of coral life and death assemblages in San Salvador, Bahamas: does a hurricane leave a signature in the fossil record? Geological Society of America Abstracts with Programs 33(4): 51.
Gamble, V.C., S.J. Carpenter & L.A. Gonzalez. 2001. Using carbon and oxygen isotopic values from acroporid corals to interpret temperature fluctuations around an unconformable surface on San Salvador Island, Bahamas. Geological Society of America Abstracts with Programs 33(4): 52.
Gardiner, L. 2001. Stability of Late Pleistocene reef mollusks from San Salvador Island, Bahamas. Palaios 16: 372-386.
Ogarek, S.A., C.K. Carney & M.R. Boardman. 2001. Paleoenvironmental analysis of the Holocene sediments of Pigeon Creek, San Salvador, Bahamas. Geological Society of America Abstracts with Programs 33(4): 17.
Schmidt, D.A., C.K. Carney & M.R. Boardman. 2001. Pleistocene reef facies diagenesis within two shallowing-upward sequences at Cockburntown, San Salvador, Bahamas. Geological Society of America Abstracts with Programs 33(4): 42.
2002. The 11th Symposium on the Geology of the Bahamas and Other Carbonate Regions, June 6th-June 10, 2002, Abstracts and Program. 29 pp.
2004. The 12th Symposium on the Geology of the Bahamas and Other Carbonate Regions, June 3-June 7, 2004, Abstracts and Program. 33 pp.
2004. Proceedings of the 11th Symposium on the Geology of the Bahamas and Other Carbonate Regions, June 6-10, 2002. 240 pp.
Martin, A.J. 2006. Trace Fossils of San Salvador. 80 pp.
2006. Proceedings of the 12th Symposium on the Geology of the Bahamas and Other Carbonate Regions, June 3-7, 2004. 249 pp.
2006. The 13th Symposium on the Geology of the Bahamas and Other Carbonate Regions, June 8-June 12, 2006, Abstracts and Program. 27 pp.
Mylroie, J.E. & J.L. Carew. 2008. Field Guide to the Geology and Karst Geomorphology of San Salvador Island. 88 pp.
2008. Proceedings of the 13th Symposium on the Geology of the Bahamas and Other Carbonate Regions, June 8-12, 2006. 223 pp.
2008. The 14th Symposium on the Geology of the Bahamas and Other Carbonate Regions, June 12-June 16, 2006, Abstracts and Program. 26 pp.
2010. Proceedings of the 14th Symposium on the Geology of the Bahamas and Other Carbonate Regions, June 12-16, 2008. 249 pp.
2010. The 15th Symposium on the Geology of the Bahamas and Other Carbonate Regions, June 17-June 21, 2010, Abstracts and Program. 36 pp.
2012. Proceedings of the 15th Symposium on the Geology of the Bahamas and Other Carbonate Regions, June 17-21, 2010. 183 pp.
2012. The 16th Symposium on the Geology of the Bahamas and Other Carbonate Regions, June 14-June 18, 2012, Abstracts with Program. 45 pp.
Astronauts from five space agencies around the world take part in ESA’s CAVES training course– Cooperative Adventure for Valuing and Exercising human behaviour and performance Skills.
The six cavenauts of this edition of CAVES are ESA astronaut Alexander Gerst, NASA astronauts Joe Acaba and Jeanette Epps, Roscosmos’ cosmonaut Nikolai Chub, Canadian Space Agency astronaut Josh Kutryk and Japan’s space agency Takuya Onishi.
The three-week course prepares astronauts to work safely and effectively in multicultural teams in an environment where safety is critical.
As they explore caves they encounter caverns, underground lakes and strange microscopic life. They test new technology and conduct science – just as if they were living on the International Space Station.
The six astronauts have to rely on their own skills, teamwork and ground control to achieve their mission goals – the course is designed to foster effective communication, decision-making, problem-solving, leadership and team dynamics.
Credits: ESA – A. Romeo
38-year Syncrude employee Richard Maslanko conducts a lab test at the Syncrude Research & Development Centre in Edmonton, AB. Syncrude has invested an average of $114-million over the past five years in R&D. The majority of which is spent on environmental initiatives. For more information visit www.research.syncrude.ca
Researcher Phil Sharer works on AUTONOMIE, Argonne's next-generation plug-and-play software architecture for evaluating the fuel consumption benefits of both components and powertrain throughout the different phases of model-based design, from modeling to hardware implementation.
