complete number scale

bütün sayı ölçeği

1) окалина; нагар; изгарь; плена; накипь

2) образовывать окалину или накипь

3) удалять окалину или накипь

4) неочищенный парафин

5) шкала

6) штриховая мера длины; масштабная линейка

7) масштаб || определять масштаб, масштабировать; изменять масштаб; сводить к определённому масштабу

8) мн. ч. весы

9) чашка весов

10) взвешивать

11) электрон. степень интеграции

12) вчт. система счисления

13) пищ. чешуя; чешуйка; шелуха || удалять чешую; отделяться чешуйками

1) окалина || образовывать окалину

2) нагар; изгарина

3) накипь || образовывать накипь

4) удалять окалину или накипь

5) шкала || шкалировать, градуировать

6) масштабная линейка

7) масштаб || определять масштаб, масштабировать; изменять масштаб; сводить к определённому масштабу

8) весы || взвешивать

9) чешуя; чешуйка || чешуйчатый

10) дозатор

11) размах

12) размер

13) солеотложение

14) энт. червец

•

- absolute scale

- acidity scale

- air scale

- analytical scale

- angular scale

- arbitrary scale

- arc scale

- base scale

- Baume scale

- boiler scale

- center-zero scale

- check scale

- circular scale

- complete number scale

- convergence scale

- conversion scale

- corrected scale

- cumulative scale

- curvilinear scale

- depth scale

- diagonal scale

- direct scale

- distance scale

- double scale

- draftsman's scale

- edge-wise scale

- equatorial scale

- equidistant scale

- estimation scale

- evenly divided scale

- expanded scale

- fixed scale

- floating scale

- focusing scale

- full scale

- functional scale

- fundamental scale

- furnace scale

- graphical scale

- gray scale

- hardness scale

- height scale

- image scale

- international temperature scale

- interval scale

- inverse scale

- local scale

- log scale

- logarithmic scale

- loose scale

- magnitude scale

- main scale

- maximal scale

- mesh scale

- mill scale

- minimal scale

- nominal scale

- nonlinear scale

- non-uniform scale

- normal scale

- number scale

- numerical scale

- observational scale

- ordinal scale

- percentile scale

- perspective scale

- plotting scale

- point scale

- power scale

- predetermined scale

- pressure scale

- probability scale

- projective scale

- proportional scale

- quadratic scale

- radix scale

- range scale

- ratio scale

- reciprocal scale

- recorder scale

- rectilinear scale

- reduced scale

- reduction scale

- reference scale

- regular scale

- relative scale

- representative scale

- rolled-in scale

- scale of alephs

- scale of comparison

- scale of decrease

- scale of distance

- scale of height

- scale of imaginaries

- scale of increase

- scale of integration

- scale of N

- scale of probability

- scale of slope

- scale of surveying

- scoring scale

- similarity scale

- size scale

- sliding scale

- small scale

- snap-in scale

- space-time scale

- stellar temperature scale

- stereographic scale

- straight scale

- suppressed-zero scale

- tight scale

- unified scale

- universal scale

- utility scale

- value scale

- variable scale

- vast scale

- visibility scale

- wavelength scale

- welding scale

1) система || системный

2) система; установка; устройство; комплекс

3) программа

•

- accounting system

- high-availability system

- homogeneous system

- legacy operating system

- level-signal system

- multiagent system

- multichannel system

- rewrite system

- right-handed coordinate system

   1) Programs and the Complexity of Human Mental Processes

   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)

   2) Artificial Intelligence Is an Engineering Discipline

   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)

   3) A Sceptical View of Artificial Intelligence

   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)

   5) Problems in Machine Intelligence Arise Because Things Obvious to Any Person Are Not Represented in the Program

   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)

   6) The Meaning of a Symbolic Description

   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)

   7) The Principle of Artificial Intelligence

   [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)

   8) Artificial Intelligence Recognizes the Need for Knowledge in Its Systems

   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)

    10) There Are Many Types of Reasoning

   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)

    11) Programs Are Beginning to Do Things That Critics Have Asserted to Be Impossible

   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)

    12) The Synthesis of Man and Machine

   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)

    13) The Thesis of Good Old-Fashioned Artificial Intelligence (GOFAI)

   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)

    15) Four Kinds of Artificial Intelligence

   We might distinguish among four kinds of AI.

