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1、2023年泰安市肥城市考研英语一临考冲刺试题Section I Use of EnglishDirections:Read the following text. Choose the best word(s) for each numbered blank and mark A, B, C or D on the ANSWER SHEET. (10 points) If youve always hoped to give up your 9-to-5 job to move to Maine to operate a restaurant, then the Center Lovell I
2、nn, built in 1805, could make your dream come true. All you need to make it yours is $125, a postage stamp, and a 200-word essay. In 1993, the then-owner of then inn, Smith, 1 an essay contest to find a successor(继任者). He chose Jack, who had been 2 a 50,000-square-foot restaurant in Maryland 3 winni
3、ng ownership of the inn. But after more than two decades of managing the Center Lovell Inn, Jack, now 68, is ready to 4 . And hes planning his own essay contest of find the inns new owner. “There are a lot of very 5 people in the restaurant business who would like to have their own place but cant 6
4、it,” Jack told the Press Herald. “This is a way for them to have the 7 to try.” Jack hopes to 8 at least 7,500 participants, which would earn him more than $900,000. Jack has promised to stop 9 limits to the participants after the number of them 10 that figure 7,500. “If I get more participants, all
5、 the better,” he said. To apply, applicants(18 years of age and older) can 11 a 200-or-less-word essay on the 12 of why theyre the right fit, as well as a check for $125, to the Center Lovell Inn postmarked by May 7. Jack well 13 the applicants to a top 20 and from there, two anonymous(匿名) judges wi
6、ll select the new 14 of the inn. Jack is hopping to 15 the successor on May 1st, 2017. Jack is 16 that just as it did last time, the essay contest will 17 a fitting new owner for the centuries-old inn. He said people often asked him, “ What if you get the 18 person or what if this person lies to you
7、? Our answer was and is, We trust. It was part of the 19 of this whole thing. And it 20 we were right.”1、AelectedBheldCattainedDundertook2、AobtainingBbotheringCadoringDmanaging3、AbeforeBafterCwhileDfor4、AdistinguishBretireCpauseDoperate5、AmiserableBthoughtfulCtalentedDarbitrary6、AaffordBaccumulateCa
8、djustDpurchase7、AjusticeBprocedureCassumptionDopportunity8、AgovernBastonishCattractDcomfort9、AindicatingBsettingCreflectingDacknowledging10、AarrivesBstrikesCassociatesDreaches11、AsendBprepareCdonateDpreserve12、AreformBbreakthroughCcompetenceDtopic13、Akeep upBnarrow downCapply forDcast down14、AownerB
9、authorityCinterpreterDvolunteer15、AidentifyBremarkCannounceDresist16、AreasonableBthankfulCconfidentDconservative17、Apick outBteam up withCbring backDcall up18、AcurrentBinnocentCcautiousDwrong19、AmeansBdrawbackCmagicDhardship20、Alived onBturned outCmarked outDset asideSection II Reading Comprehension
10、Part ADirections:Read the following four texts. Answer the questions below each text by choosing A, B, C or D. Mark your answers on the ANSWER SHEET. (40 points)Text 1Hit songs are big business, so there is an incentive for composers to get those ingredients that might increase their chances of succ
11、ess. But songs are complex mixtures of features. How to analyse them is made more difficult by the fact that what is popular changes over time. But Natalia Komarova, a mathematician at the University of California, Irvine, thinks she has cracked the problem. Her computer analysis suggests that the s
12、ongs currently preferred by consumers are danceable, party-like numbers. Unfortunately, those actually writing songs prefer something else.She and her colleagues collected information on music released in Britain between 1985 and 2015. They looked in music “metadata (元数据) that are used by music love
13、rs and are often tapped into by academics. Metadata are information about the nature of a song that can give listeners an idea of what that song is like before they hear it. Dr. Komarova and her team were presented with more than 500,000 songs to detect numerous musical features. The team fed all of
14、 this information into a computer and compared the features of songs that had made it into the charts (排行榜) with those of songs that had not.Overall, the teams results suggested that chart successes were happier and brighter than the average songs released during the same year. Chart toppers were al
15、so more likely than average songs to have been performed by women.Dr. Komarova used these results to train her computer to try to predict whether a randomly presented song was likely to have been a hit in a given year. The machine correctly predicted success 75% of the time, compared with that from the music database.Content isnt everything. As might be expected, circumstances, particularly any fame already attached to a recording artist or artists, had an effect too.
