User Trajectory Prediction in Cellular Networks Using Multi-Step LSTM Approaches: Case Study and Performance Evaluation by Iskandar, Hajiar Yuliana, Hendrawan, Adriel Timoteo, Fabian Rafinanda Benyamin, Naufal Bhanu Anargyarahman

“…While LSTM excels in capturing sequential temporal patterns, Transformer introduces multi-head attention mechanisms to model complex spatial and temporal dependencies, filling a significant research gap in trajectory prediction. …”
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Reconstructed hyperspectral imaging for in-situ nutrient prediction in pine needles by Yuanhang Li, Yuanhang Li, Jun Du, Jun Du, Chuangjie Zeng, Chuangjie Zeng, Yongshan Wu, Yongshan Wu, Junxian Chen, Junxian Chen, Teng Long, Teng Long, Yongbing Long, Yongbing Long, Yubin Lan, Yubin Lan, Xiaoliang Che, Tianyi Liu, Jing Zhao, Jing Zhao

“…However, its high cost and complexity hinder practical field applications.MethodsTo overcome these limitations, we propose a deep-learning-based method to reconstruct hyperspectral images from RGB inputs for in situ needle nutrient prediction. The model reconstructs hyperspectral images with a spectral range of 400–1000 nm (3.4 nm resolution) and spatial resolution of 768×768. …”
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Quantifying 3D coral reef structural complexity from 2D drone imagery using artificial intelligence by Aviv Suan, Simone Franceschini, Joushua Madin, Elizabeth Madin

“…The validation of our model resulted in R2 values of 0.71, 0.65, and 0.56 for each metric, respectively, indicating a robust predictive capability. …”
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Integrated analysis of single-cell, spatial and bulk RNA-sequencing identifies a cell-death signature for predicting the outcomes of head and neck cancer by Yue Pan, Lei Fei, Shihua Wang, Hua Chen, Changqing Jiang, Hong Li, Changsong Wang, Yao Yang, Qinggao Zhang, Yongwen Chen

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PM2.5 prediction and its influencing factors in the Beijing-Tianjin-Hebei urban agglomeration using spatial temporal graph convolutional networks by Yawen Zhao

“…To address this, this study uses spatiotemporal analysis and Spatial Temporal Graph Convolutional Networks (ST-GCN) to evaluate the variation and driving factors of PM _2.5 concentrations in the Beijing-Tianjin-Hebei (BTH) urban agglomeration from 2014 to 2024, and to make predictions. …”
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Current and future climate suitability for the hazel dormouse in the UK and the impact on reintroduced populations by Emma L. Cartledge, Joe Bellis, Ian White, Jane L. Hurst, Paula Stockley, Sarah Dalrymple

“…We find no effect of climate suitability on adult dormouse counts at reintroduction sites, but dormouse counts decline with time since reintroduction. Future projections predict that climate change may lead to more widespread climate suitability for dormice in the UK, reflecting predicted changes in seasonality, winter temperature and precipitation. …”
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Improved digital mapping of soil texture using the kernel temperature–vegetation dryness index and adaptive boosting by Xu Zhai, Yuzhong Liu, Yuanyuan Hong, Yunjie Yang, Pengju Wang, Zhicheng Ye, Xiaoyan Liu, Tianlong She, Lihui Wang, Chen Xu, Lili Zhang, Qiang Wang

“…In this study, we collected 399 soil samples collected from Mingguang City in southeast China and made spatial predictions of soil texture based on remote sensing indices such as the kernel normalized difference vegetation index computed from Landsat8 data and topographic attributes computed via digital elevation model as environmental covariates. …”
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Spatiotemporal prediction of soil organic carbon density in Europe (2000–2022) using earth observation and machine learning by Xuemeng Tian, Sytze de Bruin, Rolf Simoes, Mustafa Serkan Isik, Robert Minarik, Yu-Feng Ho, Murat Şahin, Martin Herold, Davide Consoli, Tomislav Hengl

“…Prediction accuracy varies by land cover, depth interval and year of prediction with the worst accuracy for shrubland and deeper soils 100–200 cm. …”
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IMPROVE OF UNCERTAIN MICROSATELLITE MAGNETIC CLEANLINESS BASED ON MAGNETIC FIELD SPATIAL HARMONICS COMPENSATION by Б.І. Кузнецов, Т.Б. Нікітіна, І.В. Бовдуй, К.В. Чуніхін, В.В. Коломієць, Б.Б. Кобилянський

