model experiments

model experiment

model experiments

1) Construction: mock-up study

2) Oil: model investigations

3) Geophysics: model study, modeling investigations, simulation study

4) Ecology: model experiment, model investigation

5) Makarov: model experiments

scale-model experiment

scale-model experiments

viability.

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► nombre femenino

1 viability

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plan de viabilidad viability study

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1) [de un plan] viability, feasibility

2) (Aut) road conditions [pl]

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femenino ( de proyecto) viability, feasibility; ( de bebé) viability

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= feasibility, viability, practicability, workability, satisfiability, tractability.

Ex. However, even with selective indexing in combination, the limit of feasibility is usually reckoned to be compound subjects consisting of five terms.

Ex. Witness the viability and influence of Panizzi's ideology despite the vulnerability of many of his rules = Véase como ejemplo la viabilidad e influencia de la ideología de Panizzi a pesar de la vulnerabilidad de muchas de sus reglas.

Ex. This article analyses the feasibility, practicability, overall applicability and financial issues of constructing a library network.

Ex. A unified data base of the Republic of Ukraine's information system illustrates the workability of the principles.

Ex. The satisfiability of an information model in a universe of discourse can be formally checked by complementing the information model with a representation of concrete knowledge which is assumed to be complete.

Ex. Experiments in automated reasoning have substantially broadened the meaning of 'practical tractability'.

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* estudio de viabilidad = demonstration project, feasibility study.

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femenino ( de proyecto) viability, feasibility; ( de bebé) viability

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1 (de un proyecto) viability, feasibility

2 (de un bebé) viability

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viabilidad nf

viability, feasibility

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f viability, feasibility

SUBJECT AREA: Electricity

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When he was 14 years old Davenport was apprenticed to a blacksmith for seven years. At the close of his apprenticeship in 1823 he opened a blacksmith's shop in Brandon, Vermont. He began experimenting with electromagnets after observing one in use at the Penfield Iron Works at Crown Point, New York, in 1831. He saw the device as a possible source of power and by July 1834 had constructed his first electric motor. Having totally abandoned his regular business, Davenport built and exhibited a number of miniature machines; he utilized an electric motor to propel a model car around a circular track in 1836, and this became the first recorded instance of an electric railway. An application for a patent and a model were destroyed in a fire at the United States Patent Office in December 1836, but a second application was made and Davenport received a patent the following year for Improvements in Propelling Machinery by Magnetism and Electromagnetism. A British patent was also obtained. A workshop and laboratory were established in New York, but Davenport had little financial backing for his experiments. He built a total of over one hundred motors but was defeated by the inability to obtain an inexpensive source of power. Using an electric motor of his own design to operate a printing press in 1840, he undertook the publication of a journal, The Electromagnet and Mechanics' Intelligencer. This was the first American periodical on electricity, but it was discontinued after a few issues. In failing health he retired to Vermont where in the last year of his life he continued experiments in electromagnetism.

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SUBJECT AREA: Ports and shipping

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Symington was the son of the Superintendent of the Mines Company in Lanarkshire, and attended the local school. When he was 22 years old he was sent by Gilbert Meason, Manager of the Wanlockhead mines, to Edinburgh University. In 1779 he was working on the assembly of a Watt engine as an apprentice to his brother, George, and in 1786 he started experiments to modify a Watt engine in order to avoid infringing the separate condenser patent. He sought a patent for his alternative, which was paid for by Meason. He constructed a model steam road carriage which was completed in 1786; it was shown in Edinburgh by Meason, attracting interest but inadequate financial support. It had a horizontal cylinder and was non-condensing. No full-sized engine was ever built but the model secured the interest of Patrick Miller, an Edinburgh banker, who ordered an engine from Symington to drive an experimental boat, 25 ft (7.6 m) long with a dual hull, which performed satisfactorily on Dalswinton Loch in 1788. In the following year Miller ordered a larger engine for a bigger boat which was tried on the Forth \& Clyde Canal in December 1789, the component parts having been made by the Carron Company. The engine worked perfectly but had the effect of breaking the paddle wheels. These were repaired and further trials were successful but Miller lost interest and his experiments lapsed. Symington devoted himself thereafter to building stationary engines. He built other engines for mine pumping at Sanquhar and Leadhills before going further afield. In all, he built over thirty engines, about half of them being rotary. In 1800–1 he designed the engine for a boat for Lord Dundas, the Charlotte Dundas; this was apparently the first boat of that name and sailed on both the Forth and Clyde rivers. A second Charlotte Dundas with a horizontal cylinder was to follow and first sailed in January 1803 for the Forth \& Clyde Canal Company. The speed of the boat was only 2 mph (3 km/h) and much was made by its detractors of the damage said to be caused to the canal banks by its wash. Lord Dundas declined to authorize payment of outstanding accounts; Symington received little reward for his efforts. He died in the house of his son-in-law, Dr Robert Bowie, in Wapping, amidst heated controversy about the true inventor of steam navigation.

