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Flattop (Dick Tracy villain)

Flattop Jones, Sr. is a fictional villain created by Chester Gould for the Dick Tracy comic strip. His nickname comes from his large head that is perfectly flat on the top.

Background of fictional character
Gould revealed little about Flattop's personal life in the comic strip, but the background references that he did give the character share similarities to real-life Depression-era gangster Pretty Boy Floyd. For example, Flattop claims in the strip to be a freelance hitman from "Crookston Hills", a parody version of Floyd's hometown of Cookson Hills in Oklahoma. The comic strip also references Flattop's involvement in the "Kansas City Massacre," a 1933 incident in which Floyd was alleged to have been involved.

Appearances in the comic strip
Gould's character leads a gang of three hoods and is known as an "ace killer" in one newspaper headline shown in the comic strip (having committed five murders). In the storyline in which Flattop is the featured villain, black marketeers hire him to eliminate Dick Tracy for a fee of $5000: five times his regular rate.

Flattop proves his value by quickly orchestrating a successful setup involving an argument over money to capture Tracy in the killer's car, to be transported somewhere where he can be murdered undisturbed. Fortunately for Tracy, Flattop stops just short of doing the deed when he realizes that if his employers are willing to pay such a high price for Tracy's death, then they would surely be desperate enough to pay much more. As such, Flattop decides to blackmail his employers for an additional $50,000, lest he release the detective with the information Tracy needs to arrest them.

Hiding in a boarding room while the transaction is completed, Tracy learns that a resident living with her mother downstairs is a WAC who is learning Morse code. Taking advantage of an available piano in the room, he plays it and stamps on the floor in code as if keeping time to his music, hoping that the WAC below, Margie Elong, will recognize the call for help. Sure enough, Elong and her mother get the message and alert the police.

However, even as Tracy's partner, Pat Patton, is preparing for a rescue; Flattop, having closed his blackmail deal, prepares to shoot Tracy while he is surrounded by his henchmen. On Flattop's count of three, Tracy interrupts the assassination by lunging at the killer and wrestles for his gun. In the struggle, Tracy manages to kill a henchman, "Machine Gun" Eddie, with a Tommy gun, using it to kill the rest of Flattop's gang even as the police were storming the hideout. While the police tend to Tracy, the detective suggests that it be publicly announced that he was found dead, anticipating that Flattop's employers would contact the killer soon afterward. The black marketeers take the bait, call Flattop's number to invite him for a celebration and the police quickly arrive to arrest the lot of them.

However, Flattop escapes and becomes a hunted fugitive hampered by his extremely distinctive appearance. This leads to a long chase, during which Flattop stays in a boarding house while being extorted by the crooked child, Bud Jenkins, who is hiding him. The child drowns in a lake while skating with expensive new skates he buys with the blackmail money. This leads Tracy to the boarding house, forcing Flattop to flee. In a comic relief episode Flattop decides to hide his $50,000 in an old family album that he has hollowed out, as he is afraid that if he goes out with this much money he might be mugged; when Tracy visits the boarding house to question the dead boy's mother about her son flashing $50 bills around town, a reporter in need of a good picture of the deceased finds the money in the album.

When questioned, the mother realizes that the money could only have come from her roomer, forcing Flattop to flee the scene just as Tracy storms the room. This begins a chase sequence that lasts for six months beginning at the end of 1943. First, Flattop hides in a chimney and is stung by bees in a hive that he disturbs there. Then, slipping away stung and covered in honey, Flattop forces a street hawker selling gasoline as fraudulent cleaning fluid to use it to clean him, then murders the hawker to fake his own death and to steal his victim's war bonds. Eventually, Flattop hides out in a hotel, but the war bonds blow out a window and betray his location. In desperation as the police officer reports this to headquarters, he hides in the room of Vitamin Flintheart, an aging ham actor.

After attempting to explain his trespass, Flattop assaults Flintheart and uses his makeup supplies for a disguise. Tracy, Patton and Flintheart continue the search and battle Flattop after almost being fooled by a child to whom Flattop had passed off his disguise in hopes of making him a decoy. Flattop is severely wounded, but is successfully treated in custody.

Loath to let Flattop slip into oblivion, Gould managed to find a way to continue the story. The comic strip details Flattop's escape from custody, the taking of Flintheart hostage, and his hiding in the harbor area. Eventually, on May 14, 1944, Flattop is drowned attempting to flee Tracy, after getting trapped between two struts of a replica of the Santa Maria.

In the original story, comic relief is afforded by Vitamin Flintheart, who cons Officer Murphy and then Flattop into buying him vitamin pills and, when asked to repay, always answers, "Charge it". More relief is afforded by Flintheart who, while trapped on the Santa Maria replica, claims he cannot swim since he fell into his aunt's lily pond at a tender age.

In a 1986 storyline, reporter Wendy Wichel approaches Dick Tracy with new information concerning Flattop and an incident in World War II which was kept secret and in which Tracy was involved. Before Flattop encountered Tracy in 1944, Flattop was hired by the Nazi spy Boche, a.k.a. Pruneface, to kidnap FBI Agent Jim Trailer, a close friend of Dick Tracy's, and a scientist Trailer and Tracy were protecting: Professor Roloc Bard. Flattop's instructions were to kill Trailer and hand the professor over to Pruneface. Bard warned Flattop that he will be shipped overseas to make deadly weapons for the Axis cause, which caused Flattop to double-cross Pruneface and auction Bard and Trailer off to the highest bidder. Authorities in Washington, DC, ostensibly participating in the auction, authorized Tracy to negotiate while they traced Flattop's call. Tracy and his partner, Pat Patton, broke into Flattop's hideout and freed Trailer, while Flattop escaped with the Professor. Flattop tied Bard up in the same replica of the Santa Maria where he would later drown. Flattop's last appearance in this storyline was his meeting with Pruneface to collect his $50,000 and tell him where to find Bard. Pruneface swore that he would kill Flattop if he ever dared to betray him again.

Characters created because of Flattop
As a response to Flattop's popularity, Gould created a large family for the character, most of whom were also equally bizarre hardened criminals. These included:
 * Flattop Jr. – The son of Flattop, an engineering genius who had customized his car into an arsenal of equipment designed to keep him ahead of the law. His accomplice during his crime wave was Joe Period, who Flattop Jr. left to be captured by Dick Tracy and partner Sam Catchem. Flattop Jr. was driven insane by the ghost of a girl he killed, to the point that his hair turned white. He was killed in self-defense by Tracy's new partner Lizz Worthington who was also the sister of the girl who was murdered by Flattop Jr.
 * Stilleta "Mrs. Flattop" Jones – The widow of Flattop Jones and the mother of Flattop Jr. and Angelica "Angeltop" Jones and a former circus performer bent on revenge for the deaths of her husband and children. She was recently killed while fighting with Sprocket Nitrate over trying to force her to be an accomplice on the planned kidnapping of Dick Tracy's grandson Dick "Trey" Tracy III, after which Sprocket killed her by throwing a 100-pound metal film can at her.
 * Unnamed sister – Looks exactly like Flattop and Sharptop (possible triplet), real name was never given, but was referred to by Tracy as "Auntie Flattop". Took care of her nephew Flattop Jr. after Flattop drowned and, like her other cousin Sharptop, seemed like a law-abiding person. She genuinely cared about her nephew and was heartbroken when he turned to crime.
 * Blowtop – A brother of Flattop whose distinctive feature was an explosive temper that caused him to rage at the slightest provocation, but only on trivialities; he took legitimate criticism on important matters without complaint. Blowtop was responsible for the kidnapping of Junior Tracy and the arson of Tracy's home. Like his brother Flattop, he has dealt with Vitamin Flintheart, who traded his shirt full of money for a shrunken head. He served 20 years in jail and later reformed. He rejected his niece Angeltop's plan to kill Tracy with the Brow's son and has not been seen since. He recently resurfaced by having his sister-in-law Stilleta living with him and was nearly murdered by her after an argument with her on a planned  kidnapping of Dick Tracy's newborn grandson Dick "Trey" Tracy III.
 * Angeltop – Angelica "Angeltop" Jones, the daughter of Flattop who has a head of equivalent dimensions, not including her fuller hair, sought to murder Dick Tracy in revenge for the death of her father and Policewoman Lizz for killing her brother. Angelica had arrived to do a television interview about her book My Life as Flattop's Daughter. Junior Tracy and Vitamin Flintheart were in the same studio, and Angelica's boyfriend, the Brow's son, shot Vitamin Flintheart. After a fire on the Santa Maria replica in the lake (the same place where her father drowned under) nearly killed her after an attempt to murder Tracy, she had plastic surgery by crooked doctor Willis Carver, whom she later killed. She was arrested later but was broken out of jail by terrorists who wished to sabotage industrialist Diet Smith's nuclear power plant. When that scheme went awry, Angeltop committed suicide with dynamite.
 * Hi-Top – The son of Angeltop and an unnamed African-American jazz musician who was murdered by Angeltop after she found out that he was planning to divorce her and also planned to take sole custody of the infant Hi-Top. Hi-Top is, consequently, the grandson of Flattop. He is introduced as the leader of a black street gang, and is recruited by "Little Boy", the alleged grandson of Tracy's original arch-enemy "Big Boy" Caprice, alongside Restless Mahoney, the niece of another iconic Tracy villain; Breathless Mahoney. Together, the three form a brief triumvirate of legacies to Tracy's rogues gallery.
 * Sharptop – A third Jones brother (Flattop's identical twin) who grew up right, became a college professor, and was apparently possessed by the ghost of his brother during a seance held by Gravel Gertie's vegan, female chauvinistic, New Age enthusiast niece, Crystal.
 * Poptop – The beer-guzzling father of Flattop, Auntie Flattop, Blowtop, and Sharptop, the father-in-law of Stilleta, the grandfather of Flattop Jr. and Angeltop, the uncle of Frizzletop, and the great-grandfather of Hi-Top. Considered the criminal members of the family a disgrace and kept his distance from all of them.
 * Croptop - Stylistically-challenged sister of Flattop and daughter of Poptop. She sports short hair on top, a buzz cut on the sides, and a mullet. She has the puckered lips and general face shape of her family. She wears a crop top showing her midriff. She was first introduced in April, 2024, freshly released from prison with a backstory not previously covered by the strip. With this introduction so long after her siblings in the timeline of the family, her brother Blowtop indicated that there were only five siblings, making her the final sibling to be introduced.