“…Both vector game solution calculated based on particles multi-swarm optimization (PMSO) algorithms from Pareto optimal solutions taking into account binary preference relations. Prediction model and location of compensating units in spherical coordinates as well as multipole harmonic coefficients of dipoles, quadrupoles and octupoles are calculated during prediction and control of uncertain microsatellite MC. …”
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A Moroccan soil spectral library use framework for improving soil property prediction: Evaluating a geostatistical approach by Tadesse Gashaw Asrat, Timo Breure, Ruben Sakrabani, Ron Corstanje, Kirsty L. Hassall, Abdellah Hamma, Fassil Kebede, Stephan M. Haefele

“…A soil spectrum generated by any spectrometer requires a calibration model to estimate soil properties from it. To achieve best results, the assumption is that locally calibrated models offer more accurate predictions. …”
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A dynamic adaptive graph convolutional recurrent network model for efficient mid-short term prediction of global sea surface salinity by Guangwen Peng, Yingbing Liu, Cong Xiao, Wenying Du, Changjiang Xiao

“…AGCRUs dynamically construct topological relationships via graph convolution to model spatial variations, while GRUs capture temporal dependencies. …”
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A Robot Error Prediction and Compensation Method Using Joint Weights Optimization Within Configuration Space by Fantong Meng, Jinhua Wei, Qianyi Feng, Zhigang Dong, Renke Kang, Dongming Guo, Jiankun Yang

“…A spatial-interpolation-based unbiased estimation method with joint weights optimization is proposed for robot errors prediction, and the particle filter method is utilized to search for the optimal joint weights, enhancing the anisotropic characteristics of the prediction model. …”
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Predicting cortical bone resorption in the mouse tibia under disuse conditions caused by transient muscle paralysis by Himanshu Shekhar, Sanjay Singh, Jitendra Prasad

“…Dissipation energy density induced by loading, based on interstitial fluid flow, has been adopted as the mechanotransduction-triggering stimulus. The developed model uses the loss of stimulus due to the disuse of bone as an input and predicts the quantity of bone loss with spatial accuracy. …”
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Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins by Senlin Tang, Fubao Sun, Wenbin Liu, Hong Wang, Yao Feng, Ziwei Li

“…Abstract Streamflow prediction in ungauged basins (PUB) is challenging, and Long Short‐Term Memory (LSTM) is widely used to for such predictions, owing to its excellent migration performance. …”
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Modeling the effects of land use change on agricultural carrying capacity and food security by R. Harini, R. Rijanta, E.H. Pangaribowo, R.F. Putri, I. Sukri

“…Predictions of spatial land changes will reveal changes in land function, carrying capacity and food security between regions.METHODS: Land changes were studied using remote sensing imagery-based mapping methods and spatial simulations using the cellular automata approach. …”
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Information-Theoretic Modeling of Categorical Spatiotemporal GIS Data by David Percy, Martin Zwick

“…An NLCD tool reports how much change occurred for each category of land use; for the study area examined, the most dynamic class is Evergreen Forest (EFO), so the presence or absence of EFO in 2021 was chosen as the dependent variable that our data modeling attempts to predict. RA predicts the outcome with approximately 80% accuracy using a sparse set of cells from a spacetime data cube consisting of neighboring lagged-time cells. …”
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Attention-Enhanced CNN-LSTM Model for Exercise Oxygen Consumption Prediction with Multi-Source Temporal Features by Zhen Wang, Yingzhe Song, Lei Pang, Shanjun Li, Gang Sun

“…Across all models, prediction errors grew during high-intensity bouts, highlighting a bottleneck in capturing non-linear physiological responses under heavy load. …”
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Pedestrian Movement Prediction and Pattern Analysis by V. Toshev, K. Karamitov, D. Petrova-Antonova, D. Petrova-Antonova

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FEN-MRMGCN: A Frontend-Enhanced Network Based on Multi-Relational Modeling GCN for Bus Arrival Time Prediction by Ting Qiu, Chan-Tong Lam, Bowie Liu, Benjamin K. Ng, Xiaochen Yuan, Sio Kei Im

“…The proposed module captures spatial relationships in dense, multi-route areas by using graph convolution layers based on multi-relational modeling to aggregate spatial information. …”
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Research on traffic state prediction method based on traffic flow prediction under multi-time granularity by Yue Chen, Jian Lu

“…In order to improve the accuracy of traffic state prediction model, a traffic state prediction method based on multi-time granularity traffic flow prediction is proposed. …”
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