was the lead ship of ten s, and first of six to be built for the Imperial Japanese Navy under the Circle One Program (Maru Ichi Keikaku).

History
The Shiratsuyu-class destroyers were modified versions of the , and were designed to accompany the Japanese main striking force and to conduct both day and night torpedo attacks against the United States Navy as it advanced across the Pacific Ocean, according to Japanese naval strategic projections. Despite being one of the most powerful classes of destroyers in the world at the time of their completion, none survived the Pacific War.
Shiratsuyu, built at the Sasebo Naval Arsenal was laid down on 14 November 1933, launched on 5 April 1935 and commissioned on 7 September 1936.

Operational history
At the time of the attack on Pearl Harbor, Shiratsuyu was assigned to Destroyer Division 27 of Destroyer Squadron 1 of the IJN 1st Fleet, and remained in Japanese home waters as part of the protection of the Japanese battleship forces. From mid-January 1942, she was assisted to escorting convoys between Japan and Taiwan, and in mid-February escorted the aircraft carrier  to Davao and back to Hashirajima. In April, the destroyer escorted the aircraft carriers  and  from Mako to Truk, from which it joined Admiral Takeo Takagi's Strike Force at the Battle of the Coral Sea on 7–8 May. At the end of May, the ship escorted the cruisers  and  back to Kure, from which it was assigned to Admiral Shirō Takasu's, Aleutians Guard Force during the Battle of Midway on 4–6 June. On 14 July she was assigned back to the IJN 2nd Fleet and returned to Truk in mid-August, from which she was deployed as a high speed transport to carry troops to reoccupy Makin Atoll after the Makin Raid.

Until mid-September, Shiratsuyu remained based out of Jaluit in the Marshall Islands, but from October was deployed to the Solomon Islands, where she made several "Tokyo Express" high speed transport runs to Guadalcanal. On an attack mission to Guadalcanal on 25 October, she assisted in sinking the American tugboat  and damaging the high-speed minesweeper . She participated in the First Naval Battle of Guadalcanal on the night of 12–13 November 1942, rescuing survivors from the torpedoed battleship , but was not involved in combat. On 23 November, while on a transport run to Lae, she rescued survivors from the destroyer , which she then scuttled with a torpedo.

However, on her next mission to Buna on 28 November, Shiratsuyu took a direct hit from a bomb during an attack by USAAF B-17 Flying Fortress bombers in her bow, which required emergency repairs at Rabaul, Truk, and Saipan before she should reach Sasebo on 25 February 1943 for complete repairs. She returned to active duty on 20 July 1943 with the IJN 2nd Fleet, escorting the aircraft carrier  from Yokosuka to Truk and back in late August. She returned to Rabaul in mid-October, after which she was assigned to troop transport runs to Qavuvu.

During the Battle of Empress Augusta Bay on 2 November, she collided with the destroyer , after which she was strafed by American aircraft, leaving four dead and two wounded, and necessitating a return to Sasebo in November. During this refit, one gun turret was removed and replaced by two triple  25-mm anti-aircraft guns.

She returned to Truk at the end of December as part of the escort for the cruisers Myōkō, Haguro and , continuing on to Kavieng in early January. On 31 January, she rescued survivors of torpedoed Yasukuni Maru at Truk. From February through April, she was part of the escort for the battleship . From the end of April, she was reassigned to Take Ichi convoy duty, escorting troop convoys from China to the Philippines and other points in southeast Asia.

Shiratsuyu was attacked on 8 June off of Biak by US Navy aircraft, with four killed and five crewmen injured. On the night of 14 June, she collided with the Japanese tanker Seiyo Maru  southeast of Surigao Strait  (), after which her depth charges exploded among survivors. Of her crew, 104 were killed, including her Captain (Cdr Kuro Matsuda). She was removed from the navy list on 10 August 1944.

Notes

References

OCLC 77257764

External links

Shiratsuyu-class destroyers
World War II destroyers of Japan
World War II shipwrecks in the Philippine Sea
Ships built by Sasebo Naval Arsenal
World War II shipwrecks in the Pacific Ocean
1935 ships
Maritime incidents in June 1944

Japanese destroyer Shiratsuyu (1935)


The United Methodist Church (UMC) is a Methodist–Christian denomination that is one of the largest Protestant churches in the United States. It was founded in 1968 by the union of the Methodist Church (USA) and the Evangelical United Brethren Church. The UMC traces its roots back to the teachings of John and Charles Wesley in England. With over 8 million members and 35,000 churches it is second only to the Southern Baptist Convention among Protestant denominations. As of 2009, worldwide membership was about 12 million. It is a member of the World Council of Churches, the World Methodist Council, and other religious associations.

References

Methodism
Christian organizations based in the United States

United Methodist Church


A weed is a plant that people think is bad, because it is growing in the wrong place. Only smaller plants are often called weeds.

A person may grow a plant because it has beautiful flowers, but other people may think that this plant is a weed for some reason. This may be for one of these reasons:
It has pollen that makes them sneeze,
It spreads seeds that will grow quickly,
The roots go too far under the ground and can cause damage to houses or pipes,
The roots spread quickly,
The plant has poisonous berries,
The plant has liquid (sap) in it that can hurt a person's skin,

If someone is farming a plant and it then moves outside the farm, the plant may then be called a weed.

Some plants are very hard to stop once they start growing. These are called invasive weeds.

Weed is also a name for any wild plant, one that people do not use for anything.

References

Types of plants

Weed


Peter Maxwell Davies (born 8t September 1934 an deid 14t Mairch 2016) wis a Inglis componer an conductor o European classical muisic. He wis Master of the Queen's Music fae 2004 til his daith.

Muisic
In his muisic, he melt different styles, whiles even in the same piece o muisic.

Personal life
He steyed in Orkney fae 1971 on. He wis homosexual an steyed wi his ither hauf til 2012.

Politics
He wis fur environmentalism an writ a piece o muisic agin a uranium mine that didnae haupin.

1934 births
2016 daiths
20t-century clessical componers
21st-century clessical componers
20t-century Inglis muisickers
Academics o the Royal Academy of Music
Alumni o the Royal Northern College of Music
Ballet componers
Cancer daiths in Scotland
Commanders o the Order o the Breetish Empire
Componers awairdit knichtheids
Componers for pipe organ
Deaths frae leukemia
Inglis conductors (muisic)
Inglis male clessical componers
Inglis opera componers
Fellows o Homerton College, Cambridge
Gay muisickers
Harkness Fellows
Honorar Members o the Royal Academy of Music
Honorar Members o the Royal Philharmonic Society
Ivor Novello Awaird winners
Knichts Bachelor
LGBT componers
LGBT muisickers frae Ingland
Masters o the Queen's Music
Members o the Order o the Companions o Honour
Muisic in Salford
Muisickers frae Manchester
Fowk associatit wi Orkney
Fowk frae Salford, Greater Manchester
Princeton Varsity alumni missin graduation year
Pupils o Earl Kim
Pupils o Goffredo Petrassi
Pupils o Milton Babbitt
Pupils o Olivier Messiaen
Pupils o Roger Sessions

Peter Maxwell Davies


Hugues-Adhémar Cuénod (born 26 June 1902; died 6 December 2010) was a Swiss opera singer. He was a tenor.  He was particularly famous for singing comic roles in opera.  He had an extremely long career, lasting over 65 years.  He was still singing in opera at the age of 92.  He died at the age of 108.

Career
Hugues Cuénod was born in Coiseaux-sur-Vevey.  He studied at the Ribaupierre Institute in Lausanne, at the conservatories in Geneva and Basel, and in Vienna. At first he sang in concerts.  Then, in 1928, he got a part in the jazz opera Jonny spielt auf by  Ernst Krenek when it was performed in Paris.  The next year he sang for the first time in the United States in Noël Coward's Bitter Swee t.  He worked in Geneva until 1933, then he went to Paris where he met the music teacher Nadia Boulanger.  She had a big influence on his musical development.  He made a concert tour of North America. From 1940 to 1946 he taught at the Geneva Conservatory. In 1943 he started singing in opera again, singing in Johann Strauss II's Die Fledermaus in Geneva. Then he sang at Milan's La Scala (1951), the Glyndebourne Festival (from 1954 on) and London's Royal Opera House, Covent Garden (1954, 1956 and 1958).

Cuénod sang music by many different composers.  He was famous for singing the part of  Basilio in Mozart's The Marriage of Figaro, the Astrologer in Rimsky-Korsakov's The Golden Cockerel, and Sellem in Stravinsky's The Rake's Progress.  He sang with a light tenor voice, not a heavy heroic voice.

One of the most famous opera houses in the world is the Metropolitan Opera in New York.  He did sing there, but not until he was 84.  By that age almost all singers have been long retired. He sang the part of the Emperor Altoum in Puccini's Turandot in 14 performances there.

The very last time he sang on stage was in 1994 when he was 92.  He sang M. Triquet in Tchaikovsky's Eugene Onegin when it was given by the Lausanne Opera at the Théâtre du Jorat in Mézières.

Personal life
Cuénod lived with his male partner, Alfred Augustin, in the Vaud region of Switzerland. They lived in an 18th-century castle that had belonged to members of his family for several generations. The couple were in a civil union.

On his last birthday on 26 June 2010 he turned 108. He died in Vevey on 6 December that year.

References

1902 births
2010 deaths
Gay men
European opera singers
Swiss centenarians
Swiss singers
Tenors
Riviera-Pays-d'Enhaut District
People from Vaud

Hugues Cuénod


Monte Carlo methods, or Monte Carlo experiments, are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. The underlying concept is to use randomness to solve problems that might be deterministic in principle. They are often used in physical and mathematical problems and are most useful when it is difficult or impossible to use other approaches. Monte Carlo methods are mainly used in three problem classes: optimization, numerical integration, and generating draws from a probability distribution.

In physics-related problems, Monte Carlo methods are useful for simulating systems with many coupled degrees of freedom, such as fluids, disordered materials, strongly coupled solids, and cellular structures (see cellular Potts model, interacting particle systems, McKean–Vlasov processes, kinetic models of gases).

