Data Mining
A class of database applications that look for hidden patterns in a group ofdata that can be used to predict future behavior. For example, data mining software can help retail companies find customers with common interests. The term is commonly misused to describe software that presents data in new ways. True data mining software doesn’t just change the presentation, but actually discovers previously unknown relationships among the data.
Data mining is popular in the science and mathematical fields but also is utilized increasingly by marketers trying to distill useful consumer data from Web sites. [ Definition from webopedia ]
Overview
Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information – information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
Continuous Innovation
Although data mining is a relatively new term, the technology is not. Companies have used powerful computers to sift through volumes of supermarket scanner data and analyze market research reports for years. However, continuous innovations in computer processing power, disk storage, and statistical software are dramatically increasing the accuracy of analysis while driving down the cost.
Example
For example, one Midwest grocery chain used the data mining capacity of Oracle software to analyze local buying patterns. They discovered that when men bought diapers on Thursdays and Saturdays, they also tended to buy beer. Further analysis showed that these shoppers typically did their weekly grocery shopping on Saturdays. On Thursdays, however, they only bought a few items. The retailer concluded that they purchased the beer to have it available for the upcoming weekend. The grocery chain could use this newly discovered information in various ways to increase revenue. For example, they could move the beer display closer to the diaper display. And, they could make sure beer and diapers were sold at full price on Thursdays.
Data, Information, and Knowledge
Data
Data are any facts, numbers, or text that can be processed by a computer. Today, organizations are accumulating vast and growing amounts of data in different formats and different databases. This includes:
- operational or transactional data such as, sales, cost, inventory, payroll, and accounting
- nonoperational data, such as industry sales, forecast data, and macro economic data
- meta data – data about the data itself, such as logical database design or data dictionary definitions
Information
The patterns, associations, or relationships among all this data can provide information. For example, analysis of retail point of sale transaction data can yield information on which products are selling and when.
Knowledge
Information can be converted into knowledge about historical patterns and future trends. For example, summary information on retail supermarket sales can be analyzed in light of promotional efforts to provide knowledge of consumer buying behavior. Thus, a manufacturer or retailer could determine which items are most susceptible to promotional efforts.
Data Warehouses
Dramatic advances in data capture, processing power, data transmission, and storage capabilities are enabling organizations to integrate their various databases into data warehouses. Data warehousing is defined as a process of centralized data management and retrieval. Data warehousing, like data mining, is a relatively new term although the concept itself has been around for years. Data warehousing represents an ideal vision of maintaining a central repository of all organizational data. Centralization of data is needed to maximize user access and analysis. Dramatic technological advances are making this vision a reality for many companies. And, equally dramatic advances in data analysis software are allowing users to access this data freely. The data analysis software is what supports data mining.
What can data mining do?
Data mining is primarily used today by companies with a strong consumer focus – retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among “internal” factors such as price, product positioning, or staff skills, and “external” factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to “drill down” into summary information to view detail transactional data.
With data mining, a retailer could use point-of-sale records of customer purchases to send targeted promotions based on an individual’s purchase history. By mining demographic data from comment or warranty cards, the retailer could develop products and promotions to appeal to specific customer segments.
For example, Blockbuster Entertainment mines its video rental history database to recommend rentals to individual customers. American Express can suggest products to its cardholders based on analysis of their monthly expenditures.
WalMart is pioneering massive data mining to transform its supplier relationships. WalMart captures point-of-sale transactions from over 2,900 stores in 6 countries and continuously transmits this data to its massive 7.5 terabyte Teradata data warehouse. WalMart allows more than 3,500 suppliers, to access data on their products and perform data analyses. These suppliers use this data to identify customer buying patterns at the store display level. They use this information to manage local store inventory and identify new merchandising opportunities. In 1995, WalMart computers processed over 1 million complex data queries.
The National Basketball Association (NBA) is exploring a data mining application that can be used in conjunction with image recordings of basketball games. The Advanced Scout software analyzes the movements of players to help coaches orchestrate plays and strategies. For example, an analysis of the play-by-play sheet of the game played between the New York Knicks and the Cleveland Cavaliers on January 6, 1995 reveals that when Mark Price played the Guard position, John Williams attempted four jump shots and made each one! Advanced Scout not only finds this pattern, but explains that it is interesting because it differs considerably from the average shooting percentage of 49.30% for the Cavaliers during that game.
By using the NBA universal clock, a coach can automatically bring up the video clips showing each of the jump shots attempted by Williams with Price on the floor, without needing to comb through hours of video footage. Those clips show a very successful pick-and-roll play in which Price draws the Knick’s defense and then finds Williams for an open jump shot.
How does data mining work?
