Applications of mutagenesis methods on affinity maturation of antibodies in vitro by LIU Yuan, LIN Manman, ZHANG Xiao, XU Chongxin, JIAO Linxia, ZHONG Jianfeng, WU Aihua, LIU Xianjin

“…At the mutagenesis level, strategies to affinity maturation in vitro can be grouped in five categories: error prone PCR, DNA shuffling, mutator strains, site directed mutagenesis and chain shuffling. …”
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An EfficientNetv2-based method for coal conveyor belt foreign object detection by Tao Hu, Deyu Zhuang, Jinbo Qiu

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Innovative Lightweight Detection for Airborne Remote Sensing: Integrating G-Shuffle and Dynamic Multiscale Pyramid Networks by Ruofei Liang, Yigang Cen, Linna Zhang, Fugui Zhang, Yansen Huang, Fei Gan

“…First, a structural reparameterization strategy is applied to optimize depthwise separable convolutions, simplifying complex structures during the inference stage, which improves inference speed and memory utilization. Second, the G-Shuffle module is designed to significantly enhance feature extraction efficiency and interchannel information interaction, balancing computational complexity and detection accuracy. …”
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An Improved Shuffled Frog Leaping Algorithm for Assembly Sequence Planning of Remote Handling Maintenance in Radioactive Environment by Jianwen Guo, Hong Tang, Zhenzhong Sun, Song Wang, Xuejun Jia, Haibin Chen, Zhicong Zhang

“…This study proposes an improved shuffled frog leaping algorithm (SFLA) for the combinatorial optimization problem of ASP. …”
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MPPT Control Strategy of PV Based on Improved Shuffled Frog Leaping Algorithm under Complex Environments by Xiaohua Nie, Haoyao Nie

“…This work presents a maximum power point tracking (MPPT) based on the particle swarm optimization (PSO) improved shuffled frog leaping algorithm (PSFLA). The swarm intelligence algorithm (SIA) has vast computing ability. …”
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MFFSNet: A Lightweight Multi-Scale Shuffle CNN Network for Wheat Disease Identification in Complex Contexts by Mingjin Xie, Jiening Wu, Jie Sun, Lei Xiao, Zhenqi Liu, Rui Yuan, Shukai Duan, Lidan Wang

“…MFFSNet incorporates a multi-scale feature extraction and fusion module (MFEF), utilizing inflated convolution to efficiently capture diverse features, and its main constituent units are improved by ShuffleNetV2 units. …”
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Study on Fault Diagnosis Method of Bearing based on Shuffled Frog Leaping Algorithm to Optimize the BP Neural Network by Wang Yu, Wei Xiuye

“…Based on the background of rolling bearing fault diagnosis,taking the JZQ250 type transfer-box as test object,the shuffled frog leaping algorithm（ SFLA） is combined with back propagation（ BP） neural network,by using the efficient computing performance and the excellent ability of global optimization of shuffled frog leaping algorithm,the network structure of BP neural network is optimized. …”
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Transformer fault diagnosis method based on Gramian Angular Field and optimized parallel ShuffleNetV2 by Qiang Guo, Haiyan Yao, Yuefei Xu, Bin Lu, Zhengshu Ma, Yuanjun Huang, Mingyu Shi

“…Then, an optimized dual-branch parallel ShuffleNetV2 model was built to simultaneously perform efficient feature extraction on the two types of images, and the Convolutional Block Attention Module was introduced to achieve adaptive weighted fusion of features. …”
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SAFH-Net: A Hybrid Network With Shuffle Attention and Adaptive Feature Fusion for Enhanced Retinal Vessel Segmentation by Yang Zhou Ling Ou, Joon Huang Chuah, Hua Nong Ting, Shier Nee Saw, Jun Zhao

“…Additionally, cross-channel and cross-spatial shuffle operations enhance the interaction between local details and global information efficiency while suppressing redundant information. …”
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An electricity price optimization model considering time-of-use and active distribution network efficiency improvements by Yan Li, Yaheng Su, Qixin Zhao, Bala Wuda, Kaibo Qu, Lei Tang

