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Файл:Scientific English. Учебно-методическое пособие для подготовки аспирантов к сдаче кандидатского минимума по иностранному языку
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that invaded and colonized much of Britain during the 5th and 6th centuries. The
―ish‖ part means ―belonging to'': in this case, the language than belonged to the
Angles - the ―Anglesish‖ language.
The Angles lived in northern Germany alongside a number of kindred
tribes, including the Saxons and the Jutes. Beginning about the year 450,
members of these three tribes, joining in the widespread barbarian migrations
that marked the end of the western Roman Empire, crossed the North Sea to find
new homes in Britain. For the next 50 or 60 years the would-be colonizers, aided
by reinforcements, fought with the original inhabitants of the island, the Britons,
and pushed them back to the north and west into present-day Scotland and
Wales. The territory that Angles, Saxons, and Jutes thus carved out for
themselves can be called the land of the Anglo-Saxons, or, for short, ―Angleland‖ - England. Similarly, their language can be called Anglo-Saxons or OLD
ENGLISH.
The Britons spoke one of the closely related languages termed Celtic.
CELTIC LANGUAGES spoken today include Irish, Welsh, and Scottish Gaelic,
all found in Great Britain, and Breton, the native language of Brittany, on the
northwest coast of France. The Anglo-Saxons spoke a form of Germanic. By the
5th century, however, speakers of these two branches of Indo-European would
have been totally unintelligible to each other.
At first the Anglo-Saxons who went to Britain continued to speak the
same tongue as their cousins whom they had left behind in northern Germany.
With each passing generation, however, the speech patterns of the two peoples,
now separated by the North Sea, grew less and less alike. After a while it could
be said that they spoke different dialects of the same language - much as today
the inhabitants of Montreal and Paris speak different dialects of French. Finally,
at that time (probably during the 7th century or even later) when the AngloSaxons and people of Germany could no longer understand each other, the
English language came into its own: the Anglo-Saxons spoke English - Old
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English to be precise - and the people of Germany spoke early forms of the
German language (3228).
Text 7
Old English
Present-day English descends directly from the speech of the AngloSaxons. English has, however, changed so much during the course of the past
thousand years that today Old English seems like a foreign tongue to us. The
Norman Conquest explains many of the shifts in vocabulary that have taken
place since the time of the Anglo-Saxons. Before 1066 only a handful of words
had been borrowed from French; since then, tens of thousands of French words
have entered the English Language. Instead of the Germanic word rice (compare
present-day Germanic Reich), we might say ―realm‖, ―dominion‖, ―region‖, or
―possessions‖, all of which are French loanwords that first appeared in English
during the course of the 16th and 14th centuries.
William the Conqueror and his French-speaking court influenced English
mostly from the top down. The vast majority of the inhabitants of England
continued to speak English after the Conquest, although Henry IV, who
succeeded Richard II on the throne in 1399, was the first king since Harold II
whose mother tongue was English rather than French. It is not surprising that our
words veal, beef, mutton, and pork for the prepared meats that would have been
eaten by the Normans inside their castles are all of French origin, while our names
calf, ox, sheep, and swine for the corresponding animals raised and slaughtered by
the English-speaking farmers outside the castle walls are all of Anglo-Saxons
origin.
Despite the many French loanwords, English remained English, not a
dialect of French. English grammar, as opposed to vocabulary, remained
virtually unaffected by French, and grammatical developments that had begun
much earlier during Anglo-Saxon times continued without interruption through
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the Conquest. Even today it is still obvious that the grammatical structure of
English resembles that of German far more than it resembles that of French.
Middle English
Although 1066 in no way marks a change of languages for the people of
England, the date nonetheless serves as a convenient divider between two
periods of English: Old English and MIDDLE ENGLISH. Middle English is
characterized both by its greater French vocabulary and, more importantly, by
the loss of inflections. By the close of the Middle English period, however, only
the two of these inflections remained in use: -еs for plural nouns (descended
from -as) and the past tense marker -еd (from -od). The poet Geoffrey
CHAUCER, for example, who died in the year 1400, was no longer able to
indicate by means of the inflection -ne that the phrase urne ... half was a direct
object even though it preceded its verb sele (―give‖). Chaucer‘s equivalent
phrase, our loof-like present-day English ―our loaf‖(or ―our bread‖) - could
function either as a subject of as an object. To show that the phrase was the
object in a sentence, Chaucer, like us, had to place it after the verb (2886).
Text 8
Effect of Printing on English
The year 1476, a date not nearly so well remembered as 1066, was every
bit as important for the English language. Just as the earlier date can serve as a
dividing line between Old English and Middle English, the later date is often
used to separate conveniently Middle English from the third and the most recent
period of our language, Modern English. In 1476 the first English printer,
William CAXTON, set up his press in London. Previously, spelling had changed
to reflect changes in pronunciation. Printing froze spelling: we spell essentially
the way Caxton did. The Anglo-Saxons wrote half because they pronounced the
h; Chaucer wrote loof or lof because he no longer did so. Although we say
―nite‖, we write ―knite‖ because Caxton still pronounced both the k at the
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beginning of the word and the gh, which sounded something like the ch in the
present-day Scottish pronunciation of loch or in the German word ich.