While large-scale information technology has been evolving separate transaction and analytical systems, data mining provides the link between the two. Data mining software analyzes relationships and patterns in stored transaction data based on open-ended user queries. Several types of analytical software are available: statistical, machine learning, and neural networks. Generally, any of four types of relationships are sought:
- Classes: Stored data is used to locate data in predetermined groups. For example, a restaurant chain could mine customer purchase data to determine when customers visit and what they typically order. This information could be used to increase traffic by having daily specials.
- Clusters: Data items are grouped according to logical relationships or consumer preferences. For example, data can be mined to identify market segments or consumer affinities.
- Associations: Data can be mined to identify associations. The beer-diaper example is an example of associative mining.
- Sequential patterns: Data is mined to anticipate behavior patterns and trends. For example, an outdoor equipment retailer could predict the likelihood of a backpack being purchased based on a consumer’s purchase of sleeping bags and hiking shoes.
Data mining consists of five major elements:
- Extract, transform, and load transaction data onto the data warehouse system.
- Store and manage the data in a multidimensional database system.
- Provide data access to business analysts and information technology professionals.
- Analyze the data by application software.
- Present the data in a useful format, such as a graph or table.
Different levels of analysis are available:
- Artificial neural networks: Non-linear predictive models that learn through training and resemble biological neural networks in structure.
- Genetic algorithms: Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of natural evolution.
- Decision trees: Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset. Specific decision tree methods include Classification and Regression Trees (CART) and Chi Square Automatic Interaction Detection (CHAID) . CART and CHAID are decision tree techniques used for classification of a dataset. They provide a set of rules that you can apply to a new (unclassified) dataset to predict which records will have a given outcome. CART segments a dataset by creating 2-way splits while CHAID segments using chi square tests to create multi-way splits. CART typically requires less data preparation than CHAID.
- Nearest neighbor method: A technique that classifies each record in a dataset based on a combination of the classes of the k record(s) most similar to it in a historical dataset (where k 1). Sometimes called the k-nearest neighbor technique.
- Rule induction: The extraction of useful if-then rules from data based on statistical significance.
- Data visualization: The visual interpretation of complex relationships in multidimensional data. Graphics tools are used to illustrate data relationships.
What technological infrastructure is required?
Today, data mining applications are available on all size systems for mainframe, client/server, and PC platforms. System prices range from several thousand dollars for the smallest applications up to $1 million a terabyte for the largest. Enterprise-wide applications generally range in size from 10 gigabytes to over 11 terabytes. NCR has the capacity to deliver applications exceeding 100 terabytes. There are two critical technological drivers:
- Size of the database: the more data being processed and maintained, the more powerful the system required.
- Query complexity: the more complex the queries and the greater the number of queries being processed, the more powerful the system required.
Relational database storage and management technology is adequate for many data mining applications less than 50 gigabytes. However, this infrastructure needs to be significantly enhanced to support larger applications. Some vendors have added extensive indexing capabilities to improve query performance. Others use new hardware architectures such as Massively Parallel Processors (MPP) to achieve order-of-magnitude improvements in query time. For example, MPP systems from NCR link hundreds of high-speed Pentium processors to achieve performance levels exceeding those of the largest supercomputers.
Reference Links:
Introduction to Data Mining
http://www-pcc.qub.ac.uk/tec/courses/datamining/stu_notes/dm_book_1.html
Information about data mining research, applications, and tools:
http://www.cs.bham.ac.uk/~anp/TheDataMine.html
http://www.think.com/html/data_min/data_min.htm
http://direct.boulder.ibm.com/bi/
http://www.software.ibm.com/data/
http://coral.postech.ac.kr/~swkim/software.html
http://www.cs.uah.edu/~infotech/mineproj.html
http://info.gte.com/~kdd/index.html
Data Sets to test data mining algorithms:
Data mining journal (Read Usama M. Fayyad’s editorial.):
Interesting application of data mining:
Data mining papers:
http://www.satafe.edu/~kurt/index.shtml
Data mining conferences:
Conference on very large databases:
Sites for datamining vendors and products:
American Heuristics (Profiler)
Angoss software (Knowledge Seeker)
Attar Software (XpertRule Profiler)
Business Objects (BusinessMiner)
DataMind (DataMind Professional)
HNC Software (DataMarksman, Falcon)
HyperParallel (Discovery)
Information Discovery Inc. (Information Discovery System)
Integral Solutions (Clementine)
IBM (Intelligent Data Miner)
Lucent Technologies (Interactive Data Visualization)
NCR (Knowledge Discovery Benchmark)
NeoVista Sloutions (Decision Series)
Nestor (Prism)
Pilot Software (Pilot Discovery Server)
Seagate Software Systems (Holos 5.0)
SPSS (SPSS)
Thinking Machines (Darwin)
Today’s Word : impetuous
Word: impetuous
PRONUNCIATION:
MEANING:
ETYMOLOGY:
USAGE:
Eric Andersson; Why Taylor Can’t Find Love; Us Weekly (New York); Nov 19, 2012.
