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3,100 | It is a vast and recently developed area of computer science. computer network Also data network. A digital telecommunications network which allows nodes to share resources. | Glossary of computer science | 0.831867 |
3,101 | computational physics Is the study and implementation of numerical analysis to solve problems in physics for which a quantitative theory already exists. Historically, computational physics was the first application of modern computers in science, and is now a subset of computational science. computational science Also scientific computing and scientific computation (SC). | Glossary of computer science | 0.831867 |
3,102 | computational model A mathematical model in computational science that requires extensive computational resources to study the behavior of a complex system by computer simulation. computational neuroscience Also theoretical neuroscience or mathematical neuroscience. A branch of neuroscience which employs mathematical models, theoretical analysis, and abstractions of the brain to understand the principles that govern the development, structure, physiology, and cognitive abilities of the nervous system. | Glossary of computer science | 0.831867 |
3,103 | Computational biology is different from biological computing, which is a subfield of computer science and computer engineering using bioengineering and biology to build computers. computational chemistry A branch of chemistry that uses computer simulation to assist in solving chemical problems. It uses methods of theoretical chemistry, incorporated into efficient computer programs, to calculate the structures and properties of molecules and solids. | Glossary of computer science | 0.831867 |
3,104 | The study of computation is paramount to the discipline of computer science. computational biology Involves the development and application of data-analytical and theoretical methods, mathematical modelling and computational simulation techniques to the study of biological, ecological, behavioural, and social systems. The field is broadly defined and includes foundations in biology, applied mathematics, statistics, biochemistry, chemistry, biophysics, molecular biology, genetics, genomics, computer science, and evolution. | Glossary of computer science | 0.831867 |
3,105 | Compilers are a type of translator that support digital devices, primarily computers. The name compiler is primarily used for programs that translate source code from a high-level programming language to a lower-level language (e.g. assembly language, object code, or machine code) to create an executable program. computability theory also known as recursion theory, is a branch of mathematical logic, of computer science, and of the theory of computation that originated in the 1930s with the study of computable functions and Turing degrees. | Glossary of computer science | 0.831867 |
3,106 | Codes are studied by various scientific disciplines—such as information theory, electrical engineering, mathematics, linguistics, and computer science—for the purpose of designing efficient and reliable data transmission methods. This typically involves the removal of redundancy and the correction or detection of errors in the transmitted data. cognitive science The interdisciplinary, scientific study of the mind and its processes. | Glossary of computer science | 0.831867 |
3,107 | cleanroom software engineering A software development process intended to produce software with a certifiable level of reliability. The cleanroom process was originally developed by Harlan Mills and several of his colleagues including Alan Hevner at IBM. | Glossary of computer science | 0.831867 |
3,108 | Contrary to elementary algebra, where the values of the variables are numbers and the prime operations are addition and multiplication, the main operations of Boolean algebra are the conjunction and (denoted as ∧), the disjunction or (denoted as ∨), and the negation not (denoted as ¬). It is thus a formalism for describing logical relations in the same way that elementary algebra describes numeric relations. byte A unit of digital information that most commonly consists of eight bits, representing a binary number. | Glossary of computer science | 0.831867 |
3,109 | Boolean expression An expression used in a programming language that returns a Boolean value when evaluated, that is one of true or false. A Boolean expression may be composed of a combination of the Boolean constants true or false, Boolean-typed variables, Boolean-valued operators, and Boolean-valued functions. Boolean algebra In mathematics and mathematical logic, the branch of algebra in which the values of the variables are the truth values true and false, usually denoted 1 and 0, respectively. | Glossary of computer science | 0.831867 |
3,110 | A raster graphics image file format used to store bitmap digital images independently of the display device (such as a graphics adapter), used especially on Microsoft Windows and OS/2 operating systems. Boolean data type A data type that has one of two possible values (usually denoted true and false), intended to represent the two truth values of logic and Boolean algebra. It is named after George Boole, who first defined an algebraic system of logic in the mid-19th century. | Glossary of computer science | 0.831867 |
3,111 | Some authors allow the binary tree to be the empty set as well. bioinformatics An interdisciplinary field that combines biology, computer science, information engineering, mathematics, and statistics to develop methods and software tools for analyzing and interpreting biological data. Bioinformatics is widely used for in silico analyses of biological queries using mathematical and statistical techniques. | Glossary of computer science | 0.831867 |
3,112 | artificial intelligence (AI) Also machine intelligence. Intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. In computer science, AI research is defined as the study of "intelligent agents": devices capable of perceiving their environment and taking actions that maximize the chance of successfully achieving their goals. | Glossary of computer science | 0.831867 |
3,113 | In software engineering and computer science, the process of removing physical, spatial, or temporal details or attributes in the study of objects or systems in order to more closely attend to other details of interest; it is also very similar in nature to the process of generalization. 2. | Glossary of computer science | 0.831867 |
3,114 | This glossary of computer science is a list of definitions of terms and concepts used in computer science, its sub-disciplines, and related fields, including terms relevant to software, data science, and computer programming. | Glossary of computer science | 0.831867 |
3,115 | In other words, "runtime" is the running phase of a program. run time error A runtime error is detected after or during the execution (running state) of a program, whereas a compile-time error is detected by the compiler before the program is ever executed. Type checking, register allocation, code generation, and code optimization are typically done at compile time, but may be done at runtime depending on the particular language and compiler. Many other runtime errors exist and are handled differently by different programming languages, such as division by zero errors, domain errors, array subscript out of bounds errors, arithmetic underflow errors, several types of underflow and overflow errors, and many other runtime errors generally considered as software bugs which may or may not be caught and handled by any particular computer language. | Glossary of computer science | 0.831867 |
3,116 | reliability engineering A sub-discipline of systems engineering that emphasizes dependability in the lifecycle management of a product. Reliability describes the ability of a system or component to function under stated conditions for a specified period of time. Reliability is closely related to availability, which is typically described as the ability of a component or system to function at a specified moment or interval of time. | Glossary of computer science | 0.831867 |
