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https://en.wikipedia.org/wiki/De%20Bruijn%20notation | In mathematical logic, the De Bruijn notation is a syntax for terms in the λ calculus invented by the Dutch mathematician Nicolaas Govert de Bruijn. It can be seen as a reversal of the usual syntax for the λ calculus where the argument in an application is placed next to its corresponding binder in the function instead of after the latter's body.
Formal definition
Terms () in the De Bruijn notation are either variables (), or have one of two wagon prefixes. The abstractor wagon, written , corresponds to the usual λ-binder of the λ calculus, and the applicator wagon, written , corresponds to the argument in an application in the λ calculus.
Terms in the traditional syntax can be converted to the De Bruijn notation by defining an inductive function for which:
All operations on λ-terms commute with respect to the translation. For example, the usual β-reduction,
in the De Bruijn notation is, predictably,
A feature of this notation is that abstractor and applicator wagons of β-redexes are paired like parentheses. For example, consider the stages in the β-reduction of the term , where the redexes are underlined:
Thus, if one views the applicator as an open paren ('(') and the abstractor as a close bracket (']'), then the pattern in the above term is '((](]]'. De Bruijn called an applicator and its corresponding abstractor in this interpretation partners, and wagons without partners bachelors. A sequence of wagons, which he called a segment, is well balanced if all its wagons are partnered.
Advantages of the De Bruijn notation
In a well balanced segment, the partnered wagons may be moved around arbitrarily and, as long as parity is not destroyed, the meaning of the term stays the same. For example, in the above example, the applicator can be brought to its abstractor , or the abstractor to the applicator. In fact, all commutatives and permutative conversions on lambda terms may be described simply in terms of parity-preserving reorderings of partnered wagons. |
https://en.wikipedia.org/wiki/Metal%20gate | A metal gate, in the context of a lateral metal–oxide–semiconductor (MOS) stack, is the gate electrode separated by an oxide from the transistor's channel – the gate material is made from a metal. In most MOS transistors since about the mid 1970s, the "M" for metal has been replaced by a non-metal gate material.
Aluminum gate
The first MOSFET (metal–oxide–semiconductor field-effect transistor) was made by Mohamed Atalla and Dawon Kahng at Bell Labs in 1959, and demonstrated in 1960. They used silicon as channel material and a non-self-aligned aluminum gate. Aluminum gate metal (typically deposited in an evaporation vacuum chamber onto the wafer surface) was common through the early 1970s.
Polysilicon
By the late 1970s, the industry had moved away from aluminum as the gate material in the metal–oxide–semiconductor stack due to fabrication complications and performance issues. A material called polysilicon (polycrystalline silicon, highly doped with donors or acceptors to reduce its electrical resistance) was used to replace aluminum.
Polysilicon can be deposited easily via chemical vapor deposition (CVD) and is tolerant to subsequent manufacturing steps which involve extremely high temperatures (in excess of 900–1000 °C), where metal was not. Particularly, metal (most commonly aluminum a Type III (P-type) dopant) has a tendency to disperse into (alloy with) silicon during these thermal annealing steps. In particular, when used on a silicon wafer with a < 1 1 1 > crystal orientation, excessive alloying of aluminum (from extended high temperature processing steps) with the underlying silicon can create a short circuit between the diffused FET source or drain areas under the aluminum and across the metallurgical junction into the underlying substrate causing irreparable circuit failures. These shorts are created by pyramidal-shaped spikes of silicon-aluminum alloy pointing vertically "down" into the silicon wafer. The practical high-temperature limit for anneal |
https://en.wikipedia.org/wiki/Span%20%28category%20theory%29 | In category theory, a span, roof or correspondence is a generalization of the notion of relation between two objects of a category. When the category has all pullbacks (and satisfies a small number of other conditions), spans can be considered as morphisms in a category of fractions.
The notion of a span is due to Nobuo Yoneda (1954) and Jean Bénabou (1967).
Formal definition
A span is a diagram of type i.e., a diagram of the form .
That is, let Λ be the category (-1 ← 0 → +1). Then a span in a category C is a functor S : Λ → C. This means that a span consists of three objects X, Y and Z of C and morphisms f : X → Y and g : X → Z: it is two maps with common domain.
The colimit of a span is a pushout.
Examples
If R is a relation between sets X and Y (i.e. a subset of X × Y), then X ← R → Y is a span, where the maps are the projection maps and .
Any object yields the trivial span A ← A → A, where the maps are the identity.
More generally, let be a morphism in some category. There is a trivial span A ← A → B, where the left map is the identity on A, and the right map is the given map φ.
If M is a model category, with W the set of weak equivalences, then the spans of the form where the left morphism is in W, can be considered a generalised morphism (i.e., where one "inverts the weak equivalences"). Note that this is not the usual point of view taken when dealing with model categories.
Cospans
A cospan K in a category C is a functor K : Λop → C; equivalently, a contravariant functor from Λ to C. That is, a diagram of type i.e., a diagram of the form .
Thus it consists of three objects X, Y and Z of C and morphisms f : Y → X and g : Z → X: it is two maps with common codomain.
The limit of a cospan is a pullback.
An example of a cospan is a cobordism W between two manifolds M and N, where the two maps are the inclusions into W. Note that while cobordisms are cospans, the category of cobordisms is not a "cospan category": it is not the category of a |
https://en.wikipedia.org/wiki/Agile%20testing | Agile testing is a software testing practice that follows the principles of agile software development. Agile testing involves all members of a cross-functional agile team, with special expertise contributed by testers, to ensure delivering the business value desired by the customer at frequent intervals, working at a sustainable pace. Specification by example is used to capture examples of desired and undesired behavior and guide coding.
Overview
Agile development recognizes that testing is not a separate phase, but an integral part of software development, along with coding. Agile teams use a "whole-team" approach to "baking quality in" to the software product. Testers on agile teams lend their expertise in eliciting examples of desired behavior from customers, collaborating with the development team to turn those into executable specifications that guide coding. Testing and coding are done incrementally and interactively, building up each feature until it provides enough value to release to production. Agile testing covers all types of testing. The Agile Testing Quadrants provide a helpful taxonomy to help teams identify and plan the testing needed.
The model of the Agile Testing Quadrants was originally described by Brian Marick, and was popularized by Lisa Crispin and Janet Gregory in their book Agile Testing: A Practical Guide for Testers and Agile Teams. It places different test types on two axis: Technology Facing vs Business Facing, and Support Programming vs Critique Product.
Traditional testing methodologies (often employed in the Waterfall model of software development) usually involve a two-team, two-phase process in which the development team builds the product to as near perfection as possible. The software product is delivered late in the software development life cycle at which point the test team strives to find as many bugs/errors as possible. In contrast with these traditional methodologies, Agile testing focuses on repairing faults immediatel |
https://en.wikipedia.org/wiki/Fragmentation%20%28cell%20biology%29 | Fragmentation describes the process of splitting into several pieces or fragments. In cell biology, fragmentation is useful for a cell during both DNA cloning and apoptosis. DNA cloning is important in asexual reproduction or creation of identical DNA molecules, and can be performed spontaneously by the cell or intentionally by laboratory researchers. Apoptosis is the programmed destruction of cells, and the DNA molecules within them, and is a highly regulated process. These two ways in which fragmentation is used in cellular processes describe normal cellular functions and common laboratory procedures performed with cells. However, problems within a cell can sometimes cause fragmentation that results in irregularities such as red blood cell fragmentation and sperm cell DNA fragmentation.
DNA Cloning
DNA cloning can be performed spontaneously by the cell for reproductive purposes. This is a form of asexual reproduction where an organism splits into fragments and then each of these fragments develop into mature, fully grown individuals that are clones of the original organism (See reproductive fragmentation).
DNA cloning can also be performed intentionally by laboratory researchers. Here, DNA fragmentation is a molecular genetic technique that permits researchers to use recombinant DNA technology to prepare large numbers of identical DNA molecules.
In order for DNA cloning to be completed, it is necessary to obtain discrete, small regions of an organism's DNA that constitute specific genes. Only relatively small DNA molecules can be cloned in any available vector. Therefore, the long DNA molecules that compose an organism's genome must be cleaved into fragments that can be inserted into the vector DNA. Two enzymes facilitate the production of such recombinant DNA molecules:
1. Restriction Enzymes
Restriction enzymes are endonucleases produced by bacteria that typically recognize small base pair sequences (called restriction sites) and then cleave both strands of DNA |
https://en.wikipedia.org/wiki/Einstein%20Prize%20%28APS%29 | Since 2003, the Einstein Prize is a biennial prize awarded by the American Physical Society. The recipients are chosen for their outstanding accomplishments in the field of gravitational physics. The prize is named after Albert Einstein (1879-1955), who authored the theories of special and general relativity. The prize was established by the Topical Group on Gravitation at the beginning of 1999. As of 2013, the prize is valued at $10,000. The 2005 prize for Bryce DeWitt was announced shortly before his death, and awarded posthumously.
Recipients |
https://en.wikipedia.org/wiki/Fluid%20Dynamics%20Prize%20%28APS%29 | The Fluid Dynamics Prize is a prize that has been awarded annually by the American Physical Society (APS) since 1979. The recipient is chosen for "outstanding achievement in fluid dynamics research". The prize is currently valued at . In 2004, the Otto Laporte Award—another APS award on fluid dynamics—was merged into the Fluid Dynamics Prize.
Recipients
The Fluid Dynamics Prize has been awarded to:
2022: Elisabeth Charlaix
2021:
2020: Katepalli Sreenivasan
2019: Alexander Smits
2018: Keith Moffatt
2017: Detlef Lohse
2016: Howard A. Stone
2015: Morteza Gharib
2014: Geneviève Comte-Bellot
2013: Elaine Surick Oran
2012: John F. Brady
2011: Tony Maxworthy
2010: E. John Hinch
2009: Stephen B. Pope
2008:
2007:
2006: Thomas S. Lundgren
2005: Ronald J. Adrian
2004:
2003:
2002: Gary Leal
2001: Howard Brenner
2000:
1999: Daniel D. Joseph
1998: Fazle Hussain
1997: Louis Norberg Howard
1996: Parviz Moin
1995: Harry L Swinney
1994: Stephen H. Davis
1993: Theodore Yao-tsu Wu
1992: William R. Sears
1991: Andreas Acrivos
1990: John L. Lumley
1989:
1988:
1987: Anatol Roshko
1986: Robert T. Jones
1985: Chia-Shun Yih
1984: George Carrier
1983: Stanley Corrsin
1982: Howard W. Emmons
1981:
1980: Hans Wolfgang Liepmann
1979: Chia Chiao Lin
See also
List of physics awards |
https://en.wikipedia.org/wiki/Frank%20Isakson%20Prize | The Frank Isakson Prize for Optical Effects in Solids is a prize that has been awarded every second year by the American Physical Society since 1980. The recipient is chosen for "outstanding optical research that leads to breakthroughs in the condensed matter sciences.". The prize is named after Frank Isakson, and as of 2007 it is valued at $5,000.
Recipients
Source: American Physical Society
2022: Manfred Fiebig
2020: Robert W. Boyd and Vladimir M. Shalaev
2018: Andrea Cavalleri and Keith A. Nelson
2016: David Burnham Tanner and Dirk van der Marel
2014: Naomi Halas, Peter Nordlander, and Tony Heinz
2012: Dmitri Basov
2010: Duncan G. Steel
2008: Joseph Orenstein and Zeev Valentine Vardeny
2006: Roberto Merlin
2004: James Wolfe
2002: James W. Allen and Thomas Timusk
2000: Paul Linford Richards
1998: Yuen-Ron Shen
1996: David E. Aspnes
1994: Anant K. Ramdas
1992: Paul A. Fleury
1990: Miles V. Klein
1988: Albert J. Sievers
1986: Elias Burstein
1984: Manuel Cardona
1982: Jan Tauc
1980: David L. Dexter
See also
List of physics awards
External links
Frank Isakson Prize for Optical Effects in Solids, American Physical Society
Awards of the American Physical Society
Condensed matter physics awards |
https://en.wikipedia.org/wiki/George%20E.%20Pake%20Prize | The George E. Pake Prize is a prize that has been awarded annually by the American Physical Society since 1984. The recipients are chosen for "outstanding work by physicists combining original research accomplishments with leadership in the management of research or development in industry". The prize is named after George E. Pake (1924–2004), founding director of Xerox PARC, and as of 2007 it is valued at $5,000.
Recipients
Source: American Physical Society
See also
List of physics awards
External links
George E. Pake Prize, American Physical Society
Awards of the American Physical Society |
https://en.wikipedia.org/wiki/Type%20III%20secretion%20system | The type III secretion system (T3SS or TTSS), also called the injectisome, is one of the bacterial secretion systems used by bacteria to secrete their effector proteins into the host's cells to promote virulence and colonisation. The T3SS is a needle-like protein complex found in several species of pathogenic gram-negative bacteria.
Overview
The term Type III secretion system was coined in 1993. This secretion system is distinguished from at least five other secretion systems found in gram-negative bacteria. Many animal and plant associated bacteria possess similar T3SSs. These T3SSs are similar as a result of convergent evolution and phylogenetic analysis supports a model in which gram-negative bacteria can transfer the T3SS gene cassette horizontally to other species. The most researched T3SSs are from species of Shigella (causes bacillary dysentery), Salmonella (typhoid fever), Escherichia coli (Gut flora, some strains cause food poisoning), Vibrio (gastroenteritis and diarrhea), Burkholderia (glanders), Yersinia (plague), Chlamydia (sexually transmitted disease), Pseudomonas (infects humans, animals and plants) and the plant pathogens Erwinia, Ralstonia and Xanthomonas, and the plant symbiont Rhizobium.
The T3SS is composed of approximately 30 different proteins, making it one of the most complex secretion systems. Its structure shows many similarities with bacterial flagella (long, rigid, extracellular structures used for motility). Some of the proteins participating in T3SS share amino-acid sequence homology to flagellar proteins. Some of the bacteria possessing a T3SS have flagella as well and are motile (Salmonella, for instance), and some do not (Shigella, for instance). Technically speaking, type III secretion is used both for secreting infection-related proteins and flagellar components. However, the term "type III secretion" is used mainly in relation to the infection apparatus. The bacterial flagellum shares a common ancestor with the type III secre |
https://en.wikipedia.org/wiki/File%20Replication%20Service | File Replication Service (FRS) is a Microsoft Windows Server service for distributing shared files and Group Policy Objects. It replaced the (Windows NT) Lan Manager Replication service, and has been partially replaced by Distributed File System Replication. It is also known as NTFRS after the name of the executable file that runs the service.
One of the main uses of FRS is for the SYSVOL directory share. The SYSVOL directory share is particularly important in a Microsoft network as it is used to distribute files supporting Group Policy and scripts to client computers on the network. Since Group Policies and scripts are run each time a user logs on to the system, it is important to have reliability. Having multiple copies of the SYSVOL directory increases the resilience and spreads the workload for this essential service.
