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1 application complexity
оценивается обычно в тысячах строк исходного текста; влияет на планирование работ и выбор инструментальных средств для реализации проектаАнгло-русский толковый словарь терминов и сокращений по ВТ, Интернету и программированию. > application complexity
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2 application complexity
Программирование: сложность приложения (оценивается обычно в тысячах строк исходного текста; влияет на планирование работ и выбор инструментальных средств для реализации проекта)Универсальный англо-русский словарь > application complexity
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3 application
= app1) (см. тж. software application) - приложение, прикладная программазаконченная прикладная программа или пакет, которые обеспечивают пользователю решение определённой задачи, например электронная таблица или текстовый процессор. Типичные словосочетания - Windows application (Windows-приложение), Linux application (Linux-приложение). Термин широко распространился, заменив термин application program в связи с тем, что приложение подразумевает работу с ним посредством графического интерфейса пользователя (GUI), а не из командной строкиAnt:см. тж. active application, application certification, application complexity, application context, application designer, application developer, application development, application domain, application framework, application gateway, application generator, application heap, application integration, application layer, application mining, application package, application partitioning, application profiling, application programmer, application server, application software, application suite, application window, client application, command line, console application, consumer application, critical application, database application, distributed application, embedded applications, engineering applications, enterprise application, government application, graphics application, householding application, information application, legacy application, managed application, meta-application, mobile application, multimedia application, multimodal application, multithreaded application, multi-tier application, network application, notification application, rugged application, scientific application, server application, software, target application, web application2) см. hardware application3) применение, использование, употребление; применимостьнапример, for military applications - для военных применений4) заявление, заявканапример, partial application - частичное присваивание значений (если значения присвоены не всем аргументам)см. тж. argument6) наложение; нанесение; аппликациясм. тж. compositingАнгло-русский толковый словарь терминов и сокращений по ВТ, Интернету и программированию. > application
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4 clock speed
частота синхронизирующих колебаний, которые выдаёт тактовый генератор компьютера. Чаще всего термин относится к частоте работы ЦП. Измеряется в мегагерцах, а у современных ЦП - в гигагерцах.As application complexity continues to grow we have reached a limit on increasing performance by merely scaling clock speed. — Поскольку сложность приложений продолжает расти, мы уже исчерпали возможности повышения производительности путём простого увеличения тактовой частоты.
Syn:см. тж. core speedАнгло-русский толковый словарь терминов и сокращений по ВТ, Интернету и программированию. > clock speed
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5 abstraction
1) широко используемый в моделировании принцип выделения только главных свойств и характеристик проблемы и игнорирования аспектов, не оказывающих существенного влияния на её решение."Structuring your application into levels of abstraction is the first step towards controlling complexity" (Bartosz Milewski). — Структуризация вашего приложения в соответствии с уровнями абстракции - первый шаг к реализации управления сложностью см. тж. abstraction layer, abstraction mechanism, control complexity, data abstraction, procedural abstraction
2) в ООП - процесс создания суперкласса путём выделения общих свойств или общих характеристик из объектов или конкретных классов3) набор существенных характеристик объекта, отличающих его от других объектов4) в ЯВУ - скрытие деталей реализации путём конструирования "ящика" вокруг них с разрешением ограниченной проверки его содержимогоАнгло-русский толковый словарь терминов и сокращений по ВТ, Интернету и программированию. > abstraction
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6 Artificial Intelligence
In my opinion, none of [these programs] does even remote justice to the complexity of human mental processes. Unlike men, "artificially intelligent" programs tend to be single minded, undistractable, and unemotional. (Neisser, 1967, p. 9)Future progress in [artificial intelligence] will depend on the development of both practical and theoretical knowledge.... As regards theoretical knowledge, some have sought a unified theory of artificial intelligence. My view is that artificial intelligence is (or soon will be) an engineering discipline since its primary goal is to build things. (Nilsson, 1971, pp. vii-viii)Most workers in AI [artificial intelligence] research and in related fields confess to a pronounced feeling of disappointment in what has been achieved in the last 25 years. Workers entered the field around 1950, and even around 1960, with high hopes that are very far from being realized in 1972. In no part of the field have the discoveries made so far produced the major impact that was then promised.... In the meantime, claims and predictions regarding the potential results of AI research had been publicized which went even farther than the expectations of the majority of workers in the field, whose embarrassments have been added to by the lamentable failure of such inflated predictions....When able and respected scientists write in letters to the present author that AI, the major goal of computing science, represents "another step in the general process of evolution"; that possibilities in the 1980s include an all-purpose intelligence on a human-scale knowledge base; that awe-inspiring possibilities suggest themselves based on machine intelligence exceeding human intelligence by the year 2000 [one has the right to be skeptical]. (Lighthill, 1972, p. 17)4) Just as Astronomy Succeeded Astrology, the Discovery of Intellectual Processes in Machines Should Lead to a Science, EventuallyJust as astronomy succeeded astrology, following Kepler's discovery of planetary regularities, the discoveries of these many principles in empirical explorations on intellectual processes in machines should lead to a science, eventually. (Minsky & Papert, 1973, p. 11)5) Problems in Machine Intelligence Arise Because Things Obvious to Any Person Are Not Represented in the ProgramMany problems arise in experiments on machine intelligence because things obvious to any person are not represented in any program. One can pull with a string, but one cannot push with one.... Simple facts like these caused serious problems when Charniak attempted to extend Bobrow's "Student" program to more realistic applications, and they have not been faced up to until now. (Minsky & Papert, 1973, p. 77)What do we mean by [a symbolic] "description"? We do not