3,117 | relational database Is a digital database based on the relational model of data, as proposed by E. F. Codd in 1970. A software system used to maintain relational databases is a relational database management system (RDBMS). Many relational database systems have an option of using the SQL (Structured Query Language) for querying and maintaining the database. | Glossary of computer science | 0.831867 |
3,118 | The most common application of recursion is in mathematics and computer science, where a function being defined is applied within its own definition. While this apparently defines an infinite number of instances (function values), it is often done in such a way that no infinite loop or infinite chain of references can occur. reference Is a value that enables a program to indirectly access a particular datum, such as a variable's value or a record, in the computer's memory or in some other storage device. | Glossary of computer science | 0.831867 |
3,119 | The generic, umbrella term callable unit is sometimes used. symbolic computation In mathematics and computer science, computer algebra, also called symbolic computation or algebraic computation, is a scientific area that refers to the study and development of algorithms and software for manipulating mathematical expressions and other mathematical objects. Although computer algebra could be considered a subfield of scientific computing, they are generally considered as distinct fields because scientific computing is usually based on numerical computation with approximate floating point numbers, while symbolic computation emphasizes exact computation with expressions containing variables that have no given value and are manipulated as symbols. | Glossary of computer science | 0.831866 |
3,120 | Sequences can be finite, as in these examples, or infinite, such as the sequence of all even positive integers (2, 4, 6, ...). In computing and computer science, finite sequences are sometimes called strings, words or lists, the different names commonly corresponding to different ways to represent them in computer memory; infinite sequences are called streams. The empty sequence ( ) is included in most notions of sequence, but may be excluded depending on the context. | Glossary of computer science | 0.831866 |
3,121 | numerical analysis The study of algorithms that use numerical approximation (as opposed to symbolic manipulations) for the problems of mathematical analysis (as distinguished from discrete mathematics). numerical method In numerical analysis, a numerical method is a mathematical tool designed to solve numerical problems. The implementation of a numerical method with an appropriate convergence check in a programming language is called a numerical algorithm. | Glossary of computer science | 0.831866 |
3,122 | encryption In cryptography, encryption is the process of encoding information. This process converts the original representation of the information, known as plaintext, into an alternative form known as ciphertext. Ideally, only authorized parties can decipher a ciphertext back to plaintext and access the original information. | Glossary of computer science | 0.831866 |
3,123 | It examines the nature, the tasks, and the functions of cognition (in a broad sense). Cognitive scientists study intelligence and behavior, with a focus on how nervous systems represent, process, and transform information. Mental faculties of concern to cognitive scientists include language, perception, memory, attention, reasoning, and emotion; to understand these faculties, cognitive scientists borrow from fields such as linguistics, psychology, artificial intelligence, philosophy, neuroscience, and anthropology. | Glossary of computer science | 0.831866 |
3,124 | The process of programming thus often requires expertise in several different subjects, including knowledge of the application domain, specialized algorithms, and formal logic. coding theory The study of the properties of codes and their respective fitness for specific applications. Codes are used for data compression, cryptography, error detection and correction, data transmission and data storage. | Glossary of computer science | 0.831866 |
3,125 | cipher Also cypher. In cryptography, an algorithm for performing encryption or decryption—a series of well-defined steps that can be followed as a procedure. class In object-oriented programming, an extensible program-code-template for creating objects, providing initial values for state (member variables) and implementations of behavior (member functions or methods). | Glossary of computer science | 0.831866 |
3,126 | Operations associated with this data type allow:the addition of a pair to the collection the removal of a pair from the collection the modification of an existing pair the lookup of a value associated with a particular key automata theory The study of abstract machines and automata, as well as the computational problems that can be solved using them. It is a theory in theoretical computer science and discrete mathematics (a subject of study in both mathematics and computer science). automated reasoning An area of computer science and mathematical logic dedicated to understanding different aspects of reasoning. The study of automated reasoning helps produce computer programs that allow computers to reason completely, or nearly completely, automatically. Although automated reasoning is considered a sub-field of artificial intelligence, it also has connections with theoretical computer science, and even philosophy. | Glossary of computer science | 0.831866 |
3,127 | Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving". ASCII See American Standard Code for Information Interchange. assertion In computer programming, a statement that a predicate (Boolean-valued function, i.e. a true–false expression) is always true at that point in code execution. | Glossary of computer science | 0.831866 |
3,128 | aggregate function In database management, a function in which the values of multiple rows are grouped together to form a single value of more significant meaning or measurement, such as a sum, count, or max. agile software development An approach to software development under which requirements and solutions evolve through the collaborative effort of self-organizing and cross-functional teams and their customer(s)/end user(s). It advocates adaptive planning, evolutionary development, early delivery, and continual improvement, and it encourages rapid and flexible response to change. | Glossary of computer science | 0.831866 |
3,129 | routing table In computer networking a routing table, or routing information base (RIB), is a data table stored in a router or a network host that lists the routes to particular network destinations, and in some cases, metrics (distances) associated with those routes. The routing table contains information about the topology of the network immediately around it. run time Runtime, run time, or execution time is the final phase of a computer program's life cycle, in which the code is being executed on the computer's central processing unit (CPU) as machine code. | Glossary of computer science | 0.831866 |
3,130 | The goal of robotics is to design intelligent machines that can help and assist humans in their day-to-day lives and keep everyone safe. round-off error Also rounding error. The difference between the result produced by a given algorithm using exact arithmetic and the result produced by the same algorithm using finite-precision, rounded arithmetic. | Glossary of computer science | 0.831866 |
3,131 | robotics An interdisciplinary branch of engineering and science that includes mechanical engineering, electronic engineering, information engineering, computer science, and others. Robotics involves design, construction, operation, and use of robots, as well as computer systems for their perception, control, sensory feedback, and information processing. | Glossary of computer science | 0.831866 |
3,132 | As regression test suites tend to grow with each found defect, test automation is frequently involved. Sometimes a change impact analysis is performed to determine an appropriate subset of tests (non-regression analysis). requirements analysis In systems engineering and software engineering, requirements analysis focuses on the tasks that determine the needs or conditions to meet the new or altered product or project, taking account of the possibly conflicting requirements of the various stakeholders, analyzing, documenting, validating and managing software or system requirements. | Glossary of computer science | 0.831866 |
