Statistically-significant cytokine changes between the pre-infusion and post-infusion blood draws plot of the mean (dot) and confidence interval (whiskers) for the coefficients of... by Jordan K. Abbott (8934239)

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Achieving Effective Self-Driven X‑ray Detection Sensitivity via Pyroelectric–Photovoltaic Coupling in a Layered Perovskite Pyroelectric by Xi Zeng (129687)

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SCF-FPN layer overall architecture. by Ruikang Xu (18778060)

Breakdown of cortical areas and layer distribution of the responsive units. by Omri David Gilday (14475646)

Structure of the two-layer stacking ensemble model. by Yansong Liu (4373785)

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Number of experiments, sampling strategy, variation of models and layers, and performance against previous and proposed approaches. by Md. Mahadi Hasan (10431872)

Statistically significant phyla that differed in the LPP swab. by Daniela Pinto (191727)

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Spatial information is significantly decreased in dCA1 and vCA1 in APP/PS1 mice. by Udaysankar Chockanathan (18510288)

“…The spatial information in dCA1 was significantly larger than circularly shuffled spike trains with similar mean firing rates for C57BL/6 mice (mean ± std: empirical = 0.132 ± 0.048, shuffled = 0.124 ± 0.035, p < 0.001, two-sided Wilcoxon rank-sum test, n_empirical = 305 units from 5 recording sessions, n_shuffled = 30500 simulated units from 5 recording sessions), but not for APP/PS1 mice (mean ± std: empirical = 0.128 ± 0.051, shuffled = 0.123 ± .047, p = 0.39, two-sided Wilcoxon rank-sum test, n_empirical = 180 units from 4 recording sessions, n_shuffled = 18000 simulated units from 4 recording sessions). …”

Specialist fraction and the corresponding tissue for each community detected in the multilayer correlation matrix and for each community detected in the multilayer networks obtaine... by Madison Russell (17409957)

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The distribution of Gamma coefficients () of the BN layers before YOLOv8n-improved model’s sparse training. by Yawen Zhao (2815210)

weighted ensemble in layer - <i>L.</i> by Anwar Hossain Efat (19942283)

“…Our study fills this gap by introducing the Weighted Ensemble (CWE) method, which is further enhanced into a Multi-Layer Weighted Ensemble (ML-CWE) to improve model aggregation across multiple layers. …”

Performance evaluation of RN in CWE-Layer 4. by Anwar Hossain Efat (19942283)

“…Our study fills this gap by introducing the Weighted Ensemble (CWE) method, which is further enhanced into a Multi-Layer Weighted Ensemble (ML-CWE) to improve model aggregation across multiple layers. …”

Performance evaluation of RNXY in CWE-Layer 3. by Anwar Hossain Efat (19942283)

“…Our study fills this gap by introducing the Weighted Ensemble (CWE) method, which is further enhanced into a Multi-Layer Weighted Ensemble (ML-CWE) to improve model aggregation across multiple layers. …”

Detailed layered architecture of proposed Up-to-the-Minute-Net. by Shaista Khanam (21387548)

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Detailed layers’ configuration of proposed Up-to-the-Minute-Net. by Shaista Khanam (21387548)

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Structure of deep forgery detection model with two-stream feature domain fusion. by Hangchuan Zhang (13906524)

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Grouping and layering. by Shichao Hou (18585070)

OCT B-scan image of eye diseases and ambiguity index of each layer. by Hideki Shiihara (10803160)

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ResNet layer. by Wu Huiyong (21743545)

AC-LayeringNetV2 architecture module. by Wenhao Ren (2561731)

“…To address these issues, this study introduces an innovative crack detection model based on YOLOv8n. The proposed method incorporates two novel components: AC-LayeringNetV2, a hierarchical backbone network that optimizes feature extraction by integrating local, peripheral, and global contextual information, and RAK-Conv, a convolutional module that combines an attention mechanism with irregular convolution operations to enhance the model’s ability to handle complex backgrounds. …”