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Cain

Cain is a biblical figure in the Book of Genesis within Abrahamic religions. He is the elder brother of Abel, and the firstborn son of Adam and Eve, the first couple within the Bible. He was a farmer who gave an offering of his crops to God. However, God was not pleased and favored Abel's offering over Cain's. Out of jealousy, Cain killed his brother, for which he was punished by God with the curse and mark of Cain. He had several children, starting with Enoch and including Lamech.

The narrative is notably unclear on God's reason for rejecting Cain's sacrifice. Some traditional interpretations consider Cain to be the originator of evil, violence, or greed. According to Genesis, Cain was the first human born and the first murderer.

Some modern scholars view the Cain and Abel narrative as a symbolic, etiological tale to explain how agriculture replaced foraging using name puns (Abel's name is related to a word for "herder" and Cain's to a word for "metalsmith"). Scholars date the story to between the 9th century BCE and first decades of the 4th century BCE, and note the similarity to other Mesopotamian stories, especially the Sumerian myth of the Courtship of Inanna and Dumuzid.

Jewish and Christian interpretations
A question arising early in the story is why God rejected Cain's sacrifice. The text states that "In the course of time Cain brought some of the fruits of the soil as an offering to the Lord. And Abel also brought an offering—fat portions from some of the firstborn of his flock. The Lord looked with favor on Abel and his offering, but on Cain and his offering he did not look with favor." Noteworthy is the difference in the type of sacrifice: fruits of the soil are renewable and bloodless, while fat-portions are set apart for the Lord and taken from the firstborn, pointing to an act of faith, since it is not guaranteed there will be more. The Midrash suggest that although Abel brought the best meat from his flock, Cain did not set aside for God the best of his harvest.

Similar to the internalized spiritual death God warns Adam and Eve off from eating the forbidden fruit—they do not physically die immediately, but over the course of time their bodies age and die—the Lord warns Cain that his inappropriate anger is waiting to consume him: "If you do what is right, will you not be accepted? But if you do not do what is right, sin is crouching at your door. It desires to have you, but you must rule over it.

Curse and Mark
According to, Cain treacherously murdered his brother, Abel, lied about the murder to God, and as a result, was cursed and marked for life. With the earth left cursed to drink Abel's blood, Cain was no longer able to farm the land. He becomes a "fugitive and wanderer", and receives a mark from God - commonly referred to as the mark of Cain - so that no one can enact vengeance on him.

Exegesis of the Septuagint's narrative, "groaning and shaking upon the earth" has Cain suffering from body tremors. Interpretations extend Cain's curse to his descendants, where they all died in the Great Deluge as retribution for the loss of Abel's potential offspring.

The Pearl of Great Price, a Mormon book of scripture, has been interpreted to depict the descendants of Cain as dark-skinned, and church president Brigham Young stated, "What is the mark? You will see it on the countenance of every African you ever did see...." In another biblical account, Ham discovered his father Noah drunk and naked in his tent. Because of this, Noah cursed Ham's son, Canaan to be "servants of servants". Although the scriptures do not mention Ham's skin color, some doctrines associated the curse with black people and used it to justify slavery.

Islamic interpretation
Cain's name in Islamic tradition is Qabil (قابيل). His story is mentioned in the Quran, though without name, where he and his brother Abel offer sacrifices, Abel's sacrifice was accepted while Cain's was not. Cain gets angry and threatens to murder his brother, but Abel tries to consult him, saying that God only accepts from the God-fearing, and that he wouldn't try to harm Cain. In the end, Cain kills Abel. God sends a crow searching in the ground to show Cain how to hide the disgrace of his brother. Cain, in his shame, began to curse himself, and became full of guilt.

Etymology

One popular theory regarding the name of Cain connects it to the verb "kana" ( qnh), meaning "to get" and used by Eve in when she says after bearing Cain, "I have gotten a man from the Lord." In this viewpoint, articulated by Nachmanides in the thirteenth century, Cain's name presages his role of mastery, power, and sin. In one of the Legends of the Jews, Cain is the fruit of a union between Eve and Satan, who is also the angel Samael and the serpent in the Garden of Eden, and Eve exclaims at Cain's birth, "I have gotten a man through an angel of the Lord." According to the Life of Adam and Eve (c. 1st century CE), Cain fetched his mother a reed (qaneh) which is how he received his name Qayin (Cain). The symbolism of him fetching a reed may be a nod to his occupation as a farmer, as well as a commentary to his destructive nature. He is also described as "lustrous", which may reflect the Gnostic association of Cain with the sun.

Characteristics
Cain is described as a city-builder, and the forefather of tent-dwelling pastoralists, all lyre and pipe players, and bronze and iron smiths.

In an alternate translation of Genesis 4:17, endorsed by a minority of modern commentators, Cain's son Enoch builds a city and names it after his son, Irad. Such a city could correspond with Eridu, one of the most ancient cities known. Philo observes that it makes no sense for Cain, the third human on Earth, to have founded an actual city. Instead, he argues, the city symbolizes an unrighteous philosophy.

In the New Testament, Cain is cited as an example of unrighteousness in and. The Targumim, rabbinic sources, and later speculations supplemented background details for the daughters of Adam and Eve. Such exegesis of Genesis 4 introduced Cain's wife as being his sister, a concept that has been accepted for at least 1,800 years. This can be seen with Jubilees 4 which narrates that Cain settled down and married his sister Awan, who bore their first son, the first Enoch, approximately 196 years after the creation of Adam. Cain then establishes the first city, naming it after his son, builds a house, and lives there until it collapses on him, killing him on the same year of Adam's death.

Relationship with the ground
In this alternative reading of the text, the ground could be personified as a character. This reading is evidenced by given human qualities, like a mouth, in the scripture. The ground is also the only subject of an active verb in the verse that states, "It opens its mouth to take the blood." This suggests that the ground reacted to the situation. By that logic, the ground could then potentially be an accomplice to the murder of Abel (Jordstad 708). The reaction from the ground raises the question, "Does the intimate connection between humans and the ground mean that the ground mirrors or aids human action, regardless of the nature of that action?"

Other stories
In Jewish tradition, Philo, Pirke De-Rabbi Eliezer and the Targum Pseudo-Jonathan asserted that Adam was not the father of Cain. Rather, Eve was subject to adultery having been seduced by Sammael, the serpent (nahash, נחש) in the Garden of Eden, the devil himself. Christian exegesis of the "evil one" in have also led some commentators, like Tertullian, to agree that Cain was the son of the devil or some fallen angel. Thus, according to some interpreters, Cain was half-human and half-angelic, one of the Nephilim (Genesis 6). Gnostic exegesis in the Apocryphon of John has Eve seduced by Yaldabaoth. However, in the Hypostasis of the Archons, Eve is raped by a pair of Archons.

