n=140, Finn, Sawyer (25) assessed anxiety following two 5

Seeing emotions, reading emotions: Behavioral and ERPs evidence of the regulation of pictures and words

F=2.8, p>0.1, Emotional content x Hemisphere F<1

The behavioral repertoire of Drosophila melanogaster in the presence of two predator species that differ in hunting mode

fit generalised linear mixed effect model

Manipulating the reported age in earliest memories in a Dutch community sample

Vividness' (2 items; alpha=.651), (3) 'Place details' (2 items; alpha=.602), (4) 'Time perception' (1 item), (5
time' (1 item), (9) 'Surrounding happenings' (2 items; alpha=.759), (10) 'Sharing' (2 items; alpha=.697), (11) 'Visual perspective' (2 items; alpha=.484), (12) 'Coherence' (1

Identification of elite performance characteristics in a small sample of taekwondo athletes

non sport specific motor coordination test
two out of 3 general motor coordination test

Effects of methylphenidate on the ERP amplitude in youth with ADHD: A double-blind placebo-controlled cross-over EEG study

Disturbing the rhythm of thought: Speech pausing patterns in schizophrenia, with and without formal thought disorder

Romantic relationship breakup: An experimental model to study effects of stress on depression (-like) symptoms

Lingering health-related anxiety about radiation among Fukushima residents as correlated with media information following the accident at Fukushima Daiichi Nuclear Power Plant

Comparison of neuron-based, kernel-based, tree-based and curve-based machine learning models for predicting daily reference evapotranspiration

rbnn model attained satisfactory et 0 estimates
improved version of kernel based machine learning model
kernel based nonlinear extension of arps decline model
unstable model may produce inaccurate et 0 estimates
model stability of 8 machine learning model
ls svm model yielded accurate et 0 estimation
grnn model exhibited more scattered estimates
mars model produced better daily et 0 estimates

n lambda x f (x -1) ∂ L ∂ omega=0 omega=∑ x=2 n lambda x phi u x ∂ L ∂ μ=0 ∑ x=2 n lambda x=0 ∂ L ∂ e x=0 e x=lambda x gamma -1 ∂ L ∂ lambda x=0
pone.0217520.e014">removedGRAPHIC R2=[∑ i=1 n (Y i, m - Y ¯ i, m) (Y i, e - Y ¯ i, e) ] 2 ∑ i=1 n (Y i, m - Y ¯ i, m) 2 ∑ i=1 n (Y i, e - Y ¯ i, e) 2</disp formula><disp formula id="pone.0217520.e015
E=1 n ∑ i=1 n (Y i, m - Y i, e) 2</disp formula><disp formula id="pone.0217520.e016
S E=1 n ∑ i=1 n (Y i, m - Y i, e) 2 / Y ¯ i, m*100 %</disp formula><disp formula id="pone.0217520.e017
MAE=0.426 mm day^-1 during training; R2=0.862, RMSE=0.663 mm day^-1, NRMSE=0.235
MAE=0.467 mm day^-1 during training; R2=0.829, RMSE=0.718 mm day^-1, NRMSE=0.250