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the passage of the disk from rest to rotation in such a fashion that both the length of the radius and the length of the periphery, measured with respect to the comoving frame of reference, remain unchanged" 1975: Øyvind Grøn writes a classic review paper about solutions of the "paradox". 1977: Grünbaum and Janis introduce a notion of physically realizable "non-rigidity" which can be applied to the spin-up of an initially non-rotating disk (this notion is not physically realistic for real materials from which one might make a disk, but it is useful for thought experiments). 1981: Grøn notices that Hooke's law is not consistent with Lorentz transformations and introduces a relativistic generalization. 1997: T. A. Weber explicitly introduces the frame field associated with Langevin observers. 2000: Hrvoje Nikolić points out that the paradox disappears when (in accordance with general theory of relativity) each piece of the rotating disk is treated separately, as living in its own local non-inertial frame. 2002: Rizzi and Ruggiero (and Bel) explicitly introduce the quotient manifold mentioned above. 2024: Jitendra Kumar analyzes the paradox for a ring and points out that the resolution depends on how the ring is brought from rest to rotational motion, whether by keeping the rest length of the periphery constant (in which case the periphery tears) or by keeping periphery's length in the inertial frame constant (in which case the periphery physically stretches, increasing its rest length). == Resolution of the paradox == Grøn states that the resolution of the paradox stems from the impossibility of synchronizing clocks in a rotating reference frame. If observers on the rotating circumference try to synchronise their clocks around the circumference to establish disc time, there is a time difference between the two end points where they meet. The modern resolution can be briefly
|
{
"page_id": 2819556,
"source": null,
"title": "Ehrenfest paradox"
}
|
summarized as follows: Small distances measured by disk-riding observers are described by the Langevin-Landau-Lifschitz metric, which is indeed well approximated (for small angular velocity) by the geometry of the hyperbolic plane, just as Kaluza had claimed. For physically reasonable materials, during the spin-up phase a real disk expands radially due to centrifugal forces; relativistic corrections partially counteract (but do not cancel) this Newtonian effect. After a steady-state rotation is achieved and the disk has been allowed to relax, the geometry "in the small" is approximately given by the Langevin–Landau–Lifschitz metric. == See also == Born coordinates, for a coordinate chart adapted to observers riding on a rigidly rotating disk Length contraction Relativistic disk Some other "paradoxes" in special relativity Bell's spaceship paradox Ladder paradox Physical paradox Supplee's paradox Twin paradox == Notes == === Citations === == Works cited == === A few papers of historical interest === === A few classic "modern" references === === Some experimental work and subsequent discussion === === Selected recent sources === == External links == The Rigid Rotating Disk in Relativity, by Michael Weiss (1995), from the sci.physics FAQ. Einstein's Carousel (section 3.5.4), by B. Crowell
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{
"page_id": 2819556,
"source": null,
"title": "Ehrenfest paradox"
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In building design, thermal mass is a property of the matter of a building that requires a flow of heat in order for it to change temperature. Not all writers agree on what physical property of matter "thermal mass" describes. Most writers use it as a synonym for heat capacity, the ability of a body to store thermal energy. It is typically referred to by the symbol Cth, and its SI unit is J/K or J/°C (which are equivalent). However: Christoph Reinhart at MIT describes thermal mass as its volume times its volumetric heat capacity. Randa Ghattas, Franz-Joseph Ulm and Alison Ledwith, also at MIT, write that "It [thermal mass] is dependent on the relationship between the specific heat capacity, density, thickness and conductivity of a material" although they don't provide a unit, describing materials only as "low" or "high" thermal mass. Chris Reardon equates thermal mass with volumetric heat capacity . The lack of a consistent definition of what property of matter thermal mass describes has led some writers to dismiss its use in building design as pseudoscience. == Background == The equation relating thermal energy to thermal mass is: Q = C t h Δ T {\displaystyle Q=C_{\mathrm {th} }\Delta T\,} where Q is the thermal energy transferred, Cth is the thermal mass of the body, and ΔT is the change in temperature. For example, if 250 J of heat energy is added to a copper gear with a thermal mass of 38.46 J/°C, its temperature will rise by 6.50 °C. If the body consists of a homogeneous material with sufficiently known physical properties, the thermal mass is simply the mass of material present times the specific heat capacity of that material. For bodies made of many materials, the sum of heat capacities for their pure components may
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{
"page_id": 67046,
"source": null,
"title": "Thermal mass"
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|
be used in the calculation, or in some cases (as for a whole animal, for example) the number may simply be measured for the entire body in question, directly. As an extensive property, heat capacity is characteristic of an object; its corresponding intensive property is specific heat capacity, expressed in terms of a measure of the amount of material such as mass or number of moles, which must be multiplied by similar units to give the heat capacity of the entire body of material. Thus the heat capacity can be equivalently calculated as the product of the mass m of the body and the specific heat capacity c for the material, or the product of the number of moles of molecules present n and the molar specific heat capacity c ¯ {\displaystyle {\bar {c}}} . For discussion of why the thermal energy storage abilities of pure substances vary, see factors that affect specific heat capacity. For a body of uniform composition, C t h {\displaystyle C_{\mathrm {th} }} can be approximated by C t h = m c p {\displaystyle C_{\mathrm {th} }=mc_{\mathrm {p} }} where m {\displaystyle m} is the mass of the body and c p {\displaystyle c_{\mathrm {p} }} is the isobaric specific heat capacity of the material averaged over temperature range in question. For bodies composed of numerous different materials, the thermal masses for the different components can just be added together. == Heat capacity in buildings == Christoph Reinhard describes the impact of heat capacity this way: If the outside diurnal temperature swing frequently oscillates around a desired (balance point) temperature, adding thermal mass may increase the hours of comfort in a given time interval. Thermal mass may act as a liability to keep a space comfortable e.g. when it is only used intermittently. Thermal
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{
"page_id": 67046,
"source": null,
"title": "Thermal mass"
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|
mass has really no effect if the direction of heat flow through the building envelope stays constant for extended periods of time. Heat capacity is not normally calculated in the engineering of buildings. In the United States and Canada, national building codes and most state and local jurisdictions require that heating and cooling equipment be sized in accordance with Manual J of the Air Conditioning Contractors of America. The Manual J process uses detailed measurements of a building's dimensions, construction, insulation, air-tightness, features and occupant loads, but it does not take into effect the heat capacity. Some heat capacity is presumed in the Manual J process, equipment sized according to Manual J is sized to maintain comfort at the first percentile of temperature for heating and the 99th percentile of temperature for cooling. The process presumes that the building has sufficient heat capacity to maintain comfort during brief excursions outside of those extremes. === Construction examples === Earthship Rammed earth wall Trombe wall == See also == Specific heat capacity Thermal energy storage Thermal inertia == References ==
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{
"page_id": 67046,
"source": null,
"title": "Thermal mass"
}
|
Ayanna MacCalla Howard (born January 24, 1972) is an American roboticist, entrepreneur, and educator currently serving as the dean of the College of Engineering at Ohio State University. Assuming this role in March 2021, Howard became the first woman to lead the Ohio State College of Engineering. Howard previously served as the chair of the School of Interactive Computing in the Georgia Tech College of Computing, the Linda J. and Mark C. Smith Endowed Chair in Bioengineering in the School of Electrical and Computer Engineering, and the director of the Human-Automation Systems (Humans) Lab. == Early life and education == As a little girl, Howard was interested in aliens and robots. Her favorite TV show was The Bionic Woman. Howard received her B.S. in engineering from Brown University in 1993 and her M.S. and Ph.D. in electrical engineering from the University of Southern California in 1994 and 1999, respectively. Her thesis, Recursive Learning for Deformable Object Manipulation, was advised by George A. Bekey. Howard has also received an MBA from Claremont Graduate University. == Career == Howard's early interest in artificial intelligence led her to pursue a senior position at Seattle-based Axcelis Inc, where she helped develop Evolver, the first commercial genetic algorithm, and Brainsheet, a neural network developed in partnership with Microsoft. From 1993 to 2005, she worked at the NASA Jet Propulsion Laboratory, holding multiple roles such as senior robotics researcher and deputy manager in the Office of the Chief Scientist. In 2005, she joined Georgia Tech as an associate professor and founder of the Human-Automation Systems (Humans) lab. She has also served as the associate director of research for Georgia Tech's Institute for Robotics and Intelligent Machines and as chair of the multidisciplinary robotics Ph.D. program at Georgia Tech. In 2017, she became the chair of the
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{
"page_id": 16713196,
"source": null,
"title": "Ayanna Howard"
}
|
School of Interactive Computing at Georgia Tech. In 2008, Howard received worldwide attention for her SnoMote robots, designed to study the impact of global warming on the Antarctic ice shelves. In 2013, she founded Zyrobotics, which has released their first suite of therapy and educational products for children with special needs. Howard has authored 250 publications in reputable journals and conferences, including serving as co-editor/co-author of more than a dozen books and book chapters. She has also received four patents and given over 140 invited talks and keynotes. She is a fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and the Institute of Electrical and Electronics Engineers (IEEE). Among her many honors, Howard received the Computer Research Association's A. Nico Habermann Award and the Richard A. Tapia Achievement Award. In a 2020 interview on Marketplace, Howard outlined how companion robots could alleviate the effects of social distancing caused by the COVID-19 pandemic in the United States. On November 30, 2020, the Columbus Dispatch reported that Howard would become the next dean of the College of Engineering at Ohio State University on March 1, pending approval by the board of trustees. On March 1, 2021, she assumed this role, becoming the first woman to hold the position. In 2021, Howard received the Athena Lecturer Award from Association for Computing Machinery (ACM) for her Contributions to Robotics, AI and Broadening Participation in Computing. In June 2022, Howard was elected a trustee of Brown University. == Research == Howard's research interests include human-robot interaction, assistive/rehabilitation robotics, science-driven/field robotics, and perception, learning, and reasoning. Howard's research and published works span across various topics in robotics and AI, including intelligent learning, virtual reality for rehabilitation and robotics in the role of pediatric therapy. Her research is highlighted by her focus on technology
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{
"page_id": 16713196,
"source": null,
"title": "Ayanna Howard"
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|
development for intelligent agents that must interact with and in a human-centered world. Her work, which addresses issues of human-robot interaction, learning, and autonomous control, has resulted in more than 200 peer-reviewed publications. == Honors and awards == Howard's numerous accomplishments have been documented in more than a dozen featured articles. In 2003, she was named to the MIT Technology Review TR100 as one of the top 100 innovators in the world under the age of 35. She was featured in Time magazine's "Rise of the Machines" article in 2004. She was also featured in a USA Today Science & Space article. Some of Howard's notable awards include: Lew Allen Award for Excellence (formerly the Director's Research Achievement Award of the Jet Propulsion Laboratory) for significant technical contributions, 2001 MIT Technology Review Top 100 Young Innovators of the Year, 2003 NAE Gilbreth Lectureship, 2010 A. Richard Newton Educator ABIE Award, Anita Borg Institute, 2014 Computer Research Association's A. Nico Habermann Award, 2016 Brown Engineering Alumni Medal (BEAM), 2016 AAAS-Lemelson Invention Ambassador, 2016-2017 Atlanta magazine's Women Making a Mark, 2017 Walker's Legacy #WLPower25 Atlanta Award, 2017 Forbes America's Top 50 Women In Tech, 2018 ACM Athena Lecturer Award, 2021 2021 class of Fellows of the American Association for the Advancement of Science. IEEE Fellow, 2021, "for contributions to human-robot interaction systems" 2023 AAAI/EAAI Patrick Henry Winston Outstanding Educator Award == References == == External links == Home Page ECE Profile Archived 2008-04-05 at the Wayback Machine Presenter at Cusp Conference 2008 United Nations Academic Impact Podcast Interview
|
{
"page_id": 16713196,
"source": null,
"title": "Ayanna Howard"
}
|
Isoantibodies, formerly called alloantibodies, are antibodies produced by an individual against isoantigens produced by members of the same species. In the case of the species Homo sapiens, for example, there are a significant number of antigens that are different in every individual. When antigens from another individual are introduced into another's body, these isoantibodies immediately bind to and destroy them. One common example is the isohaemagglutinins, which are responsible for blood transfusion reactions. This may subjectively differ from the term 'natural' antibodies, or simply 'antibodies', as the former seem to arise from genetic control without apparent antigenic stimulation whereas the latter arise due to antigenic stimulation. == Isoantigens == A protein or other substance, such as histocompatibility or red blood cell antigens, that is present in only some members of a species and therefore able to stimulate isoantibody production in other members of the same species who lack it. When injected into another animal, they trigger an immune response aimed at eliminating them. Therefore, it can be thought of as an antigen that is present in some members of the same species, but is not common to all members of that species. If an alloantigen is presented to a member of the same species that does not have the alloantigen, it will be recognized as foreign. They are the products of polymorphic genes. == Production of isohaemagglutinins == Isoantibodies are seen in people with different blood groups. The anti-A or anti-B isoantibodies or both (also called isohaemagglutinins) are produced by an individual against the antigens (A or B) on the RBCs of other blood groups. In a person with A blood group, the plasma will contain isoantibodies against B antigens, so immediately after transfusion of blood from B group the anti-B isohemagglutinins agglutinate the foreign red blood cells. Anti-A and
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{
"page_id": 20448749,
"source": null,
"title": "Isoantibodies"
}
|
anti-B antibodies (called isohaemagglutinins), which are not present in human babies, appear in the first years of life. It is possible that food and environmental antigens (bacterial, viral or plant antigens) have epitopes similar enough to A and B glycoprotein antigens. The antibodies created against these environmental antigens in the first years of life can cross react with ABO-incompatible red blood cells when it comes in contact with during blood transfusion later in life. Anti-A and anti-B antibodies are usually IgM type. O-type individuals can produce IgG-type ABO antibodies. == See also == ABO blood group system Alloimmunity Antibodies == References ==
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{
"page_id": 20448749,
"source": null,
"title": "Isoantibodies"
}
|
John Derek Dowell FRS (born 6 January 1935) is a British physicist, emeritus professor at University of Birmingham. Born in Leicestershire, he was educated at Coalville Grammar School and the University of Birmingham (BSc, PhD). He worked as a Research fellow at Birmingham University (1958–1960) before moving to be a research associate at the European Organization for Nuclear Research near Geneva (1960–1962). He then returned to Birmingham as lecturer (1962–1970), senior lecturer (1970–1974) and reader (1974–1980). In 1980 he was appointed Professor of Elementary Particle Physics and finally retired as professor emeritus in 2002. He published results from CERN's SPS accelerator which included the first observation in Europe of the J/psi particle, which consists of charmed quarks, supporting the theory that matter is composed of quarks. After research at the Hadron-Electron Ring Accelerator (HERA) at DESY in Hamburg, he helped develop detectors for the Large Hadron Collider (LHC) at Geneva and was involved in the ATLAS experiment which discovered the Higgs boson. He won the 1988 Rutherford Medal and Prize. He was elected a Fellow of the Royal Society in 1986 and a Fellow of the American Physical Society in 2003. In July 2002, a symposium was held in his honour, as he retired in September of that year. == References ==
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{
"page_id": 34014703,
"source": null,
"title": "John Dowell"
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The molecular formula C6H8O (molar mass: 96.13 g/mol, exact mass: 96.05751 u) may refer to: Cyclohexenone 2,5-Dimethylfuran 2,3-Dimethylfuran 2,4-Dimethylfuran 3,4-Dimethylfuran 2,4-Hexadienal
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{
"page_id": 12387824,
"source": null,
"title": "C6H8O"
}
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Borate buffered saline (abbreviated BBS) is a buffer used in some biochemical techniques to maintain the pH within a relatively narrow range. Borate buffers have an alkaline buffering capacity in the 8–10 range. Boric acid has a pKa of 9.14 at 25 °C. == Applications == BBS has many uses because it is isotonic and has a strong bactericidal effect. It can be used to dilute substances and has applications in coating procedures. Additives such as Polysorbate 20 and milk powder can be used to add to BBS's functionality as a washing buffer or blocking buffer. == Contents == The following is a sample recipe for BBS: 10 mM Sodium borate 150 mM NaCl Adjust pH to pH 8.2 The simplest way to prepare a BBS solution is to use BBS tablets. They are formulated to give a ready to use borate buffered saline solution upon dissolution in 500 ml of deionized water. Concentration of borate and NaCl as well as the pH can vary, and the resulting solution would still be referred to as "borate buffered saline". Borate concentration (giving buffering capacity) can vary from 10 mM to 100 mM. As BBS is used to emulate physiological conditions (as in animal or human body), the pH value is slightly alkaline, ranging from 8.0 to 9.0. NaCl gives the isotonic (mostly used 150 mM NaCl corresponds to physiological conditions: 0.9% NaCl) salt concentration. == References ==
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{
"page_id": 23594484,
"source": null,
"title": "Borate buffered saline"
}
