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Showing 21 - 40 results of 129 for search 'python model represents', query time: 0.26s Refine Results
  1. 21
    A) Schematic representation of the extended SEIR model, incorporating additional compartments to account for disease-related mortality.

    A) Schematic representation of the extended SEIR model, incorporating additional compartments to account for disease-related mortality. by Nicolò Gozzi (8837522)

    Published 2025
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  2. 22
    Output datasets from ML–assisted bibliometric workflow in African phytochemical metabolomics research

    Output datasets from ML–assisted bibliometric workflow in African phytochemical metabolomics research by Temitope Omogbene (18615415)

    Published 2025
    “…<p dir="ltr">This collection contains supplementary datasets generated during the machine learning–assisted bibliometric workflow for metabolomics and phytochemical research. The datasets represent sequential outputs derived from the integration and harmonisation of bibliographic metadata from <b>Scopus</b>, <b>Web of Science (WoS)</b>, and <b>Dimensions</b>, processed via R and Python environments.…”
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  3. 23
    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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  4. 24
    Python code for hierarchical cluster analysis of detected R-strategies from rule-based NLP on 500 circular economy definitions

    Python code for hierarchical cluster analysis of detected R-strategies from rule-based NLP on 500 circular economy definitions by Zahir Barahmand (18008947)

    Published 2025
    “…The definitions were processed using a rule-based NLP model to extract the presence of R-strategies (R0-R9), which operationalize circularity in CE frameworks. …”
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  5. 25
    Main parameters of braking system.

    Main parameters of braking system. by Honglei Pang (22693724)

    Published 2025
    “…<div><p>Vehicle lateral stability control under hazardous operating conditions represents a pivotal challenge in intelligent driving active safety. …”
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  6. 26
    EMB and SBW system structure.

    EMB and SBW system structure. by Honglei Pang (22693724)

    Published 2025
    “…<div><p>Vehicle lateral stability control under hazardous operating conditions represents a pivotal challenge in intelligent driving active safety. …”
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  7. 27
    Raw data.

    Raw data. by Honglei Pang (22693724)

    Published 2025
    “…<div><p>Vehicle lateral stability control under hazardous operating conditions represents a pivotal challenge in intelligent driving active safety. …”
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  8. 28
    Reward function related parameters.

    Reward function related parameters. by Honglei Pang (22693724)

    Published 2025
    “…<div><p>Vehicle lateral stability control under hazardous operating conditions represents a pivotal challenge in intelligent driving active safety. …”
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  9. 29
    Code program.

    Code program. by Honglei Pang (22693724)

    Published 2025
    “…<div><p>Vehicle lateral stability control under hazardous operating conditions represents a pivotal challenge in intelligent driving active safety. …”
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  10. 30
    The HIL simulation data flowchart.

    The HIL simulation data flowchart. by Honglei Pang (22693724)

    Published 2025
    “…<div><p>Vehicle lateral stability control under hazardous operating conditions represents a pivotal challenge in intelligent driving active safety. …”
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  11. 31
    Hyperparameter Configurations in PPO Training.

    Hyperparameter Configurations in PPO Training. by Honglei Pang (22693724)

    Published 2025
    “…<div><p>Vehicle lateral stability control under hazardous operating conditions represents a pivotal challenge in intelligent driving active safety. …”
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  12. 32
    Main parameters of steering system.

    Main parameters of steering system. by Honglei Pang (22693724)

    Published 2025
    “…<div><p>Vehicle lateral stability control under hazardous operating conditions represents a pivotal challenge in intelligent driving active safety. …”
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  13. 33
    Co-simulation architecture.

    Co-simulation architecture. by Honglei Pang (22693724)

    Published 2025
    “…<div><p>Vehicle lateral stability control under hazardous operating conditions represents a pivotal challenge in intelligent driving active safety. …”
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  14. 34
    Overall framework diagram of the study.

    Overall framework diagram of the study. by Honglei Pang (22693724)

    Published 2025
    “…<div><p>Vehicle lateral stability control under hazardous operating conditions represents a pivotal challenge in intelligent driving active safety. …”
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  15. 35
    Vehicle parameters.

    Vehicle parameters. by Honglei Pang (22693724)

    Published 2025
    “…<div><p>Vehicle lateral stability control under hazardous operating conditions represents a pivotal challenge in intelligent driving active safety. …”
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  16. 36
    Cross-validation of Lasso regression of enhanced MRI images.

    Cross-validation of Lasso regression of enhanced MRI images. by Jiangfa Li (18345033)

    Published 2025
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  17. 37
    Cross-validation of Lasso regression of enhanced CT images.

    Cross-validation of Lasso regression of enhanced CT images. by Jiangfa Li (18345033)

    Published 2025
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  18. 38
    The cross-validation diagram of Lasso regression of enhanced CT combined with MRI images.

    The cross-validation diagram of Lasso regression of enhanced CT combined with MRI images. by Jiangfa Li (18345033)

    Published 2025
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  19. 39
    Fig 3 - Using artificial intelligence tools to automate data extraction for living evidence syntheses

    Fig 3 - Using artificial intelligence tools to automate data extraction for living evidence syntheses by Evan Mitchell (20995743)

    Published 2025
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  20. 40
    PTPC-UHT bounce

    PTPC-UHT bounce by David Parry (22169299)

    Published 2025
    “…<br>It contains the full Python implementation of the PTPC bounce model (<code>PTPC_UHT_bounce.py</code>) and representative outputs used to generate the figures in the paper. …”
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