entropy

简明释义

英[ˈentrəpi]美[ˈentrəpi]

n. [热] 熵（热力学函数）

英英释义

单词用法

同义词

反义词

例句

1.If you change the temperature entropy changes and so on.

如果你改变温度，熵也会变化。

2.So that's our initial entropy.

这样是我们初始的熵。

3.Entropy is the tendency of things to disorder.

熵是指物体失序的趋势。

4.But what has that to do with entropy?

不过那跟熵有什么关系？

5.The present study is to investigate the application of sample entropy (SampEn) measures.

现在的研究采用了复杂性分析中的样品熵算法。

6.Yeah. What about entropy, right?

对，熵！熵起了什么作用？

7.In thermodynamics, the increase in entropy 熵 indicates a loss of usable energy.

在热力学中，熵 熵的增加表示可用能量的损失。

8.The concept of entropy 熵 is often used to describe the disorder in a system.

概念熵 熵通常用于描述系统中的无序状态。

9.As the universe expands, its overall entropy 熵 is also increasing.

随着宇宙的扩展，其整体熵 熵也在增加。

10.In information theory, entropy 熵 measures the uncertainty in a set of data.

在信息论中，熵 熵衡量一组数据中的不确定性。

11.A more disordered state corresponds to higher entropy 熵 in a chemical reaction.

在化学反应中，更无序的状态对应于更高的熵 熵。

作文

In the realm of physics and information theory, the concept of entropy (熵) plays a crucial role in understanding the disorder and randomness of systems. At its core, entropy (熵) is a measure of uncertainty or unpredictability. In thermodynamics, it quantifies the amount of energy in a physical system that cannot be used to do work. This idea can be illustrated through the example of a hot cup of coffee left in a cool room. Over time, the heat from the coffee dissipates into the surrounding air, leading to a more uniform temperature. The initial state, with hot coffee and cooler air, has lower entropy (熵) because it is more ordered. As the coffee cools, the system becomes more disordered, and the entropy (熵) increases. Beyond physics, the concept of entropy (熵) extends into various fields including information theory, where it measures the amount of unpredictability in a set of data. For instance, a completely random string of characters has high entropy (熵) because it is difficult to predict what character comes next. Conversely, a string with repeating patterns has low entropy (熵), as the next character can be easily anticipated. This application of entropy (熵) is particularly relevant in cryptography, where high entropy (熵) is desired to ensure the security of encrypted messages. The implications of entropy (熵) stretch into the realms of philosophy and ecology as well. In philosophical discussions, entropy (熵) is often associated with the inevitable decline into disorder, reflecting a universal truth about life and the universe. For instance, as time progresses, systems tend to move towards greater entropy (熵), suggesting that order and complexity require continual effort and energy to maintain. This perspective can lead to reflections on human existence, the struggle against chaos, and the pursuit of meaning in a seemingly indifferent universe. In ecological contexts, entropy (熵) helps us understand the health and stability of ecosystems. A balanced ecosystem with diverse species exhibits lower entropy (熵) because it maintains order and resilience against disturbances. However, when biodiversity is lost, the system becomes more chaotic and unpredictable, resulting in higher entropy (熵). This understanding underscores the importance of conservation efforts to preserve ecological balance and reduce entropy (熵). In conclusion, the concept of entropy (熵) serves as a powerful tool for examining the nature of disorder across various disciplines. Whether in the cooling of a cup of coffee, the unpredictability of data, the philosophical musings on life’s chaos, or the stability of ecosystems, entropy (熵) provides insight into the fundamental processes that govern our world. Embracing this concept can enhance our understanding of the delicate balance between order and disorder, urging us to appreciate the complexities of both natural and human-made systems. As we navigate through life, recognizing the role of entropy (熵) can inspire us to seek harmony amidst chaos, striving to create and maintain order in our personal and collective endeavors.

在物理学和信息理论领域，entropy（熵）这一概念在理解系统的无序和随机性方面扮演着至关重要的角色。从本质上讲，entropy（熵）是对不确定性或不可预测性的量度。在热力学中，它量化了一个物理系统中无法用于做功的能量量。这一思想可以通过一个例子来说明：一杯热咖啡放在凉爽的房间里。随着时间的推移，咖啡的热量散发到周围的空气中，导致温度变得更加均匀。最初的状态，即热咖啡和较冷的空气，具有较低的entropy（熵），因为它更有序。当咖啡冷却时，系统变得更加无序，entropy（熵）增加。 除了物理学，entropy（熵）的概念还扩展到包括信息理论在内的多个领域，在这些领域中，它测量数据集合中不可预测性的程度。例如，一个完全随机的字符字符串具有高entropy（熵），因为很难预测下一个字符是什么。相反，一个具有重复模式的字符串则具有低entropy（熵），因为下一个字符可以很容易地被预期。这种对entropy（熵）的应用在密码学中尤其相关，因为高entropy（熵）是确保加密消息安全所需的。 entropy（熵）的影响也延伸到哲学和生态学领域。在哲学讨论中，entropy（熵）常常与不可避免的无序衰退相关联，反映了关于生活和宇宙的普遍真理。例如，随着时间的推移，系统倾向于朝着更高的entropy（熵）发展，这表明秩序和复杂性需要持续的努力和能量来维持。这种观点可以引发对人类存在的思考、对抗混乱的斗争，以及在看似冷漠的宇宙中追求意义的努力。 在生态背景下，entropy（熵）帮助我们理解生态系统的健康和稳定性。一个平衡的生态系统，具有多样的物种，表现出较低的entropy（熵），因为它保持着对干扰的秩序和韧性。然而，当生物多样性丧失时，系统变得更加混乱和不可预测，从而导致更高的entropy（熵）。这种理解强调了保护工作的重要性，以维护生态平衡并减少entropy（熵）。 总之，entropy（熵）这一概念作为一个强大的工具，能够检查不同学科中无序的性质。无论是在咖啡冷却、数据的不可预测性、对生活混乱的哲学沉思还是生态系统的稳定性方面，entropy（熵）都提供了对支配我们世界的基本过程的洞察。接受这一概念可以增强我们对秩序与无序之间微妙平衡的理解，促使我们欣赏自然和人造系统的复杂性。当我们在生活中航行时，认识到entropy（熵）的作用可以激励我们在混乱中寻求和谐，努力在个人和集体的努力中创造和维持秩序。