The Blood–Brain Barrier (BBB) Score حسب Mayuri Gupta (1886839)

"…Understanding the BBB interaction with drug molecules based on physicochemical property space can guide effective and efficient drug design. An algorithm, designated “BBB Score”, composed of stepwise and polynomial piecewise functions, is herein proposed for predicting BBB penetration based on five physicochemical descriptors: number of aromatic rings, heavy atoms, MWHBN (a descriptor comprising molecular weight, hydrogen bond donor, and hydrogen bond acceptors), topological polar surface area, and pKa. …"

The Blood–Brain Barrier (BBB) Score حسب Mayuri Gupta (1886839)

"…Understanding the BBB interaction with drug molecules based on physicochemical property space can guide effective and efficient drug design. An algorithm, designated “BBB Score”, composed of stepwise and polynomial piecewise functions, is herein proposed for predicting BBB penetration based on five physicochemical descriptors: number of aromatic rings, heavy atoms, MWHBN (a descriptor comprising molecular weight, hydrogen bond donor, and hydrogen bond acceptors), topological polar surface area, and pKa. …"

The Blood–Brain Barrier (BBB) Score حسب Mayuri Gupta (1886839)

"…Understanding the BBB interaction with drug molecules based on physicochemical property space can guide effective and efficient drug design. An algorithm, designated “BBB Score”, composed of stepwise and polynomial piecewise functions, is herein proposed for predicting BBB penetration based on five physicochemical descriptors: number of aromatic rings, heavy atoms, MWHBN (a descriptor comprising molecular weight, hydrogen bond donor, and hydrogen bond acceptors), topological polar surface area, and pKa. …"

Comparison of the performance of both models against 13 other models on the independent test dataset. حسب Aastha Vatsyayan (9981916)

Varietal predicted parameters under both water conditions per groups of varieties. حسب Maud Delattre (19106551)

Evolution of the rescaled hydraulic diameter () and complexity () in both simulations and seal tomograms as a function of simulation timestep and relative age (). حسب Jonathan E Kings (20818367)

Continuous Probability Distributions generated by the PIPE Algorithm حسب LUIS G.B. PINHO (14073372)

"…The PIPE algorithm can generate several candidate functions to fit the empirical distribution of data. …"

The algorithm for achieving the research objectives illustrating the process of developing a mathematical model of the braking moment as a function of tire deformation. حسب Bartosz Wieczorek (8098352)

Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional حسب Ryan Stocks (16867476)

"…Kohn–Sham density functional theory (KS-DFT) has become a cornerstone for studying the electronic structure of molecules and materials. …"

The impact of extrinsic noise sources on the measured SNR is plotted for both CW and TG against varying optical filter magnitudes. حسب Nicholas H. Vitale (20469289)

The skeleton algorithm identifies mitochondrial density in sample images from cells. حسب Margarita M. Ivanova (11604141)

Schematics for gmxapi operations. حسب M. Eric Irrgang (4487278)

The pseudocode for the NAFPSO algorithm. حسب Huichao Guo (14515171)

"…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …"

PSO algorithm flowchart. حسب Huichao Guo (14515171)

"…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …"

Parselmouth for bioacoustics: automated acoustic analysis in Python حسب Yannick Jadoul (11498813)

"…Five years ago, the Python package Parselmouth was released to provide easy and intuitive access to all functionality in the Praat software. …"

Prediction performance of different optimization algorithms. حسب Ali-Kemal Aydin (10968731)

"…<p>(A) 3 algorithms were compared in terms of the residuals of the cost function of the optimized TF on 7 mice datasets (Derivative free algorithm failed in optimizing a TF in a mouse). …"

Standard functions to test. حسب Kemal Gökhan Nalbant (18858009)

Comparison of different algorithms. حسب Dawei Wang (471687)

"…A sophisticated optimization model has been developed to simulate the optimal operation of machinery, aiming to maximize equipment utilization efficiency while addressing the challenges posed by worker fatigue. An innovative algorithm, the improved hybrid gray wolf and whale algorithm fused with a penalty function for construction machinery optimization (IHWGWO), is introduced, incorporating a penalty function to handle constraints effectively. …"

libEnsemble: New capabilities for coordinating dynamic ensembles of calculations حسب John-Luke Navarro (9024008)

Study proposed algorithm. حسب Ainhoa Pérez-Guerrero (21377457)

"…The index of microvascular resistance (IMR) is a specific physiological parameter used to assess microvascular function. Invasive coronary assessment has been shown to be both feasible and safe. …"