Other examples include modeling phenomena with significant uncertainty in inputs such as the calculation of risk in business and, in mathematics, evaluation of multidimensional definite integrals with complicated boundary conditions. In application to systems engineering problems (space, oil exploration, aircraft design, etc.), Monte Carlo–based predictions of failure, cost overruns and schedule overruns are routinely better than human intuition or alternative "soft" methods.

In principle, Monte Carlo methods can be used to solve any problem having a probabilistic interpretation. By the law of large numbers, integrals described by the expected value of some random variable can be approximated by taking the empirical mean ( the 'sample mean') of independent samples of the variable. When the probability distribution of the variable is parameterized, mathematicians often use a Markov chain Monte Carlo (MCMC) sampler. The central idea is to design a judicious Markov chain model with a prescribed stationary probability distribution. That is, in the limit, the samples being generated by the MCMC method will be samples from the desired (target) distribution. By the ergodic theorem, the stationary distribution is approximated by the empirical measures of the random states of the MCMC sampler.

In other problems, the objective is generating draws from a sequence of probability distributions satisfying a nonlinear evolution equation. These flows of probability distributions can always be interpreted as the distributions of the random states of a Markov process whose transition probabilities depend on the distributions of the current random states (see McKean–Vlasov processes, nonlinear filtering equation). In other instances we are given a flow of probability distributions with an increasing level of sampling complexity (path spaces models with an increasing time horizon, Boltzmann–Gibbs measures associated with decreasing temperature parameters, and many others). These models can also be seen as the evolution of the law of the random states of a nonlinear Markov chain. A natural way to simulate these sophisticated nonlinear Markov processes is to sample multiple copies of the process, replacing in the evolution equation the unknown distributions of the random states by the sampled empirical measures. In contrast with traditional Monte Carlo and MCMC methodologies, these mean-field particle techniques rely on sequential interacting samples. The terminology mean field reflects the fact that each of the samples ( particles, individuals, walkers, agents, creatures, or phenotypes) interacts with the empirical measures of the process. When the size of the system tends to infinity, these random empirical measures converge to the deterministic distribution of the random states of the nonlinear Markov chain, so that the statistical interaction between particles vanishes.

Despite its conceptual and algorithmic simplicity, the computational cost associated with a Monte Carlo simulation can be staggeringly high. In general the method requires many samples to get a good approximation, which may incur an arbitrarily large total runtime if the processing time of a single sample is high. Although this is a severe limitation in very complex problems, the embarrassingly parallel nature of the algorithm allows this large cost to be reduced (perhaps to a feasible level) through parallel computing strategies in local processors, clusters, cloud computing, GPU, FPGA, etc.

Overview
Monte Carlo methods vary, but tend to follow a particular pattern:
 Define a domain of possible inputs
 Generate inputs randomly from a probability distribution over the domain
 Perform a deterministic computation on the inputs
 Aggregate the results

For example, consider a quadrant (circular sector) inscribed in a unit square. Given that the ratio of their areas is , the value of  can be approximated using a Monte Carlo method:
 Draw a square, then inscribe a quadrant within it
 Uniformly scatter a given number of points over the square
 Count the number of points inside the quadrant, i.e. having a distance from the origin of less than 1
 The ratio of the inside-count and the total-sample-count is an estimate of the ratio of the two areas, . Multiply the result by 4 to estimate .
In this procedure the domain of inputs is the square that circumscribes the quadrant. One can generate random inputs by scattering grains over the square then perform a computation on each input (test whether it falls within the quadrant). Aggregating the results yields our final result, the approximation of .

There are two important considerations:
 If the points are not uniformly distributed, then the approximation will be poor.
 The approximation is generally poor if only a few points are randomly placed in the whole square. On average, the approximation improves as more points are placed.

Uses of Monte Carlo methods require large amounts of random numbers, and their use benefitted greatly from pseudorandom number generators, which are far quicker to use than the tables of random numbers that had been previously used for statistical sampling.

History
Before the Monte Carlo method was developed, simulations tested a previously understood deterministic problem, and statistical sampling was used to estimate uncertainties in the simulations. Monte Carlo simulations invert this approach, solving deterministic problems using probabilistic metaheuristics (see simulated annealing).

An early variant of the Monte Carlo method was devised to solve the Buffon's needle problem, in which  can be estimated by dropping needles on a floor made of parallel equidistant strips. In the 1930s, Enrico Fermi first experimented with the Monte Carlo method while studying neutron diffusion, but he did not publish this work.

In the late 1940s, Stanislaw Ulam invented the modern version of the Markov Chain Monte Carlo method while he was working on nuclear weapons projects at the Los Alamos National Laboratory. In 1946, nuclear weapons physicists at Los Alamos were investigating neutron diffusion in the core of a nuclear weapon. Despite having most of the necessary data, such as the average distance a neutron would travel in a substance before it collided with an atomic nucleus and how much energy the neutron was likely to give off following a collision, the Los Alamos physicists were unable to solve the problem using conventional, deterministic mathematical methods. Ulam proposed using random experiments. He recounts his inspiration as follows:

Being secret, the work of von Neumann and Ulam required a code name. A colleague of von Neumann and Ulam, Nicholas Metropolis, suggested using the name Monte Carlo, which refers to the Monte Carlo Casino in Monaco where Ulam's uncle would borrow money from relatives to gamble.
Monte Carlo methods were central to the simulations required for the Manhattan Project, though severely limited by the computational tools at the time. Von Neumann, Nicholas Metropolis and others programmed the ENIAC computer to perform the first fully automated Monte Carlo calculations, of a fission weapon core, in the spring of 1948. In the 1950s Monte Carlo methods were used at Los Alamos for the development of the hydrogen bomb, and became popularized in the fields of physics, physical chemistry, and operations research. The Rand Corporation and the U.S. Air Force were two of the major organizations responsible for funding and disseminating information on Monte Carlo methods during this time, and they began to find a wide application in many different fields.

The theory of more sophisticated mean-field type particle Monte Carlo methods had certainly started by the mid-1960s, with the work of Henry P. McKean Jr. on Markov interpretations of a class of nonlinear parabolic partial differential equations arising in fluid mechanics. We also quote an earlier pioneering article by Theodore E. Harris and Herman Kahn, published in 1951, using mean-field genetic-type Monte Carlo methods for estimating particle transmission energies. Mean-field genetic type Monte Carlo methodologies are also used as heuristic natural search algorithms (a.k.a. metaheuristic) in evolutionary computing. The origins of these mean-field computational techniques can be traced to 1950 and 1954 with the work of Alan Turing on genetic type mutation-selection learning machines and the articles by Nils Aall Barricelli at the Institute for Advanced Study in Princeton, New Jersey.

Quantum Monte Carlo, and more specifically diffusion Monte Carlo methods can also be interpreted as a mean-field particle Monte Carlo approximation of Feynman–Kac path integrals. The origins of Quantum Monte Carlo methods are often attributed to Enrico Fermi and Robert Richtmyer who developed in 1948 a mean-field particle interpretation of neutron-chain reactions, but the first heuristic-like and genetic type particle algorithm (a.k.a. Resampled or Reconfiguration Monte Carlo methods) for estimating ground state energies of quantum systems (in reduced matrix models) is due to Jack H. Hetherington in 1984 In molecular chemistry, the use of genetic heuristic-like particle methodologies (a.k.a. pruning and enrichment strategies) can be traced back to 1955 with the seminal work of Marshall N. Rosenbluth and Arianna W. Rosenbluth.

The use of Sequential Monte Carlo in advanced signal processing and Bayesian inference is more recent. It was in 1993, that Gordon et al., published in their seminal work the first application of a Monte Carlo resampling algorithm in Bayesian statistical inference. The authors named their algorithm 'the bootstrap filter', and demonstrated that compared to other filtering methods, their bootstrap algorithm does not require any assumption about that state-space or the noise of the system. We also quote another pioneering article in this field of Genshiro Kitagawa on a related "Monte Carlo filter", and the ones by Pierre Del Moral and Himilcon Carvalho, Pierre Del Moral, André Monin and Gérard Salut on particle filters published in the mid-1990s. Particle filters were also developed in signal processing in 1989–1992 by P. Del Moral, J. C. Noyer, G. Rigal, and G. Salut in the LAAS-CNRS in a series of restricted and classified research reports with STCAN (Service Technique des Constructions et Armes Navales), the IT company DIGILOG, and the LAAS-CNRS (the Laboratory for Analysis and Architecture of Systems) on radar/sonar and GPS signal processing problems. These Sequential Monte Carlo methodologies can be interpreted as an acceptance-rejection sampler equipped with an interacting recycling mechanism.

From 1950 to 1996, all the publications on Sequential Monte Carlo methodologies, including the pruning and resample Monte Carlo methods introduced in computational physics and molecular chemistry, present natural and heuristic-like algorithms applied to different situations without a single proof of their consistency, nor a discussion on the bias of the estimates and on genealogical and ancestral tree based algorithms. The mathematical foundations and the first rigorous analysis of these particle algorithms were written by Pierre Del Moral in 1996.

Branching type particle methodologies with varying population sizes were also developed in the end of the 1990s by Dan Crisan, Jessica Gaines and Terry Lyons, and by Dan Crisan, Pierre Del Moral and Terry Lyons. Further developments in this field were described in 1999 to 2001 by P. Del Moral, A. Guionnet and L. Miclo.

Definitions
There is no consensus on how Monte Carlo should be defined. For example, Ripley defines most probabilistic modeling as stochastic simulation, with Monte Carlo being reserved for Monte Carlo integration and Monte Carlo statistical tests. Sawilowsky distinguishes between a simulation, a Monte Carlo method, and a Monte Carlo simulation: a simulation is a fictitious representation of reality, a Monte Carlo method is a technique that can be used to solve a mathematical or statistical problem, and a Monte Carlo simulation uses repeated sampling to obtain the statistical properties of some phenomenon (or behavior). Examples:
Simulation: Drawing one pseudo-random uniform variable from the interval [0,1] can be used to simulate the tossing of a coin: If the value is less than or equal to 0.50 designate the outcome as heads, but if the value is greater than 0.50 designate the outcome as tails. This is a simulation, but not a Monte Carlo simulation.
Monte Carlo method: Pouring out a box of coins on a table, and then computing the ratio of coins that land heads versus tails is a Monte Carlo method of determining the behavior of repeated coin tosses, but it is not a simulation.
Monte Carlo simulation: Drawing a large number of pseudo-random uniform variables from the interval [0,1] at one time, or once at many different times, and assigning values less than or equal to 0.50 as heads and greater than 0.50 as tails, is a Monte Carlo simulation of the behavior of repeatedly tossing a coin.

