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process characterization

  • 1 приборы для контроля качества технологического процесса

    Русско-английский словарь по микроэлектронике > приборы для контроля качества технологического процесса

  • 2 характеристики процесса

    1) Automation: (выходные)(технологического) process quality

    Универсальный русско-английский словарь > характеристики процесса

  • 3 параметры МОП-технологии

    Универсальный русско-английский словарь > параметры МОП-технологии

  • 4 приборы для контроля качества технологического процесса

    Универсальный русско-английский словарь > приборы для контроля качества технологического процесса

  • 5 характеристики МОП-технологии

    Универсальный русско-английский словарь > характеристики МОП-технологии

  • 6 параметры МОП-технологии

    Русско-английский словарь по микроэлектронике > параметры МОП-технологии

  • 7 характеристика экологичности процесса

    1. process environmental characterization

     

    характеристика экологичности процесса
    —
    [А.С.Гольдберг. Англо-русский энергетический словарь. 2006 г.]

    Тематики

    EN

    Русско-английский словарь нормативно-технической терминологии > характеристика экологичности процесса

  • 8 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, Eventually
       Just 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)
       Many 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 Form
       The 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 Formation
       It 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 Contexts
       Even 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)
        18) The Assumption That the Mind Is a Formal System
       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 Intelligence
       The 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 Propositions
       In 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

