Multimodal imaging biomarkers of subretinal hyperreflective material accumulation in central serous chorioretinopathy by Jiaxin Pu, Guiqin He, Xiongze Zhang, Miaoling Li, Xinlei Hao, Xuenan Zhuang, Yongyue Su, Feng Wen

“…Differences were also discovered in serous retinal detachment (SRD) dimensions, presence of pigment epithelial detachment (PED) and retinal pigment epithelium (RPE) defects in OCT, gravitational tracks and hyperautofluorescence contours in FAF, leakage patterns and dye pooling area in FFA, and focal choroidal vascular hyperpermeability (CVH) area in ICGA (P < 0.05 for all). …”
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ID3RSNet: cross-subject driver drowsiness detection from raw single-channel EEG with an interpretable residual shrinkage network by Xiao Feng, Xiao Feng, Zhongyuan Guo, Zhongyuan Guo, Sam Kwong

“…With the global average pooling (GAP) layer incorporated in the residual shrinkage network structure, we introduce an EEG-based Class Activation Map (ECAM) interpretable method to enable visualization analysis of sample-wise learned patterns to effectively explain the model decision. …”
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LMSOE-Net: lightweight multi-scale small object enhancement network for UAV aerial images by Zhixing Ma, Peidong Luo, Xiaole Shen

“…Additionally, we replace the Spatial Pyramid Pooling Fast (SPPF) module in YOLOv8 with the Feature Pyramid Shared Convolution (FPSC) module. …”
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MB-MSTFNet: A Multi-Band Spatio-Temporal Attention Network for EEG Sensor-Based Emotion Recognition by Cheng Fang, Sitong Liu, Bing Gao

“…A multi-scale CNN-Inception module extracts hierarchical spatial features via diverse convolutional kernels and pooling operations, capturing localized sensor activations and global brain network interactions. …”
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Key features of observational research data preparation for evidence synthesis by A. A. Lazarev, K. V. Sapozhnikov, D. G. Tolkacheva, N. A. Sableva

“…This work was conducted within a review of asymptomatic hyperuricemia (AH) and was intended to lead to the consensus among rheumatologists on the management patterns of patients with AH in the Russian Federation.Materials and methods. …”
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DSAT: a dynamic sparse attention transformer for steel surface defect detection with hierarchical feature fusion by Shouluan Wu, Hui Yang, Liefa Liao, Chao Song, Yating Fang, Jianglong Fu, Tan Li

“…These defects exhibit diverse morphological characteristics and complex patterns, which pose substantial challenges to traditional detection models, particularly regarding multi-scale feature extraction and information retention across network depths. …”
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Graph Neural Network Learning on the Pediatric Structural Connectome by Anand Srinivasan, Rajikha Raja, John O. Glass, Melissa M. Hudson, Noah D. Sabin, Kevin R. Krull, Wilburn E. Reddick

“…Architecture exploration experiments were conducted to evaluate the impact of network depth, pooling techniques, and skip connections on the ability of GNN models to capture connectomic patterns. …”
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Multisectoral coordination during the COVID-19 pandemic: practices, challenges and recommendations for future preparedness—a systematic literature review protocol by Themba Ginindza, Javan Solomon Okello, Julius N Odhiambo

“…Studies with serious or critical risk will be excluded from pooling. Subgroup analyses (LMIC vs HIC), sensitivity analyses (model and risk) and confidence grading using Confidence in the Evidence from Reviews of Qualitative Research and Grading of Recommendations, Assessment, Development and Evaluations will be conducted.Ethics and dissemination No primary data will be collected; thus additional Research Ethics Committee approval is unnecessary. …”
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A Comprehensive Review on the Application of 3D Convolutional Neural Networks in Medical Imaging by Satyam Tiwari, Goutam Jain, Dasharathraj K. Shetty, Manu Sudhi, Jayaraj Mymbilly Balakrishnan, Shreepathy Ranga Bhatta

“…CNNs are particularly adept in automatically identifying complex patterns and features in pictures like X-rays, CT scans, and MRIs. …”
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Regional Glacier Mapping and Change Analysis in the Gangshika Region of Qilian Mountain Based on Deep Learning and Satellite Data Time-Series by Lihao Chen, Wei Xu, Zhen Cao, Jiaqi Zhang, Zhizhong Kang