“…We aimed to optimize energy costs, improve system response speed, and reduce price volatility, thereby achieving more efficient energy utilization and economic benefits.…”
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Fault Diagnosis Method for Vacuum Contactor Based on Time-Frequency Graph Optimization Technique and ShuffleNetV2 by Haiying Li, Qinyang Wang, Jiancheng Song

“…Finally, considering the advantages of the channel split and channel shuffle methods, the ShuffleNetV2 network is adopted to improve the feature learning ability and identify fault categories. …”
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GaitCSF: Multi-Modal Gait Recognition Network Based on Channel Shuffle Regulation and Spatial-Frequency Joint Learning by Siwei Wei, Xiangyuan Xu, Dewen Liu, Chunzhi Wang, Lingyu Yan, Wangyu Wu

“…The channel shuffle-based feature selective regulation module achieves cross-channel information interaction and feature enhancement through channel grouping and feature shuffling strategies. …”
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Dense-ShuffleGCANet: An Attention-Driven Deep Learning Approach for Diabetic Foot Ulcer Classification Using Refined Spatio-Dimensional Features by Armaano Ajay, Akshaj Singh Bisht, R. Karthik

“…This research introduces a novel model, Dense-ShuffleGCANet, for DFU detection by leveraging DenseNet-169, Channel-Centric Depth-wise Group Shuffle (CCDGS) block, and triplet attention. …”
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Lévy Flight Shuffle Frog Leaping Algorithm Based on Differential Perturbation and Quasi-Newton Search by Xinming Zhang, Zihao Fu, Haiyan Chen, Wentao Mao, Shangwang Liu, Guoqi Liu

“…Lévy flight Shuffle Frog Leaping Algorithm (LSFLA) is a SFLA variant and enhances the performance of SFLA largely, however, it still has some defects, such as poor convergence and low efficiency. …”
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Shuffle window transformer DeepLabV3+: a lightweight convolutional neural network and transformer based hybrid semantic segmentation network by Yane Li, Zhichao Chen, Hongxia Qi, Ming Fan, Lihua Li

“…When the window size is fixed, by integrating window attention (WA) and shuffle WA mechanisms, cross-window global context modeling with linear computational complexity is achieved. …”
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Development of robust constitutive synthetic promoter using genetic resources of plant pararetroviruses by Tsheten Sherpa, Tsheten Sherpa, Nrisingha Dey

“…In this work, we attempted to increase the constitutive promoter library by developing efficient synthetic promoters suitable for high-level gene expression. …”
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Adaptive Taylor Kolmogorov–Arnold Network for Hyperspectral Image Classification by Yichen Liu, Xin Zhang, Zitong Zhang, Hanlin Feng

“…In addition, ATKAN incorporates adaptive feature shifts along the spatial axis, channel grouping, and shuffling mechanisms to improve information processing efficiency and reduce computational consumption. …”
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A CNN-SA-GRU Model with Focal Loss for Fault Diagnosis of Wind Turbine Gearboxes by Liqiang Wang, Shixian Dai, Zijian Kang, Shuang Han, Guozhen Zhang, Yongqian Liu

“…Specifically, a CNN-SA-GRU network is constructed to extract both spatial and temporal features, in which CNN is employed to extract local spatial features from SCADA data, Shuffle Attention is integrated to efficiently fuse channel and spatial information and enhance spatial representation, and GRU is utilized to capture long-term spatiotemporal dependencies. …”
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Rapid and accurate detection of peanut pod appearance quality based on lightweight and improved YOLOv5_SSE model by Zhixia Liu, Xilin Zhong, Chunyu Wang, Guozhen Wu, Fengyu He, Jing Wang, Dexu Yang

“…The CSPDarkNet53 network of the YOLOv5s model was substituted with the ShuffleNetv2 backbone network to reduce the model’s weight. …”
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TSAS—YOLOv8: An Optimization Detection Model for Capturing Small Target Features and Processing Key Information by Yongbo Yuan, Linlin Cao

“…In summary, TSAS-YOLOv8 exhibits efficiency and feasibility in small target detection tasks.…”
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