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関連する概念動画

Machines01:19

Machines

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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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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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Thalesram Izidoro Pinotti1, Fábio Sandro Dos Santos1, Yanka Manoelly Dos Santos Gaspar1

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まとめ

微生物バイオ製品の安定性は、マイクロカプセル化と人工ニューラルネットワークを使用して強化されました。このアプローチは、保管中の真菌の生存率を予測し、バイオ製品の開発を最適化するのに役立ちます。

キーワード:
農業の持続可能性人工ニューラルネットワークバイオ殺菌剤真菌の生存率予測モデリング

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科学分野:

  • 微生物学
  • 計算生物学
  • バイオテクノロジー

背景:

  • 微生物バイオ製品の開発には、保管中の細胞の安定性と生存率の理解が必要です。
  • トリコデルマ属は、さまざまなバイオ製品に応用される価値のある微生物です。
  • ピアウイ・セラードの在来植物は、多様な微生物群集を宿しています。

研究 の 目的:

  • ピアウイ・セラードのトリコデルマ属菌株の生存率を評価すること。
  • 安定性評価のためにマイクロカプセル化技術と計算モデリングを統合すること。
  • 時間経過に伴う微生物の生存率を予測する上での人工ニューラルネットワークの可能性を探求すること。

主な方法:

  • アルギン酸ナトリウムマトリックス中のイオンゲル化によるトリコデルマ属菌株のマイクロカプセル化。
  • 60日間にわたる培養および分生子計数による真菌生存率のモニタリング。
  • 特定の正則化およびドロップアウト技術を用いた多層パーセプトロン（MLP）人工ニューラルネットワークのトレーニング。

主要な成果:

  • 菌株UFPI07、UFPI10、UFPI11、UFPI16は、優れた生存率（> 7 log CFU mL⁻¹）を示しました。
  • 菌株UFPI06およびUFPI18は、生存率の低下がより顕著でした。
  • MLPモデルは、実験期間を超えた微生物の傾向の探索的予測を提供しました。

結論:

  • マイクロカプセル化と機械学習の組み合わせは、時間的微生物挙動を推定するための有望なツールを提供します。
  • この統合アプローチは、バイオ製品製剤の最適化と実験コストの削減に役立ちます。
  • これは、予測微生物学およびバイオ製品開発における探索的な方法論的進歩を表します。