Kalos and Whitlock point out that such distinctions are not always easy to maintain. For example, the emission of radiation from atoms is a natural stochastic process. It can be simulated directly, or its average behavior can be described by stochastic equations that can themselves be solved using Monte Carlo methods. "Indeed, the same computer code can be viewed simultaneously as a 'natural simulation' or as a solution of the equations by natural sampling."

Monte Carlo and random numbers

The main idea behind this method is that the results are computed based on repeated random sampling and statistical analysis. The Monte Carlo simulation is, in fact, random experimentations, in the case that, the results of these experiments are not well known.
Monte Carlo simulations are typically characterized by many unknown parameters, many of which are difficult to obtain experimentally. Monte Carlo simulation methods do not always require truly random numbers to be useful (although, for some applications such as primality testing, unpredictability is vital). Many of the most useful techniques use deterministic, pseudorandom sequences, making it easy to test and re-run simulations. The only quality usually necessary to make good simulations is for the pseudo-random sequence to appear "random enough" in a certain sense.

What this means depends on the application, but typically they should pass a series of statistical tests. Testing that the numbers are uniformly distributed or follow another desired distribution when a large enough number of elements of the sequence are considered is one of the simplest and most common ones. Weak correlations between successive samples are also often desirable/necessary.

Sawilowsky lists the characteristics of a high-quality Monte Carlo simulation:
the (pseudo-random) number generator has certain characteristics (e.g. a long "period" before the sequence repeats)
the (pseudo-random) number generator produces values that pass tests for randomness
there are enough samples to ensure accurate results
the proper sampling technique is used
the algorithm used is valid for what is being modeled
it simulates the phenomenon in question.

Pseudo-random number sampling algorithms are used to transform uniformly distributed pseudo-random numbers into numbers that are distributed according to a given probability distribution.

Low-discrepancy sequences are often used instead of random sampling from a space as they ensure even coverage and normally have a faster order of convergence than Monte Carlo simulations using random or pseudorandom sequences. Methods based on their use are called quasi-Monte Carlo methods.

In an effort to assess the impact of random number quality on Monte Carlo simulation outcomes, astrophysical researchers tested cryptographically-secure pseudorandom numbers generated via Intel's RDRAND instruction set, as compared to those derived from algorithms, like the Mersenne Twister, in Monte Carlo simulations of radio flares from brown dwarfs. RDRAND is the closest pseudorandom number generator to a true random number generator. No statistically significant difference was found between models generated with typical pseudorandom number generators and RDRAND for trials consisting of the generation of 107 random numbers.

Monte Carlo simulation versus "what if" scenarios
There are ways of using probabilities that are definitely not Monte Carlo simulations – for example, deterministic modeling using single-point estimates. Each uncertain variable within a model is assigned a "best guess" estimate. Scenarios (such as best, worst, or most likely case) for each input variable are chosen and the results recorded.

By contrast, Monte Carlo simulations sample from a probability distribution for each variable to produce hundreds or thousands of possible outcomes. The results are analyzed to get probabilities of different outcomes occurring. For example, a comparison of a spreadsheet cost construction model run using traditional "what if" scenarios, and then running the comparison again with Monte Carlo simulation and triangular probability distributions shows that the Monte Carlo analysis has a narrower range than the "what if" analysis. This is because the "what if" analysis gives equal weight to all scenarios (see quantifying uncertainty in corporate finance), while the Monte Carlo method hardly samples in the very low probability regions. The samples in such regions are called "rare events".

Applications
Monte Carlo methods are especially useful for simulating phenomena with significant uncertainty in inputs and systems with many coupled degrees of freedom. Areas of application include:

Physical sciences

Monte Carlo methods are very important in computational physics, physical chemistry, and related applied fields, and have diverse applications from complicated quantum chromodynamics calculations to designing heat shields and aerodynamic forms as well as in modeling radiation transport for radiation dosimetry calculations. In statistical physics, Monte Carlo molecular modeling is an alternative to computational molecular dynamics, and Monte Carlo methods are used to compute statistical field theories of simple particle and polymer systems. Quantum Monte Carlo methods solve the many-body problem for quantum systems. In radiation materials science, the binary collision approximation for simulating ion implantation is usually based on a Monte Carlo approach to select the next colliding atom. In experimental particle physics, Monte Carlo methods are used for designing detectors, understanding their behavior and comparing experimental data to theory. In astrophysics, they are used in such diverse manners as to model both galaxy evolution and microwave radiation transmission through a rough planetary surface. Monte Carlo methods are also used in the ensemble models that form the basis of modern weather forecasting.

Engineering
Monte Carlo methods are widely used in engineering for sensitivity analysis and quantitative probabilistic analysis in process design. The need arises from the interactive, co-linear and non-linear behavior of typical process simulations. For example,
 In microelectronics engineering, Monte Carlo methods are applied to analyze correlated and uncorrelated variations in analog and digital integrated circuits.
 In geostatistics and geometallurgy, Monte Carlo methods underpin the design of mineral processing flowsheets and contribute to quantitative risk analysis.
 In fluid dynamics, in particular rarefied gas dynamics, where the Boltzmann equation is solved for finite Knudsen number fluid flows using the direct simulation Monte Carlo method in combination with highly efficient computational algorithms.
 In autonomous robotics, Monte Carlo localization can determine the position of a robot. It is often applied to stochastic filters such as the Kalman filter or particle filter that forms the heart of the SLAM (simultaneous localization and mapping) algorithm.
 In telecommunications, when planning a wireless network, the design must be proven to work for a wide variety of scenarios that depend mainly on the number of users, their locations and the services they want to use. Monte Carlo methods are typically used to generate these users and their states. The network performance is then evaluated and, if results are not satisfactory, the network design goes through an optimization process.
 In reliability engineering, Monte Carlo simulation is used to compute system-level response given the component-level response.
 In signal processing and Bayesian inference, particle filters and sequential Monte Carlo techniques are a class of mean-field particle methods for sampling and computing the posterior distribution of a signal process given some noisy and partial observations using interacting empirical measures.

Climate change and radiative forcing
The Intergovernmental Panel on Climate Change relies on Monte Carlo methods in probability density function analysis of radiative forcing.

Computational biology
Monte Carlo methods are used in various fields of computational biology, for example for Bayesian inference in phylogeny, or for studying biological systems such as genomes, proteins, or membranes.
The systems can be studied in the coarse-grained or ab initio frameworks depending on the desired accuracy.
Computer simulations allow us to monitor the local environment of a particular molecule to see if some chemical reaction is happening for instance. In cases where it is not feasible to conduct a physical experiment, thought experiments can be conducted (for instance: breaking bonds, introducing impurities at specific sites, changing the local/global structure, or introducing external fields).

Computer graphics
Path tracing, occasionally referred to as Monte Carlo ray tracing, renders a 3D scene by randomly tracing samples of possible light paths. Repeated sampling of any given pixel will eventually cause the average of the samples to converge on the correct solution of the rendering equation, making it one of the most physically accurate 3D graphics rendering methods in existence.

Applied statistics
The standards for Monte Carlo experiments in statistics were set by Sawilowsky. In applied statistics, Monte Carlo methods may be used for at least four purposes:
To compare competing statistics for small samples under realistic data conditions. Although type I error and power properties of statistics can be calculated for data drawn from classical theoretical distributions (e.g., normal curve, Cauchy distribution) for asymptotic conditions (i. e, infinite sample size and infinitesimally small treatment effect), real data often do not have such distributions.
To provide implementations of hypothesis tests that are more efficient than exact tests such as permutation tests (which are often impossible to compute) while being more accurate than critical values for asymptotic distributions.
To provide a random sample from the posterior distribution in Bayesian inference. This sample then approximates and summarizes all the essential features of the posterior.
To provide efficient random estimates of the Hessian matrix of the negative log-likelihood function that may be averaged to form an estimate of the Fisher information matrix.

Monte Carlo methods are also a compromise between approximate randomization and permutation tests. An approximate randomization test is based on a specified subset of all permutations (which entails potentially enormous housekeeping of which permutations have been considered). The Monte Carlo approach is based on a specified number of randomly drawn permutations (exchanging a minor loss in precision if a permutation is drawn twice—or more frequently—for the efficiency of not having to track which permutations have already been selected).

Artificial intelligence for games

Monte Carlo methods have been developed into a technique called Monte-Carlo tree search that is useful for searching for the best move in a game. Possible moves are organized in a search tree and many random simulations are used to estimate the long-term potential of each move. A black box simulator represents the opponent's moves.

The Monte Carlo tree search (MCTS) method has four steps:
Starting at root node of the tree, select optimal child nodes until a leaf node is reached.
Expand the leaf node and choose one of its children.
Play a simulated game starting with that node.
Use the results of that simulated game to update the node and its ancestors.

The net effect, over the course of many simulated games, is that the value of a node representing a move will go up or down, hopefully corresponding to whether or not that node represents a good move.

Monte Carlo Tree Search has been used successfully to play games such as Go, Tantrix, Battleship, Havannah, and Arimaa.

Design and visuals
Monte Carlo methods are also efficient in solving coupled integral differential equations of radiation fields and energy transport, and thus these methods have been used in global illumination computations that produce photo-realistic images of virtual 3D models, with applications in video games, architecture, design, computer generated films, and cinematic special effects.

