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https://en.wikipedia.org/wiki/Software%20release%20life%20cycle | The software release life cycle is the process of developing, testing, and distributing a software product. It typically consists of several stages, such as pre-alpha, alpha, beta, and release candidate, before the final version, or "gold", is released to the public.
Pre-alpha refers to the early stages of development, when the software is still being designed and built. Alpha testing is the first phase of formal testing, during which the software is tested internally using white-box techniques. Beta testing is the next phase, in which the software is tested by a larger group of users, typically outside of the organization that developed it. The beta phase is focused on reducing impacts on users and may include usability testing.
After beta testing, the software may go through one or more release candidate phases, in which it is refined and tested further, before the final version is released.
Some software, particularly in the internet and technology industries, is released in a perpetual beta state, meaning that it is continuously being updated and improved, and is never considered to be a fully completed product. This approach allows for a more agile development process and enables the software to be released and used by users earlier in the development cycle.
Stages of development
Pre-alpha
Pre-alpha refers to all activities performed during the software project before formal testing. These activities can include requirements analysis, software design, software development, and unit testing. In typical open source development, there are several types of pre-alpha versions. Milestone versions include specific sets of functions and are released as soon as the feature is complete.
Alpha
The alpha phase of the release life cycle is the first phase of software testing (alpha is the first letter of the Greek alphabet, used as the number 1). In this phase, developers generally test the software using white-box techniques. Additional validation is then performe |
https://en.wikipedia.org/wiki/Encapsulation%20%28computer%20programming%29 | In software systems, encapsulation refers to the bundling of data with the mechanisms or methods that operate on the data. It may also refer to the limiting of direct access to some of that data, such as an object's components. Encapsulation allows developers to present a consistent and usable interface which is independent of how a system is implemented internally. As one example, encapsulation can be used to hide the values or state of a structured data object inside a class, preventing direct access to them by clients in a way that could expose hidden implementation details or violate state invariance maintained by the methods.
All object-oriented programming (OOP) systems support encapsulation , but encapsulation is not unique to OOP. Implementations of abstract data types, modules, and libraries, among other systems, also offer encapsulation. The similarity has been explained by programming language theorists in terms of existential types.
Meaning
In object-oriented programming languages, and other related fields, encapsulation refers to one of two related but distinct notions, and sometimes to the combination thereof:
A language mechanism for restricting direct access to some of the object's components.
A language construct that facilitates the bundling of data with the methods (or other functions) operating on those data.
Some programming language researchers and academics use the first meaning alone or in combination with the second as a distinguishing feature of object-oriented programming, while some programming languages that provide lexical closures view encapsulation as a feature of the language orthogonal to object orientation.
The second definition is motivated by the fact that in many object-oriented languages, and other related fields, the components are not hidden automatically and this can be overridden; thus, information hiding is defined as a separate notion by those who prefer the second definition.
The features of encapsulation ar |
https://en.wikipedia.org/wiki/Hsiang%E2%80%93Lawson%27s%20conjecture | In mathematics, Lawson's conjecture states that the Clifford torus is the only minimally embedded torus in the 3-sphere S3. The conjecture was featured by the Australian Mathematical Society Gazette as part of the Millennium Problems series.
In March 2012, Simon Brendle gave a proof of this conjecture, based on maximum principle techniques.
References
Geometric topology
Theorems in differential geometry
Conjectures that have been proved
Theorems in topology |
https://en.wikipedia.org/wiki/Kernel%20panic | A kernel panic (sometimes abbreviated as KP) is a safety measure taken by an operating system's kernel upon detecting an internal fatal error in which either it is unable to safely recover or continuing to run the system would have a higher risk of major data loss. The term is largely specific to Unix and Unix-like systems. The equivalent on Microsoft Windows operating systems is a stop error, often called a "blue screen of death".
The kernel routines that handle panics, known as panic() in AT&T-derived and BSD Unix source code, are generally designed to output an error message to the console, dump an image of kernel memory to disk for post-mortem debugging, and then either wait for the system to be manually rebooted, or initiate an automatic reboot. The information provided is of a highly technical nature and aims to assist a system administrator or software developer in diagnosing the problem. Kernel panics can also be caused by errors originating outside kernel space. For example, many Unix operating systems panic if the init process, which runs in user space, terminates.
History
The Unix kernel maintains internal consistency and runtime correctness with assertions as the fault detection mechanism. The basic assumption is that the hardware and the software should perform correctly and a failure of an assertion results in a panic, i.e. a voluntary halt to all system activity. The kernel panic was introduced in an early version of Unix and demonstrated a major difference between the design philosophies of Unix and its predecessor Multics. Multics developer Tom van Vleck recalls a discussion of this change with Unix developer Dennis Ritchie:
I remarked to Dennis that easily half the code I was writing in Multics was error recovery code. He said, "We left all that stuff out. If there's an error, we have this routine called panic, and when it is called, the machine crashes, and you holler down the hall, 'Hey, reboot it.'"
The original panic() function was essenti |
https://en.wikipedia.org/wiki/Parallel%20Virtual%20Machine | Parallel Virtual Machine (PVM) is a software tool for parallel networking of computers. It is designed to allow a network of heterogeneous Unix and/or Windows machines to be used as a single distributed parallel processor. Thus large computational problems can be solved more cost effectively by using the aggregate power and memory of many computers. The software is very portable; the source code, available free through netlib, has been compiled on everything from laptops to Crays.
PVM enables users to exploit their existing computer hardware to solve much larger problems at less additional cost. PVM has been used as an educational tool to teach parallel programming but has also been used to solve important practical problems. It was developed by the University of Tennessee, Oak Ridge National Laboratory and Emory University. The first version was written at ORNL in 1989, and after being rewritten by University of Tennessee, version 2 was released in March 1991. Version 3 was released in March 1993, and supported fault tolerance and better portability.
PVM was a step towards modern trends in distributed processing and grid computing but has, since the mid-1990s, largely been supplanted by the much more successful MPI standard for message passing on parallel machines. PVM is free software, released under both the BSD License and the GNU General Public License.
Design
PVM is a software system that enables a collection of heterogeneous computers to be used as a coherent and flexible concurrent computational resource, or a "parallel virtual machine".
The individual computers may be shared-memory or local-memory multiprocessors, vector supercomputers, specialized graphics engines, or scalar workstations and PCs, that may be interconnected by a variety of networks, such as Ethernet or FDDI.
PVM consists of a run-time environment and library for message passing, task and resource management, and fault notification. While PVM will not automatically make a commercial |
https://en.wikipedia.org/wiki/Math%20rock | Math rock is a style of alternative and indie rock with roots in bands such as King Crimson and Rush. It is characterized by complex, atypical rhythmic structures (including irregular stopping and starting), counterpoint, odd time signatures, and extended chords. It bears similarities to post-rock.
Characteristics
Math rock is typified by its rhythmic complexity, seen as mathematical in character by listeners and critics. While most rock music uses a meter (however accented or syncopated), math rock makes use of more non-standard, frequently changing time signatures such as , , , or .
As in traditional rock, the sound is most often dominated by guitars and drums. However, drums play a greater role in math rock in providing driving, complex rhythms. Math rock guitarists make use of tapping techniques and loop pedals to build on these rhythms, as illustrated by songs like those of "math rock supergroup" Battles.
Lyrics are generally not the focus of math rock; the voice is treated as just another instrument in the mix. Often, vocals are not overdubbed, and are positioned less prominently, as in the recording style of Steve Albini. Many of math rock's best-known groups are entirely instrumental such as Don Caballero or Hella.
The term began as a joke but has developed into the accepted name for the musical style. One advocate of this is Matt Sweeney, singer with Chavez, a group often linked to the math rock scene. Despite this, not all critics see math rock as a serious sub-genre of rock.
A significant intersection exists between math rock and emo, exemplified by bands such as Tiny Moving Parts or American Football, whose sound has been described as "twinkly, mathy rock, a sound that became one of the defining traits of the emo scene throughout the 2000s".
Bands
Early
The albums Red and Discipline by King Crimson, Spiderland by Slint are generally considered seminal influences on the development of math rock. The Canadian punk rock group Nomeansno (founded |
https://en.wikipedia.org/wiki/Right-hand%20rule | In mathematics and physics, the right-hand rule is a convention and a mnemonic for deciding the orientation of axes in three-dimensional space. It is a convenient method for determining the direction of the cross product of two vectors.
There are two ways of applying the right hand rule. The first one is conventionally called the Right hand rule or the Flemming's right hand rule. It involves the index finger, the middle finger and the thumb of the right hand. By arranging them as shown in the diagram, the direction of cross product or vector product can be calculated.
The other way, known as Amperes right hand grip rule, right-hand screw rule, coffee-mug rule or the corkscrew-rule involves pointing all fingers of the right hand along the first vector and curling the fingers along the second vector, the direction which the thumb makes is the direction of vector product.
For example, If the curling motion of the fingers represents a movement from the first (x-axis) to the second (y-axis), then the third (z-axis) can point along either thumb in a right handed coordinate system.
Both these rules can be used interchangeably.
The rule can be used to find the direction of the magnetic field, rotation, spirals, electromagnetic fields, mirror images, and enantiomers in mathematics and chemistry.
The sequence is often: index finger along the first vector, then middle finger along the second, then thumb along the third. Two other sequences also work because they preserve the cyclic nature of the cross product (and the underlying Levi-Civita symbol):
Middle finger, thumb, index finger.
Thumb, index finger, middle finger.
Coordinates
For right-handed coordinates, if the thumb of a person's right hand points along the z-axis in the positive direction (third coordinate vector), then the fingers curl from the positive x-axis (first coordinate vector) toward the positive y-axis (second coordinate vector). When viewed at a position along the positive z-axis, the ¼ tu |
https://en.wikipedia.org/wiki/Cotangent%20bundle | In mathematics, especially differential geometry, the cotangent bundle of a smooth manifold is the vector bundle of all the cotangent spaces at every point in the manifold. It may be described also as the dual bundle to the tangent bundle. This may be generalized to categories with more structure than smooth manifolds, such as complex manifolds, or (in the form of cotangent sheaf) algebraic varieties or schemes. In the smooth case, any Riemannian metric or symplectic form gives an isomorphism between the cotangent bundle and the tangent bundle, but they are not in general isomorphic in other categories.
Formal definition via diagonal morphism
There are several equivalent ways to define the cotangent bundle. One way is through a diagonal mapping Δ and germs.
Let M be a smooth manifold and let M×M be the Cartesian product of M with itself. The diagonal mapping Δ sends a point p in M to the point (p,p) of M×M. The image of Δ is called the diagonal. Let be the sheaf of germs of smooth functions on M×M which vanish on the diagonal. Then the quotient sheaf consists of equivalence classes of functions which vanish on the diagonal modulo higher order terms. The cotangent sheaf is defined as the pullback of this sheaf to M:
By Taylor's theorem, this is a locally free sheaf of modules with respect to the sheaf of germs of smooth functions of M. Thus it defines a vector bundle on M: the cotangent bundle.
Smooth sections of the cotangent bundle are called (differential) one-forms.
Contravariance properties
A smooth morphism of manifolds, induces a pullback sheaf on M. There is an induced map of vector bundles .
Examples
The tangent bundle of the vector space is , and the cotangent bundle is , where denotes the dual space of covectors, linear functions .
Given a smooth manifold embedded as a hypersurface represented by the vanishing locus of a function with the condition that the tangent bundle is
where is the directional derivative . By definition |
https://en.wikipedia.org/wiki/Scrapie | Scrapie () is a fatal, degenerative disease affecting the nervous systems of sheep and goats. It is one of several transmissible spongiform encephalopathies (TSEs), and as such it is thought to be caused by a prion. Scrapie has been known since at least 1732 and does not appear to be transmissible to humans. However, it has been found to be experimentally transmissible to humanised transgenic mice and non-human primates.
The name scrapie is derived from one of the clinical signs of the condition, wherein affected animals will compulsively scrape off their fleeces against rocks, trees or fences. The disease apparently causes an itching sensation in the animals. Other clinical signs include excessive lip smacking, altered gaits and convulsive collapse.
Scrapie is infectious and transmissible among conspecifics, so one of the most common ways to contain it (since it is incurable) is to quarantine and kill those affected. However, scrapie tends to persist in flocks and can also arise apparently spontaneously in flocks that have not previously had cases of the disease. The mechanism of transmission between animals and other aspects of the biology of the disease are only poorly understood, and are active areas of research. Recent studies suggest prions may be spread through urine and persist in the environment for decades.
Scrapie usually affects sheep around three to five years of age. The potential for transmission at birth and from contact with placental tissues is apparent.
Regulation
The disease has been notifiable in the EU since 1993, but unlike bovine spongiform encephalopathy (BSE, commonly known as mad cow disease), there was no evidence as of 1999 to suggest that scrapie is a risk to human health. In July 2003, a Canadian Food Inspection Agency officer said that while scrapie shows up every year on Canadian farms, "We've had a lot of experience with scrapie and there's never been a link between scrapie and human illness." As of 2004, the USDA made no menti |
https://en.wikipedia.org/wiki/Language%20code | A language code is a code that assigns letters or numbers as identifiers or classifiers for languages. These codes may be used to organize library collections or presentations of data, to choose the correct localizations and translations in computing, and as a shorthand designation for longer forms of language names.
Difficulties of classification
Language code schemes attempt to classify the complex world of human languages, dialects, and variants. Most schemes make some compromises between being general and being complete enough to support specific dialects.
For example, Spanish is spoken in over 20 countries in North America, Central America, the Caribbean, and Europe. Spanish spoken in Mexico will be slightly different from Spanish spoken in Peru. Different regions of Mexico will have slightly different dialects and accents of Spanish. A language code scheme might group these all as "Spanish" for choosing a keyboard layout, most as "Spanish" for general usage, or separate each dialect to allow region-specific variation.
Common schemes
See also
Accept-Language
Codes for constructed languages
Country code
Flag icons for languages
List of ISO 639-1 codes - codes for common languages
List of ISO 639-2 codes - expanded 3 character code list of all languages coded by ISO
Locale (computer software)
References
External links
List of usual language codes and its variants
Language Tags in HTML and XML
Language Identifiers in the Markup Context
Identifiers
Internationalization and localization |
https://en.wikipedia.org/wiki/Euler%27s%20four-square%20identity | In mathematics, Euler's four-square identity says that the product of two numbers, each of which is a sum of four squares, is itself a sum of four squares.
Algebraic identity
For any pair of quadruples from a commutative ring, the following expressions are equal:
Euler wrote about this identity in a letter dated May 4, 1748 to Goldbach (but he used a different sign convention from the above). It can be verified with elementary algebra.
The identity was used by Lagrange to prove his four square theorem. More specifically, it implies that it is sufficient to prove the theorem for prime numbers, after which the more general theorem follows. The sign convention used above corresponds to the signs obtained by multiplying two quaternions. Other sign conventions can be obtained by changing any to , and/or any to .
If the and are real numbers, the identity expresses the fact that the absolute value of the product of two quaternions is equal to the product of their absolute values, in the same way that the Brahmagupta–Fibonacci two-square identity does for complex numbers. This property is the definitive feature of composition algebras.
Hurwitz's theorem states that an identity of form,
where the are bilinear functions of the and is possible only for n = 1, 2, 4, or 8.
Proof of the identity using quaternions
Comment: The proof of Euler's four-square identity is by simple algebraic evaluation. Quaternions derive from the four-square identity, which can be written as the product of two inner products of 4-dimensional vectors, yielding again an inner product of 4-dimensional vectors: . This defines the quaternion multiplication rule , which simply reflects Euler's identity, and some mathematics of quaternions. Quaternions are, so to say, the "square root" of the four-square identity. But let the proof go on:
Let and be a pair of quaternions. Their quaternion conjugates are and . Then
and
The product of these two is , where is a real number, so it can comm |
https://en.wikipedia.org/wiki/Blastulation | Blastulation is the stage in early animal embryonic development that produces the blastula. In mammalian development the blastula develops into the blastocyst with a differentiated inner cell mass and an outer trophectoderm. The blastula (from Greek βλαστός ( meaning sprout)) is a hollow sphere of cells known as blastomeres surrounding an inner fluid-filled cavity called the blastocoel. Embryonic development begins with a sperm fertilizing an egg cell to become a zygote, which undergoes many cleavages to develop into a ball of cells called a morula. Only when the blastocoel is formed does the early embryo become a blastula. The blastula precedes the formation of the gastrula in which the germ layers of the embryo form.
A common feature of a vertebrate blastula is that it consists of a layer of blastomeres, known as the blastoderm, which surrounds the blastocoel. In mammals, the blastocyst contains an embryoblast (or inner cell mass) that will eventually give rise to the definitive structures of the fetus, and a trophoblast which goes on to form the extra-embryonic tissues.
During blastulation, a significant amount of activity occurs within the early embryo to establish cell polarity, cell specification, axis formation, and to regulate gene expression. In many animals, such as Drosophila and Xenopus, the mid blastula transition (MBT) is a crucial step in development during which the maternal mRNA is degraded and control over development is passed to the embryo. Many of the interactions between blastomeres are dependent on cadherin expression, particularly E-cadherin in mammals and EP-cadherin in amphibians.
The study of the blastula, and of cell specification has many implications in stem cell research, and assisted reproductive technology. In Xenopus, blastomeres behave as pluripotent stem cells which can migrate down several pathways, depending on cell signaling. By manipulating the cell signals during the blastula stage of development, various tissues can be fo |
https://en.wikipedia.org/wiki/Interphase | Interphase is the portion of the cell cycle that is not accompanied by visible changes under the microscope, and includes the G1, S and G2 phases. During interphase, the cell grows (G1), replicates its DNA (S) and prepares for mitosis (G2). A cell in interphase is not simply quiescent. The term quiescent (i.e. dormant) would be misleading since a cell in interphase is very busy synthesizing proteins, copying DNA into RNA, engulfing extracellular material, processing signals, to name just a few activities. The cell is quiescent only in the sense of cell division (i.e. the cell is out of the cell cycle, G0). Interphase is the phase of the cell cycle in which a typical cell spends most of its life. Interphase is the 'daily living' or metabolic phase of the cell, in which the cell obtains nutrients and metabolizes them, grows, replicates its DNA in preparation for mitosis, and conducts other "normal" cell functions.
Interphase was formerly called the resting phase. However, interphase does not describe a cell that is merely resting; rather, the cell is living and preparing for later cell division, so the name was changed. A common misconception is that interphase is the first stage of mitosis, but since mitosis is the division of the nucleus, prophase is actually the first stage.
In interphase, the cell gets itself ready for mitosis or meiosis. Somatic cells, or normal diploid cells of the body, go through mitosis in order to reproduce themselves through cell division, whereas diploid germ cells (i.e., primary spermatocytes and primary oocytes) go through meiosis in order to create haploid gametes (i.e., sperm and ova) for the purpose of sexual reproduction.
Stages of interphase
There are three stages of cellular interphase, with each phase ending when a cellular checkpoint checks the accuracy of the stage's completion before proceeding to the next. The stages of interphase are:
G1 (Gap 1), in which the cell grows and functions normally. During this time, a high a |
https://en.wikipedia.org/wiki/Western%20Digital | Western Digital Corporation (WDC, commonly known as Western Digital or WD) is an American computer drive manufacturer and data storage company, headquartered in San Jose, California. It designs, manufactures and sells data technology products, including data storage devices, data center systems and cloud storage services.
Western Digital has a long history in the electronics industry as an integrated circuit maker and a storage products company. It is one of the largest computer hard disk drive manufacturers, along with producing solid state drives and flash memory devices. Its competitors include the data management and storage companies Seagate Technology and Micron Technology.
