An End-to-End CRSwNP Prediction with Multichannel ResNet on Computed Tomography by Shixin Lai, Weipiao Kang, Yaowen Chen, Jisheng Zou, Siqi Wang, Xuan Zhang, Xiaolei Zhang, Yu Lin

“…Compared to the limited learning capacity of single-channel neural networks, our proposed multichannel feature adaptive fusion model captures multiscale spatial features, enhancing the model’s focus on crucial sinus information within the CT images to maximize detection accuracy. …”
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Exploring the potentialities and challenges of deep learning for simulation and prediction of urban sprawl features by Ange Gabriel Belinga, Stéphane Cedric Tékouabou Koumetio, Mohamed El Haziti

“…Through an examination of DL methodologies, we aim to highlight their effectiveness in capturing the complex spatial patterns and relationships associated with US. …”
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Spatio-temporal risk prediction of leptospirosis: A machine-learning-based approach. by Rodrigue Govan, Romane Scherrer, Baptiste Fougeron, Christine Laporte-Magoni, Roman Thibeaux, Pierre Genthon, Philippe Fournier-Viger, Cyrille Goarant, Nazha Selmaoui-Folcher

“…<h4>Methodology/principal findings</h4>This study aims to establish a precise spatio-temporal risk map of leptospirosis at a national scale, using binarized incidence rates as the variable to predict. The spatial analysis was conducted at a finer resolution than the city level, while the temporal analysis was performed on a monthly basis from 2011 to 2022. …”
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Analysis and Prediction of Coverage and Channel Rank for UAV Networks in Rural Scenarios With Foliage by Donggu Lee, Ozgur Ozdemir, Ram Asokan, Ismail Guvenc

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Numerical Prediction of Fatigue Life for Landing Gear Considering the Shock Absorber Travel by Haihong Tang, Panglun Liu, Jianbin Ding, Jinsong Cheng, Yiyao Jiang, Bingyan Jiang

“…On the basis of the whole geometric model of a large passenger aircraft’s main landing gear (MLG), the quasi-static finite element model (FEM) of the whole MLG is established, and the high-cycle fatigue issue of the Main Fitting (MF) is studied by considering the variation in shock absorber travel (SAT). …”
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A Physics-Enhanced Network for Predicting Sequential Satellite Images of Typhoon Clouds by Jiawei Yuan, Liling Zhao, Runling Yu, Xiaoqin Lu, Min Xia, Yi Liu, Yuru Wang, Xinyue Wang

“…To further improve the fine structural details in the predicted typhoon cloud images, a concurrent spatial and channel squeeze-and-excitation attention mechanism is incorporated into both the encoder and decoder modules. …”
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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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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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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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