The SYSVOL directory can be accessed by using a network share to any server that has a copy of the SYSVOL directory (normally a Domain Controller) as shown below:
\\server\SYSVOL
Or by accessing it using the domain name:
\\domain.com\SYSVOL
Servers that work together to provide this service are called Replication Partners.
To control file replication:
Use the Active Directory Sites and Services from Administrative Tools.
Select the Sites container to view a list of sites.
Expand the site that to be viewed. This will provide the list of servers in that site.
Expand the server to be viewed, right click the NTDS settings, and select Properties.
Under the Connections tab, the list of servers that are being replicated can be seen.
DFS Replication
In Windows Server 2003 R2 and Windows Server 2008, DFS Replication is available as well as the File Replication Service. DFS Replication is a state-based replication engine for file replication among DFS shares, which supports replication scheduling and bandwidth throttling. It uses Remote Differential Compression to detect and replicate only the change to files, rather than replicating enti |
https://en.wikipedia.org/wiki/Canadian%20Telework%20Association | The Canadian Telework Association (CTA) is an organization promoting telework and telecommuting in Canada. It was founded in 1997, and since then, it has grown to include over 1000 members, most of which are individuals, corporations, and academic institutions. The association does not accept funding or donations and does not charge fees for membership.
The founder of this association has since retired and the websites have been closed.
External links
Home page |
https://en.wikipedia.org/wiki/Etienne%20Vermeersch | Etienne Vermeersch (2 May 1934, Sint-Michiels, Bruges – 18 January 2019, Ghent) was a Belgian moral philosopher, skeptic, opinion maker and debater. He is one of the founding fathers of the abortion, euthanasia law, and the Law on Patients' Rights in Belgium. Vermeersch became an atheist after five years with the Society of Jesus (Jesuits). Later he became a philosophical materialist. In January 2008, Vermeersch was chosen by hundred prominent Flemings as the most influential intellectual of Flanders. He died in a hospital in Ghent on 18 January 2019 by euthanasia after a long illness.
Career
Etienne Vermeersch had an MA in classical philology and in philosophy. In 1965 he obtained his PhD on the philosophical implications of information theory and cybernetics at Ghent University, Belgium. He became a professor at Ghent University in 1967. For decades he taught Philosophy of science, History of philosophy, 20th-century philosophy, Philosophical anthropology and History of christianity.
He worked on the foundations of the social sciences, on the philosophical aspects of research into informatics, on Artificial Intelligence, and on general social and ethical problems, mainly with regard to Bioethics, Environmental philosophy, and Cultural philosophy.
He was a Vice-Rector at Ghent University from 1993 until 1997. He was, among others, a member of the Flemish Board for Scientific Policy, of the governmental board of the Flemish Institute for Biotechnology, member of the Environmental Board of Flanders, and member of the Federal Board for Science Policy. He was also President of the Advisory Committee of Bioethics. The Medical Assisted Reproduction Act, the Scientific Research Act, even the Transgender Act would not be what they are today in Belgium without Vermeersch's input. The legalization of therapeutic cloning, which sparked heated debate in the Senate, overcame it thanks to its ethical considerations.
Publications
Etienne Vermeersch published:
80 Bio-ethica |
https://en.wikipedia.org/wiki/Beyer%20Professor%20of%20Applied%20Mathematics | The Beyer Chair of Applied Mathematics is an endowed professorial position in the Department of Mathematics, University of Manchester, England. The endowment came from the will of the celebrated locomotive designer and founder of locomotive builder Beyer, Peacock & Company, Charles Frederick Beyer. He was the university's largest single donor.
The first appointment in 1881 was of Arthur Schuster who held the position until 1888. After Schuster’s departure, the chair of Mathematics to which Horace Lamb had been appointed in 1885 became the Beyer Professorship of Mathematics and remained so until Lamb’s retirement in 1920. At this point an existing chair, of Mathematics and Natural Philosophy to which Sydney Chapman had been appointed in 1919, was renamed the Beyer Professorship of Mathematics and Natural Philosophy. After Chapman’s resignation, the Beyer title was applied to the chair of Applied Mathematics. There was no incumbent between 1937-1945.
Most of the holders of the post were elected as Fellows of the Royal Society, an honour bestowed on a small minority of UK mathematics professors. Lamb, Champman, Milne and Goldstein all received the Smith's Prize and indication of early career promise.
The other endowed chairs in mathematics at the University of Manchester are the Richardson Chair of Applied Mathematics, and the Fielden Chair of Pure Mathematics as well as the named Sir Horace Lamb Chair.
Beyer Professors
1881–1888 Arthur Schuster
1888–1920 Horace Lamb
1920–1924 Sydney Chapman, Beyer Professor of Mathematics and Natural Philosophy
1924–1928 Edward Arthur Milne
1929–1937 Douglas Hartree
1945–1950 Sydney Goldstein
1950–1959 James Lighthill
1961–1990 Fritz Ursell
1991–1996 Philip Hall
1996–2017 David Abrahams
2017– Pending appointment |
https://en.wikipedia.org/wiki/Differential%20equations%20of%20addition | In cryptography, differential equations of addition (DEA) are one of the most basic equations related to differential cryptanalysis that mix additions over two different groups (e.g. addition modulo 232 and addition over GF(2)) and where input and output differences are expressed as XORs.
Examples
Differential equations of addition (DEA) are of the following form:
where and are -bit unknown variables and , and are known variables. The symbols and denote addition modulo and bitwise exclusive-or respectively. The above equation is denoted by .
Let a set
for integer denote a system of DEA where is a polynomial in . It has been proved that the satisfiability of an arbitrary set of DEA is in the complexity class P when a brute force search requires an exponential time.
In 2013, some properties of a special form of DEA were reported by Chengqing Li et al., where and is assumed known. Essentially, the special DEA can be represented as . Based on the found properties, an algorithm for deriving was proposed and analyzed.
Applications
Solution to an arbitrary set of DEA (either in batch and or in adaptive query model) was due to Souradyuti Paul and Bart Preneel. The solution techniques have been used to attack the stream cipher Helix.
Further reading
Souradyuti Paul and Bart Preneel, Solving Systems of Differential Equations of Addition, ACISP 2005. Full version (PDF)
Souradyuti Paul and Bart Preneel, Near Optimal Algorithms for Solving Differential Equations of Addition With Batch Queries, Indocrypt 2005. Full version (PDF)
Helger Lipmaa, Johan Wallén, Philippe Dumas: On the Additive Differential Probability of Exclusive-Or. FSE 2004: 317-331. |
https://en.wikipedia.org/wiki/Multiplexed%20display | Multiplexed displays are electronic display devices where the entire display is not driven at one time.
Instead, sub-units of the display (typically, rows or columns for a dot matrix display or individual characters for a character oriented display, occasionally individual display elements) are multiplexed, that is, driven one at a time, but the high switching frequency and the persistence of vision combine to make the viewer believe the entire display is continuously active.
A multiplexed display has several advantages compared to a non-multiplexed display:
fewer wires (often, far fewer wires) are needed
simpler driving electronics can be used
both lead to reduced cost
reduced power consumption
Multiplexed displays can be divided into two broad categories:
character-oriented displays
pixel-oriented displays
Character-oriented displays
Most character-oriented displays (such as seven-segment displays, fourteen-segment displays, and sixteen-segment displays) display an entire character at one time. The various segments of each character are connected in a two-dimensional diode matrix and will only illuminate if both the "row" and "column" lines of the matrix are at the correct electrical potential. The light-emitting element normally takes the form of a light-emitting diode (LED) so electricity will only flow in one direction, keeping the individual "row" and "column" lines of the matrix electrically isolated from each other. For liquid crystal displays, the intersection of the row and column is not conductive at all.
In the example of the VCR display shown above, the illuminated elements are the plates of many individual triode vacuum tubes sharing the same vacuum enclosure. The grids of the triodes are arranged so that only one digit is illuminated at a time. All of the similar plates in all of the digits (for example, all of the lower-left plates in all of the digits) are connected in parallel. One by one, the microprocessor driving the display enables |
https://en.wikipedia.org/wiki/POPLmark%20challenge | In programming language theory, the POPLmark challenge (from "Principles of Programming Languages benchmark", formerly Mechanized Metatheory for the Masses!) (Aydemir, 2005) is a set of benchmarks designed to evaluate the state of automated reasoning (or mechanization) in the metatheory of programming languages, and to stimulate discussion and collaboration among a diverse cross section of the formal methods community. Very loosely speaking, the challenge is about measurement of how well programs may be proven to match a specification of how they are intended to behave (and the many complex issues that this involves). The challenge was initially proposed by the members of the PL club at the University of Pennsylvania, in association with collaborators around the world. The Workshop on Mechanized Metatheory is the main meeting of researchers participating in the challenge.
The design of the POPLmark benchmark is guided by features common to reasoning about programming languages. The challenge problems do not require the formalisation of large programming languages, but they do require sophistication in reasoning about:
Binding Most programming languages have some form of binding, ranging in complexity from the simple binders of simply typed lambda calculus to complex, potentially infinite binders needed in the treatment of record patterns.
Induction Properties such as subject reduction and strong normalisation often require complex induction arguments.
Reuse Furthering collaboration being a key aim of the challenge, the solutions are expected to contain reusable components that would allow researchers to share language features and designs without requiring them to start from scratch every time.
The problems
, the POPLmark challenge is composed of three parts. Part 1 concerns solely the types of System F<: (System F with subtyping), and has problems such as:
Checking that the type system admits transitivity of subtyping.
Checking the transitivity of subt |
https://en.wikipedia.org/wiki/Addiction%20module | Addiction modules are toxin-antitoxin systems. Each consists of a pair of genes that specify two components: a stable toxin and an unstable antitoxin that interferes with the lethal action of the toxin. Found first in Escherichia coli on low copy number plasmids, addiction modules are responsible for a process called the postsegregational killing effect. When bacteria lose these plasmid(s) (or other extrachromosomal elements), the cured cells are selectively killed because the unstable antitoxin is degraded faster than the more stable toxin. The term "addiction" is used because the cell depends on the de novo synthesis of the antitoxin for cell survival. Thus, addiction modules are implicated in maintaining the stability of extrachromosomal elements.
Proteic addiction modules
Proteic addiction modules use proteins as toxins and antitoxins, as opposed to RNA or other methods. The known proteic addiction modules all have similar shared characteristics, including placement of the antitoxin gene relative to the toxin gene, method of toxin neutralization by the antitoxin, and autoregulation of the addiction module by the antitoxin or toxin:antitoxin complex.
Transcriptional control of antitoxin:toxin ratios
In protein-based addiction modules, the genes encoding the toxin and antitoxin lie adjacent to each other and are continuously expressed under one operon. To ensure survival of the host when the addiction module is present, more antitoxin must be produced than toxin (to counter the shorter lifespan of the antitoxin molecules). Safe ratios of the toxin and antitoxin are maintained at least in part by both this overexpression and by having the antitoxin-encoding gene encoded upstream from the toxin gene, so that the antitoxin is available to immediately neutralize the toxin. This upstream placement of the antitoxin gene is found in all proteic addiction modules.
In addition, the transcription of the whole addiction module is often negatively autoregulated (i.e. the |
https://en.wikipedia.org/wiki/VEGF%20receptor | VEGF receptors (VEGFRs) are receptors for vascular endothelial growth factor (VEGF). There are three main subtypes of VEGFR, numbered 1, 2 and 3. Depending on alternative splicing, they may be membrane-bound (mbVEGFR) or soluble (sVEGFR).
Inhibitors of VEGFR are used in the treatment of cancer.
VEGF
Vascular endothelial growth factor (VEGF) is an important signaling protein involved in both vasculogenesis (the formation of the circulatory system) and angiogenesis (the growth of blood vessels from pre-existing vasculature). As its name implies, VEGF activity is restricted mainly to cells of the vascular endothelium, although it does have effects on a limited number of other cell types (e.g. stimulation monocyte/macrophage migration). In vitro, VEGF has been shown to stimulate endothelial cell mitogenesis and cell migration. VEGF also enhances microvascular permeability and is sometimes referred to as vascular permeability factor.
Receptor biology
All members of the VEGF family stimulate cellular responses by binding to tyrosine kinase receptors (the VEGFRs) on the cell surface, causing them to dimerize and become activated through transphosphorylation. The VEGF receptors have an extracellular portion consisting of 7 immunoglobulin-like domains, a single transmembrane spanning region and an intracellular portion containing a split tyrosine-kinase domain.
VEGF-A binds to VEGFR-1 (Flt-1) and VEGFR-2 (KDR/Flk-1). VEGFR-2 appears to mediate almost all of the known cellular responses to VEGF. The function of VEGFR-1 is less well defined, although it is thought to modulate VEGFR-2 signaling. Another function of VEGFR-1 is to act as a dummy/decoy receptor, sequestering VEGF from VEGFR-2 binding (this appears to be particularly important during vasculogenesis in the embryo). In fact, an alternatively spliced form of VEGFR-1 (sFlt1) is not a membrane bound protein but is secreted and functions primarily as a decoy. A third receptor has been discovered (VEGFR-3), however |
https://en.wikipedia.org/wiki/Alexander%20Zaitsev%20%28astronomer%29 | Aleksandr Leonidovich Zaitsev (; 19 May 1945 – 29 November 2021) was a Russian and Soviet radio engineer and astronomer from Fryazino. He worked on radar astronomy devices, near-Earth asteroid radar research, and SETI.
Education
Zaitsev received his M.Sc. degree in radio engineering from the Moscow Mining University in 1967 and his Ph.D. (1981) and his postdoctoral lecture qualification (1997) in radar astronomy from the Institute of Radio Engineering and Electronics, Russian Academy of Science in Moscow. He was a member of the Space Guard Foundation, the SETI League, and The European Radio Astronomy Club (E.R.A.C.).
Career
Zaitsev was the chief scientist at the Russian Academy of Science's Institute of Radio Engineering and Electronics. He headed the group transmitting Team Encounter's interstellar messages using the Yevpatoria (Evpatoria) Deep Space Center (EDSC). Zaitsev was also serving as the SETI League's Regional Coordinator for Russia.
Zaitsev's career has focused on three main topics: the theory, the design and implementation of radar devices used in the study of Venus, Mars, and Mercury; near-Earth asteroid radar research; and interstellar radio messaging, his later field of research. He retired in 2013.
Zaitsev observed the asteroid 4179 Toutatis in December 1992 using the 70-m Yevpatorian Planetary Radar in Crimea (Ukraine), as a sounding signal transmitter, and the 100-m radio telescope in Effelsberg, Germany, as a receiver of the asteroid's radar echo.