History
1970s
Western Digital was founded on April 23, 1970, by Alvin B. Phillips, a Motorola employee, as General Digital Corporation, initially a manufacturer of MOS test equipment. It was originally based in Newport Beach, California, shortly thereafter moving to Santa Ana, California, and would go on to become one of the largest technology firms headquartered in Orange County. It rapidly became a specialty semiconductor maker, with start-up capital provided by several individual investors and industrial giant Emerson Electric. Around July 1971, it adopted its current name and soon introduced its first product, the WD1402A UART.
During the early 1970s, the company focused on making and selling calculator chips, and by 1975, Western Digital was the largest independent calculator chip maker in the world. The oil crisis of the mid-1970s and the bankruptcy of its biggest calculator customer, Bowmar Instrument, changed its fortunes, however, and in 1976 Western Digital declared Chapter 11 bankruptcy. After this, Emerson Electric withdrew their support of the company. Chuck Missler joined Western Digital as chairman and chief executive in June 1977, and became the largest shareholder of Western Digital.
In 1973, Western Digital established its Malaysian plant, initially to manufacture s |
https://en.wikipedia.org/wiki/Kamal%20%28navigation%29 | A kamal, often called simply khashaba (wood in Arabic), is a celestial navigation device that determines latitude. The invention of the kamal allowed for the earliest known latitude sailing, and was thus the earliest step towards the use of quantitative methods in navigation. It originated with Arab navigators of the late 9th century, and was employed in the Indian Ocean from the 10th century. It was adopted by Indian navigators soon after, and then adopted by Chinese navigators some time before the 16th century.
Description
Since the Polaris is currently close to the celestial pole, its elevation is equal to the latitude of the observer. The kamal consists of a rectangular wooden card about , to which a string with several equally spaced knots is attached through a hole in the middle of the card. The kamal is used by placing one end of the string in the teeth while the other end is held away from the body roughly parallel to the ground. The card is then moved along the string, positioned so the lower edge is even with the horizon, and the upper edge is occluding a target star, typically Polaris because its angle to the horizon does not change with longitude or time. The angle can then be measured by counting the number of knots from the teeth to the card, or a particular knot can be tied into the string if travelling to a known latitude.
The knots were typically tied to measure angles of one finger-width. When held at arm's length, the width of a finger measures an angle that remains fairly similar from person to person. This was widely used (and still is today) for rough angle measurements, an angle known as issabah إصبع in Arabic or a zhi 指 in Chinese (both meaning 'finger'). By modern measure, this is about 1 degree, 36 minutes, and 25 seconds, or just over 1.5 degrees. It is equal to the arcsine of the ratio of the width of the finger to the length of the arm. In Chinese navigation, the unit of jiao 角 is also used to represent a quarter 指 (an angle of 24 mi |
https://en.wikipedia.org/wiki/Flynn%27s%20taxonomy | Flynn's taxonomy is a classification of computer architectures, proposed by Michael J. Flynn in 1966 and extended in 1972. The classification system has stuck, and it has been used as a tool in the design of modern processors and their functionalities. Since the rise of multiprocessing central processing units (CPUs), a multiprogramming context has evolved as an extension of the classification system. Vector processing, covered by Duncan's taxonomy, is missing from Flynn's work because the Cray-1 was released in 1977: Flynn's second paper was published in 1972.
Classifications
The four initial classifications defined by Flynn are based upon the number of concurrent instruction (or control) streams and data streams available in the architecture. Flynn defined three additional sub-categories of SIMD in 1972.
Single instruction stream, single data stream (SISD)
A sequential computer which exploits no parallelism in either the instruction or data streams. Single control unit (CU) fetches a single instruction stream (IS) from memory. The CU then generates appropriate control signals to direct a single processing element (PE) to operate on a single data stream (DS) i.e., one operation at a time.
Examples of SISD architectures are the traditional uniprocessor machines like older personal computers (PCs) (by 2010, many PCs had multiple cores) and mainframe computers.
Single instruction stream, multiple data streams (SIMD)
A single instruction is simultaneously applied to multiple different data streams. Instructions can be executed sequentially, such as by pipelining, or in parallel by multiple functional units.
Flynn's 1972 paper subdivided SIMD down into three further categories:
Array processor – These receive the one (same) instruction but each parallel processing unit has its own separate and distinct memory and register file.
Pipelined processor – These receive the one (same) instruction but then read data from a central resource, each processes fragments of |
https://en.wikipedia.org/wiki/Multivalued%20function | In mathematics, a multivalued function is a set-valued function with additional properties depending on context. The terms multifunction and many-valued function are sometimes also used.
A multivalued function of sets f : X → Y is a subset
Write f(x) for the set of those y ∈ Y with (x,y) ∈ Γf. If f is an ordinary function, it is a multivalued function by taking its graph
They are called single-valued functions to distinguish them.
Motivation
The term multivalued function originated in complex analysis, from analytic continuation. It often occurs that one knows the value of a complex analytic function in some neighbourhood of a point . This is the case for functions defined by the implicit function theorem or by a Taylor series around . In such a situation, one may extend the domain of the single-valued function along curves in the complex plane starting at . In doing so, one finds that the value of the extended function at a point depends on the chosen curve from to ; since none of the new values is more natural than the others, all of them are incorporated into a multivalued function.
For example, let be the usual square root function on positive real numbers. One may extend its domain to a neighbourhood of in the complex plane, and then further along curves starting at , so that the values along a given curve vary continuously from . Extending to negative real numbers, one gets two opposite values for the square root—for example for —depending on whether the domain has been extended through the upper or the lower half of the complex plane. This phenomenon is very frequent, occurring for th roots, logarithms, and inverse trigonometric functions.
To define a single-valued function from a complex multivalued function, one may distinguish one of the multiple values as the principal value, producing a single-valued function on the whole plane which is discontinuous along certain boundary curves. Alternatively, dealing with the multivalued function allo |
https://en.wikipedia.org/wiki/Table%20of%20prime%20factors | The tables contain the prime factorization of the natural numbers from 1 to 1000.
When n is a prime number, the prime factorization is just n itself, written in bold below.
The number 1 is called a unit. It has no prime factors and is neither prime nor composite.
Properties
Many properties of a natural number n can be seen or directly computed from the prime factorization of n.
The multiplicity of a prime factor p of n is the largest exponent m for which pm divides n. The tables show the multiplicity for each prime factor. If no exponent is written then the multiplicity is 1 (since p = p1). The multiplicity of a prime which does not divide n may be called 0 or may be considered undefined.
Ω(n), the big Omega function, is the number of prime factors of n counted with multiplicity (so it is the sum of all prime factor multiplicities).
A prime number has Ω(n) = 1. The first: 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37 . There are many special types of prime numbers.
A composite number has Ω(n) > 1. The first: 4, 6, 8, 9, 10, 12, 14, 15, 16, 18, 20, 21 . All numbers above 1 are either prime or composite. 1 is neither.
A semiprime has Ω(n) = 2 (so it is composite). The first: 4, 6, 9, 10, 14, 15, 21, 22, 25, 26, 33, 34 .
A k-almost prime (for a natural number k) has Ω(n) = k (so it is composite if k > 1).
An even number has the prime factor 2. The first: 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24 .
An odd number does not have the prime factor 2. The first: 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23 . All integers are either even or odd.
A square has even multiplicity for all prime factors (it is of the form a2 for some a). The first: 1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144 .
A cube has all multiplicities divisible by 3 (it is of the form a3 for some a). The first: 1, 8, 27, 64, 125, 216, 343, 512, 729, 1000, 1331, 1728 .
A perfect power has a common divisor m > 1 for all multiplicities (it is of the form am for some a > 1 and m > 1). The first: 4, 8, 9, 16, 25, |
https://en.wikipedia.org/wiki/Table%20of%20divisors | The tables below list all of the divisors of the numbers 1 to 1000.
A divisor of an integer n is an integer m, for which n/m is again an integer (which is necessarily also a divisor of n). For example, 3 is a divisor of 21, since 21/7 = 3 (and therefore 7 is also a divisor of 21).
If m is a divisor of n then so is −m. The tables below only list positive divisors.
Key to the tables
d(n) is the number of positive divisors of n, including 1 and n itself
σ(n) is the sum of the positive divisors of n, including 1 and n itself
s(n) is the sum of the proper divisors of n, including 1, but not n itself; that is, s(n) = σ(n) − n
a deficient number is greater than the sum of its proper divisors; that is, s(n) < n
a perfect number equals the sum of its proper divisors; that is, s(n) = n
an abundant number is lesser than the sum of its proper divisors; that is, s(n) > n
a highly abundant number has a sum of positive divisors greater than any lesser number's sum of positive divisors; that is, s(n) > s(m) for every positive integer m < n. Counterintuitively, the first seven highly abundant numbers are not abundant numbers.
a prime number has only 1 and itself as divisors; that is, d(n) = 2. Prime numbers are always deficient as s(n)=1.
a composite number has more than just 1 and itself as divisors; that is, d(n) > 2
a highly composite number has more divisors than any lesser number; that is, d(n) > d(m) for every positive integer m < n. Counterintuitively, the first two highly composite numbers are not composite numbers.
a superior highly composite number has more divisors than any other number scaled relative to some positive power of the number itself; that is, there exists some ε such that for every other positive integer m. Superior highly composite numbers are always highly composite numbers.
a weird number is an abundant number that is not semiperfect; that is, no subset of the proper divisors of n sum to n
1 to 100
101 to 200
201 to 300
301 to 400
401 to 50 |
https://en.wikipedia.org/wiki/McCarthy%2091%20function | The McCarthy 91 function is a recursive function, defined by the computer scientist John McCarthy as a test case for formal verification within computer science.
The McCarthy 91 function is defined as
The results of evaluating the function are given by M(n) = 91 for all integer arguments n ≤ 100, and M(n) = n − 10 for n > 100. Indeed, the result of M(101) is also 91 (101 - 10 = 91). All results of M(n) after n = 101 are continually increasing by 1, e.g. M(102) = 92, M(103) = 93.
History
The 91 function was introduced in papers published by Zohar Manna, Amir Pnueli and John McCarthy in 1970. These papers represented early developments towards the application of formal methods to program verification. The 91 function was chosen for being nested-recursive (contrasted with single recursion, such as defining by means of ). The example was popularized by Manna's book, Mathematical Theory of Computation (1974). As the field of Formal Methods advanced, this example appeared repeatedly in the research literature.
In particular, it is viewed as a "challenge problem" for automated program verification.
It is easier to reason about tail-recursive control flow, this is an equivalent (extensionally equal) definition:
As one of the examples used to demonstrate such reasoning, Manna's book includes a tail-recursive algorithm equivalent to the nested-recursive 91 function. Many of the papers that report an "automated verification" (or termination proof) of the 91 function only handle the tail-recursive version.
This is an equivalent mutually tail-recursive definition:
A formal derivation of the mutually tail-recursive version from the nested-recursive one was given in a 1980 article by Mitchell Wand, based on the use of continuations.
Examples
Example A:
M(99) = M(M(110)) since 99 ≤ 100
= M(100) since 110 > 100
= M(M(111)) since 100 ≤ 100
= M(101) since 111 > 100
= 91 since 101 > 100
Example B:
M(87) = M(M(98))
= M(M(M |
https://en.wikipedia.org/wiki/Unit%20testing | In computer programming, unit testing is a software testing method by which individual units of source code—sets of one or more computer program modules together with associated control data, usage procedures, and operating procedures—are tested to determine whether they are fit for use. It is a standard step in development and implementation approaches such as Agile.
History
Before unit testing, capture and replay testing tools were the norm. In 1997, Kent Beck and Erich Gamma developed and released JUnit, a unit test framework that became popular with Java developers. Google embraced automated testing around 2005–2006.
Unit
Unit tests are typically automated tests written and run by software developers to ensure that a section of an application (known as the "unit") meets its design and behaves as intended.
Procedural programming
In procedural programming, a unit could be an entire module, but it is more commonly an individual function or procedure.
Object-oriented programming
In object-oriented programming, a unit is often an entire interface, such as a class, or an individual method. By writing tests first for the smallest testable units, then the compound behaviors between those, one can build up comprehensive tests for complex applications.
Testing criteria
During development, a software developer may code criteria, or results that are known to be good, into the test to verify the unit's correctness. During test case execution, frameworks log tests that fail any criterion and report them in a summary. For this, the most commonly used approach is test - function - expected value.
Test case
To isolate issues that may arise, each test case should be tested independently. Substitutes such as method stubs, mock objects, fakes, and test harnesses can be used to assist testing a module in isolation.
Parameterized test
Parameterized tests are a technique that claims to shorten the process of writing and maintaining unit tests . Parameterized tests allow the |
https://en.wikipedia.org/wiki/Financial%20cryptography | Financial cryptography is the use of cryptography in applications in which financial loss could result from subversion of the message system. Financial cryptography is distinguished from traditional cryptography in that for most of recorded history, cryptography has been used almost entirely for military and diplomatic purposes.
Financial cryptography includes the mechanisms and algorithms necessary for the protection of financial transfers, in addition to the creation of new forms of money. Proof of work and various auction protocols fall under the umbrella of Financial cryptography. Hashcash is being used to limit spam.
Financial cryptography has been seen to have a very broad scope of application. Ian Grigg sees financial cryptography in seven layers, being the combination of seven distinct disciplines: cryptography, software engineering, rights, accounting, governance, value, and financial applications. Business failures can often be traced to the absence of one or more of these disciplines, or to poor application of them. This views Financial cryptography as an appropriately cross-discipline subject. Indeed, inevitably so, given that finance and cryptography are each built upon multiple disciplines.
History
Cryptographers think the field originated from the work of Dr David Chaum who invented the blind signature. The blind signature is a special form of a cryptographic signature which allowed virtual coins to be signed without the signer seeing the actual coin. It permitted a form of digital token money that prevented traceability. This form is sometimes known as digital currency. Similar concepts are now being applied to modern blockchain technologies.
A system that was widely used during the 1970s-1990s and previously developed cryptographic mechanism is the Data Encryption Standard, which was used primarily for the protection of electronic funds transfers. However, it was the work of David Chaum that excited the cryptography community about the potenti |
https://en.wikipedia.org/wiki/Teratology | Teratology is the study of abnormalities of physiological development in organisms during their life span. It is a sub-discipline in medical genetics which focuses on the classification of congenital abnormalities in dysmorphology caused by teratogens. Teratogens are substances that may cause non-heritable birth defects via a toxic effect on an embryo or fetus. Defects include malformations, disruptions, deformations, and dysplasia that may cause stunted growth, delayed mental development, or other congenital disorders that lack structural malformations. The related term developmental toxicity includes all manifestations of abnormal development that are caused by environmental insult. The extent to which teratogens will impact an embryo is dependent on several factors, such as how long the embryo has been exposed, the stage of development the embryo was in when exposed, the genetic makeup of the embryo, and the transfer rate of the teratogen.
Etymology
The term was borrowed in 1842 from the French , where it was formed in 1830 from the Greek (word stem ), meaning "sign sent by the gods, portent, marvel, monster", and (-ology), used to designate a discourse, treaty, science, theory, or study of some topic.
Old literature referred to abnormalities of all kinds under the Latin term Lusus naturae (lit. "freak of nature"). As early as the 17th century, Teratology referred to a discourse on prodigies and marvels of anything so extraordinary as to seem abnormal. In the 19th century, it acquired a meaning more closely related to biological deformities, mostly in the field of botany. Currently, its most instrumental meaning is that of the medical study of teratogenesis, congenital malformations or individuals with significant malformations. Historically, people have used many pejorative terms to describe/label cases of significant physical malformations. In the 1960s, David W. Smith of the University of Washington Medical School (one of the researchers who became know |
https://en.wikipedia.org/wiki/Cork%20taint | Cork taint is a broad term referring to an off-odor and off-flavor wine fault arising from the presence of 2,4,6-trichloroanisole (TCA), a chemical compound that represents one of the strongest off-flavors, and one "generated naturally in foods/beverages", in particular wines, that considerably reduce the quality of these products.
Cork taint is characterized by a set of undesirable smells or tastes found in a bottle of wine, especially spoilage that can only be detected after bottling, aging and opening. Though modern studies have shown that other factors can also be responsible for taint—including wooden barrels, storage conditions and the transport of corks and wine—the cork stopper is normally considered to be responsible, and a wine found to be tainted on opening is said to be corked or "corky". Cork taint can affect wines irrespective of price and quality level.
The chief cause of cork taint is the presence of the chemical compounds 2,4,6-trichloroanisole (TCA) or 2,4,6-tribromoanisole (TBA) in the wine, which in many cases will have been transferred from the cork, but which also can have been transferred through the cork rather than from it. TCA is a compound which does not occur naturally; It is created when some fungi are treated with chlorinated phenolic compounds, which are a type of antimicrobial agent used in the processing of wood. This class of compounds is a chief factor responsible for the problem associated with mold liable to be found in cork; very small amounts of this compound, on the order of nanograms, can be responsible for this defect.
Corked wine containing TCA has a characteristic odor, variously described as resembling a moldy newspaper, wet dog, damp cloth, or damp basement; in almost all cases of corked wine the wine's native aromas are reduced significantly, and a very tainted wine is quite unpalatable, although harmless. While the human threshold for detecting TCA is measured in the single-digit parts per trillion, this can vary b |
https://en.wikipedia.org/wiki/Gantt%20chart | A Gantt chart is a bar chart that illustrates a project schedule. It was designed and popularized by Henry Gantt around the years 1910–1915. Modern Gantt charts also show the dependency relationships between activities and the current schedule status.
Definition
A Gantt chart is a type of bar chart that illustrates a project schedule. This chart lists the tasks to be performed on the vertical axis, and time intervals on the horizontal axis. The width of the horizontal bars in the graph shows the duration of each activity. Gantt charts illustrate the start and finish dates of the terminal elements and summary elements of a project. Terminal elements and summary elements constitute the work breakdown structure of the project. Modern Gantt charts also show the dependency (i.e., precedence network) relationships between activities. Gantt charts can be used to show current schedule status using percent-complete shadings and a vertical "TODAY" line.
Gantt charts are sometimes equated with bar charts.
Gantt charts are usually created initially using an early start time approach, where each task is scheduled to start immediately when its prerequisites are complete. This method maximizes the float time available for all tasks.
History
Widely used in project planning in the present day, Gantt charts were considered revolutionary when introduced. The first known tool of this type was developed in 1896 by Karol Adamiecki, who called it a harmonogram. Adamiecki, however, published his chart only in Russian and Polish which limited both its adoption and recognition of his authorship.
In 1912, published what could be considered Gantt charts while discussing a construction project. Charts of the type published by Schürch appear to have been in common use in Germany at the time; however, the prior development leading to Schürch's work is unclear. Unlike later Gantt charts, Schürch's charts did not display interdependencies, leaving them to be inferred by the reader. These w |
https://en.wikipedia.org/wiki/Unix%20philosophy | The Unix philosophy, originated by Ken Thompson, is a set of cultural norms and philosophical approaches to minimalist, modular software development. It is based on the experience of leading developers of the Unix operating system. Early Unix developers were important in bringing the concepts of modularity and reusability into software engineering practice, spawning a "software tools" movement. Over time, the leading developers of Unix (and programs that ran on it) established a set of cultural norms for developing software; these norms became as important and influential as the technology of Unix itself, and have been termed the "Unix philosophy."
The Unix philosophy emphasizes building simple, compact, clear, modular, and extensible code that can be easily maintained and repurposed by developers other than its creators. The Unix philosophy favors composability as opposed to monolithic design.
Origin
The Unix philosophy is documented by Doug McIlroy in the Bell System Technical Journal from 1978:
Make each program do one thing well. To do a new job, build afresh rather than complicate old programs by adding new "features".
Expect the output of every program to become the input to another, as yet unknown, program. Don't clutter output with extraneous information. Avoid stringently columnar or binary input formats. Don't insist on interactive input.