   ♦ Nonpsychological 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.

   ♦ Weak Psychological AI

   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.

   ♦ Strong Psychological AI

... 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)

   ♦ Suprapsychological AI

   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)

    17) Form and Content Are Not Fundamentally Different

   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)

    20) Mathematical Logic Provides the Basis for Theory in AI

   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)

    22) Definitions of Artificial Intelligence

   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)

    23) The Computer Can Not Be a Model of the Mind

   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)

    24) Computers Will Not Always Be Inferior to Human Brains

   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)

1) (nought; the number or figure 0: Three plus zero equals three; The figure 100 has two zeros in it.) null

2) (the point on a scale (eg on a thermometer) which is taken as the standard on which measurements may be based: The temperature was 5 degrees above/below zero.) nullpunkt

3) (the exact time fixed for something to happen, eg an explosion, the launching of a spacecraft etc: It is now 3 minutes to zero.) utløsnings-/utskytningstidspunkt

ingenting

null

subst. \/ˈzɪərəʊ\/

1) null

• 100 has two zeros

2) nullpunkt, frysepunkt

• 10 degrees below zero

3) ( overført) null, tosk, idiot

• she's a complete zero

4) (språkvitenskap, som foranstilt adjektiv) null-

absolute zero absolutt nullpunkt

be at zero ( også overført) stå på null(punktet)

verb \/ˈzɪərəʊ\/

1) nullstille

2) sikte (inn), stille (inn)

zero in on sikte på, rette ilden mot ( overført) konsentrere seg om, rette oppmerksomheten mot

1) (indicating belonging, connection, possession)

2) (indicating starting point) von

3) (indicating origin, cause)

4) (indicating material) aus

5) (indicating closer definition, identity, or contents)

6) (indicating concern, reference)

7) (indicating objective relation)

8) (indicating description, quality, condition)

9) (indicating classification, selection) von

* * *

1) (belonging to: a friend of mine.) von

2) (away from (a place etc); after (a given time): within five miles of London; within a year of his death.) von

3) (written etc by: the plays of Shakespeare.) von

4) (belonging to or forming a group: He is one of my friends.) von

5) (showing: a picture of my father.) von

6) (made from; consisting of: a dress of silk; a collection of pictures.) aus

7) (used to show an amount, measurement of something: a gallon of petrol; five bags of coal.) mit

8) (about: an account of his work.) von

9) (containing: a box of chocolates.) mit

10) (used to show a cause: She died of hunger.) an

11) (used to show a loss or removal: She was robbed of her jewels.) Genitiv

12) (used to show the connection between an action and its object: the smoking of a cigarette.) Genitiv

13) (used to show character, qualities etc: a man of courage.) mit

14) ((American) (of time) a certain number of minutes before (the hour): It's ten minutes of three.) vor

* * *

[ɒv, əv, AM ɑ:v, əv]

1. after n (belonging to) von + dat

people \of this island Menschen von dieser Insel

the language \of this country die Sprache dieses Landes

the cause \of the disease die Krankheitsursache

the colour \of her hair ihre Haarfarbe

the government \of India die indische Regierung

a friend \of mine ein Freund von mir

smoking is the worst habit \of mine Rauchen ist meine schlimmste Angewohnheit

this revolting dog \of hers ihr widerlicher Hund

the smell \of roses Rosenduft m

2. after n (expressing relationship) von + dat

an admirer \of Picasso ein Bewunderer Picassos

3. after n (expressing a whole's part) von + dat

five \of her seven kids are boys fünf ihrer sieben Kinder sind Jungen

there were ten \of us on the trip wir waren auf der Reise zu zehnt

nine \of the children came to the show neun Kinder kamen zur Vorstellung

can you please give me more \of the beans? könntest du mir noch etwas von den Bohnen geben?

