Resting-state EEG network variability predicts individual working memory behavior by Chunli Chen, Shiyun Xu, Jixuan Zhou, Chanlin Yi, Liang Yu, Dezhong Yao, Yangsong Zhang, Fali Li, Peng Xu

“…Finally, using a multivariable predictive model based on these variability metrics, we effectively predicted individual WM performances. …”
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Spatially explicit metrics improve the evaluation of species distribution models facing sampling biases by Claudio A. Bracho-Estévanez, Salvador Arenas-Castro, Juan P. González-Varo, Pablo González-Moreno

“…Furthermore, most predictions rely only on non-spatial metrics such as the AUC and the TSS to evaluate model performance. …”
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Mt or not Mt: Temporal variation in detection probability in spatial capture-recapture and occupancy models by Sollmann, Rahel

“…Here, I investigate whether closed spatial capture-recapture (SCR) and single season occupancy models are robust to ignoring temporal variation in detection probability. …”
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Correlation-regression analysis of modelled chlorine residual's spatial variability in water supply network by Sanja Marčeta, Dušan Prodanović

“…From this point of view the water quality request for minimum chlorinated and safe potable water is understandable. In numerable modeling and experimental research the spatial diversity of chlorine residual was found to correlate with the daily consumption schedule, the water temperature, the initial dose of chlorine and organic matter content in the water. …”
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Novel extensions to the Fisher copula to model flood spatial dependence over North America by D. A. Alexandre, C. Chaudhuri, J. Gill-Fortin

“…We propose novel extensions to the Fisher copula to statistically model the spatial structure of observed historical flood record data across North America. …”
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A Computing Model of Selective Attention for Service Robot Based on Spatial Data Fusion by Huanzhao Chen, Guohui Tian

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Automated Generation of Urban Spatial Structures Based on Stable Diffusion and CoAtNet Models by Dian Yu, Bo Wan, Qiang Sheng

“…We simultaneously trained two models: one is a LoRA Model based on the Stable Diffusion architecture used for generating road networks similar to those of various city road spatial structures; the other is a CoAtNet Model (Convolution + Transformer) used as an evaluation model to predict the space-syntax parameters of road structures and calculate the Mean Absolute Percentage Error (MAPE) relative to real urban samples. …”
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An Iterative Pixel-Based Dimensional Voting Model for High Spatial-Resolution Image Classification by Muhamad Asyraf Mohd Pouzi, Haza Nuzly Abdull Hamed, Muhammad Razib Othman, Hishammuddin Asmuni, Mohd Adham Isa, Husni Ruslai

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A high resolution spatial modelling framework for landscape-level, strategic conservation planning by T. Foxley, P. Lintott, S. Stonehouse, J. Flannigan, E.L. Stone

“…The aim of this study was to develop a spatial modelling framework for protecting biodiversity in the planning process. …”
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Machine Learning for Spatiotemporal Prediction of River Siltation in Typical Reach in Jiangxi, China by Yong Fu, Jin Luo, Die Zhang, Lingjia Liu, Gan Luo, Xiaofang Zu

“…This study presents an initial, yet promising attempt to apply machine learning for spatially explicit siltation prediction in data-constrained river systems. …”
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Integrative habitat analysis and multi-instance deep learning for predictive model of PD-1/PD-L1 immunotherapy efficacy in NSCLC patients: a dual-center retrospective study by Xiaoxiao Huang, Xiaoxin Huang, Yurun Xie, Kui Wang, Housheng Bai, Ruiling Ning, Xiqi Zhu, Deyou Huang, Guanqiao Jin

“…Finally, a separate PD-L1 expression dataset was used to compare the predictive performance of imaging models against PD-L1 status (positive/negative) and expression levels (high/low) to identify the optimal model for predicting immunotherapy clinical benefit. …”
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Spatial modeling of brine level and salinity in the Qarhan Salt Lake using GIS and automated machine learning algorithms by Dongmei Yu, Zitao Wang, Chao Yue, Jianping Wang

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Modelling Salmo trutta Complex Spatial Distribution in Central Italy: A Random Forest Approach Revealing Underrepresented Lowland Populations Based on Spatially‐Explicit Predictors... by Lorenzo Talarico, Elena Catucci, Marco Martinoli, Michele Scardi, Lorenzo Tancioni

“…The model shows (i) high predictive ability (K = 0.76), (ii) predicts suitable, naturally‐infrequent lowland watercourses where brown trout occurs or may occur. …”
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A New and Tested Ionospheric TEC Prediction Method Based on SegED-ConvLSTM by Yuanhang Liu, Yingkui Gong, Hao Zhang, Ziyue Hu, Guang Yang, Hong Yuan

“…In this paper, we propose a TEC prediction model, which simultaneously considers both spatial and temporal characteristics to extract spatiotemporal features of ionospheric distribution. …”
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Modelling of agricultural land dynamics using artificial neural networks and geospatial analysis in Melur Taluk, Tamil Nadu, India by Gowthaman M S, Murugasan R, Srinivasa Raju K, Makendran C

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Forecasting Day-Ahead Electricity Demand in Australia Using a CNN-LSTM Model with an Attention Mechanism by Laial Alsmadi, Gang Lei, Li Li

“…Despite advancements in various prediction models, existing approaches often struggle to capture the complex, nonlinear relationships between temperature variations and electricity consumption. …”
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Urban environmental and population factors as determinants of COVID-19 severity: A spatially-resolved probabilistic modeling approach. by Jacob Roxon, Marie-Sophie Dumont, Eric Vilain, Mircea T Sofonea, Roland J-M Pellenq

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Prediction of Airtightness Performance of Stratospheric Ships Based on Multivariate Environmental Time-Series Data by Yitong Bi, Wenkuan Xu, Lin Song, Molan Yang, Xiangqiang Zhang

“…Among the models evaluated, the NeuralProphet model demonstrated superior accuracy in long-term airtightness predictions, effectively capturing time-series dependencies and spatial interactions with environmental conditions. …”
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A Convolutional Neural Network–Long Short-Term Memory–Attention Solar Photovoltaic Power Prediction–Correction Model Based on the Division of Twenty-Four Solar Terms by Guodong Wu, Diangang Hu, Yongrui Zhang, Guangqing Bao, Ting He

“…The examination of the measured data from PV power stations and the comparison and analysis with other prediction models demonstrate that the model presented in this paper can effectively enhance the accuracy of PV power predictions.…”
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A deep learning model based on self-supervised learning for identifying subtypes of proliferative hepatocellular carcinoma from dynamic contrast-enhanced MRI by Hui Qu, Shuairan Zhang, Xuedan Li, Yuan Miao, Yuxi Han, Ronghui Ju, Xiaoyu Cui, Yiling Li

“…We developed a deep learning prediction model that employs a dynamic radiomics workflow and self-supervised learning (SSL). …”
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