A hybrid deep learning framework for global irradiance prediction using fuzzy C-Means, CNN-WNN, and Informer models by Walid Mchara, Lazhar Manai, Mohamed Abdellatif Khalfa, Monia Raissi, Wissem Dimassi, Salah Hannachi

“…Artificial intelligence (AI) is revolutionizing solar energy forecasting, enabling precise irradiance prediction for electric solar vehicles (ESVs) to optimize energy efficiency and extend driving range.This study introduces a novel AI-powered hybrid deep learning framework that synergistically combines fuzzy C-means (FCM) clustering, convolutional neural networks (CNNs), wavelet neural networks (WNNs), and an Informer model to achieve superior accuracy. …”
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SMOPCA: spatially aware dimension reduction integrating multi-omics improves the efficiency of spatial domain detection by Mo Chen, Ruihua Cheng, Jianuo He, Jun Chen, Jie Zhang

“…Despite these advancements, there is a notable lack of effective methods for modeling spatial multi-omics data. We introduce SMOPCA, a Spatial Multi-Omics Principal Component Analysis method designed to perform joint dimension reduction on multimodal data while preserving spatial dependencies. …”
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Spatiotemporal Dynamics and Potential Distribution Prediction of <i>Spartina alterniflora</i> Invasion in Bohai Bay Based on Sentinel Time-Series Data and MaxEnt Modeling by Qi Wang, Guoli Cui, Haojie Liu, Xiao Huang, Xiangming Xiao, Ming Wang, Mingming Jia, Dehua Mao, Xiaoyan Li, Yihua Xiao, Huiying Li

“…Despite this local reduction, MaxEnt modeling suggests that climate trends and habitat suitability continue to support potential northward expansion, particularly in high-risk areas such as the Binhai New District, the Shandong Yellow River Delta, and the Laizhou Bay tributary estuary. …”
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Individual mobility prediction by considering current traveling features and historical activity chain by Xiaotong Zhang, Zhipeng Gui, Yuhang Liu, Dehua Peng, Qianxi Lan, Zhangxiao Shen, Huan Chen, Yuhui Zuo, Yao Yao, Huayi Wu, Kai Li, Kun Qin

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Integrative spatial and single-cell transcriptomics elucidate programmed cell death-driven tumor microenvironment dynamics in hepatocellular carcinoma by Kai Lei, Kai Lei, Yutong Zhao, Shumin Li, Jiawei Liu, Wenhao Chen, Caihong Zhou, Yi Zhang, Jinmei Tan, Jian Wu, Qi Zhou, Qi Zhou, Jiehui Tan

“…This study aims to develop a PCD scores prediction model to evaluate the prognosis of hepatocellular carcinoma (HCC) and elucidate the tumor microenvironment differences.MethodsWe analyzed transcriptomic data from 363 HCC patients in the TCGA database and 221 patients in the GEO database to develop a PCD prediction model. …”
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Design and development of an efficient RLNet prediction model for deepfake video detection by Varad Bhandarkawthekar, T. M. Navamani, Rishabh Sharma, K. Shyamala

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Adaptive dynamic prediction model of mining subsidence aided by measured data by Yuanfei Chen, Jianfeng Zha, Lei Wang

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Performance Prediction and Evaluation of Biogas-Fed Solid Oxide Fuel Cell Plants Based on the Spatial and Temporal Fusion Model under Fluctuation Fuel Types and Processing Options by Shuai Ma, Run Jia, Yanan Li, Changyan Liu, Hao Guo

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Monthly Arctic Sea‐Ice Prediction With a Linear Inverse Model by M. Kathleen Brennan, Gregory J. Hakim, Edward Blanchard‐Wrigglesworth

“…Abstract We evaluate Linear Inverse Models (LIMs) trained on last millennium model data to predict Arctic sea‐ice concentration, thickness, and other atmospheric and oceanic variables on monthly timescales. …”
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Hybrid approaches enhance hydrological model usability for local streamflow prediction by Yiheng Du, Ilias G. Pechlivanidis