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SUBJECT AREA: Aerospace

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After serving an apprenticeship in the lace industry, Stringfellow left Nottingham in about 1820 and moved to Chard in Somerset, where he set up his own business. He had wide interests such as photography, politics, and the use of electricity for medical treatment. Stringfellow met William Samuel Henson, who also lived in Chard and was involved in lacemaking, and became interested in his "aerial steam carriage" of 1842–3. When support for this project foundered, Henson and Stringfellow drew up an agreement "Whereas it is intended to construct a model of an Aerial Machine". They built a large model with a wing span of 20 ft (6 m) and powered by a steam engine, which was probably the work of Stringfellow. The model was tested on a hillside near Chard, often at night to avoid publicity, but despite many attempts it never made a successful flight. At this point Henson emigrated to the United States. From 1848 Stringfellow continued to experiment with models of his own design, starting with one with a wing span of 10 ft (3m). He decided to test it in a disused lace factory, rather than in the open air. Stringfellow fitted a horizontal wire which supported the model as it gained speed prior to free flight. Unfortunately, neither this nor later models made a sustained flight, despite Stringfellow's efficient lightweight steam engine. For many years Stringfellow abandoned his aeronautical experiments, then in 1866 when the (Royal) Aeronautical Society was founded, his interest was revived. He built a steam-powered triplane, which was demonstrated "flying" along a wire at the world's first Aeronautical Exhibition, held at Crystal Palace, London, in 1868. Stringfellow also received a cash prize for one of his engines, which was the lightest practical power unit at the Exhibition. Although Stringfellow's models never achieved a really successful flight, his designs showed the way for others to follow. Several of his models are preserved in the Science Museum in London.

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SUBJECT AREA: Mining and extraction technology

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After Menzies conceived his idea of breaking off coal with machines in 1761, many inventors subsequently followed his proposals through into the practice of underground working. More than one century later, Garforth became one of the principal pioneers of machine-holing combined with the longwall method of working in order to reduce production costs and increase the yield of coal. Having been appointed agent to Pope \& Pearson's Collieries, West Yorkshire, in 1879, of which company he later became Managing Director and Chairman, he gathered a great deal of experience with different methods of cutting coal. The first disc machine was exhibited in London as early as 1851, and ten years later a pick machine was invented. In 1893 he introduced an improved type of deep undercutting machine, his "diamond" disc coal-cutter, driven by compressed air, which also became popular on the European continent.

Besides the considerable economic advantages it created, the use of machinery for mining coal increased the safety of working in hard and thin seams. The improvement of safety in mining technology was always his primary concern, and as a result of his inventions and his many publications he became the leading figure in the British coal mining industry at the beginning of the twentieth century; safety lamps still carry his name. In 1885 he invented a firedamp detector, and following a severe explosion in 1886 he concentrated on coal-dust experiments. From the information he obtained of the effect of stone-dust on a coal-dust explosion he proposed the stone-dust remedy to prevent explosions of coal-dust. As a result of discussions which lasted for decades and after he had been entrusted with the job of conducting the British coal-dust experiments, in 1921 an Act made it compulsory in all mines which were not naturally wet throughout to treat all roads with incombustible dust so as to ensure that the dust always consisted of a mixture containing not more than 50 per cent combustible matter. In 1901 Garforth erected a surface gallery which represented the damaged roadways of a mine and could be filled with noxious fumes to test self-contained breathing apparata. This gallery formed the model from which all the rescue-stations existing nowadays have been developed.

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   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)

