Neuroprotective effects of MIP-15 peptide from Morchella importuna against ischemic-hypoxic brain damage in rats by Chuan Xiong, Ping Li, Qiang Luo, Chia Wei Phan, Xin Jin, Wenli Huang

“…Subsequently, an oxygen glucose deprivation/reoxygenation (OGD/R) model was established in rat primary hippocampal neurons to explore the mechanism of MIP-15 action. …”
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Multi‐stage attention network for monaural speech enhancement by Kunpeng Wang, Wenjing Lu, Peng Liu, Juan Yao, Huafeng Li

“…To tackle such a problem, we divide the speech enhancement model into multiple stages, with a special attention mechanism introduced in each stage so that the detailed speech information is retained and the features of high levels are captured. …”
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Future Drought Change Characteristics and Population Exposure Estimation in the Xijiang River Basin of Guangxi Based on CMIP6 by Qin Nianxiu, Wen Feng, Wang Junneng, He Jiye, Jiang Tong

“…The findings are as follows: (1) By employing Taylor diagrams to evaluate the multi-model ensemble mean method (MME) of 18 CMIP6 climate models, we found that the method effectively simulated temperature and precipitation in the Xijiang River Basin, reducing the uncertainty associated with single-model simulations. …”
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Extraction of typical oyster pile columns in the Maowei Sea, Beibu Gulf, based on unmanned aerial vehicle laser point cloud orthophotos by Jinze Du, Jinze Du, Meiqin Huang, Zhenjun Kang, Zhenjun Kang, Zhenjun Kang, Yichao Tian, Yichao Tian, Yichao Tian, Jin Tao, Jin Tao, Qiang Zhang, Qiang Zhang, Yutong Xie, Yutong Xie, Yutong Xie, Jinying Mo, Jinying Mo, LiYan Huang, LiYan Huang, Yusheng Feng, Yusheng Feng

“…By employing band features and texture indices extracted from UAV’s multi-spectral images as data sources and combining them with a classification and prediction model based on deep learning convolutional neural networks (CNN), we successfully extract the desired oyster pile columns. …”
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Multi-source Data-driven Analysis of Deformation and Influencing Factors for Expansive Soil Canal Slopes by ZHANG Yuhan, HU Jiang, LI Xing

“…Furthermore, a self-explaining neural network (SENN) model incorporating an attention mechanism is developed to predict canal slope deformation. …”
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HIBRID: histology-based risk-stratification with deep learning and ctDNA in colorectal cancer by Chiara M. L. Loeffler, Hideaki Bando, Srividhya Sainath, Hannah Sophie Muti, Xiaofeng Jiang, Marko van Treeck, Nic Gabriel Reitsam, Zunamys I. Carrero, Asier Rabasco Meneghetti, Tomomi Nishikawa, Toshihiro Misumi, Saori Mishima, Daisuke Kotani, Hiroya Taniguchi, Ichiro Takemasa, Takeshi Kato, Eiji Oki, Yuan Tanwei, Wankhede Durgesh, Sebastian Foersch, Hermann Brenner, Michael Hoffmeister, Yoshiaki Nakamura, Takayuki Yoshino, Jakob Nikolas Kather

“…Circulating tumor DNA (ctNDA) detects molecular residual disease (MRD), but lacks spatial and tumor microenvironment information. Here, we develop a deep learning (DL) model to predict disease-free survival from hematoxylin & eosin stained whole slide images in stage II-IV colorectal cancer. …”
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Does forest management certification halt forest loss at country level? A global analysis by Saskia Dröge, Charline Depoorter, Axel Marx, Bart Muys

“…No significant association between certification coverage and forest loss reduction was found at the country level. These findings were limited by the lack of publicly available data on certified forests at high spatial resolution and the use of aggregated country-level certification coverage, which might obscure regional (sub-country) effects. …”
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Facial Feature Extraction Algorithm Based on Improved YOLOv7-Tiny by Yining Yao, Yawen Wang, Changyuan Wang, Yibo Zhang, Tingting Liu, Gaofeng Wang

“…The proposed algorithm achieves a detection accuracy of 59.8% mAP on the Drowsy-Driving-Det dataset, marking an 8.5% improvement over the original method, alongside a 60.9% reduction in model parameters. This improved algorithm not only meets real-time deployment requirements but also maintains high detection accuracy, making it well-suited for facial fatigue feature extraction in complex driving environments and edge deployment scenarios.…”
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Ultra‐Flexible µ‐ECoG Arrays Based on PEDOT:PSS Micropillars by Alice Lunghi, Michele Bianchi, Pierpaolo Greco, Riccardo Viaro, Michele Di Lauro, Luciano Fadiga, Fabio Biscarini

“…Advances in microfabrication techniques allow a dramatic reduction in electrode size, leading to highly‐dense microelectrode arrays with increased spatial resolution. …”
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Assessing Land Use Intensity and Ecosystem Service Dynamics in Citarum Watershed, Indonesia by Irmadi Nahib, Widiatmaka Widiatmaka, Suria Darma Tarigan, Wiwin Ambarwulam, Fadhlullah Ramadhani