  • 9 приводить

    reduce, reduce to, bring, cite, deduce, list, adduce, enter
    • Были приведены дальнейшие аргументы, показавшие, что... - Further arguments were given which showed that...
    • Было бы легко привести значительно больше примеров... - It would be easy to give many more examples of...
    • В основном, различные подходы приводят к... - Different approaches will, in general, lead to...
    • В свою очередь это может привести к тому, что... - This in turn can lead to...
    • В таблице 1 мы приводим вместе все данные относительно... - In Table 1 we summarize the...
    • В этом приложении мы приводим результаты... - In this appendix we present the results of...
    • Все эти данные приводили к очевидному требованию, что... - All this evidence led to a clear requirement that...
    • Дальнейшее рассуждение затем привело бы к идее... - Further argument would then lead to the idea of...
    • Данная процедура может быть продолжена, она приводит к... - The procedure can be continued, yielding...
    • Здесь мы приводим другой пример (чего-л). - We give here another example of...
    • Здесь мы приводим некоторые формулы для... - Неге we give some formulae for...
    • Исследование каждого случая отдельно приводит к... - Examination of each individual case leads to...
    • Можно привести еще одно замечание. - One further observation may be made.
    • Мы можем привести геометрическую интерпретацию для... -It is possible to give a geometric interpretation of...
    • Мы не можем привести здесь полный ответ. - We cannot give a complete answer here.
    • Мы не приводим это рассуждение со всеми подробностями по следующим причинам. - We do not present this argument in detail for the following reasons.
    • Мы приводим ниже значения для... - We quote below the values of...
    • Мы теперь приведем приложение уравнения (5). - We now give an application of (5).
    • Это привело нас к предложению, что... - We are led to the suggestion that...
    • Наши рассуждения в предыдущем параграфе могли бы привести нас к предположению, что... - Our work in the previous section might lead us to suspect that...
    • Однако здесь можно привести очень грубый довод. - A very rough reason, however, can be given here.
    • Описанный здесь метод всегда приводит... - The procedure described here always yields...
    • Перед тем как продолжить приводить примеры, мы приведем важное замечание, что... - Before proceeding to give examples, we make the important observation that...
    • Подобные повреждения могут привести к потере... - Such injuries can result in a loss of...
    • Понятно, что только один этот процесс не мог бы привести к... - Clearly such a process alone could not lead to...
    • Предыдущее обсуждение приводит к идее, что... - The preceding discussion leads to the idea that...
    • Приведем более полное доказательство, данное Гильбертом [2]. - A fuller proof, given by Hilbert [2], is as follows.
    • Приведем исключения, которые указывает Смит [1]. - Smith [1] points out certain exceptions as follows.
    • Приведем некоторый основной критерий для... - Let us list some major criteria for...
    • Приведем соответствующие численные величины:... - The corresponding numerical values are as follows:...
    • Приведем теперь пример, в котором... - We now give an example in which...
    • Процесс приводит к замене в... - The process leads to a change in...
    • Следовательно, мы обязаны попытаться развить теорию, которая приводит к... - Hence, we must try to develop a theory that leads to...
    • Смит [1] приводит убедительный пример существования... - Smith [l] makes a persuasive case for the existence of...
    • Сначала мы приведем некоторый дополнительный материал относительно... - We begin with some additional material relating to...
    • Сначала мы приведем один результат из... - We first quote a result from...
    • Такая практика приводит к серьезным недоразумениям. - This practice leads to serious confusion.
    • Тем не менее эта формальная работа привела к конкретному результату. - Nevertheless, this formal work has produced a concrete result. I
    • Тем самым нас довольно настойчиво приводит к идее, что... - This suggests quite strongly that...
    • Теперь мы приведем некоторые экспериментальные данные относительно... - We shall now give some experimental data concerning...
    • Теперь мы приведем список наиболее важных тождеств, включающих... - We shall now list the most important identities involving...
    • Теперь мы приведем явную характеристику... - We now give an explicit characterization of...
    • Теперь приведем несколько конкретных примеров. - A few concrete examples are in order.
    • Чтобы привести еще более простой пример, мы можем рассмотреть... - То take an even simpler example, we can consider...
    • Элегантное доказательство, которое мы здесь приводим, в основном принадлежит Гильберту. - The elegant proof we give is essentially due to Hilbert.
    • Эти кажущиеся тривиальными результаты приводят к... - These seemingly trivial results lead to...
    • Эти результаты мы приводим в таблице 1 для трех значений г. - The results are set out in Table 1 for three values of r.
    • Это выражение можно привести к более удобному виду. - This expression can be put in a more convenient form.
    • Это доказательство слишком сложное, чтобы приводить его здесь. - The proof is too complicated to give here.
    • Это естественным образом приводило к различным схемам для... - It led naturally to various schemes for...
    • Это заключение базируется на тех же самых идеях, которые приводят к... - This conclusion is based on the same ideas that lead to...
    • Это могло бы также привести к лучшему пониманию... - This could also lead to a better understanding of...
    • Это не приведет к ошибке, потому что... - This will not give rise to confusion because...
    • Это не приводит ни к каким концептуальным трудностям, однако... - This introduces no conceptual difficulties, but...
    • Это нестрогое рассуждение приводит нас к... - This crude argument leads to...
    • Это позволяет нам привести уравнение (1) к следующему виду... - This enables us to reduce (1) to the form...
    • Это привело нескольких авторов к заключению, что... - This has led several authors to believe that...
    • Это приводит к возникновению так называемого... - This gives rise to the so-called...
    • Это приводит к возрастанию... - This involves an increase in...
    • Это приводит к выводу, что... - This carries the implication that...
    • Это приводит к концепции... - This leads to a conception in which...
    • Это приводит к новым концепциям. - This leads to new conceptions.
    • Это приводит к полезным методам обращения с... - This leads to useful ways of dealing with...
    • Это приводит к противоречию, и, следовательно, доказательство закончено. - This gives a contradiction, and the proof is complete.
    • Это приводит к рассмотрению темы... - This leads into the topic of...
    • Это приводит к следующему определению. - This motivates the following definition.
    • Это приводит к тому, что известно как... - This leads to what is known as...
    • Это приводит нас к важному свойству... - This leads us to an important property of...
    • Это приводит нас к идее постулировать существование... - This leads us to postulate the existence of...
    • Этот результат автоматически приводит к необходимости изучения... - This result automatically leads to a study of...

    Русско-английский словарь научного общения > приводить

  • 10 методом

    •

    Cobalt is purified in the manner described for arsenic ores.

    * * *
    Методом
     Thermal stress analysis can be handled by this technique.
     Characterization of anthraquinones by means of electrophoresis has been developed recently.