SUBJECT AREA: Electricity, Land transport, Railways and locomotives

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Davidson, son of an Aberdeen merchant, attended Marischal College, Aberdeen, between 1819 and 1822: his studies included mathematics, mechanics and chemistry. He subsequently joined his father's grocery business, which from time to time received enquiries for yeast: to meet these, Davidson began to manufacture yeast for sale and from that start built up a successful chemical manufacturing business with the emphasis on yeast and dyes. About 1837 he started to experiment first with electric batteries and then with motors. He invented a form of electromagnetic engine in which soft iron bars arranged on the periphery of a wooden cylinder, parallel to its axis, around which the cylinder could rotate, were attracted by fixed electromagnets. These were energized in turn by current controlled by a simple commutaring device. Electric current was produced by his batteries. His activities were brought to the attention of Michael Faraday and to the scientific world in general by a letter from Professor Forbes of King's College, Aberdeen. Davidson declined to patent his inventions, believing that all should be able freely to draw advantage from them, and in order to afford an opportunity for all interested parties to inspect them an exhibition was held at 36 Union Street, Aberdeen, in October 1840 to demonstrate his "apparatus actuated by electro-magnetic power". It included: a model locomotive carriage, large enough to carry two people, that ran on a railway; a turning lathe with tools for visitors to use; and a small printing machine. In the spring of 1842 he put on a similar exhibition in Edinburgh, this time including a sawmill. Davidson sought support from railway companies for further experiments and the construction of an electromagnetic locomotive; the Edinburgh exhibition successfully attracted the attention of the proprietors of the Edinburgh 585\& Glasgow Railway (E \& GR), whose line had been opened in February 1842. Davidson built a full-size locomotive incorporating his principle, apparently at the expense of the railway company. The locomotive weighed 7 tons: each of its two axles carried a cylinder upon which were fastened three iron bars, and four electromagnets were arranged in pairs on each side of the cylinders. The motors he used were reluctance motors, the power source being zinc-iron batteries. It was named Galvani and was demonstrated on the E \& GR that autumn, when it achieved a speed of 4 mph (6.4 km/h) while hauling a load of 6 tons over a distance of 1 1/2 miles (2.4 km); it was the first electric locomotive. Nevertheless, further support from the railway company was not forthcoming, although to some railway workers the locomotive seems to have appeared promising enough: they destroyed it in Luddite reaction. Davidson staged a further exhibition in London in 1843 without result and then, the cost of battery chemicals being high, ceased further experiments of this type. He survived long enough to see the electric railway become truly practicable in the 1880s.

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SUBJECT AREA: Aerospace

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Wenham trained as a marine engineer and later specialized in screw propellers and high-pressure engines. He had many interests. He took his steamboat to the Nile and assisted the photographer F.Frith to photograph Egyptian tombs by devising a series of mirrors to deflect sunlight into the dark recesses. He experimented with gas engines and produced a hot-air engine. Wenham was a leading, if controversial, figure in the Microscopical Society and a member of the Royal Photographic Society; he developed an enlarger.

Wenham was interested in both mechanical and lighter-than-air flight. One of his friends was James Glaisher, a well-known balloonist who made many ascents to gather scientific information. When the (Royal) Aeronautical Society of Great Britain was founded in 1866, the Rules were drawn up by Wenham, Glaisher and the Honorary Secretary, F.W.Brearey. At the first meeting of the Society, on 27 June 1866, "On aerial locomotion and the laws by which heavy bodies impelled through the air are sustained" was read by Wenham. In his paper Wenham described his experiments with a whirling arm (used earlier by Cayley) to measure lift and drag on flat surfaces inclined at various angles of incidence. His studies of birds' wings and, in particular, their wing loading, showed that they derived most of their lift from the front portion, hence a long, thin wing was better than a short, wide one. He published illustrations of his glider designs covering his experiments of c. 1858–9. One of these had five slender wings one above the other, an idea later developed by Horatio Phillips. Wenham had some success with a model, but no real success with his full-size gliders.

In 1871, Wenham and John Browning constructed the first wind tunnel designed for aeronautical research. It utilized a fan driven by a steam engine to propel the air and had a working section of 18 in. (116 cm). Wenham continued to play an important role in aeronautical matters for many years, including a lengthy exchange of ideas with Octave Chanute from 1892 onwards.

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1 opinion.

cambiar de parecer to change one's mind

2 appearance.

1 to look like.

parece un palacio it looks like a palace

parecía un sueño it was like a dream

Ella parece un payaso She looks like a clown.

2 to look, to seem.

pareces cansado you look o seem tired

es alemán, pero no lo parece he's German, but he doesn't look it

¡pareces bobo! are you stupid, or what?

Ella parece cansada She seems tired.

3 to seem to, to appear to.

Ella parece creer lo contrario She seems to believe the opposite.

Le parece sentir algo She seems to feel something.

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Conjugation model [ AGRADECER], like link=agradecer agradecer

► nombre masculino

1 (opinión) opinion, mind

■ ¿has cambiado de parecer? have you changed your mind?

► verbo intransitivo

1 to seem, look (like)

■ parece fácil it seems easy, it looks easy

■ parece de verdad it looks real

■ parece un oso it looks like a bear

2 (opinar) to think

■ me parece que sí I think so

■ ¿qué te parece? what do you think?

