Mars sand dunes shift and change annually, images show
По выполнению контрольной работы №3
По дисциплине «Английский язык»
для студентов вторых курсов специальностей:
240306.65 «Химическая технология монокристаллов, материалов и изделий электронной техники»;
240403.65 «Химическая технология природных энергоносителей и углеродных материалов»;
210601.65 «Нанотехнология в электронике»;
280201.65 «Охрана окружающей среды и рациональное использование природных ресурсов»;
260601.65 «Машины и аппараты пищевых производств»;
260303.65 «Технология молока и молочных продуктов»;
240902.65 «Пищевая биотехнология»;
260301.65 «Технология мяса и мясных продуктов»;
260501.65 «Технология продуктов общественного питания»;
230102.65 «Автоматизированные системы обработки информации и управления»;
230201.65 «Информационные системы и технологии»;
090105.65 «Комплексное обеспечение информационной безопасности автоматизированных систем»;
130503.65 «Разработка и эксплуатация нефтяных и газовых месторождений»;
130501.65 «Проектирование, сооружение и эксплуатация газонефтепроводов и газонефтехранилищ»;
130304.65 «Геология нефти и газа»;
130500.62 «Нефтегазовое дело»;
130504.65 «Бурение нефтяных и газовых скважин»;
130201.65 «Геофизические методы поисков и разведки месторождений полезных ископаемых»;
190702.65 «Организация и безопасность движения»;
190601.65 «Автомобили и автомобильное хозяйство»;
190603.65 «Сервис транспортных и технологических машин и оборудования»;
151001.65 «Технология машиностроения»;
140205.65 «Электроэнергетические системы и сети»;
140211.65 «Электроснабжение»;
210100.62 «Электроника и микроэлектроника»;
210602.65 «Промышленная электроника»;
210602.65 «Наноматериалы»;
240901.65 «Биотехнология»;
270102.65 «Промышленное и гражданское строительство»;
270105.65 «Городское строительство и хозяйство»;
270106.65 «Производство строительных материалов, изделий и конструкций»;
270109.65 «Теплогазоснабжение и вентиляция»;
270115.65 «Экспертиза и управление недвижимостью»;
280103.65 «Защита в чрезвычайных ситуациях»;
280104.65 «Пожарная безопасность»;
200503.65 «Стандартизация и сертификация (мясная, молочная и рыбная промышленность)»;
230401.65 «Прикладная математика»;
260202.65 «Технология хлеба и кондитерских и макаронных изделий»;
Ставрополь
Методические указания по выполнению контрольной работы 3 составлены в соответствии с Государственным образовательным стандартом высшего профессионального образования, рабочим учебным планом и программой дисциплины «Английский язык» для студентов специальностей: 240306.65, 240403.65, 210601.65, 280201.65, 260601.65, 260303.65, 240902.65, 260301.65, 260501.65, 230102.65, 230201.65, 090105.65, 130500.62, 130503.65, 130501.65, 130304.65, 130504.65, 130201.65, 190702.65, 190601.65, 190603.65, 151001.65, 140205.65, 210100.62, 210602.65, 210602.65, 240901.65, 270102.65, 270105.65,270106.65, 270109.65, 270115.65, 280103.65, 280104.65, 200503.65, 230401.65, 260202.65, 021100.
Методические указания включают в себя задания, рекомендации по организации работы, вопросы для самопроверки и список рекомендуемой литературы.
Составители: Морозова И. Н., Цыганская О. Г., Пронякин Д. С.
Рецензенты: Митрофаненко Л. М., Савелло Е. В.
Содержание
| Введение 1. Содержание контрольной работы по темам программы дисциплины 2. Формулировка задания и его объем Вариант 1 Вариант 2 Вариант 3 Вариант 4 3. Порядок выбора темы и освещения проблемы 4. Структура контрольной работы, общие требования к ее написанию 5. Рекомендации по организации выполнения контрольной работы, примерный календарный план выполнения контрольной работы 6. Порядок защиты и ответственность студента за выполнение контрольной работы 7. Список рекомендуемой литературы Приложение |
Введение
Контрольная работа 3 по английскому языку предназначена для студентов технических специальностей Северо-Кавказского технического университета. Целью работы является формирование основных языковых компетенций, развитие навыков самостоятельной работы с аутентичными научными текстами, подготовки вторичных документов (аннотации и реферата), необходимых для успешного осуществления устной и письменной коммуникации в профессиональной деятельности.
