Targeting Trefoil Factor Family 3 in Obstructive Airway Diseases: A Computational Approach to Novel Therapeutics by Alireza Shahriary, Mohsen Sisakht, Masoud Arabfard, Esmaeil Behmard, Ali Najafi

“…Molecular docking was conducted with AutoDock Vina (version 1.1.2), and molecular dynamics simulations were performed using Groningen Machine for Chemical Simulations (GROMACS) version 5.1. …”
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Multi-Scale Feature Fusion Model for Bridge Appearance Defect Detection by Rong Pang, Yan Yang, Aiguo Huang, Yan Liu, Peng Zhang, Guangwu Tang

“…Finally, the effectiveness of the proposed method is evaluated on the bridge disease dataset and public computational fluid dynamic dataset.…”
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Optimizing Group Activity Recognition With Actor Relation Graphs and GCN-LSTM Architectures by M. R. Tejonidhi, C. V. Aravinda, S. V. Aruna Kumar, C. K. Madhu, A. M. Vinod

“…However, accurately deciphering complex group dynamics continues to pose significant difficulties. …”
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A novel AI-driven approach to greenwashing: breakthroughs in the future fit between domain-specific Islamic enterprises with varying developmental progress and ESG landscapes by Mahdi Ghaemi Asl

“…Additionally, the study utilizes a chrono-convolutional neural network to investigate the dynamics of long-term memory concerning the nexus of innovative green fintech index and sectoral investments, thereby assessing the potential for greenwashing activities. …”
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Calibrating calving parameterizations using graph neural network emulators: application to Helheim Glacier, East Greenland by Y. Koo, Y. Koo, Y. Koo, G. Cheng, M. Morlighem, M. Rahnemoonfar, M. Rahnemoonfar

“…In this study, we adopt three standard graph neural network (GNN) architectures, including graph convolutional network, graph attention network, and equivariant graph convolutional network (EGCN), to develop surrogate models for finite-element simulations from the Ice-sheet and Sea-level System Model. …”
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An Improved CEEMDAN-FE-TCN Model for Highway Traffic Flow Prediction by Heyao Gao, Hongfei Jia, Lili Yang

“…Finally, the Temporal Convolutional Network (TCN) is adopted to predict the recombined subsequences, and the final prediction result is reconstructed. …”
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ERNIE-TextCNN: research on classification methods of Chinese news headlines in different situations by Yumin Yan

“…The MSSE attention mechanism module is proposed to multi-scale integrate global feature information output from convolutional layers, dynamically weighting convolutional feature maps to further enhance the model’s ability to capture key information. …”
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Towards consistently measuring and monitoring habitat condition with airborne laser scanning and unmanned aerial vehicles by W. Daniel Kissling, Yifang Shi, Jinhu Wang, Agata Walicka, Charles George, Jesper E. Moeslund, France Gerard

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Research on Shale Oil Well Productivity Prediction Model Based on CNN-BiGRU Algorithm by Yuan Pan, Xuewei Liu, Fuchun Tian, Liyong Yang, Xiaoting Gou, Yunpeng Jia, Quan Wang, Yingxi Zhang

“…The model incorporates dedicated input pathways: fully connected layers for feature embedding and convolutional operations for high-dimensional feature extraction. …”
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A rapid and efficient method for flash flood simulation based on deep learning by Xinying Wang, Miao Xiao, Yi Liu, Jun Guo, Yangyang Qin, Yunkang Zhang

“…Then, we developed a Temporal Convolutional Network (TCN) model to predict flash floods. …”
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AI Techniques and Applications for Online Social Networks and Media: Insights From BERTopic Modeling by Prema Nedungadi, G. Veena, Kai-Yu Tang, Remya R. K. Menon, Raghu Raman

“…Advanced AI techniques, such as Dynamic Memory Networks and Temporal Convolutional Networks, have shown strong capabilities in tracking opinion dynamics and combating misinformation. …”
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Structural Similarity-Guided Siamese U-Net Model for Detecting Changes in Snow Water Equivalent by Karim Malik, Colin Robertson