Pseudo-Philo, a Jewish work of the first century CE, narrates that Cain murdered his brother at the age of 15. After escaping to the Land of Nod, Cain fathered four sons: Enoch, Olad, Lizpha and Fosal; and two daughters: Citha and Maac (the latter five aren't mentioned in the Bible). Cain died at the age of 730, leaving his corrupt descendants spreading evil on earth. According to the Book of Jubilees, Cain murdered his brother with a stone. Afterwards, Cain was killed by the same instrument he used against his brother; his house fell on him and he was killed by its stones. A heavenly law was cited after the narrative of Cain's death saying:

"With the instrument with which a man kills his neighbour with the same shall he be killed; after the manner that he wounded him, in like manner shall they deal with him."

A Talmudic tradition says that after Cain had murdered his brother, God made a horn grow on his head. Later, Cain was killed at the hands of his great-grandson Lamech, who mistook him for a wild beast. A Christian version of this tradition from the time of the Crusades holds that the slaying of Cain by Lamech took place on a mound called "Cain Mons" (i.e. Mount Cain), which is a corruption of "Caymont", a Crusader fort in Tel Yokneam in modern-day Israel.

The story of Cain and Abel is also made reference to in chapter 19 of 1 Meqabyan, a book considered canonical in the Ethiopian Orthodox Tewahedo Church. In this text, Cain killed Abel because he desired Abel's wife.

According to the Mandaean scriptures including the Qolastā, the Book of John and Genzā Rabbā, Abel is cognate with the angelic soteriological figure Hibil Ziwa who taught John the Baptist.

In the book Aradia, or the Gospel of the Witches by Charles Godfrey Leland, Cain is a lunar figure.

According to Rashi, who cites a midrash by Rabanus Maurus, Cain died from an arrow shot by a blindfolded man.

Family tree
The following family tree of the line of Cain is compiled from a variety of biblical and extra-biblical texts.

Sisters/wives
Various early commentators have said that Cain and Abel have sisters, usually twin sisters. According to Rabbi Joshua ben Karha as quoted in Genesis Rabbah, "Only two entered the bed, and seven left it: Cain and his twin sister, Abel and his two twin sisters."

Motives
The Book of Genesis does not give a specific reason for the murder of Abel. Modern commentators typically assume that the motives were jealousy and anger due to God rejecting Cain's offering, while accepting Abel's. The First Epistle of John says the following: "Do not be like Cain, who belonged to the evil one and murdered his brother. And why did he murder him? Because his own actions were evil and his brother's were righteous.'"

Ancient exegetes, such as the Midrash and the Conflict of Adam and Eve with Satan, tell that the motive involved a desire for the most beautiful woman. According to Midrashic tradition, Cain and Abel each had twin sisters; each was to marry the other's. The Midrash states that Abel's promised wife, Aclima, was more beautiful than Awan, Cain's promised wife. And so, after Cain would not consent to this arrangement, Adam suggested seeking God's blessing by means of a sacrifice. Whoever God blessed would marry Aclima. When God openly rejected Cain's sacrifice, Cain slew his brother in a fit of jealousy and anger. Rabbinical exegetes have discussed whether Cain's incestuous relationship with his sister was in violation of halakha.

Legacy and symbolism
A millennia-old explanation for Cain being capable of murder is that he may have been the offspring of a fallen angel or Satan himself, rather than being the son of Adam.

A medieval legend has Cain arriving at the Moon, where he eternally settled with a bundle of twigs. This was originated by the popular fantasy of interpreting the shadows on the Moon as a face. An example of this belief can be found in Dante Alighieri's Inferno (XX, 126 ) where the expression "Cain and the twigs" is used as a kenning for "moon".

In Latter-day Saint theology, Cain is considered to be the quintessential Son of Perdition, the father of secret combinations (i.e. secret societies and organized crime), as well as the first to hold the title Master Mahan meaning master of [the] great secret, that [he] may murder and get gain.

In Mormon folklore a second-hand account relates that an early Mormon leader, David W. Patten, encountered a very tall, hairy, dark-skinned man in Tennessee who said that he was Cain. The account states that Cain had earnestly sought death but was denied it, and that his mission was to destroy the souls of men. The recollection of Patten's story is quoted in Spencer W. Kimball's The Miracle of Forgiveness, a popular book within the Church of Jesus Christ of Latter-day Saints. This widespread Mormon belief is further emphasized by an account from Salt Lake City in 1963 which stated that "One superstition is based on the old Mormon belief that Cain is a black man who wanders the earth begging people to kill him and take his curse upon themselves (M, 24, SLC, 1963)."

Freud's theory of fratricide is explained by the Oedipus or Electra complex through Carl Jung's supplementation.

There were other, minor traditions concerning Cain and Abel, of both older and newer date. The apocryphal Life of Adam and Eve tells of Eve having a dream in which Cain drank his brother's blood. In an attempt to prevent the prophecy from happening the two young men are separated and given different jobs.

The author Daniel Quinn, first in his book Ishmael and later in The Story of B, proposes that the story of Cain and Abel is an account of early Semitic herdsmen observing the beginnings of what he calls totalitarian agriculture, with Cain representing the first 'modern' agriculturists and Abel the pastoralists.

Cultural portrayals and references

 * In the Old English classic poem Beowulf (c. 1000 CE), the monstrous Grendel and his mother are said to be descended from Cain.
 * The expression "Cain-coloured beard" (Cain and Judas were traditionally considered to have red or yellow hair) is used in Shakespeare's The Merry Wives of Windsor (1602).
 * Lord Byron rewrote and dramatized the story in the play Cain (1821), viewing Cain as symbolic of a sanguine temperament, provoked by Abel's hypocrisy and sanctimony.
 * Victor Hugo's poem "La Conscience" (1853, part of the La Légende des siècles collection) tells of Cain and his family fleeing from God's wrath.
 * John Steinbeck's 1952 novel East of Eden (also a 1955 film) refers in its title to Cain's exile and contains discussions of the Cain and Abel story which then play out in the plot.
 * The role-playing game Vampire: the Masquerade (1991) refers to vampires as "Cainites" after Cain, who is referred to as the first vampire.
 * Country music group 4 Runner's song "Cain's Blood" (1995) uses Cain and Abel as a metaphor for the struggle between good and evil in the song's narrator.
 * A "Mark of Cain" is featured in the TV series Supernatural (2005), and Cain appears as a character.
 * Cain appears as the ultimate antagonist of the comic book series The Strange Talent of Luther Strode (2011).
 * In Darren Aronofsky's allegorical film Mother! (2017), the characters "oldest son" represent Cain and Abel.

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Pioneer movement

A pioneer movement is an organization for children operated by a communist party. Typically children enter into the organization in elementary school and continue until adolescence. The adolescents then typically join the Young Communist League. Prior to the 1990s there was a wide cooperation between pioneer and similar movements of about 30 countries, coordinated by the international organization, International Committee of Children's and Adolescents' Movements (Comité international des mouvements d'enfants et d'adolescents, CIMEA), founded in 1958, with headquarters in Budapest, Hungary.

Overview
During the Russian Civil War from 1917 to 1921, most of the Russian Scoutmasters and many Scouts fought in the ranks of the White Army against the Red Army. Between 1918 and 1920, the All-Russian Congresses of the Russian Union of the Communist Youth (Komsomol) decided to eradicate the Scout movement and create an organization of the communist type, that would take Soviet children and adolescents under its umbrella. This organization would resemble the Scout movement in its form but properly educate children with Communist teachings.