mean to suggest that our descriptions must be made of strings of ordinary language words (although they might be). The simplest kind of description is a structure in which some features of a situation are represented by single ("primitive") symbols, and relations between those features are represented by other symbols-or by other features of the way the description is put together. (Minsky & Papert, 1973, p. 11)[AI is] the use of computer programs and programming techniques to cast light on the principles of intelligence in general and human thought in particular. (Boden, 1977, p. 5)The word you look for and hardly ever see in the early AI literature is the word knowledge. They didn't believe you have to know anything, you could always rework it all.... In fact 1967 is the turning point in my mind when there was enough feeling that the old ideas of general principles had to go.... I came up with an argument for what I called the primacy of expertise, and at the time I called the other guys the generalists. (Moses, quoted in McCorduck, 1979, pp. 228-229)9) Artificial Intelligence Is Psychology in a Particularly Pure and Abstract FormThe basic idea of cognitive science is that intelligent beings are semantic engines-in other words, automatic formal systems with interpretations under which they consistently make sense. We can now see why this includes psychology and artificial intelligence on a more or less equal footing: people and intelligent computers (if and when there are any) turn out to be merely different manifestations of the same underlying phenomenon. Moreover, with universal hardware, any semantic engine can in principle be formally imitated by a computer if only the right program can be found. And that will guarantee semantic imitation as well, since (given the appropriate formal behavior) the semantics is "taking care of itself" anyway. Thus we also see why, from this perspective, artificial intelligence can be regarded as psychology in a particularly pure and abstract form. The same fundamental structures are under investigation, but in AI, all the relevant parameters are under direct experimental control (in the programming), without any messy physiology or ethics to get in the way. (Haugeland, 1981b, p. 31)There are many different kinds of reasoning one might imagine:Formal reasoning involves the syntactic manipulation of data structures to deduce new ones following prespecified rules of inference. Mathematical logic is the archetypical formal representation. Procedural reasoning uses simulation to answer questions and solve problems. When we use a program to answer What is the sum of 3 and 4? it uses, or "runs," a procedural model of arithmetic. Reasoning by analogy seems to be a very natural mode of thought for humans but, so far, difficult to accomplish in AI programs. The idea is that when you ask the question Can robins fly? the system might reason that "robins are like sparrows, and I know that sparrows can fly, so robins probably can fly."Generalization and abstraction are also natural reasoning process for humans that are difficult to pin down well enough to implement in a program. If one knows that Robins have wings, that Sparrows have wings, and that Blue jays have wings, eventually one will believe that All birds have wings. This capability may be at the core of most human learning, but it has not yet become a useful technique in AI.... Meta- level reasoning is demonstrated by the way one answers the question What is Paul Newman's telephone number? You might reason that "if I knew Paul Newman's number, I would know that I knew it, because it is a notable fact." This involves using "knowledge about what you know," in particular, about the extent of your knowledge and about the importance of certain facts. Recent research in psychology and AI indicates that meta-level reasoning may play a central role in human cognitive processing. (Barr & Feigenbaum, 1981, pp. 146-147)Suffice it to say that programs already exist that can do things-or, at the very least, appear to be beginning to do things-which ill-informed critics have asserted a priori to be impossible. Examples include: perceiving in a holistic as opposed to an atomistic way; using language creatively; translating sensibly from one language to another by way of a language-neutral semantic representation; planning acts in a broad and sketchy fashion, the details being decided only in execution; distinguishing between different species of emotional reaction according to the psychological context of the subject. (Boden, 1981, p. 33)Can the synthesis of Man and Machine ever be stable, or will the purely organic component become such a hindrance that it has to be discarded? If this eventually happens-and I have... good reasons for thinking that it must-we have nothing to regret and certainly nothing to fear. (Clarke, 1984, p. 243)The thesis of GOFAI... is not that the processes underlying intelligence can be described symbolically... but that they are symbolic. (Haugeland, 1985, p. 113)14) Artificial Intelligence Provides a Useful Approach to Psychological and Psychiatric Theory FormationIt is all very well formulating psychological and psychiatric theories verbally but, when using natural language (even technical jargon), it is difficult to recognise when a theory is complete; oversights are all too easily made, gaps too readily left. This is a point which is generally recognised to be true and it is for precisely this reason that the behavioural sciences attempt to follow the natural sciences in using "classical" mathematics as a more rigorous descriptive language. However, it is an unfortunate fact that, with a few notable exceptions, there has been a marked lack of success in this application. It is my belief that a different approach-a different mathematics-is needed, and that AI provides just this approach. (Hand, quoted in Hand, 1985, pp. 6-7)We might distinguish among four kinds of AI.Research of this kind involves building and programming computers to perform tasks which, to paraphrase Marvin Minsky, would require intelligence if they were done by us. Researchers in nonpsychological AI make no claims whatsoever about the psychological realism of their programs or the devices they build, that is, about whether or not computers perform tasks as humans do.Research here is guided by the view that the computer is a useful tool in the study of mind. In particular, we can write computer programs or build devices that simulate alleged psychological processes in humans and then test our predictions about how the alleged processes work. We can weave these programs and devices together with other programs and devices that simulate different alleged mental processes and thereby test the degree to which the AI system as a whole simulates human mentality. According to weak psychological AI, working with computer models is a way of refining and testing hypotheses about processes that are allegedly realized in human minds.... According to this view, our minds are computers and therefore can be duplicated by other computers. Sherry Turkle writes that the "real ambition is of mythic proportions, making a general purpose intelligence, a mind." (Turkle, 1984, p. 240) The authors of a major text announce that "the ultimate goal of AI research is to build a person or, more humbly, an animal." (Charniak & McDermott, 1985, p. 