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Rose water, or rosewater, is a flavoured water created by steeping rose petals in water. It is typically made as a by-product during the distillation of rose petals to create rose oil for perfumes. Rose water is widely utilized to flavour culinary dishes and enhance cosmetic products, and it is significant in religious rituals throughout Eurasia. Iran is a major producer, supplying around 90% of the world's rose water demand. Central Iran is home to the annual Golabgiri festival each spring. Thousands of tourists visit the area to celebrate the rose harvest for the production of rosewater. == History == Since ancient times, roses have been used medicinally, nutritionally, and as a source of perfume. Rose perfumes are made from rose oil, also called "attar of roses", which is a mixture of volatile essential oils obtained by steam-distilling the crushed petals of roses. Rose water is a by-product of this process. Before the development of the technique of distilling rose water, rose petals were already used in Persian cuisine to perfume and flavour dishes. Rose water likely originated in Persia, where it is known as gulāb (گلاب), from gul (گل rose) and ab (آب water). The term was adopted into Medieval Greek as zoulápin. The process of creating rose water through steam distillation was refined by Arab and Persian chemists in the medieval Islamic world, which led to more efficient and economic uses for perfume industries. == Uses == === Food === Rose water is often added to water to mask unpleasant odours and flavours. In South Asian cuisine, it is a common ingredient in sweets such as laddu, gulab jamun, and peda. It is also used to flavour milk, lassi, rice pudding, and other dairy dishes. In Southeast Asia, sweet, red-tinted rose water is mixed with milk, producing a
|
{
"page_id": 67064,
"source": null,
"title": "Rose water"
}
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pink drink called bandung. Rose water is used in various dishes, especially in sweets such as Turkish delight, nougat, and baklava. Marzipan has long been flavoured with rose water. In Cyprus, it is used to flavour a number of different desserts, including the local version of muhallebi. It is also frequently used as a halal substitute for red wine and other alcohols in cooking. The Premier League, Bahrain Grand Prix, and Abu Dhabi Grand Prix offer a rose water-based beverage as an alternative for champagne when awarding Muslim players. === Cosmetics === In medieval Europe, rose water was used to wash hands during feasts. === Religion === Rose water is used in religious ceremonies in Christianity (in the Byzantine Rite of the Catholic Church and in the Eastern Orthodox Church), Zoroastrianism, and the Baháʼí Faith (in Kitab-i-Aqdas 1:76). == Chemical composition == Depending on the origin and manufacturing method, rose water is obtained from the sepals and petals of Rosa × damascena through steam distillation. The following monoterpenoid and alkane components can be identified with gas chromatography: mostly citronellol, nonadecane, geraniol, and phenylethyl alcohol, and also henicosane, nonadecane, eicosane, linalool, citronellyl acetate, methyleugenol, heptadecane, pentadecane, docosane, nerol, disiloxane, octadecane, and pentacosane. Usually, phenylethyl alcohol is responsible for the typical odour of rose water but is not always present in derivative products. == Gallery == == See also == Orange flower water == References == == External links == The dictionary definition of rose water at Wiktionary Media related to Rose water at Wikimedia Commons
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{
"page_id": 67064,
"source": null,
"title": "Rose water"
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Hierarchical temporal memory (HTM) is a biologically constrained machine intelligence technology developed by Numenta. Originally described in the 2004 book On Intelligence by Jeff Hawkins with Sandra Blakeslee, HTM is primarily used today for anomaly detection in streaming data. The technology is based on neuroscience and the physiology and interaction of pyramidal neurons in the neocortex of the mammalian (in particular, human) brain. At the core of HTM are learning algorithms that can store, learn, infer, and recall high-order sequences. Unlike most other machine learning methods, HTM constantly learns (in an unsupervised process) time-based patterns in unlabeled data. HTM is robust to noise, and has high capacity (it can learn multiple patterns simultaneously). When applied to computers, HTM is well suited for prediction, anomaly detection, classification, and ultimately sensorimotor applications. HTM has been tested and implemented in software through example applications from Numenta and a few commercial applications from Numenta's partners. == Structure and algorithms == A typical HTM network is a tree-shaped hierarchy of levels (not to be confused with the "layers" of the neocortex, as described below). These levels are composed of smaller elements called regions (or nodes). A single level in the hierarchy possibly contains several regions. Higher hierarchy levels often have fewer regions. Higher hierarchy levels can reuse patterns learned at the lower levels by combining them to memorize more complex patterns. Each HTM region has the same basic function. In learning and inference modes, sensory data (e.g. data from the eyes) comes into bottom-level regions. In generation mode, the bottom level regions output the generated pattern of a given category. The top level usually has a single region that stores the most general and most permanent categories (concepts); these determine, or are determined by, smaller concepts at lower levels—concepts that are more restricted in time
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{
"page_id": 11273721,
"source": null,
"title": "Hierarchical temporal memory"
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and space. When set in inference mode, a region (in each level) interprets information coming up from its "child" regions as probabilities of the categories it has in memory. Each HTM region learns by identifying and memorizing spatial patterns—combinations of input bits that often occur at the same time. It then identifies temporal sequences of spatial patterns that are likely to occur one after another. == As an evolving model == HTM is the algorithmic component to Jeff Hawkins’ Thousand Brains Theory of Intelligence. So new findings on the neocortex are progressively incorporated into the HTM model, which changes over time in response. The new findings do not necessarily invalidate the previous parts of the model, so ideas from one generation are not necessarily excluded in its successive one. Because of the evolving nature of the theory, there have been several generations of HTM algorithms, which are briefly described below. === First generation: zeta 1 === The first generation of HTM algorithms is sometimes referred to as zeta 1. ==== Training ==== During training, a node (or region) receives a temporal sequence of spatial patterns as its input. The learning process consists of two stages: The spatial pooling identifies (in the input) frequently observed patterns and memorise them as "coincidences". Patterns that are significantly similar to each other are treated as the same coincidence. A large number of possible input patterns are reduced to a manageable number of known coincidences. The temporal pooling partitions coincidences that are likely to follow each other in the training sequence into temporal groups. Each group of patterns represents a "cause" of the input pattern (or "name" in On Intelligence). The concepts of spatial pooling and temporal pooling are still quite important in the current HTM algorithms. Temporal pooling is not yet well understood, and
|
{
"page_id": 11273721,
"source": null,
"title": "Hierarchical temporal memory"
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its meaning has changed over time (as the HTM algorithms evolved). ==== Inference ==== During inference, the node calculates the set of probabilities that a pattern belongs to each known coincidence. Then it calculates the probabilities that the input represents each temporal group. The set of probabilities assigned to the groups is called a node's "belief" about the input pattern. (In a simplified implementation, node's belief consists of only one winning group). This belief is the result of the inference that is passed to one or more "parent" nodes in the next higher level of the hierarchy. "Unexpected" patterns to the node do not have a dominant probability of belonging to any one temporal group but have nearly equal probabilities of belonging to several of the groups. If sequences of patterns are similar to the training sequences, then the assigned probabilities to the groups will not change as often as patterns are received. The output of the node will not change as much, and a resolution in time is lost. In a more general scheme, the node's belief can be sent to the input of any node(s) at any level(s), but the connections between the nodes are still fixed. The higher-level node combines this output with the output from other child nodes thus forming its own input pattern. Since resolution in space and time is lost in each node as described above, beliefs formed by higher-level nodes represent an even larger range of space and time. This is meant to reflect the organisation of the physical world as it is perceived by the human brain. Larger concepts (e.g. causes, actions, and objects) are perceived to change more slowly and consist of smaller concepts that change more quickly. Jeff Hawkins postulates that brains evolved this type of hierarchy to match, predict,
|
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"page_id": 11273721,
"source": null,
"title": "Hierarchical temporal memory"
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and affect the organisation of the external world. More details about the functioning of Zeta 1 HTM can be found in Numenta's old documentation. === Second generation: cortical learning algorithms === The second generation of HTM learning algorithms, often referred to as cortical learning algorithms (CLA), was drastically different from zeta 1. It relies on a data structure called sparse distributed representations (that is, a data structure whose elements are binary, 1 or 0, and whose number of 1 bits is small compared to the number of 0 bits) to represent the brain activity and a more biologically-realistic neuron model (often also referred to as cell, in the context of HTM). There are two core components in this HTM generation: a spatial pooling algorithm, which outputs sparse distributed representations (SDR), and a sequence memory algorithm, which learns to represent and predict complex sequences. In this new generation, the layers and minicolumns of the cerebral cortex are addressed and partially modeled. Each HTM layer (not to be confused with an HTM level of an HTM hierarchy, as described above) consists of a number of highly interconnected minicolumns. An HTM layer creates a sparse distributed representation from its input, so that a fixed percentage of minicolumns are active at any one time. A minicolumn is understood as a group of cells that have the same receptive field. Each minicolumn has a number of cells that are able to remember several previous states. A cell can be in one of three states: active, inactive and predictive state. ==== Spatial pooling ==== The receptive field of each minicolumn is a fixed number of inputs that are randomly selected from a much larger number of node inputs. Based on the (specific) input pattern, some minicolumns will be more or less associated with the active input
|
{
"page_id": 11273721,
"source": null,
"title": "Hierarchical temporal memory"
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values. Spatial pooling selects a relatively constant number of the most active minicolumns and inactivates (inhibits) other minicolumns in the vicinity of the active ones. Similar input patterns tend to activate a stable set of minicolumns. The amount of memory used by each layer can be increased to learn more complex spatial patterns or decreased to learn simpler patterns. ===== Active, inactive and predictive cells ===== As mentioned above, a cell (or a neuron) of a minicolumn, at any point in time, can be in an active, inactive or predictive state. Initially, cells are inactive. ====== How do cells become active? ====== If one or more cells in the active minicolumn are in the predictive state (see below), they will be the only cells to become active in the current time step. If none of the cells in the active minicolumn are in the predictive state (which happens during the initial time step or when the activation of this minicolumn was not expected), all cells are made active. ====== How do cells become predictive? ====== When a cell becomes active, it gradually forms connections to nearby cells that tend to be active during several previous time steps. Thus a cell learns to recognize a known sequence by checking whether the connected cells are active. If a large number of connected cells are active, this cell switches to the predictive state in anticipation of one of the few next inputs of the sequence. ===== The output of a minicolumn ===== The output of a layer includes minicolumns in both active and predictive states. Thus minicolumns are active over long periods of time, which leads to greater temporal stability seen by the parent layer. ==== Inference and online learning ==== Cortical learning algorithms are able to learn continuously from each new input
|
{
"page_id": 11273721,
"source": null,
"title": "Hierarchical temporal memory"
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pattern, therefore no separate inference mode is necessary. During inference, HTM tries to match the stream of inputs to fragments of previously learned sequences. This allows each HTM layer to be constantly predicting the likely continuation of the recognized sequences. The index of the predicted sequence is the output of the layer. Since predictions tend to change less frequently than the input patterns, this leads to increasing temporal stability of the output in higher hierarchy levels. Prediction also helps to fill in missing patterns in the sequence and to interpret ambiguous data by biasing the system to infer what it predicted. ==== Applications of the CLAs ==== Cortical learning algorithms are currently being offered as commercial SaaS by Numenta (such as Grok). ==== The validity of the CLAs ==== The following question was posed to Jeff Hawkins in September 2011 with regard to cortical learning algorithms: "How do you know if the changes you are making to the model are good or not?" To which Jeff's response was "There are two categories for the answer: one is to look at neuroscience, and the other is methods for machine intelligence. In the neuroscience realm, there are many predictions that we can make, and those can be tested. If our theories explain a vast array of neuroscience observations then it tells us that we’re on the right track. In the machine learning world, they don’t care about that, only how well it works on practical problems. In our case that remains to be seen. To the extent you can solve a problem that no one was able to solve before, people will take notice." === Third generation: sensorimotor inference === The third generation builds on the second generation and adds in a theory of sensorimotor inference in the neocortex. This theory proposes
|
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"page_id": 11273721,
"source": null,
"title": "Hierarchical temporal memory"
}
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that cortical columns at every level of the hierarchy can learn complete models of objects over time and that features are learned at specific locations on the objects. The theory was expanded in 2018 and referred to as the Thousand Brains Theory. == Comparison of neuron models == == Comparing HTM and neocortex == HTM attempts to implement the functionality that is characteristic of a hierarchically related group of cortical regions in the neocortex. A region of the neocortex corresponds to one or more levels in the HTM hierarchy, while the hippocampus is remotely similar to the highest HTM level. A single HTM node may represent a group of cortical columns within a certain region. Although it is primarily a functional model, several attempts have been made to relate the algorithms of the HTM with the structure of neuronal connections in the layers of neocortex. The neocortex is organized in vertical columns of 6 horizontal layers. The 6 layers of cells in the neocortex should not be confused with levels in an HTM hierarchy. HTM nodes attempt to model a portion of cortical columns (80 to 100 neurons) with approximately 20 HTM "cells" per column. HTMs model only layers 2 and 3 to detect spatial and temporal features of the input with 1 cell per column in layer 2 for spatial "pooling", and 1 to 2 dozen per column in layer 3 for temporal pooling. A key to HTMs and the cortex's is their ability to deal with noise and variation in the input which is a result of using a "sparse distributive representation" where only about 2% of the columns are active at any given time. An HTM attempts to model a portion of the cortex's learning and plasticity as described above. Differences between HTMs and neurons include: strictly
|
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"page_id": 11273721,
"source": null,
"title": "Hierarchical temporal memory"
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binary signals and synapses no direct inhibition of synapses or dendrites (but simulated indirectly) currently only models layers 2/3 and 4 (no 5 or 6) no "motor" control (layer 5) no feed-back between regions (layer 6 of high to layer 1 of low) == Sparse distributed representations == Integrating memory component with neural networks has a long history dating back to early research in distributed representations and self-organizing maps. For example, in sparse distributed memory (SDM), the patterns encoded by neural networks are used as memory addresses for content-addressable memory, with "neurons" essentially serving as address encoders and decoders. Computers store information in dense representations such as a 32-bit word, where all combinations of 1s and 0s are possible. By contrast, brains use sparse distributed representations (SDRs). The human neocortex has roughly 16 billion neurons, but at any given time only a small percent are active. The activities of neurons are like bits in a computer, and so the representation is sparse. Similar to SDM developed by NASA in the 80s and vector space models used in Latent semantic analysis, HTM uses sparse distributed representations. The SDRs used in HTM are binary representations of data consisting of many bits with a small percentage of the bits active (1s); a typical implementation might have 2048 columns and 64K artificial neurons where as few as 40 might be active at once. Although it may seem less efficient for the majority of bits to go "unused" in any given representation, SDRs have two major advantages over traditional dense representations. First, SDRs are tolerant of corruption and ambiguity due to the meaning of the representation being shared (distributed) across a small percentage (sparse) of active bits. In a dense representation, flipping a single bit completely changes the meaning, while in an SDR a single
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{
"page_id": 11273721,
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"title": "Hierarchical temporal memory"
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bit may not affect the overall meaning much. This leads to the second advantage of SDRs: because the meaning of a representation is distributed across all active bits, the similarity between two representations can be used as a measure of semantic similarity in the objects they represent. That is, if two vectors in an SDR have 1s in the same position, then they are semantically similar in that attribute. The bits in SDRs have semantic meaning, and that meaning is distributed across the bits. The semantic folding theory builds on these SDR properties to propose a new model for language semantics, where words are encoded into word-SDRs and the similarity between terms, sentences, and texts can be calculated with simple distance measures. == Similarity to other models == === Bayesian networks === Likened to a Bayesian network, an HTM comprises a collection of nodes that are arranged in a tree-shaped hierarchy. Each node in the hierarchy discovers an array of causes in the input patterns and temporal sequences it receives. A Bayesian belief revision algorithm is used to propagate feed-forward and feedback beliefs from child to parent nodes and vice versa. However, the analogy to Bayesian networks is limited, because HTMs can be self-trained (such that each node has an unambiguous family relationship), cope with time-sensitive data, and grant mechanisms for covert attention. A theory of hierarchical cortical computation based on Bayesian belief propagation was proposed earlier by Tai Sing Lee and David Mumford. While HTM is mostly consistent with these ideas, it adds details about handling invariant representations in the visual cortex. === Neural networks === Like any system that models details of the neocortex, HTM can be viewed as an artificial neural network. The tree-shaped hierarchy commonly used in HTMs resembles the usual topology of traditional neural networks.
