RfaH genetic algorithm benchmark simulations at additional mutation rates. by Lu Hong (399882)

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A Python Package for the Localization of Protein Modifications in Mass Spectrometry Data by Anthony S. Barente (14035175)

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An expectation-maximization algorithm for finding noninvadable stationary states. by Robert Marsland (8616483)

“…<p><i>(a)</i> Noninvadable states by definition can only exist in the region Ω of resource space where the growth rate <i>dN</i>_<i>i</i>/<i>dt</i> of each species <i>i</i> is zero or negative. …”

Left: the algorithm’s best estimate of where to put breakpoints for each number of breakpoints considered. by Zhengtian Li (5076704)

Reward function related parameters. by Honglei Pang (22693724)

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Average quality scores of the models of the analysis set as a function of the <i>w</i>_<i>SP</i> with which the DOPE potential has been included in the objective function... by Giacomo Janson (8138517)

Listed python codes of our algorithm with explanations (Python 3.9 recommended). by Takayuki Niizato (162226)

3D modeling qualities of the AS single-template models built by including DOPE in the objective function of MODELLER. by Giacomo Janson (8138517)

3D modeling qualities of the AM multiple-template models built by including DOPE in the objective function of MODELLER. by Giacomo Janson (8138517)

BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data by Jean-Christophe Lachance (6619307)

“…GEM-guided predictions of growth phenotypes rely on the accurate definition of a biomass objective function (BOF) that is designed to include key cellular biomass components such as the major macromolecules (DNA, RNA, proteins), lipids, coenzymes, inorganic ions and species-specific components. …”

A detailed process of iterative simulation coupled with bone density algorithm; (a) a function of stimulus and related bone density changes, and (b) iterative calculations of finit... by Hassan Mehboob (8960273)

Genetic algorithm RfaH designs exhibit greater sequence similarity to NusG-like foldswitching proteins than non-foldswitching proteins. by Lu Hong (399882)

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Python-based Monte Carlo Simulation Tool by Yangfan Jiang (9372782)

Demonstration of the combinatorial Hill functions (A and B), and the regulation function (C and D) under the regulation of an activator and a repressor. by Ruihao Li (2908991)

HIL test results of curved road: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate. by Honglei Pang (22693724)

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HIL test results of low-adhesion road: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate. by Honglei Pang (22693724)

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HIL test results of split-μ road: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate. by Honglei Pang (22693724)

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Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

“…This algorithm conducts a series of procedures: (1) fragmentation of the molecules into functional groups from SMILES, (2) calculation of activity coefficients under predetermined temperature and mole fraction conditions by employing universal quasi-chemical functional group activity coefficient (UNIFAC) model, and (3) regression of NRTL model parameters by employing UNIFAC model simulation results in the differential evolution algorithm (DEA) and Nelder–Mead method (NMM). …”

Braking performance and lateral stability on curved road surfaces: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sidesli... by Honglei Pang (22693724)

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Braking performance and lateral stability on low-adhesion road surfaces: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle s... by Honglei Pang (22693724)

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