RUL Prediction of DC Contactor Using CNN-LSTM With Channel Attention and Fusion of Dual Aggregated Features by Sai Wang, Yuanfeng Zhang, Hao Huang, Yun Shi, Jianfei Si

“…Key features were extracted, preprocessed, and used to train and evaluate the model. Results show that the DAF-CA-CNN-LSTM model significantly outperforms traditional LSTM and CNN-LSTM models in RUL prediction, achieving higher accuracy and robustness in complex, noisy environments. …”
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The Prediction of Multistep Traffic Flow Based on AST-GCN-LSTM by Fan Hou, Yue Zhang, Xinli Fu, Lele Jiao, Wen Zheng

“…Aiming at the traffic flow prediction problem of the traffic network, this paper proposes a multistep traffic flow prediction model based on attention-based spatial-temporal-graph neural network-long short-term memory neural network (AST-GCN-LSTM). …”
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Spatio-Temporal Data Augmentation Method for Network Traffic Prediction by Sung Oh, Myeong-Jun Oh, Jong-Kyung Im, Ji-Yeon Park, Joung-Sik Kim, Na-Rae Yi, Myung-Ho Kim, Sung-Ho Bae

“…Despite this need, existing studies have largely overlooked data augmentation techniques that simultaneously address spatial and temporal features. Moreover, network traffic data often exhibits localized and granular patterns, meaning that augmented data with significant spatial deviations from the original distribution can undermine structural consistency, leading to severe performance degradation in prediction models. …”
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Forecasting Lattice and Point Spatial Data: Comparison of Unilateral and Multilateral SAR Models by Carlo Grillenzoni

“…Spatial auto-regressive (SAR) models are widely used in geosciences for data analysis; their main feature is the presence of weight (W) matrices, which define the neighboring relationships between the spatial units. …”
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The Impact of Radiosounding Observations on Numerical Weather Prediction Analyses in the Arctic by T. Naakka, T. Nygård, M. Tjernström, T. Vihma, R. Pirazzini, I. M. Brooks

“…Abstract The radiosounding network in the Arctic, despite being sparse, is a crucial part of the atmospheric observing system for weather prediction and reanalysis. The spatial coverage of the network was evaluated using a numerical weather prediction model, comparing radiosonde observations from Arctic land stations and expeditions in the central Arctic Ocean with operational analyses and background fields (12‐hr forecasts) from European Centre for Medium‐Range Weather Forecasts for January 2016 to September 2018. …”
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Integration of geospatial techniques and machine learning in land parcel prediction by Nekkanti Haripavan, Subhashish Dey, Chimakurthi Harika Mani Chandana

“…Researchers and practitioners can customize their models by choosing the most pertinent variables for each land parcel forecasts from a wide range of spatial features. …”
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Examination of analytical shear stress predictions for coastal dune evolution by O. Cecil, N. Cohn, M. Farthing, S. Dutta, S. Dutta, A. Trautz

“…<p>Existing process-based models for simulating coastal foredune evolution largely use the same analytical approach for estimating wind-induced surface shear stress distributions over spatially variable topography. …”
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Impact of Thermospheric Mass Density on the Orbit Prediction of LEO Satellites by Changyong He, Yang Yang, Brett Carter, Kefei Zhang, Andong Hu, Wang Li, Florent Deleflie, Robert Norman, Suqin Wu

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Bayesian Adaptive Lasso for the Partial Functional Linear Spatial Autoregressive Model by Dengke Xu, Ruiqin Tian, Ying Lu

“…This study introduces a partial functional linear spatial autoregressive model which can explore the relationship between a scalar spatially dependent response variable and predictive variables containing both multiple scalar covariates and a functional covariate. …”
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Prediction of Landslide Susceptibility in the Karakorum under the Context of Climate Change by Yanqian Pei, Haijun Qiu, Yaru Zhu

“…In this work, we focused on static and dynamic environment factors and utilized the certainty factor-logistic regression (CF-LR) model to assess and predict landslide susceptibility in Taxkorgan County, located in the Karakorum. …”
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Optimizing Traffic Speed Prediction Using a Multi-Objective Genetic Algorithm-Enhanced RNN for Intelligent Transportation Systems by C. Swetha Priya, F. Sagayaraj Francis

“…However, developing these models involves several challenges, including understanding spatiotemporal nonlinearities, making accurate predictions, minimizing prediction time, and reducing model complexity. …”
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Change in Fractional Vegetation Cover and Its Prediction during the Growing Season Based on Machine Learning in Southwest China by Xiehui Li, Yuting Liu, Lei Wang