|
{
"page_id": 11273721,
"source": null,
"title": "Hierarchical temporal memory"
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HTMs attempt to model cortical columns (80 to 100 neurons) and their interactions with fewer HTM "neurons". The goal of current HTMs is to capture as much of the functions of neurons and the network (as they are currently understood) within the capability of typical computers and in areas that can be made readily useful such as image processing. For example, feedback from higher levels and motor control is not attempted because it is not yet understood how to incorporate them and binary instead of variable synapses are used because they were determined to be sufficient in the current HTM capabilities. LAMINART and similar neural networks researched by Stephen Grossberg attempt to model both the infrastructure of the cortex and the behavior of neurons in a temporal framework to explain neurophysiological and psychophysical data. However, these networks are, at present, too complex for realistic application. HTM is also related to work by Tomaso Poggio, including an approach for modeling the ventral stream of the visual cortex known as HMAX. Similarities of HTM to various AI ideas are described in the December 2005 issue of the Artificial Intelligence journal. === Neocognitron === Neocognitron, a hierarchical multilayered neural network proposed by Professor Kunihiko Fukushima in 1987, is one of the first deep learning neural network models. == See also == Artificial consciousness Artificial general intelligence Belief revision Cognitive architecture Convolutional neural network List of artificial intelligence projects Memory-prediction framework Multiple trace theory Neural history compressor Neural Turing machine === Related models === Hierarchical hidden Markov model == References == == Further reading == Ahmad, Subutai; Hawkins, Jeff (25 March 2015). "Properties of Sparse Distributed Representations and their Application to Hierarchical Temporal Memory". arXiv:1503.07469 [q-bio.NC]. Hawkins, Jeff (April 2007). "Learn like a Human". IEEE Spectrum. Maltoni, Davide (April 13, 2011). "Pattern Recognition by
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{
"page_id": 11273721,
"source": null,
"title": "Hierarchical temporal memory"
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Hierarchical Temporal Memory" (PDF). DEIS Technical Report. Italy: University of Bologna. Ratliff, Evan (March 2007). "The Thinking Machine". Wired. == External links == HTM at Numenta HTM Basics with Rahul (Numenta), talk about the cortical learning algorithm (CLA) used by the HTM model on YouTube
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{
"page_id": 11273721,
"source": null,
"title": "Hierarchical temporal memory"
}
|
β-Hydroxybutyryl-CoA (or 3-hydroxybutyryl-coenzyme A) is an intermediate in the fermentation of butyric acid, and in the metabolism of lysine and tryptophan. The L-3-hydroxybutyl-CoA (or (S)-3-hydroxybutanoyl-CoA) enantiomer is also the second to last intermediate in beta oxidation of even-numbered, straight chain, and saturated fatty acids. == See also == Crotonyl-coenzyme A Acetoacetyl CoA Beta-hydroxybutyryl-CoA dehydrogenase 3-hydroxybutyryl-CoA dehydratase == References ==
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{
"page_id": 11470331,
"source": null,
"title": "Β-Hydroxybutyryl-CoA"
}
|
The anorectal canal is an embryonic structure in placental mammals that develops from the posterior portion of the cloaca, after it is divided by the urorectal septum in the 6th week of embryonic development. The anterior portion becomes the urogenital sinus. The anorectal canal develops into the rectum and the anal canal. == References ==
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{
"page_id": 52495867,
"source": null,
"title": "Anorectal canal"
}
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The Facial Images National Database (FIND) was a project managed by the United Kingdom's National Policing Improvement Agency. The database was a collection of mugshots both from still and from video image sources. It was also designed to keep track of scars, tattoos, and similar markings on persons within the database to increase efficiency in identification. It was intended that FIND would provide national access to images of individuals who have been arrested for a criminal offence, linking the image with the criminal data held on the Police National Computer. The pilot went live on 6 November 2006, with Lancashire, West Yorkshire and Merseyside contributing and viewing images. Greater Manchester, North Wales, Devon and Cornwall, Thames Valley, British Transport Police (BTP) North Eastern Region, as well as one of the Metropolitan Police specialist units and eBorders had read only access to the system. The forward plan for FIND included the addition of facial recognition software (much like the United States' FERET database) to the system. Due to budget pressures, the project was cancelled in early 2008, but this decision was under review in October 2008. == References ==
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{
"page_id": 10225149,
"source": null,
"title": "Facial Images National Database"
}
|
Polar Research is a biannual peer-reviewed scientific journal covering natural and social scientific research on the polar regions. It is published by the Norwegian Polar Institute. It covers a wide range of fields from biology to oceanography, including socio-economic and management topics. According to the Journal Citation Reports, the journal has a 2014 impact factor of 1.141. == References == == External links == Official website
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{
"page_id": 10421755,
"source": null,
"title": "Polar Research"
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Organic matter, organic material or natural organic matter is the large source of carbon-based compounds found within natural and engineered, terrestrial, and aquatic environments. It is matter composed of organic compounds that have come from the feces and remains of organisms such as plants and animals. Organic molecules can also be made by chemical reactions that do not involve life. Basic structures are created from cellulose, tannin, cutin, and lignin, along with other various proteins, lipids, and carbohydrates. Organic matter is very important in the movement of nutrients in the environment and plays a role in water retention on the surface of the planet. == Formation == Living organisms are composed of organic compounds. In life, they secrete or excrete organic material into their environment, shed body parts such as leaves and roots and after organisms die, their bodies are broken down by bacterial and fungal action. Larger molecules of organic matter can be formed from the polymerization of different parts of already broken down matter. The composition of natural organic matter depends on its origin, transformation mode, age, and existing environment, thus its bio-physicochemical functions and properties vary with different environments. == Natural ecosystem functions == Organic matter is common throughout the ecosystem and is cycled through decomposition processes by soil microbial communities that are crucial for nutrient availability. After degrading and reacting, it can move into soil and mainstream water via waterflow. Organic matter provides nutrition to living organisms. Organic matter acts as a buffer in aqueous solutions to maintain a neutral pH in the environment. The buffer acting component has been proposed to be relevant for neutralizing acid rain. == Source cycle == Some organic matter not already in the soil comes from groundwater. When the groundwater saturates the soil or sediment around it, organic matter can
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{
"page_id": 1246718,
"source": null,
"title": "Organic matter"
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freely move between the phases. Groundwater has its own sources of natural organic matter including: organic matter deposits, such as kerogen and coal. soil and sediment organic matter. organic matter infiltrating into the subsurface from rivers, lakes, and marine systems." Organisms decompose into organic matter, which is then transported and recycled. Not all biomass migrates, some is rather stationary, turning only over the course of millions of years. == Soil organic matter == The organic matter in soil derives from plants, animals and microorganisms. In a forest, for example, leaf litter and woody materials fall to the forest floor. This is sometimes referred to as organic material. When it decays to the point in which it is no longer recognizable, it is called soil organic matter. When the organic matter has broken down into a stable substance that resists further decomposition it is called humus. Thus soil organic matter comprises all of the organic matter in the soil exclusive of the material that has not decayed. An important property of soil organic matter is that it improves the capacity of a soil to hold water and nutrients, and allows their slow release, thereby improving the conditions for plant growth. Another advantage of humus is that it helps the soil to stick together which allows nematodes, or microscopic bacteria, to easily decay the nutrients in the soil. There are several ways to quickly increase the amount of humus. Combining compost, plant or animal materials/waste, or green manure with soil will increase the amount of humus in the soil. Compost: decomposed organic material. Plant and animal material and waste: dead plants or plant waste such as leaves or bush and tree trimmings, or animal manure. Green manure: plants or plant material that is grown for the sole purpose of being incorporated with
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{
"page_id": 1246718,
"source": null,
"title": "Organic matter"
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soil. These three materials supply nematodes and bacteria with nutrients for them to thrive and produce more humus, which will give plants enough nutrients to survive and grow. Soil organic matter is crucial to all ecology and to all agriculture, but it is especially emphasized in organic farming, where it is relied upon especially heavily. === Priming effect === The priming effect is characterized by intense changes in the natural process of soil organic matter (SOM) turnover, resulting from relatively moderate intervention with the soil. The phenomenon is generally caused by either pulsed or continuous changes to inputs of fresh organic matter (FOM). Priming effects usually result in an acceleration of mineralization due to a trigger such as the FOM inputs. The cause of this increase in decomposition has often been attributed to an increase in microbial activity resulting from higher energy and nutrient availability released from the FOM. After the input of FOM, specialized microorganisms are believed to grow quickly and only decompose this newly added organic matter. The turnover rate of SOM in these areas is at least one order of magnitude higher than the bulk soil. Other soil treatments, besides organic matter inputs, which lead to this short-term change in turnover rates, include "input of mineral fertilizer, exudation of organic substances by roots, mere mechanical treatment of soil or its drying and rewetting." Priming effects can be either positive or negative depending on the reaction of the soil with the added substance. A positive priming effect results in the acceleration of mineralization while a negative priming effect results in immobilization, leading to N unavailability. Although most changes have been documented in C and N pools, the priming effect can also be found in phosphorus and sulfur, as well as other nutrients. Löhnis was the first to discover
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{
"page_id": 1246718,
"source": null,
"title": "Organic matter"
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the priming effect phenomenon in 1926 through his studies of green manure decomposition and its effects on legume plants in soil. He noticed that when adding fresh organic residues to the soil, it resulted in intensified mineralization by the humus N. It was not until 1953, though, that the term priming effect was given by Bingeman in his paper titled, The effect of the addition of organic material on the decomposition of an organic soil. Several other terms had been used before priming effect was coined, including priming action, added nitrogen interaction (ANI), extra N and additional N. Despite these early contributions, the concept of the priming effect was widely disregarded until about the 1980s-1990s. The priming effect has been found in many different studies and is regarded as a common occurrence, appearing in most plant soil systems. However, the mechanisms which lead to the priming effect are more complex than originally thought, and still remain generally misunderstood. Although there is a lot of uncertainty surrounding the reason for the priming effect, a few undisputed facts have emerged from the collection of recent research: The priming effect can arise either instantaneously or very shortly (potentially days or weeks) after the addition of a substance is made to the soil. The priming effect is larger in soils that are rich in C and N as compared to those poor in these nutrients. Real priming effects have not been observed in sterile environments. The size of the priming effect increases as the amount of added treatment to the soil increases. Recent findings suggest that the same priming effect mechanisms acting in soil systems may also be present in aquatic environments, which suggests a need for broader considerations of this phenomenon in the future. == Decomposition == One suitable definition of organic matter
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{
"page_id": 1246718,
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"title": "Organic matter"
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is biological material in the process of decaying or decomposing, such as humus. A closer look at the biological material in the process of decaying reveals so-called organic compounds (biological molecules) in the process of breaking up (disintegrating). The main processes by which soil molecules disintegrate are by bacterial or fungal enzymatic catalysis. If bacteria or fungi were not present on Earth, the process of decomposition would have proceeded much slower. Various factors impact the decomposition of organic matter including its chemical properties and other environmental parameters. Metabolic capabilities of the microbial communities play a crucial role on decomposition since they are highly connected with the energy availability and processing. In terrestrial ecosystems the energy status of soil organic matter has been shown to affect microbial substrate preferences. Some organic matter pools may be energetically favorable for the microbial communities resulting in their fast oxidation and decomposition, in comparison with other pools where microbial degraders get less return from the energy they invest. By extension, soil microorganisms preferentially mineralize high-energy organic matter, avoiding decomposing less energetically dense organic matter. == Organic chemistry == Measurements of organic matter generally measure only organic compounds or carbon, and so are only an approximation of the level of once living or decomposed matter. Some definitions of organic matter likewise only consider "organic matter" to refer to only the carbon content or organic compounds and do not consider the origins or decomposition of the matter. In this sense, not all organic compounds are created by living organisms, and living organisms do not only leave behind organic material. A clam's shell, for example, while biotic, does not contain much organic carbon, so it may not be considered organic matter in this sense. Conversely, urea is one of many organic compounds that can be synthesized without any
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{
"page_id": 1246718,
"source": null,
"title": "Organic matter"
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biological activity. Organic matter is heterogeneous and very complex. Generally, organic matter, in terms of weight, is: 45–55% carbon 35–45% oxygen 3–5% hydrogen 1–4% nitrogen The molecular weights of these compounds can vary drastically, depending on if they repolymerize or not, from 200 to 20,000 amu. Up to one-third of the carbon present is in aromatic compounds in which the carbon atoms form usually six-membered rings. These rings are very stable due to resonance stabilization, so they are challenging to break down. The aromatic rings are also susceptible to electrophilic and nucleophilic attacks from other electron-donating or electron-accepting material, which explains the possible polymerization to create larger molecules of organic matter. Some reactions occur with organic matter and other materials in the soil to create compounds never seen before. Unfortunately, it is challenging to characterize these because so little is known about natural organic matter in the first place. Research is currently being done to determine more about these new compounds and how many are being formed. == Aquatic == Aquatic organic matter can be further divided into two components: (1) dissolved organic matter (DOM), measured as colored dissolved organic matter (CDOM) or dissolved organic carbon (DOC), and (2) particulate organic matter (POM). They are typically differentiated by that which can pass through a 0.45 micrometre filter (DOM), and that which cannot (POM). === Detection === Organic matter is important in water and wastewater treatment and recycling, natural aquatic ecosystems, aquaculture, and environmental rehabilitation. It is, therefore, important to have reliable methods of detection and characterisation, for both short- and long-term monitoring. Various analytical detection methods for organic matter have existed for up to decades to describe and characterise organic matter. These include, but are not limited to: total and dissolved organic carbon, mass spectrometry, nuclear magnetic resonance (NMR) spectroscopy,
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{
"page_id": 1246718,
"source": null,
"title": "Organic matter"
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infrared (IR) spectroscopy, UV-Visible spectroscopy, and fluorescence spectroscopy. Each of these methods has its advantages and limitations. === Water purification === The same capability of natural organic matter that helps with water retention in the soil creates problems for current water purification methods. In water, organic matter can still bind to metal ions and minerals. The purification process does not necessarily stop these bound molecules but does not cause harm to any humans, animals, or plants. However, because of the high reactivity of organic matter, by-products that do not contain nutrients can be made. These by-products can induce biofouling, which essentially clogs water filtration systems in water purification facilities, as the by-products are larger than membrane pore sizes. This clogging problem can be treated by chlorine disinfection (chlorination), which can break down residual material that clogs systems. However, chlorination can form disinfection by-products. Water with organic matter can be disinfected with ozone-initiated radical reactions. The ozone (three oxygens) has powerful oxidation characteristics. It can form hydroxyl radicals (OH) when it decomposes, which will react with the organic matter to shut down the problem of biofouling. == Vitalism == The equation of "organic" with living organisms comes from the now-abandoned idea of vitalism, which attributed a special force to life that alone could create organic substances. This idea was first questioned after Friedrich Wöhler artificially synthesized urea in 1828. == See also == Biofact (biology) Biomass Detritus Humus Organic geochemistry Sedimentary organic matter Total organic carbon Compare with: Biological tissue Biomolecule Biotic material Cellular component Organic production == References == == Bibliography == George Aiken (2002). "Organic Matter in Ground Water". United States Geological Survey. Cabaniss, Steve, Greg Madey, Patricia Maurice, Yingping Zhou, Laura Leff, Ola Olapade, Bob Wetzel, Jerry Leenheer, and Bob Wershaw, comps. Stochastic Synthesis of Natural Organic Matter.
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{
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"source": null,
"title": "Organic matter"
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UNM, ND, KSU, UNC, USGS. 22 Apr. 2007. Cho, Min, Hyenmi Chung, and Jeyong Yoon. "Disinfection of Water Containing Natural Organic Matter by Using Ozone-Initiated Radical Reactions." Abstract. Applied and Environmental Microbiology Vol. 69 No.4 (2003): 2284–2291. Fortner, John D., Joseph B. Hughes, Jae-Hong Kim, and Hoon Hyung. "Natural Organic Matter Stabilizes Carbon Nanotubes in the Aqueous Phase." Abstract. Environmental Science & Technology Vol. 41 No. 1 (2007): 179–184. "Researchers Study Role of Natural Organic Matter in Environment." Science Daily 20 Dec. 2006. 22 Apr. 2007 https://www.sciencedaily.com/releases/2006/12/061211221222.htm. Senesi, Nicola, Baoshan Xing, and P.m. Huang. Biophysico-Chemical Processes Involving Natural Nonliving Organic Matter in Environmental Systems. New York: IUPAC, 2006. "Table 1: Surface Area, Volume, and Average Depth of Oceans and Seas." Encyclopædia Britannica. "Topic Snapshot: Natural Organic Material." American Water Works Association Research Foundation. 2007. 22 Apr. 2007 https://web.archive.org/web/20070928102105/http://www.awwarf.org/research/TopicsAndProjects/topicSnapShot.aspx?Topic=Organic. United States of America. United States Geological Survey. Earth's Water Distribution. 10 May 2007. http://ga.water.usgs.gov/edu/waterdistribution.html Water Sheds: Organic Matter. North Carolina State University. 1 May 2007 http://www.water.ncsu.edu/watershedss/info/norganics.html Archived 14 March 2014 at the Wayback Machine.
