Predicting Wheat Potential Yield in China Based on Eco-Evolutionary Optimality Principles by Shen Tan, Shengchao Qiao, Han Wang, Sheng Chang

“…However, most existing crop models for wheat PY rely on type-specific parameters that describe wheat traits, which often require calibration and, in turn, reduce prediction confidence when applied across different spatial or temporal scales. …”
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Urban Traffic Travel Time Short-Term Prediction Model Based on Spatio-Temporal Feature Extraction by Leilei Kang, Guojing Hu, Hao Huang, Weike Lu, Lan Liu

“…In order to improve the accuracy of short-term travel time prediction in an urban road network, a hybrid model for spatio-temporal feature extraction and prediction of urban road network travel time is proposed in this research, which combines empirical dynamic modeling (EDM) and complex networks (CN) with an XGBoost prediction model. …”
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A prediction model of soil organic carbon into river and its driving mechanism in red soil region by Yanhu He, Yuyin Yang, Daoguo Xu, Zirui Wang

“…This study integrates the Soil and Water Assessment Tool (SWAT) for sediment yield simulation and a Soil Organic Carbon Content (SOCC) model to quantify SOCR at the basin scale. A Random Forest-based prediction model was developed to explore the spatial-temporal variability and driving mechanisms of SOCR in the Dongjiang River Basin (DRB), a representative red soil region in southern China. …”
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Drone Attitude and Position Prediction via Stacked Hybrid Deep Learning Model for Massive MIMO Applications by Abdullah Al-Ahmadi

“…This paper presents a novel stacked hybrid deep learning model for real-time prediction of drone attitude and position, specifically designed to support applications in massive Multiple-Input Multiple-Output (MIMO) systems. …”
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Evaluating PSA dynamics for predicting androgen deprivation failure with a patient specific prostate cancer model by Shengchao Zhao, Evan T. Keller, Tyler Robinson, Jinlu Dai, Alyssa Ghose, Ajjai Alva, Trachette Jackson, Harsh Vardhan Jain

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Integration of pre-trained protein language models with equivariant graph neural networks for peptide toxicity prediction by Shihu Jiao, Xiucai Ye, Tetsuya Sakurai, Quan Zou, Wu Han, Chao Zhan

“…By combining sequence embeddings from the ProtT5 language model and 3D structural data predicted by ESMFold, StrucToxNet can capture both sequential and spatial characteristics of peptides. …”
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A CNN-Based Downscaling Model for Macau Temperature Prediction Using ERA5 Reanalysis Data by Ningqing Pang, Hoiio Kong, Chanseng Wong, Zijun Li, Yu Du, Jeremy Cheuk-Hin Leung

“…The current reanalysis of temperature data faces difficulties in providing more accurate geographical temperature data due to insufficient spatial resolution (0.25° × 0.25°). In this study, a lightweight downscaling method incorporating a convolutional neural network is proposed to construct a high-resolution temperature prediction model for the Macau region based on ERA5 reanalysis data. …”
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AP-GRIP evaluation framework for data-driven train delay prediction models: systematic literature review by Tiong Kah Yong, Zhenliang Ma, Carl-William Palmqvist

“…The framework covers six key aspects across overall, spatial, temporal, and train-specific dimensions, providing a systematic approach for the comprehensive assessment of train delay prediction models. …”
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Calculation Methods of Topographic Factors Modification Using Data Digital Elevation Model (DEM) To Predict Erosion by Hengki Simanjuntak, Hendrayanto ., Nining Puspaningsih

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A Double-Layer LSTM Model Based on Driving Style and Adaptive Grid for Intention-Trajectory Prediction by Yikun Fan, Wei Zhang, Wenting Zhang, Dejin Zhang, Li He

“…This study introduces a novel double-layer long short-term memory (LSTM) model to surmount the limitations of conventional prediction methods, which frequently overlook predicted vehicle behavior and interactions. …”
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Global lightning-ignited wildfires prediction and climate change projections based on explainable machine learning models by Assaf Shmuel, Teddy Lazebnik, Oren Glickman, Eyal Heifetz, Colin Price

