Modeling land cover changes using an enhanced Markov-future land use simulation model with spatial distribution considerations: a case study in the Yellow River Basin by Jianchen Zhang, Heying Li, Hanwen Zhang, Jiayao Wang, Guangxia Wang, JianWei Xu, Haohua Zheng, HuiLing Ma

“…The traditional Markov-future land use simulation (FLUS) model for land use prediction primarily emphasizes the quantity changes and spatial distribution of land use types, but it neglects the influence of their inherent spatial characteristics on the prediction precision. …”
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Paramètres socio-spatiaux, réseaux sociaux et budget d'activité chez un groupe de mâles chimpanzés : stabilité dans le temps et effet de la réduction spatiale by Nerea Amezcua‑Valmala, Daniel Vázquez, Álvaro Ballesteros, Marcos Bella, Catarina Casanova, Fernando Colmenares

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Landslide Susceptibility Prediction Based on a CNN–LSTM–SAM–Attention Hybrid Model by Honggang Wu, Jiabi Niu, Yongqiang Li, Yinsheng Wang, Daohong Qiu

“…In this study, we propose a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Spatial Attention Mechanism (SAM) hybrid deep learning model designed for spatial landslide susceptibility prediction. …”
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Precise spatial tuning of visually driven alpha oscillations in human visual cortex by Kenichi Yuasa, Iris IA Groen, Giovanni Piantoni, Stephanie Montenegro, Adeen Flinker, Sasha Devore, Orrin Devinsky, Werner Doyle, Patricia Dugan, Daniel Friedman, Nick F Ramsey, Natalia Petridou, Jonathan Winawer

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Alzheimer’s Disease Prediction Using Fisher Mantis Optimization and Hybrid Deep Learning Models by Sameer Abbas, Mustafa Yeniad, Javad Rahebi

“…The selected features were classified using a CNN-LSTM model, capturing both spatial and temporal patterns. …”
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Spectral Data-Driven Prediction of Soil Properties Using LSTM-CNN-Attention Model by Yiqiang Liu, Luming Shen, Xinghui Zhu, Yangfan Xie, Shaofang He

“…This study presents an LSTM-CNN-Attention model that integrates temporal and spatial feature extraction with attention mechanisms to improve predictive accuracy. …”
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Geographically Aware Air Quality Prediction Through CNN-LSTM-KAN Hybrid Modeling with Climatic and Topographic Differentiation by Yue Hu, Yitong Ding, Wenjing Jiang

“…This methodological framework provides valuable insights for addressing spatial heterogeneity in environmental modeling applications.…”
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Assessment of landscape diversity in Inner Mongolia and risk prediction using CNN-LSTM model by Yalei Yang, Hong Wang, Xiaobing Li, Tengfei Qu, Jingru Su, Dingsheng Luo, Yixiao He

“…The projected landscape diversity risk warning for 2025 mirrors the historical spatial data, with a notable reduction in local disparities and an overall decrease in the average value by 2.73%. …”
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Development of an AI model for DILI-level prediction using liver organoid brightfield images by Shiyi Tan, Yan Ding, Wei Wang, Jianhua Rao, Feng Cheng, Qiuyin Zhang, Tingting Xu, Tianmu Hu, Qinyi Hu, Ziliang Ye, Xiaopeng Yan, Xiaowei Wang, Mingyue Li, Peng Xie, Zaozao Chen, Geyu Liang, Yuepu Pu, Juan Zhang, Zhongze Gu

“…Here we show a drug-induced liver injury (DILI) level prediction model using HLO brightfield images (DILITracer) considering that DILI is the major causes of drug withdrawals. …”
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Spatial Distribution Characteristics and Driving Factors of Formicidae in Small Watersheds of Loess Hilly Regions by Yu Tian, Fangfang Qiang, Guangquan Liu, Changhai Liu, Ning Ai

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ARIMA-Kriging and GWO-BiLSTM Multi-Model Coupling in Greenhouse Temperature Prediction by Wei Zhou, Shuo Liu, Junxian Guo, Na Liu, Zhenglin Li, Chang Xie