Содержание контрольной работы по темам программы дисциплины
Контрольная работа 3 предполагает работу студентов по следующим темам программы дисциплины «Английский язык»:
1. Аннотирование научного текста.
2. Реферирование научного текста.
3. Активизация и закрепление лексики по темам «Science», «Scientific discoveries», «Inventors and inventions».
Формулировка задания и его объем
Контрольная работа 3 по дисциплине «Английский язык» направлена на практическое овладение студентами такими жанрами научной речи, как аннотация и реферат, а также на закрепление и активизацию тематической и терминологической лексики. Контрольная работа 3 содержит задания, предполагающие: 1) самостоятельную работу студентов с двумя аутентичными научными текстами, включающую чтение, перевод, анализ содержания, составление аннотации на английском языке и реферата на русском языке; 2) выполнение лексического теста по темам «Science», «Scientific discoveries», «Inventors and inventions» в объеме 40 предложений.
Вариант 1
I. Составьте аннотацию к статье на английском языке:
What is a neural network and how does its operation differ from that of a digital computer?
By Mohamad Hassoun
Artificial neural networks are parallel computational models, comprising densely interconnected adaptive processing units. These networks are composed of many but simple processors (relative, say, to a PC, which generally has a single, powerful processor) acting in parallel to model nonlinear static or dynamic systems, where a complex relationship exists between an input and its corresponding output.
A very important feature of these networks is their adaptive nature, in which «learning by example» replaces «programming» in solving problems. Here, «learning» refers to the automatic adjustment of the system's parameters so that the system can generate the correct output for a given input; this adaptation process is reminiscent of the way learning occurs in the brain via changes in the synaptic efficacies of neurons. This feature makes these models very appealing in application domains where one has little or an incomplete understanding of the problem to be solved, but where training data is available.
One example would be to teach a neural network to convert printed text to speech. Here, one could pick several articles from a newspaper and generate hundreds of training pairs – an input and its associated «desired» output sound – as follows: the input to the neural network would be a string of three consecutive letters from a given word in the text. The desired output that the network should generate could then be the sound of the second letter of the input string. The training phase would then consist of cycling through the training examples and adjusting the network parameters – essentially, learning – so that any error in output sound would be gradually minimized for all input examples. After training, the network could then be tested on new articles. The idea is that the neural network would «generalize» by being able to properly convert new text to speech.
Another key feature is the intrinsic parallel architecture, which allows for fast computation of solutions when these networks are implemented on parallel digital computers or, ultimately, when implemented in customized hardware. In many applications, however, they are implemented as programs that run on a PC or computer workstation.
Artificial neural networks are viable models for a wide variety of problems, including pattern classification, speech synthesis and recognition, adaptive interfaces between humans and complex physical systems, function approximation, image compression, forecasting and prediction, and nonlinear system modeling.
These networks are «neural» in the sense that they may have been inspired by the brain and neuroscience, but not necessarily because they are faithful models of biological, neural or cognitive phenomena. In fact, many artificial neural networks are more closely related to traditional mathematical and / or statistical models, such as nonparametric pattern classifiers, clustering algorithms, nonlinear filters and statistical regression models, than they are to neurobiological models.
