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相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

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Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
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Response Surface Methodology01:16

Response Surface Methodology

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Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
267
Turbulent Flow: Problem Solving01:09

Turbulent Flow: Problem Solving

186
Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
Temperature is a key factor in CO2 solubility. In this case, the CO2 gas and the liquid are cooled to 20°C. Lower temperatures...
186
Laminar Flow: Problem Solving01:24

Laminar Flow: Problem Solving

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Laminar flow occurs when a fluid moves smoothly in parallel layers with minimal mixing and turbulence. In fluid mechanics, ensuring laminar flow within a pipe is essential for precise control of flow characteristics, especially in engineering applications. The key factor in determining whether flow remains laminar is the Reynolds number, a dimensionless quantity that depends on the fluid's velocity, density, viscosity, and the pipe's diameter. A Reynolds number of 2100 or lower...
260
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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空间时空臭氧 (O3) 污染建模的优化,使用集体机器模型学习与基于群体的元启发算法进行集体机器模型学习.

Seyed Vahid Razavi-Termeh1, Abolghasem Sadeghi-Niaraki1, Armin Sorooshian2

  • 1Dept. of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Republic of Korea.

Ecotoxicology and environmental safety
|July 31, 2025
PubMed
概括

这项研究引入了一种新的时空模型,使用集体机器学习和Cuckoo搜索来预测臭氧 (O3) 污染. 精确的O3风险地图为公共卫生和环境政策提供了关键数据.

关键词:
大数据就是大数据.整合机器学习 机器学习臭氧 (O3) 污染造成的污染.公共卫生 公共卫生时间空间建模.

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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
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科学领域:

  • 环境科学 环境科学
  • 公共卫生 公共卫生
  • 数据科学数据科学数据科学

背景情况:

  • 臭氧 (O3) 污染对全球环境和公共卫生构成重大风险,影响呼吸道和心血管健康.
  • 准确的时空建模对于评估和预测O3污染水平至关重要,以减轻其不利影响.

研究的目的:

  • 开发一种新的时空模型,用于O3污染的评估和预测.
  • 将集体机器学习算法与基于集群的元启发式优化算法集成在一起,以进行增强的O3建模.

主要方法:

  • 利用来自伊朗 (2018-2022) 德黑兰的表面O3数据和14个环境因素.
  • 采用随机森林 (RF) 作为基组机器学习模型.
  • 使用Cuckoo Search (CS) 的元启发算法优化了射频模型.

主要成果:

  • 在不同季节的O3风险地图预测中实现了高准确性:95.2% (秋季),97% (春季),96.7% (夏季) 和95.7% (冬季).
  • 接收器运行特征 (ROC) 曲线评估证实了强大的模型性能.

结论:

  • 新的综合模型有效地预测了高精度的O3污染.
  • 调查结果为决策者和公共卫生官员提供了可操作的见解,以解决O3对健康和环境的影响.