Search and rescue
The US Coast Guard utilizes Monte Carlo methods within its computer modeling software SAROPS in order to calculate the probable locations of vessels during search and rescue operations. Each simulation can generate as many as ten thousand data points that are randomly distributed based upon provided variables. Search patterns are then generated based upon extrapolations of these data in order to optimize the probability of containment (POC) and the probability of detection (POD), which together will equal an overall probability of success (POS). Ultimately this serves as a practical application of probability distribution in order to provide the swiftest and most expedient method of rescue, saving both lives and resources.

Finance and business

Monte Carlo simulation is commonly used to evaluate the risk and uncertainty that would affect the outcome of different decision options. Monte Carlo simulation allows the business risk analyst to incorporate the total effects of uncertainty in variables like sales volume, commodity and labour prices, interest and exchange rates, as well as the effect of distinct risk events like the cancellation of a contract or the change of a tax law.

Monte Carlo methods in finance are often used to evaluate investments in projects at a business unit or corporate level, or other financial valuations. They can be used to model project schedules, where simulations aggregate estimates for worst-case, best-case, and most likely durations for each task to determine outcomes for the overall project. Monte Carlo methods are also used in option pricing, default risk analysis. Additionally, they can be used to estimate the financial impact of medical interventions.

Law
A Monte Carlo approach was used for evaluating the potential value of a proposed program to help female petitioners in Wisconsin be successful in their applications for harassment and domestic abuse restraining orders. It was proposed to help women succeed in their petitions by providing them with greater advocacy thereby potentially reducing the risk of rape and physical assault. However, there were many variables in play that could not be estimated perfectly, including the effectiveness of restraining orders, the success rate of petitioners both with and without advocacy, and many others. The study ran trials that varied these variables to come up with an overall estimate of the success level of the proposed program as a whole.

Library science
Monte Carlo approach had also been used to simulate the number of book publications based on book genre in Malaysia. The Monte Carlo simulation utilized previous published National Book publication data and book's price according to book genre in the local market. The Monte Carlo results were used to determine what kind of book genre that Malaysians are fond of and was used to compare book publications between Malaysia and Japan.

Other

Nassim Nicholas Taleb writes about Monte Carlo generators in his 2001 book Fooled by Randomness as a real instance of the reverse Turing test: a human can be declared unintelligent if their writing cannot be told apart from a generated one.

Use in mathematics
In general, the Monte Carlo methods are used in mathematics to solve various problems by generating suitable random numbers (see also Random number generation) and observing that fraction of the numbers that obeys some property or properties. The method is useful for obtaining numerical solutions to problems too complicated to solve analytically. The most common application of the Monte Carlo method is Monte Carlo integration.

Integration

Deterministic numerical integration algorithms work well in a small number of dimensions, but encounter two problems when the functions have many variables. First, the number of function evaluations needed increases rapidly with the number of dimensions. For example, if 10 evaluations provide adequate accuracy in one dimension, then 10100 points are needed for 100 dimensions—far too many to be computed. This is called the curse of dimensionality. Second, the boundary of a multidimensional region may be very complicated, so it may not be feasible to reduce the problem to an iterated integral. 100 dimensions is by no means unusual, since in many physical problems, a "dimension" is equivalent to a degree of freedom.

Monte Carlo methods provide a way out of this exponential increase in computation time. As long as the function in question is reasonably well-behaved, it can be estimated by randomly selecting points in 100-dimensional space, and taking some kind of average of the function values at these points. By the central limit theorem, this method displays  convergence—i.e., quadrupling the number of sampled points halves the error, regardless of the number of dimensions.

A refinement of this method, known as importance sampling in statistics, involves sampling the points randomly, but more frequently where the integrand is large. To do this precisely one would have to already know the integral, but one can approximate the integral by an integral of a similar function or use adaptive routines such as stratified sampling, recursive stratified sampling, adaptive umbrella sampling or the VEGAS algorithm.

A similar approach, the quasi-Monte Carlo method, uses low-discrepancy sequences. These sequences "fill" the area better and sample the most important points more frequently, so quasi-Monte Carlo methods can often converge on the integral more quickly.

Another class of methods for sampling points in a volume is to simulate random walks over it (Markov chain Monte Carlo). Such methods include the Metropolis–Hastings algorithm, Gibbs sampling, Wang and Landau algorithm, and interacting type MCMC methodologies such as the sequential Monte Carlo samplers.

Simulation and optimization

Another powerful and very popular application for random numbers in numerical simulation is in numerical optimization. The problem is to minimize (or maximize) functions of some vector that often has many dimensions. Many problems can be phrased in this way: for example, a computer chess program could be seen as trying to find the set of, say, 10 moves that produces the best evaluation function at the end. In the traveling salesman problem the goal is to minimize distance traveled. There are also applications to engineering design, such as multidisciplinary design optimization. It has been applied with quasi-one-dimensional models to solve particle dynamics problems by efficiently exploring large configuration space. Reference is a comprehensive review of many issues related to simulation and optimization.

The traveling salesman problem is what is called a conventional optimization problem. That is, all the facts (distances between each destination point) needed to determine the optimal path to follow are known with certainty and the goal is to run through the possible travel choices to come up with the one with the lowest total distance. However, let's assume that instead of wanting to minimize the total distance traveled to visit each desired destination, we wanted to minimize the total time needed to reach each destination. This goes beyond conventional optimization since travel time is inherently uncertain (traffic jams, time of day, etc.). As a result, to determine our optimal path we would want to use simulation – optimization to first understand the range of potential times it could take to go from one point to another (represented by a probability distribution in this case rather than a specific distance) and then optimize our travel decisions to identify the best path to follow taking that uncertainty into account.

Inverse problems
Probabilistic formulation of inverse problems leads to the definition of a probability distribution in the model space. This probability distribution combines prior information with new information obtained by measuring some observable parameters (data).
As, in the general case, the theory linking data with model parameters is nonlinear, the posterior probability in the model space may not be easy to describe (it may be multimodal, some moments may not be defined, etc.).

When analyzing an inverse problem, obtaining a maximum likelihood model is usually not sufficient, as we normally also wish to have information on the resolution power of the data. In the general case we may have many model parameters, and an inspection of the marginal probability densities of interest may be impractical, or even useless. But it is possible to pseudorandomly generate a large collection of models according to the posterior probability distribution and to analyze and display the models in such a way that information on the relative likelihoods of model properties is conveyed to the spectator. This can be accomplished by means of an efficient Monte Carlo method, even in cases where no explicit formula for the a priori distribution is available.

The best-known importance sampling method, the Metropolis algorithm, can be generalized, and this gives a method that allows analysis of (possibly highly nonlinear) inverse problems with complex a priori information and data with an arbitrary noise distribution.

Philosophy
Popular exposition of the Monte Carlo Method was conducted by McCracken. The method's general philosophy was discussed by Elishakoff and Grüne-Yanoff and Weirich.

See also

 Auxiliary field Monte Carlo
 Biology Monte Carlo method
 Direct simulation Monte Carlo
 Dynamic Monte Carlo method
 Ergodicity
 Genetic algorithms
 Kinetic Monte Carlo
 List of software for Monte Carlo molecular modeling
 Mean-field particle methods
 Monte Carlo method for photon transport
 Monte Carlo methods for electron transport
 Monte Carlo N-Particle Transport Code
 Morris method
 Multilevel Monte Carlo method
 Quasi-Monte Carlo method
 Sobol sequence
 Temporal difference learning

References

Citations

Sources

External links

Numerical analysis
Statistical mechanics
Computational physics
Sampling techniques
Statistical approximations
Stochastic simulation
Randomized algorithms
Risk analysis methodologies

Monte Carlo method


The Abolition of Feudal Tenure etc. (Scotland) Act 2000 was a land reform enforced by an Act of the Scottish Parliament that was passed by the Scottish Parliament on 3 May 2000, and received Royal Assent on 9 June 2000.

Provisions
The Act officially brought to an end  annual feu duties, a vestige of feudal land tenure, on 28 November 2004 (that is, Martinmas, as the Act required the "appointed day" to be one of the Scottish term days). Tommy Sheridan was one of a number of MSPs who drove this change through the Scottish Parliament.  After that date, the former vassal of an estate was the sole owner of the land, and the former superior's rights were extinguished. For a further two years, the superior had the option of claiming compensation; this was fixed at a single payment of a size that, when invested at an annual rate of 2.5%, would yield interest equal to the former feu duty. Because inflation had eroded the value of duties, which had been fixed many years before, this payment was in most cases extremely small compared with the current value of the land.

In consequence of this change in the legal basis of land-holding, the Act also reformulated the legal basis on which conditions on the use of land can be specified in the title to ownership of that land. Such title conditions (known variously as real burdens and real conditions in the prior law) were combined into "real burdens". Prior to the Act, a superior could choose to enforce title conditions, or grant a consent or waiver (usually for payment) allowing the land owner to disregard the condition even if otherwise neighbouring property owners might wish to enforce the condition. Existing conditions which were enforceable only by the superior were abolished, and only conditions enforceable by the owners of neighbouring property or by certain legal bodies on public policy grounds were retained. Transitional arrangements allowed superiors who were also neighbouring property owners to convert the old title conditions to benefit their land and hence themselves as owners of that land rather than themselves as feudal superior.

Following this change in the legal basis for title conditions, the Title Conditions (Scotland) Act 2003 was passed, reconstituting the mechanics of how new real burdens and servitudes could be created. These two Acts, together with a third Act (the Tenements (Scotland) Act 2004), commenced on 28 November 2004.

See also
Land reform in Scotland

References

External links

Legislation
 Abolition of Feudal Tenure etc (Scotland) Act 2000

Literature
 Andrew J. M. Steven: "Revolution in Scottish Land Law " in: Electronic Journal of Comparative Law 8.3 (October 2004)
 David Sellar: "Farewell to Feudalism" in: Burke's Landed Gentry of Great Britain: The Kingdom in Scotland. Edited by Peter Beauclerk Dewar. Wilmington, DE 192001, pp. xix - xxi

Land reform in Scotland
Real property law
Feudalism in Scotland
Acts of the Scottish Parliament 2000


A cloud chamber is a sealed box with a supersaturated vapour of water or alcohol. When a charged particle (e.g. an alpha or beta particle) goes through it, the vapour is ionized. The resulting ions act as nuclei, around which a mist forms from the vapour.