Photo by Wes Agresta / Courtesy Argonne National Laboratory.
Credit: Billypix
See all the latest news, analysis and opinion from this year's Dubai air show: www.flightglobal.com/events/dubai-airshow/
Materials are the building blocks of all functional structures. TWI's expertise lies in understanding the complex interactions that take place when basic materials are transformed into functional shapes and joined together in different working environments.
For more information www.twi.co.uk/technologies/material-properties/
If you wish to use this image each use should be accompanied by the credit line and notice, "Courtesy of TWI Ltd".
Female researchers carrying out research analysis in biotechnology laboratory at IITA Ibadan. Extreme left, Dr. Fisayo Kolade. Photo by IITA.
Statistics rarely give a simple view's Yes/No type answer to the question under analysis. Interpretation often comes down to the level of statistical significance applied to the numbers and often refers to the probability of a value accurately rejecting the null hypothesis (sometimes referred to as the p-value).
In this graph the black line is probability distribution for the test statistic, the critical region is the set of values to the right of the observed data point (observed value of the test statistic) and the p-value is represented by the green area.
The standard approach is to test a null hypothesis against an alternative hypothesis. A critical region is the set of values of the estimator that leads to refuting the null hypothesis. The probability of type I error is therefore the probability that the estimator belongs to the critical region given that null hypothesis is true (statistical significance) and the probability of type II error is the probability that the estimator doesn't belong to the critical region given that the alternative hypothesis is true. The statistical power of a test is the probability that it correctly rejects the null hypothesis when the null hypothesis is false.
Referring to statistical significance does not necessarily mean that the overall result is significant in real world terms. For example, in a large study of a drug it may be shown that the drug has a statistically significant but very small beneficial effect, such that the drug is unlikely to help the patient noticeably.
While in principle the acceptable level of statistical significance may be subject to debate, the p-value is the smallest significance level that allows the test to reject the null hypothesis. This is logically equivalent to saying that the p-value is the probability, assuming the null hypothesis is true, of observing a result at least as extreme as the test statistic. Therefore, the smaller the p-value, the lower the probability of committing type I error.
Some problems are usually associated with this framework (See criticism of hypothesis testing):
A difference that is highly statistically significant can still be of no practical significance, but it is possible to properly formulate tests to account for this. One response involves going beyond reporting only the significance level to include the p-value when reporting whether a hypothesis is rejected or accepted. The p-value, however, does not indicate the size or importance of the observed effect and can also seem to exaggerate the importance of minor differences in large studies. A better and increasingly common approach is to report confidence intervals. Although these are produced from the same calculations as those of hypothesis tests or p-values, they describe both the size of the effect and the uncertainty surrounding it.
Fallacy of the transposed conditional, aka prosecutor's fallacy: criticisms arise because the hypothesis testing approach forces one hypothesis (the null hypothesis) to be favored, since what is being evaluated is probability of the observed result given the null hypothesis and not probability of the null hypothesis given the observed result. An alternative to this approach is offered by Bayesian inference, although it requires establishing a prior probability.
Rejecting the null hypothesis does not automatically prove the alternative hypothesis.
As everything in inferential statistics it relies on sample size, and therefore under fat tails p-values may be seriously mis-computed.
Working from a null hypothesis, two basic forms of error are recognized:
Type I errors where the null hypothesis is falsely rejected giving a "false positive".
Type II errors where the null hypothesis fails to be rejected and an actual difference between populations is missed giving a "false negative".
Standard deviation refers to the extent to which individual observations in a sample differ from a central value, such as the sample or population mean, while Standard error refers to an estimate of difference between sample mean and population mean.
A statistical error is the amount by which an observation differs from its expected value, a residual is the amount an observation differs from the value the estimator of the expected value assumes on a given sample (also called prediction).
Mean squared error is used for obtaining efficient estimators, a widely used class of estimators. Root mean square error is simply the square root of mean squared error.
Misuse of statistics can produce subtle, but serious errors in description and interpretation—subtle in the sense that even experienced professionals make such errors, and serious in the sense that they can lead to devastating decision errors. For instance, social policy, medical practice, and the reliability of structures like bridges all rely on the proper use of statistics.