Explore “impetuous” in the Visual Thesaurus.
A Tiny Programmable Fly’s Eye
A novel curved artificial compound eye (CurvACE) has been conceived by a collaboration implying researchers from CNRS, Aix-Marseille Université, EPFL at Lausanne, Fraunhofer Institute at Jena and Université de Tuebingen. Compared to single-lens eyes, compound eyes offer lower resolution, but significantly larger fields of view, thin package, and with negligible distortion. Futhermore, CurvACE has embedded and programmable vision processing.
CurvACE, the first artificial compound eye able to measure, like a flying insect, the apparent velocity of objects as they move across the panoramic eye. (Credit: © CurvACE)
While consumer cameras are inspired from the single-lens mammalian eye, most animal species use compound eyes, which consist of a dense mosaic of tiny eyes. Compared to single-lens eyes, compound eyes offer lower resolution, but significantly larger fields of view, thin package, and with negligible distortion, all features which are very useful for motion detection in tasks such as collision avoidance, distance estimation, and landing. Attempts have recently been made to develop artificial compound eyes, but none of the solutions proposed so far included fast motion detection in a very large range of illuminations as insects do.
The novel curved artificial compound eye (CurvACE) features a panoramic, hemispherical field of view with a resolution identical to that of the fruitfly in less than 1 mm thickness. Additionally, it can extract images 3 times faster than fruitfly, and includes neuromorphic photoreceptors that allow motion perception in a wide range of environments from a sunny day to moon light.
Furthermore, the artificial compound eye possesses embedded and programmable vision processing, which allows customizable integration in a broad range of applications where motion detection is important, such as mobile robots and micro air vehicles, home automation, surveillance, medical instruments, and smart clothing.
CURVACE is funded by the Future and Emerging Technologies (FET) programme within the Seventh Framework Programme for Research of the European Commission, under FET-Open grant number: 237940. The members of the consortium CurvACE are EPFL at Lausanne, Fraunhofer Institute at Jena, Université de Tuebingen, CNRS and Aix-Marseille Université.
Understanding the Past and Predicting the Future by Looking Across Space and Time
Studying complex systems like ecosystems can get messy, especially when trying to predict how they interact with other big unknowns like climate change.In a new paper published this week (May 20) in the Proceedings of the National Academy of Sciences, researchers from the University of Wisconsin-Madison and elsewhere validate a fundamental assumption at the very heart of a popular way to predict relationships between complex variables.
Studying complex systems like ecosystems can get messy, especially when trying to predict how they interact with other big unknowns like climate change. (Credit: © arquiplay77 / Fotolia)
To model how climate changes may impact biodiversity, researchers like Jessica Blois and John W. (Jack) Williams routinely use an approach called “space-for-time substitution.” The idea behind this method is to use the information in current geographic distributions of species to build a model that can predict climate-driven ecological changes in the past or future. But does it really work?
“It’s a necessary assumption, but it’s generally untested,” says lead study author Blois, a former postdoctoral fellow with Williams at UW-Madison. She is now an assistant professor at the University of California, Merced. “Yet we’re using this every day when we make predictions about biodiversity going into the future with climate change.”
Their results should give other ecologists — and potentially others such as economists who use similar models — more confidence in their methods.
“At these spatial and temporal scales, the space-for-time assumption does work well,” says Williams, professor of geography and director of the Center for Climatic Research at the UW-Madison Nelson Institute for Environmental Studies. “Our fossil data did support the idea that you can use spatial relationships as a source of information for making these predictions for the future.”
Their research focus is paleoecology, the study of ancient ecosystems. By looking at fossilized pollen trapped in cores of sediment from the bottoms of lakes, the scientists reconstructed information about the plant communities present at locations across eastern North America during the past 21,000 years.
If climate has influenced communities the same way across space and through time, Blois explains, then a model based on the spatial data should make the same predictions as a model based on their temporal data. And in fact, they did.
The space-for-time model explained about 72 percent of the variation seen in their time data, and the remainder is likely due to other biological and environmental factors that the simplified model does not include, Blois says.
Though the testing does not capture all the ways space-for-time substitutions are used in other predictive fields, she says that the results are very encouraging for questions spanning large geographic and time scales — scales at which collecting good temporal data can be very challenging.
