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🔗 Flynn Effect
The Flynn effect is the substantial and long-sustained increase in both fluid and crystallized intelligence test scores that were measured in many parts of the world over the 20th century, named after researcher James Flynn (1934–2020). When intelligence quotient (IQ) tests are initially standardized using a sample of test-takers, by convention the average of the test results is set to 100 and their standard deviation is set to 15 or 16 IQ points. When IQ tests are revised, they are again standardized using a new sample of test-takers, usually born more recently than the first; the average result is set to 100. When the new test subjects take the older tests, in almost every case their average scores are significantly above 100.
Test score increases have been continuous and approximately linear from the earliest years of testing to the present. For example, a study published in the year 2009 found that British children's average scores on the Raven's Progressive Matrices test rose by 14 IQ points from 1942 to 2008. Similar gains have been observed in many other countries in which IQ testing has long been widely used, including other Western European countries, as well as Japan and South Korea. Improvements have also been reported for semantic and episodic memory.
There are numerous proposed explanations of the Flynn effect, such as the rise in efficiency of education, along with skepticism concerning its implications. Some researchers have suggested the possibility of a mild reversal in the Flynn effect (i.e., a decline in IQ scores) in developed countries, beginning in the 1990s. In certain cases, this apparent reversal may be due to cultural changes rendering parts of intelligence tests obsolete. Meta-analyses indicate that, overall, the Flynn effect continues, either at the same rate, or at a slower rate in developed countries.
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- "Flynn Effect" | 2025-01-12 | 14 Upvotes 9 Comments
🔗 In 1979, a Gulf of Mexico oil spill went on for 10 months at about the BP rate.
Ixtoc I was an exploratory oil well being drilled by the semi-submersible drilling rig Sedco 135 in the Bay of Campeche of the Gulf of Mexico, about 100 km (62 mi) northwest of Ciudad del Carmen, Campeche in waters 50 m (160 ft) deep. On 3 June 1979, the well suffered a blowout resulting in one of the largest oil spills in history.
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- "In 1979, a Gulf of Mexico oil spill went on for 10 months at about the BP rate." | 2010-05-28 | 88 Upvotes 41 Comments
🔗 Pantala Flavescens
Pantala flavescens, the globe skimmer, globe wanderer or wandering glider, is a wide-ranging dragonfly of the family Libellulidae. This species and Pantala hymenaea, the "spot-winged glider", are the only members of the genus Pantala. It was first described by Johan Christian Fabricius in 1798. It is considered to be the most widespread dragonfly on the planet with good population on every continent except Antarctica although rare in Europe. Globe skimmers make an annual multigenerational journey of some 18,000 km (about 11,200 miles); to complete the migration, individual globe skimmers fly more than 6,000 km (3,730 miles)—one of the farthest known migrations of all insect species.
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- "Pantala Flavescens" | 2022-08-16 | 18 Upvotes 9 Comments
🔗 Zope Object Database
The Zope Object Database (ZODB) is an object-oriented database for transparently and persistently storing Python objects. It is included as part of the Zope web application server, but can also be used independently of Zope.
Features of the ZODB include: transactions, history/undo, transparently pluggable storage, built-in caching, multiversion concurrency control (MVCC), and scalability across a network (using ZEO).
🔗 Magnets cannot exist under classical mechanics
The Bohr–van Leeuwen theorem states that when statistical mechanics and classical mechanics are applied consistently, the thermal average of the magnetization is always zero. This makes magnetism in solids solely a quantum mechanical effect and means that classical physics cannot account for diamagnetism.
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- "Magnets cannot exist under classical mechanics" | 2020-07-19 | 141 Upvotes 88 Comments
🔗 Holodomor
The Holodomor (Ukrainian: Голодомо́р; Голодомо́р в Украї́ні; derived from морити голодом, "to kill by starvation") was a man-made famine in Soviet Ukraine in 1932 and 1933 that killed millions of Ukrainians. It is also known as the Terror-Famine and Famine-Genocide in Ukraine, and sometimes referred to as the Great Famine or the Ukrainian Genocide of 1932–33. It was part of the wider Soviet famine of 1932–33, which affected the major grain-producing areas of the country. During the Holodomor, millions of inhabitants of Ukraine, the majority of whom were ethnic Ukrainians, died of starvation in a peacetime catastrophe unprecedented in the history of Ukraine. Since 2006, the Holodomor has been recognized by Ukraine and 15 other countries as a genocide of the Ukrainian people carried out by the Soviet government.
Early estimates of the death toll by scholars and government officials varied greatly. According to higher estimates, up to 12 million ethnic Ukrainians were said to have perished as a result of the famine. A U.N. joint statement signed by 25 countries in 2003 declared that 7–10 million perished. Research has since narrowed the estimates to between 3.3 and 7.5 million. According to the findings of the Court of Appeal of Kiev in 2010, the demographic losses due to the famine amounted to 10 million, with 3.9 million direct famine deaths, and a further 6.1 million birth deficits.