In June 1995, Zaitsev was responsible for initiating the world's first intercontinental radar astronomy experiment; the radar groups participating in this experiment were led by Steven Ostro at JPL, Zaitsev in Yevpatoria, and Yasuhiro Koyama in Kashima, Japan. Ostro's group transmitted and received using the Goldstone site of the Deep Space Network, while Zaitsev received using the Yevpatoria site and Koyama's group received at Kashima. The target asteroid, 6489 Golevka, was later named for th |
https://en.wikipedia.org/wiki/Persephin | Persephin is a neurotrophic factor in the glial cell line-derived neurotrophic factor (GDNF) family. Persephin shares around a 40% similarity in amino acid sequence compared to GDNF and neurturin, two members of the GDNF family.
Function
Persephin has been found to be less potent than other members of the GDNF family. It has been found to support the survival and morphological differentiation of tyrosine hydroxylase immunoreactive neurons, although less so than both GDNF and neurturin. The mRNA levels of persephin in developing neurons has been low compared to other neurotrophic factors, but relatively higher levels of persephin mRNA have been found in embryonic neurons.
Similarly to the other members of the GDNF family of ligands, persephin uses a receptor that consists of the tyrosine kinase signaling component Ret and a unit of glycosylphosphatidylinsitol (GPI)-anchored receptor (GFRα). Persephin specifically binds to GFRα4.
Persephin acts on both neurons in the CNS and PNS, but also has the ability to act as a renal ramogen.
Structure
Unlike other GDNF family of ligands, persephin only contains one RXXR cleavage site, rather than multiple, indicating that it can only make one length of functional peptide.
Therapeutics
Persephin has the potential to be used as a therapeutic treatment for neurodegenerative diseases, such as Parkinson's disease and other diseases that affect motor neurons. Because persephin acts more selectively compared to other GFLs, such as GDNF, it may produce fewer mechanism-based complications, making it a stronger therapeutic target. |
https://en.wikipedia.org/wiki/GDNF%20family%20of%20ligands | The GDNF family of ligands (GFL) consists of four neurotrophic factors: glial cell line-derived neurotrophic factor (GDNF), neurturin (NRTN), artemin (ARTN), and persephin (PSPN). GFLs have been shown to play a role in a number of biological processes including cell survival, neurite outgrowth, cell differentiation and cell migration. In particular signalling by GDNF promotes the survival of dopaminergic neurons.
Signalling complex formation
At the cell surface of target cells, a signalling complex forms, composed of a particular GFL dimer, a receptor tyrosine kinase molecule RET, and a cell surface-bound co-receptor that is a member of the GFRα protein family. The primary ligands for the co-receptors GFRα1, GFRα2, GFRα3, and GFRα4 are GDNF, NRTN, ARTN, and PSPN, respectively.
Upon initial GFL-GFRα complex formation, the complex then brings together two molecules of RET, triggering trans-autophosphorylation of specific tyrosine residues within the tyrosine kinase domain of each RET molecule. Phosphorylation of these tyrosines then initiates intracellular signal transduction processes.
It has been shown that in the case of GDNF, heparan sulfate glycosaminoglycans are also required to be present at the cell surface in order for RET mediated GDNF signalling to occur.
Clinical significance
GFLs are an important therapeutic target for several conditions:
GDNF has shown promising results in two Parkinson's disease clinical trials and in a number of animal trials. Although a different study later reported this as a 'placebo effect', work on perfecting the delivery of GDNF to the putamen is continuing. GDNF is a potent survival factor for central motoneurons and may have clinical importance for the treatment of ALS. Moreover, recent results highlight the importance of GDNF as a new target for drug addiction and alcoholism treatment.
NRTN can also be used for Parkinson’s disease therapy and for epilepsy treatment. NRTN promotes survival of basal forebrain cholinergi |
https://en.wikipedia.org/wiki/Multiprotocol%20Encapsulation | Multiprotocol Encapsulation, or MPE for short, is a Data link layer protocol defined
by DVB which has been published as part of ETSI EN 301 192. It
provides means to carry packet oriented protocols (like for instance
IP) on top of MPEG transport stream (TS).
Another encapsulation method is Unidirectional Lightweight Encapsulation (ULE) which was developed and standardized within the IETF as RFC 4326.
Protocol Outline
MPE uses MPEG-2 Private Table sections to carry the user
datagrams. The section header is used to convey:
the frame's destination MAC address
optional ISO/IEC 8802-2 Logical Link Control (LLC) and ISO/IEC 8802-1 Sub-Network Attachment Point (SNAP) information
a payload scrambling indication
a MAC address scrambling indication
MAC addresses from 1 to 6 bytes length may be used. The format of
MPEG-2 DSM-CC sections happens to be compatible with DVB MPE.
MPE-based IP Service Offerings
Service Architecture
A complete IP service offering over MPEG-2 TS can be established by
organizing MPE streams into one or more IP Platforms carried on a
broadcast network by means of the IP/MAC Notification Table
mechanism which is also defined in ETSI EN 301 192.
Commercial Offerings
Both major European satellite operators (SES and
Eutelsat) are offering commercial IP services using MPE (such as ASTRA2Connect) to both businesses and consumers.
External links
DVB Project Homepage
ETSI Homepage
ASTRA2Connect website
See also
IP over DVB
ASTRA2Connect
Interactive television
MPEG
Logical link control
Broadcast engineering
Link protocols |
https://en.wikipedia.org/wiki/Mississippi%20statistical%20areas | The U.S. currently has 30 statistical areas that have been delineated by the Office of Management and Budget (OMB). On March 6, 2020, the OMB delineated seven combined statistical areas, four metropolitan statistical areas, and 19 micropolitan statistical areas in Mississippi.
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the 30 United States statistical areas and 82 counties of the State of Mississippi with the following information:
The combined statistical area (CSA) as designated by the OMB.
The CSA population according to 2019 US Census Bureau population estimates.
The core based statistical area (CBSA) as designated |
https://en.wikipedia.org/wiki/Geometric%20median | In geometry, the geometric median of a discrete set of sample points in a Euclidean space is the point minimizing the sum of distances to the sample points. This generalizes the median, which has the property of minimizing the sum of distances for one-dimensional data, and provides a central tendency in higher dimensions. It is also known as the 1-median, spatial median, Euclidean minisum point, or Torricelli point.
The geometric median is an important estimator of location in statistics, where it is also known as the L1 estimator (after the L1 norm). It is also a standard problem in facility location, where it models the problem of locating a facility to minimize the cost of transportation.
The more general k-median problem asks for the location of k cluster centers minimizing the sum of distances from each sample point to its nearest center.
If the point is generalized into a line or a curve, the best-fitting solution is found via least absolute deviations.
The special case of the problem for three points in the plane (that is, = 3 and = 2 in the definition below) is sometimes also known as Fermat's problem; it arises in the construction of minimal Steiner trees, and was originally posed as a problem by Pierre de Fermat and solved by Evangelista Torricelli. Its solution is now known as the Fermat point of the triangle formed by the three sample points. The geometric median may in turn be generalized to the problem of minimizing the sum of weighted distances, known as the Weber problem after Alfred Weber's discussion of the problem in his 1909 book on facility location. Some sources instead call Weber's problem the Fermat–Weber problem, but others use this name for the unweighted geometric median problem.
provides a survey of the geometric median problem. See for generalizations of the problem to non-discrete point sets.
Definition
Formally, for a given set of m points with each , the geometric median is defined as
Here, arg min means the value of the a |
https://en.wikipedia.org/wiki/Arizona%20statistical%20areas | The U.S. currently has 13 statistical areas that have been delineated by the Office of Management and Budget (OMB). On March 6, 2020, the OMB delineated two combined statistical areas, seven metropolitan statistical areas, and four micropolitan statistical areas in Arizona. The most populous of these statistical areas is the Phoenix-Mesa, AZ Combined Statistical Area with a 2020 Census population of 4,899,104.
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the 13 United States statistical areas and 15 counties of the State of Arizona with the following information:
The combined statistical area (CSA) as designated by the O |
https://en.wikipedia.org/wiki/Alaska%20statistical%20areas | The U.S. currently has four statistical areas that have been delineated by the Office of Management and Budget (OMB). On March 6, 2020, the OMB delineated two metropolitan statistical areas and two micropolitan statistical areas in Alaska. The most populous of these statistical areas is the Anchorage, AK Metropolitan Statistical Area with a 2020 Census population of 398,328.
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the 4 United States statistical areas, 19 organized boroughs and 11 census areas in the State of Alaska with the following information:
The core based statistical area (CBSA) as designated by the OMB.
The |
https://en.wikipedia.org/wiki/Hybrid%20functional | Hybrid functionals are a class of approximations to the exchange–correlation energy functional in density functional theory (DFT) that incorporate a portion of exact exchange from Hartree–Fock theory with the rest of the exchange–correlation energy from other sources (ab initio or empirical). The exact exchange energy functional is expressed in terms of the Kohn–Sham orbitals rather than the density, so is termed an implicit density functional. One of the most commonly used versions is B3LYP, which stands for "Becke, 3-parameter, Lee–Yang–Parr".
Origin
The hybrid approach to constructing density functional approximations was introduced by Axel Becke in 1993. Hybridization with Hartree–Fock (HF) exchange (also called exact exchange) provides a simple scheme for improving the calculation of many molecular properties, such as atomization energies, bond lengths and vibration frequencies, which tend to be poorly described with simple "ab initio" functionals.
Method
A hybrid exchange–correlation functional is usually constructed as a linear combination of the Hartree–Fock exact exchange functional
and any number of exchange and correlation explicit density functionals. The parameters determining the weight of each individual functional are typically specified by fitting the functional's predictions to experimental or accurately calculated thermochemical data, although in the case of the "adiabatic connection functionals" the weights can be set a priori.
B3LYP
For example, the popular B3LYP (Becke, 3-parameter, Lee–Yang–Parr) exchange-correlation functional is
where , , and . is a generalized gradient approximation: the Becke 88 exchange functional and the correlation functional of Lee, Yang and Parr for B3LYP, and is the VWN local spin density approximation to the correlation functional.
The three parameters defining B3LYP have been taken without modification from Becke's original fitting of the analogous B3PW91 functional to a set of atomization energ |
https://en.wikipedia.org/wiki/Hawaii%20statistical%20areas | The U.S. currently has four statistical areas that have been delineated by the Office of Management and Budget (OMB). On March 6, 2020, the OMB delineated two metropolitan statistical areas and two micropolitan statistical areas in Hawaii. The most populous of these statistical areas is the Honolulu, HI Metropoitan Statistical Area with a 2020 Census population of 1,016,508.
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the four United States statistical areas and five counties of the State of Hawaii with the following information:
The core based statistical area (CBSA)
The CBSA population according to 2019 US Census Bur |
https://en.wikipedia.org/wiki/Landscape-scale%20conservation | Landscape-scale conservation is a holistic approach to landscape management, aiming to reconcile the competing objectives of nature conservation and economic activities across a given landscape. Landscape-scale conservation may sometimes be attempted because of climate change. It can be seen as an alternative to site based conservation.
Many global problems such as poverty, food security, climate change, water scarcity, deforestation and biodiversity loss are connected. For example, lifting people out of poverty can increase consumption and drive climate change. Expanding agriculture can exacerbate water scarcity and drive habitat loss. Proponents of landscape management argue that as these problems are interconnected, coordinated approaches are needed to address them, by focussing on how landscapes can generate multiple benefits. For example, a river basin can supply water for towns and agriculture, timber and food crops for people and industry, and habitat for biodiversity; and each one of these users can have impacts on the others.
Landscapes in general have been recognised as important units for conservation by intergovernmental bodies, government initiatives, and research institutes.
Problems with this approach include difficulties in monitoring, and the proliferation of definitions and terms relating to it.
Definitions
There are many overlapping terms and definitions, but many terms have similar meanings. A sustainable landscape, for example, meets "the needs of the present without compromising the ability of future generations to meet their own needs."
Approaching conservation by means of landscapes can be seen as "a conceptual framework whereby stakeholders in a landscape aim to reconcile competing social, economic and environmental objectives". Instead of focussing on a single use of the land it aims to ensure that the interests of different stakeholders are met.
The starting point for all landscape-scale conservation schemes must be an understandin |
https://en.wikipedia.org/wiki/Bush%20White%20House%20email%20controversy | During the 2007 Congressional investigation of the dismissal of eight U.S. attorneys, it was discovered that administration officials had been using a private Internet domain, called gwb43.com, owned by and hosted on an email server run by the Republican National Committee, for various official communications. The domain name is an abbreviation for "George W. Bush, 43rd" President of the United States. The use of this email domain became public when it was discovered that Scott Jennings, the White House's deputy director of political affairs, was using a gwb43.com email address to discuss the firing of the U.S. attorney for Arkansas. Communications by federal employees were also found on georgewbush.com (registered to "Bush-Cheney '04, Inc.") and rnchq.org (registered to "Republican National Committee"). Congressional requests for administration documents while investigating the dismissals of the U.S. attorneys required the Bush administration to reveal that not all internal White House emails were available. Conducting governmental business in this manner is a possible violation of the Presidential Records Act of 1978. Over 5 million emails may have been lost. Greg Palast claims to have come up with 500 of the Karl Rove emails, leading to damaging allegations. In 2009, it was announced that as many as 22 million emails may have been lost.
The "gwb43.com" domain name was publicized by Citizens for Responsibility and Ethics in Washington (CREW), who sent a letter to Oversight and Government Reform Committee chairman Henry A. Waxman requesting an investigation. Waxman sent a formal warning to the RNC, advising them to retain copies of all emails sent by White House employees. According to Waxman, "in some instances, White House officials were using nongovernmental accounts specifically to avoid creating a record of the communications." The Republican National Committee claims to have erased the emails, supposedly making them unavailable for Congressional investigator |
https://en.wikipedia.org/wiki/Immobilized%20enzyme | An immobilized enzyme is an enzyme, with restricted mobility, attached to an inert, insoluble material—such as calcium alginate (produced by reacting a mixture of sodium alginate solution and enzyme solution with calcium chloride). This can provide increased resistance to changes in conditions such as pH or temperature. It also lets enzymes be held in place throughout the reaction, following which they are easily separated from the products and may be used again - a far more efficient process and so is widely used in industry for enzyme catalysed reactions. An alternative to enzyme immobilization is whole cell immobilization. Immobilized enzymes are easily to be handled, simply separated from their products, and can be reused.
Enzymes are bio-catalysts which play an essential role in the enhancement of chemical reactions in cells without being persistently modified, wasted, nor resulting in the loss of equilibrium of chemical reactions. Although the characteristics of enzymes are extremely unique, their utility in the industry is limited due to the lack of re-usability, stability, and high-cost of production.
