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相关概念视频

Rolling Resistance: Problem Solving01:17

Rolling Resistance: Problem Solving

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Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
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Protein Folding Quality Check in the RER01:29

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ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...
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Rolling Resistance01:21

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When a solid cylinder rolls steadily on a rigid surface, the normal force applied by the surface on the cylinder is perpendicular to the tangent at the contact point. However, since no materials are entirely rigid, the surface's reaction to the cylinder involves a range of normal pressures.
For instance, imagine a hard cylinder rolling on a comparatively soft surface. The cylinder's weight compresses the surface beneath it. As the cylinder moves, the material in front of it slows down due to...
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The mechanical characteristics of steel are assessed through various tests that evaluate its strength, toughness, and flexibility. These tests include tension, torsion, impact, bending, and hardness assessments, each providing crucial information about steel's suitability for specific applications.
The tension test is fundamental for determining tensile strength. In this test, a steel specimen is stretched using a gripping device until it breaks. The data collected during this test are used...
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Moisture Content and Bulking of Aggregate01:10

Moisture Content and Bulking of Aggregate

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The moisture content of aggregates is a crucial factor in construction, particularly in concrete mixing, as it influences the total water required in the mix. Moisture content represents the water coated on the exterior surface of the aggregate existing in a saturated and surface-dry condition. The total water content of a moist aggregate is the sum of its moisture content and water absorption.
When aggregates are exposed to rain or sit in stockpiles, they absorb moisture, which must be...
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Residual Stresses in Bending

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In the study of elastoplastic members subjected to bending moments, understanding the loading and unloading phases is crucial for assessing material behavior and structural integrity. During the loading phase, as the bending moment increases, the material initially responds elastically, adhering to Hooke's Law, where stress is directly proportional to strain. When the load exceeds the yield strength, plastic deformation occurs, resulting in permanent strain and deformation that remains even...
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相关实验视频

Updated: Jan 17, 2026

Author Spotlight: Real-Time Imaging of Bonding in 3D-Printed Layers
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通过机器学习框架来缓解分裂,提高了滚筒压缩过程中的带质量.

Mohammad Shahab1, David Sixon1, Jayden A Pierce2

  • 1Davidson School of Chemical Engineering, Purdue University, 47907, IN, USA.

International journal of pharmaceutics
|September 21, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种机器学习框架,用于预测和理解干燥颗粒处理中的带分裂. 该模型准确地预测了带质量,确保了稳定的平板电脑生产和监管合规性.

关键词:
数据增强是数据增强的方法.设计空间 设计空间斯过程是高斯过程.丝带分开 丝带分开滚筒压缩压缩 滚筒压缩转移学习转移学习

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科学领域:

  • 制药制造业 制药制造业 制药制造业
  • 工艺工程是过程工程.
  • 数据科学在制药领域

背景情况:

  • 在滚筒压缩过程中,带分裂会损害颗粒的均性和平板质量.
  • 由于复杂的过程变量相互作用,预测带分裂是具有挑战性的.
  • 目前的方法缺乏准确性,无法完全理解和控制这种现象.

研究的目的:

  • 开发一种机器学习框架,用于模拟和表征干颗粒化中带分裂.
  • 改进带质量的预测,并确定最佳的工艺条件.
  • 通过特征重要性分析,提高带分裂现象的解释性.

主要方法:

  • 使用基于高斯过程回归 (GPR) 的神经网络与转移学习.
  • 为了模型的解释性和特征的重要性,采用了夏普利添加式解释 (SHAP).
  • 利用多变量实验数据进行模型培训和验证.

主要成果:

  • 实现了带质量 (厚度,密度) 的可靠预测,具有高R2和低MSE/MAE.
  • 确定了可行的操作区域和最佳条件,以保持一致的产品质量.
  • 证明了框架的灵活性,可扩展性和跨条件的通用性.

结论:

  • 开发的机器学习框架有效地模拟和预测了带分裂.
  • 该方法促进了早期故障检测,加速了流程开发,并支持QbD.
  • 能够为制药干颗粒的闭环控制提供可操作的见解.