On behalf of the Soviet Council of People's Commissars, Nadezhda Krupskaya (Vladimir Lenin's wife and the People's Commissar of State for Education) was one of the main contributors to the cause of the Pioneer movement. In 1922, she wrote an essay called "Russian Union of the Communist Youth and boy-Scoutism." However, it was the remaining scoutmasters themselves who supported the Komsomol and the Red Army, who introduced the name "Pioneer" to it and convinced the Komsomol to adapt the Scout symbols and rituals.

The first Pioneer organization was founded in Soviet Russia in 1922. Later, similar organizations were founded in the countries of the Eastern Bloc and other Communist states.

The Pioneer movement was modeled in many aspects on the Scout movement. The two movements share some principles like preparedness and promotion of sports and outdoor skills. The motto Always prepared! was adapted by the pioneer movement from the Scout Motto.

A member of the movement is known as a Pioneer, with the name stemming from the pioneering activity in Scouting. A neckerchief – typically red, but sometimes light blue – is the traditional item of clothing worn by a pioneer. This tradition was adapted from the Scout uniform.

But there are some distinct differences between the two movements. Most notably, the Scout movement is independent of government control and political parties. In contrast, the Pioneer movement is controlled by the Communist Party and includes teaching of communist principles. Opponents of Communism claim that this is a form of indoctrination.

Pioneer movements have existed and still exist in countries where the Communist Party is in power as well as in some countries where the Communist Party is in opposition, if the party is large enough to support a children's organization. In countries ruled by Communist Parties, membership of the pioneer movement is officially optional. However, membership provides many benefits, so the vast majority of children typically join the movement (although at different ages). During the existence of the Soviet Union, thousands of Young Pioneer camps and Young Pioneer Palaces were built exclusively for Young Pioneers, which were free of charge, sponsored by the government and trade unions. There were many newspapers and magazines published for Young Pioneers in millions of copies.

A national pioneer organization is often named after a famous party member that is considered a suitable role model for young communists, such as Vladimir Lenin in the Soviet Union, Enver Hoxha in Albania, Georgi Dimitrov in Bulgaria, José Martí in Cuba, Ernst Thälmann in East Germany, Damdin Sükhbaatar in Mongolia, and Ho Chi Minh in Vietnam.

The Movement of the First is a state-sponsored effort to initiate a follow-up of the pioneer movement in modern Russia.

Countries with current Pioneer movements

The Pioneer movement now exists in these countries:
 * Angola: Agostinho Neto Pioneer Organization
 * Armenia: Republic of Young Pioneers and Lernamerdz Soviet Pioneers
 * Belarus: Belarusian Republican Pioneer Organization
 * Belgium: Pioneers of the Workers Party of Belgium
 * People's Republic of China: Young Pioneers of China
 * Cuba: José Martí Pioneer Organization established in 1961.
 * Cyprus: United Democratic Youth Organisation
 * Finland: Democratic Union of Finnish Pioneers (Pinskut)
 * The Czech Republic: Pionýr is a non-political organisation organizing leisure time for children, part of the IFM-SEI.
 * Laos: the Young Pioneers in Laos are under the Lao People's Revolutionary Youth Union (LPRYU).
 * Mexico: Pioneers Association "Vicente Lombardo Toledano" of the Partido Popular Socialista
 * North Korea: Young Pioneer Corps of the Korean Children's Union
 * Portugal: Portuguese Communist Youth (founded in 1974), connected to the Portuguese Communist Party.
 * Russia: Pioneers of the Communist Party of the Russian Federation
 * Moldova: Pioneers of the Party of Communists of the Republic of Moldova
 * Ukraine: Pioneers of the Communist Party of Ukraine
 * Senegal: Mouvement Nationale des Pionniers Senegalese
 * Spain: Pioneers of the Communist Party of the Basque Homelands
 * Syria: Baath Party Pioneers
 * Tajikistan: King Somoni Inheritance
 * Vietnam: Ho Chi Minh Young Pioneer Organization, established on May 15, 1941

Older children could continue in other communist organizations, but that would typically be done only by a limited number of people.

The communist parties in Russia and other countries continue to run a pioneer organization, but membership tends to be quite limited.

Pioneer movements of the past
[Lansin Lu-nge Aphwe (lit. 'Programme Youth Organization') was an organisation for school students and university students to enter and learn about friendship, useful skills and the socialist politics of the Burma Socialist Programme Party. It comprises three branches: Teiza Lu-nge (lit. 'Glorious Youth'), Sheihsaung Lu-nge (lit. 'Pioneer Youth') and Lansin Lu-nge (lit. 'Programme Youth'). Teiza Lu-nge wore blue scarf and Shei-hsaung Lu-nge wore the Red scarf ].
 * Soviet Union: Vladimir Lenin All-Union Pioneer Organization, part of All-Union Leninist Young Communist League
 * Socialist People's Republic of Albania: Pioneers of Enver
 * People's Republic of Bulgaria: Dimitrovist Pioneer Organization "Septemberists". There was also an organization called chavdarcheta – these were the youngest children, which later became pioneers. The difference between the two was the distinctive scarf, which was sky-blue in the chavdarcheta movement and red in the pioneri movement.
 * Socialist Republic of the Union of Burma: Sheihsaung Lu-nge (Pioneer Youth) part of Lansin Lu-nge Aphwe
 * People's Republic of Kampuchea: The Young Pioneers in Cambodia between 1979 and 1993 were under the Kampuchean Revolutionary Youth Association (KRYA) and the Kampuchean Young Pioneers Organization (KYPO), branches of the Kampuchean United Front for National Salvation.
 * Czechoslovak Socialist Republic: Pioneer Organization of the Socialist Youth Union, part of the Socialist Youth Union.
 * German Democratic Republic: Ernst Thälmann Pioneer Organisation
 * Federal Republic of Germany: Junge Pioniere- Sozialistische Kinderorganisation
 * People's Republic of Hungary: Úttörőszövetség
 * Indonesia: Fadjar Harapan
 * Gerakan Pramuka Indonesia (in which Fadjar Harapan was forcefully merged into) was originally intended to be a nonpartisan Pioneer movement (an atypical example of a major Pioneer movement outside a communist country) and it acted as such until the New Order turned it into a mainstream Scouting organization.
 * Malawi: Malawi Youth Pioneers – like Indonesia, this is atypical as Malawi was not a communist country. Its Pioneers were linked to the Malawi Congress Party, the only political party allowed in what was at the time a one-party state.
 * Mali: Young Pioneers/Jeunes Pionniers (1960–?), Socialist youth brigades established by the US-RDA of Modibo Keïta.
 * Mongolian People's Republic: Sukhe Bator Mongolian Pioneers Organization
 * Niger: Young Pioneers/Jeunes Pionniers (1961–1974) non-communist youth paramilitary group, one of several set up in post independence Africa, funded by and modeled on the Israeli Nahal ("Fighting Pioneer Youth").
 * Burkina Faso: Pioneers of the Revolution, the youth movement of Thomas Sankara and the Council of Popular Salvation.
 * Norway: Young Pioneers
 * People's Republic of Poland: 1950–1956, later Związek Harcerstwa Polskiego, a mixture of Scouting and pioneering
 * Socialist Republic of Romania: Pioneer Organization
 * Socialist Federal Republic of Yugoslavia: Union of Pioneers of Yugoslavia (Pioniri), 1942–1992
 * United States: Young Pioneers of America (1922–1934)

