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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Binocular Vision-Based Target Detection Algorithm by Huiguo Zhang

“…The former can effectively balance the positive and negative samples to avoid over-learning of easy-to-classify samples during the training process; the latter successfully solves the problem of the failure of the detection method when there is no intersection between the prediction frame and the real detection frame, which significantly improves the robustness of the model. …”
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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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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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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

“…The results revealed strong agreement between the SSM predicted by the model and the SSM observed on the ground. …”
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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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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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Stress generation, relaxation and size control in confined tumor growth. by Huaming Yan, Daniel Ramirez-Guerrero, John Lowengrub, Min Wu

“…Our study provides insight on how the biophysical properties of the tumor and host microenvironment, mechanical feedback control and diffusion-limited differential growth act in concert to regulate spatial patterns of stress and growth. When the tumor is stiffer than the host, our model predicts tumors are more able to change their size and mechanical state autonomously, which may help to explain why increased tumor stiffness is an established hallmark of malignant tumors.…”
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AMR‐Wind: A Performance‐Portable, High‐Fidelity Flow Solver for Wind Farm Simulations by Michael B. Kuhn, Marc T. Henry de Frahan, Prakash Mohan, Georgios Deskos, Matt Churchfield, Lawrence Cheung, Ashesh Sharma, Ann Almgren, Shreyas Ananthan, Michael J. Brazell, Luis A. Martínez‐Tossas, Regis Thedin, Jon Rood, Philip Sakievich, Ganesh Vijayakumar, Weiqun Zhang, Michael Sprague

“…The verification studies, which include a novel actuator line test case, indicate that AMR‐Wind is spatially and temporally second‐order accurate. The validation studies demonstrate that the key physics capabilities implemented in the code, including actuator disk models, actuator line models, turbulence models, and large eddy simulation (LES) models for atmospheric boundary layers, perform well in comparison to reference data from established computational tools and theory. …”
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Ultradense Dark Matter Halos with Poisson Noise from Stellar-mass Primordial Black Holes by Saeed Fakhry, Javad T. Firouzjaee

“…Conversely, at higher values of n , the predicted UDMH distributions align more closely with single-component models dominated by stellar-mass PBHs. …”
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Camouflage Target Detection Method with Mutual Compensation of Local-Global Features by HE Wenhao, GE Haibo

“…In the field of camouflage object detection (COD), the latest proposed methods mainly use the local features of the camouflaged target to complete the COD task. The output prediction map has problems such as rough target contours and incomplete objects. …”
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The trans-european catchment area of common noctule bats killed by wind turbines in France by Maela Merlet, David X. Soto, Laurent Arthur, Christian C. Voigt

“…We analysed stable hydrogen isotopes in the fur keratin of 60 common noctule bats ( Nyctalus noctula ) killed by wind turbines during summer migration in four regions of France to predict their geographic origin using models based on precipitation isoscapes. …”
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Convolutional network learning of self-consistent electron density via grid-projected atomic fingerprints by Ryong-Gyu Lee, Yong-Hoon Kim

“…The effectiveness of DeepSCF is demonstrated using a complex carbon nanotube-based DNA sequencer model. This work evidences that the nearsightedness in electronic structure can be optimally represented via the spatial locality in CNNs, offering insight into the success of various machine learning-based atomistic materials simulations.…”
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