The cyclic nucleotide-gated channels CNGC2 and CNGC4 support systemic wound responses in Arabidopsis thaliana by Sarah Johns, Erin Wiegman, Arkadipta Bakshi, Simon Gilroy

“…We hypothesized that members of the cyclic nucleotide-gated family of ion channels (CNGCs) might also be involved in the systemic component of this process.MethodsAn analysis of the systemic induction of defense genes was made using qPCR and patterns of Ca2+ signaling were monitored in plants expressing the GFP-based Ca2+ sensor GCaMP. …”
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Fog Computing and Industry 4.0 for Newsvendor Inventory Model Using Attention Mechanism and Gated Recurrent Unit by Joaquin Gonzalez, Liliana Avelar Sosa, Gabriel Bravo, Oliverio Cruz-Mejia, Jose-Manuel Mejia-Muñoz

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Cyclic nucleotide-gated ion channel gene CNGC3 positively regulates immunity against Sclerotinia sclerotiorum in Arabidopsis by LIU Mengjiao, YI Hang, CAI Xinzhong

“…Functions and mechanisms of cyclic nucleotide-gated ion channel (CNGC) in plant immunity against necrotrophic pathogens remain largely unknown. …”
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Modeling Ontology-Based Decay Analysis and HBIM for the Conservation of Architectural Heritage: The Big Gate and Adjacent Curtain Walls in Ibb, Yemen by Basema Qasim Derhem Dammag, Dai Jian, Abdulkarem Qasem Dammag, Yahya Alshawabkeh, Sultan Almutery, Amer Habibullah, Ahmad Baik

“…This study introduces a comprehensive framework combining Historic Building Information Modeling (HBIM) with ontology-based modeling aligned with the CIDOC Conceptual Reference Model (CIDOC CRM). Focusing on the Big Gate and adjacent curtain walls in Ibb, Yemen, where the gate is entirely lost, the study reconstructs the structure using historical photographs, eyewitness accounts, and analogical references. …”
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PPG-Based Accurate Insomnia Detection System Using Convolutional Neural Networks With Self-Attention Mechanism and Gated Recurrent Units by Hardik Telangore, Heneel Makwana, Prithviraj Verma, Manish Sharma, Hasan S. Mir, U. Rajendra Acharya

“…This study introduces a novel approach for PPG-based insomnia detection, utilizing Convolutional Neural Network (CNN) with self-attention, CNN with Gated Recurrent Unit (GRU), and transformer-based models. …”
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DDoS classification of network traffic in software defined networking SDN using a hybrid convolutional and gated recurrent neural network by Ahmed M. Elshewey, Safia Abbas, Ahmed M. Osman, Eman Abdullah Aldakheel, Yasser Fouad

“…This paper presents a comprehensive evaluation of six deep learning models (Multilayer Perceptron (MLP), one-dimensional Convolutional Neural Network (1D-CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), and a proposed hybrid CNN-GRU model) for binary classification of network traffic into benign or attack classes. …”
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Towards sustainable architecture: Enhancing green building energy consumption prediction with integrated variational autoencoders and self-attentive gated recurrent units from mult... by Qing Zeng, Fang Peng, Xiaojuan Han

“…This study addresses these challenges by proposing an advanced deep learning framework that integrates Time-Dependent Variational Autoencoder (TD-VAE) with Adaptive Gated Self-Attention GRU (AGSA-GRU). The framework incorporates self-attention mechanisms and Multi-Task Learning (MTL) strategies to capture long-term dependencies and complex patterns in energy consumption time series data, while simultaneously optimizing prediction accuracy and anomaly detection. …”
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A novel transformer-based dual attention architecture for the prediction of financial time series by Anita Hadizadeh, Mohammad Jafar Tarokh, Majid Mirzaee Ghazani

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Functional Diversity of Wintering Waterbird Enhanced by Restored Wetland in the Lakeshore of Chaohu Lake by Shanshan Xia, Xianglin Ji, Lei Meng, Lizhi Zhou

“…Notably, restored wetlands exhibited coupled patterns of higher functional β‐diversity turnover rates and lower functional nestedness over time, forming distinctive species assemblages. …”
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An adaptive spatiotemporal dynamic graph convolutional network for traffic prediction by Zhiguo Xiao, Qi Shen, Changgen Li, Dongni Li, Qian Liu

“…Concurrently, the model synergistically integrates dynamic graphs with gated recurrent units to achieve joint modeling of complex spatiotemporal dependencies. …”
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EEG–fNIRS signal integration for motor imagery classification using deep learning and evidence theory by Mohammed E. Seno, Niladri Maiti, Maulik Patel, Mihirkumar M. Patel, Kalpesh B. Chaudhary, Ashish Pasaya, Babacar Toure

“…For EEG signals, spatiotemporal features are extracted using dual-scale temporal convolution and depthwise separable convolution, and a hybrid attention module is introduced to enhance the network's sensitivity to salient neural patterns. For fNIRS signals, spatial convolution across all channels is employed to explore activation differences among brain regions, and parallel temporal convolution combined with a gated recurrent unit (GRU) captures richer temporal dynamics of the hemodynamic response. …”
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Short Text Classification Based on Enhanced Word Embedding and Hybrid Neural Networks by Cunhe Li, Zian Xie, Haotian Wang

“…Furthermore, we propose a dual-channel hybrid model based on a Gated Convolutional Neural Network (GCNN) and Bidirectional Long Short-Term Memory (BiLSTM), which jointly captures local features and long-range global dependencies. …”
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High-speed threat detection in 5G SDN with particle swarm optimizer integrated GRU-driven generative adversarial network by R. Shameli, Sujatha Rajkumar

“…Moreover, the performance of this model is evaluated using the InSDN dataset and compared with existing DL model-based intrusion detection approaches and the results demonstrate a significantly higher accuracy rate of 98.4%, precision rate of 98%, recall rate of 98.5%, less detection time of 2.464 s, lesser Log loss rate of 1.0 and more metrics instilling confidence in the effectiveness of the proposed method.…”
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The Reliability of Diagnosing Schizophrenia Using the GRU Layer in Conjunction with EEG Rhythms by Pankaj Kumar Sahu, Karan Jain

“…The proposed study demonstrates the applicability of alpha-EEG rhythm in a Gated-Recurrent-Unit-based deep learning model for studying schizophrenia. …”
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Surface Temperature Assisted State of Charge Estimation for Retired Power Batteries by Liangyu Xu, Wenxuan Han, Jiawei Dong, Ke Chen, Yuchen Li, Guangchao Geng

“…Specifically, our method achieves 64.3–68.1% lower RMSE than traditional electrical-parameter-only approaches (V-I inputs) across 0.5 C–2 C discharge rates. Results show that the proposed method reduces SOC estimation errors compared to traditional voltage-based models, achieving RMSE values below 1.04 across all tested rates. …”
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A hybrid deep learning model EfficientNet with GRU for breast cancer detection from histopathology images by M. Pradeepa, B. Sharmila, M. Nirmala

“…A hybrid deep learning model is proposed, integrating EfficientNetV2 for multi-scale feature extraction with a Gated Recurrent Unit (GRU) enhanced by an attention mechanism to model sequential dependencies. …”
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FPGA Implementation for 24.576-Gbit/s Optical PAM4 Signal Transmission with MLP-Based Digital Pre-Distortion by Sheng Hu, Tianqi Zheng, Chengzhen Bian, Xiongwei Yang, Xinda Sun, Zonghui Zhu, Yumeng Gou, Yuanxiao Meng, Jie Zhang, Jingtao Ge, Yichen Li, Kaihui Wang

“…At the receiver, the parallel constant modulus algorithm (PCMA) was applied for signal processing. The bit error rate (BER) achieved met the 2.4 × 10⁻² threshold for soft-decision forward error correction (SD-FEC), enabling a net transmission bit rate of 24.576 Gbit/s. …”
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An enhanced fusion of transfer learning models with optimization based clinical diagnosis of lung and colon cancer using biomedical imaging by N. A. S. Vinoth, J. Kalaivani, R. Madonna Arieth, S. Sivasakthiselvan, Gi-Cheon Park, Gyanendra Prasad Joshi, Woong Cho

“…The FTLMO-BILCCD technique implements a hybrid of temporal pattern attention and bidirectional gated recurrent unit (TPA-BiGRU) for classification. …”
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A hybrid ultra-short-term photovoltaic power prediction framework integrating ant colony optimization for clustering with Bi-GRU by Xinyi Liu, Zitao Wang, Yang Wang, Shanke Liu, Lijun Yu

“…Kendall’s Rank Correlation Coefficient is utilized as the core metric for feature selection to identify the optimal predictive feature subset, resulting in a 9-feature set that achieves the best predictive performance (R2 = 0.8153). Bidirectional Gated Recurrent Unit (Bi-GRU) models are independently constructed based on these scenario labels to enable scenario-adaptive predictions. …”
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Performance Estimation of Low Power and Area-Efficient Parallel Pipelined FFT by Surya P, Arunachalaperumal C, Dhilipkumar S

“…The BI multiplier design was synthesized in a field programmable gate array (FPGA), and the results show that the area efficiency could be improved by about 30 % and the power consumption and delay could be reduced by 56 %. …”
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