Nitrogen (N) fertilizer recommendations for corn (Zea mays L.) in the US Midwest have been a puzzle for several decades, without agreement among stakeholders for which methodology is the best to balance environmental and economic outcomes. Part of the reason is the lack of long-term data of crop responses to N over multiple fields since trial data is often limited in the number of soils and years it can explore. To overcome this limitation, we designed an analytical platform based on crop simulations run over millions of farming scenarios over extensive geographies. The database was calibrated and validated using data from more than four hundred trials in the region. This dataset can have an important role for research and education in N management, machine leaching, and environmental policy analysis. The calibration and validation procedure provides a framework for future gridded crop model studies. We describe dataset characteristics and provide thorough descriptions of the model setup.
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http://dx.doi.org/10.1016/j.dib.2021.107753 | DOI Listing |
Sci Total Environ
January 2025
Universidad de Santiago de Chile, Santiago, Chile.
Assessing future snow cover changes is challenging because the high spatial resolution required is typically unavailable from climate models. This study, therefore, proposes an alternative approach to estimating snow changes by developing a super-spatial-resolution downscaling model of snow depth (SD) for Japan using a convolutional neural network (CNN)-based method, and by downscaling an ensemble of models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) dataset. After assessing the coherence of the observed reference SD dataset with independent observations, we leveraged it to train the CNN downscaling model; following its evaluation, we applied the trained model to CMIP6 climate simulations.
View Article and Find Full Text PDFJ Integr Neurosci
January 2025
Department of Brain Disease Center, The First Affiliated Hospital of Anhui University of Chinese Medicine, 230031 Hefei, Anhui, China.
Background: White matter (WM) is a principal component of the human brain, forming the structural basis for neural transmission between cortico-cortical and subcortical structures. The impairment of WM integrity is closely associated with the aging process, manifesting as the reorganization of brain networks based on graph theoretical analysis of complex networks and increased volume of white matter hyperintensities (WMHs) in imaging studies.
Methods: This study investigated changes in the robustness of WM brain networks during aging and assessed their correlation with WMHs.
Polymers (Basel)
January 2025
School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China.
Due to the complex and uncertain physics of lightning strike on carbon fiber-reinforced polymer (CFRP) laminates, conventional numerical simulation methods for assessing the residual strength of lightning-damaged CFRP laminates are highly time-consuming and far from pretty. To overcome these challenges, this study proposes a new prediction method for the residual strength of CFRP laminates based on machine learning. A diverse dataset is acquired and augmented from photographs of lightning strike damage areas, C-scan images, mechanical performance data, layup details, and lightning current parameters.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
Aiming at the problems caused by a lack of feature matching due to occlusion and fixed model parameters in cross-domain person re-identification, a method based on multi-branch pose-guided occlusion generation is proposed. This method can effectively improve the accuracy of person matching and enable identity matching even when pedestrian features are misaligned. Firstly, a novel pose-guided occlusion generation module is designed to enhance the model's ability to extract discriminative features from non-occluded areas.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Electronics and Electrical Engineering, Faculty of Science and Technology, Keio University, 3-14-1, Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan.
Traditional Vision-and-Language Navigation (VLN) tasks require an agent to navigate static environments using natural language instructions. However, real-world road conditions such as vehicle movements, traffic signal fluctuations, pedestrian activity, and weather variations are dynamic and continually changing. These factors significantly impact an agent's decision-making ability, underscoring the limitations of current VLN models, which do not accurately reflect the complexities of real-world navigation.
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