The high energies of alpha and beta particles mean that a trail is left by ions produced along the path of the charged particle. These tracks have distinctive shapes (for example, an alpha particle's track is broad and shows more evidence of deflection by collisions, while an electron's is thinner and straight). When any uniform magnetic field is applied across the cloud chamber, positively and negatively charged particles will curve in opposite directions, according to the Lorentz force law.

The cloud chamber was invented by Charles Wilson (1869–1959), a Scottish physicist who received the Nobel Prize in Physics in 1927 for this work. The first cloud chamber was made in 1911. The invention was used in the early days of atomic physics. It is often called the 'Wilson chamber'. Since then a number of other types of particle detectors have been invented.

References
 Das Gupta N.N. & Ghosh S.K. 1946. A report on the Wilson cloud chamber and its applications in physics. Reviews of Modern Physics. 18, 225–365.

Other websites
Many pictures of nuclear interaction and experiences
Video demonstration of a cloud chamber , Peter Wothers, Royal Institution, December 2012
Wilson's Original Apparatus
Radiation tracks in Cloud Chambers

Nuclear physics
Machines
Tools

Cloud chamber


Ansgar (8 September 801 – 3 February 865), also known as Anskar, Saint Ansgar, Saint Anschar or Oscar, was Archbishop of Hamburg-Bremen in the northern part of the Kingdom of the East Franks. Ansgar became known as the "Apostle of the North" because of his travels and the See of Hamburg received the missionary mandate to bring Christianity to Northern Europe.

Life
Ansgar was the son of a noble Frankish family, born near Amiens (present day France). After his mother's early death, Ansgar was brought up in Benedictine monastery of Corbie in Picardy. According to the Vita Ansgarii ("Life of Ansgar"), when the little boy learned in a vision that his mother was in the company of Mary, mother of Jesus, his careless attitude toward spiritual matters changed to seriousness. His pupil, successor, and eventual biographer Rimbert considered the visions (of which this was the first) to have been Ansgar's main life motivator.

Ansgar acted in the context of the phase of Christianization of Saxony (present day Northern Germany) begun by Charlemagne and continued by Charlemagne's son and successor, Louis the Pious. In 822 Ansgar became one of many missionaries sent to found the abbey of Corvey (New Corbie) in Westphalia, where he became a teacher and preacher. A group of monks including Ansgar were sent further north to Jutland with the king Harald Klak, who had received baptism during his exile. With Harald's downfall in 827 and Ansgar's companion Autbert having died, their school for the sons of courtiers closed and Ansgar returned to Germany. Then in 829, after the Swedish king Björn at Hauge requested missionaries for his Swedes, King Louis sent Ansgar, now accompanied by friar Witmar from New Corbie as his assistant. Ansgar preached and made converts, particularly during six months at Birka, on Lake Mälaren, where the wealthy widow Mor Frideborg extended hospitality. Ansgar organized a small congregation with her and the king's steward, Hergeir, as its most prominent members.

In 831 Ansgar returned to Louis' court at Worms and was appointed to the Archbishopric of Hamburg-Bremen. This was a new archbishopric, incorporating the bishoprics of Bremen and Verden and with the right to send missions into all the northern lands, as well as to consecrate bishops for them. Ansgar received the mission of evangelizing pagan Denmark, Norway and Sweden. The King of Sweden decided to cast lots as to whether to admit the Christian missionaries into his kingdom. Ansgar recommended the issue to the care of God, and the lot was favorable. Ansgar was consecrated as a bishop in November 831, with the approval of Gregory IV. Before traveling north once again, Ansgar traveled to Rome to receive the pallium directly from the pope's hands, and was formally named legate for the northern lands. Ebbo, Archbishop of Reims had previously received a similar commission, but would be deposed twice before his death in 851, and never actually traveled so far north, so the jurisdiction was divided by agreement, with Ebbo retaining Sweden for himself. For a time Ansgar devoted himself to the needs of his own diocese, which was still a missionary territory and had few churches. He founded a monastery and a school in Hamburg. Although intended to serve the Danish mission further north, it accomplished little.

After Louis the Pious died in 840, his empire was divided and Ansgar lost the abbey of Turholt, which Louis had given to endow Ansgar's work. Then in 845, the Danes unexpectedly raided Hamburg, destroying all the church's treasures and books. Ansgar now had neither see nor revenue, and many helpers deserted him. The new king, Louis' third son, Louis the German, did not re-endow Turholt to Ansgar, but in 847 he named the missionary to the vacant diocese of Bremen, where Ansgar moved in 848. However, since Bremen had been suffragan to the Bishop of Cologne, combining the sees of Bremen and Hamburg presented canonical difficulties. After prolonged negotiations, Pope Nicholas I would approve the union of the two dioceses in 864.

Through this political turmoil, Ansgar continued his northern mission. The Danish civil war compelled him to establish good relations with two kings, Horik the Elder and his son, Horik II. Both assisted him until his death; Ansgar was able to secure permission to build a church in Sleswick north of Hamburg and recognition of Christianity as a tolerated religion. Ansgar did not forget the Swedish mission, and spent two years there in person (848–850), averting a threatened pagan reaction. In 854, Ansgar returned to Sweden when king Olof ruled in Birka. According to Rimbert, he was well disposed to Christianity. On a Viking raid to Apuole (current village in Lithuania) in Courland, the Swedes plundered the Curonians.

Death and legacy

Ansgar was buried in Bremen in 865. His successor as archbishop, Rimbert, wrote the Vita Ansgarii. He noted that Ansgar wore a rough hair shirt, lived on bread and water, and showed great charity to the poor. Adam of Bremen attributed the Vita et miracula of Willehad (first bishop of Bremen) to Ansgar in Gesta Hammenburgensis ecclesiæ; Ansgar is also the reputed author of a collection of brief prayers Pigmenta (ed. J. M. Lappenberg, Hamburg, 1844). Pope Nicholas I declared Ansgar a saint shortly after the missionary's death. The first actual missionary in Sweden and the Nordic countries (and organizer of the Catholic church therein), Ansgar was later declared "Patron of Scandinavia".

Relics are located in Hamburg in two places: St. Mary's Cathedral (Ger.: Domkirche St. Marien) and St. Ansgar's and St. Bernard's Church (Ger.: St. Ansgar und St. Bernhard Kirche).
Statues of Bishop Ansgar stand in Hamburg, Copenhagen and Ribe, as well as a stone cross at Birka. His feast day (Lesser Festival) is 3 February, as it is in the Church of England and the Episcopal Church.

Visions

Although a historical document and primary source written by a man whose existence can be proven historically, the Vita Ansgarii ("The Life of Ansgar") aims above all to demonstrate Ansgar's sanctity. It is partly concerned with Ansgar's visions, which, according to the author Rimbert, encouraged and assisted Ansgar's remarkable missionary feats.

Through the course of this work, Ansgar repeatedly embarks on a new stage in his career following a vision.  According to Rimbert, his early studies and ensuing devotion to the ascetic life of a monk were inspired by a vision of his mother in the presence of Mary, mother of Jesus.  Again, when the Swedish people were left without a priest for some time, he begged King Horik to help him with this problem; then after receiving his consent, consulted with Bishop Gautbert to find a suitable man. The two together sought the approval of King Louis, which he granted when he learned that they were in agreement on the issue. Ansgar was convinced he was commanded by heaven to undertake this mission and was influenced by a vision he received when he was concerned about the journey, in which he met a man who reassured him of his purpose and informed him of a prophet that he would meet, the abbot Adalhard, who would instruct him in what was to happen.  In the vision, he searched for and found Adalhard, who commanded, "Islands, listen to me, pay attention, remotest peoples", which Ansgar interpreted as God's will that he go to the Scandinavian countries as "most of that country consisted of islands, and also, when 'I will make you the light of the nations so that my salvation may reach to the ends of the earth' was added, since the end of the world in the north was in Swedish territory".

See also
List of Eastern Orthodox saints
Calendar of saints (Episcopal Church)
 Hochkirchlicher Apostolat St. Ansgar
 Priory of St. Ansgar
 Sankt-Ansgar-Schule
Vita Ansgarii

References

Further reading
 Jakobsson, Sverrir. Mission Miscarried: The Narrators of the Ninth-Century Missions to Scandinavia and Central Europe. Bulgaria Medievalis 2 (2011), 49–69.
 Palmer, James T., Rimbert's Vita Anskarii and the Scandinavian Mission in the Ninth Century. Journal of Ecclesiastical History 55/2 (2004), 235–56.
 Pryce, Mark. Literary Companion to the Festivals: A Poetic Gathering to Accompany Liturgical Celebrations of Commemorations and Festivals.  Minneapolis: Fortress Press, 2003.
 Tschan, Francis J. History of the Archbishops of Hamburg-Bremen.  New York: Columbia University Press, 1959.

External links

Ansgar at Birka History of Birka
Vita Ansgari, English translation from Medieval sourcebook
German History Forum
ANSKAR The Apostle of the North (801–865). Translated from the Vita Anskarii by Bishop Rimbert his fellow missionary and successor. BY CHARLES H. ROBINSON. Im BTM format

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801 births
865 deaths
Diplomats of the Holy See
Danish Roman Catholic saints
Medieval Swedish saints
Bishops in the Carolingian Empire
Archbishops of Hamburg-Bremen
Viking Age clergy
9th-century Christian saints
Saints from East Francia
9th-century people from East Francia
Christian missionaries in Denmark
Christian missionaries in Sweden
Medieval Danish saints
9th-century writers in Latin
Writers from the Carolingian Empire
Anglican saints
Lutheran saints

Ansgar


Stephen Wesley Nease (January 15, 1925 – April 6, 2006) was an educator and president of four different institutions of higher education. He was the father of two daughters, Linda Scott and Melissa Wallace; four sons, Floyd Nease (Representative to the Vermont State House and Majority Leader), Stephen Nease, Jr., David Hardy Nease (who died at the age of 9), and David Wayne Nease.