“…Abstract Hydrological models are essential for predicting water flux dynamics, including extremes, and managing water resources, yet traditional process-based large-scale models often struggle with accuracy and process understanding due to their inability to represent complex, non-linear hydrometeorological processes, limiting their effectiveness in local conditions. …”
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RUL prediction method based on cross-view hybrid network model by Ai Yandi, Fang Dong, Tian Zhiping, Yan Kaiyang

“…To this end, this paper designs a RUL prediction framework based on a cross-view hybrid network model (CVHNet). …”
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Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction by Jingye Zhang, Ke Sun, Xiaoming Han, Ning Mao

“…Magnitude prediction is a key focus in earthquake science research, and using machine learning models to analyze seismic data, identify pre-seismic anomalies, and improve prediction accuracy is of great scientific and practical significance. …”
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Deciphering the Mechanism of Better Predictions of Regional LSTM Models in Ungauged Basins by Qiang Yu, Liguang Jiang, Raphael Schneider, Yi Zheng, Junguo Liu

“…The long short‐term memory (LSTM) model has gained popularity in rainfall‐runoff prediction in recent years and has proven applicable in PUB. …”
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An improved machine-learning model for lightning-ignited wildfire prediction in Texas by Qi Zhang, Cong Gao, Chunming Shi

“…Using this dataset, we developed an eXtreme gradient boosting-based machine learning model that integrates meteorological, soil, vegetative, lightning, topographic, and human activity variables to predict LIW probability. …”
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A Meteorology Based Particulate Matter Prediction Model for Megacity Dhaka by Sadia Afrin, Mohammad Maksimul Islam, Tanvir Ahmed

“…Models also exhibit strong predictive power in forecasting PM levels of two other CAMSs in Dhaka. …”
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External validation of risk prediction models for post-stroke mortality in Berlin by Jessica L Rohmann, Tobias Kurth, Heinrich J Audebert, Marco Piccininni, Lukas Reitzle

“…We aimed to assess the performance of two prediction models for post-stroke mortality in Berlin, Germany.Design We used data from the Berlin-SPecific Acute Treatment in Ischaemic or hAemorrhagic stroke with Long-term follow-up (B-SPATIAL) registry.Setting Multicentre stroke registry in Berlin, Germany.Participants Adult patients admitted within 6 hours after symptom onset and with a 10th revision of the International Classification of Diseases discharge diagnosis of ischaemic stroke, haemorrhagic stroke or transient ischaemic attack at one of 15 hospitals with stroke units between 1 January 2016 and 31 January 2021.Primary outcome measures We evaluated calibration (calibration-in-the-large, intercept, slope and plot) and discrimination performance (c-statistic) of Bray et al’s 30-day mortality and Smith et al’s in-hospital mortality prediction models. …”
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A lightweight hybrid model for accurate ammonia prediction in pig houses by Jacqueline Musabimana, Qiuju Xie, Hong Zhou, Ping Zheng, Honggui Liu, Tiemin Ma, Jiming Liu

“…The model improves accuracy compared to other state-of-the-art and ability for NH3 prediction.…”
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A Spatial Transformation Based Next Frame Predictor by Saad Mokssit, Daniel Bonilla Licea, Bassma Guermah, Mounir Ghogho

“…In this work, we equip autonomous cars with an object-oriented next-frame predictor that leverages Transformer architecture to extract, for each moving object in the scene, a spatial transformation applied to the object to predict its configuration in the next frame. …”
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Multivariate Segment Expandable Encoder-Decoder Model for Time Series Forecasting by Yanhong Li, David C. Anastasiu

“…Additionally, MSEED incorporates a simple vanilla encoder-decoder model for strengthening rolling predictions. The framework has been tested on four challenging real-world datasets, focusing on two critical forecasting scenarios: long-term predictions (three days ahead) and rolling predictions (every four hours) to simulate real-time decision-making in water resource management. …”
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Increasing neural network robustness improves match to macaque V1 eigenspectrum, spatial frequency preference and predictivity. by Nathan C L Kong, Eshed Margalit, Justin L Gardner, Anthony M Norcia

“…They also suggest that it may be useful to penalize slow-decaying eigenspectra or to bias models to extract features of lower spatial frequencies during task-optimization in order to improve robustness and V1 neural response predictivity.…”
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