    Русско-английский научно-технический словарь переводчика > методом

  • 11 EXPO '98

       Portugal's world's fair, held from May to October 1998, set in Lisbon. Designed to commemorate and celebrate the 500th anniversary of Vasco da Gama's 1498 discovery of an all-water route to India, this was an ambitious undertaking for a small country with a developing economy. The setting of the exposition was remote eastern Lisbon, along the banks of the Tagus estuary. To facilitate logistics, Portugal opened a new Metro station (Oriente) for the Expo and the new Vasco da Gama Bridge, just northeast of the site. More than 10 million visitors, many of them from abroad but a large proportion from Spain and Portugal, arrived at the site by Metro, bus, taxi, or car and were guided by signs in three languages: Portuguese, Spanish, and English. To the dismay of Francophones, the choice of English and Spanish reflected both the nature of the globalization process and Portugal's growing connections with Europe and the wider world.
       The theme of Expo '98 was "The Oceans, Heritage for the Future," and the official mascot-symbol was "Gil," a cartoon characterization of a drop of ocean water, based on the suggestion of schoolchildren from the small town of Barrancos. Somewhat in the spirit of Disney's Mickey Mouse, "Gil" reflected cheeriness, but his message was serious, alerting the public to the fact that the oceans were endangered and fresh drinking water increasingly in short supply for a burgeoning world population. Among the outstanding structures at Expo '98 was the Pavilion of Portugal, designed by Portuguese architect Álvaro Siza Vieira, and the Pavilion of the Oceans or the Oceanarium (which remained open to the public after the exposition closed), which was designed by an American architect.
       Despite the general success of the fair, critics gave mixed reviews to the historic commemoration of the Discoveries facets of the effort. No vessel from Vasco da Gama's 1497-99 famous voyage was reproduced at the fair's dockside exhibit—although there was a 19th-century sailing vessel and a reproduction of one of the vessels from Christopher Columbus's first voyage, constructed by Portuguese in Madeira—nor was there much else on Vasco da Gama in the Pavilion of Portugal. Instead, visitors were impressed with a multimedia show based on knowledge of a Portuguese shipwreck, a 17th-century nau, found by archaeologists in recent years. The sound and light show in this lovely space was magnificent. The most popular exhibits were the Oceanarium and the Utopia Pavilion, where lines could be hours long. Despite the fact that Expo '98 made only a weak effort to attract visitors from outside Europe, the general consensus was that it was a successful enterprise, unique in Portugal's record of historic and contemporary expositions since 1940.

    Historical dictionary of Portugal > EXPO '98

См. также в других словарях:

  • Characterization — Char ac*ter*i*za tion, n. The act or process of characterizing. [1913 Webster] …   The Collaborative International Dictionary of English

  • Characterization — For other uses, see Characterization (disambiguation). Characterization or characterisation is the art of creating characters for a narrative,[1] including the process of conveying information about them. It may be employed in dramatic works of… …   Wikipedia

  • Process corners — In semiconductor manufacturing, a process corner is an example of a design of experiments (DoE) technique that refers to a variation of fabrication parameters used in applying an integrated circuit design to a semiconductor wafer. Process corners …   Wikipedia

  • characterization — noun The act or process of characterizing …   Wiktionary

  • Wiener process — In mathematics, the Wiener process is a continuous time stochastic process named in honor of Norbert Wiener. It is often called Brownian motion, after Robert Brown. It is one of the best known Lévy processes (càdlàg stochastic processes with… …   Wikipedia

  • Polytropic process — A polytropic process is a thermodynamic process that obeys the relation::P V^n = C,where P is pressure, V is volume, n is any real number (the polytropic index), and C is a constant. This equation can be used to accurately characterize processes… …   Wikipedia

  • Ulrich S. Schubert — was born in Tübingen in 1969. He studied chemistry at the Universities of Frankfurt and Bayreuth (both Germany) and the Virginia Commonwealth University, Richmond (USA). His Ph.D. work was performed under the supervision of Professor Eisenbach… …   Wikipedia

  • Characterisation — Characterization is a process of conveying information about characters in fiction or conversation. Characters are usually present by description and through their actions, speech, and thoughts.=Characterization in Drama=In performance an actor… …   Wikipedia

  • Algorithm characterizations — The word algorithm does not have a generally accepted definition. Researchers are actively working in formalizing this term. This article will present some of the characterizations of the notion of algorithm in more detail. This article is a… …   Wikipedia

  • theatre — /thee euh teuhr, theeeu /, n. theater. * * * I Building or space in which performances are given before an audience. It contains an auditorium and stage. In ancient Greece, where Western theatre began (5th century BC), theatres were constructed… …   Universalium

  • HEBREW LITERATURE, MODERN — definition and scope beginnings periodization …   Encyclopedia of Judaism

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