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

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History of Styria

The history of Styria concerns the region roughly corresponding to the modern Austrian state of Styria and the Slovene region of Styria (Štajerska) from its settlement by Germans and Slavs in the Dark Ages until the present. This mountainous and scenic region, which became a centre for mountaineering in the 19th century, is often called the "Green March", because half of the area is covered with forests and one quarter with meadows, grasslands, vineyards and orchards. Styria is also rich in minerals, soft coal and iron, which has been mined at Erzberg since the time of the Romans. The Slovene Hills (Slovenske gorice, Windische Bühel) is a famous wine-producing district, stretching between Slovenia and Austria. Styria was for long the most densely populated and productive mountain region in Europe.

Styria's population before World War I was 68% German-speaking, 32% Slovene, bordered on (clockwise) Lower Austria, Hungary, Croatia, Carniola, Carinthia, Salzburg, and Upper Austria. In 1918 after World War I the southern, Slovene-speaking third south of the Mur River was incorporated into Slovenia in the Kingdom of Serbs, Croats and Slovenes. The remaining two-thirds became the Austrian federal state of Styria, while the Slovene-speaking third (Lower Styria) formed the informal Styria region in Slovenia, now divided into the Drava and Savinja Statistical Regions and the major part of Slovenian Carinthia. The capital both of the duchy and the Austrian state has always been Graz, which is now also the residence of the governor and the seat of the administration of the land.

Prehistory to Charlemagne
The Roman history of Styria is as part of Noricum and Pannonia, with the romanized Celtic population of the Taurisci. During the great migrations, various Germanic tribes settled and/or traversed the region using the river valleys and low passes, but about 600 CE the Slavs took possession of the area and settled assimilating the remaining autochthonous romanized population.

When Styria came under the hegemony of Charlemagne as a part of Carantania (Carinthia), erected as a border territory against the Avars and Slavs, there was a large influx of Bavarii and other Christianized Germanic peoples, whom the bishops of Salzburg and the patriarchs of Aquileia kept faithful to Rome. Bishop Vergilius of Salzburg (745-84), was largely instrumental in establishing a church hierarchy in the Duchy and gained for himself the name of "Apostle of Carantania." In 811 Charlemagne made the Drave River the boundary between the dioceses of Salzburg and Aquileia.

Middle Ages
The March of Styria was created in the Duchy of Carinthia in the late 10th century as a defence against the Magyars. Long called the Carantanian or Carinthian March it was soon ruled by a margravial dynasty called the Otakars that originated from Steyr in Upper Austria thus giving the land its name: "Steiermark". This march was raised to become a duchy by the Emperor Frederick Barbarossa in 1180 after the fall of Henry the Lion of Bavaria.

With the death of Ottokar the first line of rulers of Styria became extinct; the region fell successively to the Babenberg family, rulers of Austria, as stipulated in the Georgenberg Pact; after their extinction to the control of Hungary (1254–60); to King Ottokar of Bohemia; in 1276 to the Habsburgs, who provided it with Habsburgs for Styrian dukes during the years 1379-1439 and 1564–1619.

At the time of the Ottoman invasions in the 16th and 17th centuries the land suffered severely and was depopulated. The Turks made incursions into Styria nearly twenty times; churches, monasteries, cities, and villages were destroyed and plundered, while the population was either killed or carried away into slavery.

Modern era
The Semmering Railway, completed in 1854, was a triumph of engineering in its time, the oldest of the great European mountain railways. It was remarkable for its numerous and long tunnels and viaducts spanning mountain valleys, running from Gloggnitz in Lower Austria to Mürzzuschlag in Styria, and passing through the area's scenery. The railway brought tourists to alpine lake resorts and mineral springs at Rohitsch (today's Rogaška Slatina) and Bad Gleichenberg, the brine springs of Bad Aussee, and the thermal springs of Tuffer (today's Laško), Neuhaus am Klausenbach and Tobelbad.

Following World War I, Styria was divided by the Treaty of Saint Germain. Lower Styria with the cities of Celje and Maribor became part of the Kingdom of Serbs, Croats and Slovenes, while the rest remained with Austria as the State of Styria. Other than in Carinthia, no fighting resulted from this, in spite of a German minority in Slovenia (the larger cities of Lower Styria were largely German-speaking).

Lower Styria was reattached to the Reichsgau Steiermark from 1942 to 1945, whence it was returned to Germany. After World War II, Styria became part of the British occupation zone in Austria. The lower third was granted to Yugoslavia and today, it makes up about the eastern third of Slovenia.

Religious history
The Protestant Reformation made its way into the country about 1530. Duke Karl (ruling 1564–90), whose wife was the Catholic Duchess Maria of Bavaria, introduced the Counter-Reformation into the country; in 1573 he invited the Jesuits into Styria and in 1586 he founded the Catholic University of Graz. In 1598 his son and successor Ferdinand suppressed all Protestant schools and expelled the teachers and preachers: Protestant doctrines were maintained only in a few isolated mountain valleys, as in the valley of the Inn and the valley of the Mur. On a narrow reading of the Peace of Augsburg, 1555, with its principle of cuius regio, eius religio, only the nobility were not forced to return to the Roman Church; each could have Protestant services privately in his own house.

After Ferdinand had become Holy Roman Emperor in 1619 and had defeated his Protestant opponents in the Battle of White Mountain near Prague in 1620, he forbade all Protestant church services whatsoever (1625). In 1628 he commanded the nobility also to return to the Catholic faith. A large number of noble families, consequently, emigrated from the country. But most of them either returned, or their descendants did so, becoming Catholics and recovering their possessions.

In the second half of the 17th century renewed action against the Protestants in the isolated mountain valleys resulted in the expulsion of Protestant ministers with the peasants who would not give up Protestantism; about 30,000 chose compulsory emigration to Transylvania over conversion. Only an Edict of Toleration issued by Emperor Joseph II as late as 1781 put an end to religious repression. The Protestants then received the right to found parish communities and to exercise their religion in those enclaves undisturbed.

In 1848, all the provinces of the Habsburg monarchy received complete liberty of religion and of conscience, parity of religions, and the right to the public exercise of religion.

