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. …”

Root-mean-square fluctuation (RMSF) analysis of wild-type and p.R163C mutant αB-crystallin dimers from molecular dynamics simulations. by Aref Baharvand (21713886)

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

Correlations between genetics and environment are demonstrated through prediction but decrease when considering spatial distance. by Forrest Li (21512582)

The small blue circles show the IC-volume (mean of left and right) from 72 males and the small red squares from 51 females as a function of age. by Pingling Kwok (14896191)

Each square gives extinction probability p (AB) or median population change d (CD) for 100 simulations at . by Alexandra M. Campbell (3284094)

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Participant demographics. * P-values were determined using Fisher-Freeman-Halton test when contingency tables are larger than 2 × 2. by Elie Mulhem (22537863)

Mean parameter values for the selected crops. by Gourab Saha (8987405)

“…<div><p>As the world population is increasing day by day, so is the need for more advanced automated precision agriculture to meet the increasing demands for food while decreasing labor work and saving water for crops. Recently, there have been many studies done in this field, but very few discuss implementing smart technologies to present a combined sustainable farming system. …”

Design variables and range of values. by Xueyong Pan (20390363)

“…The <i>T</i>_max of the battery module decreased by 6.84% from 40.94°C to 38.14°C and temperature mean square deviation decreased (<i>TSD</i>) by 62.13% from 1.69 to 0.64. …”

PBP1 depletion results in a population of cells with decreased RADA fluorescence. by Gregory A. Harrison (16639446)

“…Note that the P_tet-<i>gusA</i> data from panel A (blue squares) was duplicated in panel B for comparison. >1300 cells were quantified across three independent experiments, and symbols represent the mean and standard error of the mean from three replicates.…”

Effect of sample size on reproducibility. by Jan Willem Koten (17743224)

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Comparison between actual and predicted values. by Xueyong Pan (20390363)

“…The <i>T</i>_max of the battery module decreased by 6.84% from 40.94°C to 38.14°C and temperature mean square deviation decreased (<i>TSD</i>) by 62.13% from 1.69 to 0.64. …”

Comparison between actual and predicted values of response surface. by Xueyong Pan (20390363)

Theta (Θ) vs Phi (Φ) joint contact force (JCF) vector path of increased and decreased strength conditions in comparison to default condition (100%). by Jay R. Patel (11600758)

Group-level narrow- and broad-band spectral changes after hemispherotomy reveal a marked EEG slowing of the isolated cortex, robust across patients. by Michele Angelo Colombo (22446342)

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Mean ± SD values of unipedal stance parameters evaluated at the different time points of the postural training session. by Cristina Dolciotti (16317330)

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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. …”

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. Bias decreases with increasing neurons in the population. …”

A summary of the included study characteristics. by Zahra Tajik (20752452)

“…There is no significant difference one month after NSPT in diabetic patients (SMD: -5.83, 95%CI: -15.5, 3.83, p = 0.237, I-square, 97.4%, random effects model, n = 2), but three (SMD: -2.44, 95%CI: -3.37, -1.15, p = 0.001, I-square, 75.9%, random effects model, n = 3) and six months (SMD: -2.41, 95%CI: -3.81, -1.01, p = 0.001, I-square, 78.7%, random effects model, n = 2) after the treatment, a significant decrease is observed in the mean GCF visfatin level. …”

Assessment values of machine learning models. by Bin Pan (742525)

“…The prediction results indicate that the StackBoost model excels in predicting aqueous solubility, achieving a coefficient of determination () of 0.90, a root mean square error (RMSE) of 0.29, and a mean absolute error (MAE) of 0.22, significantly outperforming the other comparative models. …”