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. …"

Differences in computing performance between ENQUIRE’s gene normalization algorithm and GNorm2-Bioformer. حسب Luca Musella (19986312)

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. …"

Results of L-SHADE based on our method, MLCCDE, EDEV, MPEDE and CoBiDE when function dimensionality is set 30. حسب Haiming Du (6691646)

Convergence characteristic of FA fitness functions for four year planning process. حسب Hamid Amini Khanavandi (19267750)

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. …"

Convergence characteristic of FA fitness functions for four years planning and partitioning process. حسب Hamid Amini Khanavandi (19267750)

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... حسب Hassan Mehboob (8960273)

ADT: A Generalized Algorithm and Program for Beyond Born–Oppenheimer Equations of “<i>N</i>” Dimensional Sub-Hilbert Space حسب Koushik Naskar (7510592)

"…In order to overcome such shortcoming, we develop a generalized algorithm, “ADT” to generate the nonadiabatic equations through symbolic manipulation and to construct highly accurate diabatic surfaces for molecular processes involving excited electronic states. …"

The traffic flow distribution diagram when traffic lights in both entrance and exit of the short-distance road section are red. حسب Yulin Tian (1457986)

The traffic flow distribution diagram when traffic lights in both entrance and exit of the short-distance road section are green. حسب Yulin Tian (1457986)

Flowchart for Algorithm 1. حسب Sagvan Y. Musa (17797611)

Flowchart for Algorithm 2. حسب Sagvan Y. Musa (17797611)

Algorithm of the brightness scale calibration experiment. حسب Krzysztof Petelczyc (3954203)

"…<p>In the algorithm, the following variables were used: “I” denotes the current luminous intensity of the reference diode, “inc” denotes the current difference between reference and target diode luminous intensity; “cnt” is the current number of performed trials, while “correct” is a counter of correct answers in cnt trials, both of them are counted separately for every settings of I and inc. …"

Data extraction and patient algorithm. حسب Jennifer M. Kwan (3350321)

Gillespie algorithm simulation parameters. حسب Nicholas H. Vitale (20469289)

"…Both the ensemble and stochastic models presented in this work have been verified using Monte Carlo molecular dynamic simulations that utilize the Gillespie algorithm. …"

Scheduling time of five algorithms. 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. …"