History
The first synthetic immobilized enzyme was made in the 1950s, performed by the inclusion of enzyme into polymeric matrices or binding onto carrier substances. Also cross-linking procedure was applied by cross-linking of protein alone or along with the addition of inert materials. Over the last decade various immobilization methods have been developed. Binding the enzyme to previously synthesized carrier materials for example is the mostly preferred method so far. Newly, the procedure of cross-linking of crystals of enzyme is also considered as an exciting substitute. Utilization rate of immobilized enzymes is growing constantly.
Considerations
Before performing any kind of immobilization techniques, some factors should be in mind. It is necessary to understand the chemical and physical effects on an enzyme following immobilization. Enzyme stabilit |
https://en.wikipedia.org/wiki/Isocenter | Isocenter in aerial photography: it is a point where a line cuts an angle of 90 degree of tier/2.
It is the point on the aerial photo platform that directly falls on a line half-way between the Principal point and the Nadir point.
In imaging physics and radiation oncology, the isocenter is termed as the point in space through which the central rays of the radiation beams pass.
In radiation oncology there is typically talk of two isocenters:
radiation isocenter and
mechanical isocenter.
Radiation isocenter is the point in space through which the central beam of radiation passes whereas the mechanical isocenter is the point where optical beams intersect. The two are closely related but differ in terms of their functionality and working in Space.
Isocenter definition in Radiotherapy:
The point in space relative to the treatment machine about which various components of the linac rotate. The gantry rotation defines
a horizontal axis which cuts a vertical axis defined by the rotation of the treatment couch. The treatment collimators also
rotate about an axis pointing through the isocentre.
The source to isocenter distance (SIsoD) can be an important parameter to control in determining patient exposure and image quality in diagnostic computed tomography and fluoroscopy. |
https://en.wikipedia.org/wiki/Hexacode | In coding theory, the hexacode is a length 6 linear code of dimension 3 over the Galois field of 4 elements defined by
It is a 3-dimensional subspace of the vector space of dimension 6 over
.
Then contains 45 codewords of weight 4, 18 codewords of weight 6 and
the zero word. The full automorphism group of the hexacode is
. The hexacode can be used to describe the Miracle Octad Generator
of R. T. Curtis. |
https://en.wikipedia.org/wiki/Montana%20statistical%20areas | The U.S. currently has seven statistical areas that have been delineated by the Office of Management and Budget (OMB). On July, 2023, the OMB delineated five metropolitan statistical areas and two micropolitan statistical areas in Montana.
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the 7 United States statistical areas and 56 counties of the State of Montana with the following information:
The combined statistical area (CSA) as designated by the OMB.
The CSA population according to 2019 US Census Bureau population estimates.
The core based statistical area (CBSA) as designated by the OMB.
The CBSA population according |
https://en.wikipedia.org/wiki/List%20of%20Unicode%20characters | As of Unicode version , there are 149,878 characters with code points, covering 161 modern and historical scripts, as well as multiple symbol sets. This article includes the 1,062 characters in the Multilingual European Character Set 2 (MES-2) subset, and some additional related characters.
Character reference overview
HTML and XML provide ways to reference Unicode characters when the characters themselves either cannot or should not be used. A numeric character reference refers to a character by its Universal Character Set/Unicode code point, and a character entity reference refers to a character by a predefined name.
A numeric character reference uses the format
&#nnnn;
or
&#xhhhh;
where nnnn is the code point in decimal form, and hhhh is the code point in hexadecimal form. The x must be lowercase in XML documents. The nnnn or hhhh may be any number of digits and may include leading zeros. The hhhh may mix uppercase and lowercase, though uppercase is the usual style.
In contrast, a character entity reference refers to a character by the name of an entity which has the desired character as its replacement text. The entity must either be predefined (built into the markup language) or explicitly declared in a Document Type Definition (DTD). The format is the same as for any entity reference:
&name;
where name is the case-sensitive name of the entity. The semicolon is required.
Because numbers are harder for humans to remember than names, character entity references are most often written by humans, while numeric character references are most often produced by computer programs.
Control codes
65 characters, including DEL. All belong to the common script.
Footnotes:
1 Control-C has typically been used as a "break" or "interrupt" key.
2 Control-D has been used to signal "end of file" for text typed in at the terminal on Unix / Linux systems. Windows, DOS, and older minicomputers used Control-Z for this purpose.
3 Control-G is an artifact of the days when tel |
https://en.wikipedia.org/wiki/California%20statistical%20areas | The U.S. currently has 39 statistical areas that have been delineated by the federal Office of Management and Budget (OMB). On March 6, 2020, the OMB delineated five combined statistical areas, 26 metropolitan statistical areas, and eight micropolitan statistical areas in California.
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the 39 United States statistical areas and 58 counties of the State of California with the following information:
The combined statistical area (CSA) as designated by the OMB.
The CSA population according to the 2020 US Census.
The core based statistical area (CBSA) as designated by the OMB.
The C |
https://en.wikipedia.org/wiki/Alabama%20statistical%20areas | The U.S. currently has 37 statistical areas that have been delineated by the Office of Management and Budget (OMB). On July 21, 2023, the OMB delineated 9 combined statistical areas, 15 metropolitan statistical areas, and 13 micropolitan statistical areas in Alabama.
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the 37 United States statistical areas and 67 counties of the State of Alabama with the following information:
The combined statistical area (CSA) as designated by the OMB.
The CSA population according to the 2020 US Census.
The core based statistical area (CBSA) as designated by the OMB.
The CBSA population acc |
https://en.wikipedia.org/wiki/Arkansas%20statistical%20areas | The U.S. currently has 27 statistical areas that have been delineated by the Office of Management and Budget (OMB). On March 6, 2020, the OMB delineated four combined statistical areas, eight metropolitan statistical areas, and 15 micropolitan statistical areas in Arkansas.
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the 27 United States statistical areas and 75 counties of the State of Arkansas with the following information:
The combined statistical area (CSA) as designated by the OMB.
The CSA population according to the 2020 US Census.
The core based statistical area (CBSA) as designated by the OMB.
The CBSA populati |
https://en.wikipedia.org/wiki/Delaware%20statistical%20areas | The U.S. currently has five statistical areas that have been delineated by the Office of Management and Budget (OMB). On March 6, 2020, the OMB delineated two combined statistical areas and three metropolitan statistical areas in Delaware.
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the five United States statistical areas and three counties of the State of Delaware with the following information:
The combined statistical area (CSA) as designated by the OMB.
The CSA population according to 2019 US Census Bureau population estimates.
The core based statistical area (CBSA) as designated by the OMB.
The CBSA population acc |
https://en.wikipedia.org/wiki/District%20of%20Columbia%20statistical%20areas | The United States is the primary city of two statistical areas that have been delineated by the Office of Management and Budget (OMB). On March 6, 2020, the OMB delineated the Washington–Arlington–Alexandria, DC–VA–MD–WV Metropolitan Statistical Area and the more extensive Washington–Baltimore–Arlington, DC–MD–VA–WV–PA Combined Statistical Area.
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the two United States statistical areas of the District of Columbia with the following information:
The combined statistical area (CSA) as designated by the OMB.
The CSA population according to 2019 US Census Bureau population estimat |
https://en.wikipedia.org/wiki/Florida%20statistical%20areas | The U.S. currently has 35 statistical areas that have been delineated by the Office of Management and Budget (OMB). On July 21, 2023, the OMB delineated 7 combined statistical areas, 22 metropolitan statistical areas, and 6 micropolitan statistical areas in Florida.
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the 35 United States statistical areas and 67 counties of the State of Florida with the following information:An out-of-state area is displayed in green.
The combined statistical area (CSA) as designated by the OMB.
The CSA population according to 2019 US Census Bureau population estimates.
The core based statisti |
https://en.wikipedia.org/wiki/Georgia%20statistical%20areas | The U.S. currently has 46 statistical areas that have been delineated by the Office of Management and Budget (OMB). On July 21, 2023, the OMB delineated 4 combined statistical areas (7, including those with components outside the state of Georgia), 14 Georgia-based metropolitan statistical areas (15 with Chattanooga, Tennessee and its Georgia suburbs), and 24 micropolitan statistical areas within Georgia.
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the 46 United States statistical areas and 159 counties of the State of Georgia with the following information:An out-of-state area's population is displayed in green.
The co |
https://en.wikipedia.org/wiki/Idaho%20statistical%20areas | The U.S. currently has 20 statistical areas that have been delineated by the Office of Management and Budget (OMB). On March 6, 2020, the OMB delineated four combined statistical areas, seven metropolitan statistical areas, and nine micropolitan statistical areas in Idaho.
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the 20 United States statistical areas and 44 counties of the State of Idaho with the following information:
The combined statistical area (CSA) as designated by the OMB.
The CSA population according to 2019 US Census Bureau population estimates.
The core based statistical area (CBSA) as designated by the OM |
https://en.wikipedia.org/wiki/DAP%20FORTRAN | DAP FORTRAN was an extension of the non IO parts of FORTRAN with constructs that supported parallel computing for the
ICL Distributed Array Processor (DAP). The DAP had a Single Instruction Multiple Data (SIMD) architecture with 64x64 single bit processors.
DAP FORTRAN had the following major features:
It had matrix and vector operations.
Assignments could be performed under a logical mask so only some elements in the target of an assignment were changed.
On the negative side - operations were performed using the size of the underlying hardware i.e. on a 64x64 matrix or 64 element vector.
In a declaration either one or two extents could be omitted as in:
C Multiply vector by matrix
REAL M(,), V(), R()
R = SUM(M*MATR(A))
C Converge to a Laplace potential in an area
REAL P(,), OLD_P(,)
LOGICAL INSIDE(,)
DO 1 K = 1, ITERATIONS
OLD_P = P
P(INSIDE) = 0.25*(P(,+)+P(,-)+P(+,)+P(-,))
IF (MAX(ABS(P-OLD_P)) .LT. EPS) RETURN
1 CONTINUE
The omitted dimension was taken as 64, the size of one side of the DAP. The speed of arithmetic operations depended strongly on the number of bits in the value. INTEGER*n reserved 8n bits where n is 1 to 8, and REAL*n reserved 8n bits where n is 3 to 8. LOGICAL reserved a single bit.
However, DAP FORTRAN fell between two conflicting objectives. It needed to effectively exploit the DAP facilities. But also had to be accessible to the scientific computing community whose primary language, with a design closely tied to serial architectures, was FORTRAN. The dialect used was ICL's 2900-series FORTRAN which was based on an early version of the FORTRAN 77 standard and had mismatches with both FORTRAN 77 and the older FORTRAN 66 standard.
DAP FORTRAN was significantly different from either standard FORTRAN and the machine was not capable of accepting or optimising standard FORTRAN programs. On the other hand, compared with other contemporary languages which were by design extensible |
https://en.wikipedia.org/wiki/Network%20for%20Earthquake%20Engineering%20Simulation | The George E. Brown, Jr. Network for Earthquake Engineering Simulation (NEES) was created by the National Science Foundation (NSF) to improve infrastructure design and construction practices to prevent or minimize damage during an earthquake or tsunami. Its headquarters were at Purdue University in West Lafayette, Indiana as part of cooperative agreement #CMMI-0927178, and it ran from 2009 till 2014. The mission of NEES is to accelerate improvements in seismic design and performance by serving as a collaboratory for discovery and innovation.
Description
The NEES network features 14 geographically distributed, shared-use laboratories that support several types of experimental work: geotechnical centrifuge research, shake table tests, large-scale structural testing, tsunami wave basin experiments, and field site research. Participating universities include: Cornell University; Lehigh University;Oregon State University; Rensselaer Polytechnic Institute; University at Buffalo, SUNY; University of California, Berkeley; University of California, Davis; University of California, Los Angeles; University of California, San Diego; University of California, Santa Barbara; University of Illinois at Urbana-Champaign; University of Minnesota; University of Nevada, Reno; and the University of Texas, Austin.
The equipment sites (labs) and a central data repository are connected to the global earthquake engineering community via the NEEShub, which is powered by the HUBzero software developed at Purdue University specifically to help the scientific community share resources and collaborate. The cyberinfrastructure, connected via Internet2, provides interactive simulation tools, a simulation tool development area, a curated central data repository, user-developed databases, animated presentations, user support, telepresence, mechanism for uploading and sharing resources and statistics about users, and usage patterns.
This allows researchers to: securely store, organize and share da |
https://en.wikipedia.org/wiki/Jackson%20integral | In q-analog theory, the Jackson integral series in the theory of special functions that expresses the operation inverse to q-differentiation.
The Jackson integral was introduced by Frank Hilton Jackson. For methods of numerical evaluation, see and .
Definition
Let f(x) be a function of a real variable x. For a a real variable, the Jackson integral of f is defined by the following series expansion:
Consistent with this is the definition for
More generally, if g(x) is another function and Dqg denotes its q-derivative, we can formally write
or
giving a q-analogue of the Riemann–Stieltjes integral.
Jackson integral as q-antiderivative
Just as the ordinary antiderivative of a continuous function can be represented by its Riemann integral, it is possible to show that the Jackson integral gives a unique q-antiderivative
within a certain class of functions (see ).
Theorem
Suppose that If is bounded on the interval for some then the Jackson integral converges to a function on which is a q-antiderivative of Moreover, is continuous at with and is a unique antiderivative of in this class of functions.
Notes |
https://en.wikipedia.org/wiki/Negafibonacci%20coding | In mathematics, negafibonacci coding is a universal code which encodes nonzero integers into binary code words. It is similar to Fibonacci coding, except that it allows both positive and negative integers to be represented. All codes end with "11" and have no "11" before the end.
Encoding method
To encode a nonzero integer X:
Calculate the largest (or smallest) encodeable number with N bits by summing the odd (or even) negafibonacci numbers from 1 to N.
When it is determined that N bits is just enough to contain X, subtract the Nth negafibonacci number from X, keeping track of the remainder, and put a one in the Nth bit of the output.
Working downward from the Nth bit to the first one, compare each of the corresponding negafibonacci numbers to the remainder. Subtract it from the remainder if the absolute value of the difference is less, AND if the next higher bit does not already have a one in it. A one is placed in the appropriate bit if the subtraction is made, or a zero if not.
Put a one in the N+1th bit to finish.
To decode a token in the code, remove the last "1", assign the remaining bits the values 1, −1, 2, −3, 5, −8, 13... (the negafibonacci numbers), and add the "1" bits.
Negafibonacci representation
Negafibonacci coding is closely related to negafibonacci representation, a positional numeral system sometimes used by mathematicians. The negafibonacci code for a particular nonzero integer is exactly that of the integer's negafibonacci representation, except with the order of its digits reversed and an additional "1" appended to the end. The negafibonacci code for all negative numbers has an odd number of digits, while those of all positive numbers have an even number of digits.
Table
The code for the integers from −11 to 11 is given below.
