Assessment and Prediction of Coastal Ecological Resilience Based on the Pressure–State–Response (PSR) Model by Zhaoyi Wan, Chengyi Zhao, Jianting Zhu, Xiaofei Ma, Jiangzi Chen, Junhao Wang

“…In this study, a new approach based on the Pressure–State–Response model is developed to assess and predict pixel-scale multi-year ecological resilience (ER) and then applied to investigate the spatiotemporal variations of ER in the China’s coastal zone (CCZ) in the past few decades and predict future ER trend under various scenarios. …”
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Cellular automata models for simulation and prediction of urban land use change: Development and prospects by Baoling Gui, Anshuman Bhardwaj, Lydia Sam

“…Among them, Cellular Automata (CA) models have become key tools for predicting urban expansion, optimizing land-use planning, and supporting data-driven decision-making. …”
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Wind Power Prediction Based on a Hybrid Model of ICEEMDAN and ModernTCN-Informer by Jun He, Zijian Cheng, Zijie Zhong, Lizhuo Liang, Jianhui Ye

“…This effectively captures potential interrelationships in wind power data from both temporal and spatial dimensions, followed by accurate and efficient predictions using the Informer model. …”
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Improved Neutral Density Predictions Through Machine Learning Enabled Exospheric Temperature Model by Richard J. Licata, Piyush M. Mehta, Daniel R. Weimer, W. Kent Tobiska

“…The newly developed EXTEMPLAR‐ML model allows for exospheric temperature predictions at any location with one model and provides performance improvements over its predecessor. …”
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Linear and Machine Learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease. by Julia Ledien, Zulma M Cucunubá, Gabriel Parra-Henao, Eliana Rodríguez-Monguí, Andrew P Dobson, Susana B Adamo, María-Gloria Basáñez, Pierre Nouvellet

“…Predictive FoI modelling frameworks are then used to understand spatial and temporal trends indicative of heterogeneity in transmission and changes effected by control interventions. …”
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Weather-Based Prediction Models for the Prevalence of Dengue Vectors Aedes aegypti and Ae. albopictus by J. M. Manel K. Herath, Hemalika T. K. Abeyasundara, W. A. Priyanka P. De Silva, Thilini C. Weeraratne, S. H. P. Parakrama Karunaratne

“…Another prediction model was developed using OVI and RH with one month lag period (R2 (sq) = 70.21%; F = 57.23; model: OVI predicted = 15.1 + 0.528∗ Lag 1 month RH; RMSE = 2.01). …”
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Bootstrapping Enhanced Model for Improving Soil Nitrogen Prediction Accuracy in Arid Wheat Fields by Qassim A. Talib Al-Shujairy, Suhad M. Al-Hedny, Mohammed A. Naser, Sadeq Muneer Shawkat, Ahmed Hatem Ali, Dinesh Panday

“…Bootstrapped RF models surpassed non-bootstrapped random forest models, demonstrating enhanced predictive capability for soil N. …”
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Burn Severity in Canada's Mountain National Parks: Patterns, Drivers, and Predictions by Weiwei Wang, Xianli Wang, Wanli Wu, Futao Guo, Jane Park, Guangyu Wang

“…The predicted burn severity potentials of the whole parks in 2002 and 2012 showed overall consistent spatial patterns, and lightning‐caused fires produced more high‐severity burn areas than prescribed fires. …”
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An interpretable coupled model (SWAT-STFT) for multispatial-multistep evapotranspiration prediction in the river basin by Zhonghui Guo, Chang Feng, Liu Yang, Qing Liu

“…This integration of physics-based and data-driven modeling not only provides valuable insights into watershed ET modeling prediction and mechanistic understanding but also underscores the broader potential for application across global watersheds and related disciplines.…”
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Fast prediction of irradiation-induced cascade defects using denoising diffusion probabilistic model by Ruihao Liao, Ke Xu, Yifan Liu, Zibo Gao, Shuo Jin, Linyun Liang, Guang-Hong Lu

“…We propose a computational scheme that combines molecular dynamic (MD) simulations with a denoising diffusion probabilistic model (DDPM) to rapidly and accurately predict the spatial coordinates of point defects at any given primary knock atom (PKA) energy, ranging from 0 to 100.0 keV. …”
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A Novel Model for Predicting PM2.5 Concentrations Utilizing Graph Convolutional Networks and Transformer by Yuan Huang, Feilong Han, Qimeng Feng

“…To enhance the model’s predictive performance, we designed a new Transformer architecture named FFPformer, which incorporates the Fast Fourier Transform into the Transformer framework. …”
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Development of an AI model for DILI-level prediction using liver organoid brightfield images by Shiyi Tan, Yan Ding, Wei Wang, Jianhua Rao, Feng Cheng, Qiuyin Zhang, Tingting Xu, Tianmu Hu, Qinyi Hu, Ziliang Ye, Xiaopeng Yan, Xiaowei Wang, Mingyue Li, Peng Xie, Zaozao Chen, Geyu Liang, Yuepu Pu, Juan Zhang, Zhongze Gu

