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

“…When they stop being introduced in further assembly events (i.e. introduced species do not carry any mutualistic interactions), their proportion slowly decreases with successive invasions. (B) Even though higher proportions of mutualism promote higher richness, introducing this type of interaction into already assembled large communities promotes a sudden drop in richness, while stopping mutualism promotes a slight boost in richness increase. …”

Distribution of final population size (A) and mean change in mean MSI over time (B) over 100 simulations for each parameter set shown in Table 3 in the absence of weevils (left) an... by Alexandra M. Campbell (3284094)

Graphs showing reproduction of selected South American annual case (a) and death (b) data using 1000 parameter sets drawn from post-burn-in distribution obtained from 4 Markov chai... by Keith Fraser (1868587)

Classification model parameter settings. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. …”

<b>Supporting data for manuscript</b> "<b>Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins</b>" by Kira Shaw (18796168)

“…The locomotion values (traces and metrics) are in arbitrary units with larger integers representing a greater displacement of the spherical treadmill, the hemodynamic (Hbt) values (traces and metrics) are a percentage change from the normalised baseline (prior to stimulus presentation), and the corresponding time series vector is presented in seconds. …”

PCA-CGAN model parameter settings. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. …”

Parameter setting table. by Bo Tong (2138632)

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

Overview of the parameters used in the model. by Albertus Constantijn Sloof (20405090)

“…<div><p>Background</p><p>Chikungunya virus (CHIKV) outbreaks, driven by the expanding habitat of the <i>Aedes albopictus</i> mosquito and global climate change, pose a significant threat to public health. Our study evaluates the effectiveness of emergency vaccination using a dynamic disease transmission model for a potential large-scale outbreak in Rome, Italy.…”

The flowchart of the large model for font style transfer. by Weijia Zhu (65481)

Subjects:

Parameter sets generated through Latin hypercube sampling. by Alexandra M. Campbell (3284094)

Parameter Settings. by Yutong Liu (4027994)

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

Data Set. by Yang Li (7082)

Simulation Parameter Settings. by Jinrong Liang (3918740)

Model parameter setting. by Yuxuan Liu (349237)

The parameters set in the experiment under two models. by Xiying Wang (4859998)

Accuracy of test sets for each model, specifically. by Hongwei Bai (1447999)

Discrete phase parameter setting. by Yuxuan Liu (349237)

Increasing fractions of mobilized and static taxa in the microbial streamwater community, with a corresponding decrease in the fraction of diluted microbial taxa, detail the shift... by Dawn R. URycki (9659702)