Analysis of Beams with a Three-dimensional Random Field of the Modulus of Elasticity Using the Stochastic Finite Element Method by Nguyen Dang Diem, Pham Van Dat, Ta Duy Hien

“…This study proposes a stochastic finite element method (SFEM) for analyzing the static response of beams with material properties modeled as three-dimensional spatial random fields. …”
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Factors Contributing to Slab Locations and Geometries in Reconstructions of Past Mantle Flow by Joshua Weber, Nicolas Flament

“…We quantify the spatial match between predicted present‐day mantle temperature anomalies and vote maps of tomographic models. …”
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Novel non-invasive algorithm to identify the origins of re-entry and ectopic foci in the atria from 64-lead ECGs: A computational study. by Erick A Perez Alday, Michael A Colman, Philip Langley, Henggui Zhang

“…Catheter ablation therapy, commonly used in the treatment of AF, requires spatial information on atrial electrical excitation. …”
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Geographic patterns and environmental factors associated with human yellow fever presence in the Americas. by Patricia Najera Hamrick, Sylvain Aldighieri, Gustavo Machado, Deise Galan Leonel, Luz Maria Vilca, Sonia Uriona, Maria Cristina Schneider

“…Geo-environmental factors associated with presence of yellow fever could help predict and adjust the limits of other risk areas of epidemiological concern.…”
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Driving Factors in the Development of Eye Movement Patterns in Chinese Reading: The Roles of Linguistic Ability and Oculomotor Maturation by Meihua Guo, Nina Liu, Jingen Wu, Chengchieh Li, Guoli Yan

“…Path analysis explored how these factors predict temporal and spatial eye movement measures. …”
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Wide-field electromagnetic method for deep hot dry rock fracturing monitoring: penetrating thick low-resistivity overburden by Shiyin Gao, Shiyin Gao, Wubing Deng, Juncheng Wang, Juncheng Wang, Juncheng Wang, Juncheng Wang, Mingzuan Xu, Mingzuan Xu

“…Key acquisition parameters were optimized via forward modeling, including transmitter-receiver distance (*r* = 15 km), current (I = 130 A), and electrode spacing (AB = 3,000 m, MN = 100 m).ResultsField data revealed distinct resistivity reduction zones (1,000→25 Ωm) spatially correlated with active fracturing wells, demonstrating WFEM’s ability to detect fluid-induced anomalies (Δρ up to 30%). …”
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Has the Three Gorges Reservoir Impacted Regional Moisture Recycling? by Jia Wei, Weiguang Wang, Mingzhu Cao, Jianyun Zhang, Junliang Jin, Guoqing Wang, Hongbin Li, Xiaolong Pan, Zongchao Ye, Adriaan J. Teuling, Shuo Wang

“…Although different forcing data and simulation time steps show good agreement in spatial and temporal variations in the recycled moisture, the local ERRs are larger when calculated from the UTrack model than from the WAM‐2layers model.…”
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LKVHAN: Multiscale Large Kernel Vertical-Horizontal Attention Network for Hyperspectral Image Classification by Xun Liu, Alex Hay-Man Ng, Xuejiao Liao, Fangyuan Lei, Jinchang Ren, Linlin Ge

“…Among deep learning-based hyperspectral image (HSI) classification models, convolutional neural networks (CNNs), transformers, Mamba, and large kernel CNNs (LKCNNs) models have been widely explored for HSI classification (HSIC). …”
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Synergistic hyperspectral and SAR imagery retrieval of mangrove leaf area index using adaptive ensemble learning and deep learning algorithms by Jun Sun, Weiguo Jiang, Bolin Fu, Hang Yao, Huajian Li

“…We further evaluated the performance of OHS (Zhuhai-1) hyperspectral and GF-3 SAR images in mapping the spatial distribution of the LAI in mangroves. Finally, the outputs of the AELR and DNNR models were interpreted, and the interactions between different image features were clarified to select the sensitive spectral ranges and vegetation indexes for estimating the mangrove LAI using SHAP (Shapley additive explanation). …”
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Dysfunctional immunity in the comorbidity of vitamin A deficiency and tuberculosis identifies therapeutic potential of vitamin A supplementation by Dr. Faye Lanni, Revanth Krishna, David Forrest Ackart, Victoria Mitcham, Sarah K Cooper, Connor King, Hadley Gary, Dr. Chuangqi Wang, Dr Brendan Podell

