Lmst

Grid-Free Approach to Partial Differential Equations on Volumetric Domains [pdf]

http://rohansawhney.io/RohanSawhneyPhDThesis.pdf

#HackerNews #GridFree #PDEs #VolumetricDomains #MathematicalModeling #ComputationalPhysics

📝 [Preprint] What if insecticidal bednets increased #mosquito attraction to humans? 🦟

We explored this counterintuitive effect using a transmission model for #malaria.

🧪 Available on medRxiv: https://doi.org/10.1101/2025.04.02.25325102

#mathematicalmodeling #vectorcontrol #publichealth

@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: https://workshops.evolbio.mpg.de/event/128/

#Evolution #ecoevo #evol_gen #MathematicalModeling #MathematicalBiology

poster for the workshop. photo of plön lake with cartoon rescue curve. details redundant with text of post.

We're hiring a Research Officer at the Modelling and Simulation Hub, Africa (MASHA) at the beautiful University of Cape Town! Join our team and contribute to impactful research on infectious disease dynamics and control.

This is a 36-month contract position. Apply by March 7th, 2025!

https://uct.ac.za/sites/default/files/2025-02/sci-id901-ro-masha.pdf

#research #hiring #mathematicalmodeling #infectiousdiseases #Africa

https://science.uct.ac.za/masha

@jmcrookston One of my careers was in the Pharma Biotech and PBM space. I've consulted for most of the major pharma biotech and PBMs I do #MathematicalModeling and #StupidNumberTricks

🐘📢 New publication in PLOS ONE!

We used SIR/SEIR modeling to simulate the Antonine Plague, testing different pathogen scenarios. Our results suggest:
✔ Ancient mortality estimates may have been exaggerated
✔ Standard epidemic models do not fully reproduce historical death tolls
✔ Unmodeled factors, such as seasonality, may explain this discrepancy

📝 Open Access:
https://doi.org/10.1371/journal.pone.0313684

#Epidemiology #HistoryOfMedicine #MathematicalModeling #AntoninePlague #OpenScience

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 https://www.journals.uchicago.edu/doi/10.1086/734003

#mathematicalModeling #model

Postdoctoral Fellow in Microbial Genetics or Genomics, UTHealth Houston

University of Texas Health Science Center at Houston

Join us in our multidisciplinary research #genetics, #genomics, #molbio, #evolution, #systems, #bigdata, & #mathematicalmodeling

See the full job description on jobRxiv: https://jobrxiv.org/job/unive...
https://jobrxiv.org/job/university-of-texas-health-science-center-at-houston-27778-postdoctoral-fellow-in-microbial-genetics-or-genomics-uthealth-houston/?feed_id=81227

Postdoctoral Fellow in Microbial Genetics or Genomics, UTHealth Houston

University of Texas Health Science Center at Houston

Join us in our multidisciplinary research #genetics, #genomics, #molbio, #evolution, #systems, #bigdata, & #mathematicalmodeling

Postdoctoral Fellow in Microbial Genetics or Genomics, UTHealth Houston

University of Texas Health Science Center at Houston

Join us in our multidisciplinary research #genetics, #genomics, #molbio, #evolution, #systems, #bigdata, & #mathematicalmodeling

#Journals | EPJ B
#CallForPapers for a #TopicalIssue on “Mathematical Modeling in Condensed Matter and Complex Systems: Limits and Pitfalls”
Guest Editors from #UniversitätSaarland
#HumboldtUniversity

📅June 30, 2024
➡️ bit.ly/3TZuu7z

#CondensedMatter #ComplexSystems #MathematicalModeling #Physics #AcademicResearch #ScientificResearch
#sciences #ScienceMastodon
@ScienceScholar @academicsunite @academicchatter

#Journals | EPJ B
#CallForPapers for a Topical Issue on “ #MathematicalModeling in #Epidemiology:
Limits and Pitfalls” Guest Editors from
#UniversitätSaarland
#HumboldtUniversitatZuBerlin

📅June 30, 2024
➡️ https://bit.ly/3TZuu7z
#EDPSciences
#TheEuropeanPhysicalJournal
#physics
@physics
@science
@academicsunite
@academicchatter
#ScienceMastodon

#Journals | EPJ B
#CallForPapers for a Topical Issue on
“ #MathematicalModeling in #epidemiology : Limits and Pitfalls” Guest Editors from
@saar_uni #HumboldtUniversitätzuBerlin

📅30 June 2024
➡️ https://bit.ly/3TZuu7z
#EDPSciences
#ScienceMastodon
@phdstudents
@academicchatter

#MathematicalBiology #evolution #evolutionarybiology #popgen #evolutionarydynamics #MathematicalModeling @Project_Telos

How does the scalability of peer learning compare to expert-led coaching ‘fellowships’?