In other media

 * An episode of the Dick Tracy radio show featured Flattop. Titled "Flattop", this broadcast is now available for free from archive.org.
 * In an episode of the early TV series starring Ralph Byrd, John Cliff portrayed Flattop in a half-hour episode based fairly closely on the comic strip story. Rather than giving the actor a flat head, or a haircut that made his head look flat, Cliff wore a large beret that suggested a flat top.
 * In the 1990 film adaptation of the comic strip, he is played by William Forsythe. In this adaptation, he was a henchman of the main villain, Alphonse "Big Boy" Caprice (Al Pacino) and is killed during a shootout with Tracy.
 * Flattop appears in the Daffy Duck short "The Great Piggy Bank Robbery", in which the animators portrayed several mini-aircraft taking off from his head, he is also seen among the dead villains falling out of the closet after Daffy shoots them.
 * In The Dick Tracy Show, he was voiced by Benny Rubin and was paired up with B.B. Eyes in almost all his heists.

title: Flattop (Dick Tracy villain)


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Henry Duff Traill

Henry Duff Traill (14 August 1842 – 21 February 1900) was a British writer and journalist.

Life
Born at Blackheath, he belonged to an old Caithness family, the Traills of Rattar, and his father, James Traill, was the stipendiary magistrate of Greenwich and Woolwich Police Court. He was sent to the Merchant Taylors' School, where he rose to be head of the school and obtained a scholarship at St John's College, Oxford. Initially destined for the profession of medicine, Traill took his degree in natural sciences in 1865 but then he read for the bar and was called in 1869. In 1871 he received an appointment as an Inspector of Returns for the Board of Education, a position which left him leisure to cultivate his gift for literature.

In 1873 he became a contributor to the Pall Mall Gazette, then under the editorship of Frederick Greenwood. He followed Greenwood to the St. James's Gazette when in 1880 the Pall Mall Gazette took for a time the Liberal side, and he continued to contribute to that paper up to 1895. In the meantime he had also joined the staff of the Saturday Review, to which he sent, among other writings, weekly verses upon subjects of the hour. Some of the best of these he republished in 1882 in a volume called Recaptured Rhymes, and others in a later collection of Saturday Songs (1890).

He was also a leader-writer for the Daily Telegraph and edited The Observer from 1889 until 1891, which experienced an increase in circulation during his time there. In 1897, he became first editor of Literature, when that weekly paper (afterwards sold and incorporated with the Academy) was established by the proprietors of The Times, and directed its fortunes until his death.

Traill's long connection with journalism must not obscure the fact that he was a man of letters rather than a journalist. He wrote best when he wrote with least sense of the burden of responsibility. His playful humour and his ready wit were given full scope only when he was writing to please himself. One of his most brilliant jeux d'esprit was a pamphlet which was published without his name soon after he had begun to write for the newspapers. It was called The Israelitish Question and the Comments of the Canaan Journals thereon (1876). This told the story of the Exodus in articles which parodied very cleverly the style of all the leading journals of the day, and was at once recognized as the work of a born humorist. Traill sustained this reputation with The New Lucian, which appeared in 1884 (2nd ed., with several new dialogues, 1900); but for the rest his labours were upon more serious lines. He directed the production of a vast work on Social England in 1893-1898; he wrote, for several series of biographies, studies of Coleridge (1884), Sterne (1882), William III (1888), Shaftesbury (1886), Strafford (1889), and Lord Salisbury (1891); he compiled a biography of Sir John Franklin the Arctic explorer (1896); after a visit to Egypt he published a volume on the country; and in 1897 appeared his book on Lord Cromer, the man who had done so much to bring it back to prosperity. Of these the literary studies are the best, for Traill possessed great critical insight. He published two collections of essays: Number Twenty (1892), and The New Fiction (1897). In 1865 his Glaucus, a tale of a Fish, was produced at the Olympic Theatre with Miss Nellie Farren in the part of Glaucus. In conjunction with Mr Robert Hichens he wrote The Medicine Man, produced at the Lyceum in 1898. He died in London on 21 February 1900.

He also edited the Centenary edition of the Works of Thomas Carlyle (30 volumes, Chapman and Hall, 1896-1907), writing introductions to the various works.

Works

 * Sterne (1882)
 * Recaptured Rhymes (1882)
 * The New Lucian (1884)
 * Coleridge (1884)
 * Shaftesbury (1886)
 * William III (1888)
 * Strafford (1889)
 * Saturday Songs (1890)
 * The Marquis of Salisbury (1890)
 * Number Twenty: Fables and Fantasies (1892)
 * The Life of Sir John Franklin, R.N. (1896)
 * The new fiction, and other essays on literary subjects (1897)

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

title: Henry Duff Traill


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File:Yellow knight cover.jpg

Description
Cover of The Yellow Knight of Oz by Ruth Plumly Thompson, illustrated by John R. Neill.

Non-free/fair use media rationale in The Yellow Knight of Oz
Although this image is subject to copyright, it is covered by US fair use laws because:
 * 1) The image is only being used in the article about the book (The Yellow Knight of Oz) for informational and educational purposes.
 * 2) Use of the book cover visually identifies the book and its author in a manner that mere prose cannot, and meets all criteria in WP:NFCC.
 * 3) This image enhances the article in which it's displayed, as it provides an immediate relevance to the reader more capably than the textual description alone.
 * 4) The image is not replaceable with an uncopyrighted or freely copyrighted image of comparable educational value.
 * 5) The image is the primary means of visual identification of the article topic.
 * 6) The image is a low resolution image of the book cover, in an inferior graphics format (JPEG).
 * 7) The image is only a small portion of the commercial product.
 * 8) The use of this image will not affect the value of the original work.
 * 9) The use of this image will not limit the copyright holder's rights or ability to distribute or make a profit from the original. In particular, copies could not be used to make illegal copies of the book.

title: File:Yellow knight cover.jpg


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User:Mooncowboy

Welcome!
Welcome!

Hello, and welcome to Wikipedia. Thank you for your contributions. I hope you like the place and decide to stay. Here are a few good links for newcomers: I hope you enjoy editing here and being a Wikipedian! By the way, you can sign your name on Talk and vote pages using three tildes, like this: ~~~. Four tildes (~~~~) produces your name and the current date. If you have any questions, see the help pages, add a question to the village pump or ask me on my Talk page.
 * The Five Pillars of Wikipedia
 * How to edit a page
 * Editing, policy, conduct, and structure tutorial
 * Picture tutorial
 * How to write a great article
 * Naming conventions
 * Manual of Style
 * Merging, redirecting, and renaming pages
 * If you're ready for the complete list of Wikipedia documentation, there's also Topical index.

Again, welcome!

 * Barrettmagic 19:56, 18 August 2005 (UTC)

title: User:Mooncowboy


text:
List of Air America Radio affiliates

This is a list of stations that were affiliated with Air America Radio at the time of its shutdown in January 2010.

title: List of Air America Radio affiliates


text:
File:Pirates cover.jpg

Summary
Cover of Pirates in Oz by Ruth Plumly Thompson, illustrated by John R. Neill.

title: File:Pirates cover.jpg


text:
File:Purple prince cover.jpg

Image Description

 * Front cover of The Purple Prince of Oz by Ruth Plumly Thompson, illustrated by John R. Neill.

Non-free/fair use media rationale in The Purple Prince of Oz
Although this image is subject to copyright, it is covered by US fair use laws because:
 * 1) The image is only being used in the article about the book (The Purple Prince of Oz) for informational and educational purposes.
 * 2) Use of the book cover visually identifies the book and its author in a manner that mere prose cannot, and meets all criteria in WP:NFCC.
 * 3) This image enhances the article in which it's displayed, as it provides an immediate relevance to the reader more capably than the textual description alone.
 * 4) The image is not replaceable with an uncopyrighted or freely copyrighted image of comparable educational value.
 * 5) The image is the primary means of visual identification of the article topic.
 * 6) The image is a low resolution image of the book cover, in an inferior graphics format (JPEG).
 * 7) The image is only a small portion of the commercial product.
 * 8) The use of this image will not affect the value of the original work.
 * 9) The use of this image will not limit the copyright holder's rights or ability to distribute or make a profit from the original. In particular, copies could not be used to make illegal copies of the book.

title: File:Purple prince cover.jpg


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Paul Demayo

Paul DeMayo (September 12, 1967 – June 2, 2005) was an American IFBB professional bodybuilder.