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"page_id": 1246718,
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Urban evolution refers to the heritable genetic changes of populations in response to urban development and anthropogenic activities in urban areas. Urban evolution can be caused by non-random mating, mutation, genetic drift, gene flow, or evolution by natural selection. In the context of Earth's living history, rapid urbanization is a relatively recent phenomenon, yet biologists have already observed evolutionary change in numerous species compared to their rural counterparts on a relatively short timescale. Strong selection pressures due to urbanization play a big role in this process. Urbanization introduces distinct challenges such as altered microclimates, pollution, habitat fragmentation, and differential resource availability. These changed environmental conditions exert unique selection pressures on their inhabitants, leading to physiological and behavioral adaptations in city-dwelling plant and animal species. However, there is also discussion on whether some of these emerging traits are truly a consequence of genetic adaptation, or examples of phenotypic plasticity. There is also a significant change in species composition between rural and urban ecosystems. Understanding how anthropogenic activity can influence the traits of other living beings can help humans better understand their effect on the environment, particularly as cities continue to grow. Shared aspects of cities worldwide give ample opportunity for scientists to study the specific evolutionary responses in these rapidly changed landscapes independently. How certain organisms adapt to urban environments while others cannot gives a live perspective on rapid evolution. == Urbanization == With urban growth, the urban-rural gradient has seen a large shift in distribution of humans, moving from low density to very high density within the last millennia. This has brought a large change to environments as well as societies. Urbanization transforms natural habitats into completely altered living spaces that sustain large human populations. Increasing congregation of humans accompanies the expansion of infrastructure, industry and housing. Natural vegetation and
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{
"page_id": 65340929,
"source": null,
"title": "Urban evolution"
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soil are mostly replaced or covered by dense grey materials. Urbanized areas continue to expand both in size and number globally; in 2018, the United Nations estimated that 68% of people globally will live in ever-expanding urban areas by 2050. == Urban evolution selective agents == Urbanization intensifies diverse stressors spatiotemporally such that they can act in concert to cause rapid evolutionary consequences such as extinction, maladaptation, or adaptation. Three factors have come to the forefront as the main evolutionary influencers in urban areas: the urban microclimate, pollution, and urban habitat fragmentation. These influence the processes that drive evolution, such as natural and sexual selection, mutation, gene flow and genetic drift. === Urban microclimate === A microclimate is defined as any area where the climate differs from the surrounding area. Modifications of the landscape and other abiotic factors contribute to a changed climate in urban areas. The use of impervious dark surfaces which retain and reflect heat, and human generated heat energy lead to an urban heat island in the center of cities, where the temperature is increased significantly. A large urban microclimate does not only affect temperature, but also rainfall, snowfall, air pressure and wind, the concentration of polluted air, and how long that air remains in the city. These climatological transformations increase selection pressure on species living in urban areas, driving evolutionary changes. Certain species have shown to be adapting to the urban microclimate. For example, a research study focused on urban thermal heterogeneity, which can lead to the formation of Urban heat islands, shows how variations in temperature due to urbanization significantly affects Feral pigeons (Columba livia) causing changes in their metabolic processes and oxidative stress levels. Specifically, pigeons in hotter areas showed elevated oxidative stress, suggesting that urban heat could compromise their health. === Urban pollution
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{
"page_id": 65340929,
"source": null,
"title": "Urban evolution"
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=== Many species have evolved over macroevolutionary timescales by adapting in response to the presence of toxins in the environment of the planet. Human activities, including urbanization, have greatly increased selection pressures due to pollution of the environment, climate change, ocean acidification, and other stressors. Species in urban settings must deal with higher concentrations of contaminants than naturally would occur. There are two main forms of pollution which lead to selective pressures: energy or chemical substances. Energy pollution can come in the form of artificial lighting, sounds, thermal changes, radioactive contamination and electromagnetic waves. Chemical pollution leads to the contamination of the atmosphere, the soil, water and food. All these polluting factors pose direct and indirect challenges to species inhabiting urban areas, altering species’ behavior and/or physiology, which in turn can lead to evolutionary changes. Air pollution and soil pollution have significant physiological impacts on both wildlife and plants. For urban animals, exposure to pollutants often results in respiratory issues, neurological damage, and skin irritations. Over time, animals may adapt to these stressors through changes in their physiological systems, such as increased lung capacity or more efficient detoxification mechanisms to cope with pollutants. However, the severity of these adaptations varies across species, with some developing resilience while others face diminished health. The peppered moth (Biston betularia) is a classic example of industrial melanism, where moth populations adapted to increased soot and pollutants by evolving darker coloration, which allowed them to better blend into the soot-darkened trees during the industrial revolution For plants, long-term exposure to pollutants like ozone can impair vital structures on their leaves, disrupting gas exchange and reducing growth. Some plants adapt by closing their stomata or producing antioxidants to mitigate the damage, while others are less equipped to cope and show signs of decline. Pollution also alters
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{
"page_id": 65340929,
"source": null,
"title": "Urban evolution"
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|
soil chemistry, affecting nutrient availability and further stressing plant growth. These physiological changes to both flora and fauna influence urban ecosystems, determining which species can survive and reproduce in polluted environments. A study on Great tits (Parus major) also found that air pollutants, in combination with local tree composition and temperature, affect their nestling physiology. Specifically, antioxidant capacity and fatty acid composition in these birds were influenced by the surrounding environmental conditions, including pollution levels. Water pollution is another major concern, to which species living in aquatic habitats, such as fish, can evolve resistance to pollutants. The Atlantic killifish (Fundulus heteroclitus) has evolved to resist toxic pollutants like polychlorinated biphenyls (PCBs), commonly found in polluted urban waters. This resistance is thought to be the result of mutations that allow the fish to tolerate high levels of chemicals that would otherwise be lethal. Noise pollution, often resulting from traffic, construction, and industrial activities, is another form of energy pollution that significantly affects urban species. Prolonged exposure to high noise levels can interfere with animals' communication, navigation, feeding behaviors, and stress response mechanisms. In particular, birds are sensitive to noise pollution, as it disrupts their ability to communicate using signals, such as calls from potential mates or warnings of predators. This disruption can lead to changes in behavior, reproduction, and survival. === Urban habitat fragmentation === The fragmentation of previously intact natural habitats into smaller pockets which can still sustain organisms leads to selection and adaptation of species. These new urban patches, often called urban green spaces, come in all shapes and sizes ranging from parks, gardens, plants on balconies, to the breaks in pavement and ledges on buildings. The diversity in habitats leads to adaptation of local organisms to their own niche. And contrary to popular belief, there is higher biodiversity
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{
"page_id": 65340929,
"source": null,
"title": "Urban evolution"
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in urban areas than previously believed. This is due to the numerous microhabitats. These remnants of wild vegetation or artificially created habitats with often exotic plants and animals all support different kinds of species, which leads to pockets of diversity inside cities. With habitat fragmentation also comes genetic fragmentation; genetic drift and inbreeding within small isolated populations results in low genetic variation in the gene pool. Low genetic variation is generally seen as bad for chances of survival. This is why probably some species aren’t able to sustain themselves in the fragmented environments of urban areas. Urban environments create new selection pressures for species, leading to rapid adaptations. Species may experience changes in behavior, morphology, or physiology due to altered resources, human-induced pollution, and fragmented habitats. For instance, city-dwelling animals like birds may evolve shorter wings to better navigate between buildings, or insects might develop resistance to pesticides commonly used in urban settings. Urban heat islands are another factor contributing to urban evolution. Cities tend to be warmer than surrounding rural areas, causing species to adapt to higher temperatures. some insects have been observed to become more heat-tolerant over time. Pollution and light exposure also play a significant role. Many species must adapt to high levels of pollution in cities or artificial light that disrupts their natural behaviors. example birds in cities often start singing earlier in the morning due to the prevalence of artificial lighting, which can affect their mating patterns. Fragmentation of habitats has led to the creation of micro-habitats within cities, which act as isolated evolutionary zones. Species in these fragmented areas often experience unique evolutionary pressures, leading to genetic drift and divergence from rural populations. In one study, researchers examined how early life experiences, particularly adverse conditions, influence behavior in European starlings (Sturnus vulgaris). The study
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{
"page_id": 65340929,
"source": null,
"title": "Urban evolution"
}
|
specifically explored how early life adversity—such as nutritional stress or challenging environmental conditions—may trigger adaptive behaviors in the starlings, including increased foraging and actively seeking out information later in life. The birds were found to be more efficient at locating food and gathering relevant information from their surroundings, suggesting that early adversity may encourage greater exploration and resource acquisition strategies as an adaptive response to uncertainty. Their findings imply that animals experiencing early adversity in fragmented environments may develop enhanced abilities to locate and exploit scattered resources. This may help explain why some species, such as starlings, are able to persist and even thrive in urban settings despite habitat degradation. Fragmented urban habitats tend to be more unpredictable, with food sources often patchy and habitats divided. In such environments, animals that have faced early adversity may become more adept at navigating these challenges. Just as the starlings in the study displayed increased cognitive flexibility in their foraging and information-gathering behaviors, animals in urban ecosystems may also adopt similar strategies to cope with the effects of habitat fragmentation. Cognitive flexibility enables animals to adapt to fluctuating conditions, such as changes in food availability or alterations to shelter and nesting sites, which are common in urbanized landscapes. === Resource Availability === Urbanization often leads to changes in the availability and distribution of food, water, and shelter, prompting behavioral, physiological, and morphological adaptations in species that can exploit new resource environments. Resource availability also acts as a selective force in urban evolution, influencing the survival and reproductive success of species living in cities. Urban areas offer a distinctive array of resources, including food sources like garbage, human waste, and crops, often differing in quantity and quality from those found in natural habitats. These variations can create evolutionary pressures on local populations. This can
|
{
"page_id": 65340929,
"source": null,
"title": "Urban evolution"
}
|
be seen in the New York City white-footed mice (Peromyscus leucopus) as its tooth rows adapt a structure that can chew on the foods and resources available. Urban Raccoons (Procyon lotor) have also adapted to urban environments by exploiting food sources like garbage, pet food, and bird feeders. These animals have developed more adaptable foraging behaviors and are known to thrive in cities due to the abundance of easily accessible food. A recent study reveals the urban raccoons ability to solve foraging challenges, demonstrating innovative problem-solving skills. The research showed that raccoons use puzzle boxes with different difficulty levels to obtain food, with some raccoons learning to solve increasingly complex tasks. The study found that younger raccoons, who were more willing to take risks, were more successful at solving the puzzles. This study shows how raccoons adapt to urban environments through learning and behavioral flexibility, and suggests that finding ways to find resources drive these cognitive adaptations. == Examples of Urban Evolution == === Adaptation and Natural Selection === The urban environment imposes different selection pressures than the typical natural setting. These stressors elicit phenotypic changes in populations of organisms which may be due to phenotypic plasticity—the ability of individual organisms to express different phenotypes from the same genotype as a result of exposure to different environmental conditions—or actual genetic changes. Mutations are genotypic changes that may result in changes in phenotype, altering the observable traits of an organism and thus potentially its interactions or relationship with its environment. Mutations produce genetic variation which can be acted upon by evolutionary processes such as natural selection. For evolution to occur through natural selection, there must be genetic variation within a population, differential survival as a consequence of the genetic variation, and selective pressure from the environment towards particular desirable or undesirable
|
{
"page_id": 65340929,
"source": null,
"title": "Urban evolution"
}
|
traits. Thus, in considering the examples of urban evolution, observed phenotypic divergences or differences in response to urbanization have to be genetically based and increase fitness in that particular environment to be tagged as evolution and adaptation, respectively. Hence, it will be appropriate to consider neutral, or non-adaptive, and adaptive urban evolution, with the later needing to be sufficiently proven. Although there is widespread agreement that adaptation is occurring in urban populations, there are few completely proven examples of evolution – almost all are cases of selection, reasoned speculation connecting to adaptive benefit, but insufficient evidence of genetically based, actual adaptive phenotype. At this time the following examples are sufficiently demonstrated: Multiple Atlantic killifish (Fundulus heteroclitus) populations have independently evolved pollution-resistant characteristics such as whole-body chemical tolerance and aryl hydrocarbon receptors. Bioaccumulating polychlorinated biphenyl (PCB) chemicals are often disposed of into the water of urban estuary habitats and cause developmental defects in many vertebrates. The killifish evolved resistance to model pollutant PCB-126. Quantitative trait locus mapping indicated that genes responsible for aryl hydrocarbon receptor signaling may potentially be responsible for this chemical resistance, with resistant F.heteroclitus populations exhibiting a desensitized signaling pathway. This genetic change was also determined to be heritable. Killifish were consequently found to be 8,000 times more resistant to environmental pollutants than other species of fish. The peppered moth is an example of industrial melanism. These moths changed color from light to dark due to anthropogenic air pollution during the industrial revolution. With soot release as a consequence of coal burning, the urban trees that the moths would reside on became darker. Additionally, the lichens died as well, leaving little cover for the moths to camouflage. The black melanism phenotype frequency saw a rise during the time of heavy air pollution and a fall after cleaner air
|
{
"page_id": 65340929,
"source": null,
"title": "Urban evolution"
}
|
became more normal again in cities. Acorn ants (Temnothorax curvispinosus) adapt to tolerate increased urban temperatures. As a consequence of abundant heat-retaining manmade materials such as concrete and steel in urban environments, cities tend to exhibit a heat island effect. Compared to rural populations, urban populations of T.curvispinosus were more tolerating of a rapid rate of temperature increase, and higher temperatures overall. The water flea (Daphnia magna) has adapted to urban settings and displays the ability to better tolerate heat. Likely as a consequence of the urban heat island and thus warmer pond water, water fleas have also evolved even more towards a "fast living" pace of life - they mature faster, reproduce quicker, produce more offspring, are smaller, and have a higher maximum population growth rate than rural populations of the same species. Ragweed (Ambrosia artemisiifolia) has very divergent flowering phenology. Urban A.artemisiifolia also exhibit a greater variance in terms of plant height than rural members of the species. Holy hawksbeard (Crepis sancta) develops larger size, later flowering, delayed senescence, higher photosynthetic capacity, higher water use efficiency, and higher leaf nitrogen in urban areas. Other claimed examples of adaptation indicative of potential urban evolution include: Genome sequencing of New York City brown rats (Rattus norvegicus) has revealed significant selective sweeps at loci for metabolic, nervous, locomotive, and diet-related genes. These sweeps were also unique to the New York City population. This indicates not only that populations are undergoing unique genetic changes in urban environments, but also that these mutations are beneficial in their environment, and increasing in frequency. This consequently demonstrates the processes of adaptation and urban evolution. Humans often attempt to curb rodent populations through the use of rodenticides. Anticoagulant-class rodenticides alter the rate of blood coagulation through its effects on the vitamin K reductase (VKOR) enzyme. Sequencing
|
{
"page_id": 65340929,
"source": null,
"title": "Urban evolution"
}
|
of the associated VKOR gene, VKORC1, in rat and mice species indicated mutations in said gene. There was also observed resistance to the rodenticides in these mice. Presence of mutation and consequential resistance to these rodenticides indicate genetic change and resulting adaptation to the anthropogenic chemical. The guppy (Poecilia reticulata) in urban environments exhibited reduced color expression and lower sperm load. In these fish, expression of bright colors is typically used to attract mates and is therefore typically vulnerable to sexual selection. Such “attractive” traits and traits that otherwise maximize fertilization potential are favored by sexual selection. However, both of these sexually favorable traits were less expressed in urban populations. This was hypothesized to be a consequence of urban pollution. Pollution alters the underwater visibility of the bright colors, making them costly to exhibit without the benefit of being noticeable to a mate. The polluted urban waters are harsher and therefore individuals may need to prioritize investment in traits for survival rather than reproduction, potentially resulting in lower sperm load. Sexual selection was weaker in urban streams than in rural streams. New York City white-footed mice (Peromyscus leucopus) had shorter upper and lower tooth rows relative to their rural counterparts. Longer tooth rows are advantageous for eating low-quality foods, which typically require more chewing. Urban mice having shorter tooth rows consequently implies that they consume softer food or food of higher quality due to differential availability of nutritional food in urban and rural environments. House finches (Carpodacus mexicanus) in urban environments showed divergence from their rural counterparts in terms of bill morphology and bite force. House finches in urban areas rely on different food sources than those in rural desert areas - urban house finches eat more sunflower seeds from bird feeders, which are larger and harder than the non-anthropogenic
