Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

8.6K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
8.6K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

580
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
580
Multiple Regression01:25

Multiple Regression

3.2K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.2K
Extraction: Advanced Methods00:56

Extraction: Advanced Methods

526
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
526
Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

12.2K
When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
12.2K

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Coal Calorific Value Prediction via Multi-View Transformer.

Sensors (Basel, Switzerland)·2026
Same author

The role of USP19 in human diseases: from molecular function to clinical relevance.

Frontiers in immunology·2026
Same author

[Risk Nomogram Prediction Model for Cerebral Edema in Patients With Hypertensive Intracerebral Hemorrhage].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition·2026
Same author

Research progress of OTUD7B: from structural function and disease mechanisms to clinical translation.

Frontiers in immunology·2026
Same author

TRIM47: molecular characteristics, disease-related mechanisms, and clinical translational value.

Frontiers in immunology·2026
Same author

Natural-language-processing and safety-engineering-based fault identification technique for electrochemical ESSs.

Innovation (Cambridge (Mass.))·2026

関連する実験動画

Updated: Sep 9, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

LIBSとエンサンブル・マシン・ラーニング技術を用いた石炭品質の高度な多パラメータ予測

Qingsong Wang1, Donglian Zhang1, Youquan Dou1

  • 1Nanjing Coal Quality Supervision and Inspection Co. Ltd, China Energy Corporation, Nanjing 210031, China.

ACS omega
|September 2, 2025
PubMed
まとめ

機械学習と組み合わせたレーザー誘発分解スペクトロシー (LIBS) は,石炭の品質パラメータを正確に予測します. この迅速な分析方法は,発電所の効率と排出量の制御を最適化するための信頼できる代替手段です.

さらに関連する動画

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
A Uniaxial Compression Experiment with CO2-Bearing Coal Using a Visualized and Constant-Volume Gas-Solid Coupling Test System
10:27

A Uniaxial Compression Experiment with CO2-Bearing Coal Using a Visualized and Constant-Volume Gas-Solid Coupling Test System

Published on: June 12, 2019

8.8K

関連する実験動画

Last Updated: Sep 9, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
A Uniaxial Compression Experiment with CO2-Bearing Coal Using a Visualized and Constant-Volume Gas-Solid Coupling Test System
10:27

A Uniaxial Compression Experiment with CO2-Bearing Coal Using a Visualized and Constant-Volume Gas-Solid Coupling Test System

Published on: June 12, 2019

8.8K

科学分野:

  • 分析化学
  • スペクトロスコーピー
  • 機械学習

背景:

  • 石炭の質の正確な評価は 効率的な燃焼と発電所の排出量の削減に不可欠です
  • 伝統的な石炭分析方法は 時間と労力を要するものです
  • 石炭の分析のための迅速で信頼性の高い方法の開発は,継続的な課題です.

研究 の 目的:

  • 石炭の主要な品質パラメータを予測するためのレーザー誘導分解スペクトロスコーピー (LIBS) ベースの枠組みを開発する.
  • 先進的な機械学習技術を統合して 予測の精度を向上させる
  • 日常的な石炭品質分析に迅速かつ効率的な代替手段を提供すること.

主な方法:

  • 炭素の元素および分子分析のためにレーザー誘導分解スペクトロスコーピー (LIBS) を利用した.
  • 偏差値の除去とベースラインの修正を含む,適用されたスペクトル前処理技術.
  • 機械学習アルゴリズムを用いた予測モデル,特に最小平方支持ベクトルマシン (LS-SVM) を開発した.

主要な成果:

  • LIBSベースのフレームワークは,炭素元素,灰含量,揮発性物質,硫黄総量,熱量などの主要な石炭品質パラメータを成功裏に予測しました.
  • 最小平方支持ベクトルマシン (LS-SVM) モデルは,元素炭素予測のR2 0.9940で高い精度を達成した.
  • 提案された方法は,石炭品質の急速な分析において信頼性と効率性を示した.

結論:

  • 統合されたLIBSと機械学習のアプローチは,リアルタイムで石炭の品質を監視するための強力なソリューションを提供します.
  • この枠組みは,石炭火力発電所の燃焼プロセスと排出量管理の最適化を大幅に改善する可能性を秘めています.
  • この研究は,インテリジェントな産業監視・制御システムにおける高度な分析技術の適用性を強調しています.