“…In this study, we present machine learning models designed to characterize and predict lightning-ignited wildfires on a global scale. …”
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A Spatiotemporal Convolutional Neural Network Model Based on Dual Attention Mechanism for Passenger Flow Prediction by Jinlong Li, Haoran Chen, Qiuzi Lu, Xi Wang, Haifeng Song, Lunming Qin

“…The integration of network units with different specialities in the proposed model allows the network to capture passenger flow data, temporal correlation, spatial correlation, and spatiotemporal correlation with the dual attention mechanism, further improving the prediction accuracy. …”
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Spatiotemporal evolution and prediction of blue–green–grey-space carbon stocks in Henan Province, China by Kai Zhou, Kai Zhou, Xinyu Wei, Yanjie Wang, Jinhui Wang, Zhifang Wang, Zhifang Wang, Yichuan Zhang, Yichuan Zhang

“…Changes in blue–green–grey spaces use greatly influenced the carbon-storage capabilities of ecosystems, which is crucial for maintaining the carbon balance of regional ecosystems.By combining the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model with the Patch-generating Land Use Simulation (PLUS) model, this study evaluates the spatiotemporal evolution of blue–green–grey spatial carbon stocks in Henan Province, China, and predicts the relationship between blue–green–grey spatial changes and carbon stocks under four future scenarios. …”
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Exploring malaria prediction models in Togo: a time series forecasting by health district and target group by Muriel Rabilloud, Nicolas Voirin, Anne Thomas, Tchaa Abalo Bakai, Tinah Atcha-Oubou, Tchassama Tchadjobo

“…Objectives Integrating malaria prediction models into malaria control strategies can help to anticipate the response to seasonal epidemics. …”
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Evaluation of Feature Selection and Regression Models to Predict Biomass of Sweet Basil by Using Drone and Satellite Imagery by Luana Centorame, Nicolò La Porta, Michela Papandrea, Adriano Mancini, Ester Foppa Pedretti

“…This study is among the first to combine multispectral data from both a drone equipped with Altum-PT camera and PlanetScope satellite imagery to predict fresh biomass in sweet basil grown in an open field, demonstrating the added value of integrating different spatial scales. …”
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Application of capsule networks based on reparameterized heterogeneous convolution in multi-scale heterogeneous environment matrix in predictive modeling of interdisciplinary compl... by Shuya Liu, Xiaoli Zhang

“…Abstract Predictive modeling of complex systems frequently encounters inadequate processing capabilities for multi-scale heterogeneous data, as conventional methods grapple with the effective integration of such data. …”
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Prediction of Vanadium Contamination Distribution Pattern Through Remote Sensing Image Fusion and Machine Learning by Zipeng Zhao, Yuman Sun, Weiwei Jia, Jinyan Yang, Fan Wang

“…The 934 nm and 464 nm wavelengths were identified as the most critical spectral bands for predicting soil vanadium contamination. This integrated approach robustly delineates the spatial distribution characteristics of V and V5+ in soils, facilitating precise monitoring and ecological risk assessments of vanadium contamination through a comparative analysis of predictive accuracy across diverse models.…”
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Cultivated Land Suitability Prediction in Southern Xinjiang Typical Areas Based on Optimized MaxEnt Model by Yilong Tian, Xiaohuang Liu, Hongyu Li, Run Liu, Ping Zhu, Chaozhu Li, Xinping Luo, Chao Wang, Honghui Zhao

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Explainable, federated deep learning model predicts disease progression risk of cutaneous squamous cell carcinoma by Juan I. Pisula, Doris Helbig, Lucas Sancéré, Oana-Diana Persa, Corinna Bürger, Anne Fröhlich, Carina Lorenz, Sandra Bingmann, Dennis Niebel, Konstantin Drexler, Jennifer Landsberg, Roman Thomas, Katarzyna Bozek, Johannes Brägelmann

“…Risk stratification systems based on clinico-pathological criteria aim to identify high-risk patients, but accurate predictions remain challenging. Deep learning models present new opportunities for patient risk prediction, yet their interpretability has been largely unexplored. …”
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High-resolution simulation and prediction of urban private vehicles energy consumption system: Agent-based modelling by Chuang Tu, Jiayi Liu, Jing Wang, Jing Bai, Guangwen Hu

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