学年

教科

質問の種類

物理 大学生・専門学校生・社会人

人が棒から受ける垂直抗力と、その反作用については考えなくてもよいのですか?どうしてですか?

8 力のモーメント ( すべる条件) Po 11/22 出題パターン S[m]〕 ―合力が 浮力 力のしくみ なめらかで鉛直な壁の前方6mのところから、 長さ10m 質量 M 〔kg〕 の一様なはしごが壁に 立てかけられてある。重力加速度の大きさをg 壁 〔m/s2〕 とし,床とはしごとの間の静止摩擦係数 =1/2とする。 A B 上向きの いま、このはしごを質量 5M 〔kg〕 の人が登り 始めた。この人はどこまで登りうるか。 床 解答のポイント! ST 力のつりあいの式の数) < (未知数の数) のとき, 未知数を求めるために力の モーメントのつりあいの式も必要になる。 棒の重心は、棒の中央である。 う。 解法 あいで、 図2-16のように, 人が下端から1〔m〕 ま AK で登ったとき, はしごの下端がすべる直前と N ずらす N' X て なり,摩擦力が最大静止摩擦力μN=12 N 1-SE になったと考える。 力のつりあいの式より, 5Mg 8 x: N' =1/ N Mg y: N = Mg +5Mg 立のたの Sg ここで,未知数の数はN,N', lの3つ に対し,式の数は2つしかない。 ずらす 2N 4 よって、力のモーメントのつりあいの式の 立て方3ステップに入る。た 0 B Mg 5 5Mg STEP1 支点は力の集中するB点。 図2-16 STEP2 各力の作用線に「うで」を下ろす。 STEP3 力のモーメントのつりあいの式より、各力をうでの位置までずら して,

解決済み 回答数: 1
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

回答募集中 回答数: 0