Even when statistical techniques are correctly applied, the results can be difficult to interpret for those lacking expertise. The statistical significance of a trend in the data—which measures the extent to which a trend could be caused by random variation in the sample—may or may not agree with an intuitive sense of its significance. The set of basic statistical skills (and skepticism) that people need to deal with information in their everyday lives properly is referred to as statistical literacy.
There is a general perception that statistical knowledge is all-too-frequently intentionally misused by finding ways to interpret only the data that are favorable to the presenter.[26] A mistrust and misunderstanding of statistics is associated with the quotation, "There are three kinds of lies: lies, damned lies, and statistics". Misuse of statistics can be both inadvertent and intentional, and the book How to Lie with Statistics[26] outlines a range of considerations. In an attempt to shed light on the use and misuse of statistics, reviews of statistical techniques used in particular fields are conducted (e.g. Warne, Lazo, Ramos, and Ritter (2012)).[27]
Ways to avoid misuse of statistics include using proper diagrams and avoiding bias.[28] Misuse can occur when conclusions are overgeneralized and claimed to be representative of more than they really are, often by either deliberately or unconsciously overlooking sampling bias.[29] Bar graphs are arguably the easiest diagrams to use and understand, and they can be made either by hand or with simple computer programs.[28] Unfortunately, most people do not look for bias or errors, so they are not noticed. Thus, people may often believe that something is true even if it is not well represented.[29] To make data gathered from statistics believable and accurate, the sample taken must be representative of the whole.[30] According to Huff, "The dependability of a sample can be destroyed by [bias]... allow yourself some degree of skepticism.
A least squares fit: in red the points to be fitted, in blue the fitted line.
Many statistical methods seek to minimize the residual sum of squares, and these are called "methods of least squares" in contrast to Least absolute deviations. The latter gives equal weight to small and big errors, while the former gives more weight to large errors. Residual sum of squares is also differentiable, which provides a handy property for doing regression. Least squares applied to linear regression is called ordinary least squares method and least squares applied to nonlinear regression is called non-linear least squares. Also in a linear regression model the non deterministic part of the model is called error term, disturbance or more simply noise. Both linear regression and non-linear regression are addressed in polynomial least squares, which also describes the variance in a prediction of the dependent variable (y axis) as a function of the independent variable (x axis) and the deviations (errors, noise, disturbances) from the estimated (fitted) curve.
Measurement processes that generate statistical data are also subject to error. Many of these errors are classified as random (noise) or systematic (bias), but other types of errors (e.g., blunder, such as when an analyst reports incorrect units) can also be important. The presence of missing data or censoring may result in biased estimates and specific techniques have been developed to address these problems.
Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data. In applying statistics to, e.g., a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model process to be studied. Populations can be diverse topics such as "all people living in a country" or "every atom composing a crystal." Statistics deals with all aspects of data including the planning of data collection in terms of the design of surveys and experiments.When census data cannot be collected, statisticians collect data by developing specific experiment designs and survey samples. Representative sampling assures that inferences and conclusions can reasonably extend from the sample to the population as a whole. An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements. In contrast, an observational study does not involve experimental manipulation.Two main statistical methods are used in data analysis: descriptive statistics, which summarize data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation (e.g., observational errors, sampling variation). Descriptive statistics are most often concerned with two sets of properties of a distribution (sample or population): central tendency (or location) seeks to characterize the distribution's central or typical value, while dispersion (or variability) characterizes the extent to which members of the distribution depart from its center and each other. Inferences on mathematical statistics are made under the framework of probability theory, which deals with the analysis of random phenomena.A standard statistical procedure involves the test of the relationship between two statistical data sets, or a data set and synthetic data drawn from idealized model. A hypothesis is proposed for the statistical relationship between the two data sets, and this is compared as an alternative to an idealized null hypothesis of no relationship between two data sets. Rejecting or disproving the null hypothesis is done using statistical tests that quantify the sense in which the null can be proven false, given the data that are used in the test. Working from a null hypothesis, two basic forms of error are recognized: Type I errors (null hypothesis is falsely rejected giving a "false positive") and Type II errors (null hypothesis fails to be rejected and an actual difference between populations is missed giving a "false negative"). Multiple problems have come to be associated with this framework: ranging from obtaining a sufficient sample size to specifying an adequate null hypothesis.Measurement processes that generate statistical data are also subject to error. Many of these errors are classified as random (noise) or systematic (bias), but other types of errors (e.g., blunder, such as when an analyst reports incorrect units) can also be important. The presence of missing data or censoring may result in biased estimates and specific techniques have been developed to address these problems.In applying statistics to a problem, it is common practice to start with a population or process to be studied. Populations can be diverse topics such as "all persons living in a country" or "every atom composing a crystal".
