1. Which one of the following linings is suitable for shield driven tunnels particularly in the subaqueous regions :






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MCQ->Which one of the following linings is suitable for shield driven tunnels particularly in the subaqueous regions :....
MCQ-> Read passage carefully. Answer the questions by selecting the most appropriate option (with reference to the passage). PASSAGE 4While majoring in computer science isn't a requirement to participate in the Second Machine Age, what skills do liberal arts graduates specifically possess to contribute to this brave new world? Another major oversight in the debate has been the failure to appreciate that a good liberal arts education teaches many skills that are not only valuable to the general world of business, but are in fact vital to innovating the next wave of breakthrough tech-driven products and services. Many defenses of the value of a liberal arts education have been launched, of course, with the emphasis being on the acquisition of fundamental thinking and communication skills, such as critical thinking, logical argumentation, and good communication skills. One aspect of liberal arts education that has been strangely neglected in the discussion is the fact that the humanities and social sciences are devoted to the study of human nature and the nature of our communities and larger societies. Students who pursue degrees in the liberal arts disciplines tend to be particularly motivated to investigate what makes us human: how we behave and why we behave as we do. They're driven to explore how our families and our public institutions-such as our schools and legal systems-operate, and could operate better, and how governments and economies work, or as is so often the case, are plagued by dysfunction. These students learn a great deal from their particular courses of study and apply that knowledge to today's issues, the leading problems to be tackled, and various approaches for analyzing and addressing those problems. The greatest opportunities for innovation in the emerging era are in applying evolving technological capabilities to finding better ways to solve human problems like social dysfunction and political corruption; finding ways to better educate children; helping people live healthier and happier lives by altering harmful behaviors; improving our working conditions; discovering better ways to tackle poverty; Improving healthcare and making it more affordable; making our governments more accountable, from the local level up to that of global affairs; and finding optimal ways to incorporate intelligent, nimble machines into our work lives so that we are empowered to do more of the work that we do best, and to let the machines do the rest. Workers with a solid liberal arts education have a strong foundation to build on in pursuing these goals. One of the most immediate needs in technology innovation is to invest products and services with more human qualities. with more sensitivity to human needs and desires. Companies and entrepreneurs that want to succeed today and in the future must learn to consider in all aspects of their product and service creation how they can make use of the new technologies to make them more humane. Still, many other liberal arts disciplines also have much to provide the world of technological innovation. The study of psychology, for example, can help people build products that are more attuned to our emotions and ways of thinking. Experience in Anthropology can additionally help companies understand cultural and individual behavioural factors that should be considered in developing products and in marketing them. As technology allows for more machine intelligence and our lives become increasingly populated by the Internet of things and as the gathering of data about our lives and analysis of it allows for more discoveries about our behaviour, consideration of how new products and services can be crafted for the optimal enhancement of our lives and the nature of our communities, workplaces and governments will be of vital importance. Those products and services developed with the keeneSt sense of how they' can serve our human needs and complement our human talents will have a distinct competitive advantage. Much of the criticism of the liberal arts is based on the false assumption that liberal arts students lack rigor in comparison to those participating in the STEM disciplines and that they are 'soft' and unscientific whereas those who study STEM fields learn the scientific method. In fact the liberal arts teach many methods of rigorous inquiry and analysis, such as close observation and interviewing in ways that hard science adherents don't always appreciate. Many fields have long incorporated the scientific method and other types of data driven scientific inquiry and problem solving. Sociologists have developed sophisticated mathematical models of societal networks. Historians gather voluminous data on centuries-old household expenses, marriage and divorce rates, and the world trade, and use data to conduct statistical analyses, identifying trends and contributing factors to the phenomena they are studying. Linguists have developed high-tech models of the evolution of language, and they've made crucial contributions to the development of one of the technologies behind the rapid advance of automation- natural language processing, whereby computers are able to communicate with the, accuracy and personality of Siri and Alexa. It's also important to debunk the fallacy that liberal arts students who don't study these quantitative analytical methods have no 'hard' or relevant skills. This gets us back to the arguments about the fundamental ways of thinking, inquiring, problem solving and communicating that a liberal arts education teaches.What is the central theme of the passage?