3,133 | Both quarks and leptons are elementary particles, and were in 2004 seen by authors of an undergraduate text as being the fundamental constituents of matter.These quarks and leptons interact through four fundamental forces: gravity, electromagnetism, weak interactions, and strong interactions. The Standard Model of particle physics is currently the best explanation for all of physics, but despite decades of efforts, gravity cannot yet be accounted for at the quantum level; it is only described by classical physics (see quantum gravity and graviton) to the frustration of theoreticians like Stephen Hawking. Interactions between quarks and leptons are the result of an exchange of force-carrying particles such as photons between quarks and leptons. | Chemical matter | 0.831863 |
3,134 | In 1909 the famous physicist J. J. Thomson (1856–1940) wrote about the "constitution of matter" and was concerned with the possible connection between matter and electrical charge.In the late 19th century with the discovery of the electron, and in the early 20th century, with the Geiger–Marsden experiment discovery of the atomic nucleus, and the birth of particle physics, matter was seen as made up of electrons, protons and neutrons interacting to form atoms. There then developed an entire literature concerning the "structure of matter", ranging from the "electrical structure" in the early 20th century, to the more recent "quark structure of matter", introduced as early as 1992 by Jacob with the remark: "Understanding the quark structure of matter has been one of the most important advances in contemporary physics." In this connection, physicists speak of matter fields, and speak of particles as "quantum excitations of a mode of the matter field". | Chemical matter | 0.831863 |
3,135 | Baryons are strongly interacting fermions, and so are subject to Fermi–Dirac statistics. Amongst the baryons are the protons and neutrons, which occur in atomic nuclei, but many other unstable baryons exist as well. The term baryon usually refers to triquarks—particles made of three quarks. Also, "exotic" baryons made of four quarks and one antiquark are known as pentaquarks, but their existence is not generally accepted. | Chemical matter | 0.831863 |
3,136 | Some of these ways are based on loose historical meanings, from a time when there was no reason to distinguish mass from simply a quantity of matter. As such, there is no single universally agreed scientific meaning of the word "matter". Scientifically, the term "mass" is well-defined, but "matter" can be defined in several ways. Sometimes in the field of physics "matter" is simply equated with particles that exhibit rest mass (i.e., that cannot travel at the speed of light), such as quarks and leptons. However, in both physics and chemistry, matter exhibits both wave-like and particle-like properties, the so-called wave–particle duality. | Chemical matter | 0.831863 |
3,137 | Matter should not be confused with mass, as the two are not the same in modern physics. Matter is a general term describing any 'physical substance'. By contrast, mass is not a substance but rather a quantitative property of matter and other substances or systems; various types of mass are defined within physics – including but not limited to rest mass, inertial mass, relativistic mass, mass–energy. While there are different views on what should be considered matter, the mass of a substance has exact scientific definitions. | Chemical matter | 0.831863 |
3,138 | Orbital Mechanics for Engineering Students is an aerospace engineering textbook by Howard D. Curtis, in its fourth edition as of 2019. The book provides an introduction to orbital mechanics, while assuming an undergraduate-level background in physics, rigid body dynamics, differential equations, and linear algebra.Topics covered by the text include a review of kinematics and Newtonian dynamics, the two-body problem, Kepler's laws of planetary motion, orbit determination, orbital maneuvers, relative motion and rendezvous, and interplanetary trajectories. The text focuses primarily on orbital mechanics, but also includes material on rigid body dynamics, rocket vehicle dynamics, and attitude control. Control theory and spacecraft control systems are less thoroughly covered.The textbook includes exercises at the end of each chapter, and supplemental material is available online, including MATLAB code for orbital mechanics projects. == References == | Orbital Mechanics for Engineering Students | 0.831859 |
3,139 | The notion of an arithmetic group is a vast generalisation based upon the fundamental example of S L d ( Z ) {\displaystyle \mathrm {SL} _{d}(\mathbb {Z} )} . In general, to give a definition one needs a semisimple algebraic group G {\displaystyle \mathbf {G} } defined over Q {\displaystyle \mathbb {Q} } and a faithful representation ρ {\displaystyle \rho } , also defined over Q , {\displaystyle \mathbb {Q} ,} from G {\displaystyle \mathbf {G} } into G L d {\displaystyle \mathrm {GL} _{d}} ; then an arithmetic group in G ( Q ) {\displaystyle \mathbf {G} (\mathbb {Q} )} is any group Γ ⊂ G ( Q ) {\displaystyle \Gamma \subset \mathbf {G} (\mathbb {Q} )} which is of finite index in the stabiliser of a finite-index sub-lattice in Z d {\displaystyle \mathbb {Z} ^{d}} . | Non-congruence subgroup | 0.83185 |
3,140 | If n ⩾ 1 {\displaystyle n\geqslant 1} is an integer there is a homomorphism π n: S L 2 ( Z ) → S L 2 ( Z / n Z ) {\displaystyle \pi _{n}:\mathrm {SL} _{2}(\mathbb {Z} )\to \mathrm {SL} _{2}(\mathbb {Z} /n\mathbb {Z} )} induced by the reduction modulo n {\displaystyle n} morphism Z → Z / n Z {\displaystyle \mathbb {Z} \to \mathbb {Z} /n\mathbb {Z} } . The principal congruence subgroup of level n {\displaystyle n} in Γ = S L 2 ( Z ) {\displaystyle \Gamma =\mathrm {SL} _{2}(\mathbb {Z} )} is the kernel of π n {\displaystyle \pi _{n}} , and it is usually denoted Γ ( n ) {\displaystyle \Gamma (n)} . Explicitly it is described as follows: Γ ( n ) = { ( a b c d ) ∈ S L 2 ( Z ): a , d ≡ 1 ( mod n ) , b , c ≡ 0 ( mod n ) } {\displaystyle \Gamma (n)=\left\{{\begin{pmatrix}a&b\\c&d\end{pmatrix}}\in \mathrm {SL} _{2}(\mathbb {Z} ):a,d\equiv 1{\pmod {n}},\quad b,c\equiv 0{\pmod {n}}\right\}} This definition immediately implies that Γ ( n ) {\displaystyle \Gamma (n)} is a normal subgroup of finite index in Γ {\displaystyle \Gamma } . The strong approximation theorem (in this case an easy consequence of the Chinese remainder theorem) implies that π n {\displaystyle \pi _{n}} is surjective, so that the quotient Γ / Γ ( n ) {\displaystyle \Gamma /\Gamma (n)} is isomorphic to S L 2 ( Z / n Z ) . | Non-congruence subgroup | 0.83185 |
3,141 | The congruence subgroups of the modular group and the associated Riemann surfaces are distinguished by some particularly nice geometric and topological properties. Here is a sample: There are only finitely many congruence covers of the modular surface which have genus zero; (Selberg's 3/16 theorem) If f {\displaystyle f} is a nonconstant eigenfunction of the Laplace-Beltrami operator on a congruence cover of the modular surface with eigenvalue λ {\displaystyle \lambda } then λ ⩾ 3 16 . {\displaystyle \lambda \geqslant {\tfrac {3}{16}}.} | Non-congruence subgroup | 0.83185 |
3,142 | Both are profinite groups and there is a natural surjective morphism Γ ^ → Γ ¯ {\displaystyle {\widehat {\Gamma }}\to {\overline {\Gamma }}} (intuitively, there are fewer conditions for a Cauchy sequence to comply with in the congruence topology than in the profinite topology). The congruence kernel C ( Γ ) {\displaystyle C(\Gamma )} is the kernel of this morphism, and the congruence subgroup problem stated above amounts to whether C ( Γ ) {\displaystyle C(\Gamma )} is trivial. The weakening of the conclusion then leads to the following problem. Congruence subgroup problem: Is the congruence kernel C ( Γ ) {\displaystyle C(\Gamma )} finite?When the problem has a positive solution one says that Γ {\displaystyle \Gamma } has the congruence subgroup property. A conjecture generally attributed to Serre states that an irreducible arithmetic lattice in a semisimple Lie group G {\displaystyle G} has the congruence subgroup property if and only if the real rank of G {\displaystyle G} is at least 2; for example, lattices in S L 3 ( R ) {\displaystyle \mathrm {SL} _{3}(\mathbb {R} )} should always have the property. | Non-congruence subgroup | 0.83185 |