“…Specifically, researchers analyze (1) the shifts in LULC and key ES components—water yield (WY), soil conservation (SC), and carbon storage (CS)—from 2010 to 2020, (2) the spatial relationship between land use intensity (LUI) and ES, and (3) the synchronization and distribution patterns of LUI and TES using a coupling coordination degree (CCD) model. …”
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Design Features of a Removable Module Intended for Securing Containers When Transported in an Open Wagon by Alyona Lovska, Juraj Gerlici, Ján Dižo, Mykhailo Pavliuchenkov, Pavlo Rukavishnikov

“…It was established based on the preformed calculations that this optimization contributes to reducing the unit mass of the frame beam by 1.5% compared to using a typical rectangular profile. A spatial model of the removable module was built, and its strength was calculated considering the results of the optimization process. …”
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A Comprehensive Analysis of the Loss Mechanism and Thermal Behavior of a High-Speed Magnetic Field-Modulated Motor for a Flywheel Energy Storage System by Qianli Mai, Qingchun Hu, Xingbin Chen

“…Experimental validation confirms model accuracy with mean absolute percentage errors below 4.2%. …”
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Spatio-temporal Variation and Influencing Factors of CO2 Emission at County Scale in Shaanxi Province Based on Land Use Change by Cao Zhouliang, Zhang Xinrong, Yuan Xuefeng, Chen Jinhong

“…The evolution of carbon emission can be divided into two stages (substantial growth and slow growth). ② The carbon emission center gradually moved to the northeast from 2000 to 2020, and the spatial distribution range showed an expansion trend. …”
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TCCDNet: A Multimodal Pedestrian Detection Network Integrating Cross-Modal Complementarity with Deep Feature Fusion by Shipeng Han, Chaowen Chai, Min Hu, Yanni Wang, Teng Jiao, Jianqi Wang, Hao Lv

“…Specifically, the efficient multi-scale attention C2f (EMAC) is designed for the backbone, which combines the C2f structure with an efficient multi-scale attention mechanism to achieve feature weighting and fusion, thereby enhancing the model’s feature extraction capacity. Subsequently, the cross-modal complementarity (CMC) module is proposed, which enhances feature discriminability and object localization accuracy through a synergistic mechanism combining channel attention and spatial attention. …”
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Built environment characteristics and driving speed in 30 km/h zones: a Dutch national analysis by Paul Schepers, Werner van Loo, Wouter Mieras, Hans Drolenga, Dick de Waard, Marco Helbich

“…This requires understanding how built environment characteristics are associated with driving speeds, but only a few studies, typically based on small samples, focus on 30 km/h streets. Using a spatial error model, this study examines the relationship between built environment factors and 85th percentile speeds on 47,000 km of Dutch 30 km/h streets (N=159.000). …”
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Semantic SLAM using laser-vision data fusion: Enhancing autonomous navigation in unstructured environments by Ning Chen, Dong Wei, Dongsheng Lin, Linhan Lin

“…For semantic segmentation, the PSPNet_CBAM network framework was developed, achieving improvements of 0.73 %, 0.14 %, and 0.92 % in mean Accuracy (mAcc), Pixel Accuracy (PAcc), and mean Intersection over Union (mIoU), respectively, on the Cityscapes dataset compared to the original PSPNet model. For the fusion of visual and laser information, NN-SLAM demonstrated a reduction in filter processing time by 0.2440 ms and 3.6497 ms on the KITTI 0011 and 0093 datasets, respectively, when compared to the Visual-Lidar Odometry and Mapping (ALOAM) algorithm. …”
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Community Engagement in the Management of Urban Green Spaces: Prospects from a Case Study in an Emerging Economy by Adriano Bressane, Anna Isabel Silva Loureiro, Ricardo Almendra

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Patterns of dolphin bycatch in a north-western Australian trawl fishery. by Simon J Allen, Julian A Tyne, Halina T Kobryn, Lars Bejder, Kenneth H Pollock, Neil R Loneragan

“…Over the entire datasets, observer reported bycatch rates (n = 52 dolphins in 4,124 trawls, or 12.6 dolphins/1,000 trawls) were ca. double those reported by skippers (n = 180 dolphins in 27,904 trawls, or 6.5 dolphins/1,000 trawls). Generalised Linear Models based on observer data, which better explained the variation in dolphin bycatch, indicated that the most significant predictors of dolphin catch were: (1) vessel--one trawl vessel caught significantly more dolphins than three others assessed; (2) time of day--the lowest dolphin bycatch rates were between 00:00 and 05:59; and (3) whether nets included bycatch reduction devices (BRDs)--the rate was reduced by ca. 45%, from 18.8 to 10.3 dolphins/1,000 trawls, after their introduction. …”
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Influence of crosspolarization of dual-polarized antenna elements on the ergodic capacity of a multichannel system by Ekaterina V. Averina, Ksenia V. Smuseva, Pavel A. Tokarev, Grigory K. Uskov

“…These effects can lead to a significant reduction in communication system capacity. Aim. Derive relationships based on the Kronecker model that allow to take into account the polarization properties of antenna elements when calculating the ergodic capacity of a multi-channel communication system. …”
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YOLO-BCD: A Lightweight Multi-Module Fusion Network for Real-Time Sheep Pose Estimation by Chaojie Sun, Junguo Hu, Qingyue Wang, Chao Zhu, Lei Chen, Chunmei Shi

“…Comparative evaluations demonstrate significant improvements over baseline models, achieving 91.7% recognition accuracy with 389 FPS processing speed while maintaining 19.2% parameter reduction and 32.1% lower computational load compared to standard YOLOv8. …”
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