Ideally, statisticians compile data about the entire population (an operation called census). This may be organized by governmental statistical institutes. Descriptive statistics can be used to summarize the population data. Numerical descriptors include mean and standard deviation for continuous data types (like income), while frequency and percentage are more useful in terms of describing categorical data (like race).
When a census is not feasible, a chosen subset of the population called a sample is studied. Once a sample that is representative of the population is determined, data is collected for the sample members in an observational or experimental setting. Again, descriptive statistics can be used to summarize the sample data. However, the drawing of the sample has been subject to an element of randomness, hence the established numerical descriptors from the sample are also due to uncertainty. To still draw meaningful conclusions about the entire population, inferential statistics is needed. It uses patterns in the sample data to draw inferences about the population represented, accounting for randomness. These inferences may take the form of: answering yes/no questions about the data (hypothesis testing), estimating numerical characteristics of the data (estimation), describing associations within the data (correlation) and modeling relationships within the data (for example, using regression analysis). Inference can extend to forecasting, prediction and estimation of unobserved values either in or associated with the population being studied; it can include extrapolation and interpolation of time series or spatial data, and can also include data mining.When full census data cannot be collected, statisticians collect sample data by developing specific experiment designs and survey samples. Statistics itself also provides tools for prediction and forecasting through statistical models. To use a sample as a guide to an entire population, it is important that it truly represents the overall population. Representative sampling assures that inferences and conclusions can safely extend from the sample to the population as a whole. A major problem lies in determining the extent that the sample chosen is actually representative. Statistics offers methods to estimate and correct for any bias within the sample and data collection procedures. There are also methods of experimental design for experiments that can lessen these issues at the outset of a study, strengthening its capability to discern truths about the population. Sampling theory is part of the mathematical discipline of probability theory. Probability is used in mathematical statistics to study the sampling distributions of sample statistics and, more generally, the properties of statistical procedures. The use of any statistical method is valid when the system or population under consideration satisfies the assumptions of the method. The difference in point of view between classic probability theory and sampling theory is, roughly, that probability theory starts from the given parameters of a total population to deduce probabilities that pertain to samples. Statistical inference, however, moves in the opposite direction—inductively inferring from samples to the parameters of a larger or total population.
The basic steps of a statistical experiment are:
Planning the research, including finding the number of replicates of the study, using the following information: preliminary estimates regarding the size of treatment effects, alternative hypotheses, and the estimated experimental variability. Consideration of the selection of experimental subjects and the ethics of research is necessary. Statisticians recommend that experiments compare (at least) one new treatment with a standard treatment or control, to allow an unbiased estimate of the difference in treatment effects.
Design of experiments, using blocking to reduce the influence of confounding variables, and randomized assignment of treatments to subjects to allow unbiased estimates of treatment effects and experimental error. At this stage, the experimenters and statisticians write the experimental protocol that will guide the performance of the experiment and which specifies the primary analysis of the experimental data.
Performing the experiment following the experimental protocol and analyzing the data following the experimental protocol.
Further examining the data set in secondary analyses, to suggest new hypotheses for future study.
Documenting and presenting the results of the study.
Experiments on human behavior have special concerns. The famous Hawthorne study examined changes to the working environment at the Hawthorne plant of the Western Electric Company. The researchers were interested in determining whether increased illumination would increase the productivity of the assembly line workers. The researchers first measured the productivity in the plant, then modified the illumination in an area of the plant and checked if the changes in illumination affected productivity. It turned out that productivity indeed improved (under the experimental conditions). However, the study is heavily criticized today for errors in experimental procedures, specifically for the lack of a control group and blindness. The Hawthorne effect refers to finding that an outcome (in this case, worker productivity) changed due to observation itself. Those in the Hawthorne study became more productive not because the lighting was changed but because they were being observed.