Biography
Paul DeMayo was nicknamed "Quadzilla" for the size of his quadriceps. Born and raised in Malden, Massachusetts, United States, DeMayo first competed in bodybuilding when he placed 1st in the Teenage Massachusetts State championships. His first National Physique Committee (NPC) event was in 1988, where he took 7th place in the NPC Junior USA heavyweight class. He took 1st place in the NPC Nationals Championship event in 1994. His only Mr. Olympia was in 1995, when he placed 12th. DeMayo appeared in many fitness and magazine articles, including being featured on the cover of Iron Man.

Death
DeMayo died on June 2, 2005. in Boston from a heroin overdose. According to people close to him, DeMayo's problems started when his wife left him and he returned to Boston after he moved to California with her to train in Gold's Gym. He then was signed to an endorsement contract by sports nutritional supplement company Met-Rx and forced to compete in the 1995 Mr. Olympia against his wishes. He placed 12th and retired shortly afterwards.

DeMayo spent two years in the Billerica House of Correction (Middlesex County Jail) for shooting his gun in the air after an argument with his girlfriend and possession of percodan. Some months before his death he told friends he was working at Gold's Gym on Lansdowne Street in Boston, but it was revealed later that he was fired several months earlier due to a substance abuse problem.

Contest history

 * 1988 NPC Junior USA HeavyWeight - 7th
 * 1989 NPC Junior Nationals HeavyWeight - 4th
 * 1990 NPC Junior Nationals HeavyWeight - 3rd
 * 1991 NPC Junior Nationals Heavyweight - 1st & Overall Winner
 * 1991 NPC HeavyWeight - 3rd
 * 1992 NPC Nationals HeavyWeight - 4th
 * 1993 NPC USA Championships HeavyWeight - 3rd
 * 1994 NPC Nationals Heavyweight - 1st & Overall Winner
 * 1995 Mr. Olympia - 12th
 * 1995 IFBB Grand Prix England - 10th
 * 1995 IFBB Grand Prix Germany - 9th
 * 1995 IFBB Grand Prix Spain - 9th

title: Paul Demayo


text:
Harold Sylvester

Harold Sylvester (born February 10, 1949) is an American film and television actor.

Early life and education
Sylvester was born in New Orleans, Louisiana. He is a graduate of New Orleans' St. Augustine High School and Tulane University. Turning down Harvard, he attended Tulane on a basketball scholarship and graduated in 1972 with a degree in theater and psychology. He was the first African American to receive an athletic scholarship from Tulane.

Career
Sylvester is best known for his role on the TV series Married... with Children as Griff, the co-worker and friend of Al Bundy at the shoe store. Harold's other TV roles include the miniseries Wheels (1978), Sylvester was in Barnaby Jones (season 8, episode 4, "A Desperate Pursuit"), the short-lived 1981 series Walking Tall, Today's F.B.I., Mary (1985), Shaky Ground, and A Different World as Dwayne Wayne's father, Woodson "Woodchuck" Wayne. The most recent TV show in which he starred was The Army Show. Sylvester had a recurring role on the TV series City of Angels. He has made guest appearances on shows, ranging from The Eddie Capra Mysteries to Hill Street Blues to Murder, She Wrote to NYPD Blue.

His first Hollywood role was as Nathan, a lead character in Part 2, Sounder, the sequel to Sounder. His better known film roles are in supporting roles in An Officer and a Gentleman (1982), Uncommon Valor (1983), Innerspace (1987), and Corrina, Corrina (1994). He was a contract player for Universal Studios.

Sylvester wrote the screenplay for the 1998 TV movie Passing Glory. This screenplay was based on some of his experiences playing basketball in high school, including a groundbreaking game of St. Augustine High School's all-black team against Jesuit High School's all-white team.

The Amistad Research Center of New Orleans, Louisiana, maintains a collection of Harold Sylvester's papers that document his correspondence, film and television scripts, and materials reflecting his involvement with the Free Southern Theater and his Blue Bayou Productions Company.

title: Harold Sylvester


text:
List of places in Idaho

Lists of places in Idaho:

 * List of places in Idaho: A–K
 * List of places in Idaho: L–Z

title: List of places in Idaho


text:
Monitor (NHS)

Monitor was an executive non-departmental public body of the Department of Health, responsible between 2004 and 2016 for ensuring healthcare provision in NHS England was financially effective. It was the sector regulator for health services in England. Its chief executive was Ian Dalton and it was chaired by Dido Harding. Monitor was merged with the NHS Trust Development Authority to form NHS Improvement on 1 April 2016.

History
The body was established on 5 January 2004 under the Health and Social Care (Community Health and Standards) Act 2003, and was formally called The Independent Regulator for Foundation Trusts. The legislation made it responsible for authorising, monitoring and regulating NHS foundation trusts. It took on the brand name Monitor from August 2004

The Health and Social Care Act 2012 formally changed the organisation's name to Monitor and gave it additional duties.

In addition to assessing NHS trusts for foundation trust status and ensuring that foundation trusts are well led, in terms of quality and finances, Monitor had a duty to:

 * set prices for NHS-funded care in partnership with NHS England;
 * enable integrated care;
 * safeguard patient choice and prevent anti-competitive behaviour which is against the interests of patients; and
 * support commissioners to protect essential health care services for patients if a provider gets into financial difficulties.

Monitor's main tool for carrying out these functions was the NHS provider licence, which contains obligations for providers of NHS services. The 2012 Act requires everyone who provides an NHS health care service to hold a licence unless they are exempt under regulations made by the Department of Health. Foundation trusts were licensed from 1 April 2013, and all other non-exempt providers were required to apply for a licence from April 2014.

It was announced in June 2015 that the chief executive posts at Monitor and the NHS Trust Development Authority were to be merged, although there would not be a complete merger of the organisations. In April 2016 both organisations became part of NHS Improvement which subsequently operationally merged with NHS England from September 2018 and was formally abolished by the Health and Care Act 2022.

Criticism
In July 2014, Monitor was criticised by the Public Accounts Committee of the House of Commons (PAC) for the lack of clinical expertise and frontline NHS experience amongst its staff. The PAC noted that: "Only 21 of Monitor's 337 staff have an NHS operational background and only 7 have a clinical background, which damages Monitor's credibility in dealing with trusts and its effectiveness in diagnosing problems and developing solutions". The PAC also criticised the proportion of Monitor's budget spent on external consultants (£9 million of Monitor's £48 million budget in 2013-14) and found that "some NHS foundation trusts had been allowed to struggle for far too long in breach of their regulatory conditions. It has taken Monitor too long to help trusts in difficulty to improve, with three trusts having been in breach of their regulatory conditions since 2009". At the time of the PAC's hearing, of 147 foundation trusts 39(26%) were expected to be in deficit by the end of 2013-14 and on 31 December 2013 25 (17%) were in breach of the conditions attached to their status. The PAC also noted: "It is wholly inappropriate that the same person acted as both Chair and Chief Executive of Monitor between March 2011 and January 2014. This was contrary to corporate governance good practice and Monitor's own guidance to NHS foundation trusts". The board of MONITOR has a majority representation from former or seconded employees of KPMG, PWC and Deloitte while still awarding contracts to these organisations worth millions of pounds.

Regulatory action
In July 2013 six NHS foundation trusts were placed in special measures as a result of the Keogh Review of patient safety:

 * Basildon and Thurrock University Hospitals NHS Foundation Trust
 * Burton Hospitals NHS Foundation Trust
 * Medway NHS Foundation Trust
 * Northern Lincolnshire and Goole Hospitals NHS Foundation Trust
 * Sherwood Forest Hospitals NHS Foundation Trust
 * Tameside Hospital NHS Foundation Trust

Subsequent trusts placed in special measures:

 * The Queen Elizabeth Hospital King's Lynn NHS Foundation Trust October 2013
 * East Kent Hospitals University NHS Foundation Trust August 2014

Enforcement action has been taken on 21 occasions in the first 10 months of 2013/4, compared to just nine instances in the whole of 2012-13. Monitor's former chief executive David Bennett admitted the regulator's "arm's length" distance from foundation trusts had become "a little shorter" as it intervened more readily and that increased monitoring and intervention was explained by "a declined appetite for risk" among "Parliament, the government and the public".

Details of the regulatory action Monitor has taken at NHS foundation trusts are available on its website: http://www.monitor.gov.uk/about-your-local-nhs-foundation-trust/regulatory-action/nhs-foundation-trusts-special-measures-or-un.

In August 2015 Monitor issued a letter to all Foundation Trusts telling them to fill vacancies "only where essential" and warning that current financial plans are "quite simply unaffordable" as NHS providers collectively forecast a deficit of £2bn in 2015-16.

Stafford Hospital investigation
In 2010 the Stafford Hospital scandal investigation recommended that Monitor de-authorise the Mid Staffordshire NHS Foundation Trust.

In line with the investigation report recommendation, Secretary of State for Health, Andy Burnham, agreed to a further Independent Inquiry of the commissioning, supervisory and regulatory bodies for Foundation Trusts.

Anti-competitive behaviour
Spire Healthcare alleged in 2013 that a block contract agreed between Blackpool Teaching Hospitals NHS Foundation Trust and the clinical commissioning groups in Blackpool, and Fylde and Wyre offered a "clear incentive" for GPs to refer patients to the foundation trust and that this was anti-competitive behaviour. The contract provided the trust with a guaranteed income regardless of the number of patients that chose to use its services. Monitor conducted an investigation and decided In September 2014 that there was no evidence to support the claim though they did conclude that Blackpool CCG's plans did not "go far enough" to ensure patients would be offered choice, or that the right to choice would be "publicised and promoted". Blackpool CCG complained that not a single GP, practice manager or patient was spoken to by the investigating team, nor was a single practice visited.

