Systematically Improvable Tensor Hypercontraction: Interpolative Separable Density-Fitting for Molecules Applied to Exact Exchange, Second- and Third-Order Møller–Plesset Perturbat... by Joonho Lee (1351974)

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Integral-Direct and Parallel Implementation of the CCSD(T) Method: Algorithmic Developments and Large-Scale Applications by László Gyevi-Nagy (2631796)

“…Consequently, high performance is also expected for systems appearing in extensive data sets used, e.g., for density functional or machine learning parametrizations, and in calculations required for certain reduced-cost or local approximations of CCSD­(T), such as in our local natural orbital scheme [LNO-CCSD­(T)]. …”

Flowchart for the genetic algorithm used in this article. by Behnam Kia (6111551)

Cumulative distribution function of different algorithms after interpolation. by Ruiyi Tang (10302578)

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)

Color-coded disparity <i>V</i>_<i>i</i>(<i>C</i>₁, <i>C</i>₂) of the <i>i</i>(<i>τ</i>) time series for <i>N</i> = 3200, <i>N</i>_<i>v</i>... by Yannick Feld (16520678)

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Eight commonly used benchmark functions. by Guangwei Liu (181992)

“…The proposed algorithm was evaluated on eight standard benchmark functions, CEC2019 benchmark functions, four engineering design problems, and a PID parameter optimization problem. …”

Benchmark functions T-test for the proposed and compared algorithms. by El-Sayed M. El-kenawy (14581088)

Proposed algorithm convergence curves versus other competing algorithms for the seven benchmark functions. by El-Sayed M. El-kenawy (14581088)

A comparison between the static Python-based visualizations of the p65 activity in activated fibroblasts and the dynamic, HTML-based visualizations that use these same reduction me... by Hector Torres (11708207)

MSE of MOP parameters with various schemes with <i>n</i> = 50 and <i>α</i> = 2, <i>θ</i> = 1.5, <i>β</i> = 0.5. by R. Alshenawy (13162866)

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MLE and Bayes point and interval estimate for real data sample. by R. Alshenawy (13162866)

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Point and interval prediction of the unobserved order statistics for scheme 3, with <i>α</i> = 0.5, <i>β</i> = 0.5, <i>θ</i> = 0.5. by R. Alshenawy (13162866)

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Point and interval prediction of the unobserved order statistics for scheme 2, with <i>α</i> = 0.5, <i>β</i> = 0.5, <i>θ</i> = 0.5. by R. Alshenawy (13162866)

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Point and interval prediction of the unobserved order statistics for scheme 1, with <i>α</i> = 0.5, <i>β</i> = 0.5, <i>θ</i> = 0.5. by R. Alshenawy (13162866)

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S1 Dataset - by R. Alshenawy (13162866)

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MSEs and biases for MLEs and bayes estimators of <i>α</i> = 2, <i>θ</i> = 1.5, <i>β</i> = 0.5. by R. Alshenawy (13162866)

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MSE of MOP parameters with various schemes with <i>n</i> = 50 and <i>α</i> = 2.5, <i>θ</i> = 1.5, <i>β</i> = 0.5. by R. Alshenawy (13162866)

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MSEs and biases for MLEs and bayes estimators of <i>α</i> = 2.5, <i>θ</i> = 1.5, <i>β</i> = 0.5. by R. Alshenawy (13162866)

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Data set of failure times of 20 mechanical components. by R. Alshenawy (13162866)

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