By connecting practitioners to learn from each other, peer learning facilitates collaborative development. ow does it compare to expert-led coaching and mentoring “fellowships” that are seen as the ‘gold standard’ for professional development in global health?

Scalability in global health matters. (See this article for a comparison of other aspects.)

Simplified mathematical modeling can compare the scalability of expert coaching (“fellowships”) and peer learning

Let N be the total number of learners and M be the number of experts available. Assuming that each expert can coach K learners effectively:

For N>>M×KN>>M×K, it is evident that expert coaching is costly and difficult to scale.

Expert coaching “fellowships” require the availability of experts, which is often optimistic in highly specialized fields.

The number of learners (N) greatly exceeds the product of the number of experts (M) and the capacity per expert (K).

Scalability of one-to-one peer learning

By comparison, peer learning turns the conventional model on its head by transforming each learner into a potential coach who can provide peer feedback.

This has significant advantages in scalability.

Let N be the total number of learners. Assuming a peer-to-peer model, where each learner can learn from any other learner:

In this context, the number of learning interactions scales quadratically with the number of learners. This means that if the number of learners doubles, the total number of learning interactions increases by a factor of four. This quadratic relationship highlights the significant increase in interactions (and potential scalability challenges) as more learners participate in the model.

However, this one-to-one model is difficult to implement: not every learner is going to interact with every other learner in meaningful ways.

A more practical ‘triangular’ peer learning model with no upper limit to scalability

In The Geneva Learning Foundation’s peer learning model, learners give feedback to three peers, and receive feedback from three peers. This is a structured, time-bound process of peer review, guided by an expert-designed rubric.

When each learner gives feedback to 3 different learners and receives feedback from 3 different learners, the model changes significantly from the one-to-one model where every learner could potentially interact with every other learner. In this specific configuration, the total number of interactions can be calculated based on the number of learners N, with each learner being involved in 6 interactions (3 given + 3 received).

The total number of interactions per learner is six. However, since each interaction involves two learners (the giver and the receiver of feedback), we do not need to double-count these interactions for the total count in the system. Hence, the total number of interactions for each learner is directly 6, without further adjustments for double-counting.

Therefore, the total number of learning interactions in the system can be represented as:

Given this setup, the complexity or scalability of the system in terms of learning interactions relative to the number of participants N is linear. This is because the total number of interactions increases directly in proportion to the number of learners. Thus, the Big O notation would be:

This indicates that the total number of learning interactions scales linearly with the number of learners. In this configuration, as the number of learners increases, the total number of interactions increases at a linear rate, which is more scalable and manageable than the quadratic rate seen in the peer-to-peer model where every learner interacts with every other learner. Learn more: There is no scale.

Share this:

#coaching #CollectiveIntelligence #fellowships #mathematicalModeling #peerLearning

How does peer learning compare to expert-led coaching fellowships

Calculating the relative effectiveness of expert coaching, peer learning, and cascade training

A formula for calculating learning efficacy, (E), considering the importance of each criterion and the specific ratings for peer learning, is:

This abstract formula provides a way to quantify learning efficacy, considering various educational criteria and their relative importance (weights) for effective learning.

Variable DefinitionDescription SScalabilityAbility to accommodate a large number of learners IInformation fidelityQuality and reliability of information CCost effectivenessFinancial efficiency of the learning method FFeedback qualityQuality of feedback received UUniformityConsistency of learning experience Summary of five variables that contribute to learning efficacy

Weights for each variables are derived from empirical data and expert consensus.

All values are on a scale of 0-4, with a “4” representing the highest level.

ScalabilityInformation fidelityCost-benefitFeedback qualityUniformity4.003.004.003.001.00Assigned weights

Here is a summary table including all values for each criterion, learning efficacy calculated with weights, and Efficacy-Scale Score (ESS) for peer learning, cascade training, and expert coaching.

The Efficacy-Scale Score (ESS) can be calculated by multiplying the efficacy (E) of a learning method by the number of learners (N).

