関連する実験動画
Updated: Jul 7, 2026

08:35
An Operant Intra-/Extra-dimensional Set-shift Task for Mice
Published on: January 22, 2016
まとめ
物理学者クニオ・タカヤナギは,電子が1ナノメートルの金線を通ってより速く移動することを計算しました. これらの新しいナノスケールワイヤーは,次世代のスーパーコンピュータの開発を可能にすることができます.
科学分野:
- 物理 物理学 物理学とは
- マテリアルサイエンス 材料科学
- ナノテクノロジー ナノテクノロジー
背景:
- 従来の電子部品は,速度と効率の限界に直面しています.
- 新しい導電性材料の開発は,コンピューティングパワーの進歩に不可欠です.
研究 の 目的:
- ナノスケールゴールドワイヤの電子輸送特性を調査するために.
- 将来の電子アプリケーションのための1ナノメートルの金のワイヤーの可能性を調査する.
主な方法:
- 1ナノメートルの金線の製造.
- 異なる直径のワイヤーを介して電子の速度を理論的に計算する.
主要な成果:
- 電子は,より大きなワイヤと比較して,1ナノメートルの金線で著しく高い速度を示します.
- 計算された速度は,従来の導体よりも数桁大きい.
結論:
- 1ナノメートルの金線は,超高速の電子輸送の可能性を示しています.
- これらの発見は,より高速なスーパーコンピュータと高度な電子回路の作成の道を開く可能性があります.
関連する概念動画
Deindividuation
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Ordinal Level of Measurement
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks in the...
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks in the...
Interval Level of Measurement
For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between the...
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between the...
z Scores and Unusual Values
The z score is one of the three measures of relative standing. It describes the location of a value in a dataset relative to the mean. z scores are obtained after the standardization of the values in a dataset. The z score for the mean is 0.
This score indicates how far a value is from the mean in terms of standard deviation. For example, if a data value has a z score of +1, the researcher can infer that the particular data value is one standard deviation above the mean. If another data value...
This score indicates how far a value is from the mean in terms of standard deviation. For example, if a data value has a z score of +1, the researcher can infer that the particular data value is one standard deviation above the mean. If another data value...
Unusual Results
Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ from the mean, μ is considered unusual.
Maximum unusual value = μ + 2σ
Minimum unusual value...
According to the range rule of thumb, any value above or below two standard deviations, 2σ from the mean, μ is considered unusual.
Maximum unusual value = μ + 2σ
Minimum unusual value...
Wald-Wolfowitz Runs Test II
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...

