Comparison of edge detector and CHT in detecting droplets. by Manibarathi Vaithiyanathan (6655274)

TV-BayesOpt algorithm performance for tracking a gradual drift in the optimal stimulation phase for phase-locked stimulation, <i>ψ</i>*. by John E. Fleming (8533956)

“…For each estimated GPR the confidence bounds observed at the predicted optimal phase value are small and become larger for values further away from this value due to the algorithm’s acquisition function prioritizing exploitation of the parameter space during the optimization process.…”

TV-BayesOpt algorithm performance for tracking a periodic drift in the optimal stimulation phase for phase-locked stimulation, <i>ψ</i>*. by John E. Fleming (8533956)

“…For each estimated GPR the confidence bounds observed at the predicted optimal phase value are small and become larger for values further away from this value due to the algorithm’s acquisition function prioritizing exploitation of the parameter space during the optimization process.…”

Efficient algorithms to discover alterations with complementary functional association in cancer by Rebecca Sarto Basso (6728921)

“…In addition, our algorithms are much faster than the state-of-the-art, allowing the analysis of large datasets of thousands of target profiles from cancer cell lines. …”

Box plots of different algorithms on CEC2017 functions (dim = 30,100) and CEC2022 functions (dim = 20). by Qingwen Meng (156386)

Subjects:

Constructing the dynamical law. by Robert Marsland (8616483)

Definitions of global parameters used for constructing random ecosystems. by Robert Marsland (8616483)

P -values for SGHHO and other reported algorithms by Wilcoxon test on the benchmark function. by Qiong Wu (84327)

Reward function related parameters. by Honglei Pang (22693724)

Subjects:

Different phases of HHO (Heidari et al.[16]). by Ting Yang (189879)

Circadian phase waves supported by mouse SCNs and their phase disintegration in the presence of TTX. by Hyun Kim (127301)

“…The star symbols mark the times for two phase maps shown in (b). The decay time constant <i>τ</i>_decay = 6.5 was obtained by fitting the data to an exponentially decaying function of . …”

(a): These systems were simulated for (0,3] and (0,3] without the prior knowledge about different phases, and the probability density function of points in feature space illustrate... by Ali Talebi (7164203)

“…(b): The dense areas are separated by removing the data less than threshold = 0.5 in the probability density function. (c): The centroid of each cluster is determined by the K-means algorithm.…”

The parameters of the algorithms. by Yu Li (183836)

Supplementary file 1_Earthworm optimization algorithm for extracting parameters for solar cells and photovoltaic modules.docx by Fatima Wardi (22052360)

“…The extracted parameters for each case study are used to reconstruct the I–V and power–voltage (P–V) characteristic curves for the respective solar cell and photovoltaic module technologies. To validate the performance and efficiency of the algorithm, various statistical criteria are computed, including individual absolute error (IAE), relative error (RE), root mean square error (RMSE), mean absolute error (MAE), standard deviation (SD), tracking signal (TS), normalized forecast measure (NFM), and the autocorrelation function (ACF). …”

Correlation between the BNA neuromarker linear scores and the mUPDRS scores. by Sharon Hassin-Baer (420354)

Subjects: “…Cell Biology…”

The BNA neuromarker characteristics. by Sharon Hassin-Baer (420354)

Subjects: “…Cell Biology…”

Features of the BNA neuromarker. by Sharon Hassin-Baer (420354)

Subjects: “…Cell Biology…”

Schematic depiction of BNA scores. by Sharon Hassin-Baer (420354)

Subjects: “…Cell Biology…”

Demographic and clinical characteristics of participants included in the BNA neuromarker. by Sharon Hassin-Baer (420354)

Subjects: “…Cell Biology…”

Results of the cognitive tasks performance. by Sharon Hassin-Baer (420354)

Subjects: “…Cell Biology…”