IKKε interacts with PML and disrupts the PML nuclear bodies. by Xiaoqian Wang (460263)

Cross-sectional dependence results. by Ozlem Kutlu Furtuna (20308206)

“…The results of the panel data analysis show a U-shaped relationship between FDI and carbon emissions which means carbon emissions decrease to a certain level with increasing FDI investment and after this level, increasing FDI increases the environmental degradation in terms of carbon emissions. …”

Autocorrelation test results. by Ozlem Kutlu Furtuna (20308206)

“…The results of the panel data analysis show a U-shaped relationship between FDI and carbon emissions which means carbon emissions decrease to a certain level with increasing FDI investment and after this level, increasing FDI increases the environmental degradation in terms of carbon emissions. …”

Pesaran’s CADF test results for Model I. by Ozlem Kutlu Furtuna (20308206)

“…The results of the panel data analysis show a U-shaped relationship between FDI and carbon emissions which means carbon emissions decrease to a certain level with increasing FDI investment and after this level, increasing FDI increases the environmental degradation in terms of carbon emissions. …”

Descriptive statistics of related variables. by Ozlem Kutlu Furtuna (20308206)

“…The results of the panel data analysis show a U-shaped relationship between FDI and carbon emissions which means carbon emissions decrease to a certain level with increasing FDI investment and after this level, increasing FDI increases the environmental degradation in terms of carbon emissions. …”

A. Representation of the flagellar pocket region in successive stages of flagellar pocket division and new flagellar pocket collar formation. by Marie Zelená (22404144)

Complexity comparison of different models. by Li Yuan (102305)

“…Therefore, the study proposes a signal automatic modulation classification model based on fixed K-mean algorithm and denoising autoencoder. The model uses fixed K-mean algorithm for feature classification and optimizes median filtering algorithm using dynamic thresholding. …”

Dynamic window based median filtering algorithm. by Li Yuan (102305)

“…Therefore, the study proposes a signal automatic modulation classification model based on fixed K-mean algorithm and denoising autoencoder. The model uses fixed K-mean algorithm for feature classification and optimizes median filtering algorithm using dynamic thresholding. …”

Flow of operation of improved KMA. by Li Yuan (102305)

“…Therefore, the study proposes a signal automatic modulation classification model based on fixed K-mean algorithm and denoising autoencoder. The model uses fixed K-mean algorithm for feature classification and optimizes median filtering algorithm using dynamic thresholding. …”

Improved DAE based on LSTM. by Li Yuan (102305)

“…Therefore, the study proposes a signal automatic modulation classification model based on fixed K-mean algorithm and denoising autoencoder. The model uses fixed K-mean algorithm for feature classification and optimizes median filtering algorithm using dynamic thresholding. …”

Autoencoder structure. by Li Yuan (102305)

“…Therefore, the study proposes a signal automatic modulation classification model based on fixed K-mean algorithm and denoising autoencoder. The model uses fixed K-mean algorithm for feature classification and optimizes median filtering algorithm using dynamic thresholding. …”

Acoustic Startle at 28 dpf. by Morgan Barnes (7876373)

“…(C) There is a significant decrease of PPI in the 48+ and 72 + fish (p < 0.0001). …”

DOC consumption at T₁₂₀ of the water samples incubations. by Daniel Cuevas-Lara (12188972)

Variation of the potential carbon mineralization (C–CO₂) at T₁₂₀ in the water samples incubations. by Daniel Cuevas-Lara (12188972)

Population dynamics reflect intention-action binding. by Jean-Paul Noel (3072192)

PDS and CEP inhibited DDX60-mediated promotion of CCHFV replication. by Yutong Sui (11151669)

Study on the expression of mRNA related to biofilm formation of <i>C. albicans</i> by CSR extract (100 μg/mL). by Xuxi Cheng (22453447)

TAD boundary stability for different noise amplitudes for all chromosomes. by Lucas Hedström (21688891)

Complementary conditioning measures in healthy subjects. by Gabriela Ribeiro (4748373)

“…<b>(I) Novelty</b> ratings significantly decreased from pre to post-conditioning (F_(1,51) = 10.2, <i>P</i> = 0.002; post hoc CS^-, <i>P</i> = 0.0001; post hoc CS⁺, <i>P</i> = 0.01) but similarly for both stimuli (F_{(1, 51)} = 0.17, <i>P</i> = 0.7; Interaction: F_{(1, 51)} = 1.1, <i>P</i> = 0.3). …”

Complementary conditioning measures across the clinical study groups. by Gabriela Ribeiro (4748373)

“…Post hoc tests showed significant decreases for the surgical group (<i>P</i> = 0.05), while in the remaining groups, results did not reach significance (Healthy, <i>P</i> = 0.4; Obese, <i>P</i> = 0.1). …”