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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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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Features of geochemical processes in the catchment area of the river Kalaus in the Stavropol territory by Anastasia Sergeevnna Bluzhina, Inna Vladimirovna Begday

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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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Ethnic differences in CKD progression among people with type 2 diabetes and rapid eGFR decline: A secondary data analysis by Thamer Alobaid, Matthew D. L. O’Connell, Janaka Karalliedde, Salma Ayis

“…Abstract Background People with type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD) exhibit diverse patterns of kidney function decline. However, there is limited evidence on the impact of ethnicity and diabetic retinopathy on the rate of estimated glomerular filtration rate (eGFR) decline and the progression of CKD. …”
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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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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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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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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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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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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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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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Conv1D-GRU-Self Attention: An Efficient Deep Learning Framework for Detecting Intrusions in Wireless Sensor Networks by Kenan Honore Robacky Mbongo, Kanwal Ahmed, Orken Mamyrbayev, Guanghui Wang, Fang Zuo, Ainur Akhmediyarova, Nurzhan Mukazhanov, Assem Ayapbergenova

“…This study proposes a hybrid IDS model combining one-dimensional Convolutional Neural Networks (Conv1Ds), Gated Recurrent Units (GRUs), and Self-Attention mechanisms. …”
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Peculiarities of Pattern Information Transmission by Evgeniy V. Yurkevich, Lidiya N. Krukova

“…The rates of modern technologies development is such that educational programs are constantly required to be enlarged with new material. …”
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The Application of BiGRU-MSTA Based on Multi-Scale Temporal Attention Mechanism in Predicting the Remaining Life of Lithium-Ion Batteries by Luping Wang, Shanze Wang

“…This mechanism is integrated with the Bidirectional Gated Recurrent Unit (BiGRU) to develop the BiGRU-MSTA framework. …”
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Forecasting Green Energy Production in Latin American Countries and Canada via Temporal Fusion Transformer by Muhammad Shoaib Saleem, Javed Rashid, Sajjad Ahmad, Ali M. Al‐Shaery, Saad Althobaiti, Muhammad Faheem

“…The performance of the proposed TFT is more authentic as compared with the gated recurrent unit (GRU), the long short‐term memory (LSTM), deep autoregression (DeepAR), and the meta graph‐based convolutional recurrent network (MegaCRN). …”
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Exploring CESEE-EMU Synchronisation Patterns by Valerija Botrić, Tanja Broz

“…We introduce a specific measure of business cycle synchronisation by comparing the evolution of the growth rates in country pairs and further seek to identify determinants of these synchronisation patterns using pooled probit model. …”
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