In September 2014 former chief executive David Bennett admitted that the organisation had yet to identify a strategy to counter those opposed to competition because it clashed with their personal ideologies. He claimed organisations were using competition regulations as an "easy excuse" for avoiding making necessary changes.

In October 2014 after the publication of NHS England's 'Five year forward view' Bennett defended Choice and competition as "one of the ways in which we can drive change and improvement for patients, and we don't see that that will cease to be the case" - though competition was not mentioned by NHS England. He also said that Monitor, the NHS Trust Development Authority and NHS England planned to develop "a whole system, geographically based intervention regime" which could, include a 'special measures' approach to areas facing serious problems with care.

title: Monitor (NHS)


text:
File:Ojo cover.jpg

Description
Cover of Ojo in Oz by Ruth Plumly Thompson, illustrated by John R. Neill.

Non-free/fair use media rationale in Ojo in Oz
Although this image is subject to copyright, it is covered by US fair use laws because:
 * 1) The image is only being used in the article about the book (Ojo in Oz) for informational and educational purposes.
 * 2) Use of the book cover visually identifies the book and its author in a manner that mere prose cannot, and meets all criteria in WP:NFCC.
 * 3) This image enhances the article in which it's displayed, as it provides an immediate relevance to the reader more capably than the textual description alone.
 * 4) The image is not replaceable with an uncopyrighted or freely copyrighted image of comparable educational value.
 * 5) The image is the primary means of visual identification of the article topic.
 * 6) The image is a low resolution image of the book cover, in an inferior graphics format (JPEG).
 * 7) The image is only a small portion of the commercial product.
 * 8) The use of this image will not affect the value of the original work.
 * 9) The use of this image will not limit the copyright holder's rights or ability to distribute or make a profit from the original. In particular, copies could not be used to make illegal copies of the book.

title: File:Ojo cover.jpg


text:
Talk:The Bunker (book)

Discrepency
''# May 1-2. The breakout. The remaining members of the bunker staff escape in three separate groups, each to a different fate. While the majority of bunker staff escaped during that time, Hentschel was not the sole occupant as I recall, specifically I seem to recall Erna Flegel talking about the Soviets entered the bunker, and warned the remaining staff to remain inside until pockets of fighting has subsided, before bringing them aboveground. Sherurcij (talk) (Terrorist Wikiproject) 23:23, 25 April 2006 (UTC)
 * 1) May 2. Around noon, Russian troops first enter the bunker, finding Hentschel the sole remaining occupant.''

title: Talk:The Bunker (book)


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No Fences

No Fences is the second studio album by the American country music artist Garth Brooks. It was released on August 27, 1990, and reached No. 1 on Billboard's Top Country Albums chart. The album also reached No. 3 on the Billboard 200. On the latter chart, it stayed in the top 40 for 126 weeks. No Fences remains Brooks' best-selling studio album to date with 18 million copies shipped in the US, and is the album that made him an international star. It was his first album issued in Europe (the original European release contained the four singles from his US debut as bonus tracks).

Singles
Some of Brooks' most famous songs appear on No Fences, including: "The Thunder Rolls" (CMA's 1991 Video of the Year), "Friends in Low Places" (Academy of Country Music's 1990 Single of the Year), "Unanswered Prayers" and "Two of a Kind, Workin' on a Full House". A cover version of The Fleetwoods' "Mr. Blue" appears on the album. The album itself was named Album of the Year by the ACM in 1990. It reached Number 1 on the British country music charts (earning Brooks his first gold album in that country) and remained charted for over five years.

The track "Victim of the Game" was later covered by Brooks's friend and future wife Trisha Yearwood for her 1991 eponymous debut album.

Brooks later re-recorded the track "Wild Horses", and released the new recording as a single in early 2001, reaching #7 on the country chart.

25th anniversary reissue
In September 2015, it was announced No Fences would be reissued later in the year to commemorate its 25-year release anniversary. The release would include a new version of "Friends in Low Places", featuring George Strait, Jason Aldean, Florida Georgia Line, and Keith Urban singing along with Brooks. The album release has since been delayed due to royalty disputes.

Personnel
The following credits are sourced from liner notes included with the album's release.

Musicians

 * Pat Alger – acoustic guitar, harmony and backing vocals
 * Bruce Bouton – pedal steel guitar, harmony and backing vocals
 * Tim Bowers – bass guitar, harmony and backing vocals
 * Garth Brooks – lead, harmony and backing vocals, acoustic guitar
 * Mark Casstevens – acoustic guitar
 * Mike Chapman – bass guitar, harmony and backing vocals
 * Johnny Christopher – acoustic guitar
 * Ty England – acoustic guitar, harmony and backing vocals
 * Dave Gant – piano, keyboard, organ, fiddle, harmony and backing vocals
 * James Garver – electric guitar, harmony and backing vocals
 * Rob Hajacos – fiddle, harmony and backing vocals
 * Chris Leuzinger – electric guitar
 * Steve McClure – electric and pedal steel guitars
 * Edgar Meyer – double bass
 * Mike Palmer – drums, percussion
 * Brian Petree – harmony and backing vocals
 * Milton Sledge – drums
 * Bobby Wood – piano, keyboards, organ, harmony and backing vocals
 * Nashville String Machine – string orchestra

Backing and harmony vocalists

 * Friends in Low Places
 * Pat Alger
 * Al "Shaggy" Barclay
 * Dewayne Blackwell
 * Bruce Bouton
 * Tim Bowers
 * Sandy Brooks
 * Stephanie C. Brown
 * Mike Chapman
 * Bob Doyle
 * The Englands (plus one)
 * Dave Gant
 * Rob Hajacos
 * Joe Harris
 * Dan Heins
 * Rusty "Race Horse" Jones

 * Steve King
 * Earl of Bud Lee
 * Pam "The Chick" Lewis
 * Buddy Mondlock
 * Steve Morley
 * Mike "Palmerman"
 * Brian Petree
 * Dale Pierce
 * Jim Rooney
 * Tami Rose
 * Lee Sartin
 * Charlie Stefl
 * Scott Stem
 * Bobby Wood

 * Unanswered Prayers
 * Wendy Johnson
 * Jennifer O'Brien
 * Hurshel Wiginton
 * Curtis Young
 * Wolves
 * Indian River
 * David McVay
 * Stephen Tolman
 * Neil Thrasher
 * Curry Worsham

Recording
 * Denny Purcell – mastering engineer
 * Mark Miller – recording and mixing engineer
 * Dan Heins – harmony and backing vocals, audio engineer

title: No Fences


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Jim Mortimore

Jim Mortimore is a British science fiction writer, who has written several spin-off novels for popular television series, principally Doctor Who, but also Farscape and Babylon 5.

When BBC Books cancelled his Doctor Who novel Campaign, he had it published independently and gave the proceeds to a charity – the Bristol Area Down's Syndrome Association. He is also the writer of the Big Finish Doctor Who audio play The Natural History of Fear and their Tomorrow People audio play Plague of Dreams. He has also done music for other Big Finish productions.

He released his first original novel in 2011, Skaldenland.

Doctor Who novels

 * Lucifer Rising (1993) (with Andy Lane)
 * Blood Heat (1993)
 * Parasite (1994)
 * Eternity Weeps (1997)
 * The Sword of Forever (1998) (a Bernice Summerfield novel)
 * Eye of Heaven (1998)
 * Beltempest (1998)
 * Campaign (2000) (published unofficially)

Cracker novels

 * The Mad Woman in the Attic (1994)
 * Men Should Weep (1995)
 * Brotherly Love (1996)

Other novels

 * Space Truckers (1996)
 * Babylon 5: Clark's Law (1996, ISBN 0-440-22229-X)
 * Farscape: Dark Side of the Sun (2000) – under pseudonym of Andrew Dymond
 * Skaldenland (2011, Obverse Books)

title: Jim Mortimore


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Wikipedia:Experimental Deletion/XD1/Example

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This page has been blanked as per Experimental_Deletion/XD1, see there for more information


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File:Speedy cover.jpg

Summary
Cover of Speedy in Oz by Ruth Plumly Thompson, illustrated by John R. Neill.

title: File:Speedy cover.jpg


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Wikipedia:Collaboration of the week/History of Education

History of education

 * Nominated on 20:03, 18 August 2005 (UTC); needs 15 votes by September 6.

At present there is a modest section on the Education page about the history of education. But the development of education and the school systems would seem to be a very rich and important topic that deserves its own much-expanded page. Thanks!!!

Support: Comment:
 * 1) RJH 20:03, 18 August 2005 (UTC)
 * 2) Bancroftian 5:02, 20 August 2005 (UTC)
 * 3) Trevor MacInnis (Talk | Contribs) 00:16, 21 August 2005 (UTC)
 * 4) – Quadell (talk) (sleuth) 03:22, August 21, 2005 (UTC)
 * 5) SimonP 13:21, August 22, 2005 (UTC)
 * 6) Junes 14:43, 22 August 2005 (UTC)
 * 7) Usrnme h8er 15:22, 23 August 2005 (UTC)
 * 8) Neutralitytalk 02:18, August 27, 2005 (UTC)
 * 9) NuclearFunk 00:36, 3 September 2005 (UTC)
 * 10) This should be a difficult one in terms of avoiding systematic bias. Although I fear it will be westocentric I'll give my vote. --Oldak Quill 12:12, 4 September 2005 (UTC)
 * 11) gren グレン 12:21, 4 September 2005 (UTC)
 * Yes, this topic deserve more elaborate treatment. --Bhadani 14:39, 5 September 2005 (UTC)
 * 1) Dkasak 07:43, 7 September 2005 (UTC)
 * Currently a redirect to education (which contains a history section). --Oldak Quill 12:12, 4 September 2005 (UTC)


text: User talk:71.240.185.188

title: User talk:71.240.185.188


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Royal Victorian Medal

The Royal Victorian Medal (RVM) is a decoration established by Queen Victoria in April 1896. A part of the Royal Victorian Order, it is a reward for personal service to the Sovereign or the royal family, and is the personal gift of the Sovereign. It differs from other grades of the order in appearance and in the way it is worn.