3,143 | On the other hand, the work of Serre on S L 2 {\displaystyle \mathrm {SL} _{2}} over number fields shows that in some cases the answer to the naïve question is "no" while a slight relaxation of the problem has a positive answer.This new problem is better stated in terms of certain compact topological groups associated to an arithmetic group Γ {\displaystyle \Gamma } . There is a topology on Γ {\displaystyle \Gamma } for which a base of neighbourhoods of the trivial subgroup is the set of subgroups of finite index (the profinite topology); and there is another topology defined in the same way using only congruence subgroups. The profinite topology gives rise to a completion Γ ^ {\displaystyle {\widehat {\Gamma }}} of Γ {\displaystyle \Gamma } , while the "congruence" topology gives rise to another completion Γ ¯ {\displaystyle {\overline {\Gamma }}} . | Non-congruence subgroup | 0.83185 |
3,144 | One can ask the same question for any arithmetic group as for the modular group: Naïve congruence subgroup problem: Given an arithmetic group, are all of its finite-index subgroups congruence subgroups? This problem can have a positive solution: its origin is in the work of Hyman Bass, Jean-Pierre Serre and John Milnor, and Jens Mennicke who proved that, in contrast to the case of S L 2 ( Z ) {\displaystyle \mathrm {SL} _{2}(\mathbb {Z} )} , when n ⩾ 3 {\displaystyle n\geqslant 3} all finite-index subgroups in S L n ( Z ) {\displaystyle \mathrm {SL} _{n}(\mathbb {Z} )} are congruence subgroups. The solution by Bass–Milnor–Serre involved an aspect of algebraic number theory linked to K-theory. | Non-congruence subgroup | 0.83185 |
3,145 | If you apply a voltage across a capacitor, it 'charges up' by storing the electrical charge as an electrical field inside the device. This means that while the voltage across the capacitor remains initially small, a large current flows. Later, the current flow is smaller because the capacity is filled, and the voltage raises across the device. Complex Analysis methods are also important in electrical engineering in fields such as signal processing, power electronics, control systems, and others A similar though opposite situation occurs in an inductor; the applied voltage remains high with low current as a magnetic field is generated, and later becomes small with high current when the magnetic field is at maximum. The voltage and current of these two types of devices are therefore out of phase, they do not rise and fall together as simple resistor networks do. The mathematical model that matches this situation is that of complex numbers, using an imaginary component to describe the stored energy. | Mathematical methods in electronics | 0.831846 |
3,146 | Electronics engineering careers usually include courses in calculus (single and multivariable), complex analysis, differential equations (both ordinary and partial), linear algebra and probability. Fourier analysis and Z-transforms are also subjects which are usually included in electrical engineering programs. Laplace transform can simplify computing RLC circuit behaviour. | Mathematical methods in electronics | 0.831846 |
3,147 | Ohm's law: the voltage across a resistor is the product of its resistance and the current flowing through it.at constant temperature. Norton's theorem: any two-terminal collection of voltage sources and resistors is electrically equivalent to an ideal current source in parallel with a single resistor. Thévenin's theorem: any two-terminal combination of voltage sources and resistors is electrically equivalent to a single voltage source in series with a single resistor. | Mathematical methods in electronics | 0.831846 |
3,148 | Fourier analysis. Deconstructing a periodic waveform into its constituent frequencies; see also: Fourier theorem, Fourier transform. Nyquist–Shannon sampling theorem. Information theory. Sets fundamental limits on how information can be transmitted or processed by any system. | Mathematical methods in electronics | 0.831846 |
3,149 | Millman's theorem: the voltage on the ends of branches in parallel is equal to the sum of the currents flowing in every branch divided by the total equivalent conductance. See also Analysis of resistive circuits.Circuit analysis is the study of methods to solve linear systems for an unknown variable. Circuit analysis | Mathematical methods in electronics | 0.831846 |
3,150 | There is no Nobel Prize in mathematics, though sometimes mathematicians have won the Nobel Prize in a different field, such as economics or physics. Prominent prizes in mathematics include the Abel Prize, the Chern Medal, the Fields Medal, the Gauss Prize, the Nemmers Prize, the Balzan Prize, the Crafoord Prize, the Shaw Prize, the Steele Prize, the Wolf Prize, the Schock Prize, and the Nevanlinna Prize. The American Mathematical Society, Association for Women in Mathematics, and other mathematical societies offer several prizes aimed at increasing the representation of women and minorities in the future of mathematics. | Applied mathematician | 0.831845 |
3,151 | Pure mathematics is mathematics that studies entirely abstract concepts. From the eighteenth century onwards, this was a recognized category of mathematical activity, sometimes characterized as speculative mathematics, and at variance with the trend towards meeting the needs of navigation, astronomy, physics, economics, engineering, and other applications. Another insightful view put forth is that pure mathematics is not necessarily applied mathematics: it is possible to study abstract entities with respect to their intrinsic nature, and not be concerned with how they manifest in the real world. Even though the pure and applied viewpoints are distinct philosophical positions, in practice there is much overlap in the activity of pure and applied mathematicians. To develop accurate models for describing the real world, many applied mathematicians draw on tools and techniques that are often considered to be "pure" mathematics. On the other hand, many pure mathematicians draw on natural and social phenomena as inspiration for their abstract research. | Applied mathematician | 0.831845 |
3,152 | An emphasis on free thinking and experimentation had begun in Britain's oldest universities beginning in the seventeenth century at Oxford with the scientists Robert Hooke and Robert Boyle, and at Cambridge where Isaac Newton was Lucasian Professor of Mathematics & Physics. Moving into the 19th century, the objective of universities all across Europe evolved from teaching the "regurgitation of knowledge" to "encourag productive thinking." In 1810, Humboldt convinced the king of Prussia, Fredrick William III, to build a university in Berlin based on Friedrich Schleiermacher's liberal ideas; the goal was to demonstrate the process of the discovery of knowledge and to teach students to "take account of fundamental laws of science in all their thinking." | Applied mathematician | 0.831845 |
3,153 | A notable feature of many scholars working under Muslim rule in medieval times is that they were often polymaths. Examples include the work on optics, maths and astronomy of Ibn al-Haytham. | Applied mathematician | 0.831845 |
3,154 | He is credited with the first use of deductive reasoning applied to geometry, by deriving four corollaries to Thales's theorem. The number of known mathematicians grew when Pythagoras of Samos (c. 582 – c. | Applied mathematician | 0.831845 |