Early life and education
Stephen W. Nease was born January 15, 1925, in Quincy, Massachusetts, to Madeline Nostrand and Floyd W. Nease while his father was president of Eastern Nazarene College. He earned his AB from Brown University and his ThB from Eastern Nazarene College in 1951. He later completed graduate work at Harvard Divinity School, and ENC awarded him an honorary Doctor of Divinity in 1966.

Career
Stephen W. Nease served as dean of men at Eastern Nazarene College in 1950. He became the executive field secretary in 1952, and was director of development in 1958. He then served as the founding president of Mount Vernon Nazarene College from 1966 to 1972, when he became the president of Bethany Nazarene College. He left the presidency at Bethany to take on the presidency of Nazarene Theological Seminary in 1976. In 1981, he left to become the president of his alma mater, Eastern Nazarene College.

In 1989, Nease was elected by the Church of the Nazarene to serve as the first Commissioner of Education. He held this position until retirement in 1994. In retirement, he served in the development office at Mount Vernon Nazarene until 2000.

Notes and references

External links
Stephen Wesley Nease funeral sermon by Russell Metcalfe

1925 births
2006 deaths
Brown University alumni
Harvard Divinity School alumni
Presidents of Eastern Nazarene College
Eastern Nazarene College alumni
Seminary presidents
People from Quincy, Massachusetts
Presidents of Mount Vernon Nazarene University
Presidents of Southern Nazarene University
Presidents of Nazarene Theological Seminary
American members of the Church of the Nazarene
20th-century Methodists

Stephen W. Nease


Ted Glenn Gray (December 31, 1924 – June 15, 2011) was a pitcher in Major League Baseball who played eight seasons with the Detroit Tigers (1946, 1948–1954), and then had short stints during the 1955 season with the Chicago White Sox, Cleveland Indians, New York Yankees, and Baltimore Orioles.

A native Detroiter, Gray was a star pitcher at Highland Park High School.  He signed with the Tigers in 1942 at age 17 and played the 1942 season with Winston-Salem in the Piedmont League, posting a 13–14 record and a 2.04 ERA. He briefly joined the Tigers at the end of the 1942 season but did not play.

Gray enlisted in the Navy when he turned 18 after the 1942 season.  Gray was assigned to the Great Lakes Naval Training Station where he pitched for the Great Lakes team managed by Mickey Cochrane. Tigers pitchers Schoolboy Rowe and Dizzy Trout also pitched for Cochrane's star-studded Great Lakes team.  Gray was transferred to the New Hebrides in the Pacific Theater, where he continued pitching for the Navy.  He won 12 straight games and averaged 17 strikeouts per game in his Navy career.  In January 1945, he pitched for the Navy All Stars.  He lost his first game against the Army All Stars 3–1 despite striking out 19 batters.  In three games against the Army All Stars, Gray had a 1–2 record and a remarkable 46 strikeouts.  After the series, The Sporting News reported: "You can't tell any of the fellows in this war sector that when peace is restored, Ted Gray won't match the records of Grove, Hubbell, Pennock, Newhouser and the other great ." (The Sporting News, February 22, 1945.)

After the war, Gray played with Buffalo before joining the Tigers for a brief stay in 1946.  He pitched only three games in the Major Leagues in 1946 (an 0–2 record) and was returned to the minors where he spent the balance of the 1946 season and the entire 1947 season.  Gray returned to the Tigers in 1948, posting a record of 6–2.

Though Gray never lived up to the expectations that were created by his wartime performance, he became part of the Tigers starting rotation from 1949 to 1953.  In 1949, Gray won 10 games and had a career-best 3.51 ERA (Adjusted ERA+ of 118).

Gray then got off to a phenomenal start in 1950, winning 10 games before the All-Star break.  He was selected for the American League All-Star team but ended up as the losing pitcher in the 1950 All-Star Game after giving up a game-winning home run to Red Schoendienst in the 14th inning.  After the All Star game, Gray failed to win another game for the remainder of the year, finishing with a 10-7 record.

Gray reportedly suffered from chronic blisters that hindered his performance.

In 1951, Gray's downward slide continued as he led the American League in losses with a record of 7–14.  And in 1952, Gray was among the league leaders in losses with 17 (third most in the AL) and earned runs allowed with 103 (third most in the AL).

Gray was a power pitcher who was known for his forkball and ranked among the American League leaders in strikeouts four consecutive years from 1950 to 1954.  He had the second-highest rate of strikeouts per 9 innings in both 1951 (5.97) and 1952 (5.88).  He was also among the league leaders in home runs allowed on three occasions, leading the league in home runs allowed in 1953 with 25.

At the end of the 1954 season, Gray was traded to the Chicago White Sox with Walt Dropo.  He was released by four teams during the 1955 season.  Only two other players have played for four American League teams in one season: Frank Huelsman and Paul Lehner.

Gray posted a career won-loss record of 59–74 with a 4.37 ERA in 222 career games.

References

External links

Baseball in Wartime - Ted Gray
Ted Gray - Baseball Biography and Highlights

1924 births
2011 deaths
Major League Baseball pitchers
Baseball players from Detroit
Detroit Tigers players
Chicago White Sox players
Cleveland Indians players
New York Yankees players
Baltimore Orioles players
People from Highland Park, Michigan
Baseball players from Wayne County, Michigan
American League All-Stars
United States Navy personnel of World War II

Ted Gray


The Haeundae Beach is at the southeast end of the city of Busan which is in South Korea. Along the 12 km of coastline is Busan's most popular beach, and one of the most famous beaches in South Korea. "Haeundae" was so named by scholar Choi Chi-Won (857~?) of the Silla Dynasty (BC 57~AD 935). When he was walking past Dongbaekdo Island, he was fascinated by it and carved the words "Hae Un Dae" on a stone wall on the island. Haeundae is 1.5 km long, 30~50m wide, and spans an area of 58,400㎡. The white sand is rough and is rather sticky. The sand of this beach is composed of sand that comes from Chuncheon Stream and shells that have been naturally eroded by the wind over time.

Haeundae used to be isolated from the large communities in Busan and Busanjin. However hotels, shopping centers and other tourist facilities have been constructed on the beach front area since the mid-1990s, and Haendae is now an rich beach front area. Haeundae New Town, a major commercial and residential redevelopment project begun in 1990 and another development project, Centum City, has been ongoing since early 2000 and is now a major feature of Busan. In Centum City there is the world's largest department store, Shinsegae Department Store and BEXCO (Busan Exhibition and Convention Center) has become a popular venue for international conventions and exhibitions. Nearby Marine City is built on land reclaimed from the sea, and has several huge, high-rise apartment blocks.

Haeundae's geographical assets, along with its tourist facilities, have led to a regular role as one of the host venues for the annual Pusan International Film Festival (PIFF).  The 2005 APEC Conference was held on Haeundae's Dongbeak Island. Haeundae is one of the most popular spots in Busan to view the first sunrise of the year on January 1. Also, one of the most famous beach events happens in the first week of January when the temperature is around 0°C, the "Polar Bear Club", held by Choseon Beach Hotel since 1988.

Dongbaek Island, at the south end of the beach, has a view of the sea by car and its coastline is famous for fishing. Oryuk Islets, a symbol of Busan to many Koreans, can be seen in the distance from Dongbaek Island. During the hot summer months, Haeundae beach becomes very crowded with thousands of people and parasols. People visit the beach from all over South Korea, as well as outside of Korea.

Busan

Haeundae


Breccia is a type of rock formed of broken fragments of minerals or rock cemented together by a fine-grained matrix. The matrix may be similar to or different from the composition of the fragments.

Rocks formed by sticking together pieces of previous rocks (clasts) are called clastic rocks. There are two types of clastic rock: conglomerates and breccias. What divides these two categories is the amount of rounding. The particles that make up conglomerates are well rounded, whereas in breccias they are angular.

Origins
A breccia may have a variety of different origins, as indicated by the named types including sedimentary breccia, tectonic breccia, igneous breccia, impact breccia and hydrothermal breccia.

The basic sequence is as follows. Sediment is laid down in the sea, and is gradually transformed into sedimentary rock. After a long period of time something violent happens to the rock, breaking it up into many jagged pieces. Later the pieces are cemented together by another deposition, forming a new sedimentary rock. This is a breccia. On examination, the rock is seen to include fragments of the original rock.

Ornament
Brecciated marble can have a marvellous appearance. The Pantheon in Rome is full of the most wonderful marble, many of which are breccias. It includes two gigantic columns of pavonazzetto, a breccia coming from Phrygia (in modern Turkey). Pavonazzetto obtains its name from its extremely colourful appearance, which is reminiscent of a peacock's feathers (pavone is "peacock" in Italian).

References

Sedimentary rocks

Breccia


Warld War I (WWI) wis a global war centred in Europe that began on 28 Julie 1914 an lastit till 11 November 1918. It wis predominantly cried the Warld War or the Great War frae its occurrence till the stairt o Warld War II in 1939, an the First Warld War or Warld War I thareefter. It involved aw the warld's great pouers, whilk war assembled in twa opposin alliances: the Allies (based on the Triple Entente o the Unitit Kingdom, Fraunce an Roushie) an the Central Pouers (originally the Triple Alliance o Germany, Austrick-Hungary an Italy; but, as Austrick–Hungary haed taken the offensive against the agreement, Italy did nae enter intae the war). These alliances war baith reorganised an expandit as mair naitions entered the war: Italy, Japan an the Unitit States jyned the Allies, an the Ottoman Empire an Bulgarie the Central Pouers. Ultimately, mair nor 70 million militar personnel, includin 60 million Europeans, war mobilised in ane o the lairgest weirs in history. Mair nor 9 million combatants war killed, lairgely acause o technological advancements that led tae enormous increases in the lethality o wappens wioot correspondin improvements in protection or mobility. It wis the fift-deadliest conflict in warld history, subsequently pavin the wey for various poleetical chynges, such as revolutions in mony o the naitions involved.

