Influence of Source Shape on Semi-Airborne Transient Electromagnetic Surveys by Lei Liu, Jianghai Xie, Wentao Liu, Jianmei Zhou

“…A three-dimensional (3D) block model is established, and a model order reduction algorithm is applied to calculate the 3D spatial distribution of electromagnetic fields generated by both an ideal linear source and a curved source. …”
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Two different mechanisms support selective attention at different phases of training. by Sirawaj Itthipuripat, Kexin Cha, Anna Byers, John T Serences

“…Accordingly, the SDT-based model required noise reduction to account for the link between the stimulus-evoked visual responses and attentional modulations of behavior. …”
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Enhancing flood susceptibility mapping in Sana’a, Yemen with random forest and eXtreme gradient boosting algorithms by Yahia Alwathaf, Ahmed M. Al-Areeq, Yousef A. Al-Masnay, Ali R. Al-Aizari, Nabil M. Al-Areeq

“…Both models demonstrated high accuracy in predicting flood-prone areas, with RF achieving an accuracy of 92% and XGBoost slightly outperforming it with an accuracy of 94%. …”
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Pathways to Turbulent Dissipation in a Submarine Canyon by Charlotte Bellerjeau, Matthew H. Alford, Arnaud Le Boyer, Giovanni Dematteis, Alberto Naveira Garabato, Gunnar Voet, Nicole Couto, Bethan L. Wynne‐Cattanach

“…Fluxes from both methods agree within a factor of 3 with dissipation estimates from a finescale parameterization which is often used in climate‐scale ocean models. Coarse graining predicts 68% of energy fluxing to dissipation from frequencies lower than 8cpd, while the weakly nonlinear method predicts 34%. …”
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Vision transformer-based diagnosis of lumbar disc herniation with grad-CAM interpretability in CT imaging by Qingsong Chu, Xingyu Wang, Hao Lv, Yao Zhou, Ting Jiang

“…The performance of the model was further validated via gradient-weighted class activation mapping (Grad-CAM), providing interpretable insights into the regions of the CT scans that contributed to the model predictions. …”
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Mapping Soil Available Nitrogen Using Crop-Specific Growth Information and Remote Sensing by Xinle Zhang, Yihan Ma, Shinai Ma, Chuan Qin, Yiang Wang, Huanjun Liu, Lu Chen, Xiaomeng Zhu

“…These remote sensing variables were combined with soil sample data, crop type information, and crop growth period data as predictive factors and input into a Random Forest (RF) model optimized using the Optuna hyperparameter tuning algorithm. …”
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Vortex Avalanches and Collective Motion in Neutron Stars by I-Kang Liu, Andrew W. Baggaley, Carlo F. Barenghi, Toby S. Wood

“…We simulate the dynamics of about 600 quantum vortices in a spinning-down cylindrical container using a Gross–Pitaevskii model. For the first time, we find convincing spatial-temporal evidence of avalanching behavior resulting from vortex depinning and collective motion. …”
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AI-driven fusion of multimodal data for Alzheimer’s disease biomarker assessment by Varuna H. Jasodanand, Sahana S. Kowshik, Shreyas Puducheri, Michael F. Romano, Lingyi Xu, Rhoda Au, Vijaya B. Kolachalama

“…Our approach achieved an AUROC of 0.79 and 0.84 in classifying Aβ and τ status, respectively. Predicted PET status was consistent with various biomarker profiles and postmortem pathology, and model-identified regional brain volumes aligned with known spatial patterns of tau deposition. …”
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TourismNeuro xLSTM: neuro-inspired xLSTM for rural tourism planning and innovation by Jing Jiang, You Li, You Li

“…Our model integrates an extended Long Short-Term Memory (xLSTM) framework with spatial and temporal attention mechanisms and a memory module, enabling it to capture both short-term fluctuations and long-term trends in tourism data. …”
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Experimental study on the influence of shale content in fault zone on fault friction coefficient based on circular shear test by Lingdong Meng, Dong Li, Xiaofei Fu, Yejun Jin, Zezhao Liu, Ziyang Li, Tong Zhang, Ruishan Du, Xiaoling Zhang

“…By integrating a 3D prediction model of clay content on fault surfaces, the spatial distribution of non-uniform friction coefficients across fault planes was determined. …”
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Evaluating the impact of watershed management on surface soil moisture in the Kulfo watershed, Ethiopia by Aklilu Assefa Tilahun, Wondafrash Atnafu Zewde, Bizuayehu Abera Ersuncho

