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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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Related Experiment Video

Updated: Jan 10, 2026

Loneliness Assuaged: Eye-Tracking an Audience Watching Barrage Videos

Loneliness Assuaged: Eye-Tracking an Audience Watching Barrage Videos

Published on: May 29, 2020

Dynamic edge-caching through content popularity and crowd prediction for short video services.

Sen Niu¹, Yuhe Liu¹, Kaili Liao²

¹School of Computer and Information Engineering, Institute for Artificial Intelligence, Shanghai Polytechnic University, Shanghai, 201209, China.

Scientific Reports

|November 26, 2025

Summary

Dynamic Edge-caching through Content Popularity and Crowd Prediction (DECC) improves mobile network caching for short videos. This AI framework enhances cache hit rates and reduces latency by predicting content popularity and user behavior.

Keywords:

Content popularity prediction Edge caching Short video services User access forecasting

Related Experiment Videos

Last Updated: Jan 10, 2026

Loneliness Assuaged: Eye-Tracking an Audience Watching Barrage Videos

Loneliness Assuaged: Eye-Tracking an Audience Watching Barrage Videos

Published on: May 29, 2020

Area of Science:

Computer Science
Network Engineering
Artificial Intelligence

Background:

Short video traffic growth necessitates efficient mobile network caching.
Traditional caching methods struggle with dynamic, personalized short video content due to reliance on static popularity metrics.

Purpose of the Study:

To propose DECC (Dynamic Edge-caching through Content Popularity and Crowd Prediction), a novel framework for optimizing edge caching in mobile networks.
To address the limitations of traditional caching by jointly modeling content popularity and user access behavior.

Main Methods:

DECC utilizes a hybrid deep learning architecture (Conv1D, LSTM, GRU) to analyze temporal dynamics of video requests and user activity.
A fusion mechanism generates cache priority scores via dual-path predictions for adaptive content placement.

Main Results:

DECC significantly improves cache hit rate compared to baseline methods.
The framework effectively reduces access latency and enhances overall resource utilization efficiency.
Experimental evaluations on real-world datasets validate DECC's performance.

Conclusions:

DECC offers a scalable and intelligent solution for edge caching in next-generation short video services.
The joint modeling of content popularity and crowd prediction optimizes caching decisions for dynamic content.
DECC demonstrates superior performance in key metrics for mobile network efficiency.