7)Research in this field, like strong psychological AI, takes seriously the functionalist view that mentality can be realized in many different types of physical devices. Suprapsychological AI, however, accuses strong psychological AI of being chauvinisticof being only interested in human intelligence! Suprapsychological AI claims to be interested in all the conceivable ways intelligence can be realized. (Flanagan, 1991, pp. 241-242)16) Determination of Relevance of Rules in Particular ContextsEven if the [rules] were stored in a context-free form the computer still couldn't use them. To do that the computer requires rules enabling it to draw on just those [ rules] which are relevant in each particular context. Determination of relevance will have to be based on further facts and rules, but the question will again arise as to which facts and rules are relevant for making each particular determination. One could always invoke further facts and rules to answer this question, but of course these must be only the relevant ones. And so it goes. It seems that AI workers will never be able to get started here unless they can settle the problem of relevance beforehand by cataloguing types of context and listing just those facts which are relevant in each. (Dreyfus & Dreyfus, 1986, p. 80)Perhaps the single most important idea to artificial intelligence is that there is no fundamental difference between form and content, that meaning can be captured in a set of symbols such as a semantic net. (G. Johnson, 1986, p. 250)Artificial intelligence is based on the assumption that the mind can be described as some kind of formal system manipulating symbols that stand for things in the world. Thus it doesn't matter what the brain is made of, or what it uses for tokens in the great game of thinking. Using an equivalent set of tokens and rules, we can do thinking with a digital computer, just as we can play chess using cups, salt and pepper shakers, knives, forks, and spoons. Using the right software, one system (the mind) can be mapped into the other (the computer). (G. Johnson, 1986, p. 250)19) A Statement of the Primary and Secondary Purposes of Artificial IntelligenceThe primary goal of Artificial Intelligence is to make machines smarter.The secondary goals of Artificial Intelligence are to understand what intelligence is (the Nobel laureate purpose) and to make machines more useful (the entrepreneurial purpose). (Winston, 1987, p. 1)The theoretical ideas of older branches of engineering are captured in the language of mathematics. We contend that mathematical logic provides the basis for theory in AI. Although many computer scientists already count logic as fundamental to computer science in general, we put forward an even stronger form of the logic-is-important argument....AI deals mainly with the problem of representing and using declarative (as opposed to procedural) knowledge. Declarative knowledge is the kind that is expressed as sentences, and AI needs a language in which to state these sentences. Because the languages in which this knowledge usually is originally captured (natural languages such as English) are not suitable for computer representations, some other language with the appropriate properties must be used. It turns out, we think, that the appropriate properties include at least those that have been uppermost in the minds of logicians in their development of logical languages such as the predicate calculus. Thus, we think that any language for expressing knowledge in AI systems must be at least as expressive as the first-order predicate calculus. (Genesereth & Nilsson, 1987, p. viii)21) Perceptual Structures Can Be Represented as Lists of Elementary PropositionsIn artificial intelligence studies, perceptual structures are represented as assemblages of description lists, the elementary components of which are propositions asserting that certain relations hold among elements. (Chase & Simon, 1988, p. 490)Artificial intelligence (AI) is sometimes defined as the study of how to build and/or program computers to enable them to do the sorts of things that minds can do. Some of these things are commonly regarded as requiring intelligence: offering a medical diagnosis and/or prescription, giving legal or scientific advice, proving theorems in logic or mathematics. Others are not, because they can be done by all normal adults irrespective of educational background (and sometimes by non-human animals too), and typically involve no conscious control: seeing things in sunlight and shadows, finding a path through cluttered terrain, fitting pegs into holes, speaking one's own native tongue, and using one's common sense. Because it covers AI research dealing with both these classes of mental capacity, this definition is preferable to one describing AI as making computers do "things that would require intelligence if done by people." However, it presupposes that computers could do what minds can do, that they might really diagnose, advise, infer, and understand. One could avoid this problematic assumption (and also side-step questions about whether computers do things in the same way as we do) by defining AI instead as "the development of computers whose observable performance has features which in humans we would attribute to mental processes." This bland characterization would be acceptable to some AI workers, especially amongst those focusing on the production of technological tools for commercial purposes. But many others would favour a more controversial definition, seeing AI as the science of intelligence in general-or, more accurately, as the intellectual core of cognitive science. As such, its goal is to provide a systematic theory that can explain (and perhaps enable us to replicate) both the general categories of intentionality and the diverse psychological capacities grounded in them. (Boden, 1990b, pp. 1-2)Because the ability to store data somewhat corresponds to what we call memory in human beings, and because the ability to follow logical procedures somewhat corresponds to what we call reasoning in human beings, many members of the cult have concluded that what computers do somewhat corresponds to what we call thinking. It is no great difficulty to persuade the general public of that conclusion since computers process data very fast in small spaces well below the level of visibility; they do not look like other machines when they are at work. They seem to be running along as smoothly and silently as the brain does when it remembers and reasons and thinks. On the other hand, those who design and build computers know exactly how the machines are working down in the hidden depths of their semiconductors. Computers can be taken apart, scrutinized, and put back together. Their activities can be tracked, analyzed, measured, and thus clearly understood-which is far from possible with the brain. This gives rise to the tempting assumption on the part of the builders and designers that computers can tell us something about brains, indeed, that the computer can serve as a model of the mind, which then comes to be seen as some manner of information processing machine, and possibly not as good at the job as the machine. (Roszak, 1994, pp. xiv-xv)The inner workings of the human mind are far more intricate than the most complicated systems of modern technology. Researchers in the field of artificial intelligence have been attempting to develop programs that will enable computers to display intelligent behavior. Although this field has been an active one for more than thirty-five years and has had many notable successes, AI researchers still do not know how to create a program that matches human intelligence. No existing program can recall facts, solve problems, reason, learn, and process language with human facility. This lack of success has occurred not because computers are inferior to human brains but rather because we do not yet know in sufficient detail how intelligence is organized in the brain. (Anderson, 1995, p. 2)Historical dictionary of quotations in cognitive science > Artificial Intelligence
