In the synthesis of

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The building block of a metal-oxide semiconductor integrated circuit is the field-effect transistor.

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These enzymes catalyze the synthesis of RNA from its building blocks: four different ribonucleoside triphosphales.

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The synthesis of... was a prelude to the advent of life.

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The synthesis of... was a prelude to the advent of life.

SUBJECT AREA: Chemical technology, Synthetic materials, Textiles

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He early showed an aptitude for chemistry and in 1853 entered the Royal College of Chemistry as a student under A.W.von Hofmann, the first Professor at the College. By the end of his first year, he had carried out his first piece of chemical research, on the action of cyanogen chloride on phenylamine, which he published in the Journal of the Chemical Society (1857). He became honorary assistant to von Hofmann in 1857; three years previously he had set up his own chemical laboratory at home, where he had discovered the first of the azo dyes, aminoazonapththalene. In 1856 Perkin began work on the synthesis of quinine by oxidizing a salt of allyl toluidine with potassium dichromate. Substituting aniline, he obtained a dark-coloured precipitate which proved to possess dyeing properties: Perkin had discovered the first aniline dye. Upon receiving favourable reports on the new material from manufacturers of dyestuffs, especially Pullars of Perth, Perkin resigned from the College and turned to the commercial exploitation of his discovery. This proved highly successful. From 1858, the dye was manufactured at his Greenford Green works as "Aniline Purple" or "Tyrian Purple". It was later to be referred to by the French as mauve. Perkin's discovery led to the development of the modern dyestuffs industry, supplanting dyes from the traditional vegetable sources. In 1869, he introduced two new methods for making the red dye alizarin, in place of the process that involved the use of the madder plant (Rubia tinctorum). In spite of German competition, he dominated the British market until the end of 1873. After eighteen years in chemical industry, Perkin retired and devoted himself entirely to the pure chemical research which he had been pursuing since the 1850s. He eventually contributed ninety papers to the Chemical Society and further papers to other bodies, including the Royal Society. For example, in 1867 he published his synthesis of unsaturated organic acids, known as "Perkin's synthesis". Other papers followed, on the structure of "Aniline Purple". In 1881 Perkin drew attention to the magnetic-rotatory power of some of the substances he had been dealing with. From then on, he devoted particular attention to the application of this phenomenon to the determination of chemical structure.

Perkin won wide recognition for his discoveries and other contributions to chemistry.

The half-centenary of his great discovery was celebrated in July 1906 and later that year he received a knighthood.

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SUBJECT AREA: Chemical technology

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Bosch spent a year as a metalworker before studying chemistry at Leipzig University, obtaining his doctorate in 1898. The following year, he entered Badische Soda-, Anilin Fabrik (BASF), the leading German manufacturer of dyestuflfs. Between 1902 and 1907 he spent much time investigating processes for nitrogen fixation. In 1908 Fritz Haber told BASF of his laboratory-scale synthesis of ammonia from its constituent elements, and in the following year Bosch was assigned to developing it to the industrial scale. Leading a large team of chemists and engineers, Bosch designed the massive pressure converter and other features of the process and was the first to use the water gas shift reaction to produce the large quantities of hydrogen that were required. By 1913 Bosch had completed the largest chemical engineering plant at BASF's works at Oppau, and soon it was producing 36,000 tons of ammonium sulphate a year. Bosch enlarged the Oppau plant and went on to construct a larger plant at Leuna.

In 1914 Bosch was appointed a Director of BASF. At the end of the First World War he became Technical Adviser to the German delegation at the peace conference. During the 1920s BASF returned to its position of pre-eminence in high-pressure technology, thanks largely to Bosch's leadership. Although increasingly absorbed in administrative matters, Bosch was able to support the synthesis of methane and the hydrogenation of coal tar and lignite to make petrol. In 1925 BASF merged with other companies to form the giant IG Farbenindustrie AG, of which Bosch became Chairman of the Managing Board. His achievements received international recognition in 1931 when he was awarded, with F. Bergius, the Nobel Prize in Chemistry for high-pressure synthesis.