“We found that at these broad time scales we’re looking at, that space does substitute for time relatively well,” Blois says. “It makes me more confident in my analyses going forward.”
courtesy: sciencedaily
Detection of the Cosmic Gamma Ray Horizon
How much light has been emitted by all galaxies since the cosmos began? After all, almost every photon (particle of light) from ultraviolet to far infrared wavelengths ever radiated by all galaxies that ever existed throughout cosmic history is still speeding through the Universe today. If we could carefully measure the number and energy (wavelength) of all those photons — not only at the present time, but also back in time — we might learn important secrets about the nature and evolution of the Universe, including how similar or different ancient galaxies were compared to the galaxies we see today.
The attached figure illustrates how energetic gamma rays (dashed lines) from a distant blazar strike photons of extragalactic background light (wavy lines) and produce pairs of electrons and positrons. The energetic gamma rays that are not attenuated by this process strike the upper atmosphere, producing a cascade of charged particles which make a cone of erenkov light that is detected by the array of imaging atmospheric erenkov telescopes on the ground. (Credit: Nina McCurdy and Joel R. Primack/UC-HiPACC; Blazar: Frame from a conceptual animation of 3C 120 created by Wolfgang Steffen/UNAM)
That bath of ancient and young photons suffusing the Universe today is called the extragalactic background light (EBL). An accurate measurement of the EBL is as fundamental to cosmology as measuring the heat radiation left over from the Big Bang (the cosmic microwave background) at radio wavelengths. A new paper, called “Detection of the Cosmic γ-Ray Horizon from Multiwavelength Observations of Blazars,” by Alberto Dominguez and six coauthors, just published today by theAstrophysical Journal – based on observations spanning wavelengths from radio waves to very energetic gamma rays, obtained from several NASA spacecraft and several ground-based telescopes — describes the best measurement yet of the evolution of the EBL over the past 5 billion years.
Directly measuring the EBL by collecting its photons with a telescope, however, poses towering technical challenges — harder than trying to see the dim band of the Milky Way spanning the heavens at night from midtown Manhattan. Earth is inside a very bright galaxy with billions of stars and glowing gas. Indeed, Earth is inside a very bright solar system: sunlight scattered by all the dust in the plane of Earth’s orbit creates the zodiacal light radiating across the optical spectrum down to long-wavelength infrared. Therefore ground-based and space-based telescopes have not succeeded in reliably measuring the EBL directly.
So, astrophysicists developed an ingenious work-around method: measuring the EBL indirectly through measuring the attenuation of — that is, the absorption of — very high energy gamma rays from distant blazars. Blazars are supermassive black holes in the centers of galaxies with brilliant jets directly pointed at us like a flashlight beam. Not all the high-energy gamma rays emitted by a blazar, however, make it all the way across billions of light-years to Earth; some strike a hapless EBL photon along the way. When a high-energy gamma ray photon from a blazar hits a much lower energy EBL photon, both are annihilated and produce two different particles: an electron and its antiparticle, a positron, which fly off into space and are never heard from again. Different energies of the highest-energy gamma rays are waylaid by different energies of EBL photons. Thus, measuring how much gamma rays of different energies are attenuated or weakened from blazars at different distances from Earth indirectly gives a measurement of how many EBL photons of different wavelengths exist along the line of sight from blazar to Earth over those different distances.
Observations of blazars by NASA’s Fermi Gamma Ray Telescope spacecraft for the first time detected that gamma rays from distant blazars are indeed attenuated more than gamma rays from nearby blazars, a result announced on November 30, 2012, in a paper published in Science, as theoretically predicted.
Now, the big news — announced in today’s Astrophysical Journal paper — is that the evolution of the EBL over the past 5 billion years has been measured for the first time. That’s because looking farther out into the Universe corresponds to looking back in time. Thus, the gamma ray attenuation spectrum from farther distant blazars reveals how the EBL looked at earlier eras.
This was a multistep process. First, the coauthors compared the Fermi findings to intensity of X-rays from the same blazars measured by X-ray satellites Chandra, Swift, Rossi X-ray Timing Explorer, and XMM/Newton and lower-energy radiation measured by other spacecraft and ground-based observatories. From these measurements, Dominguez et al. were able to calculate the blazars’ original emitted, unattenuated gamma-ray brightnesses at different energies.