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7 factor
2) фактор3) показатель•factor of earthing — коэффициент заземленияfactor of merit — 1. критерий качества 2. добротностьfactor of quality — 1. критерий качества 2. добротностьfactor of safety — 1. коэффициент запаса (прочности), запас прочности 2. коэффициент (фактор) безопасности 3. коэффициент надёжностиfactor of safety against overturning — коэффициент запаса устойчивости против опрокидывания ( при расчёте подпорных стенок)factor of safety against sliding — коэффициент запаса устойчивости против плоского сдвига по основанию ( при расчёте подпорных стенок)factor of safety against ultimate stress — коэффициент запаса прочности по пределу прочности-
2T pulse K factor
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absorption factor
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acceleration factor
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accumulation factor
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acoustic insulation factor
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acoustic reduction factor
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acoustic reflection factor
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acoustical absorption factor
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activity factor
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additional secondary phase factor
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additional secondary factor
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aerodrome utilization factor
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aircraft acceleration factor
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aircraft load factor
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aircraft safety factor
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aircraft usability factor
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amplification factor
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amplitude factor
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anisotropy factor
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annual growth factor
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annual plant factor
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anthropogenic factor
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aperture shape factor
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application factor
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array factor
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ASTM stability factor
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atmospheric factor
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atomic factor
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attenuation factor
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automatic scale factor
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availability factor
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available heat factor
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available-lime factor
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average noise factor
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balance factor
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bandwidth factor
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barrier factor
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base-transport factor
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basin shape factor
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beam shape factor
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bed-formation factor
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belt differential factor
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belt factor
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belt sag factor
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biological quality factor N
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biological quality factor
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biotic factor
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blast-penetration factor
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blockage factor
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brake factor
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break-even load factor
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bulk factor
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bulking factor
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burnup factor
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calibration factor
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Callier factor
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capacitance factor
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capacity factor
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car capacity utilization factor
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cargo load factor
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catalyst carbon factor
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catalyst gas factor
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cement factor
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cementation factor
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characteristic factors
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chemotactic factor
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climatic factor
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clotting factor
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CNI factor
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coil magnification factor
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coincidence factor
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coke-hardness factor
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coke-permeability factor
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Colburo heat-transfer factor
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colicinogenic factor
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colicin factor
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comfort factor
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common factor
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compacting factor
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compensation factor
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complexity factor
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compressibility factor
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concentration factor
