High-Fat-Diet-Induced Metabolic Disorders: An Original Cause for Neurovascular Uncoupling Through the Imbalance of Glutamatergic Pathways by Manon Haas, Maud Petrault, Patrick Gele, Thavarak Ouk, Vincent Berezowski, Olivier Petrault, Michèle Bastide

“…Here we report that glutamatergic vasoactive pathways are a key feature of high-fat-diet (HFD)-induced neurogliovascular uncoupling in mice. …”
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Underground personnel detection and tracking using improved YOLOv7 and DeepSORT by Weiqiang FAN, Xuejin WANG, Yinghui ZHANG, Xiaoyu LI

“…The underground monitoring video is susceptible to interference factors such as artificial light source, dust and spray, which leads to the poor real-time performance, high missed & false detection rates and poor tracking accuracy of existing underground personnel monitoring methods using computer vision. …”
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Investigation of an Efficient Multi-Class Cotton Leaf Disease Detection Algorithm That Leverages YOLOv11 by Fangyu Hu, Mairheba Abula, Di Wang, Xuan Li, Ning Yan, Qu Xie, Xuedong Zhang

“…Specifically, the U-Net v2 module is embedded in the backbone network to boost the multi-scale feature extraction performance in YOLOv11. Meanwhile, the CBAM attention mechanism is integrated to emphasize critical disease-related features. …”
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DualBranch-AMR: A Semi-Supervised AMR Method Based on Dual-Student Consistency Regularization with Dynamic Stability Evaluation by Jiankun Ma, Zhenxi Zhang, Linrun Zhang, Yu Li, Haoyue Tan, Xiaoran Shi, Feng Zhou

“…Modulation recognition, as one of the key technologies in the field of wireless communications, holds significant importance in applications such as spectrum resource management, interference suppression, and cognitive radio. While deep learning has substantially improved the performance of Automatic Modulation Recognition (AMR), it heavily relies on large amounts of labeled data. …”
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Adversarial Sample Generation Method Based on Frequency Domain Transformation and Channel Awareness by Yalin Gao, Dongwei Xu, Huiyan Zhu, Qi Xuan

“…In OFDM wireless communication systems, low-resolution channel characteristics and noise interference pose significant challenges to accurate channel estimation. …”
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The Spatio-Temporal Equalization Sliding-Window Distribution Distance Maximization Based on Unsupervised Learning for Online Event-Related Potential-Based Brain–Computer Interfaces... by Haoye Wang, Jing Jin, Xinjie He, Shurui Li, Andrzej Cichocki

“…STE estimates and removes colored noise interference in background noise to enhance the signal-to-noise ratio of inputs for sDDM. …”
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ACD-Net: An Abnormal Crew Detection Network for Complex Ship Scenarios by Zhengbao Li, Heng Zhang, Ding Gao, Zewei Wu, Zheng Zhang, Libin Du

“…The CBAM attention mechanism is introduced to reduce the interference of background features and improve the accuracy of real-time detection of crew abnormal behavior. …”
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A Novel Dynamic Characteristic for Detecting Breathing Cracks in Blades Based on Vibration Response Envelope Analysis by Minghao Pan, Yongmin Yang, Fengjiao Guan, Haifeng Hu, Zifang Bian, Wenkang Huang, Bohao Xiao, Ang Li

“…Moreover, this characteristic is less susceptible to signal noise interference compared to other dynamic characteristics, enhancing its potential for crack diagnosis in engineering applications.…”
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Terahertz-ViSAR-based Imaging of Passive Jamming Objects by Lei FAN, Qi YANG, Hongqiang WANG, Jun YI

“…Next, considering rotating corner reflectors as an example of moving passive jamming objects, their characteristics regarding suppressive interference are analyzed. Considering that stationary scenes feature similarity under adjacent apertures, rotating corner reflectors can be directly detected by incoherent image subtraction after inter-frame image and amplitude registrations, followed by the extraction of signals of interest and non-parametrical compensation. …”
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FASI-Net: Frequency-Domain Information Assisted Semantic Interaction Network for Bitemporal Remote Sensing Images Change Detection by Fenglei Chen, Haijun Liu, Zhihong Zeng, Xiaoheng Tan