The coauthors then compared those calculations of unattenuated gamma-ray flux at different energies with direct measurements from special ground-based telescopes of the actual gamma-ray flux received at Earth from those same blazars. When a high-energy gamma ray from a blazar strikes air molecules in the upper regions of Earth’s atmosphere, it produces a cascade of charged subatomic particles. This cascade of particles travels faster than the speed of light in air (which is slower than the speed of light in a vacuum). This causes a visual analogue to a “sonic boom”: bursts of a special light called Čerenkov radiation. This Čerenkov radiation was detected by imaging atmospheric Čerenkov telescopes (IACTs), such as HESS (High Energy Stereoscopic System) in Namibia, MAGIC (Major Atmospheric Gamma Imaging Čerenkov) in the Canary Islands, and VERITAS (Very Energetic Radiation Imaging Telescope Array Systems) in Arizona.
Comparing the calculations of the unattenuated gamma rays to actual measurements of the attenuation of gamma rays and X-rays from blazars at different distances allowed Dominquez et al. to quantify the evolution of the EBL — that is, to measure how the EBL changed over time as the Universe aged — out to about 5 billion years ago (corresponding to a redshift of about z = 0.5). “Five billion years ago is the maximum distance we are able to probe with our current technology,” Domínguez said. “Sure, there are blazars farther away, but we are not able to detect them because the high-energy gamma rays they are emitting are too attenuated by EBL when they get to us — so weakened that our instruments are not sensitive enough to detect them.” This measurement is the first statistically significant detection of the so-called “Cosmic Gamma Ray Horizon” as a function of gamma-ray energy. The Cosmic Gamma Ray Horizon is defined as the distance at which roughly one-third (or, more precisely, 1/e — that is, 1/2.718 — where e is the base of the natural logarithms) of the gamma rays of a particular energy have been attenuated.
This latest result confirms that the kinds of galaxies observed today are responsible for most of the EBL over all time. Moreover, it sets limits on possible contributions from many galaxies too faint to have been included in the galaxy surveys, or on possible contributions from hypothetical additional sources (such as the decay of hypothetical unknown elementary particles).
courtesy: sciencedaily
Scientists found Billion-Year-Old Water
Scientists working 2.4 kilometers below Earth’s surface in a Canadian mine have tapped a source of water that has remained isolated for at least a billion years. The researchers say they do not yet know whether anything has been living in it all this time, but the water contains high levels of methane and hydrogen — the right stuff to support life.
The ancient water contains chemicals that could support life without sunlight. ( Pic courtesy: National Geographic )
Micrometer-scale pockets in minerals billions of years old can hold water that was trapped during the minerals’ formation. But no source of free-flowing water passing through interconnected cracks or pores in Earth’s crust has previously been shown to have stayed isolated for more than tens of millions of years.
“We were expecting these fluids to be possibly tens, perhaps even hundreds of millions of years of age,” says Chris Ballentine, a geochemist at the University of Manchester, UK. He and his team carefully captured water flowing through fractures in the 2.7-billion-year-old sulphide deposits in a copper and zinc mine near Timmins, Ontario, ensuring that the water did not come into contact with mine air.
To date the water, the team used three lines of evidence, all based on the relative abundances of various isotopes of noble gases present in the water. The authors determined that the fluid could not have contacted Earth’s atmosphere — and so been at the planet’s surface — for at least 1 billion years, and possibly for as long as 2.64 billion years, not long after the rocks it flows through formed.
Teeming With Life?
Geologists have long known that a lot of water can be present in continental crust, locked away in microscopic voids in minerals, pore spaces between minerals, and veins and fractures in the rock. But what’s been unclear is the age of such water, said geochemist Steven Shirey, a senior scientist at the Carnegie Institution for Science.
“The question is how old is it? Is it water that’s part of current circulation with surface water? Or is it water that retains old chemistry and potential biota?” said Shirey, who was not involved in the study.
The new findings, detailed in this week’s issue of the journal Nature, is evidence that ancient pockets of water can remain isolated in the Earth’s crust for billions of years.
“That’s the really exciting part about this study,” Shirey said.
Sherwood Lollar and her team are testing the mine water to see if they can find evidence of living microbes. If life does exist in the water, she said, it could be similar to microbes previously found in far younger water flowing from a mine located 1.74 miles (2.8 kilometers) beneath South Africa.
Those microbes could survive without light from the sun, subsisting instead on chemicals created through the interactions between water and rock.
Such “buried” microbial communities are rare, and fascinating for scientists because they are often not interconnected.
“Each one of them may have a different age and a different history,” Sherwood Lollar said. “It will be fascinating for us to look at the microbiology in each of them … It’ll tell us something about the evolution of life and the colonization of the subsurface.”
Expanding Horizons
The Timmins Mine water could also help scientists understand how much of the subsurface of the Earth is actually inhabited by life. The answer to that question has implications for life on other planets, such as Mars, scientists say.
“It opens up your horizons for what’s possible,” Shirey said. “If you think that you can have microbial life throughout the entire crust of the Earth, then all of a sudden it becomes very possible that life could live on other planets under the right condition.”