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confidence factor
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consumer load coincidence factor
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contrast factor
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control factor
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conversion factor
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conveyance factor
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core factor
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correction factor
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correlation factor
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coupling factor
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cover factor
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crack susceptibility factor
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crest factor
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critical stress intensity factor
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cross-modulation factor
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current amplification factor
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current amplitude factor
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current transformer correction factor
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current unbalance factor
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current waveform distortion factor
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cyclic duration factor
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damage factor
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damage severity factor
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damping factor
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daylight factor
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dc conversion factor
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decontamination factor
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defective factor
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deflection factor
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deflection uniformity factor
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degeneration factor
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degradation factor
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degree-day melting factor
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demagnetization factor
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demand factor
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depolarization factor
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derating factor
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design factor
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design load factor
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detuning factor
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deviation factor
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dielectric loss factor
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differential diffraction factor
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diffuse reflection factor
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diffuse transmission factor
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dilution factor
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dimensionless factor
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directivity factor
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discharge factor
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displacement factor
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displacement power factor
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dissipation factor
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distortion factor
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distribution factor
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diversity factor
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division factor
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dose buildup factor
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dose reduction factor
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drainage factor
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drug resistance factor
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duty cycle factor
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duty factor
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ecological factor
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edaphic factor
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effective demand factor
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effective multiplication factor
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effective-volume utilization factor
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efficiency factor
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electromechanical coupling factor
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elimination factor
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elongation factor
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emission factor
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emissivity factor
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engineering factors
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enlargement factor
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enrichment factor
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environmental factor
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etch factor
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excess air factor
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excess multiplication factor
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expansion factor
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exponential factor
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exposure factor
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external factor
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extraction factor
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extraneous factor
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F factor
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Fanning friction factor
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fatigue notch factor
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feedback factor
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field form factor
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field length factor
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field water-distribution factor
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fill factor
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filter factor
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filtration factor
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fineness factor
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flux factor
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food factor
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force factor
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form factor
