unpaired

简明释义

英[ʌnˈpeəd]美[ʌnˈperd]

adj. 不成双的；无对手的；无配偶的

英英释义

单词用法

同义词

反义词

例句

1.And I cannot have an unpaired electron in the same orbital.

我不可能在同一个轨道，得到不成对电子。

2.Free radicals develop when atoms in the body's cells have unpaired electrons, which can lead to damage to different parts of the cell, including DNA.

自由基发生是当身体细胞中的原子有单电子，能导致包括DNA在内的细胞不同部分的破坏。

3.Literally, a radical is a molecule that reacts easily with other chemicals because of an unpaired electron.

精确地说，因为有孤电子，所以分子中的基团与其他化学物质反应更加容易。

4.Three unpaired electrons in nitrogen.

有三个未成键电子在氮原子中。

5.S is the concentration of unpaired nucleotides.

是不配对的核苷酸的浓度。

6.When the atom absorbs energy from light, one electron may move to a higher energy orbit, leaving an unpaired electron.

当原子从光吸收能量，一个电子可移动到一个更高的能量轨道，留下一个未成对电子。

7.So the only way I can get an unpaired is to put it alone in another orbital.

所以获得不成对电子的唯一方法是，把它放在另一个轨道上。

8.My headphones are currently unpaired with my phone.

我的耳机目前与手机处于未配对状态。

9.After resetting, the printer was unpaired from all devices.

重置后，打印机与所有设备处于未配对状态。

10.You must ensure that your smartwatch is not unpaired before starting the update.

在开始更新之前，您必须确保您的智能手表没有处于未配对状态。

11.The device showed an error because it was unpaired from the network.

设备显示错误，因为它与网络处于未配对状态。

12.I need to find a way to connect the unpaired speakers to my laptop.

我需要找到一种方法将未配对的扬声器连接到我的笔记本电脑。

作文

In the world of technology and data analysis, the term unpaired often arises in various contexts. It refers to situations where items, data points, or observations are not matched or grouped with corresponding counterparts. Understanding the concept of unpaired data is essential for researchers and analysts alike, as it can significantly impact the results of statistical tests and experiments.For instance, consider a clinical trial designed to evaluate the effectiveness of a new medication. If researchers collect data from two separate groups of patients—one receiving the medication and the other receiving a placebo—the data from these groups would be considered unpaired. This means that each patient's response is independent of the others, making it crucial to use appropriate statistical methods to analyze the results.On the other hand, paired data occurs when there is a direct relationship between two sets of observations. For example, if researchers measure the blood pressure of a group of patients before and after administering a treatment, the data points are paired because they correspond to the same individuals. In contrast, unpaired data would involve measurements taken from two different groups of patients, which can introduce variability and complicate the analysis.The distinction between paired and unpaired data is vital for selecting the correct statistical tests. For paired data, researchers might use tests like the paired t-test or Wilcoxon signed-rank test, which account for the dependency between observations. However, for unpaired data, tests such as the independent t-test or Mann-Whitney U test are more appropriate, as they assume that the samples are independent of each other.Moreover, the implications of working with unpaired data extend beyond just statistical testing. In fields like machine learning, for example, unpaired datasets can pose challenges in training models effectively. When using algorithms that rely on paired inputs, such as those used in supervised learning, having unpaired data may lead to less accurate predictions and insights. Therefore, understanding how to handle unpaired data is critical for practitioners in these fields.In summary, the term unpaired signifies a lack of correspondence between data points, which can have significant ramifications in research and analytical processes. Whether in clinical trials, statistical analysis, or machine learning, recognizing the nature of the data being used is key to deriving valid conclusions and making informed decisions. As we continue to navigate an increasingly data-driven world, the ability to discern between paired and unpaired data will remain an invaluable skill for professionals across various domains.

在科技和数据分析的世界中，术语unpaired经常出现在各种上下文中。它指的是项目、数据点或观察结果没有与相应的对应项匹配或分组的情况。理解unpaired数据的概念对于研究人员和分析师来说至关重要，因为这可能会显著影响统计测试和实验的结果。例如，考虑一个旨在评估新药物有效性的临床试验。如果研究人员从两个不同的患者组收集数据——一个接受药物治疗，另一个接受安慰剂——那么这些组的数据将被视为unpaired。这意味着每个患者的反应独立于其他患者，因此必须使用适当的统计方法来分析结果。另一方面，配对数据发生在两个观察集之间存在直接关系的情况下。例如，如果研究人员在施用治疗之前和之后测量一组患者的血压，那么这些数据点是配对的，因为它们对应于同一个人。然而，unpaired数据将涉及来自两个不同患者组的测量，这可能引入变异性并使分析复杂化。配对数据和unpaired数据之间的区别对于选择正确的统计测试至关重要。对于配对数据，研究人员可能会使用配对t检验或Wilcoxon符号秩检验等测试，这些测试考虑到观察值之间的依赖关系。然而，对于unpaired数据，独立t检验或Mann-Whitney U检验等测试更为合适，因为它们假设样本彼此独立。此外，处理unpaired数据的影响不仅限于统计测试。在机器学习等领域，unpaired数据集可能在有效训练模型时带来挑战。当使用依赖于配对输入的算法（例如用于监督学习的算法）时，拥有unpaired数据可能导致预测和洞察不够准确。因此，了解如何处理unpaired数据对于这些领域的从业者至关重要。总之，术语unpaired表示数据点之间缺乏对应关系，这在研究和分析过程中可能产生重大影响。无论是在临床试验、统计分析还是机器学习中，识别所使用数据的性质是得出有效结论和做出明智决策的关键。随着我们继续在一个日益以数据驱动的世界中航行，区分配对数据和unpaired数据的能力将始终是各领域专业人士的重要技能。