<strong>Hybrid neural network with genetic algorithms for predicting distribution pattern of <em>Tetranychus urticae</em> (Acari: Tetranychidae) in cucumbers field of Ramhormoz, Ir... by Alireza Shabaninejad, Bahram Tafaghodinia, Nooshin Zandi Sohani

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Early Fault Diagnosis and Prediction of Marine Large-Capacity Batteries Based on Real Data by Yifan Liu, Huabiao Jin, Xiangguo Yang, Telu Tang, Qijia Song, Yuelin Chen, Lin Liu, Shoude Jiang

“…Furthermore, the fault prediction method based on the iTransformer model is introduced to forecast variations in battery cluster voltages. …”
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Potential Distribution Prediction of Terminalia chebula Retz. in China Under Current and Future Climate Scenarios by Zhang‐Hong Dong, Hua Jiang, Wei Zhang, Jianhua Wu, Yanping Yang, Taoming Yang, Jiangping Zhao, Cunzhen Luo, Xiaoxia Yang, Guilin Li

“…Utilizing GIS and MaxEnt model, we predicted the spatial distribution of Terminalia chebula Retz. in China for the current and for the future (2050s and 2070s) under the RCP4.5 and RCP8.5 representative concentration pathways. …”
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Using Earth observation to support the predictive accuracy of species distribution models in ecological restoration: a case study of Poland beavers (Castor fiber) by H. A. Cheriyanda Raveendra, L. Picco, F. Pirotti, F. Pirotti

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Study and Prediction of Land Use/Cover Change in Small and Medium-Sized River Basins in Typical Urbanization Areas by LI Liujie, LIU Bingjun, WEI Qiuying

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Improving National Forest Mapping in Romania Using Machine Learning and Sentinel-2 Multispectral Imagery by Mohamed Islam Keskes, Aya Hamed Mohamed, Stelian Alexandru Borz, Mihai Daniel Niţă

“…To ensure the reliability of the model predictions, extensive field campaigns were conducted across representative Romanian forests. …”
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Application of deep learning in cloud cover prediction using geostationary satellite images by Yeonjin Lee, Seyun Min, Jihyun Yoon, Jongsung Ha, Seungtaek Jeong, Seonghyun Ryu, Myoung-Hwan Ahn

“…We explore the effectiveness of advanced deep learning techniques – specifically 3D Convolutional Neural Networks, Long Short-Term Memory networks, and Convolutional Long Short-Term Memory (ConvLSTM) – using GK2A cloud detection data, which provides updates every 10 minutes at 2 km spatial resolution. Our model utilizes training sequences of four past hourly images to predict cloud cover up to 4 hours ahead. …”
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A comparative framework to develop transferable species distribution models for animal telemetry data by Joshua A. Cullen, Camila Domit, Margaret M. Lamont, Christopher D. Marshall, Armando J. B. Santos, Christopher R. Sasso, Mehsin Al Ansi, Kristen M. Hart, Mariana M. P. B. Fuentes

“…In predictive modeling, practitioners often use correlative SDMs that only evaluate a single spatial scale and do not account for differences in life stages. …”
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Predictive study of machine learning combined with serum Neuregulin 4 levels for hyperthyroidism in type II diabetes mellitus by Huilan Gu, Ye Lu

“…Machine learning techniques have garnered widespread attention due to their advantages in modeling high-dimensional, heterogeneous data.ObjectiveThis study was to evaluate the predictive capability of a support vector machine (SVM) model based on serum NRG4 combined with a convolutional neural network (CNN) and long short-term memory network (LSTM)-based ultrasound feature classification (SVM-CNN+LSTM) model for predicting the occurrence of FT in patients with T2DM.MethodsStudied 500 T2DM patients (60 with FT, 440 without), and 200 healthy controls. …”
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A dissimilarity-adaptive cross-validation method for evaluating geospatial machine learning predictions with clustered samples by Yanwen Wang, Mahdi Khodadadzadeh, Raúl Zurita-Milla

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Prediction, Prevention, and Control of “Overall–Local” Coal Burst of Isolated Working Faces Prior to Mining by Ming Zhang, Shiji Yang

“…Numerical simulations are used to validate the effectiveness of borehole stress relief, while field monitoring further confirms the accuracy of the proposed model, leading to the development of the “overall–local” coal burst prediction method. …”
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LinU-Mamba: Visual Mamba U-Net with Linear Attention to Predict Wildfire Spread by Henintsoa S. Andrianarivony, Moulay A. Akhloufi

“…In this study, we develop a deep learning model to predict wildfire spread using remote sensing data. …”
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Artificial intelligence-assisted magnetic resonance imaging technology in the differential diagnosis and prognosis prediction of endometrial cancer by Xinyu Qi