“…The predicted spatial change trends were consistent with the MODIS-MOD13A3-FVC and FY3D-MERSI-FVC, although the predicted FVC values were slightly higher but closer to the MODIS-MOD13A3-FVC. …”
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Crop Yield Prediction: Data Structure and Ai-Powered Methods by V. K. Kalichkin, K. Yu. Maksimovich, O. A. Aleshchenko, V. V. Aleshchenko

“…(Results and discussion) The study presents the core data structure and methods for data acquisition, along with a typical workflow for implementing predictive analytics models for crop yield prediction. …”
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Penalized Composite Likelihood Estimation for Spatial Generalized Linear Mixed Models by Mohsen Mohammadzadeh, Leyla Salehi

“…When discussing non-Gaussian spatially correlated variables, generalized linear mixed models have enough flexibility for modeling various data types. …”
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A novel approach to skin disease segmentation using a visual selective state spatial model with integrated spatial constraints by Yu Bai, Hai Zhou, Hongjie Zhu, Shimin Wen, Binbin Hu, Haotian Li, Huazhang Wang, Daji Ergu, Fangyao Liu

“…Additionally, we introduce a spatially-constrained loss function that mitigates gradient stability issues by considering the distance between label and prediction boundaries. …”
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Ecological epidemiology insights into clonorchiosis endemicity in Guangxi, China and Vietnam: a comprehensive machine learning analysis by Jin-Xin Zheng, Hui‐Hui Zhu, Shang Xia, Men‐Bao Qian, Robert Bergquist, Hung Manh Nguyen, Xiao‐Nong Zhou

“…Logistic regression achieved the highest predictive accuracy (AUC = 0.941). Climatic comparisons showed that Vietnam had a higher annual mean temperature (Bio1: 23.37 °C vs. 20.86 °C), greater temperature seasonality (Bio4: 609.33 vs. 464.92), and higher annual precipitation (Bio12: 1731.64 mm vs. 1607.56 mm) than Guangxi, contributing to spatial differences in endemicity. …”
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Evidential deep learning-based drug-target interaction prediction by Yanpeng Zhao, Yuting Xing, Yixin Zhang, Yifei Wang, Mengxuan Wan, Duoyun Yi, Chengkun Wu, Shangze Li, Huiyan Xu, Hongyang Zhang, Ziyi Liu, Guowei Zhou, Mengfan Li, Xuanze Wang, Zhengshan Chen, Ruijiang Li, Lianlian Wu, Dongsheng Zhao, Peng Zan, Song He, Xiaochen Bo

“…Through EDL, EviDTI provides uncertainty estimates for its predictions. Experimental results on three benchmark datasets demonstrate the competitiveness of EviDTI against 11 baseline models. …”
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Prediction of Nitrogen Responses of Corn by Soil Nitrogen Mineralization Indicators by R.R. Simard, N. Ziadi, M.C. Nolin, A.N. Cambouris

“…Soil nitrogen mineralization potential (Nmin) has to be spatially quantified to enable farmers to vary N fertilizer rates, optimize crop yields, and minimize N transfer from soils to the environment. …”
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Predicting future evapotranspiration based on remote sensing and deep learning by Xin Zheng, Sha Zhang, Shanshan Yang, Jiaojiao Huang, Xianye Meng, Jiahua Zhang, Yun Bai

“…Study focus: This study validates the efficiency of Convolutional Long Short-Term Memory Network (ConvLSTM) models for site-scale ETa prediction. We enhanced the ConvLSTM model by adding a Spatial Pyramid Pooling module (SPPM) and a Multi-head Self-Attention Module (MSA-Module), creating the Multi-head Self-Attention ConvLSTM (MSA-ConvLSTM) model, which we applied to predicting regional-scale actual evapotranspiration (ETa). …”
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A Simple Predictive Enhancer Syntax for Hindbrain Patterning Is Conserved in Vertebrate Genomes. by Joseph Grice, Boris Noyvert, Laura Doglio, Greg Elgar

“…These sequences tend to be located near developmental transcription factors and are enriched in known hindbrain activating elements, demonstrating the predictive power of this simple model.<h4>Conclusion</h4>Our findings support the theory that hundreds of CNEs, and perhaps thousands of regions across the human genome, function to coordinate gene expression in the developing hindbrain. …”
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