Ecclesiastically the province was historically divided into two Catholic prince-bishoprics, Seckau and Lavant. From the time of their foundation both were suffragans of the Archdiocese of Salzburg. The Prince-Bishopric of Seckau was established in 1218; since 1786 the see of the prince-bishop has been Graz. The Prince-Bishopric of Lavant with its bishop's seat at Sankt Andrä in the Carinthian Lavant Valley was founded as a bishopric in 1228 and raised to a prince-bishopric in 1446. In 1847 the bishop's seat was transferred from St. Andrä to Maribor, and after World War I the see's boundaries were adapted to the new political frontiers. A short-lived third Salzburg suffragan diocese of Leoben comprising 157 parishes in the districts of Leoben and Bruck an der Mur existed on Styrian soil from 1786 but was incorporated into the diocese of Graz-Seckau in 1856 Today the see of the bishop of Graz-Seckau is identical in territory with the Austrian State of Styria.

title: History of Styria


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Monte Carlo method

Monte Carlo methods, or Monte Carlo experiments, are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. The underlying concept is to use randomness to solve problems that might be deterministic in principle. The name comes from the Monte Carlo Casino in Monaco, where the primary developer of the method, physicist Stanislaw Ulam, was inspired by his uncle's gambling habits.

Monte Carlo methods are mainly used in three distinct problem classes: optimization, numerical integration, and generating draws from a probability distribution. They can also be used to model phenomena with significant uncertainty in inputs, such as calculating the risk of a nuclear power plant failure. Monte Carlo methods are often implemented using computer simulations, and they can provide approximate solutions to problems that are otherwise intractable or too complex to analyze mathematically.

Monte Carlo methods are widely used in various fields of science, engineering, and mathematics, such as physics, chemistry, biology, statistics, artificial intelligence, finance, and cryptography. They have also been applied to social sciences, such as sociology, psychology, and political science. Monte Carlo methods have been recognized as one of the most important and influential ideas of the 20th century, and they have enabled many scientific and technological breakthroughs.

Monte Carlo methods also have some limitations and challenges, such as the trade-off between accuracy and computational cost, the curse of dimensionality, the reliability of random number generators, and the verification and validation of the results.

Overview
Monte Carlo methods vary, but tend to follow a particular pattern:
 * 1) Define a domain of possible inputs
 * 2) Generate inputs randomly from a probability distribution over the domain
 * 3) Perform a deterministic computation of the outputs
 * 4) Aggregate the results

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

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

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

Application
Monte Carlo methods are often used in physical and mathematical problems and are most useful when it is difficult or impossible to use other approaches. Monte Carlo methods are mainly used in three problem classes: optimization, numerical integration, and generating draws from a probability distribution.

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

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

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

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

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

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

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

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

"The first thoughts and attempts I made to practice [the Monte Carlo Method] were suggested by a question which occurred to me in 1946 as I was convalescing from an illness and playing solitaires. The question was what are the chances that a Canfield solitaire laid out with 52 cards will come out successfully? After spending a lot of time trying to estimate them by pure combinatorial calculations, I wondered whether a more practical method than 'abstract thinking' might not be to lay it out say one hundred times and simply observe and count the number of successful plays. This was already possible to envisage with the beginning of the new era of fast computers, and I immediately thought of problems of neutron diffusion and other questions of mathematical physics, and more generally how to change processes described by certain differential equations into an equivalent form interpretable as a succession of random operations. Later [in 1946], I described the idea to John von Neumann, and we began to plan actual calculations."

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

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

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

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

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

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

Definitions
There is no consensus on how Monte Carlo should be defined. For example, Ripley defines most probabilistic modeling as stochastic simulation, with Monte Carlo being reserved for Monte Carlo integration and Monte Carlo statistical tests. Sawilowsky distinguishes between a simulation, a Monte Carlo method, and a Monte Carlo simulation: a simulation is a fictitious representation of reality, a Monte Carlo method is a technique that can be used to solve a mathematical or statistical problem, and a Monte Carlo simulation uses repeated sampling to obtain the statistical properties of some phenomenon (or behavior).

Here are the examples:
 * Simulation: Drawing one pseudo-random uniform variable from the interval [0,1] can be used to simulate the tossing of a coin: If the value is less than or equal to 0.50 designate the outcome as heads, but if the value is greater than 0.50 designate the outcome as tails. This is a simulation, but not a Monte Carlo simulation.
 * Monte Carlo method: Pouring out a box of coins on a table, and then computing the ratio of coins that land heads versus tails is a Monte Carlo method of determining the behavior of repeated coin tosses, but it is not a simulation.
 * Monte Carlo simulation: Drawing a large number of pseudo-random uniform variables from the interval [0,1] at one time, or once at many different times, and assigning values less than or equal to 0.50 as heads and greater than 0.50 as tails, is a Monte Carlo simulation of the behavior of repeatedly tossing a coin.

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

Convergence of the Monte Carlo simulation can be checked with the Gelman-Rubin statistic.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Integration

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

Monte Carlo methods provide a way out of this exponential increase in computation time. As long as the function in question is reasonably well-behaved, it can be estimated by randomly selecting points in 100-dimensional space, and taking some kind of average of the function values at these points. By the central limit theorem, this method displays $$\scriptstyle 1/\sqrt{N}$$ convergence—i.e., quadrupling the number of sampled points halves the error, regardless of the number of dimensions.

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

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

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

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

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

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

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

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

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

title: Monte Carlo method


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Red dwarf

A red dwarf is the smallest kind of star on the main sequence. Red dwarfs are by far the most common type of fusing star in the Milky Way, at least in the neighborhood of the Sun. However, due to their low luminosity, individual red dwarfs cannot be easily observed. From Earth, not one star that fits the stricter definitions of a red dwarf is visible to the naked eye. Proxima Centauri, the star nearest to the Sun, is a red dwarf, as are fifty of the sixty nearest stars. According to some estimates, red dwarfs make up three-quarters of the fusing stars in the Milky Way. The coolest red dwarfs near the Sun have a surface temperature of about $2,000 K$ and the smallest have radii about 9% that of the Sun, with masses about 7.5% that of the Sun. These red dwarfs have spectral types of L0 to L2. There is some overlap with the properties of brown dwarfs, since the most massive brown dwarfs at lower metallicity can be as hot as $3,600 K$ and have late M spectral types.

Definitions and usage of the term "red dwarf" vary on how inclusive they are on the hotter and more massive end. One definition is synonymous with stellar M dwarfs (M-type main sequence stars), yielding a maximum temperature of $3,900 K$ and. One includes all stellar M-type main-sequence and all K-type main-sequence stars (K dwarf), yielding a maximum temperature of $5,200 K$ and. Some definitions include any stellar M dwarf and part of the K dwarf classification. Other definitions are also in use. Many of the coolest, lowest mass M dwarfs are expected to be brown dwarfs, not true stars, and so those would be excluded from any definition of red dwarf.

Stellar models indicate that red dwarfs less than are fully convective. Hence, the helium produced by the thermonuclear fusion of hydrogen is constantly remixed throughout the star, avoiding helium buildup at the core, thereby prolonging the period of fusion. Low-mass red dwarfs therefore develop very slowly, maintaining a constant luminosity and spectral type for trillions of years, until their fuel is depleted. Because of the comparatively short age of the universe, no red dwarfs yet exist at advanced stages of evolution.