The medal has three levels – bronze, silver, and gold. Medal bars may be awarded to each level of medal to denote subsequent awards. Recipients may continue to wear their original medal if they are awarded a higher level for further service. The medal may also be worn in addition to the insignia of the Royal Victorian Order if the latter is subsequently awarded. Recipients are entitled to use the post-nominal letters RVM.

History
In creating the Royal Victorian Order in 1896, Queen Victoria decided to include a medal as part of this dynastic order. The medal would be used to reward those who had rendered faithful service to the monarch or the royal family, but were not eligible to be appointed to any orders due to their position or class. This creation followed the precedent of other European monarchs, who had royal household medals with which to reward servants.

The first medals were received on 7 July 1896 and were presented to Russians by the Duke of Connaught while he was in Russia for the coronation of Tsar Nicholas II. On 14 May 1912, King George V further confirmed the institution of the medal with an additional royal warrant.

The medal has been historically used to recognise the service of police officers who work in protective services, gardeners, housekeepers, drivers, valets, and other similar type of staff. Non-commissioned members of the military may also be awarded the medal for services. It is often given for service to the monarch or royal family during royal visits. The medal may be looked upon as a long service medal, but the service must be of a meritorious character to warrant its award.

Originally ranked near the end, the medal now ranks ahead of campaign, jubilee, efficiency, and long service medals in the United Kingdom Order of Wear. This gradual increase in importance was marked by the July 1980 approval, by Queen Elizabeth II, to allow the use of the postnominal RVM by recipients of the medal.

Description
The medal is circular and 28 mm in diameter. It is struck in bronze, silver, or silver-gilt (for the gold medal). The obverse bears the effigy of a young Queen Elizabeth II. Surrounding the effigy is the inscription Elizabeth II Dei Gratia Regina F D. The reverse depicts the royal cypher, surrounded by a wreath or ribbons and laurel leaves. A curled ribbon at the bottom bears the inscription Royal Victorian Medal.

Attached by a ring suspension, the medal is borne by a blue ribbon with thin stripes of white and red on each edge. Awards to foreigners have a white central stripe. The ribbon is the same as that of the Royal Victorian Order. Additional awards of the medal are indicated by bars attached to the ribbon.

Precedence
Some orders of precedence are as follows:

title: Royal Victorian Medal


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Algorithmic trading

Algorithmic trading is a method of executing orders using automated pre-programmed trading instructions accounting for variables such as time, price, and volume. This type of trading attempts to leverage the speed and computational resources of computers relative to human traders. In the twenty-first century, algorithmic trading has been gaining traction with both retail and institutional traders. A study in 2019 showed that around 92% of trading in the Forex market was performed by trading algorithms rather than humans.

It is widely used by investment banks, pension funds, mutual funds, and hedge funds that may need to spread out the execution of a larger order or perform trades too fast for human traders to react to. However, it is also available to private traders using simple retail tools.

The term algorithmic trading is often used synonymously with automated trading system. These encompass a variety of trading strategies, some of which are based on formulas and results from mathematical finance, and often rely on specialized software.

Examples of strategies used in algorithmic trading include systematic trading, market making, inter-market spreading, arbitrage, or pure speculation, such as trend following. Many fall into the category of high-frequency trading (HFT), which is characterized by high turnover and high order-to-trade ratios. HFT strategies utilize computers that make elaborate decisions to initiate orders based on information that is received electronically, before human traders are capable of processing the information they observe. As a result, in February 2012, the Commodity Futures Trading Commission (CFTC) formed a special working group that included academics and industry experts to advise the CFTC on how best to define HFT. Algorithmic trading and HFT have resulted in a dramatic change of the market microstructure and in the complexity and uncertainty of the market macrodynamic, particularly in the way liquidity is provided.

Early developments
Computerization of the order flow in financial markets began in the early 1970s, when the New York Stock Exchange introduced the "designated order turnaround" system (DOT). SuperDOT was introduced in 1984 as an upgraded version of DOT. Both systems allowed for the routing of orders electronically to the proper trading post. The "opening automated reporting system" (OARS) aided the specialist in determining the market clearing opening price (SOR; Smart Order Routing).

With the rise of fully electronic markets came the introduction of program trading, which is defined by the New York Stock Exchange as an order to buy or sell 15 or more stocks valued at over US$1 million total. In practice, program trades were pre-programmed to automatically enter or exit trades based on various factors. In the 1980s, program trading became widely used in trading between the S&P 500 equity and futures markets in a strategy known as index arbitrage.

At about the same time, portfolio insurance was designed to create a synthetic put option on a stock portfolio by dynamically trading stock index futures according to a computer model based on the Black–Scholes option pricing model.

Both strategies, often simply lumped together as "program trading", were blamed by many people (for example by the Brady report) for exacerbating or even starting the 1987 stock market crash. Yet the impact of computer driven trading on stock market crashes is unclear and widely discussed in the academic community.

Refinement and growth
The financial landscape was changed again with the emergence of electronic communication networks (ECNs) in the 1990s, which allowed for trading of stock and currencies outside of traditional exchanges. In the U.S., decimalization changed the minimum tick size from 1/16 of a dollar (US$0.0625) to US$0.01 per share in 2001, and may have encouraged algorithmic trading as it changed the market microstructure by permitting smaller differences between the bid and offer prices, decreasing the market-makers' trading advantage, thus increasing market liquidity.

This increased market liquidity led to institutional traders splitting up orders according to computer algorithms so they could execute orders at a better average price. These average price benchmarks are measured and calculated by computers by applying the time-weighted average price or more usually by the volume-weighted average price.

A further encouragement for the adoption of algorithmic trading in the financial markets came in 2001 when a team of IBM researchers published a paper at the International Joint Conference on Artificial Intelligence where they showed that in experimental laboratory versions of the electronic auctions used in the financial markets, two algorithmic strategies (IBM's own MGD, and Hewlett-Packard's ZIP) could consistently out-perform human traders. MGD was a modified version of the "GD" algorithm invented by Steven Gjerstad & John Dickhaut in 1996/7; the ZIP algorithm had been invented at HP by Dave Cliff (professor) in 1996. In their paper, the IBM team wrote that the financial impact of their results showing MGD and ZIP outperforming human traders "...might be measured in billions of dollars annually"; the IBM paper generated international media coverage.

In 2005, the Regulation National Market System was put in place by the SEC to strengthen the equity market. This changed the way firms traded with rules such as the Trade Through Rule, which mandates that market orders must be posted and executed electronically at the best available price, thus preventing brokerages from profiting from the price differences when matching buy and sell orders.

As more electronic markets opened, other algorithmic trading strategies were introduced. These strategies are more easily implemented by computers, as they can react rapidly to price changes and observe several markets simultaneously.

Many broker-dealers offered algorithmic trading strategies to their clients – differentiating them by behavior, options and branding. Examples include Chameleon (developed by BNP Paribas), Stealth (developed by the Deutsche Bank), Sniper and Guerilla (developed by Credit Suisse). These implementations adopted practices from the investing approaches of arbitrage, statistical arbitrage, trend following, and mean reversion.

In modern global financial markets, algorithmic trading plays a crucial role in achieving financial objectives. For nearly 30 years, traders, investment banks, investment funds, and other financial entities have utilized algorithms to refine and implement trading strategies. The use of algorithms in financial markets has grown substantially since the mid-1990s, although the exact contribution to daily trading volumes remains imprecise.

Technological advancements and algorithmic trading have facilitated increased transaction volumes, reduced costs, improved portfolio performance, and enhanced transparency in financial markets. According to the Foreign Exchange Activity in April 2019 report, foreign exchange markets had a daily turnover of US$6.6 trillion, a significant increase from US$5.1 trillion in 2016.

Case studies
Profitability projections by the TABB Group, a financial services industry research firm, for the US equities HFT industry were US$1.3 billion before expenses for 2014, significantly down on the maximum of US$21 billion that the 300 securities firms and hedge funds that then specialized in this type of trading took in profits in 2008, which the authors had then called "relatively small" and "surprisingly modest" when compared to the market's overall trading volume. In March 2014, Virtu Financial, a high-frequency trading firm, reported that during five years the firm as a whole was profitable on 1,277 out of 1,278 trading days, losing money just one day, demonstrating the benefits of trading millions of times, across a diverse set of instruments every trading day.

A third of all European Union and United States stock trades in 2006 were driven by automatic programs, or algorithms. As of 2009, studies suggested HFT firms accounted for 60–73% of all US equity trading volume, with that number falling to approximately 50% in 2012. In 2006, at the London Stock Exchange, over 40% of all orders were entered by algorithmic traders, with 60% predicted for 2007. American markets and European markets generally have a higher proportion of algorithmic trades than other markets, and estimates for 2008 range as high as an 80% proportion in some markets. Foreign exchange markets also have active algorithmic trading, measured at about 80% of orders in 2016 (up from about 25% of orders in 2006). Futures markets are considered fairly easy to integrate into algorithmic trading, with about 40% of options trading done via trading algorithms in 2016. Bond markets are moving toward more access to algorithmic traders.