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formation volume factor
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formation-resistivity factor
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formation factor
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fouling factor
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F-prime factor
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frequency factor
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frequency multiplication factor
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friction factor
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fuel factor
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fundamental factor
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gage factor
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gain factor
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gamma factor
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gas factor
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gas multiplication factor
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gas producing factor
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gas recovery factor
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gas saturation factor
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geometrical structure factor
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geometrical weighting factor
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g-factor
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grading factor
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granulation factor
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grindability factor
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growth factor
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harmonic distortion factor
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harmonic factor
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heat conductivity factor
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heat gain factor
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heat leakage factor
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heat loss factor
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heat-stretch factor
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heat-transfer factor
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host factor
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hot-channel factor
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hot-spot factor
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hull-efficiency factor
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human factor
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hysteresis factor
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improvement factor
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inductance factor
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infinite multiplication factor
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inhibitory factor
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innovation factor
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institutional factor
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integer factor
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integrating factor
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interlace factor
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intermodulation factor
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K bar factor
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Kell factor
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lamination factor
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leakage factor
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lethal factor
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light-transmission factor
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lime factor
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limit load factor
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linear expansion factor
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literal factor
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load curve irregularity factor
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load factor
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loading factor
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longitudinal load distribution factor
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Lorentz factor
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loss factor
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luminance factor
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luminosity factor
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magnetic form factor
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magnetic leakage factor
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magnetic loss factor
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magnification factor
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maximum enthalpy rise factor
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membrane swelling factor
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minimum noise factor
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mismatch factor
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mode I stress intensity factor
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mode II stress intensity factor
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mode III stress intensity factor
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modifying factor
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modulation factor
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modulus factor of reflux
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moment intensity factor
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mu factor
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multiplication factor
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multiplicity factor
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multiplying factor
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Murphree efficiency factor
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mutual coupling factor
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mutual inductance factor
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natural factor
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negative phase-sequence current factor
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negative phase-sequence voltage factor
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neutron multiplication factor
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noise factor
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nonlinearity factor
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notch concentration factor
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notch factor
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numerical factor
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obturation factor
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oil factors
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oil recovery factor
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oil saturation factor