3 (Used only in the 3rd pers, it does not take a subject) (aparentar) to look as if

■ parece que va a llover it looks as if it's going to rain

► verbo pronominal parecerse

1 to be alike, look like

■ se parecen mucho they're very much alike

■ el pequeño se parece mucho a su padre the little one looks just like his father

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a lo que parece apparently

al parecer apparently

parecer bien to seem right

parecer mal to seem wrong

¡parece mentira! I can't believe it!

según parece apparently

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1) opinion, view

2) appearance

1) to seem

2) look

3) appear

4) think

•

- parecerse

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1. SM

1) (=opinión) opinion, view

2) † (=aspecto)

2. VI

1) [uso copulativo]

a) [por el aspecto] + adj to look; + sustantivo to look like

b) (=por el carácter, el comportamiento) to seem

2) [uso impersonal] (=dar la impresión de) to seem

3) [indicando opinión]

4) † (=aparecer) to appear; [objeto perdido] to turn up

parecerse

* * *

verbo intransitivo

1) ( aparentar ser)

2) ( expresando opinión) (+ me/te/le etc)

3) (haber indicios, señales) (en 3^a pers)

parecerse v pron

a) ( asemejarse)

b) (recípr) to be alike

a) ( opinión) opinion

b)

* * *

verbo intransitivo

1) ( aparentar ser)

2) ( expresando opinión) (+ me/te/le etc)

3) (haber indicios, señales) (en 3^a pers)

parecerse v pron

a) ( asemejarse)

b) (recípr) to be alike

a) ( opinión) opinion

b)

* * *

parecer¹

¹ = view.

parecer²

² = loom, seem, sound (like), sound + like, strike + Pronombre Personal, look, smack of, come off as.

* * *

parecer¹ [E3 ]

(aparentar ser): parece mucho mayor de lo que es she looks much older than she is

parece muy simpática she seems very nice

pareces tonto, no te enteras de nada are you stupid or something? you never know what's going on

vestida así parece una artista de cine she looks like a movie star dressed like that

no pareces tú en esta foto this picture doesn't look like you (at all), it's not a good likeness of you

es de plástico pero parece de cuero it's plastic but it looks like leather

B parecer + INF to seem to + INF

el problema parece no tener solución there appears o seems o ( frml) would seem to be no solution to the problem

parece tener más habilidad de la que creímos al principio she seems to be o it seems she is more skillful than we thought at first

todo parece indicar que … everything seems to o appears to o ( frml) would seem to indicate that …

C (expresando opinión) (+ me/te/le etc):

sus comentarios me parecieron muy acertados I thought his remarks (were) very apt, his remarks seemed very apt to me

elegí la que me pareció mejor I chose the one that I thought was the best o the one that seemed the best

todo le parece mal he's never happy with anything

¿qué te parecieron mis primos? what did you think of my cousins?

su interpretación me pareció pobrísima I thought o felt she gave a very poor performance, to my mind her performance was very poor

1 ( en tercera persona):

según parece or al pareceror a lo que parece todo marcha viento en popa it looks as though everything's going smoothly, everything seems to be going smoothly

¿por fin se van? — así parece or parece que sí are they finally going? — it looks like it o it would seem so

aunque no lo parezca, estuve limpiando toda la mañana it might not look like it, but I spent the whole morning cleaning

¿le gusta? — parece que no does he like it? — apparently not

parece que no, pero cansa muchísimo you wouldn't think so, but it's very tiring

2 (+ me/te/le etc):

hazlo como mejor te parezca or como te parezca mejor do it however o as you think best

como a usted le parezca whatever you think best

creo que deberíamos invitarlos — ¿te parece? I think we ought to invite them — do you think so?

vamos a la playa ¿te parece? let's go to the beach, would you like to?, do you fancy going to the beach? ( BrE colloq)

podemos reunirnos mañana, si te parece bien we could meet up tomorrow if that's alright o OK with you o if that suits you

¿habrán entendido? — me parece que sí do you think they understood? — I think so

creo que así está bien ¿a ti qué te parece? I think it's alright like that, what do you think? o ( colloq) what do you reckon?

1 ( en tercera persona) parecer QUE + INDIC:

parece que va a llover it looks like (it's going to) rain

parece que fue ayer it seems like only yesterday

parece (ser) que tiene razón she appears to be right, it seems she's right

parece (ser) que ha habido un malentendido there appears to have been o it seems there has been a misunderstanding

parecería que ahora están dipuestos a negociar it would seem that they are now ready to negotiate

2 (+ me/te/le etc):

me/nos parece que tiene razón I/we think she's right

me pareció que no era necesario llamarlo I didn't think it necessary to phone him

¿te parece que éstas son horas de llegar a casa? what do you mean by coming home at this time?, what sort of time is this to be coming home?

F ( en tercera persona)

1 (+ subj):

parece increíble que hayan sobrevivido el accidente it seems incredible that they survived the accident

parece mentira que ya tenga 20 años it's hard to believe o I can't believe o it seems incredible that she's 20 already

(+ me/te/le etc): me parece difícil que venga I think it's unlikely she'll come

me parece raro que no te lo haya comentado it seems odd o I find it odd o I think it's odd that he hasn't mentioned it to you

me parece importante que ella esté presente I think it's important that she (should) be here

2 (+ inf), (+ me/te/le etc):

me parece importante dejar esto claro I think it's important to make this clear

¿te parece bonito contestarle así a tu madre? is that any way to speak to your mother?