I don't want to hear any more \of that! ich will nichts mehr davon hören!

he's the best-looking \of the three brothers er sieht von den drei Brüdern am besten aus

a third \of the people ein Drittel der Leute

the whole \of the garden der ganze Garten

the best \of friends die besten Freunde

the days \of the week die Wochentage

all \of us wir alle

all \of us were tired wir waren alle müde

\of all von allen

best \of all, I liked the green one am besten gefiel mir der grüne

that \of all his films is my favourite er gefällt mir von allen seinen Filmen am besten

both \of us wir beide

most \of them die meisten von ihnen

one \of the cleverest eine(r) der Schlauesten

he's one \of the smartest \of the smart er ist einer der Klügsten unter den Klugen

4. after n (expressing quantities)

a bunch \of parsley ein Bund Petersilie nt

a clove \of garlic eine Knoblauchzehe

a cup \of tea eine Tasse Tee

a drop \of rain ein Regentropfen

hundreds \of people Hunderte von Menschen

a kilo \of apples ein Kilo Äpfel nt

a litre \of water ein Liter Wasser m

a lot \of money eine Menge Geld

a piece \of cake ein Stück Kuchen

a pride \of lions ein Rudel Löwen [o Löwenrudel] nt

5. after vb (consisting of) aus + dat

the sweater is made \of the finest lambswool der Pullover ist aus feinster Schafwolle

a land \of ice and snow ein Land aus Eis und Schnee

dresses \of lace and silk Kleider aus Spitze und Seide

a house \of stone ein Steinhaus, ein Haus aus Stein

6. after n (containing) mit + dat

a book \of short stories ein Buch mit Kurzgeschichten

7. after adj (done by) von + dat

that was stupid \of me das war dumm von mir

8. after n (done to)

the massacre \of hundreds \of innocent people das Massaker an Hunderten von Menschen

the destruction \of the rain forest die Zerstörung des Regenwalds

9. after n (suffered by) von + dat

the anguish \of the murdered child's parents die Qualen der Eltern des ermordeten Kindes

the suffering \of millions das Leiden von Millionen

10. (expressing cause)

to die \of sth an etw dat sterben

he died \of cancer er starb an Krebs

\of one's own free will aus freien Stücken, freiwillig

\of oneself von selbst

she would never do such a thing \of herself so etwas würde sie nie von alleine tun

11. (expressing origin)

the works \of Shakespeare die Werke Shakespeares

she is \of noble birth sie ist adliger Abstammung

12. after vb (concerning)

we will notify you \of any further changes wir werden Sie über alle Änderungen informieren

he was accused \of fraud er wurde wegen Betrugs angeklagt

I know \of a guy who could fix that for you ich kenne jemanden, der das für dich reparieren kann

\of her childhood, we know very little wir wissen nur sehr wenig über ihre Kindheit

let's not speak \of this matter lass uns nicht über die Sache reden

speaking \of sb/sth,... wo [o da] wir gerade von jdm/etw sprechen,...

speaking \of time, do you have a watch on? da wir gerade von der Zeit reden, hast du eine Uhr?

she's often unsure \of herself sie ist sich ihrer selbst oft nicht sicher

I'm really appreciative \of all your help ich bin dir für all deine Hilfe wirklich dankbar

he was worthy \of the medal er hatte die Medaille verdient

I am certain \of that ich bin mir dessen sicher

this is not uncharacteristic \of them das ist für sie nichts Ungewöhnliches

to be afraid \of sb/sth vor jdm/etw Angst haben

to be fond \of swimming gerne schwimmen

to be jealous \of sb auf jdn eifersüchtig sein

to be sick \of sth etw satthaben, von etw dat genug haben

there was no warning \of the danger es gab keine Warnung vor der Gefahr

he has a love \of music er liebt die Musik

he's a doctor \of medicine er ist Doktor der Medizin

the idea \of a just society die Idee einer gerechten Gesellschaft

the memories \of her school years die Erinnerungen an ihre Schuljahre

the pain \of separation der Trennungsschmerz

it's a problem \of space das ist ein Raumproblem

his promises \of loyalty seine Treueversprechen

to be in search \of sb/sth auf der Suche nach jdm/etw sein

she's in search \of a man sie sucht einen Mann

thoughts \of revenge Rachegedanken pl

▪ what \of sb? was ist mit jdm?

and what \of Adrian? was macht eigentlich Adrian?

what \of it? was ist schon dabei?, na und?

