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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Multi-species Conserved Sequences02:51

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Insertion of Multi-pass Transmembrane Proteins in the RER01:29

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The rough ER membrane synthesizes, assembles, and embeds transmembrane proteins in diverse topologies. These proteins function as transporters or channels and can remain in the ER membrane or are sent to the Golgi complex, lysosome, and cell membrane.
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Multi-pass Transmembrane Proteins and β-barrels01:09

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In multi-pass transmembrane proteins, the polypeptide chain crosses the membrane more than once. The transmembrane polypeptide chain either forms an α-helix or β-strand structure. α-Helix containing multi-pass transmembrane proteins are ubiquitous, whereas β-strand containing ones are mainly found in gram-negative bacteria, mitochondria, and chloroplasts.
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Cardiac Output II: Effect of Stroke Volume on Cardiac Output01:22

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Related Experiment Video

Updated: Feb 4, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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Multi-output support vector machine for regional multi-step-ahead PM2.5 forecasting.

Yanlai Zhou1, Fi-John Chang1, Li-Chiu Chang2

  • 1Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan, ROC.

The Science of the Total Environment
|September 23, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces the MM-SVM framework for accurate multi-step-ahead PM2.5 forecasts, outperforming traditional methods by integrating Multi-output Support Vector Machines and Multi-Task Learning to reduce forecasting errors.

Keywords:
Multi-output SVMMulti-step-ahead forecastMulti-task learning algorithmPM(2.5) concentrationsTaipei City

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Area of Science:

  • Environmental Science
  • Data Science
  • Atmospheric Science

Background:

  • Urbanization accelerates air quality deterioration, necessitating precise PM2.5 forecasts for public health.
  • Regional multi-step-ahead PM2.5 forecasting faces challenges with error accumulation and propagation.

Purpose of the Study:

  • To develop and validate a novel MM-SVM framework for enhanced regional multi-step-ahead PM2.5 forecasting accuracy.
  • To address limitations of single-site forecasting models by proposing an integrated approach.

Main Methods:

  • Utilized Kendall tau coefficient for spatiotemporal factor extraction from meteorological and air quality data.
  • Employed Multi-Task Learning (MTL) to train Multi-output Support Vector Machines (M-SVM) for capturing complex relationships.
  • Validated the MM-SVM model using long-term hourly PM2.5 concentration, meteorological, and air quality datasets from Taipei City.

Main Results:

  • The MM-SVM model demonstrated superior performance compared to the Single-output SVM (S-SVM) benchmark.
  • MM-SVM requires a single model for multiple sites, unlike site-specific S-SVM models.
  • Forecasts from MM-SVM showed better consistency with observed PM2.5 concentrations during both training and testing phases.

Conclusions:

  • The MM-SVM framework offers a promising, integrative technique for improving regional PM2.5 forecast accuracy and spatiotemporal stability.
  • This approach effectively tackles error accumulation in multi-step-ahead forecasting.
  • MM-SVM is recommended for practical application in air quality management and health risk mitigation.