The term Holodomor emphasises the famine's man-made and intentional aspects, such as rejection of outside aid, confiscation of all household foodstuffs, and restriction of population movement. Whether the Holodomor was genocide is still the subject of academic debate, as are the causes of the famine and intentionality of the deaths. Some scholars believe that the famine was planned by Joseph Stalin to eliminate a Ukrainian independence movement. The loss of life has been compared to that of the Holocaust. However, some historians dispute its characterization as a genocide.
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- "Holodomor" | 2022-11-26 | 50 Upvotes 13 Comments
🔗 Resistor–Transistor Logic (RTL)
Resistor–transistor logic (RTL) (sometimes also transistor–resistor logic (TRL)) is a class of digital circuits built using resistors as the input network and bipolar junction transistors (BJTs) as switching devices. RTL is the earliest class of transistorized digital logic circuit; it was succeeded by diode–transistor logic (DTL) and transistor–transistor logic (TTL).
RTL circuits were first constructed with discrete components, but in 1961 it became the first digital logic family to be produced as a monolithic integrated circuit. RTL integrated circuits were used in the Apollo Guidance Computer, whose design begun in 1961 and which first flew in 1966.
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- "Resistor–Transistor Logic (RTL)" | 2023-08-23 | 84 Upvotes 34 Comments
🔗 Exploding Head Syndrome
Exploding head syndrome (EHS) is a condition in which a person experiences unreal noises that are loud and of short duration when falling asleep or waking up. The noise may be frightening, typically occurs only occasionally, and is not a serious health concern. People may also experience a flash of light. Pain is typically absent.
The cause is unknown. Potential explanations include ear problems, temporal lobe seizure, nerve dysfunction, or specific genetic changes. Potential risk factors include psychological stress. It is classified as a sleep disorder or headache disorder. People often go undiagnosed.
There is no high quality evidence to support treatment. Reassurance may be sufficient. Clomipramine and calcium channel blockers have been tried. While the frequency of the condition is not well studied, some have estimated that it occurs in about 10% of people. Females are reportedly more commonly affected. The condition was initially described at least as early as 1876. The current name came into use in 1988.
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- "Exploding Head Syndrome" | 2023-11-23 | 67 Upvotes 94 Comments
- "Exploding Head Syndrome" | 2020-03-08 | 87 Upvotes 70 Comments
- "Exploding head syndrome. I kid you not." | 2011-12-27 | 19 Upvotes 25 Comments
🔗 Wringing
Gauge blocks (also known as gage blocks, Johansson gauges, slip gauges, or Jo blocks) are a system for producing precision lengths. The individual gauge block is a metal or ceramic block that has been precision ground and lapped to a specific thickness. Gauge blocks come in sets of blocks with a range of standard lengths. In use, the blocks are stacked to make up a desired length.
An important feature of gauge blocks is that they can be joined together with very little dimensional uncertainty. The blocks are joined by a sliding process called wringing, which causes their ultra-flat surfaces to cling together. A small number of gauge blocks can be used to create accurate lengths within a wide range. By using 3 blocks at a time taken from a set of 30 blocks, one may create any of the 1000 lengths from 3.000 to 3.999 mm in 0.001 mm steps (or .3000 to .3999 inches in 0.0001 inch steps). Gauge blocks were invented in 1896 by Swedish machinist Carl Edvard Johansson. They are used as a reference for the calibration of measuring equipment used in machine shops, such as micrometers, sine bars, calipers, and dial indicators (when used in an inspection role). Gauge blocks are the main means of length standardization used by industry.
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- "Wringing" | 2019-07-05 | 356 Upvotes 77 Comments
🔗 Extreme Learning Machine
Extreme learning machines are feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning with a single layer or multiple layers of hidden nodes, where the parameters of hidden nodes (not just the weights connecting inputs to hidden nodes) need not be tuned. These hidden nodes can be randomly assigned and never updated (i.e. they are random projection but with nonlinear transforms), or can be inherited from their ancestors without being changed. In most cases, the output weights of hidden nodes are usually learned in a single step, which essentially amounts to learning a linear model. The name "extreme learning machine" (ELM) was given to such models by its main inventor Guang-Bin Huang.
According to their creators, these models are able to produce good generalization performance and learn thousands of times faster than networks trained using backpropagation. In literature, it also shows that these models can outperform support vector machines (SVM) and SVM provides suboptimal solutions in both classification and regression applications.
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- "Extreme Learning Machine" | 2019-04-19 | 50 Upvotes 7 Comments