Observational study
An example of an observational study is one that explores the association between smoking and lung cancer. This type of study typically uses a survey to collect observations about the area of interest and then performs statistical analysis. In this case, the researchers would collect observations of both smokers and non-smokers, perhaps through a cohort study, and then look for the number of cases of lung cancer in each group.[15] A case-control study is another type of observational study in which people with and without the outcome of interest (e.g. lung cancer) are invited to participate and their exposure histories are collected.Various attempts have been made to produce a taxonomy of levels of measurement. The psychophysicist Stanley Smith Stevens defined nominal, ordinal, interval, and ratio scales. Nominal measurements do not have meaningful rank order among values, and permit any one-to-one transformation. Ordinal measurements have imprecise differences between consecutive values, but have a meaningful order to those values, and permit any order-preserving transformation. Interval measurements have meaningful distances between measurements defined, but the zero value is arbitrary (as in the case with longitude and temperature measurements in Celsius or Fahrenheit), and permit any linear transformation. Ratio measurements have both a meaningful zero value and the distances between different measurements defined, and permit any rescaling transformation.
Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically, sometimes they are grouped together as categorical variables, whereas ratio and interval measurements are grouped together as quantitative variables, which can be either discrete or continuous, due to their numerical nature. Such distinctions can often be loosely correlated with data type in computer science, in that dichotomous categorical variables may be represented with the Boolean data type, polytomous categorical variables with arbitrarily assigned integers in the integral data type, and continuous variables with the real data type involving floating point computation. But the mapping of computer science data types to statistical data types depends on which categorization of the latter is being implemented.
Other categorizations have been proposed. For example, Mosteller and Tukey (1977)[distinguished grades, ranks, counted fractions, counts, amounts, and balances. Nelder (1990)[described continuous counts, continuous ratios, count ratios, and categorical modes of data. See also Chrisman (1998), van den Berg (1991). The issue of whether or not it is appropriate to apply different kinds of statistical methods to data obtained from different kinds of measurement procedures is complicated by issues concerning the transformation of variables and the precise interpretation of research questions. "The relationship between the data and what they describe merely reflects the fact that certain kinds of statistical statements may have truth values which are not invariant under some transformations. Whether or not a transformation is sensible to contemplate depends on the question one is trying to answer".Consider independent identically distributed (IID) random variables with a given probability distribution: standard statistical inference and estimation theory defines a random sample as the random vector given by the column vector of these IID variables. The population being examined is described by a probability distribution that may have unknown parameters.
A statistic is a random variable that is a function of the random sample, but not a function of unknown parameters. The probability distribution of the statistic, though, may have unknown parameters.
Consider now a function of the unknown parameter: an estimator is a statistic used to estimate such function. Commonly used estimators include sample mean, unbiased sample variance and sample covariance.
A random variable that is a function of the random sample and of the unknown parameter, but whose probability distribution does not depend on the unknown parameter is called a pivotal quantity or pivot. Widely used pivots include the z-score, the chi square statistic and Student's t-value.
Between two estimators of a given parameter, the one with lower mean squared error is said to be more efficient. Furthermore, an estimator is said to be unbiased if its expected value is equal to the true value of the unknown parameter being estimated, and asymptotically unbiased if its expected value converges at the limit to the true value of such parameter.
Other desirable properties for estimators include: UMVUE estimators that have the lowest variance for all possible values of the parameter to be estimated (this is usually an easier property to verify than efficiency) and consistent estimators which converges in probability to the true value of such parameter.
This still leaves the question of how to obtain estimators in a given situation and carry the computation, several methods have been proposed: the method of moments, the maximum likelihood method, the least squares method and the more recent method of estimating equations.
..... ( In dimensional analysis we are only concerned with the nature of the dimension ; its quality not its quantity.....)
Head of SAL Chemical Analysis Unit conferring with Euratom inspectors in the Uranium Laboratory. (Seibersdorf Analytical Laboratory, Seibersdorf, Austria, 12 March 2007)
Photo Credit: Dean Calma/IAEA
Andreas Schleicher, SDirector and Special Advisor on Education Policy to the OECD's Secretary-General.
Vesuvio Café in North Beach, San Francisco.