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- synteza myślowa mental synthesis

- dokonać syntezy to make a synthesis

- synteza związków organicznych the synthesis of organic compounds

- □ synteza termojądrowa Fiz. thermonuclear synthesis

   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)

► verbo pronominal

1 (hacer frente) to face (a/con, -), confront (a/con, -)

■ tuvo que enfrentarse con los sindicatos he had to confront the unions

2 DEPORTE to meet (a/con, -)

3 (pelearse) to have an argument (a, with), fall out (a, with); (chocar) to clash (a/con, with)

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1) (=pelear) [personas] to have a confrontation; [equipos] to face each other

2)

a) [+ persona] to confront

b) [+ problema, dificultad] to face (up to), confront

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(v.) = struggle, tackle, come to + terms with, engage, come + face to face

Ex. The chemist, struggling with the synthesis of an organic compound, has all the chemical literature before him in his laboratory.

Ex. Chapter 2 tackles books, pamphlets and printed sheets, and chapter 3 is dedicated to cartographic materials.

Ex. Much of the conventional wisdom of librarianship is going to have to undergo what is so aptly described as an 'agonizing reappraisal' before we can come to terms with the new information age.

Ex. Australian destroyers engaged the Japanese shore guns and the mine sweepers carried out their task successfully, but not without loss.

Ex. If they come face to face in a fight to death, is it really that hard to imagine who would win?.

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vpr

1. [afrontar]

nos enfrentamos a una grave crisis we are facing a serious crisis;

enfrentarse a los hechos to face the facts;

se enfrentó a su enfermedad con valor she faced up to her illness bravely

2. [en contienda] [dos bandos] to meet, to clash;

los dos equipos se enfrentarán por el campeonato the two teams will play each other for the championship;

enfrentarse a o [m5] con alguien to confront sb;

nos enfrentamos al enemigo we confronted the enemy;

los manifestantes se enfrentaron con la policía the demonstrators clashed with the police;

a Brasil le toca enfrentarse con Suecia Brazil has been drawn against Sweden

3. [discutir] to clash

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1 DEP meet

enfrentarse con alguien confront s.o.

enfrentarse a algo face (up to) sth

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vr

enfrentarse con : to clash with

enfrentarse a : to face up to

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enfrentarse vb

1. (hacer frente) to face

2. (jugar) to play

3. (pelearse) to argue

► verbo pronominal

1 (físicamente) to make an effort, exert oneself; (moralmente) to try hard, strive

■ se esfuerza en ser educado he tries hard to be polite

■ debes esforzarte si quieres aprobar you'll have to work hard if you want to pass

■ se ha esforzado para llegar a la cumbre she has striven to get to the top

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VPR to exert o.s., make an effort

* * *

= put forth + effort, put forth + energy, strive, strain, labour [labor, -USA], toil, struggle, work + hard, slave away, try + hard.

Ex. Because of this human characteristic of dislike of work, most people must be coerced, controlled, directed, threatened with punishment to get them to put forth adequate effort.

Ex. The goal may be of little value or of high scientific or cultural significance, but energy is put forth to accomplish a task.

Ex. The abstractor must resist the temptation to use long sentences in striving to avoid repetition.

Ex. His small foreign-made car strained with the added burden of an interior packed to capacity with personal belongings and a heavily laden U-Haul trailor attached to the rear.

Ex. So we see many wits and ingenuities lying scattered up and down the world, whereof some are now labouring to do what is already done and puzzling themselves to reinvent what is already invented.

Ex. His novels reflect the story of the spirit of man, undaunted and ceaselessly toiling and achieving ever higher levels of culture.

Ex. The chemist, struggling with the synthesis of an organic compound, has all the chemical literature before him in his laboratory.

Ex. Not only are the standards written, but there is a body called the Peer Council which works very hard at enforcing the standards.

Ex. Anyone who's spoken to me recently is probably aware that on most nights I'm up slaving away to the wee hours of the morning on my project.

