Decrease in loss during the training process. by Muhammad Idrees (92773)

The introduction of mutualisms into assembled communities increases their connectance and complexity while decreasing their richness. by Gui Araujo (22170819)

“…Parameter values: interaction strengths were drawn from a half-normal distribution of zero mean and a standard deviation of 0.2, and strength for consumers was made no larger than the strength for resources. …”

iKIR score decreases protection associated with protective class II genotypes in T1D. by Laura Mora-Bitria (20477525)

This is the data set that we used to reach the conclusions drawn in the manuscript with related metadata and methods. by Farayi Kaseke (19560197)

An illustration for the lineages identification procedure. Left panel: Each leaf represents a cell, characterized by its measured set of barcodes, denoted as . Each individual barc... by Nava Leibovich (12908477)

Scheme of g-λ model with larger values λ. by Zhanfeng Fan (20390992)

“…And if the value of λ assumes larger values, the distortion in the shape of the transmitted wave is associated with the plastic deformation in the uncoupled rock mass. …”

Market structure of e-commerce platforms. by Min Zhao (66793)

“…The research results show that when the reputation information of e-commerce products is symmetrical, the reputation mechanism of the e-commerce platform can play a positive role, the platform income decreases, and the consumer surplus and the total social welfare level increase. …”

Minimal data set. by Wei Huang (36889)

“…Each10 units increase in BRI, ASM/ BMI decreased by 29% (β = −0.29,95% CI: −0.31, −0.28, <i><i>p</i></i> value < 0.0001). …”

Biases in larger populations. by Sander W. Keemink (21253563)

“…<p>(<b>A</b>) Maximum absolute bias vs the number of neurons in the population for the Bayesian decoder. …”

Benchmarking DFT Accuracy in Predicting O 1s Binding Energies on Metals by Elizabeth E. Happel (22391811)

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Benchmarking DFT Accuracy in Predicting O 1s Binding Energies on Metals by Elizabeth E. Happel (22391811)

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Setting of experimental environment and model parameters. by Guozhu Sui (21672798)

Input parameter ranking for discrete measurements: Each column displays parameters (P) and their influence values (E) calculated using Eq 11, shown in decreasing order of influence. Results are presented for progressively expanding discrete measurement sets (from Set 1 to Set 3F). Only parameters with influence values greater than 0.01 are shown. by Harry Saxton (19165441)

“…Results are presented for progressively expanding discrete measurement sets (from Set 1 to Set 3F). Only parameters with influence values greater than 0.01 are shown.…”

Comparison of WEI values for two scenarios. by Danuta Roman-Liu (8532645)

Class-wise performance on the test set. by Muhammad Zubair (728141)

Kolmogorov-Smirnov (KS) test for the different parameter value pairs at a 95% confidence level. We tested the null hypothesis for all parameters: The RMSEs obtained for one pair of... by Cyrine Chenaoui (22177098)

Impact of changes in competitive strength of direct marketing channels on pharmaceutical pricing and profits. by Yan Wen (416285)

Validation results for the different scenario parameter sets and the resulting KGE_mean value. by Fabian Mitze (21084251)

Prediction (a-d) and infusion deviation (e-f) results under different training sets and test sets. by Leijie Wang (21485157)

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Experimental parameter information setting table. by Wenhui Wang (34265)