Photographs in this collection have been produced by Heather Do, Connor Rowe, Kathleen Markham, Alison Lowrie, Kenneth Chiu, Katie Salmond, Diana Chavez, Elena Toffalori, Ashley Vink, Aimee O'Dea, Liz Dolinar, Allison Barden, Justine Khoury, Daniele Alaniz-Roux, and Justin Thach at the request of Michael Ashley for the UC Berkeley Anthropology 136e class, Spring 2011. The purpose was to digitally document the cultural heritage of Vesuvio Café to not only document the cultural history embeded into the ageless walls but also to connect spatially the symbiotic relationship that preserves the legacy of beatnik culture today.
Vesuvio Cafe, (37.79757°N 122.40625°W), located in the North Beach region of San Francisco Bay, is a cultural bastion preserving the cultural heritage of bohemian era and the beatnik culture that generated its establishment by Henri Lenoir in 1949 and made infamous by the renown authors such as Jack Kerouac from which the adjacent alley is named. The building in which the bar is housed is otherwise known as the Cavalri building built in 1913 and expanded to a second story in 1918 and designed by Zanolini with Italian Renaissance revival elements. The transient existence of these unkempt literary members and their constituents is reflected in the liminal location of the former saloon restaurant at the border between the vagrant Chinese- Italian communities; by 1970[1], most of the diverse cultures regressed into economical housing . Vesuvio Café despite its rich history back to the 1950’s , are not historically preserved site; in fact, they were rented until 1999[2] by managers Chris and Janet Clyde, whose proprietary hopes to protect the building from other commercial interest. Over the years, Vesuvio has undergone its share of renovations and damages such as the 1999 retrofitting for earthquake safety or even the 1973 damage dealt to the building by an errant bus[3]. Over the years, the "I'll never forget after the retro-fitting, one man came in, he was about 55 years old and in a business suit," Clyde said. "He actually had tears in his eyes when he looked at the place. He said, `You didn't change anything.' Vesuvio has kept its character as a neighborhood bar.”[4]
Photographs in this collection were shot on April 11, 2011 between 7:30 am and 5:00 pm Pacific Time under variable natural lighting due to cloudy skies with intermittent periods of morning exposure conditions. Photos were captured on the following cameras: Canon DSLR XTI/T2i, S95, Sony Cybershot, Canon Powershot. Lenses used include: Macro 60mm, Telephoto 70-200, Canon T2i 18-55mm, Canon XTI 17-85mm. A tripod was used for timelapse, Gigapan, macro, telephoto, HDR, and photogrammetry shots. iPhones were also used for documentation shots and Geo-tagging. The photos were post-processed in Adobe Photoshop Lightroom 3.
Description written by Kenneth Chiu, following Addison’s proposed virtual heritage metadata format in his chapter “The Vanishing Virtual” in New Heritage: New Media and Cultural Heritage, edited by Kalay, et al., and published by Routledge in 2007.
All photos Copyright ©2011 Center for Digital Archaeology, Berkeley CA, licensed under Creative Commons BY-NC 3.0 For more information contact Center for Digital Archaeology, Berkeley, CA, 94720 or visit www.codifi.info/licensing
All photos Copyright ©2011 Center for Digital Archaeology, Berkeley CA
Creative Commons creativecommons.org/licenses/by-nc/3.0/
For more information contact Center for Digital Archaeology, Berkeley, CA,
94720 or visit www.codifi.info/licensing
For more facts and information about Alcatraz, please visit
[1] news.google.com/newspapers?id=WKI_AAAAIBAJ&sjid=JlYMA...
[2] news.google.com/newspapers?id=M0IfAAAAIBAJ&sjid=yc8EA...
[3] news.google.com/newspapers?id=hwsrAAAAIBAJ&sjid=cZoFA...
[4] www.highbeam.com/doc/1P2-4550312.html
Original Filename:
ANTHRO136SP11_VVO_Cam25-24.dng
Ordnance Survey is a partner in the CityVerve project, offering a geospatial framework. We helped visualise various use cases. This is a frame from an animation that showed how you might analyse parking trends in a city using a dashboard view.
IFPRI Research Fellow, Danielle Resnick introduces the speakers at the even, Donor Approaches to Political Economy Analysis.
IFPRI hosted a policy seminar titled “Donor Approaches to Political Economy Analysis” on February 5, 2015. For more information, please visit: www.ifpri.org/event/donor-approaches-political-economy-an...
©IFPRI/Xinyuan Shang
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