|
{
"page_id": 65340929,
"source": null,
"title": "Urban evolution"
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|
seeds found naturally occurring in the native desert habitat that rural finches continue to reside in. Thus, urban house finches have evolved longer and wider beaks compared to the shorter beaks of desert house finches. It was discovered that the urban finches express bone morphogenetic proteins at larger doses and earlier on in their development, a likely biochemical cause to their larger beaks. The common blackbird (Turdus merula) may be the first example of actual speciation by urban evolution, due to the urban heat island and food abundance the urban blackbird has become non-migratory in urban areas. The birds also sing higher and at different times, and they breed earlier than their rural counterparts which leads to sexual selection and a separated gene pool. Natural behavioral differences have also formed between urban and rural birds. Urban Anole lizards (Anolis) have evolved longer limbs and more lamellae compared with anolis lizards from forest habitats. This because the lizards can navigate the artificial building materials used in cities better. The urban Hawksbeard plant (Crepis) has evolved a higher percentage of heavier nondispersing seeds compared to rural hawksbeard plants, because habitat fragmentation leads to a lower chance of dispersing seeds to settle. White clover (Trifolium repens) has repeatedly adapted to urban environments on a global scale due to genetic changes in a heritable antiherbivore defense trait (hydryogen cyanide) in response to urban-rural changes in drought stress, vegetation and winter temperatures. The London Underground mosquito (Culex pipiens f. molestus) has undergone reproductive isolation in populations at higher latitudes, including the London Underground railway populations, where attempted hybridizations between molestus and the surface-living Culex pipiens pipiens are not viable in contrast to populations of pipiens and molestus in cities at lower latitudes where hybrids are found naturally. Urban Peromyscus leucopus, microtus pennylvanicus, Eptesicus fuscus, and
|
{
"page_id": 65340929,
"source": null,
"title": "Urban evolution"
}
|
Sorex cinereus all showed a statistically significant larger cranial capacity relative to rural members of the same respective species. An increased cranial size may be associated with development of novel behaviors to cope with the new stresses of the urban environment. However, it is not entirely certain whether this is an example of true evolution or behavioral plasticity - no sustained cranial capacity increase over time was observed. In fact, the cranial size decreased over time in urban populations. It is important to note that while these examples show genetic change and/or adaptation, they are not completely proven to be examples of evolution, whether due to insufficient evidence of heritability, or being a possible result of something else, such as plasticity, or because of insufficient evidence. Some interesting cases of possible adaptation which remain insufficiently proven are: Bobcats (Lynx rufus) in Los Angeles, CA, USA were selected for immune genetics loci by an epidemic of mange there, however Serieys et al. 2014 does not provide proof of resistant phenotype. Water dragon lizards (Intellagama lesueurii) in Brisbane, Australia do show divergence. Littleford-Colquhoun et al. 2017 find divergence of both morphology and genetics, but remind readers that they have not demonstrated that this is adaptive. In one case selection is widely expected to occur and yet is not found: Coyotes (Canis latrans) in New York City, USA show no immune selection in the work of DeCandia et al. 2019. == Genetic Drift and Gene Flow == Evolution is not strictly the result of natural selection and beneficial adaptation. Evolution may also result from genetic drift due to population bottlenecks. In a population bottleneck, the population size is reduced randomly and significantly; there is no selection and therefore random alleles may be kept whereas others decreased in the population. The bottlenecked population may
|
{
"page_id": 65340929,
"source": null,
"title": "Urban evolution"
}
|
thus show different allele frequencies and phenotypic frequencies than the original population. A population bottleneck may arise from anthropogenic factors common in urban areas, such as habitat fragmentation from abundant infrastructure. Habitat fragmentation may also lead to reduction in gene flow, further isolating populations of the same species from one another. Cities have been found to both increase genetic drift and decrease gene flow. In an overview of 167 different studies, over 90% indicated a correlation between genetic drift, gene flow, and urbanization. This genetic isolation of urban populations can result in divergence from the original and rural populations of the same species, leading to nonadaptive evolution. An example of nonadaptive change related to genetic drift and gene flow is the burrowing owl (Athene cunicularia) in urban Argentina. Each of the three studied cities was independently colonized by a unique population of owls, and there was minimal gene flow between urban owls and those of nearby rural populations. Moreover, there was no gene flow between the owl populations of the three different cities. Gene sequencing revealed that there was less variation present in single nucleotide polymorphisms (SNPS) in urban populations relative to rural populations, and the different cities had different rare SNPS. The different urban populations were genetically isolated from each other and exhibited genetic divergence when compared to both other urban populations and rural populations. This was also seen in New York City white-footed mice. Urbanization limited their habitat to predominantly city parks, and the independent city park populations were genetically discrete. == Phenotypic Plasticity == When species show apparent adaptation to an urban or other environment, that adaptation is not necessarily a consequence of evolution, or even genetic change. One genotype may be able to produce various phenotypes adaptive to different environmental conditions. In other words, divergent observable
|
{
"page_id": 65340929,
"source": null,
"title": "Urban evolution"
}
|
traits may arise from one set of genes and therefore, genetic change did not occur to produce these traits, and evolution did not occur. However, genetic evolution, phenotypic plasticity, and even other factors such as learning may all contribute in varying degrees to form the apparent phenotypic difference. For example, when 3,768 bird species were assessed in multiple urban environments, it was determined that urban species are generally smaller in size, occupy less specific niches, live longer, have more eggs, and are less aggressive in defending territory. While there are statistically significant differences between the urban and rural birds of various species, this cannot be assumed to be purely genetic, especially since this study did not explore the potential genetic background of the phenotypic variations. Another study examines how urbanization influences plant responses to herbivory, using the common dandelion (Taraxacum officinale) along an urbanization gradient. Plants from different urban, suburban, and rural areas were raised under similar conditions and exposed to herbivory (locust grazing). While all plants increased their resistance to herbivores with repeated exposure, urban plants showed reduced early seed production compared to rural and suburban plants. This study suggests that urbanization affects plant defenses and fitness, with urban populations showing different reaction norms in response to herbivory. A more specific example of phenotypic plasticity is behavioral plasticity, which is often observed in urban areas. In the dark-eyed junco (Junco hyemalis), it was determined that phenotypic plasticity was in part responsible for the differential nesting behaviors of urban dwellers. In order to adapt to the noise pollution abundantly present in urbanized areas, city-dwelling dark-eyed junco birds utilized higher frequency songs to communicate with one another relative to rural birds. It was determined that even in experimental conditions the birds from urbanized areas continued to sing at louder frequencies even
|
{
"page_id": 65340929,
"source": null,
"title": "Urban evolution"
}
|
without noise present. While this could have been indicative of a genetic basis and thus evolution, it was also observed that prior to capture, birds would exhibit sharing of song with one another. The higher frequency song in the captured experimental population could have therefore been a result of learning from other birds. However, the birds also show significant genetic variation in multiple traits related to reproductive and endocrine systems. This example shows demonstrates the complex interrelation between genetic change, phenotypic and behavioral plasticity, adaptation, and learning in the formation of a novel or changed phenotype. == Species Composition == As a region urbanizes the species composition generally undergoes change. The new conditions associated with urban infrastructure, air and noise pollution, habitat fragmentation, differential food availability, humans and cars, and so on may be difficult for certain species to adapt to. In birds, for instance, rare species generally disappear in urban areas, with species that are more adaptable tend to dominate. This results in homogenization. In plants, urbanization reduces species richness and introduces homogeny. It also decreases the amount of pollinators, which may increase reproductive difficulty. == References ==
|
{
"page_id": 65340929,
"source": null,
"title": "Urban evolution"
}
|
The European Union System for the Evaluation of Substances (EUSES) is a mathematical model for calculation of Predicted Environmental Concentrations (PEC) and human exposure. It may be used in Chemical Safety Assessments (CSA) and be cited in Chemical Safety Reports (CSR). EUSES is provided free of charge from the European Chemical Bureau website and has the form of a Microsoft Excel sheet. == External links == Ex-ECB EUSES 2.1.1 User Manual
|
{
"page_id": 25626113,
"source": null,
"title": "European Union System for the Evaluation of Substances"
}
|
Mutage MEW-tij is a wine making technique for making sweet wines. == Typical mechanism == The typical process involves the addition of alcohol to the must so that the fermentation process is prematurely stopped. Most yeasts die when the alcohol content in their environment is raised to approximately 13–15%. By stopping the fermentation of sugars, a sweet taste of the wine is achieved. This technique is used to make port wine and other sweet wines with high alcohol content. == Types of mutage == Two types of mutage are sometimes distinguished. A distinction being made between adding alcohol to the must before fermentation and adding during fermentation. Mutage sur grain: Where the mutage takes place during maceration on the skins. This is described as mutage on the cap of the marc and produces vin de liqueur Mutage after the traditional maceration and pressing producing vin doux naturel. == Noted wines referred to as having been made by mutage == Reds Banyuls Maury Rivesaltes Whites Muscat de Beaumes de Venise Muscat de Rivesaltes Muscat de Frontignan == Other techniques == Other techniques for making sweet wines exist such as vendange tardive, the noble rot, various filtration techniques or early heating of the must, and adding sweet musts after fermentation. == See also == Fortified wine Vin de liqueur Vin doux naturel == References ==
|
{
"page_id": 28837379,
"source": null,
"title": "Mutage"
}
|
Armor or armour in animals is a rigid cuticle or exoskeleton that provides exterior protection against attack by predators, formed as part of the body (rather than the behavioural utilization of external objects for protection) usually through the thickening and hardening of superficial tissues, outgrowths or skin secretions. It is often found in prey species that are too slow or clumsy to outrun predators, or those that would stand their ground and fight, thus needing to protect vital organs against claw, talon or bite injuries. == Composition == Armoured structures are usually composed of hardened mineral deposits, chitin, bone, or keratin. == Species with armour == Armour is evident in numerous animal species from both current and prehistoric times. Dinosaurs such as Ankylosaurus, as well as other Thyreophora (armoured dinosaurs such as Ankylosauria and Stegosauria), grew thick plate-like armour on their bodies as well as offensive armour appendages such as the thagomizer or a tail club. The armour took many forms, including osteoderms, spikes, horns, and plates. Other dinosaurs such as ceratopsian dinosaurs as well as some sauropods such as Saltasaurus, grew armour to defend themselves, although armour in sauropods overall is uncommon. In modern times, some molluscs employ the use of shells as armour, and armour is evident in the chitinous exoskeleton of arthropods. Fish use armour in the form of scales, whether 'cosmoid', 'ganoid' or 'placoid' and in some cases spines, such as on fish such as the stickleback. The chalky plate, or cuttlebone, of cuttlefish also acts as armour. Most reptiles have scaly skin which protects them from predators in addition to water retention; the crocodile's scutes and the shells of the Chelonia: tortoises, turtles and terrapins. Numerous mammals employ the use of spines and body armour, although not as sturdy as reptilian armour, like the spines
|
{
"page_id": 5768710,
"source": null,
"title": "Armour (zoology)"
}
|
of the echidnas and of porcupines and hedgehogs. The bony shell of the armadillos and the extinct Glyptodon were very much like Ankylosaurus' armour and some modern armadillos curl up into a ball when threatened, making them unexposed due to their armour. Similarly, the hairy plate-like scales of the pangolin are employed in the same way and are constructed of the same material used in the offensive armour, the horn, of the rhinoceros. == Usage == Armour, although all used for the sole intent to ward off attackers, can be split into defensive and offensive armour. Examples of offensive armour are horns, hooves, antlers, claws, beaks, clubs and pincers, as developed in some mammals, birds, reptiles (including dinosaurs, such as the dromaeosaurid claw and the ceratopsian horn) and arthropods. Offensive armour is often used in conjunction with defensive armour and in some cases makes an animal almost unassailable. == See also == Armour (disambiguation) Carapace Neck frill Osteoderms Scute Animal weapon == References ==
|
{
"page_id": 5768710,
"source": null,
"title": "Armour (zoology)"
}
|
In organosulfur chemistry, a Bunte salt is an archaic name for salts with the formula R−S−SO−3Na+. They are also called S-alkylthiosulfates or S-arylthiosulfates. These compounds are typically derived from alkylation on the pendant sulfur of sodium thiosulfate: RX + Na2S2O3 → RS−SO−3Na+ + NaX They have been used as intermediates in the synthesis of thiols. They are also used to generate unsymmetrical disulfides: RS−SO−3Na+ + NaSR' → RS−SR' + Na2SO3 According to X-ray crystallography, they adopt the expected structure with tetrahedral sulfur(VI) atom, a sulfur-sulfur single bond, and three equivalent sulfur-oxygen bonds. == See also == Thiosulfonates are organosulfur compounds with the formula RSO2S− and RSO2SR' == References ==
|
{
"page_id": 43910664,
"source": null,
"title": "Bunte salt"
}
|
The molecular formula C8H18S (molar mass: 146.29 g/mol, exact mass: 146.1129 u) may refer to: 2-Methyl-2-heptanethiol 1-Octanethiol, or 1-mercaptooctane n-Octyl mercaptan or Octyl mercaptan
|
{
"page_id": 56034825,
"source": null,
"title": "C8H18S"
}
|
Yeast artificial chromosomes (YACs) are genetically engineered chromosomes derived from the DNA of the yeast, Saccharomyces cerevisiae [1], which is then ligated into a bacterial plasmid. By inserting large fragments of DNA, from 100–1000 kb, the inserted sequences can be cloned and physically mapped using a process called chromosome walking. This is the process that was initially used for the Human Genome Project, however due to stability issues, YACs were abandoned for the use of bacterial artificial chromosome [2] The bakers' yeast S. cerevisiae is one of the most important experimental organisms for studying eukaryotic molecular genetics. Beginning with the initial research of the Rankin et al., Strul et al., and Hsaio et al., the inherently fragile chromosome was stabilized by discovering the necessary autonomously replicating sequence (ARS); a refined YAC utilizing this data was described in 1983 by Murray et al. The primary components of a YAC are the ARS, centromere [3], and telomeres [4] from S. cerevisiae. Additionally, selectable marker genes, such as antibiotic resistance and a visible marker, are utilized to select transformed yeast cells. Without these sequences, the chromosome will not be stable during extracellular replication, and would not be distinguishable from colonies without the vector. == Construction == A YAC is built using an initial circular DNA plasmid, which is typically cut into a linear DNA molecule using restriction enzymes; DNA ligase is then used to ligate a DNA sequence or gene of interest into the linearized DNA, forming a single large, circular piece of DNA. [5] The basic generation of linear yeast artificial chromosomes can be broken down into 6 main steps: == Full chromosome III == Chromosome III is the third smallest chromosome in S. cerevisiae; its size was estimated from pulsed-field gel electro- phoresis studies to be 300–360 kb This chromosome has
|
{
"page_id": 394765,
"source": null,
"title": "Yeast artificial chromosome"
}
|
been the subject of intensive study, not least because it contains the three genetic loci involved in mating-type control: MAT, HML and HMR. In March 2014, Jef Boeke of the Langone Medical Centre at New York University, published that his team has synthesized one of the S. cerevisiae 16 yeast chromosomes, the chromosome III, that he named synIII. The procedure involved replacing the genes in the original chromosome with synthetic versions and the finished synthesized chromosome was then integrated into a yeast cell. It required designing and creating 273,871 base pairs of DNA - fewer than the 316,667 pairs in the original chromosome. == Uses in biotechnology == Yeast expression vectors, such as YACs, YIps (yeast integrating plasmids), and YEps (yeast episomal plasmids), have an advantage over bacterial artificial chromosomes (BACs) in that they can be used to express eukaryotic proteins that require posttranslational modification. By being able to insert large fragments of DNA, YACs can be utilized to clone and assemble the entire genomes of an organism. With the insertion of a YAC into yeast cells, they can be propagated as linear artificial chromosomes, cloning the inserted regions of DNA in the process. With this completed, two processes can be used to obtain a sequenced genome, or region of interest: Physical Mapping [6] Chromosome Walking This is significant in that it allows for the detailed mapping of specific regions of the genome. Whole human chromosomes have been examined, such as the X chromosome, generating the location of genetic markers for numerous genetic disorders and traits. == The Human Genome Project == In the United States, the Human Genome Project first took clear form in February of 1988, with the release of the National Research Council (NRC) report Mapping and Sequencing the Human Genome. YACs are significantly less stable than
|
{
"page_id": 394765,
"source": null,
"title": "Yeast artificial chromosome"
}
|
BACs, producing "chimeric effects" : artifacts where the sequence of the cloned DNA actually corresponds not to a single genomic region but to multiple regions. Chimerism may be due to either co-ligation of multiple genomic segments into a single YAC, or recombination of two or more YACs transformed in the same host Yeast cell. The incidence of chimerism may be as high as 50%. Other artifacts are deletion of segments from a cloned region, and rearrangement of genomic segments (such as inversion). In all these cases, the sequence as determined from the YAC clone is different from the original, natural sequence, leading to inconsistent results and errors in interpretation if the clone's information is relied upon. Due to these issues, the Human Genome Project ultimately abandoned the use of YACs and switched to bacterial artificial chromosomes, where the incidence of these artifacts is very low. In addition to stability issues, specifically the relatively frequent occurrence of chimeric events, YACs proved to be inefficient when generating the minimum tiling path covering the entire human genome. Generating the clone libraries is time consuming. Also, due to the nature of the reliance on sequence tagged sites (STS) as a reference point when selecting appropriate clones, there are large gaps that need further generation of libraries to span. It is this additional hindrance that drove the project to utilize BACs instead. This is due to two factors: BACs are much quicker to generate, and when generating redundant libraries of clones, this is essential BACs allow more dense coverage with STSs, resulting in more complete and efficient minimum tiling paths generated in silico. However, it is possible to utilize both approaches, as was demonstrated when the genome of the nematode, C. elegans. There majority of the genome was tiled with BACs, and the gaps filled