13. after n (expressing condition)

on the point [or verge] \of doing sth kurz davor [o im Begriff] sein, etw zu tun

I'm on the point \of telling him off ich werde ihn jetzt gleich rausschmeißen

14. after n (expressing position) von + dat

in the back \of the car hinten im Auto

the zipper was on the back \of the dress der Reißverschluss war hinten am Kleid

on the corner \of the street an der Straßenecke

on the left \of the picture links auf dem Bild

a lake north/south \of the city ein See im Norden/Süden der Stadt

I've never been north \of Edinburgh ich war noch nie nördlich von Edinburgh

on the top \of his head [oben] auf seinem Kopf

15. after n (with respect to scale) von + dat

a rise \of 2% in inflation ein Inflationsanstieg von 2 Prozent

the stocks experienced an average rise \of 5% die Aktien sind im Durchschnitt um 5 % gestiegen

16. (expressing age) von + dat

at the age \of six im Alter von sechs Jahren

he's a man \of about 50 er ist um die 50 Jahre alt

17. after n (denoting example of category)

I hate this kind \of party ich hasse diese Art von Party

the city \of Prague die Stadt Prag

18. after n (typical of)

she has the face \of an angel sie hat ein Gesicht wie ein Engel

the grace \of a dancer die Anmut einer Tänzerin

the love \of a good woman die Liebe einer guten Frau

19. after n (expressing characteristic)

she gave a scream \of terror sie stieß einen Schrei des Entsetzens aus

a man \of honour ein Mann von Ehre

a moment \of silence ein Moment m der Stille

I want a few minutes \of quiet! ich will ein paar Minuten Ruhe!

a subject \of very little interest ein sehr wenig beachtetes Thema

a woman \of great charm and beauty eine Frau von großer Wärme und Schönheit

20. after n (away from) von + dat

we live within a mile \of the city centre wir wohnen eine Meile vom Stadtzentrum entfernt

she came within two seconds \of beating the world record sie hat den Weltrekord nur um zwei Sekunden verfehlt

21. after n (in time phrases)

I got married back in June \of 1957 ich habe im Juni 1957 geheiratet

the eleventh \of March der elfte März

the first \of the month der erste [Tag] des Monats

the most memorable events \of the past decade die wichtigsten Ereignisse des letzten Jahrzehnts

22. after vb (expressing removal)

they were robbed \of all their savings ihnen wurden alle Ersparnisse geraubt

I've him \of that nasty little habit ich habe ihm diese dumme Angewohnheit abgewöhnt

his mother had deprived him \of love seine Mutter hat ihm ihre Liebe vorenthalten

to get rid \of sb jdn loswerden

the room was devoid \of all furnishings der Raum war ganz ohne Möbel

free \of charge kostenlos, SCHWEIZ, ÖSTERR meist gratis

23. after n (apposition) von + dat

this complete idiot \of a man dieser Vollidiot

the month \of June der Monat Juni

the name \of Brown der Name Brown

24. ( dated: during) an + dat

she died \of a Sunday morning sie starb an einem Sonntagmorgen

I like to relax with my favourite book \of an evening ich entspanne mich abends gerne mit meinem Lieblingsbuch

\of late in letzter Zeit

25. AM (to) vor

it's quarter \of five es ist viertel vor fünf [o BRD drei viertel fünf

▶ \of all gerade

Jane, \of all people, is the last one I'd expect to see at the club gerade Jane ist die letzte, die ich in dem Klub erwartet hätte

I can't understand why you live in Ireland, \of all places ich kann nicht verstehen, warum du ausgerechnet in Irland lebst

today \of all days ausgerechnet heute

▶ \of all the cheek [or nerve] das ist doch die Höhe!

▶ to be \of sth:

she is \of the opinion that doctors are only out to experiment sie glaubt, Ärzte möchten nur herumexperimentieren

this work is \of great interest and value diese Arbeit ist sehr wichtig und wertvoll

* * *

1) (indicating possession or relation) von (+dat), use of gen

2)

3)

4)

5) (indicating material) aus

6)

7)

8)

9)

10)

11) (obs, liter

12)