“…Across different prediction horizons (10 min and 30 min intervals), the GWO-BiLSTM model demonstrated superior performance with key metrics reaching a coefficient of determination (R²) of 0.97, root mean square error (RMSE) of 0.79–0.89 °C (41.7% reduction compared to the PSO-BP model), mean absolute percentage error (MAPE) of 4.94–8.5%, mean squared error (MSE) of 0.63–0.68 °C, and mean absolute error (MAE) of 0.62–0.65 °C, significantly outperforming the BiLSTM, LSTM, and PSO-BP models. …”
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Early Modeling of the Upcoming Landsat Next Constellation for Soybean Yield Prediction Under Varying Levels of Water Availability by Luís Guilherme Teixeira Crusiol, Marcos Rafael Nanni, Rubson Natal Ribeiro Sibaldelli, Liang Sun, Renato Herrig Furlanetto, Sergio Luiz Gonçalves, Norman Neumaier, José Renato Bouças Farias

“…Early modeling of the upcoming Landsat Next products for soybean yield prediction is essential for long-term satellite monitoring strategies. …”
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A Dynamic Spatio-Temporal Deep Learning Model for Lane-Level Traffic Prediction by Bao Li, Quan Yang, Jianjiang Chen, Dongjin Yu, Dongjing Wang, Feng Wan

“…In this paper, we propose a deep learning model for lane-level traffic prediction. Specifically, we take advantage of the graph convolutional network (GCN) with a data-driven adjacent matrix for spatial feature modeling and treat different lanes of the same road segment as different nodes. …”
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Housing, travel, and energy spatial-temporal simulation of Riyadh: Impacts of the New Murabba Project by Abdel Rahman Muhsen, John Abraham, Geraldine Fuenmayor, Paul McMillan, J. D. Hunt

“…The model, called the Riyadh PECAS model, was used to analyze the housing, travel, energy consumption, and related spatial impacts of a proposed megaproject, the New Murabba, consisting of 104,000 residences, 9,000 hotel rooms, and 4.8 million square meters of other non-residential space, anchored by a 400-meter cube-shaped megastructure with partially open interior space. …”
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Integrative Framework for Decoding Spatial and Temporal Drivers of Land Use Change in Malaysia: Strategic Insights for Sustainable Land Management by Guanqiong Ye, Kehao Chen, Yiqun Yang, Shanshan Liang, Wenjia Hu, Liuyue He

“…Integrating the land use transfer matrix, GeoDetector model, and Structural Equation Modeling (SEM), we reveal a significant expansion of farmland and urban areas alongside a decline in forest cover, with notable regional variations in Malaysia. …”
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Spatial carbon emission and fiscal incentive: Considering spatial spillover boundaries and industrial linkages by Ye Cao, Hongzhen Zhang, Qiaoling Shi, Zhi Cao, Yuhuan Zhao, Zhonghua Zhang

“…To fulfill the existing research, this study employs a spatial difference-in-differences (SDID) model across 274 Chinese cities from 2005 to 2022 to explore the spatial effect of the ESERF policy on carbon emissions, its mediating and heterogeneous mechanisms, and delve into the performance of spatial spillover boundaries and production linkages. …”
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Spatial-temporal characteristics and influencing factors of farmland carbon emissions in Guangdong Province, China by Zihao Chao, Zihao Chao, Zihao Chao, Ziyang Zhu, Ziyang Zhu, Ziyang Zhu, Yuchen Li

“…This study offers policy recommendations to promote the reduction and sequestration of farmland carbon emissions in Guangdong.…”
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Spatial Distribution Pattern of Aromia bungii Within China and Its Potential Distribution Under Climate Change and Human Activity by Liang Zhang, Ping Wang, Guanglin Xie, Wenkai Wang

“…Hot spot distribution areas were identified using Getis‐Ord Gi*. An optimized MaxEnt model was used to predict the potential distribution areas of A. bungii within China under four shared economic pathways by combining multivariate environmental data: (1) prediction of natural environmental variables predicted under current climate models; (2) prediction of natural environmental variables + human activities under current climate models; and (3) prediction of natural environmental variables under the future climate models (2050s and 2070s). …”
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A new water temperature modeling approach to predict thermal habitat suitability for nonnative cichlids in Florida rivers by Alexandra M. Scott, Andrew K. Carlson

“…To understand how water temperature changes may affect the spatial distribution of these nonnative species, more effective water temperature prediction models are necessary. …”
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Spatial pattern characteristics and influencing factors of state-level sports industry bases in China by Qiuying Li, Laibing Lu

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