“…Methods: Using the guinea pig model of vitamin A deficiency and TB comorbidity, we evaluated immune parameters across multiple stages of TB disease progression. …”
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Sensitivity Analysis and Optimization of Urban Roundabout Road Design Parameters Based on CFD by Hangyu Zhang, Sihui Dong, Shiqun Li, Shuai Zheng

“…Secondly, the response surface model between the road design parameters of the roundabout and the pressure in the ring is constructed. …”
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Enhancing Regional Topsoil Total Nitrogen Mapping Through Differentiated Fusion of Ground Hyperspectral Data and Satellite Images Under Low Vegetation Cover by Rongpeng He, Jihua Meng, Yanfei Du, Zhenxin Lin, Xinyan You, Xinyu Gao

“…However, due to the weak spectral signals of STN and its spatial heterogeneity, hyperspectral imaging technology presents significant potential for high-resolution measurements and precise characterization of STN heterogeneity. …”
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Algebraic Non-Hermitian Skin Effect and Generalized Fermi Surface Formula in Arbitrary Dimensions by Kai Zhang, Chang Shu, Kai Sun

“…We demonstrate this framework in both tight-binding and continuum models in two and three dimensions. This investigation not only unveils a novel category of the non-Hermitian skin effect but also offers a comprehensive theoretical structure that describes skin effects in any non-Hermitian system, irrespective of its spatial dimensionality.…”
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WCMU-net: An Effective Method for Reducing the Impact of Speckle Noise in SAR Image Change Detection by Yu Duan, Kaimin Sun, Wangbin Li, Jinjiang Wei, Song Gao, Yingjiao Tan, Wanghui Zhou, Jun Liu, Junyi Liu

“…To address the issue of low SNR in SAR images, we construct a deep learning network focused on feature exploration and utilization to effectively distinguish between noise and change information. The model initially employs discrete wavelet decomposition to extract high-frequency bands for noise reduction. …”
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Expanding the boundaries: regional connectivity in green turtle (Chelonia mydas) populations across Micronesia by Michael P. Jensen, Michael P. Jensen, Erin L. LaCasella, Donald R. Kobayashi, Amy Frey, Tammy M. Summers, Jessy R. Hapdei, George H. Balazs, Shawn K. K. Murakawa, T. Todd Jones, Summer L. Martin, Alexander R. Gaos, Peter H. Dutton

“…To address this gap, our study conducted genetic analyses of green turtles in foraging grounds from four Micronesian sites and predictive modelling of hatchling dispersal patterns based on ocean currents to explore connectivity between foraging grounds and rookeries across Micronesia. …”
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Land Use Optimization and Simulation in the Dongjiang River Basin with a Low-Carbon Orientation by He Yanhu, Wu Luyan, Lin Zeyu

“…This study first optimizes the land use structure in the basin under two low-carbon development scenarios, maximizing carbon sinks and minimizing carbon emissions. A CA-Markov model was employed to simulate the land use spatial pattern in the river basin for 2025. …”
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Adjusting for seasonal harvest bias in the lactation index for white‐tailed deer management by Kamen L. Campbell, Bronson K. Strickland, Stephen Demarais, Phillip D. Jones, Guiming Wang, Chad M. Dacus, Christopher Cook, W. Matt Knox

“…We grouped populations into 18 regions based on median conception date and tested 12 models using year and region as possible random effects. …”
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Urban buzz or urban bust? Beekeeping challenges, suitability, and survival insights in Montreal, Canada by Navid Mahdizadeh Gharakhanlou, Julien Vadnais, Liliana Perez, Nico Coallier

“…These results confirm RSF's superior performance and suggest that broader spatial context may enhance prediction accuracy. Additionally, our findings revealed that the surrounding hive count was the strongest predictor of beehive survival in both buffer scenarios. …”
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Multitask Change-Aware Network and Semisupervised Enhanced Multistep Training for Semantic Change Detection by Yifei Si, Jie Jiang

“…Compared to binary change detection, which only predicts the location of changes, SCD provides detailed from-to change information, helping to gain a comprehensive understanding and analysis of land cover and land use. …”
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Methodology of nonlinearity in the research of the phenomenon of the innovation space of higher school expansion by O. Voeykova

“…It allows combining both stationary and dynamic components of the nonlinear process under study in one model, revealing the nature of changes in both spatial and temporal properties accompanying the modification of higher education integrated into the global innovation space.The study’s results are presented as the parameters of the nonlinearity of the university innovation space expansion. …”
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