G ( en tercera persona)

1 parecer QUE + IMPERF SUBJ:

parece que para él no pasaran los años he never seems to get any older

tiene 40 años — parece que tuviera muchos menos she's 40 — she looks much younger o you'd think she was much younger

2 no parecer QUE + SUBJ:

no parecía que la situación fuera a cambiar it didn't look as though the situation was going to change

no parece que le haya hecho mucha gracia la idea it doesn't look as though he liked the idea much, he doesn't seem to have been very taken with the idea

(+ me/te/le etc): no me parece que esté tan mal I don't think it's that bad

1 (asemejarse) parecerse A algn/algo (en lo físico) to look like sb/sth, to be like sb/sth; (en el carácter) to be like sb/sth

esa casa se parece bastante a la nuestra that house is rather like ours o fairly similar to ours

no son millonarios ni nada que se le parezca they're not millionaires, not by any means o ( colloq) not by a long shot ( AmE) o ( BrE) chalk

quien a los suyos se parece en nada los desmerece like breeds like

2 ( recípr) to be alike

no se parecen en nada they're not/they don't look in the least bit alike

estos cuadros se parecen mucho these pictures are very similar

1 (opinión) opinion

a mi parecer in my opinion

son del mismo parecer they're of the same opinion

es del parecer de que el asunto debería reconsiderarse she believes o she is of the opinion that the matter should be reconsidered ( frml)

ello me hizo cambiar de parecer it made me change my mind

de buen parecer ( ant); handsome

* * *

◊ parece fácil it looks easy;

◊ todo le parece mal he's never happy with anything;

◊ aunque no lo parezca, está limpio it might not look like it, but it's clean;

a) ( asemejarse) parecerse A algn/algo ( en lo físico) to look o to be like sb/sth;

b) ( recípr) to be alike;

◊ no se parecen en nada they're not/they don't look in the least bit alike;

◊ a mi parecer in my opinion;

* * *

♦ nm

1. [opinión] opinion;

somos de igual o [m5] del mismo parecer we are of the same opinion;

a mi/nuestro/ etc[m5]. parecer in my/our/ etc opinion;

cambiar de parecer to change one's mind

2. [apariencia]

♦ vi

parece un palacio it looks like a palace;

parecía un sueño it was like a dream

♦ v copulativo

to look, to seem;

pareces cansado you look o seem tired;

en la tele parece más joven she looks younger on the TV;

el casero parece buena persona the landlord seems nice o seems like a nice person;

parece de metal it looks like it's made of metal;

es alemán, pero no lo parece he's German, but he doesn't look it;

¡pareces bobo! are you stupid, or what?

♦ v impersonal

1. [indica opinión]

me parece que viven juntos I think o believe they live together;

me parece que no voy a aprobar I don't think I'm going to pass;

me parece que sí/no I think/don't think so;

el examen me pareció bastante complicado I found the exam rather difficult, I thought the exam was rather difficult;

no me pareció interesante I didn't find it interesting, I didn't think it was interesting;

¿qué te parece mi vestido? what do you think of my dress?;

¿qué te parece si vamos a mi casa? why don't we go to my place?, what do you say we go to my place?;

¿qué te parece la idea? – me parece bien/mal what do you think of the idea? – it seems OK to me/I don't think much of it;

nada le parece bien she's never happy with anything;

todo le parece bien he always says yes to everything;

no me parece bien que llegues tan tarde I'm not pleased about you arriving so late;

me parece mal que se experimente con animales I don't agree with experiments on animals;

no me parece mal que venga I don't see anything wrong with her coming;

haz lo que te parezca [lo que quieras] do what you like;

haz lo que te parezca mejor do as you see fit, do what you think best;

parece mentira que todavía no haya dimitido it's incredible that he hasn't resigned yet;

es bastante caro, ¿no te parece? it's rather expensive, don't you think?;

si te parece (bien) quedamos el lunes we can meet on Monday, if that's all right by you;

podemos comer fuera, ¿te parece? why don't we go out for a meal?, what do you say we go out for a meal?;

¿te parece bonito lo que has hecho? are you pleased with yourself o satisfied now?