* * *

of [ɒv; əv; US əv; ɑv] präp

1. allg von

2. zur Bezeichnung des Genitivs:

the tail of the dog der Schwanz des Hundes;

the tail of a dog der oder ein Hundeschwanz;

the folly of his action die Dummheit seiner Handlung

3. Ort: bei:

4. Entfernung, Trennung, Befreiung:

a) von:

south of London südlich von London;

within ten miles of London im Umkreis von 10 Meilen um London;

cure (rid) of sth von etwas heilen (befreien)

b) (gen) he was robbed of his wallet er wurde seiner Brieftasche beraubt, ihm wurde die Brieftasche geraubt

c) um:

5. Herkunft: von, aus:

of good family aus einer guten Familie;

Mr X of London Mr. X aus London

6. Teil: von oder gen:

the best of my friends;

a friend of mine ein Freund von mir, einer meiner Freunde;

that red nose of his seine rote Nase

7. Eigenschaft: von, mit:

a man of courage ein mutiger Mann, ein Mann mit Mut;

a man of no importance ein unbedeutender Mensch;

a fool of a man ein (ausgemachter) Narr

8. Stoff: aus, von:

a dress of silk ein Kleid aus oder von Seide, ein Seidenkleid;

(made) of steel aus Stahl (hergestellt), stählern, Stahl…

9. Urheberschaft, Art und Weise: von:

the works of Byron;

it was clever of him;

of o.s. von selbst, von sich aus;

he has a son of his first marriage er hat einen Sohn aus erster Ehe

10. Ursache, Grund:

a) von, an (dat):

die of cancer an Krebs sterben

b) aus:

c) vor (dat): → academic.ru/1052/afraid">afraid

d) auf (akk):

e) über (akk):

f) nach:

11. Beziehung: hinsichtlich (gen):

it is true of every case das trifft in jedem Fall zu

12. Thema:

a) von, über (akk):

b) an (akk):

13. Apposition, im Deutschen nicht ausgedrückt:

a) the city of London die Stadt London;

the month of April der Monat April

b) Maß:

a piece of meat ein Stück Fleisch

14. Genitivus obiectivus:

a) zu:

b) vor (dat): → fear A 3

c) bei:

an audience of the king eine Audienz beim König

15. Zeit:

a) umg an (dat), in (dat):

of an evening eines Abends;

of late years in den letzten Jahren

b) von:

your letter of March 3rd Ihr Schreiben vom 3. März

c) US vor (bei Zeitangaben):

* * *

1) (indicating belonging, connection, possession)

2) (indicating starting point) von

3) (indicating origin, cause)

4) (indicating material) aus

5) (indicating closer definition, identity, or contents)

6) (indicating concern, reference)

7) (indicating objective relation)

8) (indicating description, quality, condition)

9) (indicating classification, selection) von

* * *

aus präp.

von präp.

vor präp.

über präp.