“…Here we show a drug-induced liver injury (DILI) level prediction model using HLO brightfield images (DILITracer) considering that DILI is the major causes of drug withdrawals. …”
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Spatiotemporal Soil Moisture Prediction Using a Causal-Guided Deep Learning Model by Tingtao Wu, Lei Xu, Ziwei Pan, Ruinan Cai, Jin Dai, Shuang Yang, Xihao Zhang, Xi Zhang, Nengcheng Chen

“…The spatiotemporal prediction of RZSM refers to the process of estimating its future spatial distribution and temporal variations using predictive models. …”
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Ecological risk assessment and prediction of riparian zones in the Jiangsu section of the Yangtze River from a spatiotemporal perspective by Zihan Zhu, Cheng Zhang, Yangyang Lu, Jian Ye, Guohua Fang, Changran Sun, Yun Yang

“…Focusing on the Yangtze River riparian zone in Jiangsu Province, an ecological risk assessment system with 20 indicators was developed based on a systematic analysis of the ecological risk exposure–response process. The temporal and spatial characteristics of ecological risks in the riparian zone from 2003 to 2023 were analysed using the ecological risk composite index model, Moran index, and Getis-Ord Gi* cold hotspot analysis method, while ecological risks for 2028 and 2033 were predicted using the Grey–Markov chain and PLUS models. …”
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Computational predictions of magnetic resonance acoustic radiation force imaging for breast cancer focused ultrasound therapy by Chloe K. Nelson, Michelle Kline, Allison Payne, Christopher R. Dillon

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DINOV2-FCS: a model for fruit leaf disease classification and severity prediction by Chunhui Bai, Chunhui Bai, Chunhui Bai, Lilian Zhang, Lilian Zhang, Lilian Zhang, Lutao Gao, Lutao Gao, Lutao Gao, Lin Peng, Lin Peng, Lin Peng, Peishan Li, Peishan Li, Peishan Li, Linnan Yang, Linnan Yang, Linnan Yang

“…However, the current prediction of disease degree by machine learning methods still faces challenges, including suboptimal accuracy and limited generalizability.MethodsIn light of the growing application of large model technology across a range of fields, this study draws upon the DINOV2 visual large vision model backbone network to construct the DINOV2-Fruit Leaf Classification and Segmentation Model (DINOV2-FCS), a model designed for the classification and severity prediction of diverse fruit leaf diseases. …”
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A cloud-based framework for the quantification of the spatially-explicit uncertainty of remotely sensed benthic habitats by Spyridon Christofilakos, Alina Blume, Avi Putri Pertiwi, Chengfa Benjamin Lee, Dimosthenis Traganos, Peter Reinartz

“…The calculation of the spatially-explicit uncertainty is based on the Shannon Entropy equation and the probability values of a successful prediction according to the ML model. …”
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Spatial Dynamics of Harbour Porpoise Phocoena phocoena Relative to Local Hydrodynamics and Environmental Conditions by Robert Mzungu Runya, Chris McGonigle, Rory Quinn, Morgane Pommier, Christian Armstrong

“…Using data derived from multibeam echosounders (MBES), particle size analysis of sediments, hydrodynamic modelling, and theodolite tracking observations, the study examines the influence of local hydrodynamics and environmental conditions on the spatial distribution of harbour porpoises. …”
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Multi‐Satellite Data Assimilation for Large‐Scale Hydrological‐Hydrodynamic Prediction: Proof of Concept in the Amazon Basin by S. Wongchuig, R. Paiva, V. Siqueira, F. Papa, A. Fleischmann, S. Biancamaria, A. Paris, M. Parrens, A. Al Bitar

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Feature fusion ensemble classification approach for epileptic seizure prediction using electroencephalographic bio-signals by Yazeed Alkhrijah, Yazeed Alkhrijah, Shehzad Khalid, Shehzad Khalid, Syed Muhammad Usman, Amina Jameel, Muhammad Zubair, Haya Aldossary, Aamir Anwar, Saad Arif

“…The output of these classifiers is then fed into the model-agnostic meta learner ensemble classifier with LSTM as the base classifier for the final prediction of interictal and preictal states.ResultsThe proposed methodology is trained and tested on the publicly available CHB-MIT dataset while achieving 99.34% sensitivity, 98.67% specificity, and a false positive alarm rate of 0.039.DiscussionThe proposed method not only outperforms the existing methods in terms of sensitivity and specificity but is also computationally efficient, making it suitable for real-time epileptic seizure prediction systems.…”
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