Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

87
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...
87

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Hereditary spastic paraplegia in three siblings with distinct genetic mutations.

The Journal of international medical research·2026
Same author

Preferential Upregulation of AMOT-p80 Is Associated with YAP-Linked Resistance to 5-Fluorouracil and Oxaliplatin in Colorectal Cancer Cells.

Biomolecules·2026
Same author

Increased mobility, toxicity, and bioaccessibility of arsenate adsorbed onto UV-aged polyvinyl chloride microplastics.

Ecotoxicology and environmental safety·2026
Same author

Functional differentiation of glomalin-related soil protein fractions reveals dual pathways for carbon storage in organic farming systems.

The Science of the total environment·2026
Same author

Deep-eutectic-solvent synthesis of ferrocene-derived Fe-functionalized activated carbon for adsorption- assisted heterogeneous fenton oxidation in complex dye matrices.

Journal of environmental management·2026
Same author

Microbial efficiency enhancement drives carbon sequestration in long-term organic farming systems: linking taxonomic succession to carbon use efficiency.

Frontiers in microbiology·2026

相关实验视频

Updated: Sep 15, 2025

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

10.3K

基于机器学习的矩阵特定PFAS来源分配模型:识别土壤和水系统中的差异指标.

Jin-Kyung Hong1, Sungjik Oh2, Tae Kwon Lee3

  • 1Department of Environment and Energy Engineering, Chnonnam National University, Gwangju, 61186, Republic of Korea.

Environmental research
|July 12, 2025
PubMed
概括

新的机器学习模型能够准确地识别土壤和水中的多基物质 (PFAS) 的来源. 这种特定于矩阵的方法简化了PFAS来源跟踪,并通过识别关键指标化合物来降低分析成本.

关键词:
指标指标 指标指标指标指标指标机器学习是机器学习.在PFAS中,有很多方法.来源分配分配的分配方法水水水水水,这是一个很好的方法.土壤土壤土壤土壤土壤

更多相关视频

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.1K
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K

相关实验视频

Last Updated: Sep 15, 2025

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

10.3K
Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.1K
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K

科学领域:

  • 环境化学环境化学
  • 环境科学 环境科学
  • 毒理学 毒理学 毒理学

背景情况:

  • 聚甲基物质 (PFAS) 是持久性环境污染物,具有显著的人类健康风险.
  • 目前的PFAS源分配方法由于广泛的数据要求和忽视矩阵特定行为的原因,成本昂贵且容易出现错误.

研究的目的:

  • 开发和验证特定于矩阵的机器学习分类器,用于在土壤和水中区分PFAS来源 (水性薄膜形成泡[AFFF]与非AFFF).
  • 为了确定PFAS指标的关键化合物,以有效地跟踪来源并减少分析负担.

主要方法:

  • 从同行评审文献 (2012-2024) 中编制了PFAS度的综合数据集,用于土壤,水和AFFF配方.
  • 通过H2O.AutoML应用了15个分类算法,对12个遗留的PFAS化合物的日志转换度进行了分析.
  • 利用特征重要性分析和逐步变量减小来确定最佳指标化合物并评估模型性能.

主要成果:

  • 最佳水模型 (梯度增强机) 实现了AUC 0.9864;最佳土壤模型 (分布式随机森林) 实现了AUC 0.9936.
  • 特性重要性确定了PFOS,PFHxS和PFPeS作为关键水指标,PFHxS,PFPeA和PFOS作为关键土壤指标.
  • 源分配精度>0.92,在水中的PFAS只有9个,在土壤中的PFAS只有6个,大大减少了数据需求.

结论:

  • 特定于矩阵的机器学习模型提供了在土壤和水中准确和高效的PFAS来源分配.
  • 识别关键的"哨兵"PFAS指标可以大幅减少分析要求,而不会影响分类性能.
  • 这种方法增强了法医来源追踪,并为更有效的环境修复策略提供了信息.