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英語 中学生

中3 英語 長文読解 問1 本文の内容から考えて、とありますがどこで判断して分かりますか?答えはウです

5 次の英文は, ジュディ (Judy) と健 (Ken) がボランティア活動をする (do volunteer work) ことについて対話しているもので す。 これを読んで問いに答えなさい。 Judy Ken, do you know children in the U.S. often do volunteer work? Ken: Oh, really? I didn't know about that. I'm interested in it, but I haven't ( ①) volunteer work yet. I want to know (1) Judy Well, in the U.S. they usually clean the parks and beaches, help at *animal shelters, or even read books to *elderly people in *nursing homes. Ken Great! I think we can do the same things in Japan. Judy: You're right. My mother and I like the animals at the zoo. A few years ago, we did volunteer work at the local zoo in my town. Ken: Really? I've never heard of ( 2 ) volunteer work at the zoo. What did you do there? Judy We helped to *prepare food for the animals and clean their *cages. Ken: I think you ( 3 ) a lot for the zoo. Judy: Yes. We also learned a lot about the animals. Ken: I think that was a good experience. I also like animals. So, I want to do some useful volunteer work at the zoo someday. Judy, in Japan, I've heard that *volunteers (help) local people after some of the serious *earthquakes since 2011. I think getting help from volunteers is a great help to them. Judy: Yes. (ways/to/are/many/help/there) other people. Ken: That's right. Oh, I remembered our *neighborhood association will have a cleaning day next weekend. I'll join it. Judy That's a good idea. We don't have such an event in U.S. Can I join you? Ken: Sure! We will start at 10 in the morning. Let's meet at Midori Park. Please bring some *work gloves. Judy : I see. ! (注) animal shelter (s) ・・・ 動物保護施設 ... elderly ... お年寄りの おり cage (s) volunteer (s) nursing homes ・・・ 高齢者福祉施設 ボランティアをする人 ... work gloves PF prepare... 〜の準備をする earthquake (s) ... neighborhood association J 問1 本文の内容から考えて,(1) (③)に入る語の組み合わせとして最も適当なものを, ア~エから選びなさい。 ア (1) do (2) did (3) done ウ (1) done 2 doing (3) did イ (1) did (2) done 3 doing I 1 doing (2) did done

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物理 高校生

単振動 (2)でばねの力はk(a+b)にはなりませんか?l -xになる理由がわかりません。

図に示すように, 水平面と角度0をなす滑らかな斜面に沿って自然長][m], ばね定数 [N/m〕 のばねが置かれている。 そのばねの左端は固定されており,右端には質量 M[kg]の 板が取り付けられている。 斜面上でこの板の上に質量m[kg] の小球を乗せる。重力加速度を 9lm/s2] とし,ばねの質量,板の厚み、小球の大きさ、空気抵抗は無視できるものとする。 (1) ばねは自然長からs [m] だけ縮んだところで釣り合った。s をk, m, M, g, 0 を用い て表せ ばねをつり合いの位置から更にd[m] だけ縮めて静かに放す。 dが小さい場合には小球は板から離れず斜面上で振動を始めた。 (2) 板と小球の運動方程式をそれぞれ示せ。但し、斜面に沿ってばねの固定点から測った板 及び小球までの距離を〔m〕 とし, 板と小球との間に働く抗力の大きさを N[N], 斜面に 沿った上向き方向の板及び小球の加速度を α[m/s2] とする。 (3)抗力Nをm,M, g, 0, k, l, xを用いて表せ。 (4)この振動の周期を求めよ。 dが大きい場合には小球は板から離れて飛び出した。 5) 小球が板から離れるときのばねの長さを求めよ。 5) 小球が板から離れるときの速度をm, M, k, d, s を用いて表せ。 I 板 小球 www.fbo

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物理 高校生

(3)です 120度なんてどっから出てきたんですか

n ① 基本例題16 仕事 解説動画 基本問題129 第Ⅰ章 運動とエネルギー 図のような, 水平となす角が30° のなめらかな斜面 AC がある。 質量 40kgの物体を斜面上でゆっくりと AからCまで引き上げた。 重力加速度の大きさを 9.8 m/s2 として,次の各問に答えよ。 (1) 物体を引き上げる力Fの大きさは何Nか。 (2) 力Fがした仕事は何Jか。 10 (3) 物体にはたらく重力がした仕事は何Jか。 指針 (1) 「ゆっくりと引き上げた」とは, 力がつりあったままの状態で, 物体を引き上げ たことを意味する。 斜面に平行な方向の力のつ りあいの式を立て,Fの大きさを求める。 (2)(3) 「W=Fxcose」 を用いる。 ■解説 (1) 物体にはたらく力は,図のよ うになる。 斜面に平行な方向の力のつりあいか ら、 F=mgsin30° =40×9.8× 8×1/2 = 1.96 × 102N mgsin30° mgcos30° 130° 130° mg 2.0×10N A 130° 10m、 (2)物体は,力Fの向きに10m移動しているの 仕事は, W= (1.96×102 ) ×10=1.96×10° J 2.0×10J (3) 重力と物体が移動する向きとのなす角は 120° である。 重力がする仕事 W' は, W' = (40×9.8)×10×cos120° =-1.96×10 J -2.0×10% J 別解 (3) 重力は保存力であり,その仕 事は, 重力による位置エネルギーの差から求め られる。 点Aを高さの基準とすると,点Cの高 さは10sin30°=5.0mであり, 仕事 W' は, W'=0-mgh=0-40×9.8×5.0 =-1.96×10'J -2.0×10 J I ぞ

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TOEIC・英語 大学生・専門学校生・社会人

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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