Algorithmic trading and HFT have been the subject of much public debate since the U.S. Securities and Exchange Commission and the Commodity Futures Trading Commission said in reports that an algorithmic trade entered by a mutual fund company triggered a wave of selling that led to the 2010 Flash Crash. The same reports found HFT strategies may have contributed to subsequent volatility by rapidly pulling liquidity from the market. As a result of these events, the Dow Jones Industrial Average suffered its second largest intraday point swing ever to that date, though prices quickly recovered. (See List of largest daily changes in the Dow Jones Industrial Average.) A July 2011 report by the International Organization of Securities Commissions (IOSCO), an international body of securities regulators, concluded that while "algorithms and HFT technology have been used by market participants to manage their trading and risk, their usage was also clearly a contributing factor in the flash crash event of May 6, 2010." However, other researchers have reached a different conclusion. One 2010 study found that HFT did not significantly alter trading inventory during the Flash Crash. Some algorithmic trading ahead of index fund rebalancing transfers profits from investors.

Trading ahead of index fund rebalancing
Most retirement savings, such as private pension funds or 401(k) and individual retirement accounts in the US, are invested in mutual funds, the most popular of which are index funds which must periodically "rebalance" or adjust their portfolio to match the new prices and market capitalization of the underlying securities in the stock or other index that they track. Profits are transferred from passive index investors to active investors, some of whom are algorithmic traders specifically exploiting the index rebalance effect. The magnitude of these losses incurred by passive investors has been estimated at 21–28bp per year for the S&P 500 and 38–77bp per year for the Russell 2000. John Montgomery of Bridgeway Capital Management says that the resulting "poor investor returns" from trading ahead of mutual funds is "the elephant in the room" that "shockingly, people are not talking about".

Pairs trading
Pairs trading or pair trading is a long-short, ideally market-neutral strategy enabling traders to profit from transient discrepancies in relative value of close substitutes. Unlike in the case of classic arbitrage, in case of pairs trading, the law of one price cannot guarantee convergence of prices. This is especially true when the strategy is applied to individual stocks – these imperfect substitutes can in fact diverge indefinitely. In theory, the long-short nature of the strategy should make it work regardless of the stock market direction. In practice, execution risk, persistent and large divergences, as well as a decline in volatility can make this strategy unprofitable for long periods of time (e.g. 2004-2007). It belongs to wider categories of statistical arbitrage, convergence trading, and relative value strategies.

Delta-neutral strategies
In finance, delta-neutral describes a portfolio of related financial securities, in which the portfolio value remains unchanged due to small changes in the value of the underlying security. Such a portfolio typically contains options and their corresponding underlying securities such that positive and negative delta components offset, resulting in the portfolio's value being relatively insensitive to changes in the value of the underlying security.

Arbitrage
In economics and finance, arbitrage is the practice of taking advantage of a price difference between two or more markets: striking a combination of matching deals that capitalize upon the imbalance, the profit being the difference between the market prices. When used by academics, an arbitrage is a transaction that involves no negative cash flow at any probabilistic or temporal state and a positive cash flow in at least one state; in simple terms, it is the possibility of a risk-free profit at zero cost. Example: One of the most popular arbitrage trading opportunities is played with the S&P futures and the S&P 500 stocks. During most trading days, these two will develop disparity in the pricing between the two of them. This happens when the price of the stocks which are mostly traded on the NYSE and NASDAQ markets either get ahead or behind the S&P Futures which are traded in the CME market.

Conditions for arbitrage
Arbitrage is possible when one of three conditions is met:
 * The same asset does not trade at the same price on all markets (the "law of one price" is temporarily violated).
 * Two assets with identical cash flows do not trade at the same price.
 * An asset with a known price in the future does not today trade at its future price discounted at the risk-free interest rate (or, the asset does not have negligible costs of storage; as such, for example, this condition holds for grain but not for securities).

Arbitrage is not simply the act of buying a product in one market and selling it in another for a higher price at some later time. The long and short transactions should ideally occur simultaneously to minimize the exposure to market risk, or the risk that prices may change on one market before both transactions are complete. In practical terms, this is generally only possible with securities and financial products which can be traded electronically, and even then, when first leg(s) of the trade is executed, the prices in the other legs may have worsened, locking in a guaranteed loss. Missing one of the legs of the trade (and subsequently having to open it at a worse price) is called 'execution risk' or more specifically 'leg-in and leg-out risk'. In the simplest example, any good sold in one market should sell for the same price in another. Traders may, for example, find that the price of wheat is lower in agricultural regions than in cities, purchase the good, and transport it to another region to sell at a higher price. This type of price arbitrage is the most common, but this simple example ignores the cost of transport, storage, risk, and other factors. "True" arbitrage requires that there be no market risk involved. Where securities are traded on more than one exchange, arbitrage occurs by simultaneously buying in one and selling on the other. Such simultaneous execution, if perfect substitutes are involved, minimizes capital requirements, but in practice never creates a "self-financing" (free) position, as many sources incorrectly assume following the theory. As long as there is some difference in the market value and riskiness of the two legs, capital would have to be put up in order to carry the long-short arbitrage position.

Mean reversion
Mean reversion is a mathematical methodology sometimes used for stock investing, but it can be applied to other processes. In general terms the idea is that both a stock's high and low prices are temporary, and that a stock's price tends to have an average price over time. An example of a mean-reverting process is the Ornstein-Uhlenbeck stochastic equation.

Mean reversion involves first identifying the trading range for a stock, and then computing the average price using analytical techniques as it relates to assets, earnings, etc.

When the current market price is less than the average price, the stock is considered attractive for purchase, with the expectation that the price will rise. When the current market price is above the average price, the market price is expected to fall. In other words, deviations from the average price are expected to revert to the average.

The standard deviation of the most recent prices (e.g., the last 20) is often used as a buy or sell indicator.

Stock reporting services (such as Yahoo! Finance, MS Investor, Morningstar, etc.), commonly offer moving averages for periods such as 50 and 100 days. While reporting services provide the averages, identifying the high and low prices for the study period is still necessary.

Scalping
Scalping is liquidity provision by non-traditional market makers, whereby traders attempt to earn (or make) the bid-ask spread. This procedure allows for profit for so long as price moves are less than this spread and normally involves establishing and liquidating a position quickly, usually within minutes or less.

A market maker is basically a specialized scalper and also referred to as dealers. The volume a market maker trades is many times more than the average individual scalper and would make use of more sophisticated trading systems and technology. However, registered market makers are bound by exchange rules stipulating their minimum quote obligations. For instance, NASDAQ requires each market maker to post at least one bid and one ask at some price level, so as to maintain a two-sided market for each stock represented.

Transaction cost reduction
Most strategies referred to as algorithmic trading (as well as algorithmic liquidity-seeking) fall into the cost-reduction category. The basic idea is to break down a large order into small orders and place them in the market over time. The choice of algorithm depends on various factors, with the most important being volatility and liquidity of the stock. For example, for a highly liquid stock, matching a certain percentage of the overall orders of stock (called volume inline algorithms) is usually a good strategy, but for a highly illiquid stock, algorithms try to match every order that has a favorable price (called liquidity-seeking algorithms).

The success of these strategies is usually measured by comparing the average price at which the entire order was executed with the average price achieved through a benchmark execution for the same duration. Usually, the volume-weighted average price is used as the benchmark. At times, the execution price is also compared with the price of the instrument at the time of placing the order.

A special class of these algorithms attempts to detect algorithmic or iceberg orders on the other side (i.e. if you are trying to buy, the algorithm will try to detect orders for the sell side). These algorithms are called sniffing algorithms. A typical example is "Stealth".

Some examples of algorithms are VWAP, TWAP, Implementation shortfall, POV, Display size, Liquidity seeker, and Stealth. Modern algorithms are often optimally constructed via either static or dynamic programming.

Strategies that only pertain to dark pools
As of 2009, HFT, which comprises a broad set of buy-side as well as market making sell side traders, has become more prominent and controversial. These algorithms or techniques are commonly given names such as "Stealth" (developed by the Deutsche Bank), "Iceberg", "Dagger", " Monkey", "Guerrilla", "Sniper", "BASOR" (developed by Quod Financial) and "Sniffer". Dark pools are alternative trading systems that are private in nature—and thus do not interact with public order flow—and seek instead to provide undisplayed liquidity to large blocks of securities. In dark pools, trading takes place anonymously, with most orders hidden or "iceberged". Gamers or "sharks" sniff out large orders by "pinging" small market orders to buy and sell. When several small orders are filled the sharks may have discovered the presence of a large iceberged order.

"Now it's an arms race," said Andrew Lo, director of the Massachusetts Institute of Technology's Laboratory for Financial Engineering in 2006. "Everyone is building more sophisticated algorithms, and the more competition exists, the smaller the profits."

Market timing
Strategies designed to generate alpha are considered market timing strategies. These types of strategies are designed using a methodology that includes backtesting, forward testing and live testing. Market timing algorithms will typically use technical indicators such as moving averages but can also include pattern recognition logic implemented using finite-state machines.

Backtesting the algorithm is typically the first stage and involves simulating the hypothetical trades through an in-sample data period. Optimization is performed in order to determine the most optimal inputs. Steps taken to reduce the chance of over-optimization can include modifying the inputs +/- 10%, shmooing the inputs in large steps, running Monte Carlo simulations and ensuring slippage and commission is accounted for.

Forward testing the algorithm is the next stage and involves running the algorithm through an out of sample data set to ensure the algorithm performs within backtested expectations.