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oil shrinkage factor
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opening mode stress intensity factor
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operating factor
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operating load factor
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operational factor
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operation factor
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optimum noise factor
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orbit burden factor
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output factor
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overcurrent factor
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overload factor
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pacing factor
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packing factor
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paratypic factor
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partial safety factor for load
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partial safety factor for material
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particle-reduction factor
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passenger load factor
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peak factor
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peak responsibility factor
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peak-load effective duration factor
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penetration factor
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performance factor
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permeability factor
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phase factor
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phase-angle correction factor
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phasor power factor
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physiographic factor
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pitch differential factor
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pitch factor
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plain-strain stress intensity factor
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plane-earth factor
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plant capacity factor
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plant-load factor
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plant-use factor
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porosity factor
-
positive phase-sequence current factor
-
positive phase-sequence voltage factor
-
potential transformer correction factor
-
powder factor
-
power factor
-
power filling factor
-
primary phase factor
-
primary factor
-
prime factor
-
proof/ultimate factor
-
propagation factor
-
propagation meteorological factor
-
propagation terrain factor
-
proportionality factor
-
proximity factor
-
pulsation factor
-
quality factor
-
R factor
-
radiance factor
-
radio-interference suppression factor
-
readiness factor
-
recombinogenic factor
-
recovery factor
-
rectification factor
-
reduction factor
-
redundancy improvement factor
-
reflection factor
-
reflectivity factor
-
refraction factor
-
refrigerating factor
-
reheat factor
-
relative loss factor
-
relative severity factor
-
release factor
-
reliability demonstration factor
-
reliability factor
-
relocation factor
-
repairability factor
-
repeatability factor
-
reservoir volume factor
-
reset factor of relay
-
resistance transfer factor
-
restorability factor
-
revenue load factor
-
ripple factor
-
risk factor
-
rolling shape factor
-
roll-off factor
-
roughness factor
-
runoff factor
-
safety factor for dropout of relay
-
safety factor for pickup of relay
-
safety factor of insulation
-
safety factor
-
sag factor
-
saturation factor
-
scale factor
-
scaling factor
-
screening factor
-
screen factor
-
secondary-electron-emission factor
-
self-transmissible factor
-
separation factor
-
service factor
-
sex factor
-
shadow factor
-
shape factor
-
sheet ratio factor
-
shielding factor
-
shield factor
-
shrinkage factor
-
signal-to-noise improvement factor
-
size factor
-
skew factor
-
slant-range correction factor
-
sliding factor
-
slip factor
-
smoothing factor
-
snagging factor
-
soap factor
-
social factor
-
socioeconomic factor
-
solubility factor
-
sound absorption factor
-
space factor of winding
-
space factor
-
spreading factor
-
squeezing factor
-
stability factor
-
stacking factor
-
stage amplification factor
-
standing-wave factor
-
steam reduction factor
-
steam-zone shape factor
-
storage factor
-
stowage factor
-
strain concentration factor
-
streamflow formation factor
-
strength factor
-
stress concentration factor
-
stress intensity factor
-
stretch factor
-
structure factor
-
submergence factor
-
summability factor
-
superficial friction factor
-
support factor
-
surface correction factor
-
surface-area factor
-
tapping factor
-
technical preparedness factor
-
telephone influence factor
-
termination factor
-
terrain factor
-
thermal eta factor
-
thermal factor
-
thermal utilization factor
-
thermodynamic factor
-
thrust-deduction factor
-
time factor
-
time-scale factor
-
tire size factor
-
tooth factor
-
transfer factor
-
transmission factor
-
transport factor
-
traveling-wave factor
-
trigger factor
-
truck service factor
-
tuning factor
-
turbidity factor
-
turbulence factor
-
twist factor
-
U-factor
-
unavailability factor
-
unbalance factor
-
unit conversion factor
-
usage factor
-
utilization factor
-
vacuum factor
-
velocity gain factor
-
velocity factor
-
viscosity factor
-
void factor
-
voltage amplification factor
-
voltage amplitude factor
-
voltage ripple factor
-
voltage unbalance factor
-
voltage waveform distortion factor
-
volume-utilization factor
-
wake factor
-
water encroachment factor
-
water saturation factor
-
waveform distortion factor
-
wear factor
-
weather-forming factor
-
weight load factor
-
weighting factor
-
weight factor
-
winding factor
-
wobble factor
-
wood swelling factor
-
work factor
-
yield factor
-
zero phase-sequence current factor
-
zero phase-sequence voltage factor -
8 cryptography
1) криптография; криптографическая защита, криптографическое закрытие информации- computational cryptography- computational complexity based cryptographyАнгло-русский словарь по компьютерной безопасности > cryptography
-
9 security
1) безопасность; служба безопасности2) защита; защищенностьАнгло-русский словарь по компьютерной безопасности > security
-
10 complex adaptive system
Gen Mgta system that overrides conventional human controls because those controls will subdue inevitable change and development within that system. Complex adaptive systems are a product of the application of chaos theory (see chaos) and complexity theory to the world of organizations. According to writers such as Richard Pascale, organizations that are subject to too much control are at risk of failure. The bureaucracy has been cited as an example of extreme control and the top down approach to management. However, if a bureaucracy is left to adapt naturally, it could become capable of self-organization and of creating new methods of operating.
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