(«Scientific American», May, 2007)
II. Составьте реферат статьи на русском языке:
Mars sand dunes shift and change annually, images show
By Jason Palmer
Vast sand dunes near the northern pole of Mars are not frozen relics of a distant past, but shift and change every Martian year, data have shown. A hi-tech camera aboard Nasa's Mars Reconnaissance Orbiter has spotted UK-sized dune fields that are among the most dynamic on the Red Planet. Causes, says a report in Science, include carbon dioxide gas that freezes solid onto the dunes each winter. As it thaws in spring, the gas released destabilises, causing sand avalanches. The dune fields at high northern latitudes of Mars were first spotted by the Mariner 9 mission, launched in 1971. But only with the benefit of the High-Resolution Imaging Science Experiment (Hirise) orbiting Mars has the dynamic nature of the dunes finally been revealed. «Hirise has been monitoring seasonal processes for several years now and we've seen for a long time these strange spots and streaks that form, particularly on the sand dunes when they're defrosting», said Alfred McEwen, a planetary geologist at the University of Arizona who lead the Hirise team. A series of images taken of the dune fields over two Martian years – nearly four years on Earth – after the departure of the annual ice clearly show a changing picture of the Martian surface.
«What we've noticed more recently though is in looking at these sand dunes from year to year there are new gullies, new channels that form on the dunes, and we're seeing gullies only a year-old that have been repaired again – so there's a lot of activity we weren't aware of», Professor McEwen told BBC News. There's lots of debate about whether features we see on Mars could be produced in the current Mars climate or whether they require different conditions.
These findings lead to understanding where and when sand is moving, what that implies for both the weather and surface properties on Mars, and tweaking and calibrating various models that can be used to understand Mars in the past as well as today.
(«Science and technology report», BBC News)
III. Перепишите предложения, заполнив пропуски подходящим по смыслу словом:
1. It’s important to maintain proper operation of the …. .
a) reactor; b) nuclear power station; c) engine; d) electricity.
2. Radioactive … are harmful to health.
a) chemicals; b) particles; c) substances; d) dust.
3. Nuclear weapons continue to pose a … .
a) danger; b) catastrophe; c) threat; d) problem.
4. The rise in sea levels has been predicted as a … of global warming.
a) consequence; b) result; c) cause; d) reason.
5. The 1987 hurricane was the worst natural … to hit England for decades.
a) accident; b) catastrophe; c) tragedy; d) disaster.
6. Britain is committed to a 30 per cent reduction in carbon dioxide … by 2005.
a) release; b) emissions; c) generation; d) production.
7. Mrs. Thatcher began to sell into private hands many publicly-owned production and service … .
a) plants; b) works; c) enterprises; d) firms.
8 The President knew that some congressmen would … him.
a) support; b) copy; c) agree with; d) change.
9. Industrial and nuclear waste … in water rapidly.
a) lives; b) spreads; c) extends; d) stretches.
10. … and pesticides pollute the environment.
a) Substances; b) Remedies; c) Fertilizes; d) Chemicals.
11. We … to live in a small town but now we live in London.
a) used; b) get used; c) started; d) have.
12. He was … because he didn’t break the law.
a) imprisoned; b) arrested; c) taken to the prison; d) justified.
13. A doctor must … the wishes of patients.
a) ignore; b) respect; c) improve; d) change.
14. The summer was very dry and there was a … of fires in the forest.
a) threat; b) hope; c) expectance; d) believe.
15. He studied … physics at the university.
a) elementary; b) good; c) nuclear; d) well.
16. International Children’s Fund was … to improve the living conditions of children.
a) formed; b) closed; c) forgotten; d) managed.
17. A polyglot is a person who has … some languages.
a) invented; b) mastered; c) opened; d) heard.
18. They used … in road building.
a) nuclear bombs; b) chemical substances; c) explosives; d) instruments.
19. This … won the Nobel Prize for his discovery in Physics.
a) shop-assistant; b) engineer; c) pianist; d) scientist.
20. Alfred Nobel tried to … publicity.
a) avoid; b) enjoy; c) win; d) respect.
21. Alfred Nobel often thought about the … of his life.
a) meaning; b) beautiful; c) difficulties; d) end.
22. Michael Faraday is an English .… who was born in a poor labouring family.
a) computer programmer; b) artist; c) plumber; d) scientist.
23. Teach your children how to … their pets.
a) wait for; b) care for; c) laugh at; d) think of.
24. What … you leave the town so early?
a) makes; b) helps; c) hopes; d) walks.
25. They used … to cut the tunnel through the mountain.
a) wars; b) explosives; c) weapons; d) spades.
