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Related Concept Videos

Diffusion01:21

Diffusion

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Diffusion is a type of passive transport. In passive transport, a substance tends to move from an area of high concentration to an area of low concentration until the concentration is equal across the space. For example, take the diffusion of substances through the air. When someone opens a perfume bottle in a room filled with people, the perfume is at its highest concentration in the bottle and is at its lowest at the edges of the room. The perfume vapor will diffuse, or spread away, from the...
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Diffusion01:12

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Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Predicting Reaction Outcomes02:24

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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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Passive Diffusion: Overview and Kinetics01:17

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Passive diffusion is a critical process that allows small lipophilic drugs to cross the cell membrane along a concentration gradient. This mechanism's efficiency depends on four primary factors: the membrane's surface area, the drug's lipid-water partition coefficient, the concentration gradient, and the membrane's thickness.
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Facilitated Diffusion01:16

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The plasma membrane, a critical structure in cellular biology, houses an array of transporters, or carrier proteins, interspersed within its lipid bilayer. These proteins play a crucial role in solute transport through facilitated diffusion, a form of passive diffusion that uses transporters to move the molecules across the membrane.
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Related Experiment Video

Updated: Apr 9, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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Relation-Centric knowledge graph generation for recommendation based on conditional diffusion model.

Lei Tian1, Nan Li1, Zhong Zhang2

  • 1College of Computer Science and Technology, National University of Defense Technology, Changsha, 410073, Hunan, China.

Neural Networks : the Official Journal of the International Neural Network Society
|April 7, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces RKGRec, a novel framework to address knowledge incompleteness in recommendation systems. By generating an auxiliary knowledge graph, RKGRec enhances recommendation accuracy and diversity, especially for cold-start scenarios.

Keywords:
Conditional diffusion modelKnowledge graph generationKnowledge incompletenessKnowledge-Aware recommendation

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Area of Science:

  • Artificial Intelligence
  • Information Retrieval
  • Data Science

Background:

  • Recommendation systems face challenges like data sparsity and cold-start problems.
  • Knowledge graphs (KGs) improve recommendations but often neglect knowledge incompleteness.
  • Existing KG augmentation methods struggle with missing facts and relation sparsity.

Purpose of the Study:

  • To propose RKGRec, a relation-guided conditional diffusion framework.
  • To generate a relation-centric auxiliary KG to mitigate knowledge incompleteness.
  • To enhance recommendation performance by addressing structural gaps in KGs.

Main Methods:

  • Utilizing a relation-attention network for multi-hop entity-relation pattern extraction.
  • Employing a relation-guided conditional diffusion model for KG refinement via noise injection and denoising.
  • Implementing a joint prediction and optimization module for integrated training.

Main Results:

  • RKGRec significantly outperforms baseline methods across multiple datasets.
  • The model achieves leading predictive accuracy and competitive predictive diversity.
  • Demonstrates robust performance in cold-start users, long-tail items, and noisy/sparse KG conditions.

Conclusions:

  • RKGRec effectively alleviates knowledge incompleteness in knowledge-aware recommendation.
  • The proposed framework offers a promising approach to improve recommendation system robustness and performance.
  • Future work can explore further refinements of the diffusion process and KG generation.