“…This study used remotely sensed data (Landsat images) to construct and apply the soil moisture index (SMI) model. The land surface temperature and vegetation index (LST-VI) spatial pixel distribution are interpreted via the trapezoid approach, which forms the basis of the model. …”
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Water-Body Monitoring and Erosion–Deposition Analysis of East Dongting Lake Based on Long Time-Series Remote Sensing Data by Lihua Wang, Jian Xu, Sunan Shi, Guozhong Li, Shiming Yao, Hualong Luan, Xiao Xiao, Zican He

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Evaluating stream power distribution along river longitudinal profiles using Log S – log A plots by Jui-Tien Tsai, Yen-Yu Chiu, Su-Chin Chen

“…A new two-parameter regression model is proposed, addressing inaccuracies in traditional models and providing a more-precise representation of river profiles. …”
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BathyFormer: A Transformer-Based Deep Learning Method to Map Nearshore Bathymetry with High-Resolution Multispectral Satellite Imagery by Zhonghui Lv, Julie Herman, Ethan Brewer, Karinna Nunez, Dan Runfola

“…The model learns to predict water depths by analyzing the spectral signatures and spatial patterns present in the multispectral imagery. …”
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Size Effect on Energy Characteristics of Axial Flow Pump Based on Entropy Production Theory by Hongliang Wang, Xiaofeng Wu, Xiao Xu, Suhao Bian, Fan Meng

“…By solving the unsteady Reynolds-averaged Navier–Stokes (URANS) equations with the Shear Stress Transport (SST) k-omega turbulence model, the external characteristic parameters and internal flow field structures were predicted. …”
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WetCH₄: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022 by Q. Ying, Q. Ying, B. Poulter, J. D. Watts, K. A. Arndt, A.-M. Virkkala, L. Bruhwiler, Y. Oh, Y. Oh, B. M. Rogers, S. M. Natali, H. Sullivan, A. Armstrong, A. Armstrong, E. J. Ward, E. J. Ward, L. D. Schiferl, C. D. Elder, C. D. Elder, O. Peltola, A. Bartsch, A. R. Desai, E. Euskirchen, M. Göckede, B. Lehner, M. B. Nilsson, M. Peichl, O. Sonnentag, E.-S. Tuittila, T. Sachs, T. Sachs, A. Kalhori, M. Ueyama, Z. Zhang, Z. Zhang

“…The most important predictor<span id="page2508"/> variables included near-surface soil temperatures (top 40 cm), vegetation spectral reflectance, and soil moisture. Our results, modeled from 138 site years across 26 sites, had relatively strong predictive skill, with a mean <span class="inline-formula"><i>R</i>²</span> of 0.51 and 0.70 and a mean absolute error (MAE) of 30 and 27 nmol m<span class="inline-formula">⁻²</span> s<span class="inline-formula">⁻¹</span> for daily and monthly fluxes, respectively. …”
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AI Painting Effect Evaluation of Artistic Improvement with Cross-Entropy and Attention by Yihuan Tian, Shiwen Lai, Zuling Cheng, Tao Yu

“…DBAM enhances the feature fusion capability of the model by explicitly focusing on the important channel and spatial features, and it enables the model to more accurately recognize and differentiate between changes in the creative abilities of different users before and after using AI painting tools. …”
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Determinants of variation in home range of wild pigs by Peter E. Schlichting, Sarah R. Fritts, John J. Mayer, Philip S. Gipson, C. Brad Dabbert

“…We used mixed‐effects linear‐regression models to assess how allometric effects of body size and environmental variables (temp, elevation, latitude, and rainfall) could be used to predict home range size at a local scale. …”
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What Helps to Detect What? Explainable AI and Multisensor Fusion for Semantic Segmentation of Simultaneous Crop and Land Cover Land Use Delineation by Saman Ebrahimi, Saurav Kumar

“…Interclass-Grad-CAM provides insights into interactions between land cover classes, revealing complex spatial arrangements, while Spectral-Grad-CAM quantifies the contributions of individual spectral bands to model predictions, optimizing spectral data use. …”
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A linear perception-action mapping accounts for response range-dependent biases in heading estimation from optic flow. by Qi Sun, Ling-Hao Xu, Alan A Stocker

“…This simple perception-action model accurately predicts participants' estimates both in terms of mean and variance across all experimental conditions. …”
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