That raises questions about potential life in relatively warm rock located beneath the cold surface of Mars, where liquid water could still exist.
“We’re looking at billion-year-old rock here and we can still find flowing water that’s full of the kind of energy that can support life,” Sherwood Lollar said.
“If we find Martian rocks of the same age and in places of similar geology and mineralogy to our site, then there’s every reason to think that we might be able to find the same thing in the deep subsurface of Mars.”
courtesy: Nature
Fragments of Continents Hidden Under Lava in Indian Ocean
The islands Reunion and Mauritius, both well-known tourist destinations, are hiding a micro-continent, which has now been discovered. The continent fragment known as Mauritia detached about 60 million years ago while Madagascar and India drifted apart, and had been hidden under huge masses of lava.
The coloured track (left colour scale) west of Reunion is the calculated movement of the Reunion hotspot. The black lines with yellow circles and the red circle indicate the corresponding calculated track on the African plate and the Indian plate, respectively. The numbers in the circles are ages in millions of years. The areas with topography just below the sea surface are now regarded as continental fragments. (Credit: © GFZ/Steinberger)
Such micro-continents in the oceans seem to occur more frequently than previously thought, says a study in the latest issue of Nature Geoscience.
The break-up of continents is often associated with mantle plumes: These giant bubbles of hot rock rise from the deep mantle and soften the tectonic plates from below, until the plates break apart at the hotspots. This is how Eastern Gondwana broke apart about 170 million years ago. At first, one part was separated, which in turn fragmented into Madagascar, India, Australia and Antarctica, which then migrated to their present position.
Plumes currently situated underneath the islands Marion and Reunion appear to have played a role in the emergence of the Indian Ocean. If the zone of the rupture lies at the edge of a land mass (in this case Madagascar / India), fragments of this land mass may be separated off. The Seychelles are a well-known example of such a continental fragment.
A group of geoscientists from Norway, South Africa, Britain and Germany have now published a study that suggests, based on the study of lava sand grains from the beach of Mauritius, the existence of further fragments. The sand grains contain semi-precious zircons aged between 660 and 1970 million years, which is explained by the fact that the zircons were carried by the lava as it pushed through subjacent continental crust of this age.
This dating method was supplemented by a recalculation of plate tectonics, which explains exactly how and where the fragments ended up in the Indian Ocean. Dr. Bernhard Steinberger of the GFZ German Research Centre for Geosciences and Dr. Pavel Doubrovine of Oslo University calculated the hotspot trail: “On the one hand, it shows the position of the plates relative to the two hotspots at the time of the rupture, which points towards a causal relation,” says Steinberger. “On the other hand, we were able to show that the continent fragments continued to wander almost exactly over the Reunion plume, which explains how they were covered by volcanic rock.” So what was previously interpreted only as the trail of the Reunion hotspot, are continental fragments which were previously not recognized as such because they were covered by the volcanic rocks of the Reunion plume. It therefore appears that such micro-continents in the ocean occur more frequently than previously thought.
Courtesy: science Daily
Discussing the Divine Comedy with Dante- World’s Famous People Art Image
Discussing the Divine Comedy with Dante
( Click on image to enlarge)
There are totally 103 famous people present in the image( a Taiwanese Oil Painting by Dai Dudu, Li Tiezi, and Zhang in the year 2006 ), You may recognise some of them, but not all of them. I want to share this art along with their names , here it is
( Click on image to enlarge)
1 Bill Gates, Microsoft founder
2 Homer, Greek poet
3 Cui Jian, Chinese singer
4 Vladimir Lenin, Russian revolutionary
5 Pavel Korchagin, Russian artist
6 Bill Clinton, former US President
7 Peter the Great, Russian leader
8 Margaret Thatcher, former British Prime Minister
9 Bruce Lee, martial arts actor
10 Winston Churchill, former British Prime Minister
11 Henri Matisse, French artist
12 Gengis Khan, Mongolian warlord
13 Napoleon Bonaparte, French military leader
14 Che Guevara, Marxist revolutionary
15 Fidel Castro, former Prime Minister and President of Cuba
16 Marlon Brando, actor
