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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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了解和量化网络对随机输入的稳定性

Hwai-Ray Tung1, Sean D Lawley2

  • 1Department of Mathematics, University of Utah, Salt Lake City, UT, 84112, USA.

Bulletin of mathematical biology
|April 12, 2024
PubMed
概括

我们开发了一个新的统计数据来衡量生物网络如何抵御随机波动. 这种基于随机步行通道时间的网络稳定性指标有助于理解为什么系统尽管有噪音输入,但仍然保持稳定的输出.

科学领域:

  • 生物物理学的生物物理.
  • 系统生物学 系统生物学
  • 生物化学 生化学

背景情况:

  • 生物医疗系统通常使用带有随机输入的微分方程进行建模.
  • 稳定性,即能够保持不变的输出,尽管输入波动,在生物系统 (例如,生物化学网络,药物疗效) 中至关重要.
  • 由于复杂的网络参数和输入类型,了解网络对噪声的稳定性背后的机制是一个重大挑战.

研究的目的:

  • 为量化线性微分方程网络 (第一阶质量作用系统) 的稳定性提出一个新的总结统计.
  • 分析特定的网络模式如何有助于提高可靠性.
  • 为观察到的网络稳定性提供直观的解释.

主要方法:

  • 开发了基于网络上特定随机步行通行时间的方差的总结统计.
  • 应用统计学来分析线性微分方程网络.
  • 利用计算方法在大型网络上进行快速计算.
  • 证明了关于网络模式对稳定性的影响的定理.

主要成果:

  • 拟议的统计数据有效量化了网络对随机噪声的稳定性.
  • 对于拥有数千个节点的复杂网络,统计数据可以有效计算.
  • 确定了特定的网络模式,可以明显提高稳定性.
关键词:
恒常状态 (Homeostasis) 是一种恒常状态.药物治疗的坚持 药物治疗的坚持药物不坚持的情况.药理动力学是什么 药理动力学药理动力学 药理动力学坚固性 坚固性

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  • 分析提供了对生物网络强度来源的明确见解.
  • 结论:

    • 随机步行通行时间的方差是网络稳定性的强大和计算效率高的指标.
    • 这种方法为设计原则提供了有价值的直觉,这些原则是强大的生物系统的基础.
    • 这些发现对理解和设计生物和药理系统有意义.