An Improved YOLOv8n-Based Method for Detecting Rice Shelling Rate and Brown Rice Breakage Rate by Zhaoyun Wu, Yehao Zhang, Zhongwei Zhang, Fasheng Shen, Li Li, Xuewu He, Hongyu Zhong, Yufei Zhou

“…The backbone’s C2f module is replaced with C2f-Faster-CGLU, integrating partial convolution (PConv) local convolution and convolutional gated linear unit (CGLU) gating to reduce computational redundancy via sparse interaction and enhance small-target feature extraction. …”
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Codomains for the Cauchy-Riemann and Laplace operators in ℝ2 by Lloyd Edgar S. Moyo

“…A codomain for a nonzero constant-coefficient linear partial differential operator P(∂) with fundamental solution E is a space of distributions T for which it is possible to define the convolution E*T and thus solving the equation P(∂)S=T. …”
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GPC-YOLO: An Improved Lightweight YOLOv8n Network for the Detection of Tomato Maturity in Unstructured Natural Environments by Yaolin Dong, Jinwei Qiao, Na Liu, Yunze He, Shuzan Li, Xucai Hu, Chengyan Yu, Chengyu Zhang

“…This study proposes a C2f-PC module based on partial convolution (PConv) for less computation, which replaced the original C2f feature extraction module of YOLOv8n. …”
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Fast real-time detection and counting of thrips in greenhouses with multi-level feature attention and fusion by Zhangzhang He, Xinyue Chen, Ying Gao, Yu Zhang, Yuheng Guo, Tong Zhai, Xiaochen Wei, Huan Li, Haipeng Zhu, Yongkun Fu, Zhiliang Zhang, Zhiliang Zhang

“…First, we propose a lightweight backbone network, PartialNeXt, which optimizes convolution layers through Partial Convolution (PConv), ensuring both network performance and reduced complexity. …”
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Smoke Detection Transformer: An Improved Real-Time Detection Transformer Smoke Detection Model for Early Fire Warning by Baoshan Sun, Xin Cheng

“…This approach improves Partial Convolution (PConv) by using a selection strategy that selects channels containing more information for each convolution, thereby increasing the network’s ability to learn smoke features. …”
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A one-stage anchor-free keypoints detection model for fast electric vehicle charging port detection and pose extraction by Feifei Hou, Qiwen Meng, Xinyu Fan, Yijun Wang

“…To address these issues, this study introduces FasterEVPoints, a state-of-the-art convolutional neural network (CNN) model integrating partial convolution (PConv) with FasterNet. …”
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Application of Modified Laplace Variational Iteration Hybrid Approach for Solving Time-Fractional Fourth-Order Parabolic PDEs by Mehari Fentahun Endalew, Xiaoming Zhang

“…This approach is essential for specifying the Lagrange multiplier without applying integration or convolution methods via recurrence relations. Ultimately, we observed that the method employed for tackling fractional-order partial differential equations is more precise, simple, and computationally efficient. …”
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MGL-YOLO: A Lightweight Barcode Target Detection Algorithm by Yuanhao Qu, Fengshou Zhang

“…However, deploying deep convolutional neural networks on embedded and some edge devices is very challenging due to limited storage space and computational resources. …”
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YOLO-UIR: A Lightweight and Accurate Infrared Object Detection Network Using UAV Platforms by Chao Wang, Rongdi Wang, Ziwei Wu, Zetao Bian, Tao Huang

“…Compared with the existing C2f module, the introduction of Partial Convolution reduces the model’s parameter count while maintaining high detection accuracy. …”
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Airport Clearance Detection Based on Vision Transformer and Multi-Scale Feature Fusion by Yutong Chen, Yufen Liu, Zhixiong Guo, Qiang Gao

“…Secondly, to improve the feature extraction ability, partial convolution is replaced with dynamic convolution, and attention is introduced to the convolution kernel from four dimensions. …”
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PA2E: Real-Time Anomaly Detection With Hyperspectral Imaging for Food Safety Inspection by Jungi Lee, Myounghwan Kim, Jiseong Yoon, Kwangsun Yoo, Seok-Joo Byun