26. The hardest work in … is now performed by robots.
a) mines; b) schools; c) games; d) plays.
27. His … to work day and night was known to his colleagues.
a) knowledge; b) ability; c) behavior; d) fact.
28. I don’t know this word. Do you know … of this word?
a) the meaning; b) the plenty of; c) many; d) the influence.
29. She will … be here today. She promised to come.
a) never; b) probably; c) usually; d) too.
30. You shouldn’t … spiders just because you are afraid of them.
a) kill; b) like; c) avoid; d) admit.
31. The car accident took place in the street and many people were … .
a) found; b) respected; c) injured; d) avoided.
32. He realized that without the experiment his work would be ... .
a) useless; b) useful; c) successful; d) necessary.
33. I will finish my work … you are playing chess.
a) however; b) therefore; c) so; d) while.
34. If you learn by your own mistakes you will be able to … problems in future.
a) avoid; b) respect; c) occur; d) deserve.
35. Economists … the economy to grow by 5 % next year.
a) install; b) expect; c) threaten; d) abolish.
36. This student … an excellent mark. He knows so much.
a) deserves; b) develops; c) chooses; d) improves.
37. Atomic ice-breaker works on … energy.
a) electric; b) sun; c) nuclear; d) natural.
38. You must … the correct answer.
a) choose; b) avoid; c) restore; d) win.
39. Alfred Nobel’s wish was … a fund.
a) to justify; b) to form; c) to change; d) to decorate.
40. A Nobel did much for the … of permanent armies.
a) abolition; b) strengthening; c) development; d) improving.
IV. Вопросы для самопроверки:
1. Как излагается информация в реферате?
2. Какая дополнительная информация может указываться в реферате?
3. Что содержит текст реферата?
4. Каковы особенности употребления терминов и имен собственных в реферате?
5. Как составляется план реферата?
6. Можно ли использовать доказательства, рассуждения и исторические экскурсы при составлении реферата?
7. Что такое библиографическое описание?
Вариант 2
I. Составьте аннотацию к статье на английском языке:
DNA Computer Works in Human Cells
By JR Minkel
Researchers have designed a new type of DNA computer that works in human cells, perhaps paving the way for a distant technology capable of picking out diseased cells from otherwise healthy tissue. The system runs on a process called RNA interference (RNAi) in which small molecules of RNA prevent a gene from producing protein.
The goal is to inject human cells with DNA that can determine whether a cell is cancerous or otherwise diseased, based solely on the mix of molecules inside the cell. Sensing disease, the DNA might trigger a pinpoint dose of treatment in response. That technology, however, is a long way off. For now, researchers are testing different ways of turning DNA into versatile computers that can detect certain combinations of molecules and respond by producing other molecules.
«The central challenge is how do you create a 'molecular computer' capable of making decisions», says bioengineer Yaakov Benenson of Harvard University. Researchers have designed powerful test tube DNA computers that could play tic-tac-toe or perform the basic tasks of logic, but getting them to work in human cells was likely to be tricky, Benenson says.
RNAi is something that cells do naturally. Cells produce what are known as short interfering RNA (siRNA) molecules, which recognize corresponding DNA sequences in genes and cause them to shut down.
Benenson and colleagues engineered a target gene to be sensitive to several different siRNAs of their own design. In the simplest case, they introduced a single siRNA molecule to switch off a target gene that encoded a fluorescent protein. In more complex cases, a pair of siRNAs or either of two siRNAs switched off another target gene, which in turn switched off a gene for a fluorescent protein. To make sure the system worked as intended, the researchers based their siRNAs on those of other species, they report in a paper published online today by Nature Biotechnology.
In principle, the RNAi technique can reach great heights of complexity, Benenson says, by making genes sensitive to more and more siRNAs in various combinations. «The scalability is very important, because eventually you want to make complex decisions», he says.
He says the next step is figuring out how to make the molecules inside a cell – such as those that are overproduced in cancer – trigger the production of siRNAs.
(«Scientific American», May, 2007)
II. Составьте реферат статьи на русском языке: