Research on quantitative prediction method of structural fractures in metamorphic rock reservoirs by Qinghong Yang, Qi Cheng, Guanjie Zhang, Xinwu Liao, Ning Shi, Lei Zhang, Xiang Wang, Wenchao Liu, Jingshou Liu

“…On the basis of 3D seismic and rock mechanics experimental data, geomechanical heterogeneity models of the study area in different periods were established, the 3D distributions of the tectonic stress field in different periods were clarified through finite element simulation, and a quantitative relationship between the tectonic stress and fracture linear density was established to quantitatively predict the spatial distribution characteristics of multistage tectonic fractures. …”
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Predicted deep-sea coral habitat suitability for the U.S. West coast. by John M Guinotte, Andrew J Davies

“…Regional scale habitat suitability models provide finer scale resolution and more focused predictions of where organisms may occur. …”
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Predicting insect migration density and speed in the daytime convective boundary layer. by James R Bell, Prabhuraj Aralimarad, Ka-Sing Lim, Jason W Chapman

“…Overall, our findings suggest that predicting migrating insects at altitude at distances of ≈ 100 km is promising, but additional radars are needed to parameterise spatial covariance.…”
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TBM Advanced Geological Prediction via Ellipsoidal Positioning Velocity Analysis by Zhen Gao, Xin Rong, Wei Wang, Bin Huang, Junqiang Liu

“…However, given the fast excavation speed and limited prediction space in tunnel boring machine (TBM) construction tunnels, traditional methods have significant technical limitations. …”
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LLM-Augmented Linear Transformer–CNN for Enhanced Stock Price Prediction by Lei Zhou, Yuqi Zhang, Jian Yu, Guiling Wang, Zhizhong Liu, Sira Yongchareon, Nancy Wang

“…In this study, we propose a novel hybrid deep learning framework that integrates a large language model (LLM), a Linear Transformer (LT), and a Convolutional Neural Network (CNN) to enhance stock price prediction using solely historical market data. …”
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Predicting the first seasonal occurrence of <i>Lobesia botrana</i> and <i>Eupoecilia ambiguella</i> in Austria using new multiple linear regression models by Kerstin Kolkmann, Sylvia Blümel, Josef Eitzinger

“…The validation results showed high prediction accuracy for all six newly generated MLR models for L. botrana and for two out of six newly generated MLR models for E. ambiguella (R2 > 0.6; RMSE < 4.0; | BIAS | < 2.5). …”
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Probabilistic Interference Prediction for Dynamic 6G In-X Sub-Networks by Pramesh Gautam, Carsten Bockelmann, Armin Dekorsy

“…The effectiveness of the proposed predictors is evaluated using a spatially consistent 3GPP channel model incorporating realistic mobility and two distinct extreme traffic models. …”
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Human Mobility Prediction Based on Social Media with Complex Event Processing by Fernando Terroso-Sáenz, Jesús Cuenca-Jara, Aurora González-Vidal, Antonio F. Skarmeta

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Unsupervised feature correlation-based spatial stratification for local context-aware modelling by Jinyu Meng, Zengchuan Dong, Yongze Song

“…Context-aware modelling improves the accuracy of spatial inferences through using local environmental conditions, spatial dependency, and heterogeneity. …”
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Pattern mining and prediction techniques for user behavioral trajectories in e-commerce. by Xin Wang, Dong-Feng Liu

“…Analyzing user behaviour sequences requires balancing detail with data reduction while addressing challenges such as excessive spatial complexity and potential null results in predictions. …”
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Building the optimal hybrid spatial Data-Driven Model: Balancing accuracy and complexity by Emanuele Barca, Maria Clementina Caputo, Rita Masciale

“…Based on these findings, we have developed a methodology that employs a series of statistical tests and data analytics to identify essential features hidden in spatial data in order to assess the predictive model (of white/grey kind) that best approximates underlying spatial processes. …”
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Transient Stability Assessment Model With Sample Selection Method Based on Spatial Distribution by Yongbin Li, Yiting Wang, Jian Li, Huanbei Zhao, Huaiyuan Wang, Litao Hu

“…Sample selection aims to optimize the training set to speed up the training process while improving the preference of the TSA model. The typical samples which can accurately express the spatial distribution of the raw dataset are selected by the proposed method. …”
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A Consistent Differential Privacy Dynamic Trajectory Flow Prediction Method by Hongzhi Pan

“…The proposed method constructs a trajectory flow graph and integrates Laplace noise‐based differential privacy with consistency constraint adjustments to enhance privacy while maintaining data utility. A CNN‐LSTM hybrid model also extracts spatial and temporal features, improving prediction performance through feature fusion. …”
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Deep learning-based prediction of multi-level just noticeable distortion by Haifeng XU, Hongkui WANG, Haibing YIN, Chuqiao CHEN

“…Visual just noticeable distortion (JND) directly reflects the sensitivity of the human visual system to visual signal noise, and is widely used in image and video processing.Aiming at the multilevel prediction problem of video JND threshold, it was transformed into the prediction problem of satisfied user ratio (SUR) curve, and a feature fusion-based SUR curve prediction model was proposed.The model was mainly divided into key frame extraction module, feature extraction and fusion module, and SUR score regression module.In the key frame extraction module, according to the visual perception mechanism, the spatial-temporal domain perception complexity was proposed and used as the video key frame judgment index.In the feature extraction and fusion module, a multi-scale dense residual network was proposed based on dense residual block (RDB) to realize image feature extraction and multi-scale fusion.The experimental results show that the proposed SUR curve prediction model is overall better than the existing models in terms of JND prediction accuracy and reduces the time cost by 8.1% on average in terms of operational efficiency.Meanwhile, the model can also be used to predict other layers of JND thresholds, which can be directly applied to video multilevel perceptual coding optimization.…”
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Using publicly available data for predicting socioeconomic values in urban context by Maximiliano Ojeda, Juan Reutter

“…We leverage Graph Neural Network (GNN) models to capture the spatial relationships inherent in network data while integrating perceptual features extracted from images to enhance predictive accuracy. …”
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Demand prediction for shared bicycles around metro stations incorporating STAGCN. by Xue Xing, Le Wan, Fahui Luo

“…Experiments conducted on shared bike and metro datasets in Shenzhen demonstrate that the proposed model achieves a coefficient of determination (R2) of 0.893, outperforming baseline models by 6.7% in prediction accuracy. …”
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A Strategy for Predicting Transonic Compressor Performance at Low Reynolds Number by Dalin Shi, Tianyu Pan, Xingyu Zhu, Zhiping Li

“…Ultimately, this strategy improves the radial spatial resolution compared to the original method and is able to predict the compressor performance at a low Re with pressure ratio and efficiency errors of 0.23% and 1.8%, respectively.…”
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Fast Multimodal Trajectory Prediction for Vehicles Based on Multimodal Information Fusion by Likun Ge, Shuting Wang, Guangqi Wang

“…We propose a novel trajectory prediction model that adopts the encoder–decoder paradigm. …”
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A spatiotemporal graph wavelet neural network for traffic flow prediction by Linjie Zhang, Jianfeng Ma

“…The traffic flow prediction is fast becoming a key instrument in the transportation system, which has achieved impressive performance for traffic management. …”
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Surface water quality prediction based on BOA-BiLSTM model(基于BOA-BiLSTM模型的地表水水质预测) by 章佩丽(ZHANG Peili), 赵文雅(ZHAO Wenya), 许旭敏(XU Xumin), 包鑫磊(BAO Xinlei)

“…The results indicate that the predicted RMSE of NH3—N by the BOA-BiLSTM model for the next four hours is respectively 0.213 2, 0.368 9, 0.332 7 and 0.374 0, the predicted RMSE of TP is respectively 0.024 6, 0.032 1, 0.042 2 and 0.033 4. …”
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