Deep-Learning-Driven Insights into Nitrogen Leaching for Sustainable Land Use and Agricultural Practices by Caixia Hu, Jie Li, Yaxu Pang, Lan Luo, Fang Liu, Wenhao Wu, Yan Xu, Houyu Li, Bingcang Tan, Guilong Zhang

“…A machine learning (ML) model for predicting nitrate leaching was then developed, with the random forest (RF) model outperforming the support vector machine (SVM), extreme gradient boosting (XGBoost), and convolutional neural network (CNN) models, achieving an R² of 0.75. …”
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Simulation of the African ITCZ during austral summer seasons and ENSO phases: application of an RCM derived from stretched grid ESM by Teke S. Ramotubei, Teke S. Ramotubei, Willem A. Landman, Mohau J. Mateyisi, Shingirai S. Nangombe, Asmerom F. Beraki, Asmerom F. Beraki

“…IntroductionClimate predictability across timescales in a changing climate presents a unique opportunity and challenges for state-of-the-art climate models. …”
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Analyzing the Impact of Binaural Beats on Anxiety Levels by a New Method Based on Denoised Harmonic Subtraction and Transient Temporal Feature Extraction by Devika Rankhambe, Bharati Sanjay Ainapure, Bhargav Appasani, Avireni Srinivasulu, Nicu Bizon

“…Initially, a novel Wiener Fused Convo Filter is introduced to capture spatial features and eliminate linear noise in EEG signals. …”
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Influence of Multisensory Perceptions on Thermal Comfort in Heat Wave Weather with Different Heat Pressures by Chang Liu, Wei Wu, Jingyi Ouyang, Jinshan Yan, Lina Tang

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Spaceborne remote sensing effectively maps species richness across taxonomic groups in a mountain landscape by Cornelius Senf, Lisa Geres, Tobias Richter, Kristin Braziunas, Felix Glasmann, Rupert Seidl, Sebastian Seibold

“…Our results showed that a combination of EnMAP/Sentinel-1/2 performed as well or even better than airborne LiDAR data for predicting species richness, but predictive accuracies of individual spaceborne models were substantially lower. …”
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Megalopolitan-scale rooftop solar photovoltaic potential estimation of the Greater Bay Area in China on 3D buildings with deep learning by Bochen Zhang, Zhuoxi Li, Zhen Peng, Siting Xiong, Teng Zhong, Jan-Peter Muller

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Method of automatic coregistration of digital remote sensing images from different sources by A.N. Borisov, V.V. Myasnikov, V.V. Sergeev

“…The method is designed to align color, grayscale, multispectral, and radar images, as well as their combinations, with potential differences in spatial resolution of up to four times (optionally – sixteen times). …”
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The potential of wetlands and barrier islands as a coastal defense in mitigating the storm surge by Aditya Gupta, Matthew V. Bilskie, C. Brock Woodson

“…The study introduces an attenuation rate function based on the prediction factor that improves surge attenuation modeling by integrating real-world scenarios with idealized theoretical functions. …”
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Analysis of space solar array arc images based on deep learning techniques by Afaf M. Abd El-Hameed, Ahmed S. Farahat, Khaled Y. Youssef, M. Elfarran, I. M. Selim

“…Leveraging advanced Deep Learning (DL) methodologies, including Convolutional Neural Networks (CNN) and Transfer Learning, a robust predictive model has been developed to analyze arc behavior and identify defective cells based on image data. …”
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A large-scale study of academic specialization and sex effects on Corsi block performance by Sergey Malykh, Anna Pavlova, Artem Malykh, Timofey Adamovich, Tatiana Tikhomirova

“…The results highlight the importance of considering both cognitive and contextual factors in the assessment of spatial working memory.…”
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A topological approach to understanding crack initiation and propagation in carbon fiber reinforced polymer composites under an opening load by Mayrene Uy, Daiki Kido, Masao Kimura

“…From a materials perspective, this methodology allows integration of not just crack length but also 3D crack shape into predictive models, offering valuable insights for optimizing the performance and extending the lifetime of carbon fiber/epoxy composites.…”
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Comparing US state resident IQ, socioeconomic status, and racial-ethnic composition as predictors of state violent crime rates by Stewart J. H. McCann

