Late-Life Alcohol Exposure Does Not Exacerbate Age-Dependent Reductions in Mouse Spatial Memory and Brain TFEB Activity by Hao Chen, Kaitlyn Hinz, Chen Zhang, Yssa Rodriguez, Sha Neisha Williams, Mengwei Niu, Xiaowen Ma, Xiaojuan Chao, Alexandria L. Frazier, Kenneth E. McCarson, Xiaowan Wang, Zheyun Peng, Wanqing Liu, Hong-Min Ni, Jianhua Zhang, Russell H. Swerdlow, Wen-Xing Ding

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Exploring and Modeling the Spatial Variability of Soil Erosion in Tana Basin, Northwestern Ethiopia by Derege Tsegaye Meshesha, Anteneh Wubet Belay, Gizachew Ayalew Tiruneh, Enyew Adgo, Tiringo Yilak Alemayehu, M. Chandrakala, José Miguel Reichert

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Modeling Foreground Spatial Variations in 21 cm Gaussian Process Component Separation by Kangning Diao, Richard D. P. Grumitt, Yi Mao

“…Further improvements to the HGP model will require more physically-motivated modeling of foreground spatial variations. …”
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CALFUSE-KAN: A Multi-Scale Feature Fusion Network for Aircraft Trajectory Prediction by Yalan Zhang, Ruxin Sun, Fang Liu, Yongchao Wei

Subjects: “…Trajectory prediction…”
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Solar and Wind 24 H Sequenced Prediction Using L-Transform Component and Deep LSTM Learning in Representation of Spatial Pattern Correlation by Ladislav Zjavka

“…Spatiotemporal correlations between meteo-inputs and wind–solar outputs in an optimal regional scale are crucial for developing robust models, reliable in mid-term prediction time horizons. …”
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Spatial Modeling of Douglas‐Fir Plantations in Italy After 120 Years of Experimentation by Maurizio Marchi, Martina Cocozza, Gabriele Bucci, Paolo Iovieno

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Comparative Analysis of Different Interpolation Methods in Modeling Spatial Distribution of Monthly Precipitation by Yılmaz İçağa, Emin Taş

“…It is the main objective of the study that Geographic Information Systems (GIS) techniques are used to compare widely preferred interpolation methods and to model the spatial distribution of monthly precipitation values for prediction in ungauged areas in Akarcay Sinanpasa and Suhut sub-basins, Turkey. …”
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An Actor-Oriented and Architecture-Driven Approach for Spatially Explicit Agent-Based Modeling by Eric Fotsing, Severin Vianey Tuekam Kakeu, Eric Desire Kameni, Marcellin Julius Antonio Nkenlifack

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Machine learning-driven framework for realtime air quality assessment and predictive environmental health risk mapping by M. Rajesh, R. Ganesh Babu, Usha Moorthy, Sathishkumar Veerappampalayam Easwaramoorthy

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Predicting high-quality ecologically suitable areas of Astragalus mongholicus Bunge based on secondary metabolites content using Biomod2 model by Jingyi Zhou, Xiaoting Zhang, Tongtong Huo, Huizhong Xu, Fanyun Meng, Ning Xu, Cheng Peng

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Unravelling the importance of spatial and temporal resolutions in modeling urban air pollution using a machine learning approach by Alireza Zhalehdoost, Mohammad Taleai

“…In the spatial phase, emission inventory data are aggregated at three spatial resolutions (500 m, 750 m, and 1000 m) to evaluate their effect on model performance in predicting PM and NOx concentrations. …”
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The effect of green credit policy on carbon emissions based on China’s provincial panel data by Xuerui Yang, Lianmei Zhu, Tong Wei

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Corrigendum: Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm by Sanglin Zhao, Zhetong Li, Hao Deng, Xing You, Jiaang Tong, Bingkun Yuan, Zihao Zeng

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A spatiotemporal model for urban taxi Origin–Destination prediction based on Multi-hop GCN and Hierarchical LSTM by Jiang Rong, Wangtu Xu, Yanjie Wen

“…On the PEMS08, the single-path version of DBSTNet outperforms state-of-the-art demand prediction models with an 8.29% increase in SMAPE, underscoring the scalability of the proposed MS-HT for spatial–temporal computation. …”
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INFORMATION SYSTEM OF FIRE SAFETY IN EDUCATIONAL INSTITUTION by Andrey A. PETRIKIN

“…The information system role in the fire safety structure of the educational institution is considered. A model of spatial fire spread according to Monte Carlo method is offered. …”
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SceneDiffusion: Scene Generation Model Embedded with Spatial Constraints by Shanshan Yu, Jiaxin Zhu, Jiaqi Li, Xunchun Li, Kai Wang, Jian Tu, Danhuai Guo

“…The advancement of Geospatial Artificial Intelligence (GeoAI) offers a new technical pathway for the intelligent modeling of spatial scenes. Against this backdrop, we propose SceneDiffusion, a scene generation model embedded with spatial constraints, and construct a geospatial scene dataset incorporating spatial relationship descriptions and geographic semantics, aiming to enhance the understanding and modeling capabilities of GeoAI models for spatial information. …”
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Bolstering spatial learning in the primary classroom: identifying the factors underlying primary teachers’ spatial pedagogical practices by Kelsey Rocha, Catherine M. Lussier, Kinnari Atit

“…Teachers’ cognition and affect can influence their pedagogical choices, which in turn can shape the development of their students’ spatial skills. Here we examined the relations between primary teachers’ spatial cognition, affect, and their inclination for choosing teaching practices, such as models and diagrams, that engage and facilitate spatial learning. …”
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Predicting fine-scale downstream migratory movement of Atlantic salmon smolt (Salmo salar) in front of a hydropower plant by Kim M. Bærum, Ana T. Silva, Henrik Baktoft, Karl Ø. Gjelland, Finn Økland, Torbjørn Forseth

“…We present a spatially explicit individual-based model for predicting the movement of Atlantic salmon smolts in regulated rivers in Norway, parameterised for smolt movements in the River Mandal and the River Orkla. …”
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MEA‐seqX: High‐Resolution Profiling of Large‐Scale Electrophysiological and Transcriptional Network Dynamics by Brett Addison Emery, Xin Hu, Diana Klütsch, Shahrukh Khanzada, Ludvig Larsson, Ionut Dumitru, Jonas Frisén, Joakim Lundeberg, Gerd Kempermann, Hayder Amin

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Research on short-term traffic flow prediction based on the PCC-IGA-LSTM model by Junxi Zhang, Shiru Qu, Yang Bi, Lijing Ma

“…To effectively address the spatial–temporal feature mining problem in short-term traffic flow prediction for complex road networks, a new method that combined the Pearson correlation coefficient (PCC) and improved genetic algorithm to optimize the long short-term memory model (IGA-LSTM) was constructed. …”
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