Ex. Over the years the profession has tried hard to ignore the steady stream of library school closings.

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* esforzarse al máximo = do + Posesivo + utmost, stretch + Reflexivo, stretch + Nombre + to the limit, give + Posesivo + utmost, lean over + backwards, work + hard, give + Posesivo + best.

* esforzarse por = endeavour [endeavor, -USA], try + Posesivo + best, go out of + Posesivo + way to + Infinitivo, do + Posesivo + best, exert + effort, try + Posesivo + heart out, give + Posesivo + best, take + (great) pains to.

* esforzarse por conseguir = strive for, work toward(s).

* esforzarse por lograr = strive for.

* * *

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vpr

to make an effort;

tienes que esforzarte más si quieres aprobar you'll have to make more of an effort if you want to pass;

nos esforzamos, pero fue imposible ganarlos we tried very hard, but they were impossible to beat;

no te esfuerces, no puede oírte don't bother (shouting), she can't hear you;

se esforzaron enormemente en la tarea they put a huge amount of effort into the task;

esforzarse en o [m5] por hacer algo to make an effort to do sth;

me esforcé por ayudarlos I made a real effort o did my best to help them;

nos hemos esforzado mucho por ti we've made a real effort for you, we've really put ourselves out for you;

se esforzó en contener las lágrimas she tried hard to hold back the tears

* * *

v/r make an effort, try hard

* * *

vr

: to make an effort

* * *

esforzarse vb to try hard [pt. & pp. tried] / to work hard

se esforzó mucho he tried very hard / he worked very hard

to have a hard time.

* * *

(económicos) to be hard up 2 (dificultades) to be in a tight spot

* * *

(v.) = struggle, pass through + adversity, have + a thin time, be under strain, bear + hardship, be hard pressed, feel + the pinch, have + a hard time, the wolves + be + at the door, have + a tough time

Ex. The chemist, struggling with the synthesis of an organic compound, has all the chemical literature before him in his laboratory.

Ex. The personnel officer could see that the director was passing through adversity.

Ex. But the week by week publication of details of companies' accounts in the Bookseller cannot but show that many publishing houses have been having a very thin time indeed.

Ex. Sources of domestic supply of periodicals in the socialist countries are also under strain or have collapsed.

Ex. So we see extraordinary hardships cheerfully borne (indeed, apparently enjoyed) by zealous mountaineers, earnest single-handed yachtsmen floating round the world, and all-weather fishing-hobbyists sit patiently at the side of, and sometimes in, rivers, undeterred by the paucity of their catches.

Ex. Patent lawyers would be hard pressed if they had to operate without abstracts to the millions upon millions of patents issued for centuries all around the world.

Ex. Not unlike many municipalities in these inflationary times, Earnscliffe is feeling the pinch of a severely high general property tax -- i.e., the tax on real estate and personal property, both tangible and intangible.

Ex. Scholars are going to have a hard time finding that reference.

Ex. Yes, I know it's late, but there has been 'trouble at mill' -- the wolves have been at the doors, and the natives are nervous.

Ex. He had a tough time lugging his lumpy, oversized travelbag onto the plane and stuffing it in the overhead bin.

* * *

to be having troubles, to go through a lot of trouble, to be having a lot of trouble, to go through difficulties.

* * *

(v.) = struggle, be under strain, bear + hardship, have + a difficult time, experience + difficult times, pass through + difficult times, face + difficult times

Ex. The chemist, struggling with the synthesis of an organic compound, has all the chemical literature before him in his laboratory.

Ex. Sources of domestic supply of periodicals in the socialist countries are also under strain or have collapsed.

Ex. So we see extraordinary hardships cheerfully borne (indeed, apparently enjoyed) by zealous mountaineers, earnest single-handed yachtsmen floating round the world, and all-weather fishing-hobbyists sit patiently at the side of, and sometimes in, rivers, undeterred by the paucity of their catches.

