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  2. 2422

    Optimization of surface roughness on duplex stainless steel in dry milling by Nurul Hidayah, Razak, Mohammad Rizal, Md Ali

    Published 2024
    “…An optimum machining parameters speed of cutting of 78.283 mm/min, rate of feed of 0.100 mm/tooth and axial depth of 0.834 mm is identified as the optimum values.…”
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    Conference or Workshop Item
  3. 2423

    Artificial neural network-salp-swarm algorithm for stock price prediction by Zuriani, Mustaffa, Mohd Herwan, Sulaiman, Azlan, Abdul Aziz

    Published 2024
    “…Before training, the dataset is normalized using the min-max normalization technique to reduce the influence of noise. …”
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    Article
  4. 2424

    Optimizing targeted vaccination across cyber–physical networks: an empirically based mathematical simulation stud by Mones, Enys, Stopczynski, Arkadiusz, Pentland, Alex, Hupert, Nathaniel, Lehmann, Sune

    Published 2021
    “…Here, we study the digital communication activity of more than 500 individuals along with their person-to-person contacts at a 5-min temporal resolution. We then simulate different disease transmission scenarios on the person-to-person physical contact network to determine whether cyber communication networks can be harnessed to advance the goal of targeted vaccination for a disease spreading on the network of physical proximity. …”
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    Article
  5. 2425

    Optimizing targeted vaccination across cyber–physical networks: an empirically based mathematical simulation study by Mones, Enys, Stopczynski, Arkadiusz, Pentland, Alex, Hupert, Nathaniel, Lehmann, Sune

    Published 2021
    “…Here, we study the digital communication activity of more than 500 individuals along with their person-to-person contacts at a 5-min temporal resolution. We then simulate different disease transmission scenarios on the person-to-person physical contact network to determine whether cyber communication networks can be harnessed to advance the goal of targeted vaccination for a disease spreading on the network of physical proximity. …”
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    Article
  6. 2426

    Query lower bounds for log-concave sampling by Chewi, Sinho, de Dios Pont, Jaume, Li, Jerry, Lu, Chen, Narayanan, Shyam

    Published 2024
    “…In this work, we establish the following query lower bounds: (1) sampling from strongly log-concave and log-smooth distributions in dimension ≥ 2 requires Ω(log) queries, which is sharp in any constant dimension, and (2) sampling from Gaussians in dimension (hence also from general log-concave and log-smooth distributions in dimension) requires Ωe(min( √ log,)) queries, which is nearly sharp for the class of Gaussians. …”
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    Article
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