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#mathematicalmodeling — Public Fediverse posts

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  1. Resource selection function (Ecology 🏞️)

    Resource selection functions are a class of functions that are used in spatial ecology to assess which habitat characteristics are important to a specific population or species of animal, by assessing a probability of that animal using a certain resource proportional to the availability of that resource in the environment.

    en.wikipedia.org/wiki/Resource

    #ResourceSelectionFunction #Ecology #MathematicalModeling

  2. Resource selection function (Ecology 🏞️)

    Resource selection functions are a class of functions that are used in spatial ecology to assess which habitat characteristics are important to a specific population or species of animal, by assessing a probability of that animal using a certain resource proportional to the availability of that resource in the environment.

    en.wikipedia.org/wiki/Resource

    #ResourceSelectionFunction #Ecology #MathematicalModeling

  3. Resource selection function (Ecology 🏞️)

    Resource selection functions are a class of functions that are used in spatial ecology to assess which habitat characteristics are important to a specific population or species of animal, by assessing a probability of that animal using a certain resource proportional to the availability of that resource in the environment.

    en.wikipedia.org/wiki/Resource

    #ResourceSelectionFunction #Ecology #MathematicalModeling

  4. Resource selection function (Ecology 🏞️)

    Resource selection functions are a class of functions that are used in spatial ecology to assess which habitat characteristics are important to a specific population or species of animal, by assessing a probability of that animal using a certain resource proportional to the availability of that resource in the environment.

    en.wikipedia.org/wiki/Resource

    #ResourceSelectionFunction #Ecology #MathematicalModeling

  5. Resource selection function (Ecology 🏞️)

    Resource selection functions are a class of functions that are used in spatial ecology to assess which habitat characteristics are important to a specific population or species of animal, by assessing a probability of that animal using a certain resource proportional to the availability of that resource in the environment.

    en.wikipedia.org/wiki/Resource

    #ResourceSelectionFunction #Ecology #MathematicalModeling

  6. I turned a Raspberry Pi into a tiny weather intelligence powerhouse

    A Raspberry Pi Zero 2 W paired with a Sense HAT V2 runs a fully self-contained, edge-native machine learning weather station with no cloud, GPU, or heavy ML frameworks. The system uses pure NumPy implementations of Recursive Least Squares, Kalman filtering, conformal prediction, and drift detection, staying under 150 MB RAM. The station continuously updates its own statistical model of local atmospheric conditions, produces calibrated uncertainty intervals, and renders animated forecasts on […]

    kemal.yaylali.uk/i-turned-a-ra

  7. I turned a Raspberry Pi into a tiny weather intelligence powerhouse

    A Raspberry Pi Zero 2 W paired with a Sense HAT V2 runs a fully self-contained, edge-native machine learning weather station with no cloud, GPU, or heavy ML frameworks. The system uses pure NumPy implementations of Recursive Least Squares, Kalman filtering, conformal prediction, and drift detection, staying under 150 MB RAM. The station continuously updates its own statistical model of local atmospheric conditions, produces calibrated uncertainty intervals, and renders animated forecasts on […]

    kemal.yaylali.uk/i-turned-a-ra

  8. I turned a Raspberry Pi into a tiny weather intelligence powerhouse

    A Raspberry Pi Zero 2 W paired with a Sense HAT V2 runs a fully self-contained, edge-native machine learning weather station with no cloud, GPU, or heavy ML frameworks. The system uses pure NumPy implementations of Recursive Least Squares, Kalman filtering, conformal prediction, and drift detection, staying under 150 MB RAM. The station continuously updates its own statistical model of local atmospheric conditions, produces calibrated uncertainty intervals, and renders animated forecasts on […]

    kemal.yaylali.uk/i-turned-a-ra

  9. I turned a Raspberry Pi into a tiny weather intelligence powerhouse

    A Raspberry Pi Zero 2 W paired with a Sense HAT V2 runs a fully self-contained, edge-native machine learning weather station with no cloud, GPU, or heavy ML frameworks. The system uses pure NumPy implementations of Recursive Least Squares, Kalman filtering, conformal prediction, and drift detection, staying under 150 MB RAM. The station continuously updates its own statistical model of local atmospheric conditions, produces calibrated uncertainty intervals, and renders animated forecasts on […]

    kemal.yaylali.uk/i-turned-a-ra

  10. From Bioreactors to Brier Scores: Building Model90, a Football Prediction Engine

    What happens when a bioreactor engineer points his state-estimation toolkit at football. Model90 is a statistical forecasting engine for the 2026 World Cup and the major leagues, built on Dixon-Coles Poisson, Elo, xG and a calibrated meta-model. An honest look at how it works and what its Brier score really means.