Live testing is the final stage of development and requires the developer to compare actual live trades with both the backtested and forward tested models. Metrics compared include percent profitable, profit factor, maximum drawdown and average gain per trade.

High-frequency trading
As noted above, high-frequency trading (HFT) is a form of algorithmic trading characterized by high turnover and high order-to-trade ratios. Although there is no single definition of HFT, among its key attributes are highly sophisticated algorithms, specialized order types, co-location, very short-term investment horizons, and high cancellation rates for orders. In the U.S., high-frequency trading (HFT) firms represent 2% of the approximately 20,000 firms operating today, but account for 73% of all equity trading volume. As of the first quarter in 2009, total assets under management for hedge funds with HFT strategies were US$141 billion, down about 21% from their high. The HFT strategy was first made successful by Renaissance Technologies.

High-frequency funds started to become especially popular in 2007 and 2008. Many HFT firms are market makers and provide liquidity to the market, which has lowered volatility and helped narrow bid–offer spreads making trading and investing cheaper for other market participants. HFT has been a subject of intense public focus since the U.S. Securities and Exchange Commission and the Commodity Futures Trading Commission stated that both algorithmic trading and HFT contributed to volatility in the 2010 Flash Crash. Among the major U.S. high frequency trading firms are Chicago Trading Company, Optiver, Virtu Financial, DRW, Jump Trading, Two Sigma Securities, GTS, IMC Financial, and Citadel LLC.

There are four key categories of HFT strategies: market-making based on order flow, market-making based on tick data information, event arbitrage and statistical arbitrage. All portfolio-allocation decisions are made by computerized quantitative models. The success of computerized strategies is largely driven by their ability to simultaneously process volumes of information, something ordinary human traders cannot do.

Market making
Market making involves placing a limit order to sell (or offer) above the current market price or a buy limit order (or bid) below the current price on a regular and continuous basis to capture the bid-ask spread. Automated Trading Desk, which was bought by Citigroup in July 2007, has been an active market maker, accounting for about 6% of total volume on both NASDAQ and the New York Stock Exchange.

Statistical arbitrage
Another set of HFT strategies in classical arbitrage strategy might involve several securities such as covered interest rate parity in the foreign exchange market which gives a relation between the prices of a domestic bond, a bond denominated in a foreign currency, the spot price of the currency, and the price of a forward contract on the currency. If the market prices are different enough from those implied in the model to cover transaction cost then four transactions can be made to guarantee a risk-free profit. HFT allows similar arbitrages using models of greater complexity involving many more than 4 securities. The TABB Group estimates that annual aggregate profits of low latency arbitrage strategies currently exceed US$21 billion.

A wide range of statistical arbitrage strategies have been developed whereby trading decisions are made on the basis of deviations from statistically significant relationships. Like market-making strategies, statistical arbitrage can be applied in all asset classes.

Event arbitrage
A subset of risk, merger, convertible, or distressed securities arbitrage that counts on a specific event, such as a contract signing, regulatory approval, judicial decision, etc., to change the price or rate relationship of two or more financial instruments and permit the arbitrageur to earn a profit.

Merger arbitrage also called risk arbitrage would be an example of this. Merger arbitrage generally consists of buying the stock of a company that is the target of a takeover while shorting the stock of the acquiring company. Usually the market price of the target company is less than the price offered by the acquiring company. The spread between these two prices depends mainly on the probability and the timing of the takeover being completed, as well as the prevailing level of interest rates. The bet in a merger arbitrage is that such a spread will eventually be zero, if and when the takeover is completed. The risk is that the deal "breaks" and the spread massively widens.

Spoofing
One strategy that some traders have employed, which has been proscribed yet likely continues, is called spoofing. It is the act of placing orders to give the impression of wanting to buy or sell shares, without ever having the intention of letting the order execute to temporarily manipulate the market to buy or sell shares at a more favorable price. This is done by creating limit orders outside the current bid or ask price to change the reported price to other market participants. The trader can subsequently place trades based on the artificial change in price, then canceling the limit orders before they are executed.

Suppose a trader desires to sell shares of a company with a current bid of $20 and a current ask of $20.20. The trader would place a buy order at $20.10, still some distance from the ask so it will not be executed, and the $20.10 bid is reported as the National Best Bid and Offer best bid price. The trader then executes a market order for the sale of the shares they wished to sell. Because the best bid price is the investor's artificial bid, a market maker fills the sale order at $20.10, allowing for a $.10 higher sale price per share. The trader subsequently cancels their limit order on the purchase he never had the intention of completing.

Quote stuffing
Quote stuffing is a tactic employed by malicious traders that involves quickly entering and withdrawing large quantities of orders in an attempt to flood the market, thereby gaining an advantage over slower market participants. The rapidly placed and canceled orders cause market data feeds that ordinary investors rely on to delay price quotes while the stuffing is occurring. HFT firms benefit from proprietary, higher-capacity feeds and the most capable, lowest latency infrastructure. Researchers showed high-frequency traders are able to profit by the artificially induced latencies and arbitrage opportunities that result from quote stuffing.

Low latency trading systems
Network-induced latency, a synonym for delay, measured in one-way delay or round-trip time, is normally defined as how much time it takes for a data packet to travel from one point to another. Low latency trading refers to the algorithmic trading systems and network routes used by financial institutions connecting to stock exchanges and electronic communication networks (ECNs) to rapidly execute financial transactions. Most HFT firms depend on low latency execution of their trading strategies. Joel Hasbrouck and Gideon Saar (2013) measure latency based on three components: the time it takes for (1) information to reach the trader, (2) the trader's algorithms to analyze the information, and (3) the generated action to reach the exchange and get implemented. In a contemporary electronic market (circa 2009), low latency trade processing time was qualified as under 10 milliseconds, and ultra-low latency as under 1 millisecond.

Low-latency traders depend on ultra-low latency networks. They profit by providing information, such as competing bids and offers, to their algorithms microseconds faster than their competitors. The revolutionary advance in speed has led to the need for firms to have a real-time, colocated trading platform to benefit from implementing high-frequency strategies. Strategies are constantly altered to reflect the subtle changes in the market as well as to combat the threat of the strategy being reverse engineered by competitors. This is due to the evolutionary nature of algorithmic trading strategies – they must be able to adapt and trade intelligently, regardless of market conditions, which involves being flexible enough to withstand a vast array of market scenarios. As a result, a significant proportion of net revenue from firms is spent on the R&D of these autonomous trading systems.

Strategy implementation
Most of the algorithmic strategies are implemented using modern programming languages, although some still implement strategies designed in spreadsheets. Increasingly, the algorithms used by large brokerages and asset managers are written to the FIX Protocol's Algorithmic Trading Definition Language (FIXatdl), which allows firms receiving orders to specify exactly how their electronic orders should be expressed. Orders built using FIXatdl can then be transmitted from traders' systems via the FIX Protocol. Basic models can rely on as little as a linear regression, while more complex game-theoretic and pattern recognition or predictive models can also be used to initiate trading. More complex methods such as Markov chain Monte Carlo have been used to create these models.

Issues and developments
Algorithmic trading has been shown to substantially improve market liquidity among other benefits. However, improvements in productivity brought by algorithmic trading have been opposed by human brokers and traders facing stiff competition from computers.

Cyborg finance
Technological advances in finance, particularly those relating to algorithmic trading, has increased financial speed, connectivity, reach, and complexity while simultaneously reducing its humanity. Computers running software based on complex algorithms have replaced humans in many functions in the financial industry. Finance is essentially becoming an industry where machines and humans share the dominant roles – transforming modern finance into what one scholar has called, "cyborg finance".

Concerns
While many experts laud the benefits of innovation in computerized algorithmic trading, other analysts have expressed concern with specific aspects of computerized trading.

"'The downside with these systems is their black box-ness,' Mr. Williams said. 'Traders have intuitive senses of how the world works. But with these systems you pour in a bunch of numbers, and something comes out the other end, and it's not always intuitive or clear why the black box latched onto certain data or relationships.'"

"The Financial Services Authority has been keeping a watchful eye on the development of black box trading. In its annual report the regulator remarked on the great benefits of efficiency that new technology is bringing to the market. But it also pointed out that 'greater reliance on sophisticated technology and modelling brings with it a greater risk that systems failure can result in business interruption'."

UK Treasury minister Lord Myners has warned that companies could become the "playthings" of speculators because of automatic high-frequency trading. Lord Myners said the process risked destroying the relationship between an investor and a company.

Other issues include the technical problem of latency or the delay in getting quotes to traders, security and the possibility of a complete system breakdown leading to a market crash.

"'Goldman spends tens of millions of dollars on this stuff. They have more people working in their technology area than people on the trading desk...The nature of the markets has changed dramatically.'"

On August 1, 2012 Knight Capital Group experienced a technology issue in their automated trading system, causing a loss of $440 million.

"This issue was related to Knight's installation of trading software and resulted in Knight sending numerous erroneous orders in NYSE-listed securities into the market. This software has been removed from the company's systems. ... Clients were not negatively affected by the erroneous orders, and the software issue was limited to the routing of certain listed stocks to NYSE. Knight has traded out of its entire erroneous trade position, which has resulted in a realized pre-tax loss of approximately $440 million."

Algorithmic and high-frequency trading were shown to have contributed to volatility during the May 6, 2010 Flash Crash, when the Dow Jones Industrial Average plunged about 600 points only to recover those losses within minutes. At the time, it was the second largest point swing, 1,010.14 points, and the biggest one-day point decline, 998.5 points, on an intraday basis in Dow Jones Industrial Average history.