“…However, existing approaches are susceptible to interference from irrelevant factors such as shadows, noise, and unrelated changes in a single temporal, which limits the model's ability to accurately perceive bitemporal crucial semantic information. …”
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DEDANet: Mountainous Cropland Extraction From Remote Sensing Imagery With Detail Enhancement and Distance Attenuation by Liang Huang, Zixuan Zhang, You Yu, Bo-Hui Tang

“…Into the encoding stage, introduced is a detail enhancement convolution module that amplifies high-frequency edge information; through a multibranch feature extraction pathway fusing five distinct convolutional types, the model significantly enhances its sensitivity and representational capacity for irregular cropland boundaries. …”
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A Novel Parallel Multi-Scale Attention Residual Network for the Fault Diagnosis of a Train Transmission System by Yong Chang, Tengfei Gao, Juanhua Yang, Zongyao Liu, Biao Wang

“…However, the noise interference and multi-scale signal characteristics generated by the train transmission system under non-stationary conditions make it difficult for the network model to effectively learn fault features, resulting in a decrease in the accuracy and robustness of the network. …”
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Automatic Detection of Landslide Surface Cracks from UAV Images Using Improved U-Network by Hao Xu, Li Wang, Bao Shu, Qin Zhang, Xinrui Li

“…The model enhances the extraction of crack features by integrating residual blocks and attention mechanisms within the encoder. …”
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SSUM: Spatial–Spectral Unified Mamba for Hyperspectral Image Classification by Song Lu, Min Zhang, Yu Huo, Chenhao Wang, Jingwen Wang, Chenyu Gao

“…The SSUM model is composed of two branches, i.e., the Spectral Mamba branch and the Spatial Mamba branch, designed to extract the features of HSIs from both spectral and spatial perspectives. …”
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Research on Innovative Apple Grading Technology Driven by Intelligent Vision and Machine Learning by Bo Han, Jingjing Zhang, Rolla Almodfer, Yingchao Wang, Wei Sun, Tao Bai, Luan Dong, Wenjing Hou

“…The system aims to reduce human interference and enhance grading efficiency and accuracy. …”
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Meta surface Assisted Open radio access networks by 7haneenabdulrahman7 ajeel, Ismail Hburi

“…This approach can achieve flexibility in the environment of a high-traffic transmission while lowering the interference between radio units and the signalling burden required for beamforming tasks. …”
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An enhanced lightweight model for apple leaf disease detection in complex orchard environments by Ge Wang, Wenjie Sang, Fangqian Xu, Yuteng Gao, Yue Han, Qiang Liu

“…In order to enhance the network’s anti-interference ability in complex backgrounds and its capacity to differentiate between similar diseases, we incorporate an Efficient Multi-Scale Attention (EMA) within the deep structure of the network for in-depth feature extraction. …”
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RDAU-Net: A U-Shaped Semantic Segmentation Network for Buildings near Rivers and Lakes Based on a Fusion Approach by Yipeng Wang, Dongmei Wang, Teng Xu, Yifan Shi, Wenguang Liang, Yihong Wang, George P. Petropoulos, Yansong Bao

“…First, we designed a residual dynamic short-cut down-sampling (RDSC) module to minimize the interference of complex building shapes and building scale differences on the segmentation results; second, we reduced the semantic and resolution gaps between multi-scale features using a multi-channel cross fusion transformer module (MCCT); finally, a double-feature channel-wise fusion attention (DCF) was designed to improve the model’s ability to depict building edge details and to reduce the influence of similar features on the model. …”
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YOLO-DKM: A Flame and Spark Detection Algorithm Based on Deep Learning by Linpo Shang, Xufei Hu, Zijian Huang, Qiang Zhang, Zhiyu Zhang, Xin Li, Yanzuo Chang

“…And integrate DSConv into the C2f module, relying on dynamic characteristics to adaptively adjust convolution operations according to different scene features for more flexible local region feature extraction, capturing local features of flames and sparks. …”
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Deep Learning-Based Sound Source Localization: A Review by Kunbo Xu, Zekai Zong, Dongjun Liu, Ran Wang, Liang Yu

“…In marine scenarios, complex-valued convolutional networks combined with adversarial transfer learning mitigate environmental mismatch and multipath interference through phase information fusion and domain adaptation strategies. …”
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