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Showing 1 - 20 results of 26 for search '(( python proof implementation ) OR ( python effective interventions ))', query time: 0.29s Refine Results
  1. 1
    Complex Eigenvalues, Orthogonality, and QR Factorization: Analytical Proofs and Numerical Verification

    Complex Eigenvalues, Orthogonality, and QR Factorization: Analytical Proofs and Numerical Verification by Umar Tabbsum (22058780)

    Published 2025
    “…The work includes both analytical proofs and numerical verification to ensure reproducibility and clarity.…”
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  2. 2
    Dataset and Python/R scripts used in "Effect Of Health Literacy Class on the Exercise Behavior Stage Change: An Attempt of The Self-Determination Theory-Informed Intervention with Japanese University Students."

    Dataset and Python/R scripts used in "Effect Of Health Literacy Class on the Exercise Behavior Stage Change: An Attempt of The Self-Determination Theory-Informed Intervention with Japanese University Students." by Issei Ogasawara (22506893)

    Published 2025
    “…<p dir="ltr">This dataset contains the raw data used in the paper “Effect of Health Literacy Class on Exercise Behaviour Stage Change: Attempting Self-Determination Theory-Informed Intervention among Japanese University Students, doi:(later)” along with the Python and R scripts used for analysis and visualization. …”
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  3. 3
    Python source code.

    Python source code. by Zhaohui Zhu (349376)

    Published 2025
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  4. 4
    Partial Rank Correlation Coefficient (PRCC) sensitivity analysis results, obtained using LHS with 1000 samples and run in Python version 3.11.0.

    Partial Rank Correlation Coefficient (PRCC) sensitivity analysis results, obtained using LHS with 1000 samples and run in Python version 3.11.0. by Tinashe Victor Mupedza (22022124)

    Published 2025
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  5. 5
    DEMENTIA CARE APP AI POWERED SUPPORT AND EARLY DETECTION

    DEMENTIA CARE APP AI POWERED SUPPORT AND EARLY DETECTION by Govarthanan P (21508064)

    Published 2025
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  6. 6
    <b>Effects of Lifestyle and GLP-1RA based Interventions on Waist Circumference: A Systematic Review and Meta-Analysis</b>

    <b>Effects of Lifestyle and GLP-1RA based Interventions on Waist Circumference: A Systematic Review and Meta-Analysis</b> by Samit Ghosal (22024556)

    Published 2025
    “…<p dir="ltr">This dataset and code package accompanies the systematic review and meta-analysis:<br><b>“Effects of Lifestyle and GLP-1RA Based Interventions on Waist Circumference: A Systematic Review and Meta-Analysis”</b> (INPLASY202580071).…”
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  7. 7
    Bursty model and Refractory model simulation results. (A) Bursty model simulation results ( samples). The production of nascent mRNA occurs in bursts and degrade after delay time <i>τ</i>. The burst frequency , the mean burst size and . (B) Refractory model simulation results ( samples). The gene has three states, , and , and can switch from one state to another. Moreover, nascent mRNA is produced only when the gene is in state . The parameters are , , , and <i>τ</i> = 1.

    Bursty model and Refractory model simulation results. (A) Bursty model simulation results ( samples). The production of nascent mRNA occurs in bursts and degrade after delay time <... by Ziyan Jin (21021976)

    Published 2025
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  8. 8
    RNA Velocity model simulation results. (A) Simulation results for RNA Velocity model without delay ( samples). The simulation is composed of an up-regulation phase (both production and degradation) and a down-regulation phase (degradation-only). In the first phase, <i>%</i>, <i>%</i> and <i>%</i> for the reaction time . In the second phase,  ∕ 1, <i>X</i> ∼ NB ( <i>r</i> , <i>μ</i> )  and <i>μ</i>. (B) Simulation results for RNA Velocity model with delay in degradation ( samples). RNA degradation is specified as a delayed reaction. In the first (up-regulation) phase: <i>Y</i> ≡ <i>n</i>, <i>t</i> = 0 and <i>%</i>. In the second (down-regulation) phase: <i>%</i>, <i>%</i> and <i>%.</i>

    RNA Velocity model simulation results. (A) Simulation results for RNA Velocity model without delay ( samples). The simulation is composed of an up-regulation phase (both production... by Ziyan Jin (21021976)

    Published 2025
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  9. 9
    Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, the temporal molecular states <i>n</i> at specific time <i>t</i> will be generated for further visualization.

