linearised

简明释义

英[ˈlɪnəraɪzd]美[ˈlɪnərˌaɪzd]

v. （使）成直线（linearise 的过去式及过去分词，linearise 等于 linearize）

英英释义

单词用法

同义词

反义词

例句

1.Procyclic trypanosomes were electroporated with linearised plasmid DNA and selected for resistance to phleomycin.

用线性化质粒DNA 电穿孔前环锥虫体，筛选其对磷霉素的抗性。

2.Procyclic trypanosomes were electroporated with linearised plasmid DNA and selected for resistance to phleomycin.

用线性化质粒DNA 电穿孔前环锥虫体，筛选其对磷霉素的抗性。

3.The model requires a linearised version to solve the equations efficiently.

该模型需要一个线性化版本以有效解决方程。

4.By linearising the function, we can better understand its behavior near the origin.

通过线性化函数，我们可以更好地理解其在原点附近的行为。

5.In our experiment, we linearised the relationship between temperature and pressure.

在我们的实验中，我们将温度和压力之间的关系线性化。

6.The data was linearised to simplify the analysis.

数据被线性化以简化分析。

7.The linearised approximation provided a quick solution to the problem.

这个线性化近似为问题提供了快速解决方案。

作文

In the field of mathematics and physics, many complex systems can be difficult to analyze due to their non-linear characteristics. However, by employing certain techniques, we can simplify these systems into a more manageable form. One such technique is the process of making the system more comprehensible by transforming it into a linear form, a method known as linearised (线性化). This transformation allows us to apply linear algebra and other mathematical tools that are often easier to work with than their non-linear counterparts.For example, consider a simple pendulum. The motion of a pendulum is governed by a non-linear equation due to the sine function involved in its movement. However, when the angle of displacement is small, we can approximate the sine function using a Taylor series expansion, which leads us to a linear approximation. This process of approximating the pendulum's motion is an instance where we have linearised (线性化) the system. By doing so, we can derive equations that describe the pendulum's behavior more straightforwardly, allowing for easier calculations and predictions.The importance of linearised (线性化) models extends beyond simple mechanical systems. In economics, for example, many models are developed based on the assumption that relationships between variables can be approximated linearly. When analyzing supply and demand curves, economists often linearised (线性化) the relationships to predict how changes in one variable may affect another. This simplification helps in creating models that can be easily interpreted and applied in real-world scenarios.However, while linearised (线性化) models can provide valuable insights, it is essential to recognize their limitations. The accuracy of a linearised (线性化) model depends heavily on the range of values for which the linear approximation holds true. If the system behaves in a significantly non-linear manner outside this range, the predictions made by the linearised (线性化) model may lead to erroneous conclusions. Therefore, it is crucial for scientists and analysts to understand the context in which they are applying these models.Moreover, the process of linearised (线性化) does not eliminate the complexities inherent in many systems; it merely provides a tool to analyze them more effectively within certain limits. Researchers often use linearised (线性化) models as a starting point before delving into more complex, non-linear analyses. By first understanding the linear behavior of a system, they can identify key variables and relationships that may warrant further investigation.In conclusion, the concept of linearised (线性化) systems plays a critical role in various fields of study, from physics to economics. It allows researchers and practitioners to simplify complex relationships, making them more accessible for analysis and application. However, the use of linearised (线性化) models also requires caution, as their effectiveness is contingent upon the specific conditions under which they are applied. Understanding both the power and the limitations of linearised (线性化) approaches is essential for anyone looking to navigate the intricacies of complex systems effectively.

在数学和物理学领域，许多复杂系统由于其非线性特征而难以分析。然而，通过采用某些技术，我们可以将这些系统简化为更易于管理的形式。一种这样的技术是通过将系统转变为线性形式，使其更易理解，这一过程被称为linearised（线性化）。这一转变使我们能够应用线性代数和其他通常比非线性对应物更易于处理的数学工具。例如，考虑一个简单的摆。摆的运动由非线性方程控制，因为其运动中涉及到正弦函数。然而，当位移角度较小时，我们可以使用泰勒级数展开来近似正弦函数，从而得到一个线性近似。这一近似摆运动的过程就是我们对系统进行了linearised（线性化）的实例。通过这样做，我们可以推导出描述摆行为的方程，这样就可以更简单地进行计算和预测。linearised（线性化）模型的重要性不仅限于简单的机械系统。在经济学中，许多模型的开发基于变量之间的关系可以近似为线性的假设。当分析供求曲线时，经济学家们常常对关系进行linearised（线性化），以预测一个变量的变化如何影响另一个变量。这种简化有助于创建易于解释和应用于现实场景的模型。然而，尽管linearised（线性化）模型可以提供有价值的见解，但必须认识到它们的局限性。linearised（线性化）模型的准确性在很大程度上取决于线性近似成立的值范围。如果系统在此范围之外表现出显著的非线性行为，则linearised（线性化）模型所做的预测可能导致错误的结论。因此，科学家和分析师了解他们应用这些模型的背景至关重要。此外，linearised（线性化）的过程并未消除许多系统固有的复杂性；它仅仅提供了一种工具，以便在某些限制条件下更有效地分析它们。研究人员通常将linearised（线性化）模型作为起点，然后再深入研究更复杂的非线性分析。首先理解系统的线性行为，可以帮助他们识别可能需要进一步研究的关键变量和关系。总之，linearised（线性化）系统的概念在各个研究领域中发挥着关键作用，从物理学到经济学。它使研究人员和从业者能够简化复杂关系，从而使其更易于分析和应用。然而，使用linearised（线性化）模型也需要谨慎，因为它们的有效性取决于所应用的具体条件。理解linearised（线性化）方法的力量和局限性对于任何希望有效应对复杂系统的人来说都是至关重要的。