3,155 | Using their broad knowledge, actuaries help design and price insurance policies, pension plans, and other financial strategies in a manner which will help ensure that the plans are maintained on a sound financial basis. As another example, mathematical finance will derive and extend the mathematical or numerical models without necessarily establishing a link to financial theory, taking observed market prices as input. Mathematical consistency is required, not compatibility with economic theory. Thus, for example, while a financial economist might study the structural reasons why a company may have a certain share price, a financial mathematician may take the share price as a given, and attempt to use stochastic calculus to obtain the corresponding value of derivatives of the stock (see: Valuation of options; Financial modeling). | Applied mathematician | 0.831845 |
3,156 | The philosopher of computing Bill Rapaport noted three Great Insights of Computer Science: Gottfried Wilhelm Leibniz's, George Boole's, Alan Turing's, Claude Shannon's, and Samuel Morse's insight: there are only two objects that a computer has to deal with in order to represent "anything".All the information about any computable problem can be represented using only 0 and 1 (or any other bistable pair that can flip-flop between two easily distinguishable states, such as "on/off", "magnetized/de-magnetized", "high-voltage/low-voltage", etc.). Alan Turing's insight: there are only five actions that a computer has to perform in order to do "anything".Every algorithm can be expressed in a language for a computer consisting of only five basic instructions:move left one location; move right one location; read symbol at current location; print 0 at current location; print 1 at current location. Corrado Böhm and Giuseppe Jacopini's insight: there are only three ways of combining these actions (into more complex ones) that are needed in order for a computer to do "anything".Only three rules are needed to combine any set of basic instructions into more complex ones: sequence: first do this, then do that; selection: IF such-and-such is the case, THEN do this, ELSE do that; repetition: WHILE such-and-such is the case, DO this. The three rules of Boehm's and Jacopini's insight can be further simplified with the use of goto (which means it is more elementary than structured programming). | Computing science | 0.831837 |
3,157 | It has since been argued that computer science can be classified as an empirical science since it makes use of empirical testing to evaluate the correctness of programs, but a problem remains in defining the laws and theorems of computer science (if any exist) and defining the nature of experiments in computer science. Proponents of classifying computer science as an engineering discipline argue that the reliability of computational systems is investigated in the same way as bridges in civil engineering and airplanes in aerospace engineering. They also argue that while empirical sciences observe what presently exists, computer science observes what is possible to exist and while scientists discover laws from observation, no proper laws have been found in computer science and it is instead concerned with creating phenomena.Proponents of classifying computer science as a mathematical discipline argue that computer programs are physical realizations of mathematical entities and programs can be deductively reasoned through mathematical formal methods. Computer scientists Edsger W. Dijkstra and Tony Hoare regard instructions for computer programs as mathematical sentences and interpret formal semantics for programming languages as mathematical axiomatic systems. | Computing science | 0.831837 |
3,158 | Despite the word "science" in its name, there is debate over whether or not computer science is a discipline of science, mathematics, or engineering. Allen Newell and Herbert A. Simon argued in 1975, Computer science is an empirical discipline. We would have called it an experimental science, but like astronomy, economics, and geology, some of its unique forms of observation and experience do not fit a narrow stereotype of the experimental method. | Computing science | 0.831837 |
3,159 | Computer security is a branch of computer technology with the objective of protecting information from unauthorized access, disruption, or modification while maintaining the accessibility and usability of the system for its intended users. Historical cryptography is the art of writing and deciphering secret messages. Modern cryptography is the scientific study of problems relating to distributed computations that can be attacked. Technologies studied in modern cryptography include symmetric and asymmetric encryption, digital signatures, cryptographic hash functions, key-agreement protocols, blockchain, zero-knowledge proofs, and garbled circuits. | Computing science | 0.831837 |
3,160 | Artificial intelligence and machine learning aim to synthesize goal-orientated processes such as problem-solving, decision-making, environmental adaptation, planning and learning found in humans and animals. Within artificial intelligence, computer vision aims to understand and process image and video data, while natural language processing aims to understand and process textual and linguistic data. The fundamental concern of computer science is determining what can and cannot be automated. The Turing Award is generally recognized as the highest distinction in computer science. | Computing science | 0.831837 |
3,161 | Human–computer interaction investigates the interfaces through which humans and computers interact, and software engineering focuses on the design and principles behind developing software. Areas such as operating systems, networks and embedded systems investigate the principles and design behind complex systems. Computer architecture describes the construction of computer components and computer-operated equipment. | Computing science | 0.831837 |
3,162 | The fields of cryptography and computer security involve studying the means for secure communication and for preventing security vulnerabilities. Computer graphics and computational geometry address the generation of images. Programming language theory considers different ways to describe computational processes, and database theory concerns the management of repositories of data. | Computing science | 0.831837 |
3,163 | Computer science is the study of computation, information, and automation. Computer science spans theoretical disciplines (such as algorithms, theory of computation, and information theory) to applied disciplines (including the design and implementation of hardware and software). Though more often considered an academic discipline, computer science is closely related to computer programming.Algorithms and data structures are central to computer science. The theory of computation concerns abstract models of computation and general classes of problems that can be solved using them. | Computing science | 0.831837 |
3,164 | Computer Science, known by its near synonyms, Computing, Computer Studies, has been taught in UK schools since the days of batch processing, mark sensitive cards and paper tape but usually to a select few students. In 1981, the BBC produced a micro-computer and classroom network and Computer Studies became common for GCE O level students (11–16-year-old), and Computer Science to A level students. Its importance was recognised, and it became a compulsory part of the National Curriculum, for Key Stage 3 & 4. In September 2014 it became an entitlement for all pupils over the age of 4.In the US, with 14,000 school districts deciding the curriculum, provision was fractured. According to a 2010 report by the Association for Computing Machinery (ACM) and Computer Science Teachers Association (CSTA), only 14 out of 50 states have adopted significant education standards for high school computer science. According to a 2021 report, only 51% of high schools in the US offer computer science.Israel, New Zealand, and South Korea have included computer science in their national secondary education curricula, and several others are following. | Computing science | 0.831837 |
