Pipeline of the interactor algorithm. by Jose Cleydson F. Silva (8685594)

Summary of the contrasts obtained from the normalized images for both inclusions as a function of the host type and the wavelength. by María Victoria Waks-Serra (21578611)

Both Ankle fNIRS MI dataset by Hammad Gilani (8060012)

“…<p><br></p><p dir="ltr">This dataset contains functional near-infrared spectroscopy (fNIRS) signals recorded during motor imagery (MI) tasks of lower limb movements. …”

Both Knees fNIRS MI dataset by Hammad Gilani (8060012)

“…<p><br></p><p dir="ltr">This dataset contains functional near-infrared spectroscopy (fNIRS) signals recorded during motor imagery (MI) tasks of lower limb movements. …”

Ethics of algorithmic invasiveness in beauty apps: an online experimental survey of public perspectives by Yves Saint James Aquino (12311423)

“…Results show that functionalities with high degree of algorithmic invasiveness, such as AI-generated attractiveness rankings, are viewed as significantly less socially acceptable than less invasive functionalities. …”

The image of the Aekley function. by Zhengyu Xu (8550660)

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

The ALO algorithm optimization flowchart. by Wenjing Wang (181404)

“…The average running time of the proposed algorithm in Sphere function and Griebank function was 2.67s and 1.64s, respectively. …”

The IALO algorithm solution flowchart. by Wenjing Wang (181404)

“…The average running time of the proposed algorithm in Sphere function and Griebank function was 2.67s and 1.64s, respectively. …”

A) Ranking of models trained on different feature sets across all tested algorithms, sorted by their F1-score. by Jose Cleydson F. Silva (8685594)

The real NGD values, one-step ahead training set predictions, and multi-step ahead test set predictions using the AI-AFTER algorithm for Kayseri. by Burak Alakent (463512)

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

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

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

Differential expression analysis of the NAFLD and AIS datasets. by Junjie Su (368807)

The pseudocode for the NAFPSO algorithm. by 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. by 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. …”

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

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

Isologs, function and residue information of 12 key proteins in MPV replication. by Huaichuan Duan (6458477)

Comparison of different algorithms. by 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. …”