Ex. Videotext services have had a notoriously difficult time becoming accepted in the US marketplace.

Ex. Consumer publishing is experiencing difficult times and there are specific developments which are influencing the market for children's books.

Ex. The author discusses the history of and services offered by the Folger Shakespeare Library which has passed through difficult times and emerged with a new building and a new personality.

Ex. This may be a reason why the publishing industry is facing such difficult times.

* * *

SUBJECT AREA: Chemical technology

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Haber's father was a manufacturer of dyestuffs, so he studied organic chemistry at Berlin and Heidelberg universities to equip him to enter his father's firm. But his interest turned to physical chemistry and remained there throughout his life. He became Assistant at the Technische Hochschule in Karlsruhe in 1894; his first work there was on pyrolysis and electrochemistry, and he published his Grundrisse der technischen Electrochemie in 1898. Haber became famous for thorough and illuminating theoretical studies in areas of growing practical importance. He rose through the academic ranks and was appointed a full professor in 1906. In 1912 he was also appointed Director of the Institute of Physical Chemistry and Electrochemistry at Dahlem, outside Berlin.

Early in the twentieth century Haber invented a process for the synthesis of ammonia. The English chemist and physicist Sir William Crookes (1832–1919) had warned of the danger of mass hunger because the deposits of Chilean nitrate were becoming exhausted and nitrogenous fertilizers would not suffice for the world's growing population. A solution lay in the use of the nitrogen in the air, and the efforts of chemists centred on ways of converting it to usable nitrate. Haber was aware of contemporary work on the fixation of nitrogen by the cyanamide and arc processes, but in 1904 he turned to the study of ammonia formation from its elements, nitrogen and hydrogen. During 1907–9 Haber found that the yield of ammonia reached an industrially viable level if the reaction took place under a pressure of 150–200 atmospheres and a temperature of 600°C (1,112° F) in the presence of a suitable catalyst—first osmium, later uranium. He devised an apparatus in which a mixture of the gases was pumped through a converter, in which the ammonia formed was withdrawn while the unchanged gases were recirculated. By 1913, Haber's collaborator, Carl Bosch had succeeded in raising this laboratory process to the industrial scale. It was the first successful high-pressure industrial chemical process, and solved the nitrogen problem. The outbreak of the First World War directed the work of the institute in Dahlem to military purposes, and Haber was placed in charge of chemical warfare. In this capacity, he developed poisonous gases as well as the means of defence against them, such as gas masks. The synthetic-ammonia process was diverted to produce nitric acid for explosives. The great benefits and achievement of the Haber-Bosch process were recognized by the award in 1919 of the Nobel Prize in Chemistry, but on account of Haber's association with chemical warfare, British, French and American scientists denounced the award; this only added to the sense of bitterness he already felt at his country's defeat in the war. He concentrated on the theoretical studies for which he was renowned, in particular on pyrolysis and autoxidation, and both the Karlsruhe and the Dahlem laboratories became international centres for discussion and research in physical chemistry.

With the Nazi takeover in 1933, Haber found that, as a Jew, he was relegated to second-class status. He did not see why he should appoint staff on account of their grandmothers instead of their ability, so he resigned his posts and went into exile. For some months he accepted hospitality in Cambridge, but he was on his way to a new post in what is now Israel when he died suddenly in Basel, Switzerland.

[br]

to have difficulty, to find difficulty.

* * *

(v.) = struggle, experience + difficulty, be hard pressed

Ex. The chemist, struggling with the synthesis of an organic compound, has all the chemical literature before him in his laboratory.

Ex. Initially, the library staff experienced difficulties in adjusting to the new service.

Ex. Patent lawyers would be hard pressed if they had to operate without abstracts to the millions upon millions of patents issued for centuries all around the world.