“…Generally, the results underline the importance of evaluating potential crime rate predictors in a multiple regression model rather than testing their predictive capacities only as single variables.…”
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Forest Fire Hazard Assessment using Remote Sensing Data and Machine Learning, Case Study of Jijel, Algeria by Matougui Zakaria, Zouidi Mohamed

“…Evaluation metrics such as Receiver Operating Characteristic Area Under the Curve, F1 score and accuracy, supported by repeated cross-validation, were used to gauge performance. Both models performed well (with an average area under the curve of 0.977 and 0.984, respectively), with HGB maintaining a marginal but consistent advantage over KNN, with relatively low standard deviations, suggesting stability in its predictive capability. …”
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Theory of Robust Quantum Many-Body Scars in Long-Range Interacting Systems by Alessio Lerose, Tommaso Parolini, Rosario Fazio, Dmitry A. Abanin, Silvia Pappalardi

“…We analytically solve for the eigenstates of this interacting impurity model by means of a novel polaron-type canonical transformation and show their self-consistent localization in large-spin sectors of the original Hamiltonian for 0<α<d (with d=spatial dimension of the lattice). …”
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Convolutional neural network-based deep learning for landslide susceptibility mapping in the Bakhtegan watershed by Li Feng, Maosheng Zhang, Yimin Mao, Hao Liu, Chuanbo Yang, Ying Dong, Yaser A. Nanehkaran

“…Abstract Landslides pose a significant threat to infrastructure, ecosystems, and human safety, necessitating accurate and efficient susceptibility assessment methods. Traditional models often struggle to capture the complex spatial dependencies and interactions between geological and environmental factors. …”
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Exploring drought risk scenarios for bioenergy systems for adaptation to climate change in the Upper Tocantins basin, Brazil by Jazmin Campos Zeballos, Vinicius Bof Bufon, Zita Sebesvari, Jakob Rhyner

“…The IPCC AR5 Risk Assessment Framework was employed to evaluate two global climate change models, two representative concentration pathways (RCPs), and three land use scenarios. …”
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Maize crop residue cover mapping using Sentinel-2 MSI data and random forest algorithms by Jia Du, Pierre-Andre Jacinthe, Kaishan Song, Longlong Zhang, Boyu Zhao, Hua Liu, Yan Wang, Weijian Zhang, Zhi Zheng, Weilin Yu, Yiwei Zhang, Dapeng Jiang

“…Regional distribution of MRC in the Jilin Province, estimated using the RF prediction model, was higher in the central and southeast sections than in the northwest. …”
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Surface temperature change amplifies social inequality in heat exposure? Evidence from Shanghai, China by Peng Zeng, Cheng Chen, Fengyun Sun, Yaoyi Liu, Xinyu Zhang, Yue Che

“…We employed a boosted regression tree model to quantify LST changes, offering better predictive performance than traditional linear regression, and used geographically weighted regression with data from China's sixth population census to assess the city-wide and local scale inequality differences in exposure to LST changes and summer LST. …”
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Deep learning-based screening for locomotive syndrome using single-camera walking video: Development and validation study. by Junichi Kushioka, Satoru Tada, Noriko Takemura, Taku Fujimoto, Hajime Nagahara, Masahiko Onoe, Keiko Yamada, Rodrigo Navarro-Ramirez, Takenori Oda, Hideki Mochizuki, Ken Nakata, Seiji Okada, Yu Moriguchi

“…To address this, we introduced a Deep Learning (DL)-based computer vision model that employs OpenPose for pose estimation and MS-G3D for spatial-temporal graph analysis. …”
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Joint Intensity and Spatio-Temporal Representation Learning for Extreme Precipitation Nowcasting by Zefeng Pan, Renlong Hang, Qingshan Liu, Chunxiang Shi, Zhiqiang Xu, Xiao-Tong Yuan

“…However, the existing methods tend to inadequately weigh precipitation intensity features by only implicitly learning and modeling these features within the spatial distribution. …”
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