See also
Fibonacci numbers
Golden ratio base
Zeckendorf's theorem |
https://en.wikipedia.org/wiki/Shapiro%20polynomials | In mathematics, the Shapiro polynomials are a sequence of polynomials which were first studied by Harold S. Shapiro in 1951 when considering the magnitude of specific trigonometric sums. In signal processing, the Shapiro polynomials have good autocorrelation properties and their values on the unit circle are small. The first few members of the sequence are:
where the second sequence, indicated by Q, is said to be complementary to the first sequence, indicated by P.
Construction
The Shapiro polynomials Pn(z) may be constructed from the Golay–Rudin–Shapiro sequence an, which equals 1 if the number of pairs of consecutive ones in the binary expansion of n is even, and −1 otherwise. Thus a0 = 1, a1 = 1, a2 = 1, a3 = −1, etc.
The first Shapiro Pn(z) is the partial sum of order 2n − 1 (where n = 0, 1, 2, ...) of the power series
f(z) := a0 + a1 z + a2 z2 + ...
The Golay–Rudin–Shapiro sequence {an} has a fractal-like structure – for example, an = a2n – which implies that the subsequence (a0, a2, a4, ...) replicates the original sequence {an}. This in turn leads to remarkable
functional equations satisfied by f(z).
The second or complementary Shapiro polynomials Qn(z) may be defined in terms of this sequence, or by the relation Qn(z) = (1-)nz2n-1Pn(-1/z), or by the recursions
Properties
The sequence of complementary polynomials Qn corresponding to the Pn is uniquely characterized by the following properties:
(i) Qn is of degree 2n − 1;
(ii) the coefficients of Qn are all 1 or −1, and its constant term equals 1; and
(iii) the identity |Pn(z)|2 + |Qn(z)|2 = 2(n + 1) holds on the unit circle, where the complex variable z has absolute value one.
The most interesting property of the {Pn} is that the absolute value of Pn(z) is bounded on the unit circle by the square root of 2(n + 1), which is on the order
of the L2 norm of Pn. Polynomials with coefficients from the set {−1, 1} whose maximum modulus on the unit circle is close to their mean modulus are |
https://en.wikipedia.org/wiki/Iowa%20statistical%20areas | The U.S. currently has 30 statistical areas that have been delineated by the Office of Management and Budget (OMB). On March 6, 2020, the OMB delineated six combined statistical areas, nine metropolitan statistical areas, and 15 micropolitan statistical areas in Iowa.
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the 30 United States statistical areas and 99 counties of the State of Iowa with the following information:
The combined statistical area (CSA) as designated by the OMB.
The CSA population according to 2019 US Census Bureau population estimates.
The core based statistical area (CBSA) as designated by the OMB.
The |
https://en.wikipedia.org/wiki/Kansas%20statistical%20areas | The U.S. currently has 23 statistical areas that have been delineated by the Office of Management and Budget (OMB). On March 6, 2020, the OMB delineated two combined statistical areas, six metropolitan statistical areas, and 15 micropolitan statistical areas in Kansas.
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the 23 United States statistical areas and 105 counties of the State of Kansas with the following information:
The combined statistical area (CSA) as designated by the OMB.
The CSA population according to 2019 US Census Bureau population estimates.
The core based statistical area (CBSA) as designated by the OMB. |
https://en.wikipedia.org/wiki/Climactichnites | Climactichnites is an enigmatic, Cambrian fossil formed on or within sandy tidal flats around .
It has been interpreted in many different ways in the past, but is now thought to be a trace fossil of a slug-like organism that moved by crawling to on-shore surfaces, or near-shore, or burrowing into the sediment.
Morphology
There are two species within this ichnogenus, C. wilsoni and C. youngi. C. wilsoni consists of paired lateral ridges between which are undulating bars and furrows oriented at an angle to the direction of travel, whereas C. youngi lacks the paired lateral ridges and consists only of undulating transverse bars and furrows. An additional trace fossil, called Musculopodus, is sometimes found at the beginning of Climactichnites trails and represents the body imprint of the animal while it was stationary. Climactichnites range from 0.8 to 30 cm wide and may exceed ten feet long, making Climactichnites by far the largest Cambrian trace fossil. Based on measured ratios of Musculopodus imprints, the animal itself is estimated to have reached 69 cm long.
Occurrence
Currently, Climactichnites is known only from North America (Missouri, New York, Wisconsin, and Texas in the United States, and Quebec and Ontario in Canada), portions of which were submerged under a shallow equatorial sea during the Cambrian Period. The fossil is found in fine- to coarse-grained sandstones and orthoquartzites which represent sandy, intertidal beach deposits. Microbial mats probably enabled the trace to be preserved.
Interpretation
Early attempts to interpret the fossil as the body of an alga or siphonophore are easily falsified. Climactichnites is now thought to represent the trail or burrow of an organism moving, respectively, on top of or through the sediment. The animal apparently had a muscular foot and moved by extending either side of its body alternately (sometimes both sides may have been extended in unison) to produce the v-shaped transverse bars. Certainly the an |
https://en.wikipedia.org/wiki/Vacuum%20consolidation | Vacuum consolidation (or vacuum preloading) is a soft soil improvement method that has been successfully used by geotechnical engineers and specialists of ground improvement companies in countries such as Australia, China, Korea, Thailand and France for soil improvement or land reclamation. It does not necessarily require surcharge fill and vacuum loads of 80kPa or greater can, typically, be maintained for as long as required.
However, if loads of 80kPa or greater are needed in order to achieve the target soil improvement, additional surcharge may be placed on top of the vacuum system. The vacuum preloading method is cheaper and faster than the fill surcharge method for an equivalent load in suitable areas. Where the underlying ground consists of permeable materials, such as sand or sandy clay, the cost of the technique will be significantly increased due to the requirement of cut-off walls into non-permeable layers to seal off the vacuum. It has been suggested by Carter et al. (2005) that the settlement resulting from vacuum preloading is less than that from a surcharge load of the same magnitude as vacuum consolidation is influenced by drainage boundary conditions. |
https://en.wikipedia.org/wiki/Timeline%20of%20the%20nuclear%20program%20of%20Iran | This is the timeline of the nuclear program of Iran.
1956–1979
1957: The United States and Iran sign a civil nuclear co-operation agreement as part of the U.S. Atoms for Peace program.
August 9, 1963: Iran signs the Partial nuclear test ban treaty (PTBT) and ratifies it on December 23, 1963.
1967: The Tehran Nuclear Research Centre is built and run by the Atomic Energy Organization of Iran (AEOI).
September 1967: The United States supplies 5.545 kg of enriched uranium, of which 5.165 kg contain fissile isotopes for fuel in a research reactor. The United States also supplies 112 g of plutonium, of which 104 g are fissile isotopes, for use as start-up sources for research reactor.
July 1968: Iran signs the Nuclear Non-Proliferation Treaty and ratifies it. It goes into effect on March 5, 1970.
1970s: Under the rule of Mohammad Reza Shah Pahlavi, plans are made to construct up to 20 nuclear power stations across the country with U.S. support and backing. Numerous contracts are signed with various Western firms, and the West German firm Kraftwerk Union (a subsidiary of Siemens AG) begins construction on the Bushehr power plant in 1974.
1974: the Atomic Energy Act of Iran was promulgated. The Act covers the activities for which the Atomic Energy Organization of Iran was established at that period. These activities included using atomic energy and radiation in industry, agriculture and service industries, setting up atomic power stations and desalination factories, producing source materials needed in atomic industries. This creates the scientific and technical infrastructure required for carrying out the said projects, as well as co-ordinating and supervising all matters pertaining to atomic energy in the country.
1974: The Shah lent $1 billion to the French Atomic Energy Commission to help build the Eurodif uranium processing company in Europe. In exchange, Iran received rights to 10% of the enriched uranium product, a right Iran never exercised. After a bitter |
https://en.wikipedia.org/wiki/Alex%20Wilkie | Alex James Wilkie FRS (born 1948 in Northampton) is a British mathematician known for his contributions to model theory and logic. Previously Reader in Mathematical Logic at the University of Oxford, he was appointed to the Fielden Chair of Pure Mathematics at the University of Manchester in 2007.
Education
Alex Wilkie attended Aylesbury Grammar School and went on to gain his BSc in mathematics with first class honours from University College London in 1969, his MSc (in mathematical logic) from the University of London in 1970, and his PhD from the Bedford College, University of London in 1973 under the supervision of Wilfrid Hodges with a dissertation titled Models of Number Theory.
Career and research
After his PhD he went on to an appointment as a lecturer in mathematics at Leicester University from 1972 to 1973, then a research fellow at the Open University from 1973 until 1978. He spent two periods as a junior lecturer in mathematics at Oxford University (1978–80 and 1981-2) with (1980–1) as a visiting assistant professor at Yale University. In 1980 Wilkie solved Tarski's high school algebra problem.
In October 1982 Wilkie was appointed as a research fellow in the department of mathematics at the University of Paris VII, then returned to England the following year to take up a three-year SERC (now EPSRC) advanced research fellowship at the University of Manchester. After two years he was appointed lecturer in the Department of Mathematics. In 1986 he went on to Oxford where he was appointed to the readership in mathematical logic there which had become vacant upon the retirement of Robin Gandy. He remained in this post until appointment to the Fielden Chair at Manchester.
Awards and honours
Wilkie was elected a Fellow of the Royal Society in 2001. To quote the citation
Wilkie has combined logical techniques and differential-geometric techniques to establish fundamental Finiteness Theorems for sets definable using the exponential function, and more general |
https://en.wikipedia.org/wiki/Institute%20for%20Biodiversity%20and%20Ecosystem%20Dynamics | The Institute for Biodiversity and Ecosystem Dynamics (IBED) is one of the ten research institutes of the Faculty of Science of the Universiteit van Amsterdam. IBED employs more than 100 researchers, with PhD students and Postdocs forming a majority, and 30 supporting staff. The total annual budget is around 10 m€, of which more than 40 per cent comes from external grants and contracts. The main output consist of publications in peer reviewed journals and books (on average 220 per year). Each year around 15 PhD students defend their thesis and obtain their degree from the Universiteit van Amsterdam. The institute is managed by a general director appointed by the Dean of the Faculty for a period of five years, assisted by a business manager.
Mission statement
The mission of the Institute for Biodiversity and Ecosystem Dynamics is to increase our insights in the functioning and biodiversity of ecosystems in all their complexity. Knowledge of the interactions between living organisms and processes in their physical and chemical environment is essential for a better understanding of the dynamics of ecosystems at different temporal and spatial scales.
Organization of IBED Research
IBED research is organized in the following three themes:
Theme I: Biodiversity and Evolution
The main question of Theme I research is how patterns in biodiversity can be explained from underlying processes: speciation and extinction, dispersal and the (dis)appearance of geographical barriers, reproductive isolation and hybridisation of taxa. Modern reconstructions of the history of life on earth rely heavily on analyses of DNA data that contain the footprints of the past. Research related to human-made effects on biodiversity includes the identification of endangered biodiversity hotspots affected by global change, potential risks of an escape of transgenes from crops to wild species, and the consequences of habitat fragmentation for the viability and genetic diversity of populations and |
https://en.wikipedia.org/wiki/HDHomeRun | HDHomeRun is a network-attached digital television tuner box, produced by the company SiliconDust USA, Inc.
Overview
Unlike standard set-top box (or set-top unit) appliances, HDHomeRun does not have a video output that connects directly to the user's television. It instead receives a live TV signal and then streams the decoded video over a local area network to an existing smart phone, tablet computer, smart tv, set top streaming device, computer, or game console. This allows it to stream content to multiple viewing locations.
General details
There are currently a number of HDHomeRun models on the market:
single-tuner ATSC/clear QAM
dual-tuner ATSC/clear QAM
dual-tuner commercial (TECH) ATSC/clear QAM
dual-tuner DVB-T/unencrypted DVB-C
three tuner CableCard/clear QAM
All models are designed to receive unencrypted digital broadcast or cable television and stream it over a network for use by any PC on the network. HDHomeRun normally receives an IP address via DHCP but will also work via an auto IP address if no DHCP server is available.
The HDHomeRun Windows driver presents the tuners as standard BDA tuners, enabling BDA-compliant applications to work with the HDHomeRun. The HDHomeRun can also be controlled via a command-line application which is available for Windows, Mac OS X, Linux, FreeBSD, and other POSIX-compliant operating systems. The control library is open source and is available under the LGPL for use in custom applications.
Select retail packaged HDHomeRun units are distributed with Arcsoft TotalMedia Theatre.
Technical specifications
ATSC OTA models
8VSB ATSC 1.0 US over-the-air digital TV
ATSC 3.0 (HD*-4k models only)
QAM 64/256 unencrypted digital cable TV
100 Mbit RJ45 connection
CableCard models
QAM 64/256 unencrypted digital cable TV
CableCard US encrypted digital cable TV
1 Gbit RJ45 connection
ISDB model
ISDB-T South America over the air
DVB models
DVB-T / DVB-T2 over-the-air unencrypted digital TV
DVB-C QAM 64/128/256 |
https://en.wikipedia.org/wiki/Knowledge%20management%20software | Knowledge management software (KM software) is a subset of content management software, which contains a range of software that specializes in the way information is collected, stored and/or accessed. The concept of knowledge management is based on a range of practices used by an individual, a business, or a large corporation to identify, create, represent and redistribute information for a range of purposes. Software that enables an information practice or range of practices at any part of the processes of information management can be deemed to be called information management software. A subset of information management software that emphasizes an approach to build knowledge out of information that is managed or contained is often called knowledge management software.
KM software in most cases provides a means for individuals, small groups or mid-sized businesses to innovate, build new knowledge in the group, and/or improve customer experience. Knowledge management systems (software) include a range of about 1,500 or more different approaches to collect and contain information to then build knowledge that can be searched through specialised search tools. These include concept building tools and/or visual search tools that present information in a connected manner not originally conceptualised by those collecting or maintaining the information database.
One of the main categories of knowledge management software is groupware, which can be used for knowledge sharing and capture. Groupware is a combination of synchronous, asynchronous and community focused tools. Groupware can be used to exchange knowledge and expertise even when the team members are situated around the world.
Features
Features of KM software usually include:
Aggregation of content from both internal and external sources
Classification of content using taxonomies
Search
Expertise location
Views/dashboards
As business today is becoming increasingly international, the ability to access inform |
https://en.wikipedia.org/wiki/Proteinopathy | In medicine, proteinopathy ([pref. protein]; -pathy [suff. disease]; proteinopathies pl.; proteinopathic adj), or proteopathy, protein conformational disorder, or protein misfolding disease, is a class of diseases in which certain proteins become structurally abnormal, and thereby disrupt the function of cells, tissues and organs of the body. Often the proteins fail to fold into their normal configuration; in this misfolded state, the proteins can become toxic in some way (a toxic gain-of-function) or they can lose their normal function. The proteinopathies include such diseases as Creutzfeldt–Jakob disease and other prion diseases, Alzheimer's disease, Parkinson's disease, amyloidosis, multiple system atrophy, and a wide range of other disorders. The term proteopathy was first proposed in 2000 by Lary Walker and Harry LeVine.