|
{
"page_id": 394765,
"source": null,
"title": "Yeast artificial chromosome"
}
|
in with YACs. == See also == Bacterial artificial chromosome (BAC) Cosmid Fosmid Genetic engineering Human artificial chromosome Autonomously replicating sequence(ARS) Cloning Vector == References == == External links == Yeast+Artificial+Chromosomes at the U.S. National Library of Medicine Medical Subject Headings (MeSH) North Dakota State University Cloning and Cloning Vectors Resource Molecular Cell Biology 4th Edition [NCBI Database]: DNA Cloning with Plasmid Vectors, Ch. 7 Washington University Genome Institute Saccharomyces Genome Database
|
{
"page_id": 394765,
"source": null,
"title": "Yeast artificial chromosome"
}
|
Since the 1990s, biogeomorphology has developed as an established research field examining the interrelationship between organisms and geomorphic processes in a variety of environments, both marine, and terrestrial. Coastal biogeomorphology looks at the interaction between marine organisms and coastal geomorphic processes. Biogeomorphology is a subdiscipline of geomorphology. This can include not only microorganisms and plants, but animals as well. These interactions are important factors in the development of certain environments like salt marsh, mangrove and other types of coastal wetlands as well as influencing coastal and shoreline stability. == Main processes == There are three main processes related to biogeomorphology: bioerosion, bioprotection, and bioconstruction. Bioerosion is the erosion of ocean substrates by living organisms. Bioprotection refers to the protection of substrate from various forms erosion by the presence of organisms, and the structures they create (i.e. coral reefs). Finally bioconstruction refers to the physical construction of biological structures on ocean substrate. Marine biota interact with landform processes by building structures, accumulating carbonate sediments, accelerating erosion by boring or bioturbation, and marine plant life contribute to shoreline stability, especially in marsh and wetland environments. == Role in shoreline stability == The interaction between marine biota and geologic processes is important to shoreline stability, especially in soft sedimentary environments where sediments are more likely to erode away. Benthic and planktonic organisms, as well as shellfish filter, package, and even bind fine sediments together in tidal regions. This action reduces turbidity in the area by solidifying and protecting loose, soft sediments, and thus allowing more colonization by other organisms. If disturbance of these soft sediments occurs, particularly through human interaction such as shellfish harvesting, dredging, or the introduction of toxins, the environment may drastically change. If this occurs, and marine biota are removed from the environment, erosion can occur, or increase, especially in
|
{
"page_id": 20383245,
"source": null,
"title": "Coastal biogeomorphology"
}
|
regions prone to wave action and tidal re-suspension. == See also == Changes in global mangrove distributions == References ==
|
{
"page_id": 20383245,
"source": null,
"title": "Coastal biogeomorphology"
}
|
Wetting solutions are liquids containing active chemical compounds that minimise the distance between two immiscible phases by lowering the surface tension to induce optimal spreading. The two phases, known as an interface, can be classified into five categories, namely, solid-solid, solid-liquid, solid-gas, liquid-liquid and liquid-gas. Although wetting solutions have a long history of acting as detergents for four thousand plus years, the fundamental chemical mechanism was not fully discovered until 1913 by the pioneer McBain. Since then, diverse studies have been conducted to reveal the underlying mechanism of micelle formation and working principle of wetting solutions, broadening the area of applications. The addition of wetting solution to an aqueous droplet leads to the formation of a thin film due to its intrinsic spreading property. This property favours the formation of micelles which are specific chemical structures consisting of a cluster of surfactant molecules that has a hydrophobic core and a hydrophilic surface that can lower the surface tension between two different phases. In addition, wetting solutions can be further divided into four classes; non-ionic, anionic, cationic and zwitterionic. The spreading property may be examined by adding a drop of the liquid onto an oily surface. If the liquid is not a wetting solution, the droplet will remain intact. If the liquid is a wetting solution, the droplet will spread uniformly on the oily surface because the formation of the micelles lowers the surface tension of the liquid. Wetting solutions can be applied in pharmaceuticals, cosmetics and agriculture. Albeit a number of practical uses of wetting solutions, the presence of wetting solution can be a hindrance to water purification in industrial membrane distillation. == History == Wetting agent was used as soap for cleansing purposes for thousands of years. The oldest evidence of wetting solution went back to 2800 BC in
|
{
"page_id": 70321679,
"source": null,
"title": "Wetting solution"
}
|
ancient Babylon. The earliest credible reference of soap is in the writings of Galen, the Greek physician, around 200 AD. Over the following centuries, wetting solutions mainly functioned as detergents due to their wetting properties. Despite the extensive use of wetting solutions, the underlying chemical mechanism remained unknown until the emergence of McBain's proposed theory in 1913. Founded on his research on how the electrical conductivity of a solution of surfactant molecules changed with concentration, he raised the possibility of surfactant molecules in the form of self-assembled aggregates. Not until Debye published his original hypothesis in 1949 did he described the reason of micelle formation and the existence of finite-shaped micelles. McBain's discovery sparked numerous studies by Hobbs, Ooshika, Reich and Halsey from 1950 to 1956. These scholars intended to correct some of the foundational theories of the description of an equilibrium system, as well as emphasising the role of surface energy which was overlooked in Debye's prototype. In 1976, the fundamental theory for understanding the mechanism of micelle formation was developed by Tanford's free energy model. Apart from integrating all relevant physicochemical elements and explaining the growth of micells, he provided a comprehensive reasoning of why micelles are finite in terms of opposing interactional forces. == Mechanism == The chemical structure of wetting solution molecules consist of a hydrophilic head and a long hydrophobic tail. Its distinct amphiphilicity allows it to bury its hydrophilic head in an aqueous bulk phase and hydrophobic part in the organic bulk phase respectively. Wetting solution molecules break the intermolecular forces between each molecule in the organic phase and each water molecule in the aqueous phase by displacement. Due to the lowered attractive forces, the surface tension is reduced. Upon adding more wetting solution, the elevated concentration of wetting solution molecules leads to a
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"page_id": 70321679,
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further decrease in surface tension and makes the molecules at the surfaces become more crowded. The molecules will be forced to remain in the aqueous phase when there are no more vacancies for them to stay on the surface. At this point, the surface tension is maximally lowered and is termed as the critical micelle concentration (CMC). The lower the CMC, the more efficient the wetting solution is in reducing surface tension. Any additional wetting solution molecules will undergo self-aggregation into several special structures called micelles. Micelles are spheres with a hydrophobic core formed by the non-polar tail of wetting solution molecules and are surrounded by a hydrophilic layer arising from the molecules’ polar heads. Extra wetting solution molecules will be forced to form micelles instead of adhering to the surface, hence the surface tension remains constant. Due to the minimised surface tension, the droplet can now spread thoroughly and form a thin film on the surface. == Classification == Generally, the wetting solution molecules consist of a hydrophilic head and a long hydrophobic tail. The hydrophobic region usually contains saturated or unsaturated hydrocarbon chains, heterocyclic rings or aromatic rings. Despite the similar amphiphilic composition, the molecules can be divided into four classes with respect to the nature of the hydrophilic group, namely, non-ionic, anionic, cationic and zwitterionic. The following table shows the composition, special features of the corresponding classes and common examples of various forms of the respective wetting solutions. == Applications == Generally, wetting solution is applied in pharmaceuticals, cosmetics and agriculture. McBain’s research on maximising the application of wetting solutions have an important role in enabling a range of options to both manufacturers and consumers and improving product performance in the respective areas of application, such as modifying the stability of pharmaceuticals, delivery of drugs, effectiveness of
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"page_id": 70321679,
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cleansing products and water retention in soils. === Pharmaceuticals === Specific properties of different wetting solutions are able to alternate drug delivery which is beneficial in improving drug safety and patients' experiences . For example, solulan C-24, a non-ionic wetting solution, forms large bilayers of wetting solution molecules known as discosomes that have a lower risk of causing systemic adverse effects. Non-ionic wetting solutions are found to have a wider usage and are more efficient in reducing surface tension compared to ionic wetting solutions which have higher toxicity and CMC value in general. To ensure the safety, efficacy and quality of the preparations, toxicity and interaction profiles of the choice of wetting solutions are carefully investigated. ==== Dosage form: Suspensions ==== Suspension preparation is a liquid dosage form that contains insoluble solid drug particles. The suspension preparation is ideal if solid particles that have become compacted together during storage can re-disperse throughout the liquid vehicle readily with gentle shaking for a period of time that is sufficient for measuring the required dosage. Solid particles have a natural tendency to aggregate and eventually cause caking due to the presence of air film coating. A solution to this is using a wetting solution as the liquid vehicle for suspension preparation. Wetting solution increases the dispersal ability of the solid particles by replacing the air film to increase steric hindrance and minimise interactions between solid particles and resulting in a decreased rate of aggregation. ==== Topical ophthalmic solutions ==== Wetting solutions lowers the surface tension of topical ophthalmic solutions and induces instant spreading when applied onto the cornea by increasing the interaction between the two. The instant spreading increases the amount of drug molecules that are exposed to the cornea for absorption and therefore a quicker onset of action. The increased interaction allow
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"page_id": 70321679,
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the topical ophthalmic solutions to remain on the corneal surface for a longer period of time to maximise the amount of drug that can diffuse from the applied topical ophthalmic solution layer to the corneal epithelium through tear film, the protective layer of the cornea from the external environment. === Cosmetics: Skin cleansing products === Skin cleansing products including facial cleanser, body wash and shampoo consist of wetting solutions. Wetting solutions allow efficient spreading and wetting of the surface of skin and scalp by reducing the surface tension between the hydrophobic sebum secreted by the sebaceous gland in our skin. An efficient wetting solution penetrates the skin and clears any topical applications, body fluids including sebum secreted via openings of hair follicles, dead skin cells and microbes. Non-ionic wetting solutions have a low risk of causing skin irritation and are efficient in reducing surface tension between different ingredients, for example, fragrance and essential oils extracted from plants, in skin cleansing products to produce a consistent liquid formula. However, non-ionic wetting solutions are of higher cost than the other types of wetting solutions hence are less favourable for commercial products. Cationic wetting solutions cause more severe skin irritation problems hence are not used in skin cleansing products. They are used in hair conditioners that are only applied to the second half hair length and washed off after a short period of time. Anionic and amphoteric wetting solutions are often used as a mixture in body wash and shampoo. The anionic wetting solutions formulated into skin cleansing products have often undergone chemical modification as they often contain sulphur which triggers skin irritation by causing collagen in skin cells to swell and sometimes cell death. Examples of modified anionic wetting solutions include ammonium laureth sulphate and modified sulfosuccinates, both reported to exhibit low
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skin irritation. === Agriculture === Wetting solutions are widely used in Agriculture to increase crop yield which is affected by the degree of infiltration and penetration of water, nutrients and chemicals such as fertilisers and pesticides. Wetting solutions reduce surface runoff of water and nutrients and enhance water infiltration in water repelling soil by reducing surface tension. Wetting-solution-treated soil has shown to retain high water content and an even distribution of nutrients in the root zone that are in deep soil areas, benefiting crop yield and improving water efficiency. Examples of wetting solutions used in agriculture are modified alkylated polyol, mixture of polyether polyol and glycol ether and mixture of poloxalene, 2-butoxyethanol. == Industrial concerns == Membrane distillation is a water purification process that utilises a hydrophobic membrane with pores to separate water vapour from contaminants, for example, oil and unwanted chemicals. The filtration efficiency and stability of the membrane can be diminished by wetting. Wetting of the hydrophobic membrane is resulted from the presence of wetting solution in sewage due to its increasing large variety of usage in different fields, for example, pharmaceuticals, cosmetics and agriculture. A possible solution is to pretreat the sewage to remove wetting solutions, limiting the amount of wetting solution in contact with the membrane. Other possible solutions to lengthen durability of the membrane include modification of the membrane material repellent to water and oil, air-backwashing and membrane surface geometry modification. These solutions are costly and require further research and development to optimise the durability and efficiency of membrane distillation. == References ==
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"page_id": 70321679,
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The Netherlands Entomological Society (Dutch: Nederlandse Entomologische Vereniging, abbreviated NEV) was founded in 1845 for the purpose of improving and promoting entomology in the Netherlands. The society has more than 600 members. == External links == Official website: Nederlandse Entomologische Vereniging (in Dutch)
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"page_id": 11863568,
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"title": "Netherlands Entomological Society"
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In thermodynamics, thermal pressure (also known as the thermal pressure coefficient) is a measure of the relative pressure change of a fluid or a solid as a response to a temperature change at constant volume. The concept is related to the Pressure-Temperature Law, also known as Amontons's law or Gay-Lussac's law. In general pressure, ( P {\displaystyle P} ) can be written as the following sum: P total ( V , T ) = P ref ( V , T ) + Δ P thermal ( V , T ) {\displaystyle P_{\text{total}}(V,T)=P_{\text{ref}}(V,T)+\Delta P_{\text{thermal}}(V,T)} . P ref {\displaystyle P_{\text{ref}}} is the pressure required to compress the material from its volume V 0 {\displaystyle V_{0}} to volume V {\displaystyle V} at a constant temperature T 0 {\displaystyle T_{0}} . The second term expresses the change in thermal pressure Δ P thermal {\displaystyle \Delta P_{\text{thermal}}} . This is the pressure change at constant volume due to the temperature difference between T 0 {\displaystyle T_{0}} and T {\displaystyle T} . Thus, it is the pressure change along an isochore of the material. The thermal pressure γ v {\displaystyle \gamma _{v}} is customarily expressed in its simple form as γ v = ( ∂ P ∂ T ) V . {\displaystyle \gamma _{v}=\left({\frac {\partial P}{\partial T}}\right)_{V}.} == Thermodynamic definition == Because of the equivalences between many properties and derivatives within thermodynamics (e.g., see Maxwell Relations), there are many formulations of the thermal pressure coefficient, which are equally valid, leading to distinct yet correct interpretations of its meaning. Some formulations for the thermal pressure coefficient include: ( ∂ P ∂ T ) v = α κ T = γ V C V = α β T {\displaystyle \left({\frac {\partial P}{\partial T}}\right)_{v}=\alpha \kappa _{T}={\frac {\gamma }{V}}C_{V}={\frac {\alpha }{\beta _{T}}}} Where α {\displaystyle \alpha } is the volume
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"page_id": 64488973,
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thermal expansion, κ T {\displaystyle \kappa _{T}} the isothermal bulk modulus, γ {\displaystyle \gamma } the Grüneisen parameter, β T {\displaystyle \beta _{T}} the compressibility and C V {\displaystyle C_{V}} the constant-volume heat capacity. Details of the calculation: ( ∂ P ∂ T ) V = − ( ∂ V ∂ T ) p ( ∂ P ∂ V ) T = − ( V α ) ( − 1 κ T ) = α κ T {\displaystyle \left({\frac {\partial P}{\partial T}}\right)_{V}=-\left({\frac {\partial V}{\partial T}}\right)_{p}\left({\frac {\partial P}{\partial V}}\right)_{T}=-(V\alpha )\left({\frac {-1}{\kappa _{T}}}\right)=\alpha \kappa _{T}} ( ∂ P ∂ T ) V = 1 V ( ∂ V ∂ T ) p − 1 V ( ∂ V ∂ P ) T = α β {\displaystyle \left({\frac {\partial P}{\partial T}}\right)_{V}={\frac {{\frac {1}{V}}\left({\frac {\partial V}{\partial T}}\right)_{p}}{{\frac {-1}{V}}\left({\frac {\partial V}{\partial P}}\right)_{T}}}={\frac {\alpha }{\beta }}} == The utility of the thermal pressure == The thermal pressure coefficient can be considered as a fundamental property; it is closely related to various properties such as internal pressure, sonic velocity, the entropy of melting, isothermal compressibility, isobaric expansibility, phase transition, etc. Thus, the study of the thermal pressure coefficient provides a useful basis for understanding the nature of liquid and solid. Since it is normally difficult to obtain the properties by thermodynamic and statistical mechanics methods due to complex interactions among molecules, experimental methods attract much attention. The thermal pressure coefficient is used to calculate results that are applied widely in industry, and they would further accelerate the development of thermodynamic theory. Commonly the thermal pressure coefficient may be expressed as functions of temperature and volume. There are two main types of calculation of the thermal pressure coefficient: one is the Virial theorem and its derivatives; the other is the Van der Waals type and its derivatives. ==
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"page_id": 64488973,
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Thermal pressure at high temperature == As mentioned above, α κ T {\displaystyle \alpha \kappa _{T}} is one of the most common formulations for the thermal pressure coefficient. Both α {\displaystyle \alpha } and κ T {\displaystyle \kappa _{T}} are affected by temperature changes, but the value of α {\displaystyle \alpha } and κ T {\displaystyle \kappa _{T}} of a solid much less sensitive to temperature change above its Debye temperature. Thus, the thermal pressure of a solid due to moderate temperature change above the Debye temperature can be approximated by assuming a constant value of α {\displaystyle \alpha } and κ T {\displaystyle \kappa _{T}} . On the contrary, in the paper, authors demonstrated that, at ambient pressure, the pressure predicted of Au and MgO from a constant value of α κ T {\displaystyle \alpha \kappa _{T}} deviates from the experimental data, and the higher temperature, the more deviation. In addition, the authors suggested a thermal expansion model to replace the thermal pressure model. == Thermal pressure in a crystal == The thermal pressure of a crystal defines how the unit-cell parameters change as a function of pressure and temperature. Therefore, it also controls how the cell parameters change along an isochore, namely as a function of ( ∂ P ∂ T ) V {\textstyle \left({\frac {\partial P}{\partial T}}\right)_{V}} . Usually, Mie-Grüneisen-Debye and other Quasi harmonic approximation (QHA) based state functions are being used to estimate volumes and densities of mineral phases in diverse applications such as thermodynamic, deep-Earth geophysical models and other planetary bodies. In the case of isotropic (or approximately isotropic) thermal pressure, the unit cell parameter remains constant along the isochore and the QHA is valid. But when the thermal pressure is anisotropic, the unit cell parameter changes so, the frequencies of vibrational modes also change
