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

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

712
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
712

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

The PPC neurons modulate memory generalization via NR2B-type NMDA receptors in mice.

Physiology & behavior·2026
Same author

FKBP10 mitigates osteoporosis by restraining the HSPA5-coupled ERS-pyroptosis axis to enhance osteogenic differentiation in BMSCs.

International immunopharmacology·2026
Same author

Modulating burn wound immunity and vasculature with a photo-crosslinkable glycyrrhizic acid-ginsenoside hydrogel.

Biomedical materials (Bristol, England)·2026
Same author

Gut microbiota dysbiosis-induced chronic inflammation as a driver of atherosclerosis: cellular crosstalk and host-microbe interactions.

Frontiers in cellular and infection microbiology·2026
Same author

Insight into the biotransformation pathway of oat phenolic in enzyme hydrolysis coupled with Monascus fermentation.

Journal of biotechnology·2026
Same author

Effects of Organic Amendments Combined with Mineral Fertilizer on Soil Properties and Crop Yield in a Maize-Soybean Rotation System on Meadow Albic Soil.

Plants (Basel, Switzerland)·2026

相关实验视频

Updated: May 3, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K

CPDC-MFNet:有条件的点扩散完成网络与Muti规模的反精细化为3D陶战士.

Xueli Xu1,2,3, Da Song1,3, Guohua Geng4,5

  • 1School of Information Science and Technology, Northwest University, Xi'an, 710127, Shaanxi, China.

Scientific reports
|April 9, 2024
PubMed
概括

这项研究介绍了一种新的神经网络,用于使用点云完成修复受损的土木战士文物. 该方法加快了生成速度,同时保持了文化遗迹的多样化和准确的重建.

更多相关视频

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.4K
Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

548

相关实验视频

Last Updated: May 3, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.4K
Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
11:38

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

548

科学领域:

  • 数字遗产是数字遗产的一部分.
  • 计算机视觉 计算机视觉
  • 保护文化遗产 保护文化遗产

背景情况:

  • 陶战士由于古代和挖掘挑战而遭受损伤.
  • 完成点云对于恢复这些文化遗迹至关重要.
  • 现有的方法往往缺乏点云完成结果的多样性.

研究的目的:

  • 开发一种新型的神经网络,以高效多样化地完成特拉科塔战士碎片的点云.
  • 为了解决与点云重建中的扩散模型相关的缓慢生成速度问题.

主要方法:

  • 一个新的神经网络架构被提议用于Terracotta Warriors碎片的完成.
  • 该模型在反向扩散阶段采用了减少采样策略,以更快地生成粗略结果.
  • 一个多尺度的精炼网络和分区注意力采样被用于增强的特征表示和精炼.

主要成果:

  • 拟议的模型与现有实体和公共数据集的现有方法相比,显示出具有竞争力的性能.
  • 实验验证实了该模型在修复文化遗迹的点云完成方面的有效性.
  • 这种方法可以实现更快的生成速度,同时保持多样化和准确的输出.

结论:

  • 开发的神经网络提供了一个有效的解决方案,用于完成受损的土木战士的点云.
  • 该方法成功地平衡了生成速度与重建的文化遗产的多样性和准确性.
  • 这项工作有助于通过创新的AI应用程序推进数字遗产保护技术.