The concept of proteopathy can trace its origins to the mid-19th century, when, in 1854, Rudolf Virchow coined the term amyloid ("starch-like") to describe a substance in cerebral corpora amylacea that exhibited a chemical reaction resembling that of cellulose. In 1859, Friedreich and Kekulé demonstrated that, rather than consisting of cellulose, "amyloid" actually is rich in protein. Subsequent research has shown that many different proteins can form amyloid, and that all amyloids show birefringence in cross-polarized light after staining with the dye Congo red, as well as a fibrillar ultrastructure when viewed with an electron microscope. However, some proteinaceous lesions lack birefringence and contain few or no classical amyloid fibrils, such as the diffuse deposits of amyloid beta (Aβ) protein in the brains of people with Alzheimer's. Furthermore, evidence has emerged that small, non-fibrillar protein aggregates known as oligomers are toxic to the cells of an affected organ, and that amyloidogenic proteins in their fibrillar form may be relatively benign.
Pathophysiology
In most, if not all proteinopathies, a change in the 3-dime |
https://en.wikipedia.org/wiki/Material%20flow%20management | Material flow management (MFM) is a method of efficiently managing materials.
The triad of environmental, social and economical orientation makes MFM a tool of high importance in the field of sustainable development and circular economy.
Seen historically, material flow management is a tool that can be understood as an implementation-orientated advancement of the methodology of material flow analysis (MFA). MFM was established as a policy tool after the UN Earth Summit conference in Rio de Janeiro 1992. The German Bundestag outlined the targets and specific goals of MFM in a special report by an Enquete Commission.
See also
Material flow accounting |
https://en.wikipedia.org/wiki/Kantorovich%20theorem | The Kantorovich theorem, or Newton–Kantorovich theorem, is a mathematical statement on the semi-local convergence of Newton's method. It was first stated by Leonid Kantorovich in 1948. It is similar to the form of the Banach fixed-point theorem, although it states existence and uniqueness of a zero rather than a fixed point.
Newton's method constructs a sequence of points that under certain conditions will converge to a solution of an equation or a vector solution of a system of equation . The Kantorovich theorem gives conditions on the initial point of this sequence. If those conditions are satisfied then a solution exists close to the initial point and the sequence converges to that point.
Assumptions
Let be an open subset and a differentiable function with a Jacobian that is locally Lipschitz continuous (for instance if is twice differentiable). That is, it is assumed that for any there is an open subset such that and there exists a constant such that for any
holds. The norm on the left is some operator norm that is compatible with the vector norm on the right. This inequality can be rewritten to only use the vector norm. Then for any vector the inequality
must hold.
Now choose any initial point . Assume that is invertible and construct the Newton step
The next assumption is that not only the next point but the entire ball is contained inside the set . Let be the Lipschitz constant for the Jacobian over this ball (assuming it exists).
As a last preparation, construct recursively, as long as it is possible, the sequences , , according to
Statement
Now if then
a solution of exists inside the closed ball and
the Newton iteration starting in converges to with at least linear order of convergence.
A statement that is more precise but slightly more difficult to prove uses the roots of the quadratic polynomial
,
and their ratio
Then
a solution exists inside the closed ball
it is unique inside the bigger ball
and the convergenc |
https://en.wikipedia.org/wiki/Red%20Forest | The Red Forest (, , ) is the area surrounding the Chernobyl Nuclear Power Plant within the Exclusion Zone, located in Polesia. The name "Red Forest" comes from the ginger-brown colour of the pine trees after they died following the absorption of high levels of ionizing radiation as a consequence of the Chernobyl nuclear disaster on 26 April 1986. In the post-disaster cleanup operations, the Red Forest was bulldozed and buried in "waste graveyards"; the site remains one of the most contaminated areas in the world today.
Disaster and cleanup
The Red Forest is located in the zone of alienation; this area received the highest doses of radiation from the Chernobyl disaster and the resulting clouds of smoke and dust, heavily polluted with radioactive contamination. The trees died from this radiation. The explosion and fire at the Chernobyl No. 4 reactor contaminated the soil, water and atmosphere with radioactive material equivalent to that of 20 times the atomic bombings of Hiroshima and Nagasaki.
In the post-disaster cleanup operations, a majority of the pine trees were bulldozed and buried in trenches by the "liquidators". The trenches were then covered with a thick carpet of sand and planted with pine saplings. Many fear that as the trees decay, radioactive contaminants will leach into the ground water. People have evacuated the contaminated zone around the Red Forest.
Wildlife refuge
As humans were evacuated from the area in 1986, animals moved in despite the radiation. The flora and fauna of the Red Forest have been dramatically affected by the accident. It seems that the biodiversity of the Red Forest has increased in the years following the disaster. There are reports of stunted plants in the area. Wild boar multiplied eightfold between 1986 and 1988.
The site of the Red Forest remains one of the most contaminated areas in the world. However, it has proved to be an astonishingly fertile habitat for many endangered species. The evacuation of the area surroun |
https://en.wikipedia.org/wiki/Virginia%20statistical%20areas | The United States currently has 19 statistical areas that have been delineated by the Office of Management and Budget (OMB). On March 6, 2020, the OMB delineated four combined statistical areas, 11 metropolitan statistical areas, and four micropolitan statistical areas in Virginia.
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the 19 United States statistical areas, 95 counties, and 38 independent cities of the Commonwealth of Virginia with the following information:
The combined statistical area (CSA) as designated by the OMB.
The CSA population according to 2019 US Census Bureau population estimates.
The core based stat |
https://en.wikipedia.org/wiki/Maryland%20statistical%20areas | The U.S. currently has 12 statistical areas that have been delineated by the Office of Management and Budget (OMB). On March 6, 2020, the OMB delineated three combined statistical areas, seven metropolitan statistical areas, and two micropolitan statistical areas in Maryland.
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the 12 United States statistical areas, 23 counties, and 1 independent city of the State of Maryland with the following information:
The combined statistical area (CSA) as designated by the OMB.
The CSA population according to 2019 US Census Bureau population estimates.
The core based statistical area (CB |
https://en.wikipedia.org/wiki/Realization%20%28systems%29 | In systems theory, a realization of a state space model is an implementation of a given input-output behavior. That is, given an input-output relationship, a realization is a quadruple of (time-varying) matrices such that
with describing the input and output of the system at time .
LTI System
For a linear time-invariant system specified by a transfer matrix, , a realization is any quadruple of matrices such that .
Canonical realizations
Any given transfer function which is strictly proper can easily be transferred into state-space by the following approach (this example is for a 4-dimensional, single-input, single-output system)):
Given a transfer function, expand it to reveal all coefficients in both the numerator and denominator. This should result in the following form:
.
The coefficients can now be inserted directly into the state-space model by the following approach:
.
This state-space realization is called controllable canonical form (also known as phase variable canonical form) because the resulting model is guaranteed to be controllable (i.e., because the control enters a chain of integrators, it has the ability to move every state).
The transfer function coefficients can also be used to construct another type of canonical form
.
This state-space realization is called observable canonical form because the resulting model is guaranteed to be observable (i.e., because the output exits from a chain of integrators, every state has an effect on the output).
General System
D = 0
If we have an input , an output , and a weighting pattern then a realization is any triple of matrices such that where is the state-transition matrix associated with the realization.
System identification
System identification techniques take the experimental data from a system and output a realization. Such techniques can utilize both input and output data (e.g. eigensystem realization algorithm) or can only include the output data (e.g. frequency domain decom |
https://en.wikipedia.org/wiki/West%20Virginia%20statistical%20areas | The U.S. currently has 22 statistical areas that have been delineated by the Office of Management and Budget (OMB). On March 6, 2020, the OMB delineated five combined statistical areas, 11 metropolitan statistical areas, and six micropolitan statistical areas in .
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the 22 United States statistical areas and 55 counties of the State of West Virginia with the following information:
The combined statistical area (CSA) as designated by the OMB.
The CSA population according to 2019 US Census Bureau population estimates.
The core-based statistical area (CBSA) as designated by the OMB. |
https://en.wikipedia.org/wiki/Massachusetts%20statistical%20areas | The United States currently has eight statistical areas that have been delineated by the Office of Management and Budget (OMB). On March 6, 2020, the OMB delineated one combined statistical area, six metropolitan statistical areas, and one micropolitan statistical area in Massachusetts.
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the 8 United States statistical areas and 14 counties of the Commonwealth of Massachusetts with the following information:
The combined statistical area (CSA) as designated by the OMB.
The CSA population according to 2019 US Census Bureau population estimates.
The core based statistical area (C |
https://en.wikipedia.org/wiki/Missouri%20statistical%20areas | The U.S. currently has 34 statistical areas that have been delineated by the Office of Management and Budget (OMB). On March 6, 2020, the OMB delineated seven combined statistical areas, eight metropolitan statistical areas, and 19 micropolitan statistical areas in Missouri.
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the 34 United States statistical areas, 114 counties, and 1 independent city of the State of Missouri with the following information:
The combined statistical area (CSA) as designated by the OMB.
The CSA population according to 2019 US Census Bureau population estimates.
The core based statistical area (CB |
https://en.wikipedia.org/wiki/Nevada%20statistical%20areas | The U.S. currently has 11 statistical areas that have been delineated by the Office of Management and Budget (OMB). On March 6, 2020, the OMB delineated two combined statistical areas, three metropolitan statistical areas, and six micropolitan statistical areas in Nevada.
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the 11 United States statistical areas, 16 counties, and 1 independent city of the State of Nevada with the following information:
The combined statistical area (CSA) as designated by the OMB.
The CSA population according to the 2020 US Census.
The core based statistical area (CBSA) as designated by the OMB.
|
https://en.wikipedia.org/wiki/Agaricus%20augustus | Agaricus augustus, known commonly as the prince, is a basidiomycete fungus of the genus Agaricus.
Taxonomy
According to Heinemann's (1978) popular division of Agaricus, A. augustus belongs to section Arvenses. The system proposed by Wasser (2002) classifies A. augustus within subgenus Flavoagaricus, section Majores, subsection Flavescentes. Moreover, there have been attempts to recognise distinct varieties, namely A. augustus var. augustus Fr., and A. augustus var. perrarus (Schulzer) Bon & Cappelli. The specific epithet augustus is a Latin adjective meaning noble.
Description
The fruiting bodies of Agaricus augustus are large and distinctive agarics. The cap shape is hemispherical during the so-called button stage, and then expands, becoming convex and finally flat, with a diameter from . The cap cuticle is dry, and densely covered with concentrically arranged, brown-coloured scales on a white to yellow background.
The flesh is thick, firm and white and may discolour yellow when bruised. The gills are crowded and pallid at first, and turn pink then dark brown with maturity. The gills are not attached to the stem—they are free. Immature specimens bear a delicate white partial veil with darker-coloured warts, extending from the stem to the cap periphery.
The stem is clavate and tall, and thick. In mature specimens, the partial veil is torn and left behind as a pendulous ring adorning the stem. Above the ring, the stem is white to yellow and smooth. Below, it is covered with numerous small scales. Its flesh is thick, white and sometimes has a narrow central hollow. The stem base extends deeply into the substrate.
The mushroom's odour is strong and sweet, similar to almond extract, marzipan or maraschino cherry, due to the presence of benzaldehyde and benzyl alcohol. Its taste has been described as not distinctive.
Under a microscope, the ellipsoid-shaped spores are seen characteristically large at 7–10 by 4.5–6.5 μm. The basidia are 4-spored. The spore mass |
https://en.wikipedia.org/wiki/Damping%20off | Damping off (or damping-off) is a horticultural disease or condition, caused by several different pathogens that kill or weaken seeds or seedlings before or after they germinate. It is most prevalent in wet and cool conditions.
Symptoms
There are various symptoms associated with damping off; these reflect the variety of different pathogenic organisms which can cause the condition. However, all symptoms result in the death of at least some seedlings in any given population.
Groups of seedlings may die in roughly circular patches, the seedlings sometimes having stem lesions at ground level. Stems of seedlings may also become thin and tough ("wire-stem") resulting in reduced seedling vigor. Leaf spotting sometimes accompanies other symptoms, as does a grey mold growth on stems and leaves. Roots sometimes rot completely or back to just discolored stumps.
Causal agents
A number of different fungi and fungi-like organisms cause the symptoms of damping off, including:
Alternaria – a genus of fungi that can cause leaf spotting.
Botrytis cinerea a fungus, also known as "grey mould". Symptoms caused by this often accompany other symptoms.
Fusarium – a genus of fungi.
Macrophomina phaseoli a fungus that causes charcoal rot on many plant species including Zea mays and Pinus elliottii.
Phyllosticta – a genus of fungi that can cause leaf spotting.
Phytophthora a genus of plant-damaging oomycetes (water molds), whose member species are capable of causing enormous economic losses on crops worldwide, as well as environmental damage in natural ecosystems.
Pseudomonas – a genus of bacteria that can cause leaf spotting.
Pythium a genus of parasitic oomycete. Once classified as fungi, and consequently sometimes still treated as such. Along with Rhizoctonia solani, attacks by Pythium are most associated with producing roughly circular patches of dead seedlings.
Rhizoctonia a genus of fungi with a wide host range and worldwide distribution.
Sclerotium rolfsii a corticioid fun |
https://en.wikipedia.org/wiki/Realization%20%28probability%29 | In probability and statistics, a realization, observation, or observed value, of a random variable is the value that is actually observed (what actually happened). The random variable itself is the process dictating how the observation comes about. Statistical quantities computed from realizations without deploying a statistical model are often called "empirical", as in empirical distribution function or empirical probability.
Conventionally, to avoid confusion, upper case letters denote random variables; the corresponding lower case letters denote their realizations.
Formal definition
In more formal probability theory, a random variable is a function X defined from a sample space Ω to a measurable space called the state space. If an element in Ω is mapped to an element in state space by X, then that element in state space is a realization. Elements of the sample space can be thought of as all the different possibilities that could happen; while a realization (an element of the state space) can be thought of as the value X attains when one of the possibilities did happen. Probability is a mapping that assigns numbers between zero and one to certain subsets of the sample space, namely the measurable subsets, known here as events. Subsets of the sample space that contain only one element are called elementary events. The value of the random variable (that is, the function) X at a point ω ∈ Ω,
is called a realization of X.
See also
Errors and residuals
Outcome (probability)
Random variate
Raw data
Notes |
https://en.wikipedia.org/wiki/Louisiana%20statistical%20areas | The U.S. currently has 25 statistical areas that have been delineated by the Office of Management and Budget (OMB). On March 6, 2020, the OMB delineated six combined statistical areas, nine metropolitan statistical areas, and ten micropolitan statistical areas in Louisiana.