    Design and implementation of DelaySSA. The input data for DelaySSA are reaction information, initial states, parameters and delays. Combining these into the simulation function, th... by Ziyan Jin (21021976)

    Published 2025
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  10. 10
    Modeling lung cancer AST network and its perturbation with a SOX2 degrader. (A) Lung Cancer AST Network and the corresponding stochastic equations. Parameters are , , , , , , , , with different . The control parameter is . (B) The potential landscape is approximated by simulating a large number of molecules corresponding to concentrations ( samples) with parameter values , , and , respectively representing the adenocarcinoma state, the intermediate state, and the squamous carcinoma state. FOXA2 and SOX2 amounts are chosen to visualize the landscape. (C) Principle of SOX2-targeted degradation by the PROTAC technique. The PROTAC molecule here is designed to recognize SOX2 proteins and to recruit E3 ligases to induce an addition of ubiquitin (Ub) proteins onto SOX2, which causes degradation of SOX2 subsequently. (D) Application of a SOX2 degrader to the squamous carcinoma state () reprogram the AST network back to the adenocarcinoma state. Here the treatment effect of the SOX2 degrad

    Modeling lung cancer AST network and its perturbation with a SOX2 degrader. (A) Lung Cancer AST Network and the corresponding stochastic equations. Parameters are , , , , , , , , w... by Ziyan Jin (21021976)

    Published 2025
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  11. 11
    Study workflow diagram.

    Study workflow diagram. by Alemu Birara Zemariam (17540938)

    Published 2025
    “…Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of stunting among adolescent girls in Ethiopia.…”
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  12. 12
    Stunting final dataset.

    Stunting final dataset. by Alemu Birara Zemariam (17540938)

    Published 2025
    “…Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of stunting among adolescent girls in Ethiopia.…”
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  13. 13
    A mean SHAP value report.

    A mean SHAP value report. by Alemu Birara Zemariam (17540938)

    Published 2025
    “…Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of stunting among adolescent girls in Ethiopia.…”
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  14. 14
    A waterfall plot analysis.

    A waterfall plot analysis. by Alemu Birara Zemariam (17540938)

    Published 2025
    “…Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of stunting among adolescent girls in Ethiopia.…”
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  15. 15
    Map of cumulative daily root-mean-square errors for each province in China in the test set.

    Map of cumulative daily root-mean-square errors for each province in China in the test set. by Xiaoyi Wang (90589)

    Published 2025
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  16. 16
    S2 Fig -

    S2 Fig - by Philippos Michaelides (18463232)

    Published 2025
    “…Future studies could explore the combined effects of multiple interventions and validate the model with empirical data.…”
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  17. 17
    Experimental set-up.

    Experimental set-up. by Philippos Michaelides (18463232)

    Published 2025
    “…Future studies could explore the combined effects of multiple interventions and validate the model with empirical data.…”
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  18. 18
    Metrics and statistics for Scenarios 1-7.

    Metrics and statistics for Scenarios 1-7. by Philippos Michaelides (18463232)

    Published 2025
    “…Future studies could explore the combined effects of multiple interventions and validate the model with empirical data.…”
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  19. 19
    OLS regression results—Distances under 9 ft per image.

    OLS regression results—Distances under 9 ft per image. by Matthew Martell (18536273)

    Published 2024
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  20. 20
    Core-Based Smart Sampling Framework: A Theoretical and Experimental Study on Randomized Partitioning for SAT Problems

    Core-Based Smart Sampling Framework: A Theoretical and Experimental Study on Randomized Partitioning for SAT Problems by DURGHAM QARALLEH (21904172)

    Published 2025
    “…We provide theoretical guarantees on complexity reduction and probabilistic completeness, apply the method to SAT instances, and evaluate its performance using experimental Python implementations. The results show that smart sampling drastically reduces the effective complexity of SAT problems and offers new insights into the structure of NP-complete problems.…”
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