|
{
"page_id": 64488973,
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"title": "Thermal pressure"
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even in constant volume and the QHA is no longer valid. The combined effect of a change in pressure and temperature is described by the strain tensor ε i j {\displaystyle \varepsilon _{ij}} : ε i j = α i j d T − β i j d P {\displaystyle \varepsilon _{ij}=\alpha _{ij}dT-\beta _{ij}dP} Where α i j {\displaystyle \alpha _{ij}} is the volume thermal expansion tensor and β i j {\displaystyle \beta _{ij}} is the compressibility tensor. The line in the P-T space which indicates that the strain ϵ i j {\displaystyle \epsilon _{ij}} is constant in a particular direction within the crystal is defined as: ( ∂ P ∂ T ) V = α i j β i j {\displaystyle \left({\frac {\partial P}{\partial T}}\right)_{V}={\frac {\alpha _{ij}}{\beta _{ij}}}} Which is an equivalent definition of the isotropic degree of thermal pressure. == See also == Isochoric process Pressure Hydrostatic equilibrium == References ==
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"page_id": 64488973,
"source": null,
"title": "Thermal pressure"
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Astrostatistics is a discipline which spans astrophysics, statistical analysis and data mining. It is used to process the vast amount of data produced by automated scanning of the cosmos, to characterize complex datasets, and to link astronomical data to astrophysical theory. Many branches of statistics are involved in astronomical analysis including nonparametrics, multivariate regression and multivariate classification, time series analysis, and especially Bayesian inference. The field is closely related to astroinformatics. == References ==
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{
"page_id": 37815827,
"source": null,
"title": "Astrostatistics"
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ParaHoxozoa (or Parahoxozoa) is a clade of animals that consists of Bilateria, Placozoa, and Cnidaria. == Phylogeny == The relationship of Parahoxozoa relative to the two other animal lineages Ctenophora and Porifera is debated. Some phylogenomic studies have presented evidence supporting Ctenophora as the sister to Parahoxozoa and Porifera as the sister group to the rest of animals (e.g. ). Other studies have presented evidence supporting Porifera as the sister to Parahoxozoa and Ctenophora as the sister group to the rest of animals (e.g. ), finding that nervous systems either evolved independently in ctenophores and parahoxozoans, or were secondarily lost in poriferans. If ctenophores are taken to have diverged first, Eumetazoa is sometimes used as a synonym for ParaHoxozoa. The cladogram, which is congruent with the vast majority of these phylogenomic studies, conveys this uncertainty with a polytomy. == ParaHoxozoa or Parahoxozoa == "ParaHox" genes are usually referred to in CamelCase and the original paper that named the clade used "ParaHoxozoa"; the single initial capital format "Parahoxozoa" has also come to be used in the literature. == Characteristics == Parahoxozoa was defined by the presence of several gene (sub)classes (HNF, CUT, PROS, ZF, CERS, K50, S50-PRD), as well as Hox/ParaHox-ANTP from which the name of this clade originated. It was later proposed and contested that a gene of the same class (ANTP) as the Hox/ParaHox, the NK gene and the Cdx Parahox gene, is also present in Porifera, the sponges. Regardless of whether a ParaHox gene is ever definitively identified, Parahoxozoa, as originally defined, is monophyletic and therefore continues to be used as such. == Planula-acoel, triploblasty, and bilaterian similarities == The original bilaterian is hypothesized to be a bottom dwelling worm with a single body opening. A through-gut may already have developed with the Ctenophora. The through-gut may have
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{
"page_id": 41813523,
"source": null,
"title": "ParaHoxozoa"
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developed from the corners of a single opening with lips fusing. E.g. Acoela resemble the planula larvae of some Cnidaria, which exhibit some bilaterian symmetry. They are vermiform, just as the cnidarian Buddenbrockia is. Placozoans have been noted to resemble planula. Usually, "Planulozoa" is a Cnidaria–Bilateria clade that excludes Placozoa. Otherwise, when including all three lineages, it is synonymous with Parahoxozoa. Triploblasty may have developed before the Cnidaria–Bilateria radiation. == References ==
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{
"page_id": 41813523,
"source": null,
"title": "ParaHoxozoa"
}
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The standard atomic weight of a chemical element (symbol Ar°(E) for element "E") is the weighted arithmetic mean of the relative isotopic masses of all isotopes of that element weighted by each isotope's abundance on Earth. For example, isotope 63Cu (Ar = 62.929) constitutes 69% of the copper on Earth, the rest being 65Cu (Ar = 64.927), so A r ° ( 29 Cu ) = 0.69 × 62.929 + 0.31 × 64.927 = 63.55. {\displaystyle A_{\text{r}}{\text{°}}(_{\text{29}}{\text{Cu}})=0.69\times 62.929+0.31\times 64.927=63.55.} Relative isotopic masses dimensionless, and so is the weighted average. It can be converted into a measure of mass (with dimension M) by multiplying it with the atomic mass constant dalton. Among various variants of the notion of atomic weight (Ar, also known as relative atomic mass) used by scientists, the standard atomic weight (Ar°) is the most common and practical. The standard atomic weight of each chemical element is determined and published by the Commission on Isotopic Abundances and Atomic Weights (CIAAW) of the International Union of Pure and Applied Chemistry (IUPAC) based on natural, stable, terrestrial sources of the element. The definition specifies the use of samples from many representative sources from the Earth, so that the value can widely be used as the atomic weight for substances as they are encountered in reality—for example, in pharmaceuticals and scientific research. Non-standardized atomic weights of an element are specific to sources and samples, such as the atomic weight of carbon in a particular bone from a particular archaeological site. Standard atomic weight averages such values to the range of atomic weights that a chemist might expect to derive from many random samples from Earth. This range is the rationale for the interval notation given for some standard atomic weight values. Of the 118 known chemical elements, 80 have stable isotopes
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and 84 have this Earth-environment based value. Typically, such a value is, for example helium: Ar°(He) = 4.002602(2). The "(2)" indicates the uncertainty in the last digit shown, to read 4.002602±0.000002. IUPAC also publishes abridged values, rounded to five significant figures. For helium, Ar, abridged°(He) = 4.0026. For fourteen elements the samples diverge on this value, because their sample sources have had a different decay history. For example, thallium (Tl) in sedimentary rocks has a different isotopic composition than in igneous rocks and volcanic gases. For these elements, the standard atomic weight is noted as an interval: Ar°(Tl) = [204.38, 204.39]. With such an interval, for less demanding situations, IUPAC also publishes a conventional value. For thallium, Ar, conventional°(Tl) = 204.38. == Definition == The standard atomic weight is a special value of the relative atomic mass. It is defined as the "recommended values" of relative atomic masses of sources in the local environment of the Earth's crust and atmosphere as determined by the IUPAC Commission on Atomic Weights and Isotopic Abundances (CIAAW). In general, values from different sources are subject to natural variation due to a different radioactive history of sources. Thus, standard atomic weights are an expectation range of atomic weights from a range of samples or sources. By limiting the sources to terrestrial origin only, the CIAAW-determined values have less variance, and are a more precise value for relative atomic masses (atomic weights) actually found and used in worldly materials. The CIAAW-published values are used and sometimes lawfully required in mass calculations. The values have an uncertainty (noted in brackets), or are an expectation interval (see example in illustration immediately above). This uncertainty reflects natural variability in isotopic distribution for an element, rather than uncertainty in measurement (which is much smaller with quality instruments). Although there is
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an attempt to cover the range of variability on Earth with standard atomic weight figures, there are known cases of mineral samples which contain elements with atomic weights that are outliers from the standard atomic weight range. For synthetic elements the isotope formed depends on the means of synthesis, so the concept of natural isotope abundance has no meaning. Therefore, for synthetic elements the total nucleon count of the most stable isotope (i.e., the isotope with the longest half-life) is listed in brackets, in place of the standard atomic weight. When the term "atomic weight" is used in chemistry, usually it is the more specific standard atomic weight that is implied. It is standard atomic weights that are used in periodic tables and many standard references in ordinary terrestrial chemistry. Lithium represents a unique case where the natural abundances of the isotopes have in some cases been found to have been perturbed by human isotopic separation activities to the point of affecting the uncertainty in its standard atomic weight, even in samples obtained from natural sources, such as rivers. === Terrestrial definition === An example of why "conventional terrestrial sources" must be specified in giving standard atomic weight values is the element argon. Between locations in the Solar System, the atomic weight of argon varies as much as 10%, due to extreme variance in isotopic composition. Where the major source of argon is the decay of 40K in rocks, 40Ar will be the dominant isotope. Such locations include the planets Mercury and Mars, and the moon Titan. On Earth, the ratios of the three isotopes 36Ar : 38Ar : 40Ar are approximately 5 : 1 : 1600, giving terrestrial argon a standard atomic weight of 39.948(1). However, such is not the case in the rest of the universe. Argon produced
|
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"page_id": 10356246,
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directly, by stellar nucleosynthesis, is dominated by the alpha-process nuclide 36Ar. Correspondingly, solar argon contains 84.6% 36Ar (according to solar wind measurements), and the ratio of the three isotopes 36Ar : 38Ar : 40Ar in the atmospheres of the outer planets is 8400 : 1600 : 1. The atomic weight of argon in the Sun and most of the universe, therefore, would be only approximately 36.3. === Causes of uncertainty on Earth === Famously, the published atomic weight value comes with an uncertainty. This uncertainty (and related: precision) follows from its definition, the source being "terrestrial and stable". Systematic causes for uncertainty are: Measurement limits. As always, the physical measurement is never finite. There is always more detail to be found and read. This applies to every single, pure isotope found. For example, today the mass of the main natural fluorine isotope (fluorine-19) can be measured to the accuracy of eleven decimal places: 18.998403163(6). But a still more precise measurement system could become available, producing more decimals. Imperfect mixtures of isotopes. In the samples taken and measured the mix (relative abundance) of those isotopes may vary. For example, copper. While in general its two isotopes make out 69.15% and 30.85% each of all copper found, the natural sample being measured can have had an incomplete 'stirring' and so the percentages are different. The precision is improved by measuring more samples of course, but there remains this cause of uncertainty. (Example: lead samples vary so much, it can not be noted more precise than four figures: 207.2) Earthly sources with a different history. A source is the greater area being researched, for example 'ocean water' or 'volcanic rock' (as opposed to a 'sample': the single heap of material being investigated). It appears that some elements have a different isotopic mix per
|
{
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source. For example, thallium in igneous rock has more lighter isotopes, while in sedimentary rock it has more heavy isotopes. There is no Earthly mean number. These elements show the interval notation: Ar°(Tl) = [204.38, 204.39]. For practical reasons, a simplified 'conventional' number is published too (for Tl: 204.38). These three uncertainties are accumulative. The published value is a result of all these. == Determination of relative atomic mass == Modern relative atomic masses (a term specific to a given element sample) are calculated from measured values of atomic mass (for each nuclide) and isotopic composition of a sample. Highly accurate atomic masses are available for virtually all non-radioactive nuclides, but isotopic compositions are both harder to measure to high precision and more subject to variation between samples. For this reason, the relative atomic masses of the 22 mononuclidic elements (which are the same as the isotopic masses for each of the single naturally occurring nuclides of these elements) are known to especially high accuracy. The calculation is exemplified for silicon, whose relative atomic mass is especially important in metrology. Silicon exists in nature as a mixture of three isotopes: 28Si, 29Si and 30Si. The atomic masses of these nuclides are known to a precision of one part in 14 billion for 28Si and about one part in one billion for the others. However the range of natural abundance for the isotopes is such that the standard abundance can only be given to about ±0.001% (see table). The calculation is Ar(Si) = (27.97693 × 0.922297) + (28.97649 × 0.046832) + (29.97377 × 0.030872) = 28.0854 The estimation of the uncertainty is complicated, especially as the sample distribution is not necessarily symmetrical: the IUPAC standard relative atomic masses are quoted with estimated symmetrical uncertainties, and the value for silicon is 28.0855(3).
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The relative standard uncertainty in this value is 1×10–5 or 10 ppm. To further reflect this natural variability, in 2010, IUPAC made the decision to list the relative atomic masses of 10 elements as an interval rather than a fixed number. == Naming controversy == The use of the name "atomic weight" has attracted a great deal of controversy among scientists. Objectors to the name usually prefer the term "relative atomic mass" (not to be confused with atomic mass). The basic objection is that atomic weight is not a weight, that is the force exerted on an object in a gravitational field, measured in units of force such as the newton or poundal. In reply, supporters of the term "atomic weight" point out (among other arguments) that: the name has been in continuous use for the same quantity since it was first conceptualized in 1808; for most of that time, atomic weights really were measured by weighing (that is by gravimetric analysis) and the name of a physical quantity should not change simply because the method of its determination has changed; the term "relative atomic mass" should be reserved for the mass of a specific nuclide (or isotope), while "atomic weight" be used for the weighted mean of the atomic masses over all the atoms in the sample; it is not uncommon to have misleading names of physical quantities which are retained for historical reasons, such as electromotive force, which is not a force resolving power, which is not a power quantity molar concentration, which is not a molar quantity (a quantity expressed per unit amount of substance). It could be added that atomic weight is often not truly "atomic" either, as it does not correspond to the property of any individual atom. The same argument could be made against
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"relative atomic mass" used in this sense. == Published values == IUPAC publishes one formal value for each stable chemical element, called the standard atomic weight.: Table 1 Any updates are published biannually (in uneven years). In 2015, the atomic weight of ytterbium was updated. Per 2017, 14 atomic weights were changed, including argon changing from single number to interval value. The value published can have an uncertainty, like for neon: 20.1797(6), or can be an interval, like for boron: [10.806, 10.821]. Next to these 84 values, IUPAC also publishes abridged values (up to five digits per number only), and for the twelve interval values, conventional values (single number values). Symbol Ar is a relative atomic mass, for example from a specific sample. To be specific, the standard atomic weight can be noted as Ar°(E), where (E) is the element symbol. === Abridged atomic weight === The abridged atomic weight, also published by CIAAW, is derived from the standard atomic weight, reducing the numbers to five digits (five significant figures). The name does not say 'rounded'. Interval borders are rounded downwards for the first (low most) border, and upwards for the upward (upmost) border. This way, the more precise original interval is fully covered.: Table 2 Examples: Calcium: Ar°(Ca) = 40.078(4) → Ar, abridged°(Ca) = 40.078 Helium: Ar°(He) = 4.002602(2) → Ar, abridged°(He) = 4.0026 Hydrogen: Ar°(H) = [1.00784, 1.00811] → Ar, abridged°(H) = [1.0078, 1.0082] === Conventional atomic weight === Fourteen chemical elements – hydrogen, lithium, boron, carbon, nitrogen, oxygen, magnesium, silicon, sulfur, chlorine, argon, bromine, thallium, and lead – have a standard atomic weight that is defined not as a single number, but as an interval. For example, hydrogen has Ar°(H) = [1.00 784, 1.00811]. This notation states that the various sources on Earth have substantially different isotopic
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}
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constitutions, and that the uncertainties in all of them are just covered by the two numbers. For these elements, there is not an 'Earth average' constitution, and the 'right' value is not its middle (which would be 1.007975 for hydrogen, with an uncertainty of (±0.000135) that would make it just cover the interval). However, for situations where a less precise value is acceptable, for example in trade, CIAAW has published a single-number conventional atomic weight. For hydrogen, Ar, conventional°(H) = 1.008.: Table 3 === A formal short atomic weight === By using the abridged value, and the conventional value for the fourteen interval values, a short IUPAC-defined value (5 digits plus uncertainty) can be given for all stable elements. In many situations, and in periodic tables, this may be sufficiently detailed.: Tables 2 and 3 == List of atomic weights == === In the periodic table === == See also == International Union of Pure and Applied Chemistry (IUPAC) Commission on Isotopic Abundances and Atomic Weights (CIAAW) == References == == External links == IUPAC Commission on Isotopic Abundances and Atomic Weights Atomic Weights of the Elements 2011
|
{
"page_id": 10356246,
"source": null,
"title": "Standard atomic weight"
}
|
Graphene foam is a solid, open-cell foam made of single-layer sheets of graphene. It is a candidate substrate for the electrode of lithium-ion batteries. == Synthesis == The foam can be manufactured using vapor deposition to coat a metal foam, a three-dimensional mesh of metal filaments. The metal is then removed. == Applications == === Electrode === A physically flexible battery was created using the foam for electrodes. The anode was made by coating the foam with a lithium-titanium compound (Li4Ti5O12) and the cathode by coating the foam with LiFePO4. Both electrodes were lightweight and their large surface area provided high energy density of 110 Wh/kg, comparable to commercial batteries. Power density was much greater than a typical battery. At a rate that completely discharged the material in 18 seconds, power delivered was 80 percent of what it produced during an hour-long discharge. Performance remained stable through 500 charge/discharge cycles. === Support === In 2017 researchers used carbon nanotubes to reinforce a foam. The latter material supports 3,000 times its own weight and can return to its original shape when unweighted. Nanotubes, a powdered nickel catalyst and sugar were mixed. Dried pellets of the substance were then compressed in a steel die in the shape of a screw. The nickel was removed, leaving a screw-shaped piece of foam. The nanotubes' outer layers split and bonded with the graphene. == See also == Aerographene Foam Lithium-ion battery == References == == Further reading == Paronyan, Tereza M.; Thapa, Arjun Kumar; Sherehiy, Andriy; Jasinski, Jacek B.; Jangam, John Samuel Dilip (6 January 2017). "Incommensurate Graphene Foam as a High Capacity Lithium Intercalation Anode". Scientific Reports. 7 (1): 39944. Bibcode:2017NatSR...739944P. doi:10.1038/srep39944. PMC 5216342. PMID 28059110.