    kemal.yaylali.uk/model90-footb

  11. From Bioreactors to Brier Scores: Building Model90, a Football Prediction Engine

    What happens when a bioreactor engineer points his state-estimation toolkit at football. Model90 is a statistical forecasting engine for the 2026 World Cup and the major leagues, built on Dixon-Coles Poisson, Elo, xG and a calibrated meta-model. An honest look at how it works and what its Brier score really means.

    kemal.yaylali.uk/model90-footb

  12. From Bioreactors to Brier Scores: Building Model90, a Football Prediction Engine

    What happens when a bioreactor engineer points his state-estimation toolkit at football. Model90 is a statistical forecasting engine for the 2026 World Cup and the major leagues, built on Dixon-Coles Poisson, Elo, xG and a calibrated meta-model. An honest look at how it works and what its Brier score really means.

    kemal.yaylali.uk/model90-footb

  13. From Bioreactors to Brier Scores: Building Model90, a Football Prediction Engine

    What happens when a bioreactor engineer points his state-estimation toolkit at football. Model90 is a statistical forecasting engine for the 2026 World Cup and the major leagues, built on Dixon-Coles Poisson, Elo, xG and a calibrated meta-model. An honest look at how it works and what its Brier score really means.

    kemal.yaylali.uk/model90-footb

  14. The most accurate carotid artery model to date — the first to capture both the soft, low-pressure behavior and the stiff, high-pressure response of the vessel.

    Built on the same principles as Fung’s law, but improved: our 2014 α–β framework fits strain energy first, then derives pressure — like how \( F = \frac{dE}{dx} \) gives the force in a spring. Here \(E\) is the strain energy — the quantity Fung’s law was originally built around. Strain energy is differentiated to give force, and in the fits below that force corresponds to pressure.

    The 1987 plot below (Fung-type) fits well only at high pressures; the 2019 plot fits low pressures. Ours is the first to capture both perfectly.

    #Biomechanics #ContinuumMechanics #MathematicalModeling #StrainEnergy #FungsLaw #ConstitutiveModeling #Mechanics #NSFResearch #ScienceCommunication #ArterialMechanics

  15. The most accurate carotid artery model to date — the first to capture both the soft, low-pressure behavior and the stiff, high-pressure response of the vessel.

    Built on the same principles as Fung’s law, but improved: our 2014 α–β framework fits strain energy first, then derives pressure — like how \( F = \frac{dE}{dx} \) gives the force in a spring. Here \(E\) is the strain energy — the quantity Fung’s law was originally built around. Strain energy is differentiated to give force, and in the fits below that force corresponds to pressure.

    The 1987 plot below (Fung-type) fits well only at high pressures; the 2019 plot fits low pressures. Ours is the first to capture both perfectly.

    #Biomechanics #ContinuumMechanics #MathematicalModeling #StrainEnergy #FungsLaw #ConstitutiveModeling #Mechanics #NSFResearch #ScienceCommunication #ArterialMechanics

  16. The most accurate carotid artery model to date — the first to capture both the soft, low-pressure behavior and the stiff, high-pressure response of the vessel.

    Built on the same principles as Fung’s law, but improved: our 2014 α–β framework fits strain energy first, then derives pressure — like how \( F = \frac{dE}{dx} \) gives the force in a spring. Here \(E\) is the strain energy — the quantity Fung’s law was originally built around. Strain energy is differentiated to give force, and in the fits below that force corresponds to pressure.

    The 1987 plot below (Fung-type) fits well only at high pressures; the 2019 plot fits low pressures. Ours is the first to capture both perfectly.

    #Biomechanics #ContinuumMechanics #MathematicalModeling #StrainEnergy #FungsLaw #ConstitutiveModeling #Mechanics #NSFResearch #ScienceCommunication #ArterialMechanics

  17. The most accurate carotid artery model to date — the first to capture both the soft, low-pressure behavior and the stiff, high-pressure response of the vessel.

    Built on the same principles as Fung’s law, but improved: our 2014 α–β framework fits strain energy first, then derives pressure — like how \( F = \frac{dE}{dx} \) gives the force in a spring. Here \(E\) is the strain energy — the quantity Fung’s law was originally built around. Strain energy is differentiated to give force, and in the fits below that force corresponds to pressure.

    The 1987 plot below (Fung-type) fits well only at high pressures; the 2019 plot fits low pressures. Ours is the first to capture both perfectly.

    #Biomechanics #ContinuumMechanics #MathematicalModeling #StrainEnergy #FungsLaw #ConstitutiveModeling #Mechanics #NSFResearch #ScienceCommunication #ArterialMechanics

  18. The most accurate carotid artery model to date — the first to capture both the soft, low-pressure behavior and the stiff, high-pressure response of the vessel.

    Built on the same principles as Fung’s law, but improved: our 2014 α–β framework fits strain energy first, then derives pressure — like how \( F = \frac{dE}{dx} \) gives the force in a spring. Here \(E\) is the strain energy — the quantity Fung’s law was originally built around. Strain energy is differentiated to give force, and in the fits below that force corresponds to pressure.

