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TOEIC・English Undergraduate

このプリントの穴埋めをして英文和英しなさいという問題です。助けてください

英語2A レポート課題(2026年前期) 以下の英文中の( 内に入れるのに適切と思われる1語を、 下の 入れなさい。 そのうえで全文を和訳しなさい。 の中から選んで ite of national diger Most funny stories are based on comic situations. In spite of national differences, certain funny situations have a ( 1 ) appeal. No matter ( 2 ) you live, you would find (3) difficult not to laugh at, say, Charlie Chaplin's early films. However, a new type of humor, called 'sick humor', has come into fashion. The following example of 'sick humor' will enable you to judge for yourself. A man ( 4 ) had broken his right leg was taken to a hospital a few days before Christmas. From the moment he arrived there, he kept on annoying his doctor to tell him ( 5 ) he would be able to go home. He felt afraid ( 6 ) having to spend Christmas in the hospital. On Christmas Day, the man still had his right leg in plaster. He spent a miserable day in bed thinking of all the ( 7 ) he was missing. The following day, however, the doctor consoled him by telling that his chances of being able to leave the hospital ( 8 ) time for New Year Celebrations were ( 9 ). The man took heart and, sure enough, on New Year's Eve he managed to walk along to a party. To ( 10 ) for his unpleasant experiences in the hospital, the man drank a little more than was good for him. He was still grumbling about hospitals at the end of the party when he slipped on a piece of ice and broke his left leg. blame compensate money yourself where of in at by with fun good whose who it when special universal

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TOEIC・English Undergraduate

この長文問題の答えと解説をお願いします。

15 語数: 398 語 出題校 法政大 5 We are already aware that our every move online is tracked and analyzed. But you 2-53 couldn't have known how much Facebook can learn about you from the smallest of social interactions - a 'like'*. (1) Researchers from the University of Cambridge designed (2) a simple machine-learning 2-54 system to predict Facebook users' personal information based solely on which pages they had liked. E "We were completely surprised by the accuracy of the predictions," says Michael 2-55 Kosinski, lead researcher of the project. Kosinski and colleagues built the system by scanning likes for a sample of 58,000 volunteers, and matching them up with other 10 profile details such as age, gender, and relationship status. They also matched up those likes with the results of personality and intelligence tests the volunteers had taken. The team then used their model to make predictions about other volunteers, based solely on their likes. The system can distinguish between the profiles of black and white Facebook users, 15 getting it right 95 percent of the time. It was also 90 percent accurate in separating males and females, Democrats and Republicans. Personality traits like openness and intelligence were also estimated based on likes, and were as accurate in some areas as a standard personality test designed for the task. Mixing what a user likes with many kinds of other data from their real-life activities could improve these predictions even more. 20 Voting records, utility bills and marriage records are already being added to Facebook's database, where they are easier to analyze. Facebook recently partnered with offline data companies, which all collect this kind of information. This move will allow even deeper insights into the behavior of the web users. 25 30 (3) - Sarah Downey, a lawyer and analyst with a privacy technology company, foresees insurers using the information gained by Facebook to help them identify risky customers, and perhaps charge them with higher fees. But there are potential benefits for users, too. Kosinski suggests that Facebook could end up as an online locker for your personal information, releasing your profiles at your command to help you with career planning. Downey says the research is the first solid example of the kinds of insights that can be made through Facebook. "This study is a great example of how the little things you do online show so much about you,” she says. "You might not remember liking things, " but Facebook remembers and (4) it all adds up.", * a 'like': フェイスブック上で個人の好みを表示する機能。 日本語版のフェイスブックでは「いいね!」 と表記される。 2-56 2-57 2-58 36

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TOEIC・English Undergraduate

動詞の使い分けがわからないです😭

EXERCISE 20➤Looking at grammar. (Chart 5-6) Choose the correct completions. 1. The United States has have a population of around 325 million. an established and respected newspaper. a branch of mathematics. (2.) The New York Times: C:C/DC Caree 3. Statistics is/are 4. The statistics in that report on oil production :C:MKEKECRETKEY) incorrect.* 5. Fifty minutes is are the maximum length of time for the test. 6. Rabies (is) are an infectious and often fatal disease. The blind wants / want us to treat them the same way we treat everyone else. talking with people 8. French is are somewhat similar to Spanish, isn't it / aren't they? The French is are proud, independent people. 10. Does/Do the police have training in mental health issues? 1. Thirty dollars is an unreasonable price for that T-shirt. 12. Four hours of skiing provides / provide plenty of exercise. EXERCISE 21 ▸ Game. are (Chart 5-6) Work in teams. Choose the correct words (or numbers). Then complete the sentences with is or are. 1. The Scots /The Irish The English and Cambridge. 2. Statistics/Linguistics / Physics 3. Diabetes/Measles / Mumps 4. English / French / Afrikaans 5. People from Canada. called are famous for educational institutions like Oxford the study of the structure and nature of language. a blood-sugar illness. the official language of Namibia. Canadas/Canadians / Canadese. covered by water, but drinkable. 6. Approximately 60% / 70% / 80% of the earth only 1% / 10% / 20% of the earth's water 7. 312 x .5+ 100 227/275/256. 8. The United Arab Emirates/The Netherlands/The Philippines Hemisphere (i.e., north of the equator). in the Northern 9. Fish/Whales / Cattle not mammals. 10. Five hundred thousand + five hundred thousand ten hundred / one million / one billion. 11. Macy's/Harrods / Hudson's Bay a department store that began in London. *Statistics is singular when it refers to a field of study (e.g., Statistics is an interesting field of study.). When it refers to particular numbers, it is used as a count noun: singular one statistic (no final -s); plural = two statistics. For example, This