Design and build software, even operating systems, to be tried early, ideally within weeks. Don't hesitate to throw away the clumsy parts and rebuild them.
Use tools in preference to unskilled help to lighten a programming task, even if you have to detour to build the tools and expect to throw some of them out after you've finished using them.
It was later summarized by Peter H. Salus in A Quarter-Century of Unix (1994):
Write programs that do one thing and do it well.
Write programs to work together.
Write programs to handle text streams, because that is a universal interface.
In their award-winning Unix pap |
https://en.wikipedia.org/wiki/Worse%20is%20better | Worse is better (also called the New Jersey style) is a term conceived by Richard P. Gabriel in a 1989 essay to describe the dynamics of software acceptance. It refers to the argument that software quality does not necessarily increase with functionality: that there is a point where less functionality ("worse") is a preferable option ("better") in terms of practicality and usability. Software that is limited, but simple to use, may be more appealing to the user and market than the reverse.
As to the oxymoronic title, Gabriel calls it a caricature, declaring the style bad in comparison with "The Right Thing". However he also states that "it has better survival characteristics than the-right-thing" development style and is superior to the "MIT Approach" with which he contrasted it.
The essay was included into the 1994 book The UNIX-HATERS Handbook, and has been referred to as the origin of the notion of a conceptual split between developers on the east and west coasts of the United States.
Origin
Gabriel was a Lisp programmer when he formulated the concept in 1989, presenting it in his essay "Lisp: Good News, Bad News, How to Win Big". A section of the article, titled "The Rise of 'Worse is Better, was widely disseminated beginning in 1991, after Jamie Zawinski found it in Gabriel's files at Lucid Inc. and emailed it to friends and colleagues.
Characteristics
New Jersey style
In The Rise of Worse is Better, Gabriel identified a "Worse is Better" (also the "New Jersey style", "Berkeley", or "West coast") model of software design and implementation which has the characteristics (in approximately descending order of importance):
Simplicity The design must be simple, both in implementation and interface. It is more important for the implementation to be simpler than the interface. Simplicity is the most important consideration in a design.
Correctness The design should be correct in all observable aspects. It is slightly better to be simple than correct.
Consi |
https://en.wikipedia.org/wiki/Software%20design | Software design is the process by which an agent creates a specification of a software artifact intended to accomplish goals, using a set of primitive components and subject to constraints. The term is sometimes used broadly to refer to "all the activity involved in conceptualizing, framing, implementing, commissioning, and ultimately modifying" the software, or more specifically "the activity following requirements specification and before programming, as ... [in] a stylized software engineering process."
Software design usually involves problem-solving and planning a software solution. This includes both a low-level component and algorithm design and a high-level, architecture design.
Overview
Software design is the process of envisioning and defining software solutions to one or more sets of problems. One of the main components of software design is the software requirements analysis (SRA). SRA is a part of the software development process that lists specifications used in software engineering.
If the software is "semi-automated" or user centered, software design may involve user experience design yielding a storyboard to help determine those specifications. If the software is completely automated (meaning no user or user interface), a software design may be as simple as a flow chart or text describing a planned sequence of events. There are also semi-standard methods like Unified Modeling Language and Fundamental modeling concepts. In either case, some documentation of the plan is usually the product of the design. Furthermore, a software design may be platform-independent or platform-specific, depending upon the availability of the technology used for the design.
The main difference between software analysis and design is that the output of a software analysis consists of smaller problems to solve. Additionally, the analysis should not be designed very differently across different team members or groups. In contrast, the design focuses on capabilities, a |
https://en.wikipedia.org/wiki/Alternator | An alternator is an electrical generator that converts mechanical energy to electrical energy in the form of alternating current. For reasons of cost and simplicity, most alternators use a rotating magnetic field with a stationary armature. Occasionally, a linear alternator or a rotating armature with a stationary magnetic field is used. In principle, any AC electrical generator can be called an alternator, but usually the term refers to small rotating machines driven by automotive and other internal combustion engines.
An alternator that uses a permanent magnet for its magnetic field is called a magneto. Alternators in power stations driven by steam turbines are called turbo-alternators. Large 50 or 60 Hz three-phase alternators in power plants generate most of the world's electric power, which is distributed by electric power grids.
History
Alternating current generating systems were known in simple forms from the discovery of the magnetic induction of electric current in the 1830s. Rotating generators naturally produced alternating current but, since there was little use for it, it was normally converted into direct current via the addition of a commutator in the generator. The early machines were developed by pioneers such as Michael Faraday and Hippolyte Pixii. Faraday developed the "rotating rectangle", whose operation was heteropolar – each active conductor passed successively through regions where the magnetic field was in opposite directions. Lord Kelvin and Sebastian Ferranti also developed early alternators, producing frequencies between 100 and 300 Hz.
The late 1870s saw the introduction of first large scale electrical systems with central generation stations to power Arc lamps, used to light whole streets, factory yards, or the interior of large warehouses. Some, such as Yablochkov arc lamps introduced in 1878, ran better on alternating current, and the development of these early AC generating systems was accompanied by the first use of the word "a |
https://en.wikipedia.org/wiki/Power%20inverter | A power inverter, inverter or invertor is a power electronic device or circuitry that changes direct current (DC) to alternating current (AC). The resulting AC frequency obtained depends on the particular device employed. Inverters do the opposite of rectifiers which were originally large electromechanical devices converting AC to DC.
The input voltage, output voltage and frequency, and overall power handling depend on the design of the specific device or circuitry. The inverter does not produce any power; the power is provided by the DC source.
A power inverter can be entirely electronic or maybe a combination of mechanical effects (such as a rotary apparatus) and electronic circuitry.
Static inverters do not use moving parts in the conversion process.
Power inverters are primarily used in electrical power applications where high currents and voltages are present; circuits that perform the same function for electronic signals, which usually have very low currents and voltages, are called oscillators. Circuits that perform the opposite function, converting AC to DC, are called rectifiers.
Input and output
Input voltage
A typical power inverter device or circuit requires a stable DC power source capable of supplying enough current for the intended power demands of the system. The input voltage depends on the design and purpose of the inverter. Examples include:
12 V DC, for smaller consumer and commercial inverters that typically run from a rechargeable 12 V lead acid battery or automotive electrical outlet.
24, 36 and 48 V DC, which are common standards for home energy systems.
200 to 400 V DC, when power is from photovoltaic solar panels.
300 to 450 V DC, when power is from electric vehicle battery packs in vehicle-to-grid systems.
Hundreds of thousands of volts, where the inverter is part of a high-voltage direct current power transmission system.
Output waveform
An inverter may produce a square wave, sine wave, modified sine wave, pulsed sine wa |
https://en.wikipedia.org/wiki/Switched-mode%20power%20supply | A switched-mode power supply (SMPS), also called switching-mode power supply, switch-mode power supply, switched power supply, or simply switcher, is an electronic power supply that incorporates a switching regulator to convert electrical power efficiently.
Like other power supplies, an SMPS transfers power from a DC or AC source (often mains power, see AC adapter) to DC loads, such as a personal computer, while converting voltage and current characteristics. Unlike a linear power supply, the pass transistor of a switching-mode supply continually switches between low-dissipation, full-on and full-off states, and spends very little time in the high dissipation transitions, which minimizes wasted energy. A hypothetical ideal switched-mode power supply dissipates no power. Voltage regulation is achieved by varying the ratio of on-to-off time (also known as duty cycles). In contrast, a linear power supply regulates the output voltage by continually dissipating power in the pass transistor. The switched-mode power supply's higher electrical efficiency is an important advantage.
Switched-mode power supplies can also be substantially smaller and lighter than a linear supply because the transformer can be much smaller. This is because it operates at a high switching frequency which ranges from several hundred kHz to several MHz in contrast to the 50 or 60 Hz mains frequency. Despite the reduced transformer size, the power supply topology and the requirement for electromagnetic interference (EMI) suppression in commercial designs result in a usually much greater component count and corresponding circuit complexity.
Switching regulators are used as replacements for linear regulators when higher efficiency, smaller size or lighter weight is required. They are, however, more complicated; switching currents can cause electrical noise problems if not carefully suppressed, and simple designs may have a poor power factor.
History
1836 Induction coils use switches to generate h |
https://en.wikipedia.org/wiki/Public%20key%20certificate | In cryptography, a public key certificate, also known as a digital certificate or identity certificate, is an electronic document used to prove the validity of a public key. The certificate includes the public key and information about it, information about the identity of its owner (called the subject), and the digital signature of an entity that has verified the certificate's contents (called the issuer). If the device examining the certificate trusts the issuer and finds the signature to be a valid signature of that issuer, then it can use the included public key to communicate securely with the certificate's subject. In email encryption, code signing, and e-signature systems, a certificate's subject is typically a person or organization. However, in Transport Layer Security (TLS) a certificate's subject is typically a computer or other device, though TLS certificates may identify organizations or individuals in addition to their core role in identifying devices. TLS, sometimes called by its older name Secure Sockets Layer (SSL), is notable for being a part of HTTPS, a protocol for securely browsing the web.
In a typical public-key infrastructure (PKI) scheme, the certificate issuer is a certificate authority (CA), usually a company that charges customers a fee to issue certificates for them. By contrast, in a web of trust scheme, individuals sign each other's keys directly, in a format that performs a similar function to a public key certificate. In case of key compromise, a certificate may need to be revoked.
The most common format for public key certificates is defined by X.509. Because X.509 is very general, the format is further constrained by profiles defined for certain use cases, such as Public Key Infrastructure (X.509) as defined in .
Types of certificate
TLS/SSL server certificate
The Transport Layer Security (TLS) protocol – as well as its outdated predecessor, the Secure Sockets Layer (SSL) protocol – ensures that the communication between a cli |
https://en.wikipedia.org/wiki/Index%20of%20biology%20articles | Biology is the study of life and its processes. Biologists study all aspects of living things, including all of the many life forms on earth and the processes in them that enable life. These basic processes include the harnessing of energy, the synthesis and duplication of the materials that make up the body, the reproduction of the organism and many other functions. Biology, along with chemistry and physics is one of the major disciplines of natural science.
A
ABO blood group system – abscisic acid – absorption spectrum – abyssal zone – acetylcholine – acetyl-CoA – acid – acid precipitation – acoelomate – acrosome – actin – action potential – active site – adaptive radiation – address-message concept – adenosine 5'-triphosphate – adenylyl cyclase – adrenal gland – adrenodoxin – aerobic organism – age structure – agonist – AIDS – albumin – aldehydes – aldosterone – algae – allantois – allele – allometry – allopatric speciation – allosteric binding site – allosteric effector – allosteric enzyme – allosteric site – allozyme – alpha helix – amino acid – aminoacyl tRNA synthetase – amino group – amniocentesis – amniote – amphipathic molecule – anabolism – anaerobic organism – anaerobic respiration – androgen – anemia – aneuploidy – angiosperm – anther – anthrax – antibiotic – antibody – anticodon – antidiuretic hormone – antigen – apical dominance – apical meristem – apolipoprotein – apoplast – apoptosis – aquaporin – Archaea – archegonium – arteriosclerosis – artery – arthritis – ascus – asexual reproduction – atomic number – ATP – ATP synthase – atrioventricular valve – atrium – autoimmune disease – autonomic nervous system – autosome – auxin – axillary bud – axon
B
bacillary band – bacteria – bacteriochlorin – bark – Barr body – basal body – basal metabolic rate – base – base pair – basement membrane – basidiomycetes – basidium – B cell – benthic zone – beta sheet – binary fission – binding site – bioassay – biodiversity – bioenergetics – biogeochemical cycle – b |
https://en.wikipedia.org/wiki/Industrial%20ecology | Industrial ecology (IE) is the study of material and energy flows through industrial systems. The global industrial economy can be modelled as a network of industrial processes that extract resources from the Earth and transform those resources into by-products, products and services which can be bought and sold to meet the needs of humanity. Industrial ecology seeks to quantify the material flows and document the industrial processes that make modern society function. Industrial ecologists are often concerned with the impacts that industrial activities have on the environment, with use of the planet's supply of natural resources, and with problems of waste disposal. Industrial ecology is a young but growing multidisciplinary field of research which combines aspects of engineering, economics, sociology, toxicology and the natural sciences.
Industrial ecology has been defined as a "systems-based, multidisciplinary discourse that seeks to understand emergent behavior of complex integrated human/natural systems". The field approaches issues of sustainability by examining problems from multiple perspectives, usually involving aspects of sociology, the environment, economy and technology. The name comes from the idea that the analogy of natural systems should be used as an aid in understanding how to design sustainable industrial systems.
Overview
Industrial ecology is concerned with the shifting of industrial process from linear (open loop) systems, in which resource and capital investments move through the system to become waste, to a closed loop system where wastes can become inputs for new processes.
Much of the research focuses on the following areas:
material and energy flow studies ("industrial metabolism")
dematerialization and decarbonization
technological change and the environment
life-cycle planning, design and assessment
design for the environment ("eco-design")
extended producer responsibility ("product stewardship")
eco-industrial parks |
https://en.wikipedia.org/wiki/Rootkit | A rootkit is a collection of computer software, typically malicious, designed to enable access to a computer or an area of its software that is not otherwise allowed (for example, to an unauthorized user) and often masks its existence or the existence of other software. The term rootkit is a compound of "root" (the traditional name of the privileged account on Unix-like operating systems) and the word "kit" (which refers to the software components that implement the tool). The term "rootkit" has negative connotations through its association with malware.
Rootkit installation can be automated, or an attacker can install it after having obtained root or administrator access. Obtaining this access is a result of direct attack on a system, i.e. exploiting a vulnerability (such as privilege escalation) or a password (obtained by cracking or social engineering tactics like "phishing"). Once installed, it becomes possible to hide the intrusion as well as to maintain privileged access. Full control over a system means that existing software can be modified, including software that might otherwise be used to detect or circumvent it.
Rootkit detection is difficult because a rootkit may be able to subvert the software that is intended to find it. Detection methods include using an alternative and trusted operating system, behavioral-based methods, signature scanning, difference scanning, and memory dump analysis. Removal can be complicated or practically impossible, especially in cases where the rootkit resides in the kernel; reinstallation of the operating system may be the only available solution to the problem. When dealing with firmware rootkits, removal may require hardware replacement, or specialized equipment.
History
The term rootkit or root kit originally referred to a maliciously modified set of administrative tools for a Unix-like operating system that granted "root" access. If an intruder could replace the standard administrative tools on a system with a rootki |
https://en.wikipedia.org/wiki/Generalised%20logistic%20function | The generalized logistic function or curve is an extension of the logistic or sigmoid functions. Originally developed for growth modelling, it allows for more flexible S-shaped curves. The function is sometimes named Richards's curve after F. J. Richards, who proposed the general form for the family of models in 1959.
Definition
Richards's curve has the following form:
where = weight, height, size etc., and = time. It has six parameters:
: the left horizontal asymptote;
: the right horizontal asymptote when . If and then is called the carrying capacity;
: the growth rate;
: affects near which asymptote maximum growth occurs.
: is related to the value
: typically takes a value of 1. Otherwise, the upper asymptote is
The equation can also be written:
where can be thought of as a starting time, at which . Including both and can be convenient:
this representation simplifies the setting of both a starting time and the value of at that time.
The logistic function, with maximum growth rate at time , is the case where .
Generalised logistic differential equation
A particular case of the generalised logistic function is:
which is the solution of the Richards's differential equation (RDE):
with initial condition
where
provided that ν > 0 and α > 0.
The classical logistic differential equation is a particular case of the above equation, with ν =1, whereas the Gompertz curve can be recovered in the limit provided that:
In fact, for small ν it is
The RDE models many growth phenomena, arising in fields such as oncology and epidemiology.
Gradient of generalized logistic function
When estimating parameters from data, it is often necessary to compute the partial derivatives of the logistic function with respect to parameters at a given data point (see). For the case where ,
Special cases
The following functions are specific cases of Richards's curves:
Logistic function
Gompertz curve
Von Bertalanffy function
Monomolecular curve
Footnotes
Referen |
https://en.wikipedia.org/wiki/Tarpit%20%28networking%29 | A tarpit is a service on a computer system (usually a server) that purposely delays incoming connections. The technique was developed as a defense against a computer worm, and the idea is that network abuses such as spamming or broad scanning are less effective, and therefore less attractive, if they take too long. The concept is analogous with a tar pit, in which animals can get bogged down and slowly sink under the surface, like in a swamp.
The original tarpit idea
Tom Liston developed the original tarpitting program LaBrea. It can protect an entire network with a tarpit run on a single machine.
The machine listens for Address Resolution Protocol requests that go unanswered (indicating unused addresses), then replies to those requests, receives the initial SYN packet of the scanner and sends a SYN/ACK in response. It does not open a socket or prepare a connection, in fact it can forget all about the connection after sending the SYN/ACK. However, the remote site sends its ACK (which gets ignored) and believes the 3-way-handshake to be complete. Then it starts to send data, which never reaches a destination. The connection will time out after a while, but since the system believes it is dealing with a live (established) connection, it is conservative in timing it out and will instead try to retransmit, back-off, retransmit, etc. for quite a while.
Later versions of LaBrea also added functionality to reply to the incoming data, again using raw IP packets and no sockets or other resources of the tarpit server, with bogus packets that request that the sending site "slow down". This will keep the connection established and waste even more time of the scanner.
SMTP tarpits
One of the possible avenues that were considered to battle bulk-spam at one time, was to mandate a small fee for every submitted mail. By introducing such artificial cost, with negligible impact on legitimate use as long as the fee is small enough, automated mass-scale spam would instantly become |
https://en.wikipedia.org/wiki/Wallace%E2%80%93Bolyai%E2%80%93Gerwien%20theorem | In geometry, the Wallace–Bolyai–Gerwien theorem, named after William Wallace, Farkas Bolyai and P. Gerwien, is a theorem related to dissections of polygons. It answers the question when one polygon can be formed from another by cutting it into a finite number of pieces and recomposing these by translations and rotations. The Wallace–Bolyai–Gerwien theorem states that this can be done if and only if two polygons have the same area.
Wallace had proven the same result already in 1807.
According to other sources, Bolyai and Gerwien had independently proved the theorem in 1833 and 1835, respectively.
Formulation
There are several ways in which this theorem may be formulated. The most common version uses the concept of "equidecomposability" of polygons: two polygons are equidecomposable if they can be split into finitely many triangles that only differ by some isometry (in fact only by a combination of a translation and a rotation). In this case the Wallace–Bolyai–Gerwien theorem states that two polygons are equidecomposable if and only if they have the same area.
Another formulation is in terms of scissors congruence: two polygons are scissors-congruent if they can be decomposed into finitely many polygons that are pairwise congruent. Scissors-congruence is an equivalence relation. In this case the Wallace–Bolyai–Gerwien theorem states that the equivalence classes of this relation contain precisely those polygons that have the same area.
Proof sketch
The theorem can be understood in a few steps. Firstly, every polygon can be cut into triangles. There are a few methods for this. For convex polygons one can cut off each vertex in turn, while for concave polygons this requires more care. A general approach that works for non-simple polygons as well would be to choose a line not parallel to any of the sides of the polygon and draw a line parallel to this one through each of the vertices of the polygon. This will divide the polygon into triangles and trapezoids, which |
https://en.wikipedia.org/wiki/GNOME%20Web | GNOME Web, called Epiphany until 2012 and still known by that code name, is a free and open-source web browser based on the GTK port of Apple's WebKit rendering engine, called WebKitGTK. It is developed by the GNOME project for Unix-like systems. It is the default and official web browser of GNOME, and part of the GNOME Core Applications.
Despite being a component of GNOME, Web has no dependency on GNOME components, so it can be potentially installed on any system supporting GTK and WebKitGTK.