17 Yasser Arafat, former leader of Palastine
18 Julius Caesar, Roman emperor
19 Claire Lee Chennault, Second World War US Lieutenant
20 Luciano Pavarotti, singer
21 George W. Bush, former US President
22 The Prince of Wales
23 Liu Xiang, Chinese hurdler
24 Kofi Annan, former UN Secretary General
25 Zhang An (the painter)
26 Mikhail Gorbachev, former Russian leader
27 Li Tiezi (the painter)
28 Dante Alighieri, Florentine poet
29 Dai Dudu (the painter)
30 Pele, footballer
31 Guan Yu, Chinese warlord
32 Ramses II, Egyptian pharoah
33 Charles De Gaulle, French general
34 Albert Nobel, Swedish chemist, founder of Nobel prizes
35 Franklin Roosevelt, former US President
36 Ernest Hemingway, American novelist
37 Elvis Presley, American singer
38 Robert Oppenheimer, American physicist
39 William Shakespeare, English playwright
40 Wolfgang Amadeus Mozart, Austrian composer
41 Steven Spielberg, American film director
42 Pablo Picasso, Spanish painter
43 Marie Curie, physicist and pioneer of radioactivity
44 Zhou Enlai, first Premier of the People’s Republic of China
45 Johann Wolfgang Von Goethe, German writer
46 Laozi, Chinese philosopher
47 Marilyn Monroe, American actress
48 Salvador Dali, Spanish painter
49 Dowager Cixi, former ruler of China
50 Ariel Sharon, former Israeli Prime Minister
51 Qi Baishi, Chinese painter
52 Qin Shi Huang, former Emperor of China
53 Mother Teresa, Roman Catholic Missionary (India- Missionaries of Charity)
54 Song Qingling, Chinese politician
55 Rabindranath Tagore, Indian poet
56 Otto Von Bismarck, German statesman
57 Run Run Shaw, Chinese media mogul
58 Jean-Jacques Rousseau, Swiss philosopher
59 Audrey Hepburn, Belgian-born actress
60 Ludwig Van Beethoven, German composer
61 Adolf Hitler, Nazi leader
62 Benito Mussolini, Italian fascist politician
63 Saddam Hussein, former President of Iraq
64 Maxim Gorky, Russian writer
65 Sun Yat-Sen, Chinese revolutionary
66 Den Xiaoping, Chinese revolutionary
67 Alexander Pushkin, Russian author
68 Lu Xun, Chinese writer
69 Joseph Stalin, former Soviet Union leader
70 Leonardo Da Vinci, Italian painter
71 Karl Marx, German philosopher
72 Friedrich Nietzche, German philosopher
73 Abraham Lincoln, former US President
74 Mao Zedong, Chinese dictator
75 Charlie Chaplin, British actor
76 Henry Ford, founder of Ford motor company
77 Lei Feng, Chinese soldier
78 Norman Bethune, Canadian physician
79 Sigmund Freud, Austrian psychiatrist
80 Juan Antonio Samaranch, former International Olympic Committee president
81 Chiang Kai Shek, Chinese general
82 Queen Elizabeth II, Queen of the United Kingdom
83 Leo Tolstoy, Russian novelist
84 Li Bai, Chinese poet
85 Corneliu Baba, Romanian painter
86 Auguste Rodin, French artist
87 Dwight Eisenhower, former US President
88 Michael Jordan, American basketball player
89 Hideki Tojo, former Japan Prime Minister
90 Michelangelo, Italian Renaissance painter
91 Yi Sun-Sin, Korean naval commander
92 Mike Tyson, American boxer
93 Vladimir Putin, Russian Prime Minister
94 Hans Christian Andersen, Danish author
95 Shirley Temple, American actress
96 Albert Einstein, German physicist
97 Moses, Hebrew religious leader
98 Confucius, Chinese philosopher
99 Mohandas Karamchand Gandhi, Indian Nationalist Movement leader
100 Vincent Van Gogh, Dutch painter
101 Toulouse Lautrec, French painter
102 Marcel Duchamp, French artist
103 Behind George Bush (former US President) is Osama bin Laden ( founder of Al Qaeda a global militant Islamist organisation )
Math Behind Growth of ‘Coffee Rings’
Last year, a team of University of Pennsylvania physicists showed how to undo the “coffee-ring effect,” a commonplace occurrence when drops of liquid with suspended particles dry, leaving a ring-shaped stain at the drop’s edges. Now the team is exploring how those particles stack up as they reach the drop’s edge, and they discovered that different particles make smoother or rougher deposition profiles at the drop edge depending on their shape.
Slightly stretched particles exhibited a rare Kardar-Parisi-Zhang growth process. (Credit: Art: Felice Macera)
These resultant growth profiles offer tests of deep mathematical ideas about growing interfaces and are potentially relevant for many commercial and industrial coating applications.
The new research was conducted by the members of the original team: professor Arjun Yodh, director of the Laboratory for Research on the Structure of Matter; doctoral candidates Peter Yunker and Matthew Lohr; and postdoctoral fellow Tim Still, all of the Department of Physics and Astronomy in Penn’s School of Arts and Sciences. New to the collaboration were professor D.J. Durian of the Department of Physics and Astronomy and Alexei Borodin, professor of mathematics at the Massachusetts Institute of Technology.