Definition
The term "red dwarf" when used to refer to a star does not have a strict definition. One of the earliest uses of the term was in 1915, used simply to contrast "red" dwarf stars from hotter "blue" dwarf stars. It became established use, although the definition remained vague. In terms of which spectral types qualify as red dwarfs, different researchers picked different limits, for example K8–M5 or "later than K5". Dwarf M star, abbreviated dM, was also used, but sometimes it also included stars of spectral type K.

In modern usage, the definition of a red dwarf still varies. When explicitly defined, it typically includes late K- and early to mid-M-class stars, but in many cases it is restricted just to M-class stars. In some cases all K stars are included as red dwarfs, and occasionally even earlier stars.

The most recent surveys place the coolest true main-sequence stars into spectral types L2 or L3. At the same time, many objects cooler than about M6 or M7 are brown dwarfs, insufficiently massive to sustain hydrogen-1 fusion. This gives a significant overlap in spectral types for red and brown dwarfs. Objects in that spectral range can be difficult to categorize.

Description and characteristics
Red dwarfs are very-low-mass stars. As a result, they have relatively low pressures, a low fusion rate, and hence, a low temperature. The energy generated is the product of nuclear fusion of hydrogen into helium by way of the proton–proton (PP) chain mechanism. Hence, these stars emit relatively little light, sometimes as little as $1/undefined$ that of the Sun, although this would still imply a power output on the order of 1022 watts (10 trillion gigawatts or 10 ZW). Even the largest red dwarfs (for example HD 179930, HIP 12961 and Lacaille 8760) have only about 10% of the Sun's luminosity. In general, red dwarfs less than transport energy from the core to the surface by convection. Convection occurs because of opacity of the interior, which has a high density compared to the temperature. As a result, energy transfer by radiation is decreased, and instead convection is the main form of energy transport to the surface of the star. Above this mass, a red dwarf will have a region around its core where convection does not occur.

Because low-mass red dwarfs are fully convective, helium does not accumulate at the core, and compared to larger stars such as the Sun, they can burn a larger proportion of their hydrogen before leaving the main sequence. As a result, red dwarfs have estimated lifespans far longer than the present age of the universe, and stars less than have not had time to leave the main sequence. The lower the mass of a red dwarf, the longer the lifespan. It is believed that the lifespan of these stars exceeds the expected 10-billion-year lifespan of the Sun by the third or fourth power of the ratio of the solar mass to their masses; thus, a red dwarf may continue burning for 10 trillion years. As the proportion of hydrogen in a red dwarf is consumed, the rate of fusion declines and the core starts to contract. The gravitational energy released by this size reduction is converted into heat, which is carried throughout the star by convection.

According to computer simulations, the minimum mass a red dwarf must have to eventually evolve into a red giant is ; less massive objects, as they age, would increase their surface temperatures and luminosities becoming blue dwarfs and finally white dwarfs.

The less massive the star, the longer this evolutionary process takes. It has been calculated that a red dwarf (approximately the mass of the nearby Barnard's Star) would stay on the main sequence for 2.5 trillion years, followed by five billion years as a blue dwarf, during which the star would have one third of the Sun's luminosity  and a surface temperature of 6,500–8,500 kelvins.

The fact that red dwarfs and other low-mass stars still remain on the main sequence when more massive stars have moved off the main sequence allows the age of star clusters to be estimated by finding the mass at which the stars move off the main sequence. This provides a lower limit to the age of the Universe and also allows formation timescales to be placed upon the structures within the Milky Way, such as the Galactic halo and Galactic disk.

All observed red dwarfs contain "metals", which in astronomy are elements heavier than hydrogen and helium. The Big Bang model predicts that the first generation of stars should have only hydrogen, helium, and trace amounts of lithium, and hence would be of low metallicity. With their extreme lifespans, any red dwarfs that were a part of that first generation (population III stars) should still exist today. Low-metallicity red dwarfs, however, are rare. The accepted model for the chemical evolution of the universe anticipates such a scarcity of metal-poor dwarf stars because only giant stars are thought to have formed in the metal-poor environment of the early universe. As giant stars end their short lives in supernova explosions, they spew out the heavier elements needed to form smaller stars. Therefore, dwarfs became more common as the universe aged and became enriched in metals. While the basic scarcity of ancient metal-poor red dwarfs is expected, observations have detected even fewer than predicted. The sheer difficulty of detecting objects as dim as red dwarfs was thought to account for this discrepancy, but improved detection methods have only confirmed the discrepancy.

The boundary between the least massive red dwarfs and the most massive brown dwarfs depends strongly on the metallicity. At solar metallicity the boundary occurs at about, while at zero metallicity the boundary is around. At solar metallicity, the least massive red dwarfs theoretically have temperatures around $1,700 K$, while measurements of red dwarfs in the solar neighbourhood suggest the coolest stars have temperatures of about $2,075 K$ and spectral classes of about L2. Theory predicts that the coolest red dwarfs at zero metallicity would have temperatures of about $3,600 K$. The least massive red dwarfs have radii of about, while both more massive red dwarfs and less massive brown dwarfs are larger.

Spectral standard stars
The spectral standards for M type stars have changed slightly over the years, but settled down somewhat since the early 1990s. Part of this is due to the fact that even the nearest red dwarfs are fairly faint, and their colors do not register well on photographic emulsions used in the early to mid 20th century. The study of mid- to late-M dwarfs has significantly advanced only in the past few decades, primarily due to development of new astrographic and spectroscopic techniques, dispensing with photographic plates and progressing to charged-couple devices (CCDs) and infrared-sensitive arrays.

The revised Yerkes Atlas system (Johnson & Morgan, 1953) listed only two M type spectral standard stars: HD 147379 (M0V) and HD 95735/Lalande 21185 (M2V). While HD 147379 was not considered a standard by expert classifiers in later compendia of standards, Lalande 21185 is still a primary standard for M2V. Robert Garrison does not list any "anchor" standards among the red dwarfs, but Lalande 21185 has survived as a M2V standard through many compendia. The review on MK classification by Morgan & Keenan (1973) did not contain red dwarf standards.

In the mid-1970s, red dwarf standard stars were published by Keenan & McNeil (1976) and Boeshaar (1976), but there was little agreement among the standards. As later cooler stars were identified through the 1980s, it was clear that an overhaul of the red dwarf standards was needed. Building primarily upon the Boeshaar standards, a group at Steward Observatory (Kirkpatrick, Henry, & McCarthy, 1991) filled in the spectral sequence from K5V to M9V. It is these M type dwarf standard stars which have largely survived as the main standards to the modern day. There have been negligible changes in the red dwarf spectral sequence since 1991. Additional red dwarf standards were compiled by Henry et al. (2002), and D. Kirkpatrick has recently reviewed the classification of red dwarfs and standard stars in Gray & Corbally's 2009 monograph. The M dwarf primary spectral standards are: GJ 270 (M0V), GJ 229A (M1V), Lalande 21185 (M2V), Gliese 581 (M3V), Gliese 402 (M4V), GJ 51 (M5V), Wolf 359 (M6V), van Biesbroeck 8 (M7V), VB 10 (M8V), LHS 2924 (M9V).