“…We conclude with a discussion on the implications of the findings from our study of snow dynamics and climate variables using gridded SWE data, computer vision metrics, and fully convolutional deep neural networks.…”
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Deep learning enhanced light sheet fluorescence microscopy for in vivo 4D imaging of zebrafish heart beating by Meng Zhang, Renjian Li, Songnian Fu, Sunil Kumar, James Mcginty, Yuwen Qin, Lingling Chen

“…Abstract Time-resolved volumetric fluorescence imaging over an extended duration with high spatial/temporal resolution is a key driving force in biomedical research for investigating spatial-temporal dynamics at organism-level systems, yet it remains a major challenge due to the trade-off among imaging speed, light exposure, illumination power, and image quality. …”
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Short-Term Passenger Flow Prediction Based on Federated Learning on the Urban Metro System by Guowen Dai, Jinjun Tang, Jie Zeng, Yuting Jiang

“…To address these issues, this study proposes a federated learning framework integrating convolutional neural networks (CNNs) and bidirectional gated recurrent units (BIGRU). …”
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Bayesian optimization of biodegradable polymers via machine learning driven features from low-field NMR data by Ryo Fujita, Yoshifumi Amamoto, Jun Kikuchi

“…Low-field NMR is one of the candidate techniques because it provides information on the higher-order structure and dynamics of polymers quickly and conveniently. Although machine learning approaches such as Bayesian optimization (BO) and convolutional neural networks (CNNs) are significant, there have been almost no reports on effective material design based on low-field nuclear magnetic resonance (NMR) data. …”
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Effective last-mile delivery using reinforcement learning and social media-based traffic prediction in underdeveloped megacities by Luis Rabelo, Cristian Rincón-Guio, Valeria Laynes, Edgar Gutierrez-Franco, Vasanth Bhat, Juan Zamora-Aguas, Marwen Elkamel

“…Leveraging a Graph Convolutional Networks and a Long Short-Term Memory model for traffic prediction, the framework incorporates multimodal data sources, such as social media sentiment analysis, to provide real-time insights into traffic dynamics. …”
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Improving Climate Bias and Variability via CNN‐Based State‐Dependent Model‐Error Corrections by William E. Chapman, Judith Berner

“…Abstract We develop an approach to correct biases in the atmospheric component of the Community Earth System Model using convolutional neural networks (CNNs) to create a corrective model parameterization for online bias reduction. …”
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A Workload Characterization Methodology Using Supervised and Unsupervised Deep Learning by Bing Hu, Karl Kempf, Nicholas Mason

“…To fill this gap, this study proposes a deep-learning workload analysis tool (DLWAT) to capture complex workload dynamics. The DLWAT tool uses a hybrid deep learning model that includes a convolutional neural network (CNN) and recurrent neural networks (RNN) to study workloads from a supervised learning perspective. …”
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Explainable Offline‐Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave‐QBO Testbed in the Small‐Data Regime by Hamid A. Pahlavan, Pedram Hassanzadeh, M. Joan Alexander

“…Here, we use a 1D model of the quasi‐biennial oscillation (QBO) and gravity wave (GW) parameterizations as testbeds. A 12‐layer convolutional NN that predicts GW forcings for given wind profiles, when trained offline in a big‐data regime (100‐year), produces realistic QBOs once coupled to the 1D model. …”
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Metal interdiffusion enhanced WOx/CuOx heterojunction optoelectronic memristive synapses for face recognition application by Hans Juliano, Stephen Ekaputra Limantoro, Firman M. Simanjuntak, Tseung-Yuen Tseng

“…The behaviors including long-term potentiation/depression, paired-pulse facilitation, spike-timing-dependent plasticity, and fully tunable relaxation time of short-term memory, mimicking the temporal dynamics of the biological neuron, are declared. A convolutional neural network operation is conducted by exploiting the synaptic functions of the device. …”
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