Ane o the lang-term causes o the war wis the resurgence o imperialism in the foreign policies o the great pouers o Europe. Mair immediately, the assassination o Airchduke Franz Ferdinand o Austrick, the heir tae the throne o Austrick-Hungary, on 28 Juin 1914 bi Yugoslav naitionalist Gavrilo Princip in Sarajevo triggered a diplomatic crisis whan Austrick-Hungary subsequently delivered an ultimatum tae the Kinrick o Serbie. Several alliances formed ower the previous decades war invoked. Within weeks, the major pouers war at war an, via thair colonies, the conflict soon spread aroond the warld.

On 28 Julie, the Austro-Hungarians fired the first shots o the war as prepairation for the invasion o Serbie. While the Roushies mobilised, the Germans invadit neutral Belgium an Luxembourg on the wey tae Fraunce, providin a casus belli for Britain's declaration o war against Germany. Efter the German mairch on Paris wis broucht tae a halt—the so-cried Miracle o the Marne—the Wastren Front settled intae a static battle o attrition wi a trench line that chynged little till 1917. On the Eastren Front, the Roushie airmy wis successful against the Austro-Hungarians, but wis stopped in its invasion o East Prussie bi the Germans. In November the Ottoman Empire jyned the war, openin up fronts in the Caucasus, Mesopotamie an the Sinai. Italy an Bulgarie went tae war in 1915 an Romanie in 1916. In Roushie, the tsar's govrenment collapsed in Mairch 1917 an a subsequent revolution in November brocht the Roushies tae terms wi the Central Powers. Efter a 1918 German offensive alang the western front, the Allies drove back the Germans in a series o successful offensives an American forces began enterin the trenches. Germany, whilk had its awn trooble wi revolutionaries, agreed tae an armistice on 11 November 1918. The war endit in victory for the Allies.

Events on the home fronts war as tumultuous as on the battle fronts, as the participants tried tae mobilize thair manpouer an economic resources tae ficht a tot war. Bi the end o the war, fower major imperial pouers—the German, Roushie, Austro-Hungarian an Ottoman empires—ceased tae exist. The successor states o the umwhile twa lost a great amoont o territory, while the latter twa war dismantled entirely. The cairt o central Europe wis redrawn intae several smawer states. The League o Naitions wis formed in the hope o preventin anither such conflict. The European naitionalism spawned bi the war an the breakup o empires, the repercussions o Germany's defeat an problems wi the Treaty o Versailles are agreed tae be factors contributin tae Warld War II.

References

Warld War I
Contemporary German history
Contemporary Italian history
Edwardian era
French Third Republic
Global conflicts
History o Austrick-Hungary
History o modren Serbie
History o Montenegro
History o the Unitit States (1865–1918)
Russo-Ottoman Wars
Wars involvin Armenie
Wars involvin Australie
Wars involvin Austrick
Wars involvin Belgium
Wars involvin Brazil
Wars involvin Breetish Indie
Wars involvin Bulgarie
Wars involvin Canadae
Wars involvin Costa Rica
Wars involvin Croatie
Wars involvin Czechoslovakie
Wars involvin Fraunce
Wars involvin Germany
Wars involvin Greece
Wars involvin Guatemala
Wars involvin Haiti
Wars involvin Hungary
Wars involvin Ireland
Wars involvin Italy
Wars involvin Japan
Wars involvin Liberia
Wars involvin Montenegro
Wars involvin New Zealand
Wars involvin Nicaragua
Wars involvin Panama
Wars involvin Portugal
Wars involvin Romanie
Wars involvin Roushie
Wars involvin Serbie
Wars involvin Sooth Africae
Wars involvin Sri Lanka
Wars involvin Thailand
Wars involvin the Ottoman Empire
Wars involvin the Republic o Cheenae
Wars involvin the states an fowks o Oceanie
Wars involvin the Unitit Kinrick
Wars involvin the Unitit States

Warld War I


A prairie is a type of habitat with mostly grasses, but also flowering plants and occasional shrubs or isolated trees. This type of habitat can be found around the world, but it goes by different names, such as steppes in Asia. What attracts tourists is the golden grass in prairie grassland. The word 'prairie' generally means grasslands in North America. There are often many animals on a prairie. They either prefer a prairie or can only live in a prairie. Well-known are the American bison (also called the buffalo) and the pronghorn antelope. Birds include the bobolink and the meadowlark.

Tall grass prairie
A tall grass prairie has plants and grasses that typically grow three to seven feet (one to two meters) high, with occasional plants growing higher.  This type of prairie grows in areas with a good amount of rain.  Illinois (the prairie state), Indiana and Iowa are good examples of where a tall grass prairie would be.  This type of habitat usually has rich soil and is very good for farming.  Much of the original prairie has been changed into crops or lost to brush and trees.

Trees can grow in this area, but most were stopped by fires that swept through. Some oaks and hickories could survive a prairie fire. If there are scattered trees, it is called a savanna, and an area in a prairie where trees grew closer together is called a grove. In the last century, fires have been stopped, and trees and brush that once did not grow in prairies are taking over. Some of these invasive species were accidentally introduced from Europe or elsewhere. In Illinois, less than 0.01% of the original prairie still exists. For this reason, conservationists in this area practice prairie restoration to keep the prairie in good condition.

Short grass prairie

Short grass prairie is in drier areas, and is usually one to two feet (0.3 to 0.6 meters) high and has more space between plants. The American West (from central Texas up into Canada, East of the arid semi-desert) is all short grass prairie. Much of this area is converted to pastures for grazing cows and sheep. Areas near rivers are irrigated for crops. Some of the original native plants are still widespread.

Mixed grass prairie
Mixed grass prairie is found between the short grass prairie and the tall grass prairie and contains more species than either the short grass prairie or the tall grass prairie.

Related pages
Prairie dog

References

Grasslands

Prairie


Stem cells are cells of the body
(somatic cells) which can divide and become differentiated.

When an organism grows, stem cells specialize, and take specific functions. For instance, mature tissues like skin, muscle, blood, bone, liver, nerves, all have different types of cells. Because stem cells are not yet differentiated, they can change to become some kind of specialized cells. Organisms also use stem cells to replace damaged cells.

Stem cells are found in most, if not all, plants and animals. They divide and differentiate into a range of cell types.Research in the stem cell field grew out of findings in the 1960s.

The two broad types of mammalian stem cells are embryonic stem cells, and adult stem cells, which are found in adult tissues. In a developing embryo, stem cells can differentiate into all of the specialised embryonic tissues. In adult organisms, stem cells act as a repair system for the body, replenishing specialized cells, but also maintain the normal turnover of blood, skin, and intestinal tissues.

Stem cells can be grown in tissue culture. In culture, they can be transformed into specialised cells, such as those of muscles or nerves. Highly plastic adult stem cells can be taken from a variety of sources, including umbilical cord blood and bone marrow. They are now used in medical therapies, and researchers expect that stem cells will be used in many future therapies.

Embryonic stem cells

Embryonic stem cells (ES cells) are stem cells taken from the inner cell mass of the early stage embryo called a blastocyst. Human embryos reach the blastocyst stage 4–5 days after fertilization. At that time, they are made up of between 50 and 150 cells.

The stem cells' state, and what the daughter cells turn into, is influenced by signals from other cells in the embryo.

Adult stem cells
Adult stem cells exist throughout body after embryonic development has completed. They are found inside different types of tissue and remain in a non-dividing state until disease or tissue injury. They have changed permanently into specialised cells and have lost the ability to divide and specialise further. An advantage of adult stem cells is that it provides a lesser probability of rejection. A disadvantage would be that there is a limited availability to get them.

Plant stem cells
Plant stem cells are unspecialised, and can develop into any type of plant cell. They become specialised into cells of roots, leaves or flowers. The stem cells are found in meristem, at root and stem apices. They make it possible for growth to continue.

Cloning plants
These are plants which have been grown from cuttings (therefore the same plant). They are dipped in hormone rooting powder to develop bigger root systems. They turn into tissues (e.g. xylem and phloem) and organs (e.g. roots, leaves and flowers), therefore becoming a completely new plant.

Cloning plants is a cheap way of producing a new plant.

Stem cells in medicine
Some cancers may be treated by stem cells. Leukemia, a cancer of white blood cells (WBC) is an example.

There are two stages to this process:
 Chemotherapy and radiotherapy to kill cancerous white blood cells and stop the production of more. However, this may leave the patient with a severely compromised immune system with a risk of infection.
 A transplant of bone marrow containing healthy stem cells (to produce normal WBC).

Other websites
 Stem cells at the National Academies
 Adult stem cells and cloning
 National Institutes of Health

References

Developmental biology
Cells

Stem cell


Pinchas Zukerman  (, (born July 16, 1948 in Tel Aviv) is a famous Israeli violinist, violist, and conductor. Since 1971 he has conducted many orchestras.

Career
Pinchas Zukerman was born in Tel Aviv.  His parents were Jews who had survived the Nazi  concentration camps and had moved from Poland to Israel in 1947.  By the time he was eight he showed great musical promise and started to learn music at the Academy of Music in Tel Aviv.  When the violinist Isaac Stern heard him play he was so impressed that he helped him to go to the Julliard School of Music in New York.  There he studied with Ivan Galanian.
In 1967 Zukerman shared a joint first prize in the Leventritt International Competition with Kyung Wha Chung.  Then Stern became ill and could not play in some concerts, so Zukerman played instead.  This made him famous.

After hearing Daniel Barenboim conduct the English Chamber Orchestra he became interested in conducting.  He started to conduct orchestras while playing the solo parts of works by Bach and Vivaldi.  This conducting (leading) from the concertmaster's chair had been the tradition in the 17th and 18th centuries.  In 1974 he conducted the English Chamber Orchestra.  Then he got opportunities to conduct New York Philharmonic, the National Symphony, Boston Symphony, and Los Angeles Philharmonic orchestras.  In the 1980s he was the musical director of the St Paul Chamber Orchestra.  For many years he continued to appear as soloist with some of the best orchestras in the world.  He won many awards, including two Grammy Awards.

In April 1998, he became music director of the National Arts Centre Orchestra in Ottawa.  He took them on a tour of Europe in 1990.