Planets
Many red dwarfs are orbited by exoplanets, but large Jupiter-sized planets are comparatively rare. Doppler surveys of a wide variety of stars indicate about 1 in 6 stars with twice the mass of the Sun are orbited by one or more of Jupiter-sized planets, versus 1 in 16 for Sun-like stars and the frequency of close-in giant planets (Jupiter size or larger) orbiting red dwarfs is only 1 in 40. On the other hand, microlensing surveys indicate that long-orbital-period Neptune-mass planets are found around one in three red dwarfs. Observations with HARPS further indicate 40% of red dwarfs have a "super-Earth" class planet orbiting in the habitable zone where liquid water can exist on the surface. Computer simulations of the formation of planets around low-mass stars predict that Earth-sized planets are most abundant, but more than 90% of the simulated planets are at least 10% water by mass, suggesting that many Earth-sized planets orbiting red dwarf stars are covered in deep oceans.

At least four and possibly up to six exoplanets were discovered orbiting within the Gliese 581 planetary system between 2005 and 2010. One planet has about the mass of Neptune, or 16 Earth masses. It orbits just 6 e6km from its star, and is estimated to have a surface temperature of 150 C, despite the dimness of its star. In 2006, an even smaller exoplanet (only ) was found orbiting the red dwarf OGLE-2005-BLG-390L; it lies 390 e6km from the star and its surface temperature is −220 C.

In 2007, a new, potentially habitable exoplanet, Gliese 581c, was found, orbiting Gliese 581. The minimum mass estimated by its discoverers (a team led by Stephane Udry) is. The discoverers estimate its radius to be 1.5 times that of Earth. Since then Gliese 581d, which is also potentially habitable, was discovered.

Gliese 581c and d are within the habitable zone of the host star, and are two of the most likely candidates for habitability of any exoplanets discovered so far. Gliese 581g, detected September 2010, has a near-circular orbit in the middle of the star's habitable zone. However, the planet's existence is contested.

On 23 February 2017 NASA announced the discovery of seven Earth-sized planets orbiting the red dwarf star TRAPPIST-1 approximately 39 light-years away in the constellation Aquarius. The planets were discovered through the transit method, meaning we have mass and radius information for all of them. TRAPPIST-1e, f, and g appear to be within the habitable zone and may have liquid water on the surface.

Habitability

Modern evidence suggests that planets in red dwarf systems are extremely unlikely to be habitable. In spite of their great numbers and long lifespans, there are several factors which may make life difficult on planets around a red dwarf. First, planets in the habitable zone of a red dwarf would be so close to the parent star that they would likely be tidally locked. For a nearly circular orbit, this would mean that one side would be in perpetual daylight and the other in eternal night. This could create enormous temperature variations from one side of the planet to the other. Such conditions would appear to make it difficult for forms of life similar to those on Earth to evolve. And it appears there is a great problem with the atmosphere of such tidally locked planets: the perpetual night zone would be cold enough to freeze the main gases of their atmospheres, leaving the daylight zone bare and dry. On the other hand, though, a theory proposes that either a thick atmosphere or planetary ocean could potentially circulate heat around such a planet.

Variability in stellar energy output may also have negative impacts on the development of life. Red dwarfs are often flare stars, which can emit gigantic flares, doubling their brightness in minutes. This variability makes it difficult for life to develop and persist near a red dwarf. While it may be possible for a planet orbiting close to a red dwarf to keep its atmosphere even if the star flares, more-recent research suggests that these stars may be the source of constant high-energy flares and very large magnetic fields, diminishing the possibility of life as we know it.

title: Red dwarf


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Ouagadougou

Ouagadougou or Wagadugu is the capital of Burkina Faso, and the administrative, communications, cultural and economic centre of the nation. It is also the country's largest city, with a population of 2,415,266 in 2019. The city's name is often shortened to Ouaga. The inhabitants are called ouagalais. The spelling of the name Ouagadougou is derived from the French orthography common in former French African colonies.

Ouagadougou's primary industries are food processing and textiles. It is served by an international airport and is linked by rail to Abidjan in the Ivory Coast and, for freight only, to Kaya. There are several highways linking the city to Niamey, Niger, south to Ghana, and southwest to Ivory Coast. Ouagadougou has one of West Africa's largest markets, which burned down in 2003 and has since reopened with better facilities and improved fire-prevention measures. Other attractions include the National Museum of Burkina Faso, the Moro-Naba Palace (site of the Moro-Naba Ceremony), the National Museum of Music, and several craft markets.

History
Ouagadougou was founded in the 11th century, being originally called Kumbee-Tenga which means "the land of princes". An eponymous kingdom was founded in the 15th century by Ninsi tribespeople. They were in constant conflict until 1441 when Wubri, a Yonyonse hero and an important figure in Burkina Faso's history, led his tribe to victory. He then renamed the area from "Kumbee-Tenga", as the Ninsi had called it, to "Wage sabre soba koumbem tenga", meaning "head war chief's village". The name later became "Wagadugu"; Ouagadougou is a Francophone spelling of this name.

An alternative theory on the origin of the name is that the town was founded by members of the Soninke diaspora after the fall of the Ghana Empire, also known as Wagadu. The name Wagadugu would therefore mean 'home of the Wagu', the Soninke subgroup that ruled Ghana.

The city became the capital of the Mossi Empire in 1441 and became the permanent residence of the Mossi emperors (Moro-Naba) in 1681. The Moro-Naba Ceremony is still performed every Friday by the Moro-Naba and his court. In 1919 the French made Ouagadougou the capital of the Upper Volta territory (basically the same area as contemporary Burkina Faso). In 1954 the railroad line from Ivory Coast reached the city. Ouagadougou's population doubled from 1954 to 1960 and has been doubling about every ten years since.

On 15 January 2016, gunmen armed with heavy weapons attacked central Ouagadougou at the Cappuccino restaurant and the Splendid Hotel. 28 people were killed, and at least 56 wounded; after a government counterattack, a total of 176 hostages were released the morning after the initial attack. Three of the perpetrators were also killed. The jihadist insurgency continued with major attacks in 2017 and 2018.

Geography
Ouagadougou, situated on the central plateau (12.4°N, -1.5°W), grew around the imperial palace of the Mogho Naaba. An administrative centre of colonial rule, it became an important urban centre in the post-colonial era. First the capital of the Mossi Kingdoms and later of Upper Volta and Burkina Faso, Ouagadougou became a veritable communal centre in 1995.

Climate
Ouagadougou's climate is hot semi-arid (BSh) under Köppen-Geiger classification, and closely borders with tropical wet and dry (Aw). The city is part of the Sudano-Sahelian area, with annual rainfall of about 800 mm. The rainy season stretches from May to September, with an average temperature of 28 C. The cool season runs from October to February, with a minimum average temperature of 16 C. The maximum temperature during the hot season, which runs from March to April, can reach 43 C. The harmattan (a dry wind) and the West African Monsoon are the two main factors that determine Ouagadougou's climate. Being further north, Ouagadougou's warmest months are slightly hotter and drier than those of Bobo-Dioulasso, the country's second most populous city.