    The 1987 plot below (Fung-type) fits well only at high pressures; the 2019 plot fits low pressures. Ours is the first to capture both perfectly.

    #Biomechanics #ContinuumMechanics #MathematicalModeling #StrainEnergy #FungsLaw #ConstitutiveModeling #Mechanics #NSFResearch #ScienceCommunication #ArterialMechanics

  19. @HildegardUecker and I are excited to be running the second edition of our #EvolutionaryRescue workshop series at the #MaxPlanck Plön, June 30-July 3. This time the focus is on bridging theory and experiments.

    Invited speakers: Helen Alexander, Lutz Becks, Robert D Holt, Laure Olazcuaga, Jitka Polechova.

    Submit an abstract by March 15 and tell your friends.

    More info: workshops.evolbio.mpg.de/event

    #Evolution #ecoevo #evol_gen #MathematicalModeling #MathematicalBiology

  20. @HildegardUecker and I are excited to be running the second edition of our #EvolutionaryRescue workshop series at the #MaxPlanck Plön, June 30-July 3. This time the focus is on bridging theory and experiments.

    Invited speakers: Helen Alexander, Lutz Becks, Robert D Holt, Laure Olazcuaga, Jitka Polechova.

    Submit an abstract by March 15 and tell your friends.

    More info: workshops.evolbio.mpg.de/event

    #Evolution #ecoevo #evol_gen #MathematicalModeling #MathematicalBiology

  21. @HildegardUecker and I are excited to be running the second edition of our #EvolutionaryRescue workshop series at the #MaxPlanck Plön, June 30-July 3. This time the focus is on bridging theory and experiments.

    Invited speakers: Helen Alexander, Lutz Becks, Robert D Holt, Laure Olazcuaga, Jitka Polechova.

    Submit an abstract by March 15 and tell your friends.

    More info: workshops.evolbio.mpg.de/event

    #Evolution #ecoevo #evol_gen #MathematicalModeling #MathematicalBiology

  22. @HildegardUecker and I are excited to be running the second edition of our #EvolutionaryRescue workshop series at the #MaxPlanck Plön, June 30-July 3. This time the focus is on bridging theory and experiments.

    Invited speakers: Helen Alexander, Lutz Becks, Robert D Holt, Laure Olazcuaga, Jitka Polechova.

    Submit an abstract by March 15 and tell your friends.

    More info: workshops.evolbio.mpg.de/event

    #Evolution #ecoevo #evol_gen #MathematicalModeling #MathematicalBiology

  23. New Historical Perspective available ahead of print: "Georgii F. Gause’s The Struggle for Existence and the Integration of Natural History and Mathematical Models" by Topaz Halperin journals.uchicago.edu/doi/10.1

    #mathematicalModeling #model

  24. New Historical Perspective available ahead of print: "Georgii F. Gause’s The Struggle for Existence and the Integration of Natural History and Mathematical Models" by Topaz Halperin journals.uchicago.edu/doi/10.1

    #mathematicalModeling #model

  25. New Historical Perspective available ahead of print: "Georgii F. Gause’s The Struggle for Existence and the Integration of Natural History and Mathematical Models" by Topaz Halperin journals.uchicago.edu/doi/10.1

    #mathematicalModeling #model

  26. New Historical Perspective available ahead of print: "Georgii F. Gause’s The Struggle for Existence and the Integration of Natural History and Mathematical Models" by Topaz Halperin journals.uchicago.edu/doi/10.1

    #mathematicalModeling #model

  27. New Historical Perspective available ahead of print: "Georgii F. Gause’s The Struggle for Existence and the Integration of Natural History and Mathematical Models" by Topaz Halperin journals.uchicago.edu/doi/10.1

    #mathematicalModeling #model

  28. Postdoctoral Fellow in Microbial Genetics or Genomics, UTHealth Houston

    University of Texas Health Science Center at Houston

    Join us in our multidisciplinary research , , , , , , &

    See the full job description on jobRxiv: jobrxiv.org/job/unive...
    jobrxiv.org/job/university-of-

  29. Postdoctoral Fellow in Microbial Genetics or Genomics, UTHealth Houston

    University of Texas Health Science Center at Houston

    Join us in our multidisciplinary research , , , , , , &

    See the full job description on jobRxiv: jobrxiv.org/job/unive...
    jobrxiv.org/job/university-of-

  30. Postdoctoral Fellow in Microbial Genetics or Genomics, UTHealth Houston

    University of Texas Health Science Center at Houston

    Join us in our multidisciplinary research , , , , , , &

    See the full job description on jobRxiv: jobrxiv.org/job/unive...
    jobrxiv.org/job/university-of-