In recent years Zukerman has worked hard to help young musicians.  As music director of the National Arts Centre Orchestra, he started the  Young Artists Program, a summer training program for talented young classical musicians. It soon became popular, with students from all over the world.

Although he had a lot of work in Ottawa  he also found time to be music director of the Ilona Feher Music Center in Holon, Israel, which he started.  He became chairman of the Pinchas Zukerman Performance Program at the Manhattan School of Music in New York City. He was also artist in residence of the Milwaukee Symphony Orchestra.

Personal life
In 1968 he married Eugenia Rich.  They had two daughters.  Later they divorced.  In 1985 he married the actress Tuesday Weld.  They later divorced.

Other websites
 http://www.answers.com/topic/pinchas-zukerman

1948 births
Living people
Grammy Award winners
Israeli conductors
Jewish Israeli musicians
Musicians from Tel Aviv
Israeli violinists

Pinchas Zukerman


There are some sciences that are used to study nature. According to these sciences, the things that can be observed in nature follow certain rules. These rules are often unknown, and natural sciences are about finding them. They often use what is called the scientific method to do this.

 Physics looks at how nature is made, and how different fields of nature relate to each other.
 Chemistry is about the elements, their combinations, and their reactions.
 Biology looks at living organisms, their development, and how they interact with each other.
 Geology looks at how the earth developed and what it looks like.

Some sciences, like Mathematics, can help in all of the above. They make it easier to talk about rules and models.

Other sciences that mainly look at how humans think and behave, and about their society are called social sciences.

Overview
The natural sciences are used to make new things (applied science). The natural sciences are guides to test new ideas. They are used to solve engineering problems and technology problems. Math helps the natural sciences to solve problems and make new things. Computers do this too. Computers and math are tools for the natural sciences.

The following sciences are natural sciences:
 Astronomy
 Biology
 Chemistry
 Earth science
 Physics
 Atmospheric science
 Oceanography
 Materials sciences

Natural sciences


Cloth, textile or fabric are similar names for manufactured material. They are often made by weaving or knitting fibres together. It is often used to make clothing or cover furniture.

Cloth can be made from natural fibres or man-made ones. Examples of natural fibres are cotton, wool, and silk.  Examples of man-made fibres are nylon, rayon, and polyester.

History
The first clothes, were worn at least 70,000 years ago and maybe much earlier. They were probably made of animal skins and helped to protect early humans from the weather. At some point, people learned to weave plant fibers into textiles.

Uses
Textiles are commonly used for clothing. They are also containers such as bags and baskets. In the household, textiles are used in carpeting, upholstered furnishings, window shades, towels, coverings for tables, beds, and other flat surfaces. Textiles are also used in art. In the workplace, textiles can be used in industrial and scientific processes such as filtering. It can also be used in flags, backpacks, tents, nets, handkerchiefs, cleaning rags. It is used in transportation devices such as balloons, kites, sails, and parachutes. Textiles are also used make composite materials such as fibreglass and industrial geotextiles stronger. Textiles are used in many traditional crafts such as sewing, quilting and embroidery.

Types and sources of fibre
Textiles are made from many materials. The fibre used in textiles are gotten from with four main sources. They are animal (wool, silk), plant (cotton, flax, jute, bamboo), mineral (asbestos, glass fibre), and synthetic (nylon, polyester, acrylic, rayon). The first three are natural.

Animal
There are many sources of animal fibers. They include:

 Wool refers to the hair of the domestic sheep or goat. It is different from other types of animal hair because each strand is coated with scales and lanolin. Wool is commonly used for thick clothing. Examples include:
 Alpaca wool, vicuña wool, llama wool, and camel hair. They are generally used to make coats, jackets, ponchos, blankets, and other warm coverings.
 Cashmere, the hair of the Indian cashmere goat, and mohair, the hair of the North African angora goat, are types of wool that are known for their softness.
 Angora refers to the long, thick, soft hair of the angora rabbit.
 Qiviut is the fine inner wool of the muskox.
 Sea silk is an extremely fine, rare, and valuable fabric. It is made from the silky filaments or byssus secreted by a gland in the foot of pen shells.
 Silk is an animal textile made from the fibres of the cocoon of the Chinese silkworm. It is spun into a smooth fabric which is soft. Around four-fifths of the silk made is made up of cultivated silk.

Plant
There are many sources of plant fibers. They include:

 Coir (coconut fibre) is used in making twine. It is also used in making doormats, brushes, mattresses, floor tiles, and bags.
 Straw and bamboo are both used to make hats. Straw is a dried form of grass. It is also used for stuffing, as is kapok.
 Fibres from pulpwood trees, cotton, rice, hemp, and nettle are used in making paper.
 Cotton, flax, jute, hemp, modal and even bamboo fibre are all used in clothing. Piña (pineapple fibre) and ramie are also fibres used in clothing.
 Acetate is used to make certain fabrics such as silks, velvets, and taffetas shinier.
 Seaweed is used in making textiles.
 Rayon is a fabric gotten from plant pulp.

Mineral
There are three sources of mineral fibers. They include:

 Asbestos and basalt fibre are mineral fibres used for vinyl tiles, sheeting and adhesives, acoustical ceilings, stage curtains, and fire blankets.
 Glass fibre is used in making ironing board and mattress covers, ropes and cables, reinforcement fibre for composite materials, insect netting, flame-retardant and protective fabric, soundproof, fireproof, and insulating fibres.
 Metal fibre, metal foil, and metal wire have lots of uses including making cloth-of-gold and jewellery.

Synthetic
There are many sources synthetic fibres. They include

 Polyester fibre is used in all types of clothing, either alone or used together with fibres such as cotton.
 Aramid fibre (e.g. Twaron) is used for flame-retardant clothing, cut-protection, and armour.
 Acrylic is a fibre used to mimic wools. It is often used to replace wool in some clothes.
 Nylon is a fibre used to mimic silk. It is used in making pantyhose. Thicker nylon fibres are used in rope.
 Spandex is a polyurethane product. It is used to make activewear, bras, and swimsuits.
 Olefin fibre is a fibre used in activewear, linings, and warm clothing. Olefins are hydrophobic, which allows them to dry quickly.
 Ingeo is a polylactide fibre mixed with other fibres such as cotton and used in clothing. It is hydrophilic.
 Lurex is a metallic fibre used in clothing embellishment.
 Milk proteins have also been used to make synthetic fabric. It is sold as a biodegradable, renewable synthetic fibre.
 Carbon fibre is mostly used in composite materials, together with resin. The fibres are made from polymer fibres through carbonization.

Gallery

References

Basic English 850 words

Textile


Manchester Liberalism (also called the Manchester School, Manchester Capitalism and Manchesterism) comprises the political, economic and social movements of the 19th century that originated in Manchester, England. Led by Richard Cobden and John Bright, it won a wide hearing for its argument that free trade would lead to a more equitable society, making essential products available to all.  Its most famous activity was the Anti-Corn Law League that called for repeal of the Corn Laws that kept food prices high. It expounded the social and economic implications of free trade and laissez-faire capitalism. The Manchester School took the theories of economic liberalism advocated by classical economists such as Adam Smith and made them the basis for government policy. It also promoted pacifism, anti-slavery, freedom of the press and separation of church and state.

Manchester background
Manchester was the hub of the world's textile manufacturing industry and had a large population of factory workers who were disadvantaged by the Corn Laws, the protectionist policy that imposed tariffs on imported wheat and therefore increased the price of food. The Corn Laws were supported by the land-owning aristocracy because they reduced foreign competition and allowed landowners to keep grain prices high. That increased the profits from agriculture as the population expanded. However, the operation of the Corn Laws meant that factory workers in the textile mills of northern England were faced with increasing food prices. In turn, mill owners had to pay higher wages, which meant that the price of finished goods was higher, and the foreign trade competitiveness of their products was reduced.

Anti-Corn Law League
Mercantilism holds that a country's prosperity is dependent on large exports, but limited imports of goods. At the beginning of the 19th century, trade in Britain was still subject to import quotas, price ceilings and other state interventions. That led to shortages of certain goods in British markets, in particular corn (grains usually requiring grinding, most often, but not always wheat).

Manchester became the headquarters of the Anti-Corn Law League from 1839. The League campaigned against the Corn Laws, which it said would reduce food prices and increase the competitiveness of manufactured goods abroad. Manchester Liberalism grew out of that movement. That has led to the situation seen in modern Britain, where the country benefits from less expensive food, imported from trading partners, and those partners in turn benefit from less expensive goods imported from Britain, in a system of globalised cooperation in production.

Manchester Liberalism has a theoretical basis in the writings of Adam Smith, David Hume and Jean-Baptiste Say.

The great champions of the Manchester School were Richard Cobden and John Bright. As well as being advocates of free trade, they were radical opponents of war and imperialism, and proponents of peaceful relations between peoples. The "Little Englander" movement saw little benefit in paying taxes to defend colonies such as Canada, which contributed little trade to Manchester manufacturers and could not supply their main raw material of cotton.

Terminology
In January 1848, Conservative Benjamin Disraeli first used the term "the Manchester School". According to historian Ralph Raico and as indicated by the German liberal Julius Faucher in 1870, the term "Manchesterism" was invented by Ferdinand Lassalle (the founder of German socialism) and was meant as an abusive term.

See also

 Birmingham School, other contemporary school associated with British industrial capitalism
 British Whig Party
 Michel Chevalier
 Thomas Thomasson

Notes and references

Further reading
 Bresiger, Gregory. "Laissez Faire and Little Englanderism: The Rise, Fall, Rise, and Fall of the Manchester School," Journal of Libertarian Studies (1997) 13#1 pp 45–79. online
 William Dyer Grampp, The Manchester School of Economics (Stanford: Stanford University Press, 1960), the standard scholarly history
 Wallace, Elisabeth. "The Political Ideas of the Manchester School," University of Toronto Quarterly (1960) 29#2 pp 122–138

History of liberalism
Liberalism in the United Kingdom
Libertarianism in the United Kingdom
History of Manchester
Schools of economic thought

Manchester Liberalism