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MCQ-> People are continually enticed by such "hot" performance, even if it lasts for brief periods. Because of this susceptibility, brokers or analysts who have had one or two stocks move up sharply, or technicians who call one turn correctly, are believed to have established a credible record and can readily find market followings. Likewise, an advisory service that is right for a brief time can beat its drums loudly. Elaine Garzarelli gained near immortality when she purportedly "called" the 1987 crash. Although, as the market strategist for Shearson Lehman, her forecast was never published in a research report, nor indeed communicated to its clients, she still received widespread recognition and publicity for this call, which was made in a short TV interview on CNBC. Still, her remark on CNBC that the Dow could drop sharply from its then 5300 level rocked an already nervous market on July 23, 1996. What had been a 40-point gain for the Dow turned into a 40-point loss, a good deal of which was attributed to her comments.The truth is, market-letter writers have been wrong in their judgments far more often than they would like to remember. However, advisors understand that the public considers short-term results meaningful when they are, more often than not, simply chance. Those in the public eye usually gain large numbers of new subscribers for being right by random luck. Which brings us to another important probability error that falls under the broad rubric of representativeness. Amos Tversky and Daniel Kahneman call this one the "law of small numbers.". The statistically valid "law of large numbers" states that large samples will usually be highly representative of the population from which they are drawn; for example, public opinion polls are fairly accurate because they draw on large and representative groups. The smaller the sample used, however (or the shorter the record), the more likely the findings are chance rather than meaningful. Yet the Tversky and Kahneman study showed that typical psychological or educational experimenters gamble their research theories on samples so small that the results have a very high probability of being chance. This is the same as gambling on the single good call of an advisor. The psychologists and educators are far too confident in the significance of results based on a few observations or a short period of time, even though they are trained in statistical techniques and are aware of the dangers.Note how readily people over generalize the meaning of a small number of supporting facts. Limited statistical evidence seems to satisfy our intuition no matter how inadequate the depiction of reality. Sometimes the evidence we accept runs to the absurd. A good example of the major overemphasis on small numbers is the almost blind faith investors place in governmental economic releases on employment, industrial production, the consumer price index, the money supply, the leading economic indicators, etc. These statistics frequently trigger major stock- and bond-market reactions, particularly if the news is bad. Flash statistics, more times than not, are near worthless. Initial economic and Fed figures are revised significantly for weeks or months after their release, as new and "better" information flows in. Thus, an increase in the money supply can turn into a decrease, or a large drop in the leading indicators can change to a moderate increase. These revisions occur with such regularity you would think that investors, particularly pros, would treat them with the skepticism they deserve. Alas, the real world refuses to follow the textbooks. Experience notwithstanding, investors treat as gospel all authoritative-sounding releases that they think pinpoint the development of important trends. An example of how instant news threw investors into a tailspin occurred in July of 1996. Preliminary statistics indicated the economy was beginning to gain steam. The flash figures showed that GDP (gross domestic product) would rise at a 3% rate in the next several quarters, a rate higher than expected. Many people, convinced by these statistics that rising interest rates were imminent, bailed out of the stock market that month. To the end of that year, the GDP growth figures had been revised down significantly (unofficially, a minimum of a dozen times, and officially at least twice). The market rocketed ahead to new highs to August l997, but a lot of investors had retreated to the sidelines on the preliminary bad news. The advice of a world champion chess player when asked how to avoid making a bad move. His answer: "Sit on your hands”. But professional investors don't sit on their hands; they dance on tiptoe, ready to flit after the least particle of information as if it were a strongly documented trend. The law of small numbers, in such cases, results in decisions sometimes bordering on the inane. Tversky and Kahneman‘s findings, which have been repeatedly confirmed, are particularly important to our understanding of some stock market errors and lead to another rule that investors should follow.Which statement does not reflect the true essence of the passage? I. Tversky and Kahneman understood that small representative groups bias the research theories to generalize results that can be categorized as meaningful result and people simplify the real impact of passable portray of reality by small number of supporting facts. II. Governmental economic releases on macroeconomic indicators fetch blind faith from investors who appropriately discount these announcements which are ideally reflected in the stock and bond market prices. III. Investors take into consideration myopic gain and make it meaningful investment choice and fail to see it as a chance of occurrence. IV. lrrational overreaction to key regulators expressions is same as intuitive statistician stumbling disastrously when unable to sustain spectacular performance.....
MCQ->Consider the following statements with respect to tunnelling methods:1. Full face excavation is suitable for small size tunnel of short length in compact rock. It is not suitable in urban areas.2. Heading and Benching method is suitable for soft rock tunnelling of medium size.3. Drift method is suitable for large tunnels in difficult or incompetent rock.Which of the statements given above are correct ?....
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