This table provides a detailed comparison of the values for each criterion across the different learning methods, the calculated learning efficacy values considering the specified weights, and the Efficacy-Scale Score (ESS) for each method.

Type of learningScalabilityInformation fidelityCost effectivenessFeedback qualityUniformityLearning efficacy# of learnersEfficacy-Scale ScorePeer learning4.002.504.002.501.003.2010003200Cascade training2.001.002.000.500.501.40500700Expert coaching0.504.001.004.003.002.2060132

Of course, there are many nuances in individual programmes that could affect the real-world effectiveness of this simple model. The model, grounded in empirical data and simplified to highlight core determinants of learning efficacy, leverages statistical weighting to prioritize key educational factors, acknowledging its abstraction from the multifaceted nature of educational effectiveness and assumptions may not capture all nuances of individual learning scenarios.

Peer learning

The calculated learning efficacy for peer learning, , is 3.20. This value reflects the weighted assessment of peer learning’s strengths and characteristics according to the provided criteria and their importance.

By virtue of scalability, ESS for peer learning is 24 times higher than expert coaching.

Cascade training

For Cascade Training, the calculated learning efficacy, , is approximately 1.40. This reflects the weighted assessment based on the provided criteria and their importance, indicating lower efficacy compared to peer learning.

Cascade training has a higher ESS than expert coaching, due to its ability to achieve scale.

Learn more: Why does cascade training fail?

Expert coaching

For Expert Coaching, the calculated learning efficacy, , is approximately 2.20. This value indicates higher efficacy than cascade training but lower than peer learning.

However, the ESS is the lowest of the three methods, primarily due to its inability to scale. Read this article for a scalability comparison between expert coaching and peer learning.

Share this:

#cascadeTraining #expertCoaching #fellowship #mathematicalModeling #peerLearning

Calculating the relative effectiveness of expert coaching, peer learning, and cascade training

Don't be smart, be lucky.

#wealth #luck #MathematicalModeling

If You’re So Smart, Why Aren’t You Rich? Turns Out It’s Just Chance. https://getpocket.com/explore/item/if-you-re-so-smart-why-aren-t-you-rich-turns-out-it-s-just-chance?utm_source=pocket-newtab-en-us

We have a fully funded (3 year) #PhD position available in #disease ecology at the University of Oslo, Norway. The topic is #Lyme disease with focus on modeling ecological interactions and processes influencing the disease dynamics.

Deadline February 29th (master students can also apply if they complete the degree before June 30).

Please help spread the word to potential applicants! #PhDposition #MathematicalModeling

For more information and to apply:

https://www.jobbnorge.no/en/available-jobs/job/257248/phd-research-fellow-in-disease-ecology

A diagram showing a simplified overview of a tick life cycle (stages of larvae, nymphs and adults) and Lyme pathogen transmission, illustrating main hosts classified as small or large.

Balanced Boulders On San Andreas Fault Suggest The 'Big One' Won't Be As Destructive As Once Thought
--
https://www.livescience.com/planet-earth/earthquakes/balanced-boulders-on-san-andreas-fault-suggest-the-big-one-wont-be-as-destructive-as-once-thought <-- shared technical article
--
https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1303303 <-- shared abstract
--
#GIS #spatial #mapping #spatialanalysis #model #modeling #earthquake #faulting #prediction #reconstruction #dating #risk #hazard #hazardassessment #shaking #seismic #earthquakeengineering #LoveJoyButtes #hazardmodel #earthquakemodel #CA #California #geology #engineeringgeology #SanAndreasFault #SanAndreas #fault #groundmotion #history #holocene #earthquakehazard #Mojave #validation #returnperiod #magnitude #mathematicalmodeling

photo - sampling - precariously balanced rocks (PBRs) at Lovejoy Buttes, Mojave, CA

photo - precariously balanced rocks (PBRs) at Lovejoy Buttes, Mojave, CA

photo - scanning marks - precariously balanced rocks (PBRs) at Lovejoy Buttes, Mojave, CA

2023 was a very positive year regarding my Julia endeavours. In short, I gave 4 talks, I started doing more general Julia videos in English, and I started working a lot more with Julia at my current job. I give more details in this post: https://abelsiqueira.com/blog/2024-01-04-personal-julia-2023-retrospective/

2024 will also be a great year for Julia, but more on that later.

#JuliaLang #JuliaProgramming #Julia #talk #optimization #mathematicalmodeling #orms

#mathematicalModeling

Client Info