“…The AC, PR, RE, and F1 values of the proposed model for postoperative recurrence prediction were visibly higher (P  < 0.05). …”
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Western North Pacific Monsoon Gyres: A New Identification Approach, Interannual Variability, and Seasonal Prediction by Yuqi Zang, Haikun Zhao, Philip J. Klotzbach, Chao Wang, Liguang Wu

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Machine learning-assisted multi-dimensional transcriptomic analysis of cytoskeleton-related molecules and their relationship with prognosis in hepatocellular carcinoma by Yuxuan Li, Mingbo Cao, Xiaorui Su, Gaoyuan Yang, Yupeng Ren, Zhiwei He, Zheng Shi, Ziyi Hu, Guirong Liang, Qi Zhang, Zhicheng Yao, Meihai Deng

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Relationships between abundances of breeding ducks and attributes of Canadian prairie wetlands by Blake Bartzen, Kevin W. Dufour, Mark T. Bidwell, Michael D. Watmough, Robert G. Clark

“…In regions where duck densities were high, there were more ducks per pond; conversely, there were fewer ducks per pond in regions where pond densities were high, indicating that mechanisms influencing local habitat use were, in part, mediated by processes occurring at larger spatial scales. Although models explained small amounts of variation of duck abundance on a per pond basis, these models explained more variation when results were aggregated to the level of survey segment, indicating reasonable performance of models for estimating duck abundance over specific areas with known pond areas. …”
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Spatial epidemiology of Tabanus (Diptera: Tabanidae) vectors of Trypanosoma by Roberta Marques, Daniel Jiménez-García, Luis E. Escobar, Tiago Kütter Krolow, Rodrigo Ferreira Krüger

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Multi‑omics identification of a novel signature for serous ovarian carcinoma in the context of 3P medicine and based on twelve programmed cell death patterns: a multi-cohort machin... by Lele Ye, Chunhao Long, Binbing Xu, Xuyang Yao, Jiaye Yu, Yunhui Luo, Yuan Xu, Zhuofeng Jiang, Zekai Nian, Yawen Zheng, Yaoyao Cai, Xiangyang Xue, Gangqiang Guo

“…Subsequently, 14 PCD-related genes were included in the PCD-gene-based CDI model. Genomics, single-cell transcriptomes, bulk transcriptomes, spatial transcriptomes, and clinical information from TCGA-OV, GSE26193, GSE63885, and GSE140082 were collected and analyzed to verify the prediction model. …”
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Near real-time LOD prediction using ConvLSTM model through integrating IGS rapid LOD and effective angular momentum by Kehao Yu, Xiaoya Wang, Zhao Li, Jian Wang, Lihua Li, Weiping Jiang, Kai Yang, Jingwen Long, Yunzhao Li

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Study on Short Term Temperature Forecast Model in Jiangxi Province based on LightGBM Machine Learning Algorithm by Kanghui SUN, An XIAO, Houjie XIA

“…In order to achieve further improvement in the forecast accuracy of station temperatures and enhance the forecast capability for extreme temperatures， this study establishes a 24-hour national station daily maximum （minimum） temperature forecast model for Jiangxi Province based on the LightGBM machine-learning algorithm and the MOS forecast framework by using the surface observation data of 91 national stations in Jiangxi Province and the upper-air and surface forecast data of the ECMWF model from 2017 to 2019.The results of the 2020 evaluation show that the LightGBM model daily maximum （minimum） temperature forecast is consistent with the observed trend， and the annual average forecast is better than that of three numerical models， ECMWF， CMA-SH9 and CMA-GFS， two machine learning products， RF and SVM， and subjective revision products.In terms of the spatial and temporal distribution of forecast errors， the model's daily maximum （minimum） temperature forecast errors in winter and spring are slightly larger than those in summer and autumn； the daily maximum temperature forecast errors show the spatial distribution characteristics of "larger in the south and smaller in the north， and larger in the periphery than in the centre"， while the opposite is true for the daily minimum temperatures.In terms of important weather processes， the LightGBM model has the best prediction effect among the seven products in the high temperature process； in the strong cold air process， the LightGBM model is still better than the three numerical model products and the other two machine-learning models， but the prediction effect of the daily minimum temperature is not as good as that of the subjective revision products.After a simple empirical correction for the low-temperature forecast error in the strong cold air process， the model low-temperature forecast effect is close to that of the subjective revision product.The model significance analysis shows that the recent surface observation features also contribute to the model construction， and the results can be used as a reference for model improvement and temperature forecast product development.At present， the LightGBM model temperature forecast products have been applied to meteorological operations in Jiangxi Province.…”
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