quantification

简明释义

英[ˌkwɒntɪfɪˈkeɪʃn]美[ˌkwɑːntɪfɪˈkeɪʃn]

n. 定量，量化

英英释义

单词用法

同义词

反义词

例句

1.QUANTIFICATION was a word Robert McNamara loved.

“定量”此词乃罗伯特·麦克纳马拉之爱。

2.Likewise, you cannot include something of the wrong type, even if the length of a quantification is acceptable.

同样，您不能包括错误类型的信息，即使量化长度是可接受的。

3.Measured results provide a reliable basis for quantification of calculated ones.

测量结果为计算结果的量化提供了可靠的依据。

4.Quantification should follow a qualitative step of hazards inventory.

量化应该根据一个对危害清单的步骤。

5.With a quantification method, the original image is not required for extracting the watermarking.

在水印嵌入时采用量化的方法，使提取时不需要原始图像。

6.Quantification of the proliferation reaction was performed using standard planimetry techniques and Micro-plan II image analysis system.

采用电子面板标准面积计算技术对再狭窄增殖组织作定量分析。

7.This is good news, as it means that style sheet authors will soon be able to take advantage of features like sequences, quantification, and stronger type control.

这是个好消息，因为它意味着样式表作者们将很快就能利用象序列、量化和更强有力的类型控制这样的特性。

8.In project management, quantification of resources is crucial for effective planning.

在项目管理中，资源的量化对有效规划至关重要。

9.The quantification of customer satisfaction helps businesses improve their services.

客户满意度的量化帮助企业改善服务。

10.Effective marketing strategies often rely on the quantification of consumer behavior.

有效的营销策略通常依赖于对消费者行为的量化。

11.The study involved the quantification of air pollution levels over several months.

该研究涉及对几个月内空气污染水平的量化。

12.The scientist emphasized the importance of quantification in her research to ensure accurate results.

科学家强调了在她的研究中进行量化的重要性，以确保结果的准确性。

作文

In today's data-driven world, the process of quantification (量化) has become increasingly important across various fields. From science and engineering to economics and social sciences, the ability to measure and express phenomena in numerical terms allows for clearer communication and more effective decision-making. This essay explores the significance of quantification (量化), its applications, and the challenges associated with it.Firstly, quantification (量化) provides a standardized way to represent complex information. In scientific research, for example, quantifying results through statistical methods enables researchers to draw meaningful conclusions and compare their findings with others. For instance, when studying the effects of a new drug, researchers quantify the impact by measuring variables such as patient recovery rates and side effects. This numerical data not only helps in assessing the drug's efficacy but also facilitates peer review and replication of the study by other scientists.Moreover, quantification (量化) plays a crucial role in economics. Economists rely on quantitative data to analyze trends, make forecasts, and formulate policies. By quantifying economic indicators like GDP, inflation rates, and unemployment figures, economists can better understand the health of an economy and devise strategies to improve it. For instance, during a recession, policymakers may look at quantified data to determine the best course of action, such as implementing stimulus packages or adjusting interest rates.In social sciences, quantification (量化) allows researchers to study human behavior and societal trends systematically. Surveys and experiments often involve quantifying responses to gauge public opinion or measure the effectiveness of social programs. For example, a study on educational outcomes might quantify student performance through standardized test scores, enabling educators to assess the impact of different teaching methods.However, while quantification (量化) is undeniably valuable, it is not without its challenges. One major issue is the potential for oversimplification. Reducing complex phenomena to mere numbers can lead to a loss of nuance and context. For instance, quantifying happiness through a single survey question may overlook the multifaceted nature of well-being, which encompasses emotional, social, and psychological dimensions. Thus, while quantification (量化) can provide insights, it is essential to consider qualitative aspects as well.Another challenge is the risk of misinterpretation. Data can be manipulated or presented in misleading ways, leading to incorrect conclusions. For instance, cherry-picking data points to support a specific argument can distort the overall picture. Therefore, it is crucial for researchers and decision-makers to approach quantification (量化) with a critical mindset, ensuring that they consider the broader context and potential biases in the data.In conclusion, quantification (量化) is a powerful tool that enhances our understanding of the world by allowing us to measure and analyze various phenomena. Its applications span across disciplines, from science to economics and social sciences. However, it is vital to recognize the limitations and challenges associated with quantification (量化) to avoid oversimplification and misinterpretation. As we continue to navigate an increasingly complex world, mastering the art of quantification (量化) will be essential for effective communication, informed decision-making, and meaningful progress.

在当今数据驱动的世界中，量化（quantification）这一过程在各个领域变得愈发重要。从科学和工程到经济学和社会科学，将现象用数字形式进行测量和表达的能力可以实现更清晰的沟通和更有效的决策。本文探讨了量化（quantification）的重要性、应用及其相关挑战。首先，量化（quantification）提供了一种标准化的方式来表示复杂信息。例如，在科学研究中，通过统计方法对结果进行量化（quantification）使研究人员能够得出有意义的结论，并与他人的发现进行比较。例如，在研究新药的效果时，研究人员通过测量患者恢复率和副作用等变量来量化（quantification）影响。这些数值数据不仅有助于评估药物的有效性，还便于其他科学家进行同行评审和研究的复制。此外，量化（quantification）在经济学中扮演着至关重要的角色。经济学家依赖定量数据来分析趋势、做出预测和制定政策。通过对GDP、通货膨胀率和失业率等经济指标进行量化（quantification），经济学家可以更好地理解经济的健康状况，并制定改善经济的策略。例如，在经济衰退期间，政策制定者可能会查看量化（quantification）数据以确定最佳行动方案，比如实施刺激计划或调整利率。在社会科学中，量化（quantification）使研究人员能够系统地研究人类行为和社会趋势。调查和实验通常涉及量化（quantification）回应，以衡量公众舆论或评估社会项目的有效性。例如，一项关于教育成果的研究可能通过标准化测试成绩来量化（quantification）学生表现，使教育工作者能够评估不同教学方法的影响。然而，尽管量化（quantification）无疑是有价值的，但它并非没有挑战。一个主要问题是可能导致的过度简化。将复杂现象简化为单纯的数字可能会导致失去细微差别和上下文。例如，通过单一的调查问题来量化（quantification）幸福感可能忽视了幸福的多维特性，包括情感、社会和心理层面。因此，虽然量化（quantification）可以提供洞见，但考虑定性方面同样重要。另一个挑战是误解的风险。数据可能被操纵或以误导性的方式呈现，从而导致错误的结论。例如，选择性地挑选数据点以支持特定论点可能会扭曲整体情况。因此，研究人员和决策者在处理量化（quantification）时应保持批判性思维，确保他们考虑数据的更广泛背景和潜在偏见。总之，量化（quantification）是一种强大的工具，通过允许我们测量和分析各种现象，增强了我们对世界的理解。它的应用跨越多个学科，从科学到经济学和社会科学。然而，认识到与量化（quantification）相关的局限性和挑战至关重要，以避免过度简化和误解。随着我们继续在日益复杂的世界中航行，掌握量化（quantification）的艺术对于有效沟通、明智决策和有意义的进步将是必不可少的。