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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Increased thermal suitability elevates the risk of dengue transmission across the mid hills of Nepal. by Bipin Kumar Acharya, Laxman Khanal, Meghnath Dhimal

“…In this study, we utilized Moderate Resolution Imaging Spectroradiometer (MODIS) satellite land surface temperature (LST) data (MOD11A2) and a temperature-dependent mechanistic model (R0) to predict the monthly suitability for dengue transmission in Nepal from 2001 to 2020 for both mosquito vectors, Aedes aegypti and Ae. albopictus. …”
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Contribution of relative humidity on aerosol optical depth role in influencing the regionalization pattern of precipitation over West Africa by Ayomide Victor Arowolo, Ayodeji Festus Ologun, Precious Ebiendele, Ayodeji Oluleye

“…Understanding the complex interactions between aerosol optical depth (AOD), relative humidity, and precipitation is essential for improving climate and weather prediction models, particularly in regions like West Africa, which are vulnerable to climate variability. …”
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Flood Hazard Assessment Using Weather Radar Data in Athens, Greece by Apollon Bournas, Evangelos Baltas

“…The GFFG system improves upon the traditional flash flood guidance (FFG) concept by better integrating the weather radar dataset’s spatial and temporal flexibility, leading to increased resolution results. …”
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Color polymorphic carnivores have faster speciation rates by Moritz M. Heuer, Klaus Fischer, Laura Tensen

“…Therefore, other selective forces than spatial niche segregation, such as predator-prey coevolution, may contribute to color polymorphism in wild carnivores.…”
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OSMlanduse a dataset of European Union land use at 10 m resolution derived from OpenStreetMap and Sentinel-2 by Michael Schultz, Hao Li, Zhaoyan Wu, Daniel Wiell, Michael Auer, Zipf Alexander

“…These labels served as training data for a classification model, predicting land use in remaining areas. Country-specific deep learning convolutional neural networks and Sentinel-2 feature space composites of 2020 at 10 m resolution were employed. …”
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A novel dual-branch network for comprehensive spatiotemporal information integration for EEG-based epileptic seizure detection. by Xiaobing Deng

“…Deep learning EEG-based detection methods have made significant progress, but still face challenges such as the underutilization of spatial relationships, inter-individual physiological variability, and sequence intricacies. …”
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Geography is a stronger predictor of diversification of monogenean parasites (Platyhelminthes) than host relatedness in characin fishes of Middle America. by Fernando Alda, Edgar F Mendoza-Franco, William Hanson-Regan, Ruth G Reina, Eldredge Bermingham, MarkE Torchin

“…At this taxonomical scale, host-switching is mediated by the geographical proximity of potential hosts, underscoring the importance of spatial factors in parasite evolution.…”
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RGB-D Image Saliency Detection Based on Multi-branch Backbone Supervised Network by WANG Wei-bing, ZHANG Xiao-zhuo, DENG Qiang

“…Each fusion group is supervised by the upper level fusion result and truth map, and finally the predicted saliency map is obtained iteratively. Experiments on four representative data sets show that compared with the current advanced RGB-D image saliency detection model. …”
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Advanced Deep Learning Algorithms for Energy Optimization of Smart Cities by Izabela Rojek, Dariusz Mikołajewski, Krzysztof Galas, Adrianna Piszcz

“…Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) facilitate detailed analysis of spatial and temporal data to better predict energy usage. …”
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Airborne sound insulation performance of lightweight double leaf walls with different stud types by Hequn Min, Bo Wang, Ting Qu

“…To support performance-based design workflows, this paper presents an analytical model based on sound transmission path decoupling, enabling architects to quickly predict the STL of commonly used lightweight double leaf wall types, including wooden, steel, and acoustical stud constructions. …”
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Plausible Vehicle Trajectory Augmentation With Hybrid Machine Learning by Junyeop Park, Hyeonsoo Jang, Byeongju Kang, Yunhyoung Hwang

“…To address this issue, this paper proposes a novel data augmentation technique, integrating Chebyshev polynomial fitting with Hierarchical Density-Based Spatial Clustering of Applications with Noise clustering and Gaussian mixture models to generate robust training data. …”
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GAN-based solar radiation forecast optimization for satellite communication networks by Chao Chen, Xin Liu, Shizhong Zhao, Muhammad Bilal

“…In such scenarios, predictions often exhibit spatial texture degradation and distortions in radiation intensity. …”
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Modern deep neural networks for Direct Normal Irradiance forecasting: A classification approach by Muhammad Saud Ul Hassan, Kashif Liaqat, Laura Schaefer, Alexander J. Zolan

“…Existing DNI forecasting models use meteorological factors, which are both challenging to estimate numerically over short prediction windows and expensive to model through data at a sufficiently high spatial and temporal resolution. …”
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