GNOME Web is the default web browser on elementary OS and Bodhi Linux version 5.
History
Naming
GNOME Web was originally named "Epiphany", but was rebranded in 2012 as part of GNOME 3.4. The name Epiphany is still used internally, as its code name, for development and in the source code. The package remains epiphany-browser in Debian (to avoid a name collision with a video game that is also called "Epiphany") and epiphany in Fedora and Arch Linux.
Development
Galeon
Marco Pesenti Gritti, the initiator of Galeon, originally developed Epiphany in 2002 as a fork of Galeon. The fork occurred because of the disagreement between Gritti and the rest of Galeon developers about new features. While Gritti regarded Galeon's monolithic design and the number of user-configurable features as factors limiting Galeon's maintainability and usability, the rest of the Galeon developers wanted to add more features.
Around the same time, the GNOME project adopted a set of human interface guidelines, which promoted simplification of user interfaces. As Galeon was oriented towards power users, most developers disapproved. As a result, Gritti created a new browser based on Galeon, with most of the non-critical features removed. He intended Epiphany to comply with the GNOME HIG. As such, Epiphany used the global GNOME theme and other settings from inception.
Gritti explained his motivations:
Galeon continued after the fork, but lost momentum due to the remaining developers' failure to keep |
https://en.wikipedia.org/wiki/Bowen%20ratio | The Bowen ratio is used to describe the type of heat transfer for a surface that has moisture. Heat transfer can either occur as sensible heat (differences in temperature without evapotranspiration) or latent heat (the energy required during a change of state, without a change in temperature). The Bowen ratio is generally used to calculate heat lost (or gained) in a substance; it is the ratio of energy fluxes from one state to another by sensible heat and latent heating respectively.
The ratio was named by Harald Sverdrup after Ira Sprague Bowen (1898–1973), an astrophysicist whose theoretical work on evaporation to air from water bodies made first use of it, and it is used most commonly in meteorology and hydrology.
Formulation
The Bowen ratio is calculated by the equation:
, where is sensible heating and is latent heating.
In this context, when the magnitude of is less than one, a greater proportion of the available energy at the surface is passed to the atmosphere as latent heat than as sensible heat, and the converse is true for values of greater than one. As , however, becomes unbounded making the Bowen ratio a poor choice of variable for use in formulae, especially for arid surfaces. For this reason the evaporative fraction is sometimes a more appropriate choice of variable representing the relative contributions of the turbulent energy fluxes to the surface energy budget.
The Bowen ratio is related to the evaporative fraction, , through the equation,
.
The Bowen ratio is an indicator of the type of surface. The Bowen ratio, , is less than one over surfaces with abundant water supplies.
References
External links
National Science Digital Library - Bowen Ratio
1926 introductions
Engineering ratios
Heat transfer
Atmospheric thermodynamics |
https://en.wikipedia.org/wiki/Mod%20perl | mod_perl is an optional module for the Apache HTTP server. It embeds a Perl interpreter into the Apache server. In addition to allowing Apache modules to be written in the Perl programming language, it allows the Apache web server to be dynamically configured by Perl programs. However, its most common use is so that dynamic content produced by Perl scripts can be served in response to incoming requests, without the significant overhead of re-launching the Perl interpreter for each request.
Slash, which runs the web site Slashdot, is written using mod_perl. Early versions of PHP were implemented in Perl using mod_perl.
mod_perl can emulate a Common Gateway Interface (CGI) environment, so that existing Perl CGI scripts can benefit from the performance boost without having to be re-written.
Unlike CGI (and most other web application environments), mod_perl provides complete access to the Apache API, allowing programmers to write handlers for all phases in the Apache request cycle, manipulate Apache's internal tables and state mechanisms, share data between Apache processes or threads, alter or extend the Apache configuration file parser, and add Perl code to the configuration file itself, among other things.
See also
CGI.pm
FastCGI
References
External links
Why mod_perl?
The magic of mod_perl
Writing Apache Modules with Perl and C
The mod_perl Developer's Cookbook
mod_perl2 User's Guide
An easy step-by-step installation guide for mod_perl2 on Unix/Linux and Windows/ReactOS
How to disabled "mod_perl"
Perl
Perl
Articles with underscores in the title
Cross-platform software |
https://en.wikipedia.org/wiki/Chain%20%28algebraic%20topology%29 | In algebraic topology, a -chain
is a formal linear combination of the -cells in a cell complex. In simplicial complexes (respectively, cubical complexes), -chains are combinations of -simplices (respectively, -cubes), but not necessarily connected. Chains are used in homology; the elements of a homology group are equivalence classes of chains.
Definition
For a simplicial complex , the group of -chains of is given by:
where are singular -simplices of . that any element in not necessary to be a connected simplicial complex.
Integration on chains
Integration is defined on chains by taking the linear combination of integrals over the simplices in the chain with coefficients (which are typically integers).
The set of all k-chains forms a group and the sequence of these groups is called a chain complex.
Boundary operator on chains
The boundary of a chain is the linear combination of boundaries of the simplices in the chain. The boundary of a k-chain is a (k−1)-chain. Note that the boundary of a simplex is not a simplex, but a chain with coefficients 1 or −1 – thus chains are the closure of simplices under the boundary operator.
Example 1: The boundary of a path is the formal difference of its endpoints: it is a telescoping sum. To illustrate, if the 1-chain is a path from point to point , where
,
and
are its constituent 1-simplices, then
Example 2: The boundary of the triangle is a formal sum of its edges with signs arranged to make the traversal of the boundary counterclockwise.
A chain is called a cycle when its boundary is zero. A chain that is the boundary of another chain is called a boundary. Boundaries are cycles,
so chains form a chain complex, whose homology groups (cycles modulo boundaries) are called simplicial homology groups.
Example 3: The plane punctured at the origin has nontrivial 1-homology group since the unit circle is a cycle, but not a boundary.
In differential geometry, the duality between the boundary operator on chains and t |
https://en.wikipedia.org/wiki/MIL-STD-1750A | MIL-STD-1750A or 1750A is the formal definition of a 16-bit computer instruction set architecture (ISA), including both required and optional components, as described by the military standard document MIL-STD-1750A (1980). Since August 1996, it has been inactive for new designs.
In addition to the core ISA, the definition defines optional instructions, such as a FPU and MMU. Importantly, the standard does not define the implementation details of a 1750A processor.
Internals
The 1750A supports 216 16-bit words of memory for the core standard. The standard defines an optional memory management unit that allows 220 16-bit words of memory using 512 page mapping registers (in the I/O space), defining separate instruction and data spaces, and keyed memory access control.
Most instructions are 16 bits, although some have a 16-bit extension. The standard computer has 16 general purpose 16-bit registers (0 through 15). Registers 1 through 15 can be used as index registers. Registers 12 through 15 can be used as base registers.
Any of the 16 registers can be used as a stack pointer for the SJS and URS instructions (stack jump subroutine and unstack return subroutine), but only register 15 is used as the stack pointer for the PSHM and POPM instructions (push multiple and pop multiple).
The computer has instructions for 16, and 32-bit binary arithmetic, as well as 32 and 48 bit floating point. I/O is generally via the I/O instructions (XIO and VIO), which have a separate 216 16-bit word address space and may have a specialized bus.
Implementations
Because MIL-STD-1750A does not define implementation details, 1750A products are available from a wide variety of companies in the form of component, board, and system-level offerings implemented in myriad technologies, often the most advanced and exotic of their respective periods (e.g. GaAs, ECL, SoS).
1750A systems often offer high levels of protection from radiation and other hazardous environments, making them particu |
https://en.wikipedia.org/wiki/Radio%20Data%20System | Radio Data System (RDS) is a communications protocol standard for embedding small amounts of digital information in conventional FM radio broadcasts. RDS standardizes several types of information transmitted, including time, station identification and program information.
The standard began as a project of the European Broadcasting Union (EBU), but has since become an international standard of the International Electrotechnical Commission (IEC). Radio Broadcast Data System (RBDS) is the official name used for the U.S. version of RDS. The two standards are only slightly different, with receivers able to work with either system with only minor inconsistencies in the displayed data.
Both versions carry data at 1,187.5 bits per second (about 1.2kbit/s) on a 57 kHz subcarrier, so there are exactly 48 cycles of subcarrier during every data bit. The RBDS/RDS subcarrier was set to the third harmonic of the 19 kHz FM stereo pilot tone to minimize interference and intermodulation between the data signal, the stereo pilot and the 38 kHz DSB-SC stereo difference signal. (The stereo difference signal extends up 38 kHz + 15 kHz = 53 kHz, leaving 4 kHz for the lower sideband of the RDS signal.)
The data is sent with an error correction code, but receivers may choose to use it only for error detection without correction. RDS defines many features including how private (in-house) or other undefined features can be "packaged" in unused program groups.
Development
RDS was inspired by the development of the Autofahrer-Rundfunk-Informationssystem (ARI) in Germany by the Institut für Rundfunktechnik (IRT) and the radio manufacturer Blaupunkt. ARI used a 57-kHz subcarrier to indicate the presence of traffic information in an FM radio broadcast.
The EBU Technical Committee launched a project at its 1974 Paris meeting to develop a technology with similar purposes to ARI, but which was more flexible and which would enable automated retuning of a receiver where a broadcast network transm |
https://en.wikipedia.org/wiki/Petri%20net | A Petri net, also known as a place/transition (PT) net, is one of several mathematical modeling languages for the description of distributed systems. It is a class of discrete event dynamic system. A Petri net is a directed bipartite graph that has two types of elements: places and transitions. Place elements are depicted as white circles and transition elements are depicted as rectangles.
A place can contain any number of tokens, depicted as black circles. A transition is enabled if all places connected to it as inputs contain at least one token. Some sources state that Petri nets were invented in August 1939 by Carl Adam Petri—at the age of 13—for the purpose of describing chemical processes.
Like industry standards such as UML activity diagrams, Business Process Model and Notation, and event-driven process chains, Petri nets offer a graphical notation for stepwise processes that include choice, iteration, and concurrent execution. Unlike these standards, Petri nets have an exact mathematical definition of their execution semantics, with a well-developed mathematical theory for process analysis.
Historical background
The German computer scientist Carl Adam Petri, after whom such structures are named, analyzed Petri nets extensively in his 1962 dissertation .
Petri net basics
A Petri net consists of places, transitions, and arcs. Arcs run from a place to a transition or vice versa, never between places or between transitions. The places from which an arc runs to a transition are called the input places of the transition; the places to which arcs run from a transition are called the output places of the transition.
Graphically, places in a Petri net may contain a discrete number of marks called tokens. Any distribution of tokens over the places will represent a configuration of the net called a marking. In an abstract sense relating to a Petri net diagram, a transition of a Petri net may fire if it is enabled, i.e. there are sufficient tokens in all of it |
https://en.wikipedia.org/wiki/Carl%20Adam%20Petri | Carl Adam Petri (12 July 1926 in Leipzig – 2 July 2010 in Siegburg) was a German mathematician and computer scientist.
Life and work
Petri created his major scientific contribution, the concept of the Petri net, in 1939 at the age of 13, for the purpose of describing chemical processes. In 1941, his father told him about Konrad Zuse's work on computing machines and Carl Adam started building his own analog computer.
After earning his Abitur at Thomasschule in 1944, he was drafted into the Wehrmacht. He was taken into British captivity until 1949, when he departed England.
Petri started studying mathematics at the Technische Hochschule Hannover (today, the Leibniz University Hannover) in 1950. He documented Petri nets in 1962 as part of his dissertation, (Communication with automata). From 1959 until 1962 he worked at the University of Bonn and received his PhD degree in 1962 from the Technische Universität Darmstadt. From 1963 to 1968 he established and directed the computing centre of Bonn University. In 1968, he became head of of the newly founded Gesellschaft für Mathematik und Datenverarbeitung (GMD). He retired in 1991.
In 1988, Petri became honorary professor of the University of Hamburg. He was a member of the Academia Europaea.
Petri's work significantly advanced the fields of parallel computing and distributed computing, and it helped define the modern studies of complex systems and workflow management systems. His contributions have been in the broader area of network theory, which includes coordination models and theories of interaction, and eventually led to the formal study of software connectors.
Books, papers, and presentations
1962
Kommunikation mit Automaten (Dissertation) by Carl Adam Petri
1976
Nicht-sequentielle Prozesse (Work report) by Carl Adam Petri
Kommunikationsdisziplinen (Internal report) by Carl Adam Petri
1977
General net theory (from "Computing System Design: Proceedings of the Joint IBM University of Newcastle upon Ty |
https://en.wikipedia.org/wiki/Silicon%20carbide | Silicon carbide (SiC), also known as carborundum (), is a hard chemical compound containing silicon and carbon. A semiconductor, it occurs in nature as the extremely rare mineral moissanite, but has been mass-produced as a powder and crystal since 1893 for use as an abrasive. Grains of silicon carbide can be bonded together by sintering to form very hard ceramics that are widely used in applications requiring high endurance, such as car brakes, car clutches and ceramic plates in bulletproof vests. Large single crystals of silicon carbide can be grown by the Lely method and they can be cut into gems known as synthetic moissanite.
Electronic applications of silicon carbide such as light-emitting diodes (LEDs) and detectors in early radios were first demonstrated around 1907. SiC is used in semiconductor electronics devices that operate at high temperatures or high voltages, or both.
Natural occurrence
Naturally occurring moissanite is found in only minute quantities in certain types of meteorite, corundum deposits, and kimberlite. Virtually all the silicon carbide sold in the world, including moissanite jewels, is synthetic.
Natural moissanite was first found in 1893 as a small component of the Canyon Diablo meteorite in Arizona by Dr. Ferdinand Henri Moissan, after whom the material was named in 1905. Moissan's discovery of naturally occurring SiC was initially disputed because his sample may have been contaminated by silicon carbide saw blades that were already on the market at that time.
While rare on Earth, silicon carbide is remarkably common in space. It is a common form of stardust found around carbon-rich stars, and examples of this stardust have been found in pristine condition in primitive (unaltered) meteorites. The silicon carbide found in space and in meteorites is almost exclusively the beta-polymorph. Analysis of SiC grains found in the Murchison meteorite, a carbonaceous chondrite meteorite, has revealed anomalous isotopic ratios of carbon and si |
https://en.wikipedia.org/wiki/Zugzwang | Zugzwang (; ) is a situation found in chess and other turn-based games wherein one player is put at a disadvantage because of their obligation to make a move; a player is said to be "in zugzwang" when any legal move will worsen their position.
Although the term is used less precisely in games such as chess, it is used specifically in combinatorial game theory to denote a move that directly changes the outcome of the game from a win to a loss. Putting the opponent in zugzwang is a common way to help the superior side win a game, and in some cases it is necessary in order to make the win possible. More generally, the term can also be used to describe a situation where none of the available options lead to a good outcome.
The term zugzwang was used in German chess literature in 1858 or earlier, and the first known use of the term in English was by World Champion Emanuel Lasker in 1905. The concept of zugzwang was known to chess players many centuries before the term was coined, appearing in an endgame study published in 1604 by Alessandro Salvio, one of the first writers on the game, and in shatranj studies dating back to the early 9th century, over 1000 years before the first known use of the term. International chess notation uses the symbol "⊙" to indicate a zugzwang position.
Positions with zugzwang occur fairly often in chess endgames, especially in king and pawn endgames. According to John Nunn, positions of reciprocal zugzwang are surprisingly important in the analysis of endgames.
Etymology
The word comes from German 'move' + 'compulsion', so that means 'being forced to make a move'. Originally the term was used interchangeably with the term 'obligation to make a move' as a general game rule. Games like chess and checkers have "zugzwang" (or "zugpflicht"): a player always make a move on their turn even if this is to their disadvantage. Over time, the term became especially associated with chess.
According to chess historian Edward Winter, the term ha |
https://en.wikipedia.org/wiki/Floradora | "Floradora", also called Keyword, was a doubly enciphered diplomatic code used by the Germans during the Second World War. The Allies used tabulating equipment, created by IBM, to break the code over period of more than a year in 1941 and 1942.
References
Budiansky, Stephen. Battle of wits: the complete story of codebreaking in World War II, p. 55. Simon and Schuster, 2000
Cryptography
Communications in Germany |
https://en.wikipedia.org/wiki/Honeywell | Honeywell International Inc. is an American publicly traded, multinational conglomerate corporation headquartered in Charlotte, North Carolina. It primarily operates in four areas of business: aerospace, building technologies, performance materials and technologies (PMT), and safety and productivity solutions (SPS).
Honeywell is a Fortune 500 company, ranked 115th in 2023. In 2022, the corporation had a global workforce of approximately 97,000 employees, down from 113,000 in 2019. The current chairman is Darius Adamczyk and the chief executive officer (CEO) is Vimal Kapur.
The corporation's current name, Honeywell International Inc., is a product of the merger of Honeywell Inc. and AlliedSignal in 1999. The corporation headquarters were consolidated with AlliedSignal's headquarters in Morristown, New Jersey; however, the combined company chose the name "Honeywell" because of the considerable brand recognition. Honeywell was a component of the Dow Jones Industrial Average index from 1999 to 2008. Prior to 1999, its corporate predecessors were included dating back to 1925, including early entrants in the computing and thermostat industries.
In 2020, Honeywell rejoined the Dow Jones Industrial Average index and the following year moved its stock listing from the New York Stock Exchange to the Nasdaq.
History
The Butz Thermo-Electric Regulator Company was founded in 1885 when the Swiss-born Albert Butz invented the damper-flapper, a thermostat used to control coal furnaces, bringing automated heating system regulation into homes. The following year he founded the Butz Thermo-Electric Regulator Company. In 1888, after a falling out with his investors, Butz left the company and transferred the patents to the legal firm Paul, Sanford, and Merwin, who renamed the company the Consolidated Temperature Controlling Company. As the years passed, CTCC struggled with debt, and the company underwent several name changes. After it was renamed the Electric Heat Regulator Company |
https://en.wikipedia.org/wiki/Program%20optimization | In computer science, program optimization, code optimization, or software optimization is the process of modifying a software system to make some aspect of it work more efficiently or use fewer resources. In general, a computer program may be optimized so that it executes more rapidly, or to make it capable of operating with less memory storage or other resources, or draw less power.
General
Although the word "optimization" shares the same root as "optimal", it is rare for the process of optimization to produce a truly optimal system. A system can generally be made optimal not in absolute terms, but only with respect to a given quality metric, which may be in contrast with other possible metrics. As a result, the optimized system will typically only be optimal in one application or for one audience. One might reduce the amount of time that a program takes to perform some task at the price of making it consume more memory. In an application where memory space is at a premium, one might deliberately choose a slower algorithm in order to use less memory. Often there is no "one size fits all" design which works well in all cases, so engineers make trade-offs to optimize the attributes of greatest interest. Additionally, the effort required to make a piece of software completely optimal incapable of any further improvement is almost always more than is reasonable for the benefits that would be accrued; so the process of optimization may be halted before a completely optimal solution has been reached. Fortunately, it is often the case that the greatest improvements come early in the process.
Even for a given quality metric (such as execution speed), most methods of optimization only improve the result; they have no pretense of producing optimal output. Superoptimization is the process of finding truly optimal output.