Printing had a decisive effect on spelling because until the development of
printing all books were copied by hand. Each copy of a book was spelled
differently because no two copyists or scribes spoke in exactly the same manner.
(If this seems strange, consider how a person with a New York City accent
might spell the word earl, if he had not been taught otherwise: oil) Thus, when
Caxton began to turn out dozens or even hundreds of virtually identical copies of
a book, his spelling system at once became familiar all over England. Because
their readers were accustomed to Caxton`s spellings, his immediate successors
decided to adapt these spellings for their books. Aside from occasional
modifications and reforms, printers have followed the same spelling system ever
since - that of Caxton`s late-15th-century London (1710).
Text 9
THE GREAT LEAP FORWARD
Plans for economic and monetary union have been circulating for 30
years, only to be blown off course by recessions, oil crises and exchange rate
turbulence. Established in Frankfurt at the end of last year, the new European
Monetary Institute will play a key role in guiding the European Union towards a
single currency and official economic unity.
The dream of a single currency for Europe by tile end of the century lives
on. Whether it can be achieved will depend not simply on political will and
greater "convergence" of economic performance among the 12 members of the
European Union. The outcome may also rest on the skills of Mr. Alexandre
Lamfalussy, a Hungarian émigré who fled Communism as a young man, became
an honorary Baron in his adopted country of Belgium, and now occupies a
central position in European monetary affairs.
Stage I of EMU was supposed to be the period when member states made
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decisive progress towards economic convergence. Their performance has been
poor on all counts other than inflation. Moreover, the impressive progress toward
price stability has been at the cost of rising unemployment and the most serious
deterioration in public finances in a generation. Economic convergence has been
replaced by economic deference.
One result was August 1993 crisis in the ERM which led to the
abandonment of narrow fluctuation margins for member currencies and the
introduction of 15 per cent bands, with the exception of the German Deutchemark and the Dutch guilder whose margins remain at 2.25 per cent. The August 2
decision marked the collapse of the old ERM, but also the end of the earlier, widelyheld view that fixed but adjustable narrow bands were the prerequisite for an early
move to European monetary union.
On January 1 1994 Mr. Lamfalussy took over as the first president of the
Frankfurt-based European Monetary Institute (EMI), the forerunner of the
European central bank which will manage the putative single currency according
to the provisions of the Maastricht treaty. His arrival coincided with the official
start of "Stage II" of economic and monetary union (EMU), the transition period of
between three and five years in which the Twelve are supposed to prepare for the
final leap to a single currency.
A deeper-than-expected recession and series of currency crises in 1992
and 1993 have made the deadline of January 1 1999 much more realistic for the
move to fully-fledged monetary union than the original goal of 1997. The
likelihood of having a longer-than-expected Stage II puts a certain onus on the
EMI as the catalyst for greater monetary co-operation and co-ordination inside
the EU.
The dilemma facing Mr. Lamfalussy is that the Maastricht treaty sets clear
limits to the EMI's powers. On the fundamental question of who retains
responsibility for running economic policy in the EU, the answer remains the
individual member states. There is a risk, therefore, that, far from marking a
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qualitative new step toward greater integration sin, EMU Stage II might turn out
to amount to little more than treading water (3095).
Text 10
A mathematical model
A mathematical model uses mathematical language to describe a system.
Mathematical models are used not only in the natural sciences and engineering
disciplines (such as physics, biology, earth science, meteorology, and
engineering) but also in the social sciences (such as economics, psychology,
sociology and political science); physicists, engineers, computer scientists, and
economists use mathematical models most extensively. The process of
developing a mathematical model is termed 'mathematical modelling' (also
modeling).
Eykhoff (1974) defined a mathematical model as 'a representation of the
essential aspects of an existing system (or a system to be constructed) which
presents knowledge of that system in usable form'.
Mathematical models can take many forms, including but not limited to
dynamical systems, statistical models, differential equations, or game theoretic
models. These and other types of models can overlap, with a given model
involving a variety of abstract structures.
Often when engineers analyze a system to be controlled or optimized,
they use a mathematical model. In analysis, engineers can build a descriptive
model of the system as a hypothesis of how the system could work, or try to
estimate how an unforeseeable event could affect the system. Similarly, in
control of a system, engineers can try out different control approaches in
simulations.
A mathematical model usually describes a system by a set of variables and
a set of equations that establish relationships between the variables. The values
of the variables can be practically anything; real or integer numbers, Boolean
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values or strings, for example. The variables represent some properties of the
system, for example, measured system outputs often in the form of signals,
timing data, counters, and event occurrence (yes/no). The actual model is the set
of functions that describe the relations between the different variables (1944).
Text 11
Classifying mathematical models
Many mathematical models can be classified in some of the following
ways:
Linear vs. nonlinear: Mathematical models are usually composed by
variables, which are abstractions of quantities of interest in the described
systems, and operators that act on these variables, which can be algebraic
operators, functions, differential operators, etc. If all the operators in a
mathematical model present linearity, the resulting mathematical model is
defined as linear. A model is considered to be nonlinear otherwise.