3,165 | Artificial intelligence (AI) aims to or is required to synthesize goal-orientated processes such as problem-solving, decision-making, environmental adaptation, learning, and communication found in humans and animals. From its origins in cybernetics and in the Dartmouth Conference (1956), artificial intelligence research has been necessarily cross-disciplinary, drawing on areas of expertise such as applied mathematics, symbolic logic, semiotics, electrical engineering, philosophy of mind, neurophysiology, and social intelligence. AI is associated in the popular mind with robotic development, but the main field of practical application has been as an embedded component in areas of software development, which require computational understanding. The starting point in the late 1940s was Alan Turing's question "Can computers think? ", and the question remains effectively unanswered, although the Turing test is still used to assess computer output on the scale of human intelligence. But the automation of evaluative and predictive tasks has been increasingly successful as a substitute for human monitoring and intervention in domains of computer application involving complex real-world data. | Computing science | 0.831837 |
3,166 | Computer science is considered by some to have a much closer relationship with mathematics than many scientific disciplines, with some observers saying that computing is a mathematical science. Early computer science was strongly influenced by the work of mathematicians such as Kurt Gödel, Alan Turing, John von Neumann, Rózsa Péter and Alonzo Church and there continues to be a useful interchange of ideas between the two fields in areas such as mathematical logic, category theory, domain theory, and algebra.The relationship between computer science and software engineering is a contentious issue, which is further muddied by disputes over what the term "software engineering" means, and how computer science is defined. David Parnas, taking a cue from the relationship between other engineering and science disciplines, has claimed that the principal focus of computer science is studying the properties of computation in general, while the principal focus of software engineering is the design of specific computations to achieve practical goals, making the two separate but complementary disciplines.The academic, political, and funding aspects of computer science tend to depend on whether a department is formed with a mathematical emphasis or with an engineering emphasis. Computer science departments with a mathematics emphasis and with a numerical orientation consider alignment with computational science. Both types of departments tend to make efforts to bridge the field educationally if not across all research. | Computing science | 0.831837 |
3,167 | For example, the study of computer hardware is usually considered part of computer engineering, while the study of commercial computer systems and their deployment is often called information technology or information systems. However, there has been exchange of ideas between the various computer-related disciplines. Computer science research also often intersects other disciplines, such as cognitive science, linguistics, mathematics, physics, biology, Earth science, statistics, philosophy, and logic. | Computing science | 0.831837 |
3,168 | "In the U.S., however, informatics is linked with applied computing, or computing in the context of another domain. "A folkloric quotation, often attributed to—but almost certainly not first formulated by—Edsger Dijkstra, states that "computer science is no more about computers than astronomy is about telescopes." The design and deployment of computers and computer systems is generally considered the province of disciplines other than computer science. | Computing science | 0.831837 |
3,169 | An alternative term, also proposed by Naur, is data science; this is now used for a multi-disciplinary field of data analysis, including statistics and databases. In the early days of computing, a number of terms for the practitioners of the field of computing were suggested in the Communications of the ACM—turingineer, turologist, flow-charts-man, applied meta-mathematician, and applied epistemologist. Three months later in the same journal, comptologist was suggested, followed next year by hypologist. | Computing science | 0.831837 |
3,170 | Despite its name, a significant amount of computer science does not involve the study of computers themselves. Because of this, several alternative names have been proposed. Certain departments of major universities prefer the term computing science, to emphasize precisely that difference. | Computing science | 0.831837 |
3,171 | Although first proposed in 1956, the term "computer science" appears in a 1959 article in Communications of the ACM, in which Louis Fein argues for the creation of a Graduate School in Computer Sciences analogous to the creation of Harvard Business School in 1921. Louis justifies the name by arguing that, like management science, the subject is applied and interdisciplinary in nature, while having the characteristics typical of an academic discipline. His efforts, and those of others such as numerical analyst George Forsythe, were rewarded: universities went on to create such departments, starting with Purdue in 1962. | Computing science | 0.831837 |
3,172 | A number of computer scientists have argued for the distinction of three separate paradigms in computer science. Peter Wegner argued that those paradigms are science, technology, and mathematics. Peter Denning's working group argued that they are theory, abstraction (modeling), and design. Amnon H. Eden described them as the "rationalist paradigm" (which treats computer science as a branch of mathematics, which is prevalent in theoretical computer science, and mainly employs deductive reasoning), the "technocratic paradigm" (which might be found in engineering approaches, most prominently in software engineering), and the "scientific paradigm" (which approaches computer-related artifacts from the empirical perspective of natural sciences, identifiable in some branches of artificial intelligence). Computer science focuses on methods involved in design, specification, programming, verification, implementation and testing of human-made computing systems. | Computing science | 0.831837 |
3,173 | For industrial use, tool support is required. However, the high cost of using formal methods means that they are usually only used in the development of high-integrity and life-critical systems, where safety or security is of utmost importance. Formal methods are best described as the application of a fairly broad variety of theoretical computer science fundamentals, in particular logic calculi, formal languages, automata theory, and program semantics, but also type systems and algebraic data types to problems in software and hardware specification and verification. | Computing science | 0.831837 |
3,174 | The use of formal methods for software and hardware design is motivated by the expectation that, as in other engineering disciplines, performing appropriate mathematical analysis can contribute to the reliability and robustness of a design. They form an important theoretical underpinning for software engineering, especially where safety or security is involved. Formal methods are a useful adjunct to software testing since they help avoid errors and can also give a framework for testing. | Computing science | 0.831837 |
3,175 | Programming language theory is a branch of computer science that deals with the design, implementation, analysis, characterization, and classification of programming languages and their individual features. It falls within the discipline of computer science, both depending on and affecting mathematics, software engineering, and linguistics. It is an active research area, with numerous dedicated academic journals. Formal methods are a particular kind of mathematically based technique for the specification, development and verification of software and hardware systems. | Computing science | 0.831837 |
3,176 | Cooperation between agents – in this case, algorithms and humans – depends on trust. If humans are to accept algorithmic prescriptions, they need to trust them. Incompleteness in formal trust criteria is a barrier to optimization. | Interpretability (machine learning) | 0.831812 |