Levels of optimization
Optimization can occur at a number of levels. Typically the higher levels have greater impact, and are harder to change later on in |
https://en.wikipedia.org/wiki/Boombox | A boombox is a transistorized portable music player featuring one or two cassette tape recorder/players and AM/FM radio, generally with a carrying handle. Beginning in the mid 1980s, a CD player was often included. Sound is delivered through an amplifier and two or more integrated loudspeakers. A boombox is a device typically capable of receiving radio stations and playing recorded music (usually cassettes or CDs usually at a high volume). Many models are also capable of recording onto cassette tapes from radio and other sources. In the 1990s, some boomboxes were available with minidisc recorders and players. Designed for portability, boomboxes can be powered by batteries as well as by line current. The boombox was introduced to the American market during the late 1970s. The desire for louder and heavier bass led to bigger and heavier boxes; by the 1980s, some boomboxes had reached the size of a suitcase. Some larger boomboxes even contained vertically mounted record turntables. Most boomboxes were battery-operated, leading to extremely heavy, bulky boxes.
The boombox quickly became associated with urban society in the United States, particularly African American and Latino youth. The wide use of boomboxes in urban communities led to the boombox being coined a "ghetto blaster". Some cities petitioned for the banning of boomboxes from public places, and over time, they became less acceptable on city streets. The boombox became closely linked to American hip hop culture and was instrumental in the rise of hip hop music.
History
The first boombox was developed by the inventor of the audio compact cassette, Philips of the Netherlands. Their first 'Radiorecorder' was released in 1966. The Philips innovation was the first time that radio broadcasts could be recorded onto cassette tapes without the cables or microphones that previous stand-alone cassette tape recorders required. Recordings of radio were still subject to interferences from automobiles and other vehicle |
https://en.wikipedia.org/wiki/Vienna%20Circle | The Vienna Circle () of logical empiricism was a group of elite philosophers and scientists drawn from the natural and social sciences, logic and mathematics who met regularly from 1924 to 1936 at the University of Vienna, chaired by Moritz Schlick. The Vienna Circle had a profound influence on 20th-century philosophy, especially philosophy of science and analytic philosophy.
The philosophical position of the Vienna Circle was called logical empiricism (German: logischer Empirismus), logical positivism or neopositivism. It was influenced by Ernst Mach, David Hilbert, French conventionalism (Henri Poincaré and Pierre Duhem), Gottlob Frege, Bertrand Russell, Ludwig Wittgenstein and Albert Einstein. The Vienna Circle was pluralistic and committed to the ideals of the Enlightenment. It was unified by the aim of making philosophy scientific with the help of modern logic. Main topics were foundational debates in the natural and social sciences, logic and mathematics; the modernization of empiricism by modern logic; the search for an empiricist criterion of meaning; the critique of metaphysics and the unification of the sciences in the unity of science.
The Vienna Circle appeared in public with the publication of various book series – Schriften zur wissenschaftlichen Weltauffassung (Monographs on the Scientific World-Conception), Einheitswissenschaft (Unified Science) and the journal Erkenntnis – and the organization of international conferences in Prague; Königsberg (today known as Kaliningrad); Paris; Copenhagen; Cambridge, UK, and Cambridge, Massachusetts. Its public profile was provided by the Ernst Mach Society (German: Verein Ernst Mach) through which members of the Vienna Circle sought to popularize their ideas in the context of programmes for popular education in Vienna.
During the era of Austrofascism and after the annexation of Austria by Nazi Germany most members of the Vienna Circle were forced to emigrate. The murder of Schlick in 1936 by former student Joh |
https://en.wikipedia.org/wiki/Secondary%20metabolite | Secondary metabolites, also called specialised metabolites, toxins, secondary products, or natural products, are organic compounds produced by any lifeform, e.g. bacteria, fungi, animals, or plants, which are not directly involved in the normal growth, development, or reproduction of the organism. Instead, they generally mediate ecological interactions, which may produce a selective advantage for the organism by increasing its survivability or fecundity. Specific secondary metabolites are often restricted to a narrow set of species within a phylogenetic group. Secondary metabolites often play an important role in plant defense against herbivory and other interspecies defenses. Humans use secondary metabolites as medicines, flavourings, pigments, and recreational drugs.
The term secondary metabolite was first coined by Albrecht Kossel, the 1910 Nobel Prize laureate for medicine and physiology. 30 years later a Polish botanist Friedrich Czapek described secondary metabolites as end products of nitrogen metabolism.
Secondary metabolites commonly mediate antagonistic interactions, such as competition and predation, as well as mutualistic ones such as pollination and resource mutualisms. Usually, secondary metabolites are confined to a specific lineage or even species, though there is considerable evidence that horizontal transfer across species or genera of entire pathways plays an important role in bacterial (and, likely, fungal) evolution. Research also shows that secondary metabolism can affect different species in varying ways. In the same forest, four separate species of arboreal marsupial folivores reacted differently to a secondary metabolite in eucalypts. This shows that differing types of secondary metabolites can be the split between two herbivore ecological niches. Additionally, certain species evolve to resist secondary metabolites and even use them for their own benefit. For example, monarch butterflies have evolved to be able to eat milkweed (Asclepias) |
https://en.wikipedia.org/wiki/Guido%20van%20Rossum | Guido van Rossum (; born 31 January 1956) is a Dutch programmer best known as the creator of the Python programming language, for which he was the "benevolent dictator for life" (BDFL) until he stepped down from the position on 12 July 2018. He remained a member of the Python Steering Council through 2019, and withdrew from nominations for the 2020 election.
Life and education
Van Rossum was born and raised in the Netherlands, where he received a master's degree in mathematics and computer science from the University of Amsterdam in 1982. He received a bronze medal in 1974 in the International Mathematical Olympiad. He has a brother, Just van Rossum, who is a type designer and programmer who designed the typeface used in the "Python Powered" logo.
Van Rossum lives in Belmont, California, with his wife, Kim Knapp, and their son. According to his home page and Dutch naming conventions, the "van" in his name is capitalized when he is referred to by surname alone, but not when using his first and last name together.
Work
Centrum Wiskunde & Informatica
While working at the Centrum Wiskunde & Informatica (CWI), Van Rossum wrote and contributed a glob() routine to BSD Unix in 1986 and helped develop the ABC programming language. He once stated, "I try to mention ABC's influence because I'm indebted to everything I learned during that project and to the people who worked on it." He also created Grail, an early web browser written in Python, and engaged in discussions about the HTML standard.
He has worked for various research institutes, including the Centrum Wiskunde & Informatica (CWI) in the Netherlands, the U.S. National Institute of Standards and Technology (NIST), and the Corporation for National Research Initiatives (CNRI). In May 2000, he left CNRI along with three other Python core developers to work for tech startup BeOpen.com, which subsequently collapsed by October of the same year. From late 2000 until 2003 he worked for Zope Corporation. In 2003 Van Rossu |
https://en.wikipedia.org/wiki/Accumulation%20point | In mathematics, a limit point, accumulation point, or cluster point of a set in a topological space is a point that can be "approximated" by points of in the sense that every neighbourhood of with respect to the topology on also contains a point of other than itself. A limit point of a set does not itself have to be an element of
There is also a closely related concept for sequences. A cluster point or accumulation point of a sequence in a topological space is a point such that, for every neighbourhood of there are infinitely many natural numbers such that This definition of a cluster or accumulation point of a sequence generalizes to nets and filters.
The similarly named notion of a (respectively, a limit point of a filter, a limit point of a net) by definition refers to a point that the sequence converges to (respectively, the filter converges to, the net converges to). Importantly, although "limit point of a set" is synonymous with "cluster/accumulation point of a set", this is not true for sequences (nor nets or filters). That is, the term "limit point of a sequence" is synonymous with "cluster/accumulation point of a sequence".
The limit points of a set should not be confused with adherent points (also called ) for which every neighbourhood of contains some point of . Unlike for limit points, an adherent point of may have a neighbourhood not containing points other than itself. A limit point can be characterized as an adherent point that is not an isolated point.
Limit points of a set should also not be confused with boundary points. For example, is a boundary point (but not a limit point) of the set in with standard topology. However, is a limit point (though not a boundary point) of interval in with standard topology (for a less trivial example of a limit point, see the first caption).
This concept profitably generalizes the notion of a limit and is the underpinning of concepts such as closed set and topological closure. In |
https://en.wikipedia.org/wiki/Comparison%20of%20multi-paradigm%20programming%20languages | Programming languages can be grouped by the number and types of paradigms supported.
Paradigm summaries
A concise reference for the programming paradigms listed in this article.
Concurrent programming – have language constructs for concurrency, these may involve multi-threading, support for distributed computing, message passing, shared resources (including shared memory), or futures
Actor programming – concurrent computation with actors that make local decisions in response to the environment (capable of selfish or competitive behaviour)
Constraint programming – relations between variables are expressed as constraints (or constraint networks), directing allowable solutions (uses constraint satisfaction or simplex algorithm)
Dataflow programming – forced recalculation of formulas when data values change (e.g. spreadsheets)
Declarative programming – describes what computation should perform, without specifying detailed state changes c.f. imperative programming (functional and logic programming are major subgroups of declarative programming)
Distributed programming – have support for multiple autonomous computers that communicate via computer networks
Functional programming – uses evaluation of mathematical functions and avoids state and mutable data
Generic programming – uses algorithms written in terms of to-be-specified-later types that are then instantiated as needed for specific types provided as parameters
Imperative programming – explicit statements that change a program state
Logic programming – uses explicit mathematical logic for programming
Metaprogramming – writing programs that write or manipulate other programs (or themselves) as their data, or that do part of the work at compile time that would otherwise be done at runtime
Template metaprogramming – metaprogramming methods in which a compiler uses templates to generate temporary source code, which is merged by the compiler with the rest of the source code and then compiled
Reflective progr |
https://en.wikipedia.org/wiki/Norton%27s%20theorem | In direct-current circuit theory, Norton's theorem, also called the Mayer–Norton theorem, is a simplification that can be applied to networks made of linear time-invariant resistances, voltage sources, and current sources. At a pair of terminals of the network, it can be replaced by a current source and a single resistor in parallel.
For alternating current (AC) systems the theorem can be applied to reactive impedances as well as resistances.
The Norton equivalent circuit is used to represent any network of linear sources and impedances at a given frequency.
Norton's theorem and its dual, Thévenin's theorem, are widely used for circuit analysis simplification and to study circuit's initial-condition and steady-state response.
Norton's theorem was independently derived in 1926 by Siemens & Halske researcher Hans Ferdinand Mayer (1895–1980) and Bell Labs engineer Edward Lawry Norton (1898–1983).
To find the equivalent, the Norton current Ino is calculated as the current flowing at the terminals into a short circuit (zero resistance between A and B). This is Ino. The Norton resistance Rno is found by calculating the output voltage produced with no resistance connected at the terminals; equivalently, this is the resistance between the terminals with all (independent) voltage sources short-circuited and independent current sources open-circuited. This is equivalent to calculating the Thevenin resistance.
When there are dependent sources, the more general method must be used. The voltage at the terminals is calculated for an injection of a 1 amp test current at the terminals. This voltage divided by the 1 A current is the Norton impedance Rno (in ohms). This method must be used if the circuit contains dependent sources, but it can be used in all cases even when there are no dependent sources.
Example of a Norton equivalent circuit
In the example, the total current Itotal is given by:
The current through the load is then, using the current divider rule:
And th |
https://en.wikipedia.org/wiki/Th%C3%A9venin%27s%20theorem | As originally stated in terms of direct-current resistive circuits only, Thévenin's theorem states that "Any linear electrical network containing only voltage sources, current sources and resistances can be replaced at terminals by an equivalent combination of a voltage source in a series connection with a resistance ."
The equivalent voltage is the voltage obtained at terminals of the network with terminals open circuited.
The equivalent resistance is the resistance that the circuit between terminals and would have if all ideal voltage sources in the circuit were replaced by a short circuit and all ideal current sources were replaced by an open circuit.
If terminals and are connected to one another, the current flowing from and will be This means that could alternatively be calculated as divided by the short-circuit current between and when they are connected together.
In circuit theory terms, the theorem allows any one-port network to be reduced to a single voltage source and a single impedance.
The theorem also applies to frequency domain AC circuits consisting of reactive (inductive and capacitive) and resistive impedances. It means the theorem applies for AC in an exactly same way to DC except that resistances are generalized to impedances.
The theorem was independently derived in 1853 by the German scientist Hermann von Helmholtz and in 1883 by Léon Charles Thévenin (1857–1926), an electrical engineer with France's national Postes et Télégraphes telecommunications organization.
Thévenin's theorem and its dual, Norton's theorem, are widely used to make circuit analysis simpler and to study a circuit's initial-condition and steady-state response. Thévenin's theorem can be used to convert any circuit's sources and impedances to a Thévenin equivalent; use of the theorem may in some cases be more convenient than use of Kirchhoff's circuit laws.
Calculating the Thévenin equivalent
The equivalent circuit is a voltage source with voltage i |
https://en.wikipedia.org/wiki/Pentagon%20%28computer%29 | The Pentagon (ru: Пентагон) home computer was a clone of the British-made Sinclair ZX Spectrum 128.
It was manufactured by amateurs in the former Soviet Union, following freely distributable documentation. Its PCB was copied all over the ex-USSR in 1991-1996, which made it a widespread ZX Spectrum clone.
The name "Pentagon" derives from the shape of the original PCB (Pentagon 48), with a diagonal cut in one of the corners.
Many simple devices (upgrades) were invented to connect to the Pentagon with some soldering.
Versions
Pentagon 48K (1989 by Vladimir Drozdov)
Pentagon 128K (1991)
Pentagon 128K 2+ (1991 by ATM)
Pentagon 128K 3+ (1993 by Solon)
Pentagon 1024SL v1.x (2005 by Alex Zhabin)
Pentagon-1024SL v2.x (2006 by Alex Zhabin)
Pentagon ver.2.666 (2009 by Alex Zhabin)
The Pentagon 1024SL v2.3 included most of the upgrades of the standard Spectrum architecture, including 1024 KB RAM, Beta 128 Disk Interface and ZX-BUS slots (especially for IDE and General Sound cards). This model also featured a "turbo" mode (7 MHz instead of the original's 3.50 MHz).
Upgrades from the original ZX Spectrum
Extra RAM ranging from 256 KB to 4 MB
Several sound card possibilities such as Covox (usually named as SounDrive) or DMA UltraSound
Additional video modes: 512x192 monochrome, 384x304, 256x192x15 (with no Attribute clash)
CMOS with persistent real-time clock
IDE Controller for hard drives
"Turbo Mode" that clocks the CPU up to 7 MHz
References
External links
Russian most popular Spectrum models
Pentagon 1024 official site
Schematic diagram of the Pentagon 48K and drive controller (DjVu)
128K Schematic diagram of the Pentagon (the DjVu)
Schematic and wiring diagrams Pentagon 128K 1991, revised and enlarged version (PNG)
Wiring diagram 128K the Pentagon (the PNG)
NEW English FaceBook Group
ZX Spectrum clones
Computer-related introductions in 1989
1989 establishments in the Soviet Union
Soviet computer systems |
https://en.wikipedia.org/wiki/Finite%20mathematics | In mathematics education, Finite Mathematics is a syllabus in college and university mathematics that is independent of calculus. A course in precalculus may be a prerequisite for Finite Mathematics.
Contents of the course include an eclectic selection of topics often applied in social science and business, such as finite probability spaces, matrix multiplication, Markov processes, finite graphs, or mathematical models. These topics were used in Finite Mathematics courses at Dartmouth College as developed by John G. Kemeny, Gerald L. Thompson, and J. Laurie Snell and published by Prentice-Hall. Other publishers followed with their own topics. With the arrival of software to facilitate computations, teaching and usage shifted from a broad-spectrum Finite Mathematics with paper and pen, into development and usage of software.
Textbooks
1957: Kemeny, Thompson, Snell, Introduction to Finite Mathematics, (2nd edition 1966) Prentice-Hall
1959: Hazelton Mirkil & Kemeny, Thompson, Snell, Finite Mathematical Structures, Prentice-Hall
1962: Arthur Schliefer Jr. & Kemeny, Thompson, Snell, Finite Mathematics with Business Applications, Prentice-Hall
1969: Marvin Marcus, A Survey of Finite Mathematics, Houghton-Mifflin
1970: Guillermo Owen, Mathematics for Social and Management Sciences, Finite Mathematics, W. B. Saunders
1970: Irving Allen Dodes, Finite Mathematics: A Liberal Arts Approach, McGraw-Hill
1971: A.W. Goodman & J. S. Ratti, Finite Mathematics with Applications, Macmillan
1971: J. Conrad Crown & Marvin L. Bittinger, Finite Mathematics: a modeling approach, (2nd edition 1981) Addison-Wesley
1977: Robert F. Brown & Brenda W. Brown, Applied Finite Mathematics, Wadsworth Publishing
1980: L.J. Goldstein, David I. Schneider, Martha Siegel, Finite Mathematics and Applications, (7th edition 2001) Prentice-Hall
1981: John J. Costello, Spenser O. Gowdy, Agnes M. Rash, Finite Mathematics with Applications, Harcourt, Brace, Jovanovich
1982: James Radlow, Understand |
https://en.wikipedia.org/wiki/Computer%20Go | Computer Go is the field of artificial intelligence (AI) dedicated to creating a computer program that plays the traditional board game Go. The field is sharply divided into two eras. Before 2015, the programs of the era were weak. The best efforts of the 1980s and 1990s produced only AIs that could be defeated by beginners, and AIs of the early 2000s were intermediate level at best. Professionals could defeat these programs even given handicaps of 10+ stones in favor of the AI. Many of the algorithms such as alpha-beta minimax that performed well as AIs for checkers and chess fell apart on Go's 19x19 board, as there were too many branching possibilities to consider. Creation of a human professional quality program with the techniques and hardware of the time was out of reach. Some AI researchers speculated that the problem was unsolvable without creation of human-like AI.
The application of Monte Carlo tree search to Go algorithms provided a notable improvement in the late 2000s decade, with programs finally able to achieve a low-dan level: that of an advanced amateur. High-dan amateurs and professionals could still exploit these programs' weaknesses and win consistently, but computer performance had advanced past the intermediate (single-digit kyu) level. The tantalizing unmet goal of defeating the best human players without a handicap, long thought unreachable, brought a burst of renewed interest. The key insight proved to be an application of machine learning and deep learning. DeepMind, a Google acquisition dedicated to AI research, produced AlphaGo in 2015 and announced it to the world in 2016. AlphaGo defeated Lee Sedol, a 9 dan professional, in a no-handicap match in 2016, then defeated Ke Jie in 2017, who at the time continuously held the world No. 1 ranking for two years. Just as checkers had fallen to machines in 1995 and chess in 1997, computer programs finally conquered humanity's greatest Go champions in 2016–2017. DeepMind did not relea |
https://en.wikipedia.org/wiki/Superorganism | A superorganism or supraorganism is a group of synergetically interacting organisms of the same species. A community of synergetically interacting organisms of different species is called a holobiont.
Concept
The term superorganism is used most often to describe a social unit of eusocial animals, where division of labour is highly specialised and where individuals are not able to survive by themselves for extended periods. Ants are the best-known example of such a superorganism. A superorganism can be defined as "a collection of agents which can act in concert to produce phenomena governed by the collective", phenomena being any activity "the hive wants" such as ants collecting food and avoiding predators, or bees choosing a new nest site. In challenging environments, micro organisms collaborate and evolve together to process unlikely sources of nutrients such as methane. This process called syntrophy ("eating together") might be linked to the evolution of eukaryote cells and involved in the emergence or maintenance of life forms in challenging environments on Earth and possibly other planets. Superorganisms tend to exhibit homeostasis, power law scaling, persistent disequilibrium and emergent behaviours.
The term was coined in 1789 by James Hutton, the "father of geology", to refer to Earth in the context of geophysiology. The Gaia hypothesis of James Lovelock, and Lynn Margulis as well as the work of Hutton, Vladimir Vernadsky and Guy Murchie, have suggested that the biosphere itself can be considered a superorganism, although this has been disputed. This view relates to systems theory and the dynamics of a complex system.