2. [tener aspecto de]

parece que le gusta it looks as if o it seems (that) she likes it;

no parece que le guste he doesn't seem to like it, it seems (that) he doesn't like it;

parece (ser) que hay un pequeño malentendido there seems to be a small misunderstanding, it seems (like) there's a small misunderstanding;

ahora parece (ser) que quieren echarse atrás it now seems they want to pull out;

a lo que parece, al parecer apparently;

tienen mucho dinero, aunque no lo parezca it may not seem like it, but they've got a lot of money;

eso parece so it seems;

parece como si estuviéramos en invierno it's as if it was still winter;

parece que fue ayer cuando nos conocimos it seems like only yesterday that we met;

¿lo ha hecho? – parece que sí has she done it? – it seems so o it seems she has;

¿te han invitado? – parece que no have they invited you? – it seems not o it doesn't seem so;

parece que no, pero se tarda en llegar hasta aquí you'd be surprised how long it takes you to get here;

según parece apparently

♦ See also the pronominal verb parecerse

* * *

I m opinion, view;

al parecer apparently;

de buen parecer well-dressed;

dar su parecer give one’s opinion

II v/i seem, look;

me parece que I think (that), it seems to me that;

me parece bien it seems fine to me;

¿qué te parece? what do you think?;

si a usted le parece if you’re agreeable, if it suits you;

parece que va a llover it looks like rain, it looks like it’s going to rain

* * *

parecer {53} vi

1) : to seem, to look, to appear to be

parece bien fácil: it looks very easy

así parece: so it seems

pareces una princesa: you look like a princess

2) : to think, to have an opinion

me parece que sí: I think so

3) : to like, to be in agreement

si te parece: if you like, if it's all right with you

♦ See also the reflexive verb parecerse

* * *

parecer vb

1. (tener el aspecto) to look

Con un nombre se dice look like

2. (dar la impresión) to seem

3. (opinar) to think [pt. & pp. thought]

4. (uso impersonal) to look / to seem

. из этого вытекает, что; приходить к выводу

•

Several conclusions were reached (or made, or drawn).

•

From these experiments it was concluded (or inferred) that in order to...

•

To draw a conclusion (or inference).

* * *

 Since sufficient knowledge concerning С for short and long bubbles is lacking, it is difficult to draw any general conclusions.

 A number of observations may be made concerning the results in Figs.....

 From these results, it was inferred that pure water was not a good model of a bird.

 From an analysis of these data, it was concluded that gelatin with 15 volume percent microballoons would make a good replica model of a bird.

 It may also be deduced from Fig. that the percentage reduction in drag is likely to be much greater in long tunnels than in short tunnels.

SUBJECT AREA: Aerospace, Ports and shipping

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He was educated at Gresham's School in Holt and at Peterhouse College, Cambridge, where he graduated in engineering in 1931; he was made an Honorary Fellow in 1974. Cockerell entered the engineering firm of W.H.Allen \& Sons of Bedford as a pupil in 1931, and two years later he returned to Cambridge to engage in radio research for a further two years. In 1935 he joined Marconi Wireless Telegraph Company, working on very high frequency (VHF) transmitters and direction finders. During the Second World War he worked on airborne navigation and communication equipment, and later he worked on radar. During this period he filed thirty six patents in the fields of radio and navigational systems.

In 1950 Cockerell left Marconi to set up his own boat-hire business on the Norfolk Broads. He began to consider how to increase the speed of boats by means of air lubrication. Since the 1870s engineers had at times sought to reduce the drag on a boat by means of a thin layer of air between hull and water. After his first experiments, Cockerell concluded that a significant reduction in drag could only be achieved with a thick cushion of air. After experimenting with several ways of applying the air-cushion principle, the first true hovercraft "took off" in 1955. It was a model in balsa wood, 2 ft 6 in. (762 mm) long and weighing 4½ oz. (27.6 g); it was powered by a model-aircraft petrol engine and could travel over land or water at 13 mph (20.8 km/h). Cockerell filed his first hovercraft patent on 12 December 1955. The following year he founded Hovercraft Ltd and began the search for a manufacturer. The government was impressed with the invention's military possibilities and placed it on the secret list. The secret leaked out, however, and the project was declassified. In 1958 the National Research and Development Corporation decided to give its backing, and the following year Saunders Roe Ltd with experience of making flying boats, produced the epoch-making SR N1, a hovercraft with an air cushion produced by air jets directed downwards and inwards arranged round the periphery of the craft. It made a successful crossing of the English Channel, with the inventor on board.

Meanwhile Cockerell had modified the hovercraft so that the air cushion was enclosed within flexible skirts. In this form it was taken up by manufacturers throughout the world and found wide application as a passenger-carrying vehicle, for military transport and in scientific exploration and survey work. The hover principle found other uses, such as for air-beds to relieve severely burned patients and for hover mowers.

The development of the hovercraft has occupied Cockerell since then and he has been actively involved in the several companies set up to exploit the invention, including Hovercraft Development Ltd and British Hovercraft Corporation. In the 1970s and 1980s he took up the idea of the generation of electricity by wavepower; he was Founder of Wavepower Ltd, of which he was Chairman from 1974 to 1982.

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SUBJECT AREA: Aerospace

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Wilbur and Orville designed and built bicycles in Dayton, Ohio. In the 1890s they developed an interest in flying which led them to study the experiments of gliding pioneers such as Otto Lilienthal in Germany, and their fellow American Octave Chanute. The Wrights were very methodical and tackled the many problems stage by stage. First, they developed a method of controlling a glider using movable control surfaces, instead of weight-shifting as used in the early hand-gliders. They built a wind tunnel to test their wing sections and by 1902 they had produced a controllable glider. Next they needed a petrol engine, and when they could not find one to suit their needs they designed and built one themselves.

