Online Pre-Diagnosis of Multiple Faults in Proton Exchange Membrane Fuel Cells by Convolutional Neural Network Based Bi-Directional Long Short-Term Memory Parallel Model with Atten... by Junyi Chen, Huijun Ran, Ziyang Chen, Trevor Hocksun Kwan, Qinghe Yao

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CNN-based vane-type vortex generator modelling by Koldo Portal-Porras, Unai Fernandez-Gamiz, Ekaitz Zulueta, Roberto Garcia-Fernandez, Xabier Uralde-Guinea

“…The simplicity and accuracy of Computational Fluid Dynamics (CFD) tools have made them the most widely used method for solving fluid dynamics problems. …”
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Pedestrian Trajectory Prediction Based on Dual Social Graph Attention Network by Xinhai Li, Yong Liang, Zhenhao Yang, Jie Li

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Dynamically Tunable Multidimensional Feature Focusing and Diffusion Networks for Water Surface Debris Detection by Chong Zhang, Jie Yue, Jianglong Fu

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Disentangled diffusion dodels for probabilistic spatio-temporal traffic forecasting by Wenyu Zhang, Kaidong Zheng

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Estimation of Vehicle Dynamic Response from Track Irregularity Using Deep Learning Techniques by Fei Yang, Xiaoli Hao, Hang Zhang, Jie Jiang, Zhixing Fan, Zilong Wei

“…To improve the quality of track maintenance work, it is a desire to estimate vehicle dynamic behavior from track geometry irregularities. …”
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Predictive Analysis of Maritime Congestion Using Dynamic Big Data and Multiscale Feature Analysis by Yalin Wu

“…This study proposes an innovative congestion prediction approach using dynamic big data analysis of vessel trajectories and multiscale feature analysis. …”
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Twofold dynamic attention guided deep network and noise-aware mechanism for image denoising by Zihao Chen, Alex Noel Joseph Raj, Vijayarajan Rajangam, Wei Li, Vijayalakshmi G.V. Mahesh, Zhemin Zhuang

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Intelligent Dynamic Trajectory Planning of UAVs: Addressing Unknown Environments and Intermittent Target Loss by Zhengpeng Yang, Suyu Yan, Chao Ming, Xiaoming Wang

“…Specifically, the system employs a bidirectional Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) network algorithm with an adaptive attention mechanism (BITCN-BIGRU-AAM) to train a model that incorporates the historical motion trajectory features of the target and motion intention the inferred by a Dynamic Bayesian Network (DBN). …”
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Dynamic Spatial–Temporal Graph Neural Network for Cooling Capacity Prediction in HVDC Systems by Hao Sun, Shaosen Li, Jianxiang Huang, Hao Li, Guanxin Jing, Ye Tao, Xincui Tian

“…Additionally, a graph attention mechanism dynamically adjusts the importance of variable relationships, improving prediction accuracy and interoperability. …”
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A Dynamic Spatio-Temporal Deep Learning Model for Lane-Level Traffic Prediction by Bao Li, Quan Yang, Jianjiang Chen, Dongjin Yu, Dongjing Wang, Feng Wan

“…Specifically, we take advantage of the graph convolutional network (GCN) with a data-driven adjacent matrix for spatial feature modeling and treat different lanes of the same road segment as different nodes. …”
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DFPF-Net: Dynamically Focused Progressive Fusion Network for Remote Sensing Change Detection by Chengming Wang, Peng Duan, Jinjiang Li

“…In recent years, various CD methods represented by convolutional neural network (CNN) and transformer have achieved significant success in effectively detecting difference areas in bitemporal remote sensing images. …”
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Redefining Urban Traffic Dynamics With TCN-FL Driven Traffic Prediction and Control Strategies by K. M. Karthick Raghunath, C. Rohith Bhat, Venkatesan Vinoth Kumar, Velmurugan Athiyoor Kannan, T. R. Mahesh, K. Manikandan, N. Krishnamoorthy

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Efficient Image Super-Resolution Using Dynamic Quality Control With Recursive Model Structures by Inho Lee, Jaemin Park, Seunghwan Lee, Tae Hyun Kim, Jiwon Seo, Hunjun Lee, Yongjun Park

“…Therefore, we propose an efficient dynamic quality control SR (DQC-SR) system that can dynamically adjust the inference rate according to the number of ROIs by introducing recursive and early-exit architectures into the baseline SR network. …”
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Reinforcement Learning With Deep Features: A Dynamic Approach for Intrusion Detection in IoT Networks by Mohamad Khayat, Ezedin Barka, Mohamed Adel Serhani, Farag Sallabi, Khaled Shuaib, Heba M. Khater

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Expression Dynamics and Genetic Compensation of Cell Cycle Paralogues in <i>Saccharomyces cerevisiae</i> by Gabriele Schreiber, Facundo Rueda, Florian Renner, Asya Fatima Polat, Philipp Lorenz, Edda Klipp

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The utility of combining deep learning with metabarcoding to model biodiversity dynamics at a national scale by Adrian Baggström, Robert Goodsell, Laura van Dijk, Ela Iwaszkiewicz-Eggebrecht, Andreia Miraldo, Ayco J.M. Tack, Tobias Andermann

“…To make informed decisions on how to effectively protect biodiversity, we need knowledge of its spatial and temporal dynamics. By combining detailed biodiversity surveys, geospatial data, and machine learning, we can model biodiversity with the aim of gaining insights into how these complex patterns behave. …”
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A dual-stream ordered topology-aware network for accurate fish counting in aquaculture environments by Jiaming Xin, Zhengmeng Wu, Tongtong Gu, Yiying Wang, Dashe Li, Bin Wang

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Design and modeling of a nanocomposite system for demineralization of sweet whey by Mina Rezapour, Mohsen Esmaiili, Mehdi Mahmoudian, Alireza Behrooz Sarand

“…In the dead-end filtration system, the highest flux was achieved for the GO-modified membrane under laboratory conditions with a pressure of 10 bar and a pH of 6. The dynamic flux behavior of whey output and salt rejection from whey was modeled using convolutional neural network (CNN) machine learning tools. …”
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Simultaneous single image super‐resolution and blind Gaussian denoising via slim ghost full‐frequency residual blocks by Saghar Farhangfar, Aryaz Baradarani, Mohammad Asadpour, Mohammad Ali Balafar, Roman Gr. Maev

“…This paper presents a model for simultaneous super‐resolution and blind additive white Gaussian noise (AWGN) denoising with two components (netdeg and netSR) that is based on a generative adversarial network (GAN) to achieve detailed results. netdeg, featuring residual and innovative cost‐effective ghost residual blocks with a frequency separation module for obtaining long‐range information, blindly restores a clean version of the LR image. netSR leverages slim ghost full‐frequency residual blocks to process low‐frequency (LF) and high‐frequency (HF) information via static large convolutions and pixel‐wise highlighted input‐adaptive dynamic convolutions, respectively. …”
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