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συντίθημι, used by Hom. only in [voice] Med., v. infr.:—[voice] Pass. (v. infr.), but σύγκειμαι is more freq. as [voice] Pass.:—

A place or put together,

τὴν οὐρὴν καὶ τὸν σπλῆνα.. συνθεὶς ὁμοῦ Hdt.2.47

, cf. 4.67;

ὅπλα ἐν τῷ ναῷ X.HG2.3.20

;

ἅπαντα εἰς ἕν E.IT 1016

;

ἐν ὀλίγῳ πάντα Id.Supp. 1126

(lyr.);

ὁ πρῶτος συνθεὶς εἰς ταὐτὸν τὰ δύο ταῦτα βιβλίδια Gal.15.109

; σ. ἱμάτια, opp. ἀνασείειν, fold them together, X. Oec.10.11; σ. σκέλη, opp. ἐκτείνειν, Id.Cyn.5.10; opp. διαιρεῖσθαι, Pl.Sph. 252b; σ. ἄρθρα στόματος close the lips, E.Cyc. 625; εἰς τὸ οὖλον ( αυλον cod.) σ. τὴν κόμην, = calamistrat, Gloss.:—[voice] Pass., τὸ συντίθεσθαι καθ' ὁντινοῦν τρόπον ῥῖγος οὐκ ἀγαθόν ἐστι any sort of combination of shivering (with other symptoms), Gal.16.746.

2 technical uses,

a Math., add together, of numbers, Hdt.3.95 ([voice] Pass.); τό τε ἀρχαῖον καὶ τὸ ἔργον principal and interest, D.27.17, cf. 29.30: Geom., of lines and figures, Archim.Spir.Praef., Papp.70.4.

b Math. also, of the transformation of a ratio componendo, Arist. EN 1131b8 ([voice] Pass.), Euc.5.18,24 ([voice] Pass.).

c Logic, combine the terms of a proposition, Arist.Metaph. 1012a4, 1024b19 ([voice] Pass.); also, use the fallacy of composition (cf.

σύνθεσις 1.2e

), Id.Rh. 1401a24.

d Rhet., accumulate, joined with ἐποικοδομεῖν (to form a climax), ib. 1365a16.

e σ. λόγον make up an account, PHib.1.48.15 (iii B.C.).

II put together constructively, so as to make a whole, πεντηκοντέρους καὶ τριήρεας (as a bridge) Hdt.7.36; λίθους, of builders, Th.4.4, IG42(1).103.59 (Epid., iv B.C.); πλίνθους, ξύλα, X.Mem.3.1.7, etc.;

τὰ ὄστρακα IG42(1).121.82

(Epid., iv B.C.);

τὰ κομισθέντα Sor. 2.64

;

ἐκ τούτων τὰ μέγιστα.. συνθεὶς τοῦτον.. τὸν λόγον ποιήσομαι Hippias Eleus 6

D.;

συλλαβάς Pl.Cra. 424e

.

2 construct, frame,

τὸ θνητὸν γένος Pl.Ti. 69d

; ὁ συνθείς the creator, ib. 33d:—[voice] Pass., to be constructed, of the material universe, opp. διαλύεσθαι, Arist.Cael. 304b30.

b σ. τι ἀπό τινος compose or make one thing of or from another, Hdt.4.23;

τὸ ἐκ τῶν νεῶν καὶ τοῦ πεζοῦ πλῆθος συντιθέμενον Id.7.184

;

ἐξ ὧν [συλλαβῶν] τὰ ὀνόματα συντίθενται Pl.Cra. 425a

, cf. 434a;

συνθεὶς πρᾶγμα πράξει δι' ὀνόματος καὶ ῥήματος Id.Sph. 262e

; εἴδωλον οὐρανοῦ ξυνθεῖσ' ἄπο (Reiske for ὕπο) E.Hel.34: metaph.,

συντιθεὶς γέλων πολύν S.Aj. 303

; δυοῖν ἅμιλλαν ξ. strive for two things at once, E.El.95.