On 17 December 1903 their Flyer was ready and Orville made the first short flight of 12 seconds; Wilbur followed with a 59-second flight covering 853 ft (260 m). An improved design, Flyer II, followed in 1904 and made about eighty flights, including circuits and simple ma-noeuvres. In 1905 Flyer III made several long flights, including one of 38 minutes covering 24½ miles (39 km). Most of the Wrights' flying was carried out in secret to protect their patents, so their achievements received little publicity. For a period of two and a half years they did not fly, but they worked to improve their Flyer and to negotiate terms for the sale of their invention to various governments and commercial syndi-cates.

In 1908 the Wright Model A appeared, and when Wilbur demonstrated it in France he astounded the European aviators by making several flights lasting more than one hour and one of 2 hours 20 minutes. Considerable numbers of the Model A were built, but the European designers rapidly caught up and overtook the Wrights. The Wright brothers became involved in several legal battles to protect their patents: one of these, with Glenn Curtiss, went on for many years. Wilbur died of typhoid fever in 1912. Orville sold his interest in the Wright Company in 1915, but retained an interest in aeronautical research and lived on to see an aeroplane fly faster than the speed of sound.

[br]

см. в процессе

* * *

 In the course of the clutch development, a new model was suggested.

 During the course of these tests, the superiority of the tangential drain was clearly demonstrated.

 Three blockage elements were employed throughout the course of the experiments.

• В обсуждениях такого рода необходимо указывать... - In discussions of this kind it is necessary to point out...

• В то же самое время данные результаты указывают, что... - At the same time, the results indicate that...

• Возможно, стоит указать, что... - It is perhaps worth pointing out that...

• Вряд ли имеется необходимость указывать, что... - It is hardly necessary to point out that...

• Все имеющиеся свидетельства указывают, что... - All available evidence indicates that...

• Все опубликованные отчеты указывают на наличие... - All the published reports point to the presence of...

• Данные эксперименты указывали, что... - These experiments indicated that...

• Для специалистов стоит указать, что... - It is worth pointing out to experts that the spaces of...

• Другие исследования также указывали, что... - Other studies have also indicated that...

• Знак минус в уравнении (4) указывает, что... - The minus sign in (4) indicates that...

• Изучение... указывает, что... - Studies of... indicate that...

• Многие исследователи это отвергают. Они указывают, что... - Many scientists object to this. They point out that...

• Можно было бы указать, что... - It may be pointed out that...

• Мы не будем обсуждать детально ни одну из этих концепций, а просто хотим указать, что... - We shall not discuss any of these concepts in detail, but merely wish to point out that...

• Мы уже указали важность (чего-л). - We have indicated the relevance of...

• Мы. хотим указать явно, что... - We wish to point out explicitly that...

• На этом этапе необходимо указать очень важный факт. - At this stage a very important fact must be pointed out.

• Наконец, данная модель должна указывать... - Finally, the model must indicate...

• Наконец, мы должны указать на полезность... - Finally, we should point out the usefulness of...

• Накопленный опыт начал нам указывать, что... - Our accumulated experience began to indicate that...

• Наши оценки указывают, что... - Our estimates indicate that...

• Несколько исследований указывают, что... - Several studies indicate that...

• Однако будет честно указать, что... - It is fair, though, to point out that...

• Однако дальнейшие проверки указывали, что... - But further tests indicated that...

• Однако существующие экспериментальные данные указывают, что... - Present experimental evidence indicates, however, that...

• Однако, как указывает Смит [1], безусловно существуют примеры... - But, as Smith [lj points out, there are certainly examples of...

• Однако, как указывает Смит [lj, недостаточно, чтобы... - But, as Smith [1] points out, it is not sufficient that...

• Одним интересным свойством этих результатов является то, что они указывают... - One interesting feature of these results is that they indicate...

• Позднее мы укажем, что... - We will indicate later that...

• Приведем исключения, которые указывает Смит в статье [1]. - Smith [1] points out certain exceptions as follows.

• Различные авторы уже указывали, что... - It has been pointed out by various writers that...

• С другой стороны, недавние данные указывают, что... - On the other hand, recent findings indicate that...

• Смит [1] указал, что... - Smith [1] has indicated that...

• Эти явления указывают на существование... - These phenomena point to the existence of...

• Это был Эйнштейн, кто указал, что... - It was Einstein who pointed out that...

• Это довольно настойчиво указывает, что... - This suggests quite strongly that...

• Это обосновывает приведенную выше интерпретацию и указывает, что... - This justifies the above interpretation and indicates that...