The concept of a superorganism raises the question of what is to be considered an individual. Toby Tyrrell's critique of the Gaia hypothesis argues that Earth's climate system does not resemble an animal's physiological system. Planetary biospheres are not tightly regulated in the same way that animal bodies are: "planets, unlike animals, |
https://en.wikipedia.org/wiki/Waverider | A waverider is a hypersonic aircraft design that improves its supersonic lift-to-drag ratio by using the shock waves being generated by its own flight as a lifting surface, a phenomenon known as compression lift.
The waverider remains a well-studied design for high-speed aircraft in the Mach 5 and higher hypersonic regime, although no such design has yet entered production. The Boeing X-51 scramjet demonstration aircraft was tested from 2010 to 2013. In its final test flight, it reached a speed of .
History
Early work
The waverider design concept was first developed by Terence Nonweiler of the Queen's University of Belfast, and first described in print in 1951 as a re-entry vehicle. It consisted of a delta-wing platform with a low wing loading to provide considerable surface area to dump the heat of re-entry. At the time, Nonweiler was forced to use a greatly simplified 2D model of airflow around the aircraft, which he realized would not be accurate due to spanwise flow across the wing. However, he also noticed that the spanwise flow would be stopped by the shockwave being generated by the aircraft, and that if the wing was positioned to deliberately approach the shock, the spanwise flow would be trapped under wing, increasing pressure, and thus increasing lift.
In the 1950s, the British started a space program based around the Blue Streak missile, which was, at some point, to include a crewed vehicle. Armstrong-Whitworth were contracted to develop the re-entry vehicle, and unlike the U.S. space program, they decided to stick with a winged vehicle instead of a ballistic capsule. Between 1957 and 1959, they contracted Nonweiler to develop his concepts further. This work produced a pyramid-shaped design with a flat underside and short wings. Heat was conducted through the wings to the upper cool surfaces, where it was dumped into the turbulent air on the top of the wing. In 1960, work on the Blue Streak was canceled as the missile was seen as being obsolete be |
https://en.wikipedia.org/wiki/Dementia%20with%20Lewy%20bodies | Dementia with Lewy bodies (DLB) is a type of dementia characterized by changes in sleep, behavior, cognition, movement, and regulation of automatic bodily functions. Memory loss is not always an early symptom. The disease worsens over time and is usually diagnosed when cognitive impairment interferes with normal daily functioning. Together with Parkinson's disease dementia, DLB is one of the two Lewy body dementias. It is a common form of dementia, but the prevalence is not known accurately and many diagnoses are missed. The disease was first described by Kenji Kosaka in 1976.
REM sleep behavior disorder (RBD)—in which people lose the muscle paralysis (atonia) that normally occurs during REM sleep and act out their dreams—is a core feature. RBD may appear years or decades before other symptoms. Other core features are visual hallucinations, marked fluctuations in attention or alertness, and parkinsonism (slowness of movement, trouble walking, or rigidity). A presumptive diagnosis can be made if several disease features or biomarkers are present; the diagnostic workup may include blood tests, neuropsychological tests, imaging, and sleep studies. A definitive diagnosis usually requires an autopsy.
Most people with DLB do not have affected family members, although occasionally DLB runs in a family. The exact cause is unknown but involves formation of abnormal clumps of protein in neurons throughout the brain. Manifesting as Lewy bodies (discovered in 1912 by Frederic Lewy) and Lewy neurites, these clumps affect both the central and the autonomic nervous systems. Heart function and every level of gastrointestinal function—from chewing to defecation—can be affected, constipation being one of the most common symptoms. Low blood pressure upon standing can also occur. DLB commonly causes psychiatric symptoms, such as altered behavior, depression, or apathy.
DLB typically begins after the age of fifty, and people with the disease have an average life expectancy, with wid |
https://en.wikipedia.org/wiki/Projected%20coordinate%20system | A projected coordinate systemalso called a projected coordinate reference system, planar coordinate system, or grid reference systemis a type of spatial reference system that represents locations on Earth using Cartesian coordinates (x, y) on a planar surface created by a particular map projection. Each projected coordinate system, such as "Universal Transverse Mercator WGS 84 Zone 26N," is defined by a choice of map projection (with specific parameters), a choice of geodetic datum to bind the coordinate system to real locations on the earth, an origin point, and a choice of unit of measure. Hundreds of projected coordinate systems have been specified for various purposes in various regions.
When the first standardized coordinate systems were created during the 20th century, such as the Universal Transverse Mercator, State Plane Coordinate System, and British National Grid, they were commonly called grid systems; the term is still common in some domains such as the military that encode coordinates as alphanumeric grid references. However, the term projected coordinate system has recently become predominant to clearly differentiate it from other types of spatial reference system. It is used in international standards such as the EPSG and ISO 19111 (also published by the Open Geospatial Consortium as Abstract Specification 2), and in most geographic information system software.
History
The map projection and the Geographic coordinate system (GCS, latitude and longitude) date to the Hellenistic period, proliferating during the Enlightenment Era of the 18th century. However, their use as the basis for specifying precise locations, rather than latitude and longitude, is a 20th century innovation.
Among the earliest was the State Plane Coordinate System (SPCS), which was developed in the United States during the 1930s for surveying and engineering, because calculations such as distance are much simpler in a Cartesian coordinate system than the three-dimensional trigo |
https://en.wikipedia.org/wiki/Chandler%20wobble | The Chandler wobble or Chandler variation of latitude is a small deviation in the Earth's axis of rotation relative to the solid earth, which was discovered by and named after American astronomer Seth Carlo Chandler in 1891. It amounts to change of about in the point at which the axis intersects the Earth's surface and has a period of 433 days. This wobble, which is an astronomical nutation, combines with another wobble with a period of one year, so that the total polar motion varies with a period of about 7 years.
The Chandler wobble is an example of the kind of motion that can occur for a freely rotating object that is not a sphere; this is called a free nutation. Somewhat confusingly, the direction of the Earth's rotation axis relative to the stars also varies with different periods, and these motions—caused by the tidal forces of the Moon and Sun—are also called nutations, except for the slowest, which are precessions of the equinoxes.
Predictions
The existence of Earth's free nutation was predicted by Isaac Newton in Corollaries 20 to 22 of Proposition 66, Book 1 of the Philosophiæ Naturalis Principia Mathematica, and by Leonhard Euler in 1765 as part of his studies of the dynamics of rotating bodies. Based on the known ellipticity of the Earth, Euler predicted that it would have a period of 305 days. Several astronomers searched for motions with this period, but none was found. Chandler's contribution was to look for motions at any possible period; once the Chandler wobble was observed, the difference between its period and the one predicted by Euler was explained by Simon Newcomb as being caused by the non-rigidity of the Earth. The full explanation for the period also involves the fluid nature of the Earth's core and oceans—the wobble, in fact, produces a very small ocean tide with an amplitude of approximately , called a pole tide, which is the only tide not caused by an extraterrestrial body. Despite the small amplitude, the gravitational effect of the |
https://en.wikipedia.org/wiki/Wilson%27s%20theorem | In algebra and number theory, Wilson's theorem states that a natural number n > 1 is a prime number if and only if the product of all the positive integers less than n is one less than a multiple of n. That is (using the notations of modular arithmetic), the factorial satisfies
exactly when n is a prime number. In other words, any number n is a prime number if, and only if, (n − 1)! + 1 is divisible by n.
History
This theorem was stated by Ibn al-Haytham (c. 1000 AD), and, in the 18th century, by the English mathematician John Wilson. Edward Waring announced the theorem in 1770, although neither he nor his student Wilson could prove it. Lagrange gave the first proof in 1771. There is evidence that Leibniz was also aware of the result a century earlier, but he never published it.
Example
For each of the values of n from 2 to 30, the following table shows the number (n − 1)! and the remainder when (n − 1)! is divided by n. (In the notation of modular arithmetic, the remainder when m is divided by n is written m mod n.)
The background color is blue for prime values of n, gold for composite values.
Proofs
The proofs (for prime moduli) below use the fact that the residue classes modulo a prime number are a field—see the article prime field for more details. Lagrange's theorem, which states that in any field a polynomial of degree n has at most n roots, is needed for all the proofs.
Composite modulus
If n is composite it is divisible by some prime number q, where . Because divides , let for some integer . Suppose for the sake of contradiction that were congruent to where n is composite. Then (n-1)! would also be congruent to −1 (mod q) as implies that for some integer which shows (n-1)! being congruent to -1 (mod q). But (n − 1)! ≡ 0 (mod q) by the fact that q is a term in (n-1)! making (n-1)! a multiple of q. A contradiction is now reached.
In fact, more is true. With the sole exception of 4, where 3! = 6 ≡ 2 (mod 4), if n is composite then (n − 1)! |
https://en.wikipedia.org/wiki/Amiga%20Unix | Amiga Unix (informally known as Amix) is a discontinued full port of AT&T Unix System V Release 4 operating system developed by Commodore-Amiga, Inc. in 1990 for the Amiga computer family as an alternative to AmigaOS, which shipped by default.
Overview
Bundled with the Amiga 2500UX and Amiga 3000UX, Commodore's Unix was one of the first ports of SVR4 to the 68k architecture. The Amiga A3000UX model even got the attention of Sun Microsystems, though ultimately nothing came of it.
Unlike Apple's A/UX compatibility layer for System 7 applications, Amiga Unix contains no compatibility layer for AmigaOS applications. With few native applications available to take advantage of the Amiga's significant multimedia capabilities, it failed to find a niche in the competitive Unix workstation market of the early 1990s. The A3000UX's price of was also not very attractive compared to other Unix workstations at the time, such as the NeXTstation ($5,000 for a base system, with a full API and many times the number of applications available), the SGI Indigo (starting at $8,000), or the Personal DECstation 5000 Model 25 (starting at $5,000). Sun, HP, and IBM had similarly priced systems. The A3000UX's 68030 was noticeably underpowered compared to most of its RISC-based competitors.
Unlike typical commercial Unix distributions of the time, Amiga Unix included the source code to the vendor-specific enhancements and platform-dependent device drivers (essentially any part that wasn't owned by AT&T), allowing interested users to study or enhance those parts of the system. However this source code was subject to the same license terms as the binary part of the system it was not free software. Amiga Unix also incorporated and depended upon many open source components, such as the GNU C Compiler and X Window System, and included their source code.
Like many other proprietary Unix variants with small market shares, Amiga Unix vanished into the mists of computer history when its vendor, C |
https://en.wikipedia.org/wiki/Septum | In biology, a septum (Latin for something that encloses; : septa) is a wall, dividing a cavity or structure into smaller ones. A cavity or structure divided in this way may be referred to as septate.
Examples
Human anatomy
Interatrial septum, the wall of tissue that is a sectional part of the left and right atria of the heart
Interventricular septum, the wall separating the left and right ventricles of the heart
Lingual septum, a vertical layer of fibrous tissue that separates the halves of the tongue
Nasal septum: the cartilage wall separating the nostrils of the nose
Alveolar septum: the thin wall which separates the alveoli from each other in the lungs
Orbital septum, a palpebral ligament in the upper and lower eyelids
Septum pellucidum or septum lucidum, a thin structure separating two fluid pockets in the brain
Uterine septum, a malformation of the uterus
Penile septum, a fibrous wall between the two corpora cavernosa penis
Septum glandis, partition of the ventral aspect of the glans penis
Scrotal septum, layer of tissue dividing the scrotum
Vaginal septum, a lateral or transverse partition inside the vagina
Intermuscular septa separating the muscles of the arms and legs
Histological septa are seen throughout most tissues of the body, particularly where they are needed to stiffen soft cellular tissue, and they also provide planes of ingress for small blood vessels. Because the dense collagen fibres of a septum usually extend out into the softer adjacent tissues, microscopic fibrous septa are less clearly defined than the macroscopic types of septa listed above. In rare instances, a septum is a cross-wall. Thus it divides a structure into smaller parts.
Cell biology
The septum (cell biology) is the boundary formed between dividing cells in the course of cell division.
Fungus
A partition dividing filamentous hyphae into discrete cells in fungi.
Botany
A partition that separates the locules of a fruit, anther, or sporangium.
Zoology
A cora |
https://en.wikipedia.org/wiki/Lennard-Jones%20potential | In computational chemistry, the Lennard-Jones potential (also termed the LJ potential or 12-6 potential; named for John Lennard-Jones) is an intermolecular pair potential. Out of all the intermolecular potentials, the Lennard-Jones potential is probably the one that has been the most extensively studied. It is considered an archetype model for simple yet realistic intermolecular interactions.
The Lennard-Jones potential models soft repulsive and attractive (van der Waals) interactions. Hence, the Lennard-Jones potential describes electronically neutral atoms or molecules. The commonly used expression for the Lennard-Jones potential is
where is the distance between two interacting particles, is the depth of the potential well (usually referred to as 'dispersion energy'), and is the distance at which the particle-particle potential energy is zero (often referred to as 'size of the particle'). The Lennard-Jones potential has its minimum at a distance of where the potential energy has the value
The Lennard-Jones potential is a simplified model that yet describes the essential features of interactions between simple atoms and molecules: Two interacting particles repel each other at very close distance, attract each other at moderate distance, and do not interact at infinite distance, as shown in Figure 1. The Lennard-Jones potential is a pair potential, i.e. no three- or multi-body interactions are covered by the potential.
Statistical mechanics and computer simulations can be used to study the Lennard-Jones potential and to obtain thermophysical properties of the 'Lennard-Jones substance'. The Lennard-Jones substance is often referred to as 'Lennard-Jonesium,' suggesting that it is viewed as a (fictive) chemical element. Moreover, its energy and length parameters can be adjusted to fit many different real substances. Both the Lennard-Jones potential and, accordingly, the Lennard-Jones substance are simplified yet realistic models, such as they accurately capt |
https://en.wikipedia.org/wiki/Rose%20hip | The rose hip or rosehip, also called rose haw and rose hep, is the accessory fruit of the various species of rose plant. It is typically red to orange, but ranges from dark purple to black in some species. Rose hips begin to form after pollination of flowers in spring or early summer, and ripen in late summer through autumn.
Propagation
Roses are propagated from rose hips by removing the achenes that contain the seeds from the hypanthium (the outer coating) and sowing just beneath the surface of the soil. The seeds can take many months to germinate. Most species require chilling (stratification), with some such as Rosa canina only germinating after two winter chill periods.
Use
Rose hips are used in bread and pies, jam,
jelly, marmalade, syrup, soup, tea, wine, and other beverages.
Rose hips can be eaten raw, like berries, if care is taken to avoid the hairs inside the fruit. The hairs are used as itching powder.
A few rose species are sometimes grown for the ornamental value of their hips, such as Rosa moyesii, which has prominent, large, red bottle-shaped fruits. Rosa macrophylla 'Master Hugh' has the largest hips of any readily available rose.
Rose hips are commonly used in herbal tea, often blended with hibiscus. An oil is also extracted from the seeds. Rose hip soup, known as in Swedish, is especially popular in Sweden. Rhodomel, a type of mead, is made with rose hips.
Rose hips can be used to make , the traditional Hungarian fruit brandy popular in Hungary, Romania, and other countries sharing Austro-Hungarian history. Rose hips are also the central ingredient of cockta, the fruity-tasting national soft drink of Slovenia.
Dried rose hips are also sold for crafts and home fragrance purposes. The Inupiat mix rose hips with wild redcurrant and highbush cranberries and boil them into a syrup.
Nutrients and research
Wild rose hip fruits are particularly rich in vitamin C, containing 426 mg per 100 g or 0.4% by weight (w/w). RP-HPLC assays of fresh rose h |
https://en.wikipedia.org/wiki/Atomic%20force%20microscopy | Atomic force microscopy (AFM) or scanning force microscopy (SFM) is a very-high-resolution type of scanning probe microscopy (SPM), with demonstrated resolution on the order of fractions of a nanometer, more than 1000 times better than the optical diffraction limit.
Overview
Atomic force microscopy (AFM) is a type of scanning probe microscopy (SPM), with demonstrated resolution on the order of fractions of a nanometer, more than 1000 times better than the optical diffraction limit. The information is gathered by "feeling" or "touching" the surface with a mechanical probe. Piezoelectric elements that facilitate tiny but accurate and precise movements on (electronic) command enable precise scanning. Despite the name, the Atomic Force Microscope does not use the Nuclear force.
Abilities
The AFM has three major abilities: force measurement, topographic imaging, and manipulation.
In force measurement, AFMs can be used to measure the forces between the probe and the sample as a function of their mutual separation. This can be applied to perform force spectroscopy, to measure the mechanical properties of the sample, such as the sample's Young's modulus, a measure of stiffness.
For imaging, the reaction of the probe to the forces that the sample imposes on it can be used to form an image of the three-dimensional shape (topography) of a sample surface at a high resolution. This is achieved by raster scanning the position of the sample with respect to the tip and recording the height of the probe that corresponds to a constant probe-sample interaction (see for more). The surface topography is commonly displayed as a pseudocolor plot.
Although the initial publication about atomic force microscopy by Binnig, Quate and Gerber in 1986 speculated about the possibility of achieving atomic resolution, profound experimental challenges needed to be overcome before atomic resolution of defects and step edges in ambient (liquid) conditions was demonstrated in 1993 by Ohnesorge a |
https://en.wikipedia.org/wiki/Stochastic%20calculus | Stochastic calculus is a branch of mathematics that operates on stochastic processes. It allows a consistent theory of integration to be defined for integrals of stochastic processes with respect to stochastic processes. This field was created and started by the Japanese mathematician Kiyosi Itô during World War II.
The best-known stochastic process to which stochastic calculus is applied is the Wiener process (named in honor of Norbert Wiener), which is used for modeling Brownian motion as described by Louis Bachelier in 1900 and by Albert Einstein in 1905 and other physical diffusion processes in space of particles subject to random forces. Since the 1970s, the Wiener process has been widely applied in financial mathematics and economics to model the evolution in time of stock prices and bond interest rates.
The main flavours of stochastic calculus are the Itô calculus and its variational relative the Malliavin calculus. For technical reasons the Itô integral is the most useful for general classes of processes, but the related Stratonovich integral is frequently useful in problem formulation (particularly in engineering disciplines). The Stratonovich integral can readily be expressed in terms of the Itô integral, and vice versa. The main benefit of the Stratonovich integral is that it obeys the usual chain rule and therefore does not require Itô's lemma. This enables problems to be expressed in a coordinate system invariant form, which is invaluable when developing stochastic calculus on manifolds other than Rn.
The dominated convergence theorem does not hold for the Stratonovich integral; consequently it is very difficult to prove results without re-expressing the integrals in Itô form.
Itô integral
The Itô integral is central to the study of stochastic calculus. The integral is defined for a semimartingale X and locally bounded predictable process H.
Stratonovich integral
The Stratonovich integral or Fisk–Stratonovich integral of a semimartingale ag |
https://en.wikipedia.org/wiki/Viterbi%20algorithm | The Viterbi algorithm is a dynamic programming algorithm for obtaining the maximum a posteriori probability estimate of the most likely sequence of hidden states—called the Viterbi path—that results in a sequence of observed events, especially in the context of Markov information sources and hidden Markov models (HMM).
The algorithm has found universal application in decoding the convolutional codes used in both CDMA and GSM digital cellular, dial-up modems, satellite, deep-space communications, and 802.11 wireless LANs. It is now also commonly used in speech recognition, speech synthesis, diarization, keyword spotting, computational linguistics, and bioinformatics. For example, in speech-to-text (speech recognition), the acoustic signal is treated as the observed sequence of events, and a string of text is considered to be the "hidden cause" of the acoustic signal. The Viterbi algorithm finds the most likely string of text given the acoustic signal.