3,177 | Scholars have suggested that explainability in AI should be considered a goal secondary to AI effectiveness, and that encouraging the exclusive development of XAI may limit the functionality of AI more broadly. Critiques of XAI rely on developed concepts of mechanistic and empiric reasoning from evidence-based medicine to suggest that AI technologies can be clinically validated even when their function cannot be understood by their operators.Moreover, XAI systems have primarily focused on making AI systems understandable to AI practitioners rather than end users, and their results on user perceptions of these systems have been somewhat fragmented. Some researchers advocate the use of inherently interpretable machine learning models, rather than using post-hoc explanations in which a second model is created to explain the first. This is partly because post-hoc models increase the complexity in a decision pathway and partly because it is often unclear how faithfully a post-hoc explanation can mimic the computations of an entirely separate model. | Interpretability (machine learning) | 0.831812 |
3,178 | Senator Robert Byrd, Democrat from West Virginia, joined critics of reform mathematics on the floor of the senate by dubbing Addison-Wesley Secondary Math: An Integrated Approach: Focus on Algebra the "Texas rainforest algebra book". It had received an "F" grade on a report card produced by Mathematically Correct, a back-to-basics group, who claimed that it had no algebraic content on the first hundred pages. | Rainforest algebra | 0.831799 |
3,179 | The multidisciplinary nature of learning engineering creates challenges. The problems that learning engineering attempts to solve often require expertise in diverse fields such as software engineering, instructional design, domain knowledge, pedagogy/andragogy, psychometrics, learning sciences, data science, and systems engineering. In some cases, an individual Learning Engineer with expertise in multiple disciplines might be sufficient. However, learning engineering problems often exceed any one person’s ability to solve. A 2021 convening of thirty learning engineers produced recommendations that key challenges and opportunities for the future of the field involve enhancing R&D infrastructure, supporting domain-based education research, developing components for reuse across learning systems, enhancing human-computer systems, better engineering implementation in schools, improving advising, optimizing for the long-term instead of short-term, supporting 21st-century skills, improved support for learner engagement, and designing algorithms for equity. | Learning engineering | 0.83179 |
3,180 | Herbert Simon, a cognitive psychologist and economist, first coined the term learning engineering in 1967. However, associations between the two terms learning and engineering began emerging earlier, in the 1940s and as early as the 1920s. Simon argued that the social sciences, including the field of education, should be approached with the same kind of mathematical principles as other fields like physics and engineering.Simon’s ideas about learning engineering continued to reverberate at Carnegie Mellon University, but the term did not catch on until businessman Bror Saxberg began marketing it in 2014 after visiting Carnegie Mellon University and the Pittsburgh Science of Learning Center, or LearnLab for short. Bror Saxberg brought his team from the for-profit education company, Kaplan, to visit CMU. The team went back to Kaplan with what we now call learning engineering to enhance, optimize, test, and sell their educational products. Bror Saxberg would later co-write with Frederick Hess, founder of the American Enterprise Institute's Conservative Education Reform Network, the 2014 book using the term learning engineering. | Learning engineering | 0.83179 |
3,181 | In 2019, PBS partnered with Kaggle to create the 2019 Data Science Bowl. The DataScience Bowl sought machine learning insights from researchers and developers, specifically into how digital media can better facilitate early-childhood STEM learning outcomes. Datasets, like those hosted by Kaggle PBS and Carnegie Learning, allow researchers to gather information and derive conclusions about student outcomes. These insights help predict student performance in courses and exams. | Learning engineering | 0.83179 |
3,182 | Datasets provide the raw material that researchers use to formulate educational insights. For example, Carnegie Mellon University hosts a large volume of learning interaction data in LearnLab's DataShop. Their datasets range from sources like Intelligent Writing Tutors to Chinese tone studies to data from Carnegie Learning’s MATHia platform. Kaggle, a hub for programmers and open source data, regularly hosts machine learning competitions. | Learning engineering | 0.83179 |
3,183 | Learning engineering teams require expertise associated with the content that learners will learn, the targeted learners themselves, the venues in which learning is expected to happen, educational practice, software engineering, and sometimes even more. Learning engineering teams employ an iterative design process for supporting and improving learning. Initial designs are informed by findings from the learning sciences. | Learning engineering | 0.83179 |
3,184 | Mixtures are not limited in either their number of substances or the amounts of those substances, though in a homogeneous mixture the solute-to-solvent proportion can only reach a certain point before the mixture separates and becomes heterogeneous. A homogeneous mixture is characterized by uniform dispersion of its constituent substances throughout; the substances exist in equal proportion everywhere within the mixture. Differently put, a homogeneous mixture will be the same no matter from where in the mixture it is sampled. For example, if a solid-liquid solution is divided into two halves of equal volume, the halves will contain equal amounts of both the liquid medium and dissolved solid (solvent and solute). In physical chemistry and materials science, "homogeneous" more narrowly describes substances and mixtures which are in a single phase. | Homogeneous and heterogeneous mixtures | 0.831783 |
3,185 | Target proteins are functional biomolecules that are addressed and controlled by biologically active compounds. They are used in the processes of transduction, transformation and conjugation. The identification of target proteins, the investigation of signal transduction processes and the understanding of their interaction with ligands are key elements of modern biomedical research. Since the interaction with target proteins is the molecular origin of most drugs, their particular importance for molecular biology, molecular pharmacy and pharmaceutical sciences is obvious. | Target protein | 0.831765 |
3,186 | In mathematics, the term maximal subgroup is used to mean slightly different things in different areas of algebra. In group theory, a maximal subgroup H of a group G is a proper subgroup, such that no proper subgroup K contains H strictly. In other words, H is a maximal element of the partially ordered set of subgroups of G that are not equal to G. Maximal subgroups are of interest because of their direct connection with primitive permutation representations of G. They are also much studied for the purposes of finite group theory: see for example Frattini subgroup, the intersection of the maximal subgroups. In semigroup theory, a maximal subgroup of a semigroup S is a subgroup (that is, a subsemigroup which forms a group under the semigroup operation) of S which is not properly contained in another subgroup of S. Notice that, here, there is no requirement that a maximal subgroup be proper, so if S is in fact a group then its unique maximal subgroup (as a semigroup) is S itself. Considering subgroups, and in particular maximal subgroups, of semigroups often allows one to apply group-theoretic techniques in semigroup theory. There is a one-to-one correspondence between idempotent elements of a semigroup and maximal subgroups of the semigroup: each idempotent element is the identity element of a unique maximal subgroup. | Maximal subgroup | 0.831765 |
3,187 | Similarly, a normal subgroup N of G is said to be a maximal normal subgroup (or maximal proper normal subgroup) of G if N < G and there is no normal subgroup K of G such that N < K < G. We have the following theorem: Theorem: A normal subgroup N of a group G is a maximal normal subgroup if and only if the quotient G/N is simple. | Maximal subgroup | 0.831765 |
