Grade

Type of questions

Science Junior High

問3がなぜアになるのか教えてください。操作のところは分かるんですけど水溶液が分かりません

化を調べた。次に、 右の図のような装置で3Vの電圧を加えて水溶液に電流が流れ るかどうかを調べた。 ⑨ピーカーB~Dについても②と同様に調べた。 ステンレス の 水溶液 電流計 問酸の水溶液とアルカリの水溶液を混 ぜ合わせると起きる。 たがいの性質を 打ち消し合う反応を何といいますか 書きなさい。 また、この反応を化学式 を用いて表しなさい。 表 うすい塩酸(cm) 10.0 うすい水酸化ナトリウム) BTB溶液の色 電流 16.0 ビーカーA ピーカーB ビーカー C ピーカーD 10.0 8.0 10.0 10.0 24.0 32.0 黄色 黄色 色 青色 流れた 流れた 流れた 流れた 問2 中性になったピーカーCでも電流が流れたが,ビーカーCにあるイオンをすべて化学式で書きなさい。 問3 ビーカーA~Cの水溶液から,それぞれ塩をとり出すための操作と、 その操作によって塩を純粋な物質としてとり出すことができる水溶液 の組み合わせとして適当なものを。 右のア~エから選びなさい。 T エ 操作 水を蒸発させる水を蒸発させる 冷却する 水溶液 A.B.C A A. B. C 冷却する A 4 この実験で使ったうすい塩酸の濃度を0.5倍にして同じ実験を行うと、 BTB溶液の色を緑色にするには、この実験で使ったうす

Waiting Answers: 1
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

Waiting for Answers Answers: 0
33/1000