History
The Viterbi algorithm is named after Andrew Viterbi, who proposed it in 1967 as a decoding algorithm for convolutional codes over noisy digital communication links. It has, however, a history of multiple invention, with at least seven independent discoveries, including those by Viterbi, Needleman and Wunsch, and Wagner and Fischer. It was introduced to Natural Language Processing as a method of part-of-speech tagging as early as 1987.
Viterbi path and Viterbi algorithm have become standard terms for the application of dynamic programming algorithms to maximization problems involving probabilities.
For example, in statistical parsing a dynamic programming algorithm can be used to discover the single most likely context-free derivation (parse) of a string, which is commonly called the "Viterbi parse". Another application is in target tracking, where the track is computed that assigns a maximum likelihood to a sequence of observations.
Extensions
A generalization of the Viterbi algorithm, termed the max-sum a |
https://en.wikipedia.org/wiki/Stress%20%28mechanics%29 | In continuum mechanics, stress is a physical quantity that describes forces present during deformation. For example, an object being pulled apart, such as a stretched elastic band, is subject to tensile stress and may undergo elongation. An object being pushed together, such as a crumpled sponge, is subject to compressive stress and may undergo shortening. The greater the force and the smaller the cross-sectional area of the body on which it acts, the greater the stress. Stress has dimension of force per area, with SI units of newtons per square meter (N/m2) or pascal (Pa).
Stress expresses the internal forces that neighbouring particles of a continuous material exert on each other, while strain is the measure of the relative deformation of the material. For example, when a solid vertical bar is supporting an overhead weight, each particle in the bar pushes on the particles immediately below it. When a liquid is in a closed container under pressure, each particle gets pushed against by all the surrounding particles. The container walls and the pressure-inducing surface (such as a piston) push against them in (Newtonian) reaction. These macroscopic forces are actually the net result of a very large number of intermolecular forces and collisions between the particles in those molecules. Stress is frequently represented by a lowercase Greek letter sigma (σ).
Strain inside a material may arise by various mechanisms, such as stress as applied by external forces to the bulk material (like gravity) or to its surface (like contact forces, external pressure, or friction). Any strain (deformation) of a solid material generates an internal elastic stress, analogous to the reaction force of a spring, that tends to restore the material to its original non-deformed state. In liquids and gases, only deformations that change the volume generate persistent elastic stress. If the deformation changes gradually with time, even in fluids there will usually be some viscous stress, o |
https://en.wikipedia.org/wiki/Young%27s%20modulus | Young's modulus (or Young modulus) is a mechanical property of solid materials that measures the tensile or compressive stiffness when the force is applied lengthwise. It is the modulus of elasticity for tension or axial compression. Young's modulus is defined as the ratio of the stress (force per unit area) applied to the object and the resulting axial strain (displacement or deformation) in the linear elastic region of the material.
Although Young's modulus is named after the 19th-century British scientist Thomas Young, the concept was developed in 1727 by Leonhard Euler. The first experiments that used the concept of Young's modulus in its modern form were performed by the Italian scientist Giordano Riccati in 1782, pre-dating Young's work by 25 years. The term modulus is derived from the Latin root term modus which means measure.
Definition
Young's modulus, , quantifies the relationship between tensile or compressive stress (force per unit area) and axial strain (proportional deformation) in the linear elastic region of a material:
Young's modulus is commonly measured in the International System of Units (SI) in multiples of the pascal (Pa) and common values are in the range of gigapascals (GPa).
Examples:
Rubber (increasing pressure: length increases quickly, meaning low )
Aluminium (increasing pressure: length increases slowly, meaning high )
Linear elasticity
A solid material undergoes elastic deformation when a small load is applied to it in compression or extension. Elastic deformation is reversible, meaning that the material returns to its original shape after the load is removed.
At near-zero stress and strain, the stress–strain curve is linear, and the relationship between stress and strain is described by Hooke's law that states stress is proportional to strain. The coefficient of proportionality is Young's modulus. The higher the modulus, the more stress is needed to create the same amount of strain; an idealized rigid body would have an |
https://en.wikipedia.org/wiki/Remote%20sensing | Remote sensing is the acquisition of information about an object or phenomenon without making physical contact with the object, in contrast to in situ or on-site observation. The term is applied especially to acquiring information about Earth and other planets. Remote sensing is used in numerous fields, including geophysics, geography, land surveying and most Earth science disciplines (e.g. exploration geophysics, hydrology, ecology, meteorology, oceanography, glaciology, geology); it also has military, intelligence, commercial, economic, planning, and humanitarian applications, among others.
In current usage, the term remote sensing generally refers to the use of satellite- or aircraft-based sensor technologies to detect and classify objects on Earth. It includes the surface and the atmosphere and oceans, based on propagated signals (e.g. electromagnetic radiation). It may be split into "active" remote sensing (when a signal is emitted by a satellite or aircraft to the object and its reflection detected by the sensor) and "passive" remote sensing (when the reflection of sunlight is detected by the sensor).
Overview
Remote sensing can be divided into two types of methods: Passive remote sensing and Active remote sensing. Passive sensors gather radiation that is emitted or reflected by the object or surrounding areas. Reflected sunlight is the most common source of radiation measured by passive sensors. Examples of passive remote sensors include film photography, infrared, charge-coupled devices, and radiometers. Active collection, on the other hand, emits energy in order to scan objects and areas whereupon a sensor then detects and measures the radiation that is reflected or backscattered from the target. RADAR and LiDAR are examples of active remote sensing where the time delay between emission and return is measured, establishing the location, speed and direction of an object.
Remote sensing makes it possible to collect data of dangerous or inaccessible areas |
https://en.wikipedia.org/wiki/Bernoulli%20polynomials | In mathematics, the Bernoulli polynomials, named after Jacob Bernoulli, combine the Bernoulli numbers and binomial coefficients. They are used for series expansion of functions, and with the Euler–MacLaurin formula.
These polynomials occur in the study of many special functions and, in particular, the Riemann zeta function and the Hurwitz zeta function. They are an Appell sequence (i.e. a Sheffer sequence for the ordinary derivative operator). For the Bernoulli polynomials, the number of crossings of the x-axis in the unit interval does not go up with the degree. In the limit of large degree, they approach, when appropriately scaled, the sine and cosine functions.
A similar set of polynomials, based on a generating function, is the family of Euler polynomials.
Representations
The Bernoulli polynomials Bn can be defined by a generating function. They also admit a variety of derived representations.
Generating functions
The generating function for the Bernoulli polynomials is
The generating function for the Euler polynomials is
Explicit formula
for n ≥ 0, where Bk are the Bernoulli numbers, and Ek are the Euler numbers.
Representation by a differential operator
The Bernoulli polynomials are also given by
where D = d/dx is differentiation with respect to x and the fraction is expanded as a formal power series. It follows that
cf. integrals below. By the same token, the Euler polynomials are given by
Representation by an integral operator
The Bernoulli polynomials are also the unique polynomials determined by
The integral transform
on polynomials f, simply amounts to
This can be used to produce the inversion formulae below.
Another explicit formula
An explicit formula for the Bernoulli polynomials is given by
That is similar to the series expression for the Hurwitz zeta function in the complex plane. Indeed, there is the relationship
where ζ(s, q) is the Hurwitz zeta function. The latter generalizes the Bernoulli polynomials, allowing for non-int |
https://en.wikipedia.org/wiki/Vanillin | Vanillin is an organic compound with the molecular formula . It is a phenolic aldehyde. Its functional groups include aldehyde, hydroxyl, and ether. It is the primary component of the extract of the vanilla bean. Synthetic vanillin is now used more often than natural vanilla extract as a flavoring in foods, beverages, and pharmaceuticals.
Vanillin and ethylvanillin are used by the food industry; ethylvanillin is more expensive, but has a stronger note. It differs from vanillin by having an ethoxy group (−O−CH2CH3) instead of a methoxy group (−O−CH3).
Natural vanilla extract is a mixture of several hundred different compounds in addition to vanillin. Artificial vanilla flavoring is often a solution of pure vanillin, usually of synthetic origin. Because of the scarcity and expense of natural vanilla extract, synthetic preparation of its predominant component has long been of interest. The first commercial synthesis of vanillin began with the more readily available natural compound eugenol (4-allyl-2-methoxyphenol). Today, artificial vanillin is made either from guaiacol or lignin.
Lignin-based artificial vanilla flavoring is alleged to have a richer flavor profile than oil-based flavoring; the difference is due to the presence of acetovanillone, a minor component in the lignin-derived product that is not found in vanillin synthesized from guaiacol.
History
Although it is generally accepted that vanilla was domesticated in Mesoamerica and subsequently spread to the Old World in the 16th century, in 2019, researchers published a paper stating that vanillin residue had been discovered inside jars within a tomb in Israel dating to the 2nd millennium BCE, suggesting the cultivation of an unidentified, Old World-endemic Vanilla species in Canaan since the Middle Bronze Age. Traces of vanillin were also found in wine jars in Jerusalem, which were used by the Judahite elite before the city was destroyed in 586 BCE.
Vanilla beans, called tlilxochitl, were discov |
https://en.wikipedia.org/wiki/Additive%20inverse | In mathematics, the additive inverse of a number (sometimes called the opposite of ) is the number that, when added to , yields zero. The operation taking a number to its additive inverse is known as sign change or negation. For a real number, it reverses its sign: the additive inverse (opposite number) of a positive number is negative, and the additive inverse of a negative number is positive. Zero is the additive inverse of itself.
The additive inverse of is denoted by unary minus: (see also below). For example, the additive inverse of 7 is −7, because , and the additive inverse of −0.3 is 0.3, because .
Similarly, the additive inverse of is which can be simplified to . The additive inverse of is , because .
The additive inverse is defined as its inverse element under the binary operation of addition (see also below), which allows a broad generalization to mathematical objects other than numbers. As for any inverse operation, double additive inverse has no net effect: .
Common examples
For a number (and more generally in any ring), the additive inverse can be calculated using multiplication by −1; that is, . Examples of rings of numbers are integers, rational numbers, real numbers, and complex numbers.
Relation to subtraction
Additive inverse is closely related to subtraction, which can be viewed as an addition of the opposite:
.
Conversely, additive inverse can be thought of as subtraction from zero:
.
Hence, unary minus sign notation can be seen as a shorthand for subtraction (with the "0" symbol omitted), although in a correct typography, there should be no space after unary "−".
Other properties
In addition to the identities listed above, negation has the following algebraic properties:
, it is an Involution operation
notably,
Formal definition
The notation + is usually reserved for commutative binary operations (operations where for all , ). If such an operation admits an identity element (such that for all ), then this element i |
https://en.wikipedia.org/wiki/Open%20formula | An open formula is a formula that contains at least one free variable.
An open formula does not have a truth value assigned to it, in contrast with a closed formula which constitutes a proposition and thus can have a truth value like true or false. An open formula can be transformed into a closed formula by applying a quantifier for each free variable. This transformation is called capture of the free variables to make them bound variables.
For example, when reasoning about natural numbers, the formula "x+2 > y" is open, since it contains the free variables x and y. In contrast, the formula "∃y ∀x: x+2 > y" is closed, and has truth value true.
Open formulas are often used in rigorous mathematical definitions of properties, like
"x is an aunt of y if, for some person z, z is a parent of y, and x is a sister of z"
(with free variables x, y, and bound variable z) defining the notion of "aunt" in terms of "parent" and "sister".
Another, more formal example, which defines the property of being a prime number, is
"P(x) if ∀m,n∈ℕ: m>1 ∧ n>1 → x≠ m⋅n",
(with free variable x and bound variables m,n).
An example of a closed formula with truth value false involves the sequence of Fermat numbers
studied by Fermat in connection to the primality. The attachment of the predicate letter P (is prime) to each number from the Fermat sequence gives a set of closed formulae. While they are true fur n = 0,...,4, no larger value of n is known that obtains a true formula, ; for example, is not a prime. Thus the closed formula ∀n P(Fn) is false.
See also
First-order logic
Higher-order logic
Quantifier (logic)
Predicate (mathematical logic)
References
Logical expressions |
https://en.wikipedia.org/wiki/Clock%20rate | In computing, the clock rate or clock speed typically refers to the frequency at which the clock generator of a processor can generate pulses, which are used to synchronize the operations of its components, and is used as an indicator of the processor's speed. It is measured in the SI unit of frequency hertz (Hz).
The clock rate of the first generation of computers was measured in hertz or kilohertz (kHz), the first personal computers (PCs) to arrive throughout the 1970s and 1980s had clock rates measured in megahertz (MHz), and in the 21st century the speed of modern CPUs is commonly advertised in gigahertz (GHz). This metric is most useful when comparing processors within the same family, holding constant other features that may affect performance.
Determining factors
Binning
Manufacturers of modern processors typically charge higher prices for processors that operate at higher clock rates, a practice called binning. For a given CPU, the clock rates are determined at the end of the manufacturing process through actual testing of each processor. Chip manufacturers publish a "maximum clock rate" specification, and they test chips before selling them to make sure they meet that specification, even when executing the most complicated instructions with the data patterns that take the longest to settle (testing at the temperature and voltage that gives the lowest performance). Processors successfully tested for compliance with a given set of standards may be labeled with a higher clock rate, e.g., 3.50 GHz, while those that fail the standards of the higher clock rate yet pass the standards of a lower clock rate may be labeled with the lower clock rate, e.g., 3.3 GHz, and sold at a lower price.
Engineering
The clock rate of a CPU is normally determined by the frequency of an oscillator crystal. Typically a crystal oscillator produces a fixed sine wave—the frequency reference signal. Electronic circuitry translates that into a square wave at the same frequency for di |
https://en.wikipedia.org/wiki/Disk%20controller | The disk controller is the controller circuit which enables the CPU to communicate with a hard disk, floppy disk or other kind of disk drive. It also provides an interface between the disk drive and the bus connecting it to the rest of the system.
Early disk controllers were identified by their storage methods and data encoding. They were typically implemented on a separate controller card. Modified frequency modulation (MFM) controllers were the most common type in small computers, used for both floppy disk and hard disk drives. Run length limited (RLL) controllers used data compression to increase storage capacity by about 50%. Priam created a proprietary storage algorithm that could double the disk storage. Shugart Associates Systems Interface (SASI) was a predecessor to SCSI.
Modern disk controllers are integrated into the disk drive as peripheral controllers. For example, disks called "SCSI disks" have built-in SCSI controllers. In the past, before most SCSI controller functionality was implemented in a single chip, separate SCSI controllers interfaced disks to the SCSI bus.
These integrated peripheral controllers communicate with a host adapter in the host system over a standardized, high-level storage bus interface. The most common types of interfaces provided nowadays by host controllers are PATA (IDE) and Serial ATA for home use. High-end disks use Parallel SCSI, Fibre Channel or Serial Attached SCSI.
Disk controllers can also control the timing of access to flash memory which is not mechanical in nature (i.e. no physical disk).
Disk controller versus host adapter
The component that allows a computer to talk to a peripheral bus is host adapter or host bus adapter (HBA, e.g. Advanced Host Controller Interface or AHDC). A disk controller allows a disk to talk to the same bus. Signals read by a disk read-and-write head are converted by a disk controller, then transmitted over the peripheral bus, then converted again by the host adapter into the suita |
https://en.wikipedia.org/wiki/SQL-Ledger | SQL-Ledger is an ERP and double entry accounting system. Accounting data is stored in an SQL database server and a standard web browser can be used as its user interface. The system uses the Perl language with a database interface module for processing and PostgreSQL for data storage which is the preferred platform. The download version also includes schemas for IBM's DB2 database server as well as Oracle.
Capabilities
SQL-Ledger offers all of the standard features of SMB accounting software. Specific customization is available as part of an enterprise support contract.
Not only is the user interface multi-lingual, but it also offers the ability to print out statements, invoices, and the like in the language of the customer, even if the user does not know the language in which the content is being printed.
Supported languages
Business model
DWS generates their revenue from selling a manual and customizations. For free, DWS provides the source code of the current and all the previous versions, installation instructions, an FAQ collection and a user forum.
Version 3.0 of this program was released under the GNU GPL 2.0 license.
Version 3.2.6 released in December 2017.
Licensing issues
At its inception, SQL-Ledger used the GNU GPL 2.0 license. In 2005, Debian legal questioned whether or not the program belonged in Free or Non-Free, due to wording in the Terms and Conditions notice in the tarball.
In late 2006, LedgerSMB was created as a secure fork of SQL-Ledger. In early 2007, SQL-Ledger 2.8 was released under the 'SQL-Ledger Open Source License, a license which retroactively revokes all previous licenses under which the covered code had been released. That version also contained an "anti-forking" clause.
However, within a month, SQL-Ledger 2.8.1 was released, under the GNU GPL 2.0.
See also
LedgerSMB, another fork of version 2.
Comparison of accounting software
List of free and open source software packages
References
External links
Free accounting |
https://en.wikipedia.org/wiki/Parazoa | Parazoa (Parazoa, gr. Παρα-, para, "next to", and ζωα, zoa, "animals") are a taxon with sub-kingdom category that is located at the base of the phylogenetic tree of the animal kingdom in opposition to the sub-kingdom Eumetazoa; they group together the most primitive forms, characterized by not having proper tissues or that, in any case, these tissues are only partially differentiated. They generally group a single phylum, Porifera, which lack muscles, nerves and internal organs, which in many cases resembles a cell colony rather than a multicellular organism itself. All other animals are eumetazoans, which do have differentiated tissues.
On occasion, Parazoa reunites Porifera with Archaeocyatha, a group of extinct sponges sometimes considered a separate phylum. In other cases, Placozoa is included, depending on the authors.
Porifera and Archaeocyatha
Porifera and Archaeocyatha show similarities such as benthic and sessile habitat and the presence of pores, with differences such as the presence of internal walls and septa in Archaeocyatha. They have been considered separate phyla, however, the consensus is growing that Archaeocyatha was in fact a type of sponge that can be classified into Porifera.
Porifera and Placozoa
Some authors include in Parazoa the poriferous or sponge phyla and Placozoa—comprising only the Trichoplax adhaerens species – on the basis of shared primitive characteristics: Both are simple, show a lack of true tissues and organs, have both asexual and sexual reproduction, and are invariably aquatic. As animals, they are a group that in various studies are at the base of the phylogenetic tree, albeit in a paraphyletic form. Of this group only surviving sponges, which belong to the phylum Porifera, and Trichoplax in the phylum Placozoa.
Parazoa do not show any body symmetry (they are asymmetric); all other groups of animals show some kind of symmetry. There are currently 5000 species, 150 of which are freshwater. The larvae are planktonic and th |
https://en.wikipedia.org/wiki/Suslin%27s%20problem | In mathematics, Suslin's problem is a question about totally ordered sets posed by and published posthumously.
It has been shown to be independent of the standard axiomatic system of set theory known as ZFC; showed that the statement can neither be proven nor disproven from those axioms, assuming ZF is consistent.
(Suslin is also sometimes written with the French transliteration as , from the Cyrillic .)
Formulation
Suslin's problem asks: Given a non-empty totally ordered set R with the four properties
R does not have a least nor a greatest element;
the order on R is dense (between any two distinct elements there is another);
the order on R is complete, in the sense that every non-empty bounded subset has a supremum and an infimum; and
every collection of mutually disjoint non-empty open intervals in R is countable (this is the countable chain condition for the order topology of R),
is R necessarily order-isomorphic to the real line R?
If the requirement for the countable chain condition is replaced with the requirement that R contains a countable dense subset (i.e., R is a separable space), then the answer is indeed yes: any such set R is necessarily order-isomorphic to R (proved by Cantor).
The condition for a topological space that every collection of non-empty disjoint open sets is at most countable is called the Suslin property.