|
{
"page_id": 39650838,
"source": null,
"title": "Graphene foam"
}
|
In machine learning, the polynomial kernel is a kernel function commonly used with support vector machines (SVMs) and other kernelized models, that represents the similarity of vectors (training samples) in a feature space over polynomials of the original variables, allowing learning of non-linear models. Intuitively, the polynomial kernel looks not only at the given features of input samples to determine their similarity, but also combinations of these. In the context of regression analysis, such combinations are known as interaction features. The (implicit) feature space of a polynomial kernel is equivalent to that of polynomial regression, but without the combinatorial blowup in the number of parameters to be learned. When the input features are binary-valued (booleans), then the features correspond to logical conjunctions of input features. == Definition == For degree-d polynomials, the polynomial kernel is defined as K ( x , y ) = ( x T y + c ) d {\displaystyle K(\mathbf {x} ,\mathbf {y} )=(\mathbf {x} ^{\mathsf {T}}\mathbf {y} +c)^{d}} where x and y are vectors of size n in the input space, i.e. vectors of features computed from training or test samples and c ≥ 0 is a free parameter trading off the influence of higher-order versus lower-order terms in the polynomial. When c = 0, the kernel is called homogeneous. (A further generalized polykernel divides xTy by a user-specified scalar parameter a.) As a kernel, K corresponds to an inner product in a feature space based on some mapping φ: K ( x , y ) = ⟨ φ ( x ) , φ ( y ) ⟩ {\displaystyle K(\mathbf {x} ,\mathbf {y} )=\langle \varphi (\mathbf {x} ),\varphi (\mathbf {y} )\rangle } The nature of φ can be seen from an example. Let d = 2, so we get the special case of the quadratic
|
{
"page_id": 37619227,
"source": null,
"title": "Polynomial kernel"
}
|
kernel. After using the multinomial theorem (twice—the outermost application is the binomial theorem) and regrouping, K ( x , y ) = ( ∑ i = 1 n x i y i + c ) 2 = ∑ i = 1 n ( x i 2 ) ( y i 2 ) + ∑ i = 2 n ∑ j = 1 i − 1 ( 2 x i x j ) ( 2 y i y j ) + ∑ i = 1 n ( 2 c x i ) ( 2 c y i ) + c 2 {\displaystyle K(\mathbf {x} ,\mathbf {y} )=\left(\sum _{i=1}^{n}x_{i}y_{i}+c\right)^{2}=\sum _{i=1}^{n}\left(x_{i}^{2}\right)\left(y_{i}^{2}\right)+\sum _{i=2}^{n}\sum _{j=1}^{i-1}\left({\sqrt {2}}x_{i}x_{j}\right)\left({\sqrt {2}}y_{i}y_{j}\right)+\sum _{i=1}^{n}\left({\sqrt {2c}}x_{i}\right)\left({\sqrt {2c}}y_{i}\right)+c^{2}} From this it follows that the feature map is given by: φ ( x ) = ( x n 2 , … , x 1 2 , 2 x n x n − 1 , … , 2 x n x 1 , 2 x n − 1 x n − 2 , … , 2 x n − 1 x 1 , … , 2 x 2 x 1 , 2 c x n , … , 2 c x 1 , c ) {\displaystyle \varphi (x)=\left(x_{n}^{2},\ldots ,x_{1}^{2},{\sqrt {2}}x_{n}x_{n-1},\ldots ,{\sqrt {2}}x_{n}x_{1},{\sqrt {2}}x_{n-1}x_{n-2},\ldots ,{\sqrt {2}}x_{n-1}x_{1},\ldots ,{\sqrt {2}}x_{2}x_{1},{\sqrt {2c}}x_{n},\ldots ,{\sqrt {2c}}x_{1},c\right)} generalizing for ( x T y + c ) d {\displaystyle \left(\mathbf {x} ^{T}\mathbf {y} +c\right)^{d}} , where x ∈ R n {\displaystyle \mathbf {x} \in \mathbb {R} ^{n}} , y ∈ R n {\displaystyle \mathbf {y} \in \mathbb {R} ^{n}} and applying the multinomial theorem: ( x T y + c ) d = ∑ j 1 + j 2 + ⋯ + j n + 1 = d d ! j 1 ! ⋯ j n ! j n + 1 ! x
|
{
"page_id": 37619227,
"source": null,
"title": "Polynomial kernel"
}
|
1 j 1 ⋯ x n j n c j n + 1 d ! j 1 ! ⋯ j n ! j n + 1 ! y 1 j 1 ⋯ y n j n c j n + 1 = φ ( x ) T φ ( y ) {\displaystyle {\begin{alignedat}{2}\left(\mathbf {x} ^{T}\mathbf {y} +c\right)^{d}&=\sum _{j_{1}+j_{2}+\dots +j_{n+1}=d}{\frac {\sqrt {d!}}{\sqrt {j_{1}!\cdots j_{n}!j_{n+1}!}}}x_{1}^{j_{1}}\cdots x_{n}^{j_{n}}{\sqrt {c}}^{j_{n+1}}{\frac {\sqrt {d!}}{\sqrt {j_{1}!\cdots j_{n}!j_{n+1}!}}}y_{1}^{j_{1}}\cdots y_{n}^{j_{n}}{\sqrt {c}}^{j_{n+1}}\\&=\varphi (\mathbf {x} )^{T}\varphi (\mathbf {y} )\end{alignedat}}} The last summation has l d = ( n + d d ) {\displaystyle l_{d}={\tbinom {n+d}{d}}} elements, so that: φ ( x ) = ( a 1 , … , a l , … , a l d ) {\displaystyle \varphi (\mathbf {x} )=\left(a_{1},\dots ,a_{l},\dots ,a_{l_{d}}\right)} where l = ( j 1 , j 2 , . . . , j n , j n + 1 ) {\displaystyle l=(j_{1},j_{2},...,j_{n},j_{n+1})} and a l = d ! j 1 ! ⋯ j n ! j n + 1 ! x 1 j 1 ⋯ x n j n c j n + 1 | j 1 + j 2 + ⋯ + j n + j n + 1 = d {\displaystyle a_{l}={\frac {\sqrt {d!}}{\sqrt {j_{1}!\cdots j_{n}!j_{n+1}!}}}x_{1}^{j_{1}}\cdots x_{n}^{j_{n}}{\sqrt {c}}^{j_{n+1}}\quad |\quad j_{1}+j_{2}+\dots +j_{n}+j_{n+1}=d} == Practical use == Although the RBF kernel is more popular in SVM classification than the polynomial kernel, the latter is quite popular in natural language processing (NLP). The most common degree is d = 2 (quadratic), since larger degrees tend to overfit on NLP problems. Various ways of computing the polynomial kernel (both exact and approximate) have been devised as alternatives to the usual non-linear SVM training algorithms, including: full expansion of the kernel prior to training/testing with a linear SVM, i.e. full computation of the mapping φ as in polynomial
|
{
"page_id": 37619227,
"source": null,
"title": "Polynomial kernel"
}
|
regression; basket mining (using a variant of the apriori algorithm) for the most commonly occurring feature conjunctions in a training set to produce an approximate expansion; inverted indexing of support vectors. One problem with the polynomial kernel is that it may suffer from numerical instability: when xTy + c < 1, K(x, y) = (xTy + c)d tends to zero with increasing d, whereas when xTy + c > 1, K(x, y) tends to infinity. == References ==
|
{
"page_id": 37619227,
"source": null,
"title": "Polynomial kernel"
}
|
The Physics Analysis Workstation (PAW) is an interactive, scriptable computer software tool for data analysis and graphical presentation in high-energy physics. The development of this software tool started at CERN in 1986, it was optimized for the processing of very large amounts of data. It was based on and intended for inter-operation with components of CERNLIB, an extensive collection of Fortran libraries. PAW had been a standard tool in high energy physics for decades, yet was essentially unmaintained. Despite continuing popularity as of 2008, it has been losing ground to the C++-based ROOT package. Conversion tutorials exist. In 2014, development and support were stopped. == Sample script == PAW uses its own scripting language. Here is sample code (with its actual output), which can be used to plot data gathered in files. * read data vector/read X,Y input_file.dat * eps plot fort/file 55 gg_ggg_dsig_dphid_179181.eps meta 55 -113 opt linx | linear scale opt logy | logarithmic scale * here goes plot set plci 1 | line color set lwid 2 | line width set dmod 1 | line type (solid, dotted, etc.) graph 32 X Y AL | 32 stands for input data lines in input file * plot title and comments set txci 1 atitle '[f] (deg)' 'd[s]/d[f]! (mb)' set txci 1 text 180.0 2e1 '[f]=179...181 deg' 0.12 close 55 == References == == External links == PAW (at CERN) The PAW History Seen by the CERN Computer News Letters CERNLIB (at CERN) ROOT (at CERN)
|
{
"page_id": 394780,
"source": null,
"title": "Physics Analysis Workstation"
}
|
This page provides supplementary chemical data on lithium tantalate. == Material Safety Data Sheet == The handling of this chemical may incur notable safety precautions. It is highly recommend that you seek the Material Safety Datasheet (MSDS) for this chemical from a reliable source such as SIRI, and follow its directions. == Structure and properties == == Thermodynamic properties == == Spectral data == == References == Yue, Wang; Yi-jian, Jiang (2003). "Crystal orientation dependence of piezoelectric properties in LiNbO3 and LiTaO3". Optical Materials. 23 (1–2). Elsevier BV: 403–408. doi:10.1016/s0925-3467(02)00328-2. ISSN 0925-3467. Smith, R. T. (1967). "Elastic, Piezoelectric, and Dielectric Properties of Lithium Tantalate". Applied Physics Letters. 11 (5). AIP Publishing: 146–148. doi:10.1063/1.1755072. ISSN 0003-6951.
|
{
"page_id": 8259104,
"source": null,
"title": "Lithium tantalate (data page)"
}
|
Dilek Z. Hakkani-Tür is a Turkish-American computer scientist focusing on speech processing, speech recognition, and dialogue systems. She is a professor of computer science at the University of Illinois Urbana-Champaign. == Education and career == Hakkani-Tür is a 1994 graduate of Middle East Technical University in Ankara, Turkey. She continued her studies at Bilkent University, also in Ankara, where she earned a master's degree in 1996 and completed her Ph.D. in 2000. She worked as a researcher at AT&T Labs from 2001 to 2005, at the International Computer Science Institute from 2006 to 2010, at Microsoft Research from 2010 to 2016, at Google Research from 2016 to 2018, and at Amazon Alexa from 2018 to 2023. At Microsoft, she was in the team of scientists that built the first prototype of the Cortana virtual assistant. While working for Amazon Alexa, she also taught at the University of California, Santa Cruz as a distinguished visiting instructor. She joined the University of Illinois Urbana-Champaign faculty in 2023. She was editor-in-chief of IEEE/ACM Transactions on Audio, Speech and Language Processing from 2019 to 2021, and is president of the Special Interest Group on Discourse and Dialogue of the Association for Computational Linguistics for the 2023–2025 term. She also serves as the co-editor-in-chief of Transactions of the Association for Computational Linguistics since 2024. == Recognition == In 2014, Hakkani-Tür was elected as an IEEE Fellow "for contributions to spoken language processing", and as a Fellow of the International Speech Communication Association "for contributions to advancing the state-of-the-art in spoken language processing, especially for human/human and human/machine conversational understanding". In 2024, she was elected as a Fellow of the Association for Computational Linguistics for her contributions to spoken dialogue systems. == References == == External links == Dilek Hakkani-Tür publications indexed by Google Scholar
|
{
"page_id": 77399585,
"source": null,
"title": "Dilek Hakkani-Tür"
}
|
The molecular formula C5H4O3 (molar mass: 112.08 g/mol, exact mass: 112.0160 u) may refer to: 2-Furoic acid Itaconic anhydride
|
{
"page_id": 47777313,
"source": null,
"title": "C5H4O3"
}
|
In nuclear physics and nuclear chemistry, a nuclear reaction is a process in which two nuclei, or a nucleus and an external subatomic particle, collide to produce one or more new nuclides. Thus, a nuclear reaction must cause a transformation of at least one nuclide to another. If a nucleus interacts with another nucleus or particle, they then separate without changing the nature of any nuclide, the process is simply referred to as a type of nuclear scattering, rather than a nuclear reaction. In principle, a reaction can involve more than two particles colliding, but because the probability of three or more nuclei to meet at the same time at the same place is much less than for two nuclei, such an event is exceptionally rare (see triple alpha process for an example very close to a three-body nuclear reaction). The term "nuclear reaction" may refer either to a change in a nuclide induced by collision with another particle or to a spontaneous change of a nuclide without collision. Natural nuclear reactions occur in the interaction between cosmic rays and matter, and nuclear reactions can be employed artificially to obtain nuclear energy, at an adjustable rate, on-demand. Nuclear chain reactions in fissionable materials produce induced nuclear fission. Various nuclear fusion reactions of light elements power the energy production of the Sun and stars. Most nuclear reactions (fusion and fission) results in transmutation of nuclei (called also nuclear transmutation). == History == In 1919, Ernest Rutherford was able to accomplish transmutation of nitrogen into oxygen at the University of Manchester, using alpha particles directed at nitrogen 14N + α → 17O + p. This was the first observation of an induced nuclear reaction, that is, a reaction in which particles from one decay are used to transform another atomic nucleus. Eventually,
|
{
"page_id": 460322,
"source": null,
"title": "Nuclear reaction"
}
|
in 1932 at Cambridge University, a fully artificial nuclear reaction and nuclear transmutation was achieved by Rutherford's colleagues John Cockcroft and Ernest Walton, who used artificially accelerated protons against lithium-7, to split the nucleus into two alpha particles. The feat was popularly known as "splitting the atom", although it was not the modern nuclear fission reaction later (in 1938) discovered in heavy elements by the German scientists Otto Hahn, Lise Meitner, and Fritz Strassmann. == Nuclear reaction equations == Nuclear reactions may be shown in a form similar to chemical equations, for which invariant mass must balance for each side of the equation, and in which transformations of particles must follow certain conservation laws, such as conservation of charge and baryon number (total atomic mass number). An example of this notation follows: To balance the equation above for mass, charge and mass number, the second nucleus to the right must have atomic number 2 and mass number 4; it is therefore also helium-4. The complete equation therefore reads: or more simply: Instead of using the full equations in the style above, in many situations a compact notation is used to describe nuclear reactions. This style of the form A(b,c)D is equivalent to A + b producing c + D. Common light particles are often abbreviated in this shorthand, typically p for proton, n for neutron, d for deuteron, α representing an alpha particle or helium-4, β for beta particle or electron, γ for gamma photon, etc. The reaction above would be written as 6Li(d,α)α. == Energy conservation == Kinetic energy may be released during the course of a reaction (exothermic reaction) or kinetic energy may have to be supplied for the reaction to take place (endothermic reaction). This can be calculated by reference to a table of very accurate particle
|
{
"page_id": 460322,
"source": null,
"title": "Nuclear reaction"
}
|
rest masses, as follows: according to the reference tables, the 63Li nucleus has a standard atomic weight of 6.015 atomic mass units (abbreviated u), the deuterium has 2.014 u, and the helium-4 nucleus has 4.0026 u. Thus: the sum of the rest mass of the individual nuclei = 6.015 + 2.014 = 8.029 u; the total rest mass on the two helium-nuclei = 2 × 4.0026 = 8.0052 u; missing rest mass = 8.029 – 8.0052 = 0.0238 atomic mass units. In a nuclear reaction, the total (relativistic) energy is conserved. The "missing" rest mass must therefore reappear as kinetic energy released in the reaction; its source is the nuclear binding energy. Using Einstein's mass-energy equivalence formula E = mc2, the amount of energy released can be determined. We first need the energy equivalent of one atomic mass unit: Hence, the energy released is 0.0238 × 931 MeV = 22.2 MeV. Expressed differently: the mass is reduced by 0.3%, corresponding to 0.3% of 90 PJ/kg is 270 TJ/kg. This is a large amount of energy for a nuclear reaction; the amount is so high because the binding energy per nucleon of the helium-4 nucleus is unusually high because the He-4 nucleus is "doubly magic". (The He-4 nucleus is unusually stable and tightly bound for the same reason that the helium atom is inert: each pair of protons and neutrons in He-4 occupies a filled 1s nuclear orbital in the same way that the pair of electrons in the helium atom occupy a filled 1s electron orbital). Consequently, alpha particles appear frequently on the right-hand side of nuclear reactions. The energy released in a nuclear reaction can appear mainly in one of three ways: kinetic energy of the product particles (fraction of the kinetic energy of the charged nuclear reaction products
|
{
"page_id": 460322,
"source": null,
"title": "Nuclear reaction"
}
|
can be directly converted into electrostatic energy); emission of very high energy photons, called gamma rays; some energy may remain in the nucleus, as a metastable energy level. When the product nucleus is metastable, this is indicated by placing an asterisk ("*") next to its atomic number. This energy is eventually released through nuclear decay. A small amount of energy may also emerge in the form of X-rays. Generally, the product nucleus has a different atomic number, and thus the configuration of its electron shells is wrong. As the electrons rearrange themselves and drop to lower energy levels, internal transition X-rays (X-rays with precisely defined emission lines) may be emitted. == Q-value and energy balance == In writing down the reaction equation, in a way analogous to a chemical equation, one may, in addition, give the reaction energy on the right side: For the particular case discussed above, the reaction energy has already been calculated as Q = 22.2 MeV. Hence: The reaction energy (the "Q-value") is positive for exothermal reactions and negative for endothermal reactions, opposite to the similar expression in chemistry. On the one hand, it is the difference between the sums of kinetic energies on the final side and on the initial side. But on the other hand, it is also the difference between the nuclear rest masses on the initial side and on the final side (in this way, we have calculated the Q-value above). == Reaction rates == If the reaction equation is balanced, that does not mean that the reaction really occurs. The rate at which reactions occur depends on the energy and the flux of the incident particles, and the reaction cross section. An example of a large repository of reaction rates is the REACLIB database, as maintained by the Joint Institute for
|
{
"page_id": 460322,
"source": null,
"title": "Nuclear reaction"
}
|
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