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関連する概念動画

Predicting Molecular Geometry02:27

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Prediction Intervals01:03

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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. 
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End Point Prediction: Gran Plot01:07

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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.
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In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
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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,...
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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:
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Updated: Jan 22, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
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Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

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画像処理を用いた森林火災予測

Yingdan Li1,2, Junting Chen1, Yaxuan Zeng1

  • 1School of Electronic Information Engineering, Guiyang University, Guiyang, China.

PloS one
|January 20, 2026
PubMed
まとめ
この要約は機械生成です。

早期の森林火災検出は安全のために不可欠です。新しいYOLOv5-PSGモデルは、リアルタイムの火災認識精度を大幅に向上させ、山火事予防と環境保護を強化します。

キーワード:
森林火災早期検出YOLOv5-PSGモデル精度向上山火事予防環境保護

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科学分野:

  • 環境科学; 計算機科学; 人工知能

背景:

  • 森林火災は、公共の安全と生態系に実質的なリスクをもたらします。; 小規模な火災が大規模な災害にエスカレートするのを防ぐためには、早期検出が不可欠です。; 従来の火災予測方法は、効果的な介入に必要な精度とリアルタイム能力を欠いています。

研究 の 目的:

  • 森林火災予測システムの精度とリアルタイム検出能力を向上させること。; より効果的な早期警戒と山火事予測のために、改良されたYOLOv5-PSGモデルを導入すること。

主な方法:

  • 本研究では、YOLOv5-PSGと名付けられたYOLOv5モデルの改良版を提案します。; モデルは300回の厳密なトレーニングと学習を受けました。

主要な成果:

  • YOLOv5-PSGモデルは、平均認識精度率93.1% (mAP) を達成しました。; モデルは、トレーニング後、約0.802の精度率と約0.965の信頼度を示しました。; これらの結果は、従来のメソッドに対する大幅な改善を示しています。

結論:

  • 改良されたYOLOv5-PSGモデルは、森林火災に対してより包括的で効果的な早期警戒と予測を提供します。; この進歩は、山火事の影響の軽減、人命と環境の保護に貢献します。