3,188 | If an integer relation is found, this suggests a possible closed-form expression for the original series, product or integral. This conjecture can then be validated by formal algebraic methods. The higher the precision to which the inputs to the algorithm are known, the greater the level of confidence that any integer relation that is found is not just a numerical artifact. | Integer relation algorithm | 0.831753 |
3,189 | Integer relation algorithms have numerous applications. The first application is to determine whether a given real number x is likely to be algebraic, by searching for an integer relation between a set of powers of x {1, x, x2, ..., xn}. The second application is to search for an integer relation between a real number x and a set of mathematical constants such as e, π and ln(2), which will lead to an expression for x as a linear combination of these constants. A typical approach in experimental mathematics is to use numerical methods and arbitrary precision arithmetic to find an approximate value for an infinite series, infinite product or an integral to a high degree of precision (usually at least 100 significant figures), and then use an integer relation algorithm to search for an integer relation between this value and a set of mathematical constants. | Integer relation algorithm | 0.831753 |
3,190 | A Bertrand curve is a regular curve in R 3 {\displaystyle \mathbb {R} ^{3}} with the additional property that there is a second curve in R 3 {\displaystyle \mathbb {R} ^{3}} such that the principal normal vectors to these two curves are identical at each corresponding point. In other words, if γ1(t) and γ2(t) are two curves in R 3 {\displaystyle \mathbb {R} ^{3}} such that for any t, the two principal normals N1(t), N2(t) are equal, then γ1 and γ2 are Bertrand curves, and γ2 is called the Bertrand mate of γ1. We can write γ2(t) = γ1(t) + r N1(t) for some constant r.According to problem 25 in Kühnel's "Differential Geometry Curves – Surfaces – Manifolds", it is also true that two Bertrand curves that do not lie in the same two-dimensional plane are characterized by the existence of a linear relation a κ(t) + b τ(t) = 1 where κ(t) and τ(t) are the curvature and torsion of γ1(t) and a and b are real constants with a ≠ 0. | Unit speed parametrization | 0.831745 |
3,191 | An axiomatization of propositional calculus is a set of tautologies called axioms and one or more inference rules for producing new tautologies from old. A proof in an axiom system A is a finite nonempty sequence of propositions each of which is either an instance of an axiom of A or follows by some rule of A from propositions appearing earlier in the proof (thereby disallowing circular reasoning). The last proposition is the theorem proved by the proof. Every nonempty initial segment of a proof is itself a proof, whence every proposition in a proof is itself a theorem. An axiomatization is sound when every theorem is a tautology, and complete when every tautology is a theorem. | Boolean algebra (logic) | 0.831744 |
3,192 | Propositional calculus restricts attention to abstract propositions, those built up from propositional variables using Boolean operations. Instantiation is still possible within propositional calculus, but only by instantiating propositional variables by abstract propositions, such as instantiating Q by Q→P in P→(Q→P) to yield the instance P→((Q→P)→P). (The availability of instantiation as part of the machinery of propositional calculus avoids the need for metavariables within the language of propositional calculus, since ordinary propositional variables can be considered within the language to denote arbitrary propositions. The metavariables themselves are outside the reach of instantiation, not being part of the language of propositional calculus but rather part of the same language for talking about it that this sentence is written in, where we need to be able to distinguish propositional variables and their instantiations as being distinct syntactic entities.) | Boolean algebra (logic) | 0.831744 |
3,193 | One motivating application of propositional calculus is the analysis of propositions and deductive arguments in natural language. Whereas the proposition "if x = 3 then x+1 = 4" depends on the meanings of such symbols as + and 1, the proposition "if x = 3 then x = 3" does not; it is true merely by virtue of its structure, and remains true whether "x = 3" is replaced by "x = 4" or "the moon is made of green cheese." The generic or abstract form of this tautology is "if P then P", or in the language of Boolean algebra, "P → P".Replacing P by x = 3 or any other proposition is called instantiation of P by that proposition. The result of instantiating P in an abstract proposition is called an instance of the proposition. | Boolean algebra (logic) | 0.831744 |
3,194 | Therefore, it was concluded that clusters of these specific numbers of rare gas atoms dominate due to their exceptional stability. The concept was also successfully applied to explain the monodispersed occurrence of thiolate-protected gold clusters; here the outstanding stability of specific cluster sizes is connected with their respective electronic configuration. The term magic numbers is also used in the field of nuclear physics. In this context, magic numbers refer to a specific number of protons or neutrons that forms complete nucleon shells. | Magic number (chemistry) | 0.831739 |
3,195 | The concept of magic numbers in the field of chemistry refers to a specific property (such as stability) for only certain representatives among a distribution of structures. It was first recognized by inspecting the intensity of mass-spectrometric signals of rare gas cluster ions. In case a gas condenses into clusters of atoms, the number of atoms in these clusters that are most likely to form varies between a few and hundreds. However, there are peaks at specific cluster sizes, deviating from a pure statistical distribution. | Magic number (chemistry) | 0.831739 |
3,196 | Stem cell genomics analyzes the genomes of stem cells. Currently, this field is rapidly expanding due to the dramatic decrease in the cost of sequencing genomes. The study of stem cell genomics has wide reaching implications in the study of stem cell biology and possible therapeutic usages of stem cells. Application of research in this field could lead to drug discovery and information on diseases by the molecular characterization of the pluripotent stem cell through DNA and transcriptome sequencing and looking at the epigenetic changes of stem cells and subsequent products. | Stem cell genomics | 0.831739 |
3,197 | Molecules and Cells is a peer-reviewed open access scientific journal of molecular and cellular biology. It was established in 1990 as the official publication of the Korean Society for Molecular and Cellular Biology, and is currently edited by Rho Hyun Seong (Seoul National University). From 1992 to 2013, the journal was published by Springer, but as of 2014 is published by the Society directly on a monthly basis. | Molecules and Cells | 0.831732 |
3,198 | Permeability is a property of porous materials that is an indication of the ability for fluids (gas or liquid) to flow through them. Fluids can more easily flow through a material with high permeability than one with low permeability. The permeability of a medium is related to the porosity, but also to the shapes of the pores in the medium and their level of connectedness. Fluid flows can also be influenced in different lithological settings by brittle deformation of rocks in fault zones; the mechanisms by which this occurs are the subject of fault zone hydrogeology. Permeability is also affected by the pressure inside a material. | Soil permeability | 0.831729 |
3,199 | For example, Marshall Hall used the near-field of order 9 given above to produce a Hall plane, the first of a sequence of such planes based on Dickson near-fields of order the square of a prime. In 1971 T. G. Room and P.B. Kirkpatrick provided an alternative development.There are numerous other applications, mostly to geometry. A more recent application of near-fields is in the construction of ciphers for data-encryption, such as Hill ciphers. | Near-field (mathematics) | 0.831728 |
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