3 construct or frame a story,

συνθέντες λόγον Id.Ba. 297

, cf. Ar.Ra. 1052 (anap.), Pl.Phdr. 260b;

οἱ τὰς τέχνας τῶν λόγων συντιθέντες Arist.Rh. 1354a12

; narrate in writing,

τὰ Ἑλληνικά Th.1.97

, cf. 21; compose, σ. μύθους, ποίησιν, μελῳδίαν, ὄρχησιν, Pl.R. 377d, Phdr. 278c, Lg. 812d, 816c;

αἴνιγμα Id.Ap. 27a

;

ὁ τὴν ἐνθάδε συνθεὶς ἀνατομήν Gal.15.147

:—[voice] Pass., [tense] pf. συντέθειται ib.797;

περὶ ὀλίγας οἰκίας αἱ.. τραγῳδίαι συντίθενται Arist.Po. 1453a19

.

4 Math., of the synthesis of a geometrical problem, opp. ἀναλύω, Id.SE 175a28, Papp.648.13; συντεθήσεται τὸ πρόβλημα οὕτως the synthesis of the problem will proceed thus, Archim.Sph.Cyl.2.1, cf. Apollon. Perg.Con.1 Praef., 2.44, al.

5 frame, devise, contrive, ὁ συνθεὶς τάδε the framer of this plot, S.OT 401, cf. Th.8.68;

ἐξ ἐπιβουλῆς σ. ταῦτα Antipho 5.25

;

σ. λόγους ψευδεῖς Id.6.9

;

ψευδεῖς αἰτίας D.25.28

;

τὴν κατηγορίαν And.1.6

, etc.; rarely in good sense,

εὖ πρᾶγμα συντεθὲν ὄψεσθε D.18.144

.

6 put together, take in, comprehend,

παιδὸς μόρον A.Supp.65

(lyr.);

ὄμνυ.. θεῶν συντιθεὶς ἅπαν γένος E.Med. 747

;

πάσας συντιθεὶς ψέγει γυναῖκας Id.Fr. 657

, cf. Hec. 1184: ἐν βραχεῖ ξυνθεὶς λέγω putting things shortly together, speaking briefly, S.El. 673.

III commit to a person's care, deliver to him for his own use or that of others, PMich.Zen.2.3,14 (iii B.C.), PCair.Zen.4.23, 6.11,64, 299.9, al. (iii B.C.);

γνώριζε οὐχ ὑπάρχον παρ' ἡμῖν ἀργύριον τοσοῦτο ὥστε ἱκανὸν συνθεῖναι Πυρρίχῳ PMich.Zen.28.18

, cf. 32.7, PSI4.392.7, 5.524.3, 6.613.8, 7.862.1, PLille 15.3 (all iii B.C.); τινὶ ὀστᾶ, ἐπιστολάς, πλῆθος χρυσίου, etc., Plb.5.10.4, 8.17.4, 15.25.16, cf. 27.7.1, 28.22.3, IG12(5).590.12 (Ceos, ii B.C.), 11(4).1056.4 (Delos, ii B.C., cf. Jahresh.24.171), OGI345.11 (Delph., i B.C.).

2 αὐτοὶ δ' ἔνοχοι εἴημεν τῷ ὅρκῳ ὁπηνίκ' ἂν εὖ συνθῶμεν perh. as soon as we have duly delivered (or executed) this declaration, BGU1738.32 (i B.C.);

συνθεὶς τούτους μου τοὺς λιβέλλους ἐπιδίδωμι τῇ σῇ λαμπρότητι PLond.3.1000.7

(vi A.D.).

IV collect, conclude, infer, Plb. 28.17.14, Arr.Ind.34.

B [voice] Med. συντίθεμαι, used by Hom. only in [tense] aor. 2 and in signf. 1:

I put together for oneself, i.e. observe, give heed to,

σύνθετο θυμῷ βουλήν Il.7.44

;

φρεσὶ σύνθετο θέσπιν ἀοιδήν Od.1.328

;

ἐμεῖο δὲ σύνθεο μῦθον 17.153

;

συνθέμενος ῥῆμα Pi.P.4.277

; and, simply, perceive, hear,

κλαιούσης ὄπα σύνθετο Od.20.92

: in Hom. mostly abs., σὺ δὲ σύνθεο do thou take heed, Il.1.76, Od.15.318, etc.; σὺ δὲ σύνθεο θυμῷ ib.27.