Their study was published in the journal Physical Review Letters.
In the “coffee-ring effect,” drop edges are “pinned” to a surface, meaning that when the liquid evaporates, the drop can’t shrink in circumference and particles are convectively pushed to its edges. The Penn team’s earlier research showed that this phenomenon was highly dependent on particle shape. Spherical particles could flow under or over each other to aggregate on the edges, but ellipsoidal particles formed loosely packed logjams as they interacted with one another on the surface of the drop.
MIT’s Borodin saw the Penn team’s earlier experimental videos online, and they reminded him of analytical and simulation work he and others in the math community had performed on interfacial growth processes. These problems had some similarity to the random-walker problem, a classic example in probability theory that involves tracing the path of an object that randomly picks a direction each time it takes a step. In the present case, however, the random motion involved the shape of a surface: the edge of the drop where new particles are added to the system. Borodin was curious about these growth processes in drying drops, especially whether particle shape had any effect.
“Interfacial growth processes are ubiquitous in nature and industry, ranging from vapor deposition coatings to growing bacterial colonies, but not all growth processes are the same,” Yunker said. “Theorists have identified several qualitatively distinct classes of these processes, but these predictions have proven difficult to test experimentally.”
The two classes of particular interest are “Poisson” and “Kardar-Parisi-Zhang” processes. Poisson processes arise when growth is random in space and time; in the context of an interfacial growth process, the growth of one individual region is independent of neighboring regions. Kardar-Parisi-Zhang, or KPZ, processes are more complicated, arising when growth of an individual region depends on neighboring regions.
A purely mathematical simulation of an interfacial growth process might look like a game of Tetris but with single square blocks. These blocks fall at random into a series of adjacent columns, forming stacks.
In a Poisson process, since individual regions are independent, a tall stack is just as likely to be next to a short stack as another tall stack. Taking the top layers of the stacks as the “surface” of the system, Poisson processes produce a very rough surface, with large changes in surface height from one column to the next.
In contrast, KPZ processes arise when the blocks are “sticky.” When these blocks fall into a column, they don’t always fall all the way to the bottom but can stick to adjacent columns at their highest point. This means that short columns quickly catch up to their tall neighbors, and the resulting growth surfaces are smoother. There will be fewer abrupt changes in height from one column to the next.
“Many theoretical simulations have demonstrated KPZ processes, a fact which might lead one to think this process should be ubiquitous in nature,” Yunker said. “However, few experiments have identified signatures of KPZ processes.”
“The relative paucity of identified KPZ processes in experiments is likely due to two main factors,” Yodh said. “First, a clean experiment is required; the presence of impurities or particle aggregation can destroy signatures of growth processes. Second, a substantial amount of data must be collected to make comparisons to theoretical predictions.
“Thus, experiments must be very precise and must characterize a wide range of size scales from the particle diameter to the growth fronts. Moreover, they must be repeated many times under exactly the same conditions to accumulate statistically meaningful amounts of homogeneous data.”
As in the previous research, the Penn team’s experiment involved drying drops of water with differently shaped plastic particles under a microscope. The team measured the growth fronts of particles at the drying edge, especially their height fluctuations — the edge’s roughness — over time. When using spherical particles, they found their deposition at the edges of the drop exhibited a classic Poisson growth process. As they tried with increasingly elongated particles, however, the deposition pattern changed.
Slightly elliptical particles — spheres stretched by 20 percent — produced the elusive KPZ class of growth. Stretching the spheres further, 250 percent out of round, produced a third growth process known as KPZQ, or Kardar-Parisi-Zhang with Quenched Disorder. It is also called the “colloidal Matthew effect” as the surface’s growth is proportional to the local particle density so that particle-rich regions get richer, while particle poor regions stay poor.
In practical terms, the experiment showed that when spheres and highly stretched particles are deposited, surface roughness grows at a high rate. However, when slightly stretched particles are deposited, surface roughness grows at a relatively slow rate.
The ability to control surface roughness can be important for industrial and commercial applications, as non-uniformity in films and coatings can lead to structural weakness or poor aesthetics. Surface roughness is controlled passively in the team’s experiments, making this process potentially attractive alternative for more costly or complicated smoothing processes currently in use.
“Experimental successes are highly valued in the math community,” Borodin said. “Not only do they demonstrate real-life applicability of very advanced yet originally purely theoretical research, but they also suggest further directions and even predict yet undiscovered mathematical phenomena.”
Courtesy: sciencedaily