“…Time-series glacier change analysis that is closely linked to global climate patterns has drawn increasing attention. However, few studies focused on regional glacier mapping with high spatial resolution and accuracy. …”
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Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism by Wangyong Guo, Shijin Liu, Liguo Weng, Xingyu Liang

“…Firstly, the CNN module is used to extract high-dimensional spatial features from the load data, and a pooling layer is applied to reduce dimensionality while retaining key information. …”
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Temporal dynamics of chicken host’s molecular response against Fowl adenovirus serotype 8b infection via RNA-sequencing by Bahiyah Azli, Nur Farhana Salim, Mohd Hair-Bejo, Abdul Rahman Omar, Norfitriah Mohamed Sohaimi, Ugwu Chidozie Clifford, Nor Asilah Wati Abdul Hamid, Nurulfiza Mat Isa

“…Results The harvested chicken livers were subjected to biological pooling and transcriptomic profiling via RNA-sequencing, according to the days of post-infections (dpi). …”
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Vowel classification with combining pitch detection and one‐dimensional convolutional neural network based classifier for gender identification by Chia‐Hung Lin, Hsiang‐Yueh Lai, Ping‐Tzan Huang, Pi‐Yun Chen, Chien‐Ming Li

“…Then, in vowel voice signal classification, a one‐dimensional (1D) convolutional neural network (CNN), consisted of a multi‐round 1D kernel convolutional layer, a 1D pooling process, and a vowel classifier that could preliminary divide feature patterns into three level ranges of F0, including adult and children groups. …”
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Intraoperative ICG Dye Monitoring for Identification of Pituitary Adenoma in Endoscopic Transsphenoidal Surgery: A Prospective Observational Study by S. Balaji

“…These variations in intensity, temporal changes in fluorescence, and tissue-specific patterns allowed differentiation of tumor margins and identification of surrounding structures. …”
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Enhancing advanced cervical cell categorization with cluster-based intelligent systems by a novel integrated CNN approach with skip mechanisms and GAN-based augmentation by Gunjan Shandilya, Sheifali Gupta, Ahmad Almogren, Salil Bharany, Ayman Altameem, Ateeq Ur Rehman, Seada Hussen

“…It includes three convolutional layers with increasing filter sizes and max-pooling layers, followed by dropout and dense layers for improved feature extraction and robust learning. …”
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Towards precision agriculture tea leaf disease detection using CNNs and image processing by Irfan Sadiq Rahat, Hritwik Ghosh, Suresh Dara, Shashi Kant

“…This stage features a dropout layer for regularization, a dense layer with 512 units for further pattern learning, and a final dense layer with 8 units and a soft max activation function, producing a probability distribution across different disease classes. …”
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SFMHANet: Surface Fitting Constrained Multidimensional Hybrid Attention Network for Aero-Optics Thermal Radiation Effect Correction by Yu Shi, ShanLin Niu, Lei Wang, Liang Ye, YaoZong Zhang, HanYu Hong

“…In practical application scenarios, the aero-optical thermal radiation patterns in degraded images are not fixed, and types of aero-optics thermal radiation are more variable and complex. …”
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Agent-based modelling of Mycobacterium tuberculosis transmission: a systematic review by Viet Long Bui, Angus E. Hughes, Romain Ragonnet, Michael T. Meehan, Alec Henderson, Emma S. McBryde, James M. Trauer

“…Abstract Background Traditional epidemiological models tend to oversimplify the transmission dynamics of Mycobacterium tuberculosis (M.tb) to replicate observed tuberculosis (TB) epidemic patterns. This has led to growing interest in advanced methodologies like agent-based modelling (ABM), which can more accurately represent the complex heterogeneity of TB transmission. …”
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Persistent Heterogeneities in the Oceanic Lithosphere Due To Differential Freezing Beneath Ridges by Shi Joyce Sim, Ting‐Ying Yu, Chris Havlin

“…In our models, some melt generated from decompression melting is frozen back into the lithosphere, forming a layered refertilization pattern. These refertilized layers are due to the stacked horizontal layering pattern of melt pooling beneath the freezing front. …”
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Transfer of Periodic Phenomena in Multiphase Capillary Flows to a Quasi-Stationary Observation Using U-Net by Bastian Oldach, Philipp Wintermeyer, Norbert Kockmann

“…Artificial Intelligence is now the state of the art for recognizing patterns in images or analyzing datasets that are too large for humans to handle. …”
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