2 set in order organize,

δύναμιν X.HG4.8.20

; τὰ τῆς πόλεως ib.6.1.17.

II agree on, conclude (cf. συνθήκη)

, ἄνδρεσσι κακοῖς συνθέμενοι φιλίην Thgn.306

; συντίθεσθαι συμμαχίην, ὁμαιχμίην τισί, Hdt.2.181, 8.140.á;

τὰς ξυνθήκας ἂς ξυνέθεντο IG12.117.4

, cf. 116.27, al.;

εἰρήνην Isoc. 15.109

; σ. ναῦλον agree upon the fare, X. An.5.1.12; ταῦτα συνθέμενοι having agreed on these points, Th.3.114, cf. Ar.Lys. 178, Plu.Alc.31;

ξυνέθεσθε κοινῇ τάδε E.Ba. 807

, cf. 808; so

τάπερ τῷ Δαρείῳ συνεθήκατο

with

D.

, Hdt.3.157;

σ. Ἴωσι ξεινίην Id.1.27

;

μισθόν τινι Pl.Grg. 520c

;

σ. τι πρός τινα Hdt.7.145

, etc.:—[voice] Pass., τοῦ συντεθέντος χρόνου agreed upon, Pl.Phdr. 254d.

2 c. inf., covenant, agree to do,

συνέθευ παρέχειν φωνάν Pi.P.11.41

(dub. l.);

σ. ἀλλήλοις μήτ' ἀδικεῖν μήτ' ἀδικεῖσθαι Pl.R. 359a

, cf. And. 4.18, Arist.Pol. 1257a35: c. inf. [tense] fut.,

ξυνέθεντο ἥξειν Th.6.65

; σ. τινί folld. by inf. [tense] fut.,

συνθέμενοι ἡμῖν.. ἀντιώσεσθαι Hdt.9.7

.β, cf. And.1.42: an inf. must be supplied in the phrases, κατὰ (i.e. καθ' ἃ) συνεθήκαντο, καθ' ὅτι ἂν συνθῶνται, etc., Hdt.3.86, Foed. ap. Th.5.18: also

σ. ὡς.. Hdt.6.84

;

ὡς δεῖ ἕκαστα γίγνεσθαι X.HG5.4.2

.

3 abs., make a covenant,

ἔβαν συνθέμενος Pi.N.4.75

(constr. uncertain in Alc.Supp.5.11): c. dat., Hdt.6.115, X.An.1.9.7, POxy.1668.12 (iii A.D.);

αὐτὸς σαυτῷ συνέθου Pl.Cra. 435a

; συνθέσθαι πρός τινα come to terms with him, Decr. ap. D.18.187, POxy.908.18 (ii/iii A.D.);

περί τινος πρὸς ἀλλήλους D.S.1.98

; also, bet, wager, Thphr. HP9.17.2, Men.Epit. 288;

πρός τινας Plu.Alc.8

.

4 vote with, support,

τούτοις Lys.Fr.68

, cf. Call.Epigr.1.14, D.H.Isoc.18, Paus. 4.15.2;

τοῖς ἀπὸ Ἡροφίλου Sor.2.53

; assent to,

πᾶσι τοῖς προκειμένοις PFay.34.20

(ii A.D.);

ἵνα μὴ δόξω συνθέσθαι τῇ τοῦ πραγματικοῦ ἀγνοίᾳ POxy.78.23

(iii A.D.).

5 conclude, infer (cf. A. IV), Stoic.2.63, Phld.Sign.2, al.:—[voice] Pass., τὰ ὕστερον -τεθησόμενα ib.28.

organic compound.

* * *

= organic compound

Ex. The chemist, struggling with the synthesis of an organic compound, has all the chemical literature before him in his laboratory.

* * *