1 to check.

tengo que comprobar si lo tengo I have to check o see if I've got it

¿podrías comprobar a qué hora sale el tren? could you check what time the train leaves?

Ricardo comprobó los resultados Richard checked the results.

2 to prove.

se ha comprobado que la vacuna es efectiva the vaccine has been proved to be effective

3 to find.

María comprobó la solución Mary found the solution.

* * *

Conjugation model [ CONTAR], like link=contar contar

► verbo transitivo

1 (verificar) to verify, check

2 (demostrar) to prove

3 (observar) to see, observe

■ como podrán ustedes comprobar as you can see for yourselves

4 (confirmar) to confirm

* * *

1) to check

2) verify, probe

* * *

1) (=examinar) [+ billete, documento, frenos] to check

2) (=confirmar) [+ teoría, existencia] to prove; [+ eficacia, veracidad] to verify, confirm

3) frm (=darse cuenta) to realize

* * *

verbo transitivo

a) ( verificar) <operación/resultado> to check

b) ( demostrar) to prove

c) ( darse cuenta) to realize

d) prueba ( confirmar) to confirm

* * *

= check, cross check, make + sure, verify, vet, double-check [doublecheck], collate, test, check out, check for.

Ex. This would be the last stage in the compilation of the scheme in order to check that the scheme seems likely to be effective.

Ex. All Allibone's work reveals how important it is for bibliographers to cross check their references and not take earlier work at its face value.

Ex. DOBIS/LIBIS first checks the borrower's number to make sure that it is in the files and is valid.

Ex. Cataloguers use the NUC and other LC catalogues to verify authors and titles and as sources of authoritative catalogue records.

Ex. All three types of material, when first received by DG XIII, are submitted to the Technological Information and Patents Division of DG XIII in order to vet items for possible patentable inventions.

Ex. Duplicates should be double-checked before being handled as duplicates.

Ex. It is no longer necessary to collate unaided, since copies can now be compared mechanically with a Hinman collating machine, which rapidly shows up all variation between them.

Ex. Inmate library workers often test a new librarian, but once he or she has passed the test, they usually become very protective and staunch promoters of the library.

Ex. Where problems do arise it is sensible to check out the training programme before blaming the assistant for poor performance of duties.

Ex. This was important before computers were invented, when calculations were all done by hand, and also were done repeatedly to check for calculation errors.

----

* comprobar con = check against.

* comprobar de antemano = pretest [pre-test], pilot-test.

* comprobar en la práctica real = field-test.

* comprobar las necesidades económicas = means test.

* comprobar la validez = pilot-test.

* comprobar la validez de = test + the validity of.

* comprobar los ingresos = means test.

* comprobar si el contenido de un vídeo es adecuado o no = vet + video.

* comprobar si un dispositivo o dirección web está activa = ping.

* comprobar una hipótesis = test + hypothesis.

* haber sido comprobado exhaustivamente = be thoroughly tested.

* sin comprobar = untested.

* volver a comprobar = check back.

* * *

verbo transitivo

a) ( verificar) <operación/resultado> to check

b) ( demostrar) to prove

c) ( darse cuenta) to realize

d) prueba ( confirmar) to confirm

* * *

* * *

comprobar [ A10 ]

1 (verificar) ‹operación/resultado› to check

¿le compruebo el nivel del aceite? shall I check the oil for you?

compruébalo tú mismo si no me crees check o see for yourself if you don't believe me

voy a comprobar si funciona I'm going to see o check if it works

2 (demostrar) to prove

¿tiene algún documento que compruebe su identidad? do you have any proof of identity o any identification?

3 (darse cuenta) to realize

al examinarlo comprobó que le faltaba una pieza when he examined it he realized that there was a part missing

comprobé con tristeza que era cierto I was sad to discover that it was true

4 «prueba» (confirmar) to confirm

* * *

a) ( verificar) ‹operación/resultado/funcionamiento› to check

b) ( demostrar) to prove

c) ( darse cuenta) to realize

d) [ hecho] ( confirmar) to confirm

* * *

comprobar vt

1. [revisar] to check;

comprueba los frenos antes de salir de viaje check your brakes before setting out on a journey;

tengo que comprobar si lo tengo I have to check o see if I've got it

2. [averiguar] to check;

¿podrías comprobar a qué hora sale el tren? could you check what time the train leaves?;

he comprobado en carne propia que estabas en lo cierto I found out o discovered through personal experience that you were right

3. [demostrar] to prove;

esto comprueba que yo tenía razón this proves that I was right;

se ha comprobado que la vacuna es efectiva the vaccine has been proved to be effective

* * *

1 check

2 ( darse cuenta de) realize

* * *

comprobar {19} vt

1) : to verify, to check

2) : to prove

* * *

comprobar vb (verificar) to check