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the 25 United States statistical areas and 64 parishes of the State of Louisiana with the following information:
The combined statistical area (CSA) as designated by the OMB.
The CSA population according to 2019 US Census Bureau population estimates.
The core based statistical area (CBSA) as designated by t |
https://en.wikipedia.org/wiki/Macdonald%20polynomials | In mathematics, Macdonald polynomials Pλ(x; t,q) are a family of orthogonal symmetric polynomials in several variables, introduced by Macdonald in 1987. He later introduced a non-symmetric generalization in 1995. Macdonald originally associated his polynomials with weights λ of finite root systems and used just one variable t, but later realized that it is more natural to associate them with affine root systems rather than finite root systems, in which case the variable t can be replaced by several different variables t=(t1,...,tk), one for each of the k orbits of roots in the affine root system. The Macdonald polynomials are polynomials in n variables x=(x1,...,xn), where n is the rank of the affine root system. They generalize many other families of orthogonal polynomials, such as Jack polynomials and Hall–Littlewood polynomials and Askey–Wilson polynomials, which in turn include most of the named 1-variable orthogonal polynomials as special cases. Koornwinder polynomials are Macdonald polynomials of certain non-reduced root systems. They have deep relationships with affine Hecke algebras and Hilbert schemes, which were used to prove several conjectures made by Macdonald about them.
Definition
First fix some notation:
R is a finite root system in a real vector space V.
R+ is a choice of positive roots, to which corresponds a positive Weyl chamber.
W is the Weyl group of R.
Q is the root lattice of R (the lattice spanned by the roots).
P is the weight lattice of R (containing Q).
An ordering on the weights: if and only if is a nonnegative linear combination of simple roots.
P+ is the set of dominant weights: the elements of P in the positive Weyl chamber.
ρ is the Weyl vector: half the sum of the positive roots; this is a special element of P+ in the interior of the positive Weyl chamber.
F is a field of characteristic 0, usually the rational numbers.
A = F(P) is the group algebra of P, with a basis of elements written eλ for λ ∈ P.
If f = eλ, then f mea |
https://en.wikipedia.org/wiki/Maine%20statistical%20areas | The U.S. currently has five statistical areas that have been delineated by the Office of Management and Budget (OMB). On March 6, 2020, the OMB delineated one combined statistical area, three metropolitan statistical areas, and one micropolitan statistical area in Maine.
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the 5 United States statistical areas and 16 counties of the State of Maine with the following information:
The combined statistical area (CSA) as designated by the OMB.
The CSA population according to 2019 US Census Bureau population estimates.
The core based statistical area (CBSA) as designated by the OMB.
|
https://en.wikipedia.org/wiki/Minnesota%20statistical%20areas | The U.S. currently has 30 statistical areas that have been delineated by the Office of Management and Budget (OMB). On March 6, 2020, the OMB delineated four combined statistical areas, eight metropolitan statistical areas, and 18 micropolitan statistical areas in Minnesota.
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the 30 United States statistical areas and 87 counties of the State of Minnesota with the following information:
The combined statistical area (CSA) as designated by the OMB.
The CSA population according to 2019 US Census Bureau population estimates.
The core based statistical area (CBSA) as designated by |
https://en.wikipedia.org/wiki/Nebraska%20statistical%20areas | The U.S. currently has 15 statistical areas that have been delineated by the Office of Management and Budget (OMB). On March 6, 2020, the OMB delineated two combined statistical areas, four metropolitan statistical areas, and nine micropolitan statistical areas in Nebraska.
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the 15 United States statistical areas and 93 counties of the State of Nebraska with the following information:
The combined statistical area (CSA) as designated by the OMB.
The CSA population according to 2019 US Census Bureau population estimates.
The core based statistical area (CBSA) as designated by th |
https://en.wikipedia.org/wiki/New%20Hampshire%20statistical%20areas | The U.S. currently has eight statistical areas that have been delineated by the Office of Management and Budget (OMB). On March 6, 2020, the OMB delineated one combined statistical area, two metropolitan statistical areas, and five micropolitan statistical areas in .
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the eight United States statistical areas and ten counties of the state of New Hampshire with the following information:
The combined statistical area (CSA) as designated by the OMB.
The CSA population as of the 2020 United States census.
The core based statistical area (CBSA) as designated by the OMB.
The CBSA po |
https://en.wikipedia.org/wiki/New%20Mexico%20statistical%20areas | The U.S. currently has 22 statistical areas that have been delineated by the Office of Management and Budget (OMB). On March 6, 2020, the OMB delineated three combined statistical areas, four metropolitan statistical areas, and 15 micropolitan statistical areas in .
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the 22 United States statistical areas and 33 counties of the State of New Mexico with the following information:
The combined statistical area (CSA) as designated by the OMB.
The CSA population according to 2019 US Census Bureau population estimates.
The core based statistical area (CBSA) as designated by the OMB. |
https://en.wikipedia.org/wiki/Laminar%20organization | A laminar organization describes the way certain tissues, such as bone membrane, skin, or brain tissues, are arranged in layers.
Types
Embryo
The earliest forms of laminar organization are shown in the diploblastic and triploblastic formation of the germ layers in the embryo. In the first week of human embryogenesis two layers of cells have formed, an external epiblast layer (the primitive ectoderm), and an internal hypoblast layer (primitive endoderm). This gives the early bilaminar disc. In the third week in the stage of gastrulation epiblast cells invaginate to form endoderm, and a third layer of cells known as mesoderm. Cells that remain in the epiblast become ectoderm. This is the trilaminar disc and the epiblast cells have given rise to the three germ layers.
Brain
In the brain a laminar organization is evident in the arrangement of the three meninges, the membranes that cover the brain and spinal cord. These membranes are the dura mater, arachnoid mater, and pia mater. The dura mater has two layers a periosteal layer near to the bone of the skull, and a meningeal layer next to the other meninges.
The cerebral cortex, the outer neural sheet covering the cerebral hemispheres can be described by its laminar organization, due to the arrangement of cortical neurons into six distinct layers.
Eye
The eye in mammals has an extensive laminar organization. There are three main layers – the outer fibrous tunic, the middle uvea, and the inner retina. These layers have sublayers with the retina having ten ranging from the outer choroid to the inner vitreous humor and including the retinal nerve fiber layer.
Skin
The human skin has a dense laminar organization. The outer epidermis has four or five layers. |
https://en.wikipedia.org/wiki/North%20Carolina%20statistical%20areas | The U.S. currently has 44 statistical areas that have been delineated by the Office of Management and Budget (OMB). On July 21, 2023, the OMB delineated 7 combined statistical areas, 16 metropolitan statistical areas, and 21 micropolitan statistical areas in .
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the 49 United States statistical areas and 100 counties of the State of North Carolina with the following information:
The combined statistical area (CSA) as designated by the OMB as of 2023.
The CSA population according to 2023 US Census Bureau population estimates.
The core based statistical area (CBSA) as designated b |
https://en.wikipedia.org/wiki/North%20Dakota%20statistical%20areas | The U.S. currently has nine statistical areas that have been delineated by the Office of Management and Budget (OMB). On March 6, 2020, the OMB delineated one combined statistical area, three metropolitan statistical areas, and five micropolitan statistical areas in .
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the 9 United States statistical areas and 53 counties of the State of North Dakota with the following information:
The combined statistical area (CSA) as designated by the OMB.
The CSA population according to 2019 US Census Bureau population estimates.
The core based statistical area (CBSA) as designated by the O |
https://en.wikipedia.org/wiki/Standard%20atomic%20weight | 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
Because relative isotopic masses are dimensionless quantities, this weighted mean is also dimensionless. It can be converted into a measure of mass (with dimension ) by multiplying it with the dalton, also known as the atomic mass constant.
Among various variants of the notion of atomic weight (Ar, also known as relative atomic mass) used by scientists, the standard atomic weight 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 archeological 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 and 84 have this Earth-environment based value. Typically, such a value is, for example helium: . The "(2)" indicates the uncertainty in the last digit shown, to read . IUPAC also publishes abridged values, round |
https://en.wikipedia.org/wiki/Oklahoma%20statistical%20areas | The U.S. currently has 26 statistical areas that have been delineated by the Office of Management and Budget (OMB). On March 6, 2020, the OMB delineated four combined statistical areas, five metropolitan statistical areas, and 17 micropolitan statistical areas in Oklahoma.
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the 26 United States statistical areas and 77 counties of the State of Oklahoma with the following information:
The combined statistical area (CSA) as designated by the OMB.
The CSA population according to 2019 US Census Bureau population estimates.
The core based statistical area (CBSA) as designated by the |
https://en.wikipedia.org/wiki/Oregon%20statistical%20areas | The U.S. currently has 24 statistical areas that have been delineated by the Office of Management and Budget (OMB). On March 6, 2020, the OMB delineated four combined statistical areas, eight metropolitan statistical areas, and 12 micropolitan statistical areas in Oregon.
Statistical areas
The Office of Management and Budget (OMB) has designated more than 1,000 statistical areas for the United States and Puerto Rico. These statistical areas are important geographic delineations of population clusters used by the OMB, the United States Census Bureau, planning organizations, and federal, state, and local government entities.
The OMB defines a core-based statistical area (commonly referred to as a CBSA) as "a statistical geographic entity consisting of the county or counties (or county-equivalents) associated with at least one core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core." The OMB further divides core-based statistical areas into metropolitan statistical areas (MSAs) that have "a population of at least 50,000" and micropolitan statistical areas (μSAs) that have "a population of at least 10,000, but less than 50,000."
The OMB defines a combined statistical area (CSA) as "a geographic entity consisting of two or more adjacent core-based statistical areas with employment interchange measures of at least 15%." The primary statistical areas (PSAs) include all combined statistical areas and any core-based statistical area that is not a constituent of a combined statistical area.
Table
The table below describes the 24 United States statistical areas and 36 counties of the State of Oregon with the following information:
The combined statistical area (CSA) as designated by the OMB.
The CSA population according to 2019 US Census Bureau population estimates.
The core based statistical area (CBSA) as designated by the OM |
https://en.wikipedia.org/wiki/TV%20Site | A TV site is a website designed for viewing on a television set.
Unlike mobile and PC access, the use of internet sites through the TV has been small, mostly due to the poor user experience of many "web on TV" deployments, and the "walled garden" or closed platform business models adopted by many TV network operators.
With the advent of IPTV and Broadband enabled TV Devices web site owners have realized, that like Mobile, TV needs to be considered as a different media, and that Mobile, TV and PC based internet access all present different user interface design challenges in order to make the services usable and acceptable to consumers on the device they are using.
TV Sites can be developed and deployed using a number of different technologies including Adobe Flash, WTVML, Java and HTML, although the same design principles apply whatever the development technology.
Typically TV Sites are differentiated from other web sites by having a "wtv." rather than a "www." subdomain.
Interfaces
The following styles of interface are often considered:
The "one foot" user experience - used to describe mobile apps, which are typically "personal" and used at a short distance from the device
The "three foot" user experience - used to describe PC applications, which are typically used "personal" and accessed via a PC, with mouse, windows and a high resolution screen
The "10-foot user interface" - used to describe TV applications, which are typically used in a "shared" environment, via a TV, with only a TV remote control as the input mechanism
These interfaces are characterized as follows:
Mobile: narrow screen 1D scrolling, simplified layout, limited graphics and media types, limited transactional capability, limited keyboard, no pointer (see Post-WIMP)
PC: wide screen, scrollable interfaces, complex layouts, complex media types, fully transactional, keyboard, pointer
TV: wide screen, explicit layout, embedded scrolling, limited media types, video support, fully transa |
https://en.wikipedia.org/wiki/Responsiveness | Responsiveness as a concept of computer science refers to the specific ability of a system or functional unit to complete assigned tasks within a given time. For example, it would refer to the ability of an artificial intelligence system to understand and carry out its tasks in a timely fashion.
In the Reactive principle, Responsiveness is one of the fundamental criteria along with resilience, elasticity and message driven.
It is one of the criteria under the principle of robustness (from a v principle). The other three are observability, recoverability, and task conformance.
Vs performance
Software which lacks a decent process management can have poor responsiveness even on a fast machine. On the other hand, even slow hardware can run responsive software.
It is much more important that a system actually spend the available resources in the best way possible. For instance, it makes sense to let the mouse driver run at a very high priority to provide fluid mouse interactions. For long-term operations, such as copying, downloading or transforming big files the most important factor is to provide good user-feedback and not the performance of the operation since it can quite well run in the background, using only spare processor time.
Delays
Long delays can be a major cause of user frustration, or can lead the user to believe the system is not functioning, or that a command or input gesture has been ignored. Responsiveness is therefore considered an essential usability issue for human-computer-interaction (HCI). The rationale behind the responsiveness principle is that the system should deliver results of an operation to users in a timely and organized manner.
The frustration threshold can be quite different, depending on the situation and the fact that user interface depends on local or remote systems to show a visible response.
There are at least three user tolerance thresholds (i.e.):
0.1 seconds
under 0.1 seconds the response is perceived as instantaneous |
https://en.wikipedia.org/wiki/Leonard%20Carlitz | Leonard Carlitz (December 26, 1907 – September 17, 1999) was an American mathematician. Carlitz supervised 44 doctorates at Duke University and published over 770 papers.
Chronology
1907 Born Philadelphia, PA, USA
1927 BA, University of Pennsylvania
1930 PhD, University of Pennsylvania, 1930 under Howard Mitchell, who had studied under Oswald Veblen at Princeton
1930–31 at Caltech with E. T. Bell
1931 married Clara Skaler
1931–32 at Cambridge with G. H. Hardy
1932 Joined the faculty of Duke University where he served for 45 years
1938 to 1973 Editorial Board Duke Mathematical Journal (Managing Editor from 1945.)
1939 Birth of son Michael
1940 Supervision of his first doctoral student E. F. Canaday, awarded 1940
1945 Birth of son Robert
1964 First James B. Duke Professor in Mathematics
1977 Supervised his 44th and last doctoral student, Jo Ann Lutz, awarded 1977
1977 Retired
1990 Death of wife Clara, after 59 years of marriage
1999 September 17 Died in Pittsburgh, PA
Mathematical work
The Carlitz module is generalized by the Drinfeld module
An identity regarding Bernoulli numbers
Carlitz wrote about Bessel polynomials
He introduced Al-Salam–Carlitz polynomials.
Carlitz' identity for bicentric quadrilaterals
He conjectured the Carlitz-Wan conjecture, later proved by Daqing Wan.
Publication
Leonard Carlitz published about 771 technical papers comprising approximately 7,000 pages. The effort to edit his collected works, undertaken originally by Professor John Brillhart, is ongoing.