Government
Ouagadougou's first municipal elections were held in 1956.

The city is divided into five arrondissements, consisting of 30 sectors, which are subdivided into districts. Districts of Ouagadougou include Gounghin, Kamsaoghin, Koulouba, Moemmin, Niogsin, Paspanga, Peuloghin, Bilbalogho, and Tiendpalogo.

Ouagadougou's communes have invested in huge city-management projects. This is largely because Ouagadougou constitutes a 'cultural centre' by merit of holding the SIAO (International Arts and Crafts fair) and the FESPACO (Panafrican Film and Television Festival of Ouagadougou). Moreover, the villages' growing affluence allows for such investment, and the population's rapid growth necessitates it.

Education
Though literacy in Ouagadougou is not high, there are three universities in the city. The largest is the state University of Ouagadougou, which was founded in 1974. In 2010 it had around 40,000 students (83% of the national population of university students).

The city's official language is French and the principal local languages are More, Dyula and Fulfulde. The bilingual program in schools (French plus one of the local languages) was established in 1994.

International schools include:
 * Lycée Saint-Exupéry de Ouagadougou (French school)
 * International School of Ouagadougou

Sport
Ouagadougou's inhabitants play a wide array of sports, including association football, basketball, and volleyball. There are tournaments and activities organized by the local authorities. The Stade du 4-Août is the home of Étoile Filante de Ouagadougou, the city's main football team.

Health
Ouagadougou has both state and private hospitals. The two state hospitals in the city are the Centre hospitalier national Yalgado Ouedraogo (CHNYO) and the Centre hospitalier national pédiatrique Charles de Gaulle (CHNP-CDG). Despite that, the local population still largely can only afford traditional local medicine and the "pharmacopée".

Transport
Many residents travel on motorcycles and mopeds. The large private vendor of motorcycles JC Megamonde sells 50,000 motorbikes and mopeds every year.

Ouagadougou's citizens also travel in green cabs, which take their passengers anywhere in town for 200 to 400 CFA, but the price is higher after 10:00 pm and can then reach 1000 CFA.

Air transport
Thomas Sankara International Airport Ouagadougou (code OUA) serves the area with flights to West Africa and Europe. Air Burkina has its head office in the Air Burkina Storey Building (Immeuble Air Burkina) in Ouagadougou.

Rail
Ouagadougou is connected by passenger rail service to Bobo-Dioulasso, Koudougou and Ivory Coast. As of June 2014, Sitarail operates a passenger train three times a week along the route from Ouagadougou to Abidjan. There are freight services to Kaya in the north of Burkina Faso and in 2014 plans were announced to revive freight services to the Manganese mine at Tambao starting in 2016.

Economy
The economy of Ouagadougou is based on industry and commerce. Some industrial facilities have relocated from Bobo-Dioulasso to Ouagadougou, which has made the city an important industrial centre of Burkina Faso. The industrial areas of Kossodo and Gounghin are home to several processing plants and factories. The industry of Ouagadougou is sector that fuels urban growth, as people move to the city from the countryside to find employment in industry. The Copromof workshop in Ouagadougou sews cotton lingerie for the French label "Atelier Augusti."

Ouagadougou is an important commercial centre. It is a centre where goods are collected and directed to rural areas. With a large consumer base, large amounts of energy sources, raw materials for buildings, agricultural products and livestock products are imported to the city.

The economy is dominated by the informal sector, which is characterized by petty commodity production, and workers not necessarily having salaries. Traditional, informal trade is widespread and concentrated around markets and major roads, as well as in outlets in neighborhoods. There are also instances of modern economic practices with workplaces having qualified, stable labor forces, or more traditional forms of business such as family businesses.

The tertiary sector is also an important part of the economy. This comprises communications, banking, transport, bars, restaurants, hotels, as well as administrative jobs.

Twin towns – sister cities
Ouagadougou is twinned with:

 * Bordeaux, France
 * Briton Ferry, Wales, United Kingdom
 * Grenoble, France
 * Kumasi, Ghana
 * Kuwait City, Kuwait
 * Leuze-en-Hainaut, Belgium
 * Lyon, France
 * Taipei, Taiwan
 * Zhengzhou, China

Parks
The Bangr-Weoogo urban park (area: 2.63 km2), before colonialism, belonged to the Mosse chiefs. Considering it a sacred forest, many went there for traditional initiations or for refuge. The French colonists, disregarding its local significance and history, established it as a park in the 1930s. In 1985, renovations were done in the park. In January 2001, the park was renamed "Parc Urbain Bangr-Weoogo", meaning "the urban park of the forest of knowledge".

Another notable park in Ouagadougou is the "L'Unité Pédagogique", which shelters animals in a semi-free state. This botanic garden/biosphere system stretches over 8 ha and also serves as a museum for the country's history.

"Jardin de l'amitié Ouaga-Loudun" (Garden of Ouaga-Loudun Friendship), with a green space that was renovated in 1996, is a symbol of the twin-city relationship between Ouagadougou and Loudun in France. It is situated in the centre of the city, near the "Nation Unies' crossroads".

Culture
There are a number of cultural and art venues, such as the Maison du Peuple and Salle des Banquets, in addition to performances of many genres of music, including traditional folk music, modern music, and rap.
 * National Museum of Music: exhibits all the musical instruments of Burkina Faso.
 * Musée de Manega: also exhibits musical instruments of Burkina Faso, Mossi rifles and other cultural items. Located 55 km northwest of the city.
 * "Naba Koom": a statue depicting a woman handling a calabash to pour water. The 6 m statue faces the railway station, welcoming travellers into Ouaga. The place bears the name of an important chief in Burkina Faso's history.
 * "Laongo": 30 km east of the city, features enormous granite slabs that were designed by various sculptors. The exhibit displays works of art from five continents.
 * "La Place du Grand Lyon": a monument that reflects the relationship between Burkina Faso's capital and Lyon in France. It is located near the French cultural centre George Melies and features an imposing lion. A zoo called "Parc Animalier de Ziniaré": located 30 km east of the city in the hometown of the former president Blaise Compaoré.

Art and crafts
Several international festivals and activities are organized within the municipality, such as FESPACO (Panafrican Film and Television Festival of Ouagadougou), which is Africa's largest festival of this type, SIAO (International Art and Craft Fair), FESPAM (Pan-African Music Festival), FITMO (International Theatre and Marionnette Festival) and FESTIVO.

Places of worship
The most common places of worship are Muslim mosques. There are also numerous Christian churches: Roman Catholic Archdiocese of Ouagadougou (Catholic Church), Association of Reformed Evangelical Church of Burkina Faso (World Communion of Reformed Churches), Assemblies of God, Deeper Life Bible Church, and the International Evangelism Center.

Notable people

 * Malika Ouattara – slam poet and activist
 * Dango Ouattara – footballer
 * Serge Oulon – journalist

title: Ouagadougou