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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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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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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.
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Activity-Integrated Hidden Markov Model to Predict Calving Time.

Kosuke Sumi1, Swe Zar Maw1, Thi Thi Zin2

  • 1Interdisciplinary Graduate School of Agriculture and Engineering, University of Miyazaki, Miyazaki 889-2192, Japan.

Animals : an Open Access Journal From MDPI
|February 6, 2021
PubMed
Summary
This summary is machine-generated.

Accurate calving prediction using video analysis and Hidden Markov Models helps dairy farms manage labor and improve cow and calf health. High frequency of posture changes is key to precise calving time prediction.

Keywords:
Hidden Markov Modelbehavior changescalvingprediction

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Area of Science:

  • Veterinary Science
  • Animal Behavior
  • Machine Learning

Background:

  • Calving is a critical period for dairy cows, impacting animal health and farm productivity.
  • Timely human intervention during calving is crucial but challenging without accurate prediction.
  • Existing monitoring methods may not adequately capture subtle behavioral cues.

Purpose of the Study:

  • To develop an integrated system for predicting calving time in dairy cows.
  • To enhance dairy farm management through early detection of calving and potential complications.
  • To improve the health and welfare of mother cows and calves.

Main Methods:

  • Behavioral activities (lying, standing, posture changes) extracted from video sequences.
  • Utilizing a Hidden Markov Model (HMM) for predictive analysis.
  • Integration of behavior extraction and HMM for calving prediction.

Main Results:

  • The proposed system demonstrates promise for practical application in dairy farms.
  • High frequency of posture changes was identified as a significant predictor of calving time.
  • The integrated approach accurately predicts calving occurrence.

Conclusions:

  • The developed video-monitoring and HMM system offers a valuable tool for dairy farm management.
  • Behavioral analysis, particularly posture changes, is vital for accurate calving prediction.
  • This technology facilitates timely intervention, improving outcomes for cows and calves.