See also
Bateman polynomials
Carlitz exponential
Carlitz polynomial (disambiguation)
Maillet's determinant
Reciprocal Fibonacci constant |
https://en.wikipedia.org/wiki/Oparin%20Medal | The Oparin/Urey Medal honours important contributions to the field of origins of life. The medal is awarded by the International Society for the Study of the Origin of Life (ISSOL). The award was originally named for Alexander Ivanovich Oparin, one of the pioneers in researching the origins of life. In 1993, the Society decided to alternate the name of the award so as to also honour the memory of Harold C. Urey, one of the first to propose the study of cosmochemistry.
List of winners
The current list of medalists is shown below: |
https://en.wikipedia.org/wiki/National%20Center%20for%20Ecological%20Analysis%20and%20Synthesis | The National Center for Ecological Analysis and Synthesis (NCEAS) is a research center at the University of California, Santa Barbara, in Santa Barbara, California. Better known by its acronym, NCEAS (pronounced “n-seas”) opened in May 1995. Funding for NCEAS is diverse and includes supporters such as the U.S. National Science Foundation, the State of California, and the University of California, Santa Barbara.
NCEAS supports cross-disciplinary research that analyzes and synthesizes existing data to address major fundamental issues in ecology and allied fields, and encourages the application of science to natural resource management and public policy decision making. To facilitate synthetic analysis, NCEAS advances new techniques in mathematical and geospatial modeling, dynamic simulation, and visualization of ecological systems through its Ecoinformatics program. Since its inception, the Center has hosted over 5,000 individuals and supported roughly 500 research projects, which have resulted in more than 2,000 publications in 300+ different journals. In addition, NCEAS engages graduate students and grade school children through a variety of outreach and education programs.
Mission
NCEAS’ core mission is to foster synthesis and analysis, and promote effective collaboration among researchers to alter how science is conducted. This mission includes 3 goals:
Advance the state of ecological knowledge through the search for general patterns and principles in existing data
Organize and synthesize ecological information in a manner useful to researchers, resource managers, and policy makers addressing important environmental issues
Influence the way ecological research is conducted by promoting a culture of synthesis, collaboration and data sharing
Research programs
Research at NCEAS focuses on three key areas: Core Ecology, Ecoinformatics, and Conservation and Resource Management.
Core Ecology - the primary research area funded by NCEAS, which includes a broad ra |
https://en.wikipedia.org/wiki/Missense%20mRNA | Missense mRNA is a messenger RNA bearing one or more mutated codons that yield polypeptides with an amino acid sequence different from the wild-type or naturally occurring polypeptide. Missense mRNA molecules are created when template DNA strands or the mRNA strands themselves undergo a missense mutation in which a protein coding sequence is mutated and an altered amino acid sequence is coded for.
Biogenesis
A missense mRNA arises from a missense mutation, in the event of which a DNA nucleotide base pair in the coding region of a gene is changed such that it results in the substitution of one amino acid for another. The point mutation is nonsynonymous because it alters the RNA codon in the mRNA transcript such that translation results in amino acid change. An amino acid change may not result in appreciable changes in protein structure depending on whether the amino acid change is conservative or non-conservative. This owes to the similar physicochemical properties exhibited by some amino acids.
Missense mRNAs may be detected as a result of two different types of point mutations - spontaneous mutations and induced mutations. Spontaneous mutations occur during the DNA replication process where a non-complementary nucleotide is deposited by the DNA polymerase in the extension phase. The consecutive round of replication would result in a point mutation. If the resulting mRNA codon is one that changes the amino acid, a missense mRNA would be detected. A hypergeometric distribution study involving DNA polymerase β replication errors in the APC gene revealed 282 possible substitutions that could result in missense mutations. When the APC mRNA was analyzed in the mutational spectrum, it showed 3 sites where the frequency of substitutions were high.
Induced mutations caused by mutagens can give rise to missense mutations. Nucleoside analogues such as 2-aminopurine and 5-bromouracil can insert in place of A and T respectively. Ionizing radiation like x-rays and γ-rays c |
https://en.wikipedia.org/wiki/Supplementum%20primum%20Prodromi%20florae%20Novae%20Hollandiae | Supplementum primum Prodromi florae Novae Hollandiae ("First supplement to the Prodromus of the flora of New Holland") is an 1830 supplement to Robert Brown's Prodromus florae Novae Hollandiae et Insulae Van Diemen. It may be referred to by its standard botanical abbreviation Suppl. Prodr. Fl. Nov. Holl.
The supplement published numerous new Proteaceae taxa, mainly those discovered by William Baxter since the publication of the original Prodromus in 1810. |
https://en.wikipedia.org/wiki/Runaway%20electrons | The term runaway electrons (RE) is used to denote electrons that undergo free fall acceleration into the realm of relativistic particles. REs may be classified as thermal (lower energy) or relativistic. The study of runaway electrons is thought to be fundamental to our understanding of High-Energy Atmospheric Physics. They are also seen in tokamak fusion devices, where they can damage the reactors.
Lightning
Runaway electrons are the core element of the runaway breakdown based theory of lightning propagation. Since C.T.R. Wilson's work in 1925, research has been conducted to study the possibility of runaway electrons, cosmic ray based or otherwise, initiating the processes required to generate lightning.
Extraterrestrial Occurrence
Electron runaway based lightning may be occurring on the four jovian planets in addition to earth. Simulated studies predict runaway breakdown processes are likely to occur on these gaseous planets far more easily on earth, as the threshold for runaway breakdown to begin is far smaller.
High Energy Plasma
The runaway electron phenomenon has been observed in high energy plasmas. They can pose a threat to machines and experiments in which these plasmas exist, including ITER. Several studies exist examining the properties of runaway electrons in these environments (tokamak), searching to better suppress the detrimental effects of these unwanted runaway electrons. Recent measurements reveal higher-than-expected impurity ion diffusion in runaway electron plateaus, possibly due to turbulence. The choice between low and high atomic number (Z) gas injections for disruption mitigation techniques requires a better understanding of the impurity ion transport, as these ions may not completely mix at impact, affecting the prevention of runaway electron wall damage in large tokamak concepts, like ITER.
Computer and Numerical Simulations
This highly complex phenomenon has proved difficult to model with traditional systems, but has been modelled in p |
https://en.wikipedia.org/wiki/Free%20lattice | In mathematics, in the area of order theory, a free lattice is the free object corresponding to a lattice. As free objects, they have the universal property.
Formal definition
Because the concept of a lattice can be axiomatised in terms of two operations and satisfying certain identities, the category of all lattices constitute a variety (universal algebra), and thus there exist (by general principles of universal algebra) free objects within this category: lattices where only those relations hold which follow from the general axioms.
These free lattices may be characterised using the relevant universal property. Concretely, free lattice is a functor from sets to lattices, assigning to each set the free lattice equipped with a set map assigning to each the corresponding element . The universal property of these is that there for any map from to some arbitrary lattice exists a unique lattice homomorphism satisfying , or as a commutative diagram:
The functor is left adjoint to the forgetful functor from lattices to their underlying sets.
It is frequently possible to prove things about the free lattice directly using the universal property, but such arguments tend to be rather abstract, so a concrete construction provides a valuable alternative presentation.
Semilattices
In the case of semilattices, an explicit construction of the free semilattice is straightforward to give; this helps illustrate several features of the definition by way of universal property. Concretely, the free semilattice may be realised as the set of all finite nonempty subsets of , with ordinary set union as the join operation . The map maps elements of to singleton sets, i.e., for all . For any semilattice and any set map , the corresponding universal morphism is given by
where denotes the semilattice operation in .
This form of is forced by the universal property: any can be written as a finite union of elements on the form for some , the equality in the universal |
https://en.wikipedia.org/wiki/Microfungi | Microfungi or micromycetes are fungi—eukaryotic organisms such as molds, mildews and rusts—which have microscopic spore-producing structures. They exhibit tube tip-growth and have cell walls composed of chitin, a polymer of N-acetylglucosamine. Microfungi are a paraphyletic group, distinguished from macrofungi only by the absence of a large, multicellular fruiting body. They are ubiquitous in all terrestrial and freshwater and marine environments, and grow in plants, soil, water, insects, cattle rumens, hair, and skin. Most of the fungal body consists of microscopic threads, called hyphae, extending through the substrate in which it grows. The mycelia of microfungi produce spores that are carried by the air, spreading the fungus.
Many microfungi species are benign, existing as soil saprotrophs, for example, largely unobserved by humans. Many thousands of microfungal species occur in lichens, forming symbiotic relationships with algae. Other microfungi, such as those of the genera Penicillium, Aspergillus and Neurospora, were first discovered as molds causing spoilage of fruit and bread.
Certain species have commercial value. Penicillium species are used in the manufacture of blue cheeses and as the source of the antibiotic penicillin, discovered by Sir Alexander Fleming in 1928, while Fusarium venenatum is used to produce Quorn, a mycoprotein food product.
Harmful microfungi
Microfungi can be harmful, causing diseases of plants, animals and humans with varying degrees of severity and economic impact. The irritating human skin disease known as athlete's foot or tinea pedis is caused by species of the microfungal genus Trichophyton. Microfungi may cause diseases of crops and trees which range in severity from mild to disastrous, and in economic importance from beneficial to seriously costly. The mould Botrytis cinerea can cause spoilage of crops including grapes, but is also responsible for the "noble rot", which concentrates sugars in the grapes used to make t |
https://en.wikipedia.org/wiki/Erbin%20%28protein%29 | Erbb2 interacting protein (ERBB2IP), also known as erbin, is a protein which in humans is encoded by the ERBB2IP gene. Discovered in 1997, erbin is a 200kDa protein containing a PDZ domain.
Function
This gene is a member of the leucine-rich repeat and PDZ domain (LAP) family. The encoded protein contains 17 leucine-rich repeats and one PDZ domain. It binds to the unphosphorylated form of the ERBB2 protein and regulates ERBB2 function and localization. It has also been shown to affect the Ras signaling pathway by disrupting Ras-Raf interaction. Alternate transcriptional splice variants encoding different isoforms have been found for this gene, but only two of them have been characterized to date.
Clinical significance
Erbin's C-terminal PDZ domain is able to bind to ErbB2, a protein tyrosine kinase which is often associated with poor prognosis in epidermal oncogenesis. Erbin's N-terminal region has been shown to disrupt Ras to Raf binding and may be, through this action, a tumor suppressing protein.
Interactions
Erbin has been shown to interact with:
Dystonin
HER2/neu
ITGB4
Mothers against decapentaplegic homolog 3
PKP4 and
KSR1 |
https://en.wikipedia.org/wiki/Coreset | In computational geometry, a coreset is a small set of points that approximates the shape of a larger point set, in the sense that applying some geometric measure to the two sets (such as their minimum bounding box volume) results in approximately equal numbers. Many natural geometric optimization problems have coresets that approximate an optimal solution to within a factor of , that can be found quickly (in linear time or near-linear time), and that have size bounded by a function of independent of the input size, where is an arbitrary positive number. When this is the case, one obtains a linear-time or near-linear time approximation scheme, based on the idea of finding a coreset and then applying an exact optimization algorithm to the coreset. Regardless of how slow the exact optimization algorithm is, for any fixed choice of , the running time of this approximation scheme will be plus the time to find the coreset. |
https://en.wikipedia.org/wiki/Stable%20vices | Stable vices are stereotypies of equines, especially horses. They are usually undesirable habits that often develop as a result of being confined in a stable with boredom, hunger, isolation, excess energy, or insufficient exercise. They present a management issue, not only leading to facility damage from chewing, kicking, and repetitive motion, but also leading to health consequences for the animal if not addressed. They also raise animal welfare concerns.
Stereotypical behaviors in animals are generally thought to be caused by artificial environments that do not allow animals to satisfy their normal behavioral needs. Rather than refer to these behaviors as abnormal, it has been suggested that they be described as "behavior indicative of an abnormal environment".
It was once thought that stable vices may be learned by observing other horses already performing the behaviors, but studies on the topic to date have failed to establish this as a cause. Stereotypies are correlated with altered behavioral response selection in the basal ganglia. Although a more enriched environment may help minimize or eliminate some stereotypical behavior, once established, it is sometimes impossible to eliminate them due to alterations in the brain.
Examples
Stereotypies in equines are usually placed into one of two classes: Locomotor or Oral. Common stable vices include:
Wood chewing (lignophagia): Gnawing on wood out of hunger or boredom. This is not to be confused with the more serious vice, cribbing.
Cribbing, also called windsucking: When the equine grabs a board or other surface with its teeth, arches its neck, and sucks in air. This can harm the teeth and may lead to colic. Cribbing can be caused either by nervousness or boredom. It was previously thought to release endorphins in the horse, but recent research suggests this is a fallacy. Additional research suggests that cribbing increases salivation and may reduce stomach discomfort. There is a direct correlation betwee |
https://en.wikipedia.org/wiki/Robotics%20suite | A robotics suite is a visual environment for robot control and simulation. They are typically an end-to-end platform for robotics development and include tools for visual programming and creating and debugging robot applications. Developers can often interact with robots through web-based or visual interfaces.
One objective of a robotics suite is to support a variety of different robot platforms through a common programming interface. The key point about a robotics suite is that the same code will run either with a simulated robot or the corresponding real robot without modification.
Some robotic suites are based in free software, free hardware and both free software and hardware.
Suites
Fedora Robotics
ArtiMinds Robot Programming Suite
Brainlab Robotic Suite
See also
AnyKode Marilou
ArduPilot
Autonomous Robot Control (ARC)
Debian Science
Evolution Robotics
Lego Mindstorms
Microsoft Robotics Studio
Player Project (formerly the Player/Stage Project or Player/Stage/Gazebo Project)
Robot software
Robot Operating System
Simbad robot simulator
URBI
Webots |
https://en.wikipedia.org/wiki/Resource | Resource refers to all the materials available in our environment which are technologically accessible, economically feasible and culturally sustainable and help us to satisfy our needs and wants. Resources can broadly be classified upon their availability — they are classified into renewable and non-renewable resources. They can also be classified as actual and potential on the basis of the level of development and use, on the basis of origin they can be classified as biotic and abiotic, and on the basis of their distribution, as ubiquitous and localised (private, community-owned, national and international resources). An item becomes a resource with time and developing technology. The benefits of resource utilization may include increased wealth, proper functioning of a system, or enhanced well-being. From a human perspective, a natural resource is anything obtained from the environment to satisfy human needs and wants. From a broader biological or ecological perspective, a resource satisfies the needs of a living organism (see biological resource).
The concept of resources has been developed across many established areas of work, in economics, biology and ecology, computer science, management, and human resources for example - linked to the concepts of competition, sustainability, conservation, and stewardship. In application within human society, commercial or non-commercial factors require resource allocation through resource management.
The concept of a resource can also be tied to the direction of leadership over resources, this can include the things leaders have responsibility for over the human resources, with management, help, support or direction such as in charge of a professional group, technical experts, innovative leaders, archiving expertise, academic management, association management, business management, healthcare management, military management, public administration, spiritual leadership and social networking administrator.
individuals exp |
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