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Related Concept Videos

First Pass Effect01:12

First Pass Effect

Presystemic elimination, or the first-pass effect, is the metabolism of drugs that reduces their effective concentration at the site of action. Apart from the first-pass effect, the systemic bioavailability of the drug is also reduced by other factors, including incomplete absorption or chemical degradation of drugs.
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Buffer Effectiveness

Buffer solutions do not have an unlimited capacity to keep the pH relatively constant . Instead, the ability of a buffer solution to resist changes in pH relies on the presence of appreciable amounts of its conjugate weak acid-base pair. When enough strong acid or base is added to substantially lower the concentration of either member of the buffer pair, the buffering action within the solution is compromised.
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Accelerating Fluids01:17

Accelerating Fluids

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Buffers: Buffer Capacity01:09

Buffers: Buffer Capacity

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Related Experiment Video

Updated: May 10, 2026

Computer-Generated Animal Model Stimuli
26:43

Computer-Generated Animal Model Stimuli

Published on: July 29, 2007

Memory-efficient single-pass GPU rendering of multifragment effects.

Wencheng Wang1, Guofu Xie

  • 1State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China. whn@ios.ac.cn

IEEE Transactions on Visualization and Computer Graphics
|June 8, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a GPU stream method for rendering complex scenes efficiently. It enables fast, accurate multifragment effects even for large models with limited memory.

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Area of Science:

  • Computer Graphics
  • Scientific Visualization

Background:

  • Rendering multifragment effects on Graphics Processing Units (GPUs) is challenging due to ordering difficulties and memory limitations.
  • Existing methods struggle with large models and suffer from CPU-to-GPU data transmission bottlenecks.

Purpose of the Study:

  • To present an efficient stream method for accurate rendering of multifragment effects on GPUs.
  • To overcome limitations of existing methods for handling large models and improve rendering speed.

Main Methods:

  • Decomposing models into parts for efficient ordering and rendering on the GPU.
  • Transmitting model data part-by-part, rendering immediately, and compositing results.
  • Utilizing a single data access pass with bounded memory requirements and packing parts for acceleration.

Main Results:

  • The proposed stream method achieves accurate rendering of multifragment effects.
  • The method significantly outperforms existing approaches in terms of speed.
  • It demonstrates the capability to handle large models of any size effectively.

Conclusions:

  • The stream method offers a viable solution for high-speed, accurate multifragment rendering on GPUs.
  • This approach overcomes memory and data transmission limitations, enabling efficient processing of large-scale models.