Implications
Any totally ordered set that is not isomorphic to R but satisfies properties 1–4 is known as a Suslin line. The Suslin hypothesis says that there are no Suslin lines: that every countable-chain-condition dense complete linear order without endpoints is isomorphic to the real line. An equivalent statement is that every tree of height ω1 either has a branch of length ω1 or an antichain of cardinality .
The generalized Suslin hypothesis says that for every infinite regular cardinal κ every tree of height κ either has a branch of length κ or an antichain of cardinality κ. The existence of Suslin lines is equivalent |
https://en.wikipedia.org/wiki/Climm |
climm (previously mICQ) is a free CLI-based instant messaging client that runs on a wide variety of platforms, including AmigaOS, BeOS, Windows (using either Cygwin or MinGW), OS X, NetBSD/OpenBSD/FreeBSD, Linux, Solaris, HP-UX, and AIX.
Functionality
climm has many of the features the official ICQ client has, and more:
It has support for SSL-encrypted direct connection compatible with licq and SIM.
It supports OTR encrypted messages.
It is internationalized; German, English, and other translations are available, and it supports sending and receiving acknowledged and non-acknowledged Unicode-encoded messages (it even understands UTF-8 messages for message types the ICQ protocol does not use them for).
It is capable of running several UINs at the same time and is very configurable (e.g. different colors for incoming messages from different contacts or for different accounts).
Due to its command-line interface, it has good usability for blind users through text-to-speech interfaces or Braille devices.
climm also supports basic functionality of the XMPP protocol.
History
Climm was originally developed as mICQ by Matt D. Smith as public domain software. Starting with mICQ 0.4.8 it was licensed under the GPLv2, not much of the original PD code remained since then. All later additions were made by Rüdiger Kuhlmann, in particular, the support for the ICQ v8 protocol. mICQ was renamed to climm ("Command Line Interface Multi Messenger") with version change to 0.6. CLimm was relicensed to include the OpenSSL exception.
See also
Comparison of instant messaging clients
References
Notes
Andreas Kneib (Feb 2004) Der direkte Draht. ICQ in der Kommandozeile (Direct Line. ICQ in the command line), LinuxUser
Further reading
Jonathan Corbet (February 18, 2003) The trojaning of mICQ, lwn.net
External links
ICQ protocol page
Instant messaging clients for Linux
MacOS instant messaging clients
Windows instant messaging clients
Amiga instant messaging clie |
https://en.wikipedia.org/wiki/Propylene%20glycol | Propylene glycol (IUPAC name: propane-1,2-diol) is a viscous, colorless liquid, which is nearly odorless but possesses a faintly sweet taste. Its chemical formula is CH3CH(OH)CH2OH.
As it contains two alcohol groups, it is classed as a diol. It is miscible with a broad range of solvents, including water, acetone, and chloroform. In general, glycols are non-irritating and have very low volatility.
It is produced on a large scale primarily for the production of polymers. In the European Union, it has E-number E1520 for food applications. For cosmetics and pharmacology, the number is E490. Propylene glycol is also present in propylene glycol alginate, which is known as E405. Propylene glycol is a compound which is GRAS (generally recognized as safe) by the US Food and Drug Administration under 21 CFR x184.1666, and is also approved by the FDA for certain uses as an indirect food additive. Propylene glycol is approved and used as a vehicle for topical, oral, and some intravenous pharmaceutical preparations in the U.S. and in Europe.
Structure
The compound is sometimes called (alpha) α-propylene glycol to distinguish it from the isomer propane-1,3-diol, known as (beta) β-propylene glycol. Propylene glycol is chiral. Commercial processes typically use the racemate. The S-isomer is produced by biotechnological routes.
Production
Industrial
Industrially, propylene glycol is mainly produced from propylene oxide (for food-grade use). According to a 2018 source, 2.16 M tonnes are produced annually. Manufacturers use either non-catalytic high-temperature process at to , or a catalytic method, which proceeds at to in the presence of ion exchange resin or a small amount of sulfuric acid or alkali.
Final products contain 20% propylene glycol, 1.5% of dipropylene glycol, and small amounts of other polypropylene glycols. Further purification produces finished industrial grade or USP/JP/EP/BP grade propylene glycol that is typically 99.5% or greater. Use of USP (US Pharmacop |
https://en.wikipedia.org/wiki/Old%20age | Old age is the range of ages for persons nearing and surpassing life expectancy. People of old age are also referred to as: old people, elderly, elders, seniors, senior citizens, or older adults.
Old age is not a definite biological stage: the chronological age denoted as "old age" varies culturally and historically. Some disciplines and domains focus on the aging and the aged, such as the organic processes of aging (senescence), medical studies of the aging process (gerontology), diseases that afflict older adults (geriatrics), technology to support the aging society (gerontechnology), and leisure and sport activities adapted to older people (such as senior sport).
Old people often have limited regenerative abilities and are more susceptible to illness and injury than younger adults. They face social problems related to retirement, loneliness, and ageism.
In 2011, the United Nations proposed a human-rights convention to protect old people.
Definitions
Definitions of old age include official definitions, sub-group definitions, and four dimensions as follows.
Official definitions
Most developed Western countries set the retirement age around the age of 65; this is also generally considered to mark the transition from middle to old age. Reaching this age is commonly a requirement to become eligible for senior social programs.
Old age cannot be universally defined because it is context-sensitive. The United Nations, for example, considers old age to be 60 years or older. In contrast, a 2001 joint report by the U.S. National Institute on Aging and the World Health Organization [WHO] Regional Office for Africa set the beginning of old age in Sub-Saharan Africa at 50. This lower threshold stems primarily from a different way of thinking about old age in developing nations. Unlike in the developed world, where chronological age determines retirement, societies in developing countries determine old age according to a person's ability to make active contributions to |
https://en.wikipedia.org/wiki/Brooks%27s%20law | Brooks' law is an observation about software project management according to which adding more individuals to a software project that is behind schedule delays it even longer. It was coined by Fred Brooks in his 1975 book The Mythical Man-Month. According to Brooks, under certain conditions, an incremental person when added to a project makes it take more, not less time.
Explanations
According to Brooks himself, the law is an "outrageous oversimplification", but it captures the general rule. Brooks points to the main factors that explain why it works this way:
It takes some time for the people added to a project to become productive. Brooks calls this the "ramp up" time. Software projects are complex engineering endeavors, and new workers on the project must first become educated about the work that has preceded them; this education requires diverting resources already working on the project, temporarily diminishing their productivity while the new workers are not yet contributing meaningfully. Each new worker also needs to integrate with a team composed of several engineers who must educate the new worker in their area of expertise in the code base, day by day. In addition to reducing the contribution of experienced workers (because of the need to train), new workers may even make negative contributions, for example, if they introduce bugs that move the project further from completion.
Communication overhead increases as the number of people increases. Due to combinatorial explosion, the number of different communication channels increases rapidly with the number of people. Everyone working on the same task needs to keep in sync, so as more people are added they spend more time trying to find out what everyone else is doing.
Adding more people to a highly divisible task, such as cleaning rooms in a hotel, decreases the overall task duration (up to the point where additional workers get in each other's way). However, other tasks including many specialties in s |
https://en.wikipedia.org/wiki/Hobbyist%20operating%20system | The development of a hobbyist operating system is one of the more involved and technical options for a computer hobbyist.
The definition of a hobby operating system can sometimes be vague. It can be from the developer's view, where the developers do it just for fun or learning; it can also be seen from the user's view, where the users are only using it as a toy; or it can be defined as an operating system which doesn't have a very big user base.
Development can begin from existing resources like a kernel, an operating system, or a bootloader, or it can also be made completely from scratch. The development platform could be a bare hardware machine, which is the nature of an operating system, but it could also be developed and tested on a virtual machine.
Since the hobbyist must claim more ownership for adapting a complex system to the ever-changing needs of the technical terrain, much enthusiasm is common amongst the many different groups attracted to operating system development.
Development
Elements of operating system development include:
Kernel:
Bootstrapping
Memory management
Process management and scheduling
Device driver management
Program API
External programs
User interface
The C programming language is frequently used for hobby operating system programming, as well as assembly language, though other languages can be used as well.
The use of assembly language is common with small systems, especially those based on eight bit microprocessors such as the MOS Technology 6502 family or the Zilog Z80, or in systems with a lack of available resources because of its small output size and low-level efficiency.
User interface
Most hobby operating systems use a command-line interface or a simple text user interface due to ease of development. More advanced hobby operating systems may have a graphical user interface. For example, AtheOS was a hobby operating system with a graphical interface written entirely by one programmer.
Examples
Use of BIOS
This s |
https://en.wikipedia.org/wiki/Propositional%20function | In propositional calculus, a propositional function or a predicate is a sentence expressed in a way that would assume the value of true or false, except that within the sentence there is a variable (x) that is not defined or specified (thus being a free variable), which leaves the statement undetermined. The sentence may contain several such variables (e.g. n variables, in which case the function takes n arguments).
Overview
As a mathematical function, A(x) or A(x, x, ..., x), the propositional function is abstracted from predicates or propositional forms. As an example, consider the predicate scheme, "x is hot". The substitution of any entity for x will produce a specific proposition that can be described as either true or false, even though "x is hot" on its own has no value as either a true or false statement. However, when a value is assigned to x , such as lava, the function then has the value true; while one assigns to x a value like ice, the function then has the value false.
Propositional functions are useful in set theory for the formation of sets. For example, in 1903 Bertrand Russell wrote in The Principles of Mathematics (page 106):
"...it has become necessary to take propositional function as a primitive notion.
Later Russell examined the problem of whether propositional functions were predicative or not, and he proposed two theories to try to get at this question: the zig-zag theory and the ramified theory of types.
A Propositional Function, or a predicate, in a variable x is an open formula p(x) involving x that becomes a proposition when one gives x a definite value from the set of values it can take.
According to Clarence Lewis, "A proposition is any expression which is either true or false; a propositional function is an expression, containing one or more variables, which becomes a proposition when each of the variables is replaced by some one of its values from a discourse domain of individuals." Lewis used the notion of propositional func |
https://en.wikipedia.org/wiki/Q%20factor | In physics and engineering, the quality factor or Q factor is a dimensionless parameter that describes how underdamped an oscillator or resonator is. It is defined as the ratio of the initial energy stored in the resonator to the energy lost in one radian of the cycle of oscillation. Q factor is alternatively defined as the ratio of a resonator's centre frequency to its bandwidth when subject to an oscillating driving force. These two definitions give numerically similar, but not identical, results. Higher Q indicates a lower rate of energy loss and the oscillations die out more slowly. A pendulum suspended from a high-quality bearing, oscillating in air, has a high Q, while a pendulum immersed in oil has a low one. Resonators with high quality factors have low damping, so that they ring or vibrate longer.
Explanation
The Q factor is a parameter that describes the resonance behavior of an underdamped harmonic oscillator (resonator). Sinusoidally driven resonators having higher Q factors resonate with greater amplitudes (at the resonant frequency) but have a smaller range of frequencies around that frequency for which they resonate; the range of frequencies for which the oscillator resonates is called the bandwidth. Thus, a high-Q tuned circuit in a radio receiver would be more difficult to tune, but would have more selectivity; it would do a better job of filtering out signals from other stations that lie nearby on the spectrum. High-Q oscillators oscillate with a smaller range of frequencies and are more stable.
The quality factor of oscillators varies substantially from system to system, depending on their construction. Systems for which damping is important (such as dampers keeping a door from slamming shut) have Q near . Clocks, lasers, and other resonating systems that need either strong resonance or high frequency stability have high quality factors. Tuning forks have quality factors around 1000. The quality factor of atomic clocks, superconducting RF cavi |
https://en.wikipedia.org/wiki/Domain%20of%20discourse | In the formal sciences, the domain of discourse, also called the universe of discourse, universal set, or simply universe, is the set of entities over which certain variables of interest in some formal treatment may range.
Overview
The domain of discourse is usually identified in the preliminaries, so that there is no need in the further treatment to specify each time the range of the relevant variables. Many logicians distinguish, sometimes only tacitly, between the domain of a science and the universe of discourse of a formalization of the science.
Examples
For example, in an interpretation of first-order logic, the domain of discourse is the set of individuals over which the quantifiers range. A proposition such as is ambiguous, if no domain of discourse has been identified. In one interpretation, the domain of discourse could be the set of real numbers; in another interpretation, it could be the set of natural numbers. If the domain of discourse is the set of real numbers, the proposition is false, with as counterexample; if the domain is the set of natural numbers, the proposition is true, since 2 is not the square of any natural number.
Universe of discourse
The term "universe of discourse" generally refers to the collection of objects being discussed in a specific discourse. In model-theoretical semantics, a universe of discourse is the set of entities that a model is based on. The concept universe of discourse is generally attributed to Augustus De Morgan (1846) but the name was used for the first time by George Boole (1854) on page 42 of his Laws of Thought. Boole's definition is quoted below. The concept, probably discovered independently by Boole in 1847, played a crucial role in his philosophy of logic especially in his principle of wholistic reference.
Boole’s 1854 definition
See also
Domain of a function
Domain theory
Interpretation (logic)
Quantifier (logic)
Term algebra
Universe (mathematics)
References
Semantics
Predicate logic |
https://en.wikipedia.org/wiki/Broadcast%20range | A broadcast range (also listening range or listening area for radio, or viewing range or viewing area for television) is the service area that a broadcast station or other transmission covers via radio waves (or possibly infrared light, which is closely related). It is generally the area in which a station's signal strength is sufficient for most receivers to decode it. However, this also depends on interference from other stations.
Legal definitions
The "primary service area" is the area served by a station's strongest signal. The "city-grade contour" is 70 dBμ (decibels relative to one microvolt per meter of signal strength) or 3.16mV/m (millivolts per meter) for FM stations in the United States, according to Federal Communications Commission (FCC) regulations. This is also significant in broadcast law, in that a station must cover its city of license within this area, except for non-commercial educational and low-power stations.
The legally protected range of a station extends beyond this range, out to the point where signal strength is expected to be 1mV/m for most stations in North America, though for class B1 stations it is 0.7mV/m, and as low as 0.5mV/m for full class B stations (the maximum allowed in densely populated areas of both Canada and the U.S.).
Practical application
In reality, radio propagation changes along with the weather and tropospheric ducting, and occasionally along with other upper-atmospheric phenomena like sunspots and even meteor showers. Thus, while a broadcasting authority might fix the range to an area with exact boundaries (defined as a series of vectors), this is rarely if ever true. When a broadcast reaches well outside of its intended range due to unusual conditions, DXing is possible.
The local terrain can also play a major role in limiting broadcast range. Mountain ranges block FM broadcasts, AM broadcasts, and TV broadcasts, and other signals in the VHF and especially UHF ranges, respectively. This terrain shielding |
https://en.wikipedia.org/wiki/Anomalous%20propagation | Anomalous propagation (sometimes shortened to anaprop or anoprop) includes different forms of radio propagation due to an unusual distribution of temperature and humidity with height in the atmosphere. While this includes propagation with larger losses than in a standard atmosphere, in practical applications it is most often meant to refer to cases when signal propagates beyond normal radio horizon.
Anomalous propagation can cause interference to VHF and UHF radio communications if distant stations are using the same frequency as local services. Over-the-air analog television broadcasting, for example, may be disrupted by distant stations on the same channel, or experience distortion of transmitted signals ghosting). Radar systems may produce inaccurate ranges or bearings to distant targets if the radar "beam" is bent by propagation effects. However, radio hobbyists take advantage of these effects in TV and FM DX.
Causes
Air temperature profile
The first assumption of the prediction of propagation of a radio wave is that it is moving through air with temperature that declines at a standard rate with height in the troposphere. This has the effect of slightly bending (refracting) the path toward the Earth, and accounts for an effective range that is slightly greater than the geometric distance to the horizon. Any variation to this stratification of temperatures will modify the path followed by the wave. Changes to the path can be separated into super and under refraction:
Super refraction
It is very common to have temperature inversions forming near the ground, for instance air cooling at night while remaining warm aloft. This happens equally aloft when a warm and dry airmass overrides a cooler one, like in the subsidence aloft cause by a high pressure intensifying. The index of refraction of air increases in both cases and the EM wave bends toward the ground instead of continuing upward.
On surface-base inversion, the beam will eventually hit the ground and |
https://en.wikipedia.org/wiki/Blanketing | Blanketing is a term used predominantly in the US to refer to receiver blocking, which is interference caused when a strong unwanted off-channel radio signal prevents the reception of another (wanted) transmission.
This problem is greatly reduced by even moderate-quality receivers, which have better selectivity (filtering) and dynamic range than poorly designed inexpensive or disposable ones.
See also
Blocking (radio)
Near–far problem
Broadcast engineering |
https://en.wikipedia.org/wiki/Delta%20operator | In mathematics, a delta operator is a shift-equivariant linear operator on the vector space of polynomials in a variable over a field that reduces degrees by one.
To say that is shift-equivariant means that if , then
In other words, if is a "shift" of , then is also a shift of , and has the same "shifting vector" .
To say that an operator reduces degree by one means that if is a polynomial of degree , then is either a polynomial of degree , or, in case , is 0.
Sometimes a delta operator is defined to be a shift-equivariant linear transformation on polynomials in that maps to a nonzero constant. Seemingly weaker than the definition given above, this latter characterization can be shown to be equivalent to the stated definition when has characteristic zero, since shift-equivariance is a fairly strong condition.
Examples
The forward difference operator
is a delta operator.
Differentiation with respect to x, written as D, is also a delta operator.
Any operator of the form
(where Dn(ƒ) = ƒ(n) is the nth derivative) with is a delta operator. It can be shown that all delta operators can be written in this form. For example, the difference operator given above can be expanded as
The generalized derivative of time scale calculus which unifies the forward difference operator with the derivative of standard calculus is a delta operator.
In computer science and cybernetics, the term "discrete-time delta operator" (δ) is generally taken to mean a difference operator
the Euler approximation of the usual derivative with a discrete sample time . The delta-formulation obtains a significant number of numerical advantages compared to the shift-operator at fast sampling.
Basic polynomials
Every delta operator has a unique sequence of "basic polynomials", a polynomial sequence defined by three conditions:
Such a sequence of basic polynomials is always of binomial type, and it can be shown that no other sequences of binomial type exist. |
https://en.wikipedia.org/wiki/Monge%20array | In mathematics applied to computer science, Monge arrays, or Monge matrices, are mathematical objects named for their discoverer, the French mathematician Gaspard Monge.
An m-by-n matrix is said to be a Monge array if, for all such that
one obtains
So for any two rows and two columns of a Monge array (a 2 × 2 sub-matrix) the four elements at the intersection points have the property that the sum of the upper-left and lower right elements (across the main diagonal) is less than or equal to the sum of the lower-left and upper-right elements (across the antidiagonal).
This matrix is a Monge array:
For example, take the intersection of rows 2 and 4 with columns 1 and 5.
The four elements are:
17 + 7 = 24
23 + 11 = 34
The sum of the upper-left and lower right elements is less than or equal to the sum of the lower-left and upper-right elements.
Properties
The above definition is equivalent to the statement
A matrix is a Monge array if and only if for all and .
Any subarray produced by selecting certain rows and columns from an original Monge array will itself be a Monge array.
Any linear combination with non-negative coefficients of Monge arrays is itself a Monge array.
One interesting property of Monge arrays is that if you mark with a circle the leftmost minimum of each row, you will discover that your circles march downward to the right; that is to say, if , then for all . Symmetrically, if you mark the uppermost minimum of each column, your circles will march rightwards and downwards. The row and column maxima march in the opposite direction: upwards to the right and downwards to the left.
The notion of weak Monge arrays has been proposed; a weak Monge array is a square n-by-n matrix which satisfies the Monge property only for all .
Every Monge array is totally monotone, meaning that its row minima occur in a nondecreasing sequence of columns, and that the same property is true for every subarray. This property allows the row minima to be found quickly |
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