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物理 高校生

物理力のモーメント F2cosθが力のモーメントの回転に無関係なのは何故ですか??

1 32 右ページ上図のような質量m の一様な長方形の板にFF2 の力がは 考えます。 このとき、ちょうど床からの抗力は0になっているとします。 点を中心とする左回りのモーメントを求めましょう。 この問題では力がいろいろな方向に向きすぎているので, 鉛直方向と水平方向に分けましょう。 力がはたらく こうすると,回転に関係する力はFicose, Fisin0, F,sine, mgの 4つを考えればよいとわかりますね。 たときの左 のぞ 考えましょう。 それでは, 0を支点として,どちら向きに回そうとしている力なのかを考えましょう。 Fcoseは右回り, Fsin0は右回り, mgは左回り, F2 sin 0は右回り というのがわかりますね。 えると そして次は「力を分解する」か 「力を移動する」 かのどちらかを考えるのですが、 最初に力を垂直に分けてかき直したのですから、また分解するのはおかしいですね。 そこで「力を移動する」 方法で求めてみましょう。 左回りのモーメントを正とすると mg・2b-Ficos ・a-F1sin0 b-F2sinθ・4b 入り組んだ問題でもモーメントを求められましたね。 一般に、力が入り組んでいるときは、 まず垂直な2方向に分解してからモーメン トを考えると解きやすくなります。 また,モーメントに関して苦手意識のある人は ・棒の問題のときは力を分解して、うでの長さはそのままで掛ける ・板の問題のときは力を移動して,カに垂直なうでの長さを掛ける というように剛体別に解法を分けると解きやすいかもしれません。 これらのコツも覚えておくといいでしょう。

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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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