- a priori method

- abbreviated method

- abridged method

- absolute method

- adaptive method

- adaptive smoothing method

- ADI method

- adjoint method

- adobe blasting method

- aero-projection method

- algebraic method

- algorithmic method

- alternating method

- alternating-variable descent method

- analog method

- analytical method

- angular modulation method

- approximate estimation method

- approximation-function method

- approximative method

- area method

- area moment method

- ascent method

- asymptotically efficient method

- averoid method

- axiomatic method

- axonometric method

- backward-sweep method

- balayage method

- Barrelet method of zeroes

- barrier method

- baseband recording method

- block-diagram method

- bordering method

- Borrmann method

- bottle method

- boundary integral method

- building-block method

- caisson method

- cascade method

- case method

- catastrophe method

- category method

- centroid method

- chain method

- chaining method

- chord method

- climb milling method

- clustering method

- cofactor method

- coincidence method

- collocation method

- colorimetric method

- combinatorial method

- comparative method

- comparison method

- complete elimination method

- complexometric method

- composite value method

- computing method

- conditionally stable method

- conductance-measuring method

- cone method

- conformal mapping method

- conjugate-gradient method

- constant-fraction method

- constructive method

- continuation method

- contour integral method

- convergence method

- convergent method

- convex simplex method

- covariant method

- crossed beam method

- crude method

- cut-and-try method

- cutting-plane method

- cyclic method

- data enrichment method

- decomposition method

- deductive method

- delta method

- depth-first method

- describing function method

- diagonalization method

- dichotomy method

- difference factorization method

- differential-equations method

- diffusion method

- digital method

- dilution method

- dimensional method

- direct method of measurement

- direct method

- direct search method

- dispersion method

- distribution-free method

- dot alloying method

- double-description method

- dry method

- dual simplex method

- duality method

- Dumas method

- dye-penetrant method

- efficient method

- efflux method

- electrophoretic method

- elementary method

- elimination method

- embedded method

- empirical method

- energy method

- equal-probability selection method

- equal-strain method

- equation method

- ergodic method

- error method

- error of method

- escalator method

- estimation method

- exact method

- exclusion method

- exhaustion method

- expansion method

- experimental method

- exterior point method

- extrapolation method

- factor comparison method

- factorial method

- factorization method

- falling body method

- fall-of-charge method

- false position method

- finitary method

- finite difference method

- fitting method

- fixed-point method

- floatation method

- floating-zone method

- forecasting method

- formal method

- fractional step method

- freehand method

- functional method

- fundamental method

- game-theoretic method

- general method

- genetic method

- geometrical method

- global balance method

- gradient-search method

- graph method

- graphic method

- grapho-numerical method

- gravimetric method

- gray-wedge method

- grid method

- Griess-Ilovai method

- grouping method

- half-and-half method

- half-deflection method

- half-split method

- heuristic method

- hill-climbing method

- Hopkinson split-bar method

- horn-and-lens method

- hydraulic fill method

- hypothetico-deductive method

- idealized method

- identification method

- immersion method

- inaccurate method

- incremental method

- index method

- indirect method of measurement

- indirect method

- inductive method

- inexact method

- infinitesimal method

- informal method

- integral estimation method

- integral method

- integro-interpolation method

- interior point method

- interpretation method

- inverse matrix method

- inverse-scattering method

- inversion method

- isocline method

- isolation method

- iterated-interpolation method

- iteration method

- jacknife method

- kernel of summation method

- kick-sorting method

- knapsack method

- laboratory method

- laborious method

- Lagrange's method of multipliers

- learning method

- least sample value method

- leveling method

- limited information method

- linking method

- load factor method

- load-factor method

- local method

- logical method

- logical-and-probabilistic method

- logistic method

- lost wax method

- machining method

- magnetic particle method

- manufacturing method

- mapping method

- marginal method

- Markowitz method

- matching method

- mathematically deductive method

- matrix method

- maximum likelihood method

- mean-value method

- measurement method

- method of abstraction

- method of acyclic models

- method of approximation

- method of arithmetic means

- method of arithmetization

- method of averages and ranges

- method of averages

- method of backward induction

- method of balayage

- method of barriers

- method of basic series

- method of bearings

- method of catastrophes

- method of characteristic functions

- method of characteristics

- method of choice

- method of collapsed strata

- method of comparison

- method of complex gradients

- method of continuation

- method of continuity

- method of contrasts

- method of convex ascent

- method of correction

- method of countable coverings

- method of counterexamples

- method of cutting and gluing

- method of determinants

- method of diagrams

- method of direct elimination

- method of estimation

- method of exhaustion

- method of extrapolation

- method of extremal metric

- method of forcing

- method of forecasting

- method of fraction levelling

- method of gisements

- method of golden section

- method of graphs

- method of harmonic balance

- method of homology

- method of incremental rates

- method of indicators

- method of induction

- method of inductive inference

- method of inversion

- method of isoclines

- method of iterations

- method of krakowians

- method of leading variables

- method of lines

- method of loci

- method of martingale

- method of mathematical induction

- method of multipliers

- method of normals

- method of numbering

- method of observation

- method of operators

- method of optimum allocation

- method of ordering

- method of orthogonal contrasts

- method of parabolas

- method of parametrix

- method of partial integration

- method of partition

- method of penultimate remainder

- method of potentials

- method of prediction

- method of principal components

- method of proof

- method of proportional control

- method of quantiles

- method of quickest descent

- method of randomization

- method of raviness

- method of rectangles

- method of reiteration

- method of representative sample

- method of residues

- method of revolution

- method of rotating coordinates

- method of scoring

- method of search

- method of selected points

- method of semiaverages

- method of separation of variable

- method of smoothing

- method of solution

- method of spherical means

- method of substitution

- method of successive corrections

- method of summability