“…However, existing methods often use fully connected layers that do not leverage local spectral similarities or convolutional layers that overlook positional importance due to transform invariance, leading to performance limitations. …”
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SWMD-YOLO: A Lightweight Model for Tomato Detection in Greenhouse Environments by Quan Wang, Ye Hua, Qiongdan Lou, Xi Kan

“…The model integrates switchable atrous convolution (SAConv) and wavelet transform convolution (WTConv) for the dynamic adjustment of receptive fields for occlusion-adaptive feature extraction and to decompose features into multi-frequency sub-bands, respectively, thus preserving critical edge details of obscured targets. …”
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Multi-Strategy Enhancement of YOLOv8n Monitoring Method for Personnel and Vehicles in Mine Air Door Scenarios by Lei Zhang, Hongjing Tao, Zhipeng Sun, Weixun Yi

“…Firstly, the Faster Block module, which incorporates partial convolution (PConv), is integrated with the C2f module of the backbone network. …”
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SPL-YOLOv8: A Lightweight Method for Rape Flower Cluster Detection and Counting Based on YOLOv8n by Yue Fang, Chenbo Yang, Jie Li, Jingmin Tu

“…Additionally, a lightweight Partial Group Convolution Detection Head (PGCD) is proposed, which employs Partial Convolution combined with Group Normalization to enable multi-scale feature interaction. …”
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YOLOv8n-SMMP: A Lightweight YOLO Forest Fire Detection Model by Nianzu Zhou, Demin Gao, Zhengli Zhu

“…Key innovations include the following: introducing the SlimNeck solution to streamline the neck network by replacing conventional convolutions with Group Shuffling Convolution (GSConv) and substituting the Cross-convolution with 2 filters (C2f) module with the lightweight VoV-based Group Shuffling Cross-Stage Partial Network (VoV-GSCSP) feature extraction module; integrating the Multi-dimensional Collaborative Attention (MCA) mechanism between the neck and head networks to enhance focus on fire-related regions; adopting the Minimum Point Distance Intersection over Union (MPDIoU) loss function to optimize bounding box regression during training; and implementing selective channel pruning tailored to the modified network architecture. …”
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A Novel Dual-Branch Global and Local Feature Extraction Network for SAR and Optical Image Registration by Xuanran Zhao, Yan Wu, Xin Hu, Zhikang Li, Ming Li

“…First, considering the ability of the transformer for long-range dependence and the advantages of convolutional neural network for local feature extraction, GLFE-Net designs two branches: one uses an attention-discarded global feature extraction transformer (ADG Transformer) to extract global features from the entire SAR and optical images, and another uses a partial convolution unit with parameter-free attention mechanism (PCU-AM) to extract local features from the patches around the keypoints. …”
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Uniqueness Results of Semilinear Parabolic Equations in Infinite-Dimensional Hilbert Spaces by Carlo Bianca, Christian Dogbe

“…Specifically, the uniqueness of the viscosity solution is established by employing the inf/sup-convolution approach in a separable infinite-dimensional Hilbert space. …”
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A novel lightweight YOLOv8-PSS model for obstacle detection on the path of unmanned agricultural vehicles by Zhijian Chen, Yijun Fang, Jianjun Yin, Shiyu Lv, Farhan Sheikh Muhammad, Lu Liu

“…Firstly, this work integrates partial convolution (PConv) into the C2f module of the backbone network to improve inference performance and minimize computing load. …”
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DP-YOLO: A Lightweight Real-Time Detection Algorithm for Rail Fastener Defects by Lihua Chen, Qi Sun, Ziyang Han, Fengwen Zhai

“…First, we design a Depthwise Separable Convolution Stage Partial (DSP) module that integrates depthwise separable convolution with a CSP residual connection strategy, reducing model parameters while enhancing recognition accuracy. …”
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Detection of Soluble Solid Content in Citrus Fruits Using Hyperspectral Imaging with Machine and Deep Learning: A Comparative Study of Two Citrus Cultivars by Yuxin Xiao, Yuanning Zhai, Lei Zhou, Yiming Yin, Hengnian Qi, Chu Zhang

“…Traditional machine learning models (support vector machines and partial least squares regression) and deep learning models (convolutional neural networks, long short-term memory, and Transformer architectures) were evaluated for SSC prediction performance. …”
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