The question of linearity and nonlinearity is dependent on context, and
linear models may have nonlinear expressions in them. For example, in a
statistical linear model, it is assumed that a relationship is linear in the
parameters, but it may be nonlinear in the predictor variables. Similarly, a
differential equation is said to be linear if it can be written with linear
differential operators, but it can still have nonlinear expressions in it. In a
mathematical programming model, if the objective functions and constraints are
represented entirely by linear equations, then the model is regarded as a linear
model. If one or more of the objective functions or constraints are represented
with a nonlinear equation, then the model is known as a nonlinear model.
Nonlinearity, even in fairly simple systems, is often associated with
phenomena such as chaos and irreversibility. Although there are exceptions,
nonlinear systems and models tend to be more difficult to study than linear ones.
A common approach to nonlinear problems is linearization, but this can be
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problematic if one is trying to study aspects such as irreversibility, which are
strongly tied to nonlinearity.
Deterministic vs. probabilistic (stochastic): A deterministic model is one
in which every set of variable states is uniquely determined by parameters in the
model and by sets of previous states of these variables. Therefore, deterministic
models perform the same way for a given set of initial conditions. Conversely,
in a stochastic model, randomness is present, and variable states are not
described by unique values, but rather by probability distributions.
Static vs. dynamic: A static model does not account for the element of
time, while a dynamic model does. Dynamic models typically are represented
with difference equations or differential equations.
Lumped vs. distributed parameters: If the model is homogeneous
(consistent state throughout the entire system) the parameters are distributed. If
the model is heterogeneous (varying state within the system), then the
parameters are lumped. Distributed parameters are typically represented with
partial differential equations (2702).
Text 12
Personal computing
Fifteen years ago, personal computing devices were expensive. The idea
that kids might have their own cell phones with text messaging or other personal
digital assistants (PDAs) was laughable. But over time, costs have dropped so
low that most people have at least one PDA that can support texting, e-mail, or
instant messaging. These devices, along with modular content and shared
computing resources, make connecting with other people fast and easy in a nowglobal economy. Add to that the fact that the old social structures common with
large enterprises ("you'll communicate when and how we want you to") are
rapidly being replaced with these easier connections ("I'll communicate when
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and how I want to"), and you have a social structure in which communication is
run by the people, not for the people.
Another key element of social computing is taken straight from the
backbone premise of Web 2.0: Collective intelligence is better than individual
intelligence. The information produced by groups of people can be used to
enhance how the system works.
In this new social structure, people take cues from their contemporaries.
They're increasingly less willing to be led by the organizations they work for
and are more likely to voice dissent. Power is shifting from institutions to the
communities within the institutions; creating value in these communities means
relinquishing control to some degree in order to encourage input. Corporations
are discovering they can no longer rely on top-down communication tactics;
they're finding more success by using the same kinds of tools their target
audiences are already using on a personal level. By becoming part of the
community, IT can target a corporation - or an architect - and use employees and
business partners to its advantage by making them part of the solution and
encouraging their input for improve the organization's collective intelligence
(1925).
Text 13
Hunter, most famous for his mathematical model of the human heart,
began his career as an engineering student and could have ended up building
bridges, dams and sewer systems. Instead he has built a career applying
mathematical laws to the liver, lungs, skin, eyes and almost every other organ
system in the human body.
For more than 20 years, Hunter has headed the Physiome Project, an
international consortium of linked research groups (including those in Oxford,
MIT and the University of California) devoted to defining all aspects of human
physiology, using databasing and computer modelling. It involves biochemistry,
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biophysics, mathematics and the anatomy of cells, tissues, organs and organ
systems. The long-term goal, hopefully achieved in the not-too-distant future, is
to provide a mathematical model for the entire human body. If it succeeds, it
could change the future of medicine. Ultimately it will allow specialists to
diagnose drug treatment for patients in a patient-specific way, that is, based on
an individual‘s entire body in all their idiosyncratic physiological detail.
But good things take time, lots of time — says Hunter. ―If there‘s one
thing you learn about developing business outcomes from science is that it‘s a
ten-year process.‖
Hunter has devoted his life to the microbiological and mathematical stuff
of life. But he‘s also notably comfortable with the business of business. ―It‘s
survival,‖ he says. ―If you want to survive as a major research institute then you
have to deal with the spectrum, from basic science to applied science to
economic outcomes.‖
―I don‘t think I have the business bones in me to want to become a leader
of a company,‖ he says. ―At heart I‘m an academic. But I also have a strong
commitment to creating employment for our graduate students who come
through the bioengineering programme.‖ (1850)
Text 14
Mathematical modelling problems
Mathematical modelling problems are often classified into black box or
white box models, according to how much a priori information is available of
the system. A black-box model is a system of which there is no a priori
information available. A white-box model (also called glass box or clear box) is
a system where all necessary information is available. Practically all systems are
somewhere between the black-box and white-box models, so this concept only
works as an intuitive guide for approach.
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