Recent developments
Financial market news is now being formatted by firms such as Need To Know News, Thomson Reuters, Dow Jones, and Bloomberg, to be read and traded on via algorithms.

"'Computers are now being used to generate news stories about company earnings results or economic statistics as they are released. And this almost instantaneous information forms a direct feed into other computers which trade on the news.'"

The algorithms do not simply trade on simple news stories but also interpret more difficult to understand news. Some firms are also attempting to automatically assign sentiment (deciding if the news is good or bad) to news stories so that automated trading can work directly on the news story. "'Increasingly, people are looking at all forms of news and building their own indicators around it in a semi-structured way,' as they constantly seek out new trading advantages said Rob Passarella, global director of strategy at Dow Jones Enterprise Media Group. His firm provides both a low latency news feed and news analytics for traders. Passarella also pointed to new academic research being conducted on the degree to which frequent Google searches on various stocks can serve as trading indicators, the potential impact of various phrases and words that may appear in Securities and Exchange Commission statements and the latest wave of online communities devoted to stock trading topics."

"'Markets are by their very nature conversations, having grown out of coffee houses and taverns,' he said. So the way conversations get created in a digital society will be used to convert news into trades, as well, Passarella said."

"'There is a real interest in moving the process of interpreting news from the humans to the machines' says Kirsti Suutari, global business manager of algorithmic trading at Reuters. 'More of our customers are finding ways to use news content to make money.'"

An example of the importance of news reporting speed to algorithmic traders was an advertising campaign by Dow Jones (appearances included page W15 of The Wall Street Journal, on March 1, 2008) claiming that their service had beaten other news services by two seconds in reporting an interest rate cut by the Bank of England.

In July 2007, Citigroup, which had already developed its own trading algorithms, paid $680 million for Automated Trading Desk, a 19-year-old firm that trades about 200 million shares a day. Citigroup had previously bought Lava Trading and OnTrade Inc.

In late 2010, The UK Government Office for Science initiated a Foresight project investigating the future of computer trading in the financial markets, led by Dame Clara Furse, ex-CEO of the London Stock Exchange and in September 2011 the project published its initial findings in the form of a three-chapter working paper available in three languages, along with 16 additional papers that provide supporting evidence. All of these findings are authored or co-authored by leading academics and practitioners, and were subjected to anonymous peer-review. Released in 2012, the Foresight study acknowledged issues related to periodic illiquidity, new forms of manipulation and potential threats to market stability due to errant algorithms or excessive message traffic. However, the report was also criticized for adopting "standard pro-HFT arguments" and advisory panel members being linked to the HFT industry.

System architecture
A traditional trading system consists primarily of two blocks – one that receives the market data while the other that sends the order request to the exchange. However, an algorithmic trading system can be broken down into three parts:
 * 1) Exchange
 * 2) The server
 * 3) Application

Exchange(s) provide data to the system, which typically consists of the latest order book, traded volumes, and last traded price (LTP) of scrip. The server in turn receives the data simultaneously acting as a store for historical database. The data is analyzed at the application side, where trading strategies are fed from the user and can be viewed on the GUI. Once the order is generated, it is sent to the order management system (OMS), which in turn transmits it to the exchange.

Gradually, old-school, high latency architecture of algorithmic systems is being replaced by newer, state-of-the-art, high infrastructure, low-latency networks. The complex event processing engine (CEP), which is the heart of decision making in algo-based trading systems, is used for order routing and risk management.

With the emergence of the FIX (Financial Information Exchange) protocol, the connection to different destinations has become easier and the go-to market time has reduced, when it comes to connecting with a new destination. With the standard protocol in place, integration of third-party vendors for data feeds is not cumbersome anymore.

Effects
One of the more ironic findings of academic research on algorithmic trading might be that individual trader introduce algorithms to make communication more simple and predictable, while markets end up more complex and more uncertain. Since trading algorithms follow local rules that either respond to programmed instructions or learned patterns, on the micro-level, their automated and reactive behavior makes certain parts of the communication dynamic more predictable. However, on the macro-level, it has been shown that the overall emergent process becomes both more complex and less predictable. This phenomenon is not unique to the stock market, and has also been detected with editing bots on Wikipedia.

Though its development may have been prompted by decreasing trade sizes caused by decimalization, algorithmic trading has reduced trade sizes further. Jobs once done by human traders are being switched to computers. The speeds of computer connections, measured in milliseconds and even microseconds, have become very important.

More fully automated markets such as NASDAQ, Direct Edge and BATS (formerly an acronym for Better Alternative Trading System) in the US, have gained market share from less automated markets such as the NYSE. Economies of scale in electronic trading have contributed to lowering commissions and trade processing fees, and contributed to international mergers and consolidation of financial exchanges.

Competition is developing among exchanges for the fastest processing times for completing trades. For example, in June 2007, the London Stock Exchange launched a new system called TradElect that promises an average 10 millisecond turnaround time from placing an order to final confirmation and can process 3,000 orders per second. Since then, competitive exchanges have continued to reduce latency with turnaround times of 3 milliseconds available. This is of great importance to high-frequency traders, because they have to attempt to pinpoint the consistent and probable performance ranges of given financial instruments. These professionals are often dealing in versions of stock index funds like the E-mini S&Ps, because they seek consistency and risk-mitigation along with top performance. They must filter market data to work into their software programming so that there is the lowest latency and highest liquidity at the time for placing stop-losses and/or taking profits. With high volatility in these markets, this becomes a complex and potentially nerve-wracking endeavor, where a small mistake can lead to a large loss. Absolute frequency data play into the development of the trader's pre-programmed instructions.

In the U.S., spending on computers and software in the financial industry increased to $26.4 billion in 2005.

Algorithmic trading has caused a shift in the types of employees working in the financial industry. For example, many physicists have entered the financial industry as quantitative analysts. Some physicists have even begun to do research in economics as part of doctoral research. This interdisciplinary movement is sometimes called econophysics. Some researchers also cite a "cultural divide" between employees of firms primarily engaged in algorithmic trading and traditional investment managers. Algorithmic trading has encouraged an increased focus on data and had decreased emphasis on sell-side research.

Communication standards
Algorithmic trades require communicating considerably more parameters than traditional market and limit orders. A trader on one end (the "buy side") must enable their trading system (often called an "order management system" or "execution management system") to understand a constantly proliferating flow of new algorithmic order types. The R&D and other costs to construct complex new algorithmic orders types, along with the execution infrastructure, and marketing costs to distribute them, are fairly substantial. What was needed was a way that marketers (the "sell side") could express algo orders electronically such that buy-side traders could just drop the new order types into their system and be ready to trade them without constant coding custom new order entry screens each time.

FIX Protocol is a trade association that publishes free, open standards in the securities trading area. The FIX language was originally created by Fidelity Investments, and the association Members include virtually all large and many midsized and smaller broker dealers, money center banks, institutional investors, mutual funds, etc. This institution dominates standard setting in the pretrade and trade areas of security transactions. In 2006–2007, several members got together and published a draft XML standard for expressing algorithmic order types. The standard is called FIX Algorithmic Trading Definition Language (FIXatdl).

title: Algorithmic trading


text:
Catalytic cycle

In chemistry, a catalytic cycle is a multistep reaction mechanism that involves a catalyst. The catalytic cycle is the main method for describing the role of catalysts in biochemistry, organometallic chemistry, bioinorganic chemistry, materials science, etc.

Since catalysts are regenerated, catalytic cycles are usually written as a sequence of chemical reactions in the form of a loop. In such loops, the initial step entails binding of one or more reactants by the catalyst, and the final step is the release of the product and regeneration of the catalyst. Articles on the Monsanto process, the Wacker process, and the Heck reaction show catalytic cycles.

A catalytic cycle is not necessarily a full reaction mechanism. For example, it may be that the intermediates have been detected, but it is not known by which mechanisms the actual elementary reactions occur.

Precatalysts
Precatalysts are not catalysts but are precursors to catalysts. Precatalysts are converted in the reactor to the actual catalytic species. The identification of catalysts vs precatalysts is an important theme in catalysis research.

The conversion of a precatalyst to a catalyst is often called catalyst activation. Many metal halides are precatalysts for alkene polymerization, see Kaminsky catalyst and Ziegler-Natta catalysis. The precatalysts, e.g. titanium trichloride, are activated by organoaluminium compounds, which function as catalyst activators. Metal oxides are often classified as catalysts, but in fact are almost always precatalysts. Applications include olefin metathesis and hydrogenation. The metal oxides require some activating reagent, usually a reducing agent, to enter the catalytic cycle.

Often catalytic cycles show the conversion of a precatalyst to the catalyst.

Sacrificial catalysts
Often a so-called sacrificial catalyst is also part of the reaction system with the purpose of regenerating the true catalyst in each cycle. As the name implies, the sacrificial catalyst is not regenerated and is irreversibly consumed, thereby not a catalyst at all. This sacrificial compound is also known as a stoichiometric catalyst when added in stoichiometric quantities compared to the main reactant. Usually the true catalyst is an expensive and complex molecule and added in quantities as small as possible. The stoichiometric catalyst on the other hand should be cheap and abundant. "Sacrificial catalysts" are more accurately referred to by their actual role in the catalytic cycle, for example as a reductant.

title: Catalytic cycle


text:
Valp

Valp can refer to

 * The Swedish name for "puppy"
 * The Volvo L-3314 car has the pet name "Valp" in Sweden
 * A Swiss comics creator, also known as Valentine Pasche

title: Valp


text: User talk:217.207.85.50

title: User talk:217.207.85.50