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Mathematics Undergraduate

確率の勉強をしている学生なのですが、この問題が分かりません。どなたか教えていただけませんか。

練習問題 1.8 (積率母関数) X を非負の確率変数とし, x(t) = Eetx は全てのt∈ に対して有限であると仮定する.さらに,全てのt∈ R に対し E [XetX] < ∞ であると仮定する.この練習問題の目的は, '(t) = E [Xetx] で あり、特に'(0)=EX であることを示すことである。 微分の定義, すなわち次式を思い出そう. 4'(t) = lim x(t) - (s) lim st t-s st EetxEesx t-s 「etx = lim E st t-s 上式の極限は,連続な変数sについて取っているが,t に収束する実数列{8}n=1を 選ぶことができ, 次を計算すればよい. 「etx e³n X lim E sn→t t-Sn これは、次の確率変数の列 etx -enx Yn = t-Sn の期待値の極限を取っていることになる.もしこの極限が, t に収束する列{Sn}=1 の選び方によらず同じ値になるならば、この極限も limotE [ex と同じで,そ れは '(t) である. .tx sx ← -e t-s 解析学の平均値の定理の主張は,もしf(t) が微分可能な関数ならば、任意の実数 s ともに対し,stの間の値の実数0で次を満たすものが存在するというものである. f(t)-f(s) =f' (0) (t-s). もしweΩを固定し,f(t) = etx(w) を定義すると,この式は, etX(w)_esx(w)=(t-s) X (w)e (w)x(w) (1.9.1) となる.ただし,(ω) はωに依存する実数 (すなわち,tとsの間の値を取る確率変 数)である. (i) 優収束定理 (14.9) (191) 式を使って,次を示せ. lim EY = Elim Yn=E [XetX] . (1.9.2) n→∞ [n→∞ このことから,求める式 4'(t) [XetX ] が導かれる. (ii) 確率変数 X は正の値も負の値も取り得、全てのt∈Rに対し Eetx < かつ E [|X|etX] < ∞ であると仮定する。 再度 '(t) = E [XetX] を示せ(ヒント: (1.3.1) 式の記号を使って X = X + - X- とせよ . )

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Chemistry Undergraduate

大学受験で、周期表はどこまで覚えた方が良いでしょうか?流石に全部覚える必要はないですか?

1 ヘリウム 4.003 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 H |2Hel 水素 1.008 Lia Bel 2 リチウム ベリリウム 6.941 9.012 典型元素 5B 6C N O F Ne 10] ホウ素 遷移元素 10.81 炭素 12.01 窒素 14.01 酸素 16.00 フッ素 ネオン 19.00 20.18 3 11.Na12Mg ナトリウム マグネシウム 22.99 24.31 13A 14S 15P 16S 17CI 19 Ar アルミニウム ケイ素 26.98 リン 硫黄 塩素 アルゴン 28.09 30.97 32.07 35.45 39.95 4 19K 20Ca 21Sc 22Ti 23V 24 Cr 25Mn 26Fe27Co 26 Ni 29Cu30Zn32Ga32Ge33As 31Se 35 Br 36Kr 39.10 カリウム カルシウム スカンジウム チタン バナジウム クロム 40.08 44.96 47.87 50.94 52.00 マンガン 20 コバルト ニッケル 54.94 55.85 58.93 58.69 63.55 65.38 69.72 鉛 ガリウム ゲルマニウム ヒ素 72.63 74.92 セレン 臭素 78.97 79.90 クリプトン 83.80 5 37Rb 39Sr 39Y 40Zr 42Nb 42 Mo 43TC 44 Ru 45 Rh 46Pd 47Ag 48Cd 49In 50Sn 51Sb52Te 531 530Xe 544 87.62 88.91 91.22 92.91 ルビジウム ストロンチウムイットリウムジルコニウム ニオブ モリブデン テクネチウムルテニウム ロジウム パラジウム 85.47 | カドミウム インジウム スズ アンチモン テルル ヨウ素 キセノン 95.95 (99) 101.1 102.9 106.4 107.9 112.4 114.8 118.7 121.8 127.6 126.9 131.3 60 55 SCs ss Bal 57~71 72Hf 73Ta 74W 75Re 76Os 77lr 78Pt 70 Au 30Hg 81 TI 02Pb 83 Bi 34 Poss At 86 Rn 80 132 178.5 セシウムバリウム ランタノイド ハフニウム タンタル タングステン レニウム オスミウム イリジウム 白金 17.3. 180.9 183.8 192.2 金 186.2 190.2 195.1 197.0 水銀 タリウム 200.6 鉛 204.4 207.2 ビスマス ポロニウム アスタチン 209.0 ラドン (210) (210) (222) |37 Fring Ral | 89~103 104Rf 105Db 106Sg 107 Bh 108HS 100Mt 110DS 12Rg 112Cn 113Nh 114F 115MC 116 Lv 117 TS 1180g | フランシウム ラジウム アクチノイドラザホージウムドブニウム シーボーギウム ボーリウム ハッシウムマイトネリウム ダームスタチウムレントゲニウム コペルニシウム ニホニウム フレロビウム モスコビウムリバモリウム テネシン オガネソン (223) (226) (268) (271) (272) (280) (285) (293) (267) (277) (276) (281) (278) (289) (289) (293) (294) 7

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