Research on Hydrological Element Analysis and Runoff Prediction in the Jinsha River Basin Based on the VIC Model by LIU Hongcai, ZHANG Huilan, SUN Jianing, PANG Jianzhuang, AN Ni

“…[Results] (1) The historical runoff simulation indicates that the Nash efficiency coefficient between the simulated and observed values exceeds 0.6, and the absolute value of BIAS is controlled within 0.15, demonstrating that the VIC model is suitable for simulating the hydrological process in the Jinsha River Basin. (2) The hydrological process components simulated by the VIC model show that precipitation increases from the upstream to the downstream regions, evapotranspiration is higher in the middle and lower reaches, and the multi-year average values of surface runoff and base flow increase from the upstream to the downstream regions with significant differences, further validating the model's applicability. (3) A comparison of single-model and MMEA simulation results using the EEMD-based (Ensemble Empirical Mode Decomposition) statistical downscaling method reveals that the MMEA matches the observed data the best. …”
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Distinguishing Resting State From Motor Imagery Swallowing Using EEG and Deep Learning Models by Sevgi Gokce Aslan, Bulent Yilmaz

“…We thoroughly investigated the impact of different preprocessing methods (Independent Component Analysis, Empirical Mode Decomposition, bandpass filtering) and visualization techniques (spectrograms, scalograms) on the classification performance of multichannel EEG signals. …”
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An xLSTM–XGBoost Ensemble Model for Forecasting Non-Stationary and Highly Volatile Gasoline Price by Fujiang Yuan, Xia Huang, Hong Jiang, Yang Jiang, Zihao Zuo, Lusheng Wang, Yuxin Wang, Shaojie Gu, Yanhong Peng

“…To address the poor fitting performance of single deep learning models on oil price data, which hampers accurate gasoline price prediction, this paper proposes a gasoline price prediction method based on a combined xLSTM–XGBoost model. …”
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Recent Trends in Proxy Model Development for Well Placement Optimization Employing Machine Learning Techniques by Sameer Salasakar, Sabyasachi Prakash, Ganesh Thakur

“…The second class, i.e., a ROM, uses proper orthogonal decomposition (POD) methods to reduce the dimensionality of the problem. …”
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DeepOmicsSurv: a deep learning-based model for survival prediction of oral cancer by Deepali, Neelam Goel, Padmavati Khandnor

“…Methods DeepOmicsSurv builds on the DeepSurv model, incorporating multi-head attention convolutional layers, dropout, pooling, and batch normalization to boost its strength and precision. …”
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Enhancing corn industry sustainability through deep learning hybrid models for price volatility forecasting. by Chengjin Yang, Yanzhong Zhai, Zehua Liu

“…The model integrates a three-layer decomposition combined dual-filter time-series denoising method (TLDCF-TSD), a bidirectional time-convolutional enhancement network (BiTCEN), a bidirectional long- and short-term memory network (BiLSTM), and a frequency-enhanced channel attention mechanism (FECAM) to improve prediction accuracy and robustness. …”
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A structural equation model analysis of the influence of learning organization construction for primary care physicians by Yunlong SUO, Chaoyi CHEN, Zhanchun FENG

“…ObjectiveTo analyze the influence of primary care physicians′ professional identity and job resilience on the construction of learning organizations, and to provide a reference for the reform of primary healthcare supply-side capacity. MethodsFrom August to October 2022, 400 primary care physicians were selected from Yichang in Hubei province, Zhongmu in Henan province, Neijiang in Sichuan province, and Jinan in Shandong province using purposive sampling. …”
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Rapid prediction algorithm for flow field in fully mechanized excavation face based on POD and machine learning by JIN Bing, ZHANG Lang, LI Wei, ZHENG Yi, LIU Yanqing, ZHANG Yibin

“…The support vector machine (SVM) model outperformed the Random Forest and Neural Network models in predicting mode coefficients. …”
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DMS-OGSTV: A Novel Infrared Small Target Detection Based on Spatial-Temporal Tensor Model by Xiaoling Ge, Weixian Qian, Zipeng Fu

“…Detecting small targets in complex infrared backgrounds is challenging due to edge aliasing and noise interference. Tensor decomposition methods show potential but have limitations in these conditions. …”
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Fault Diagnosis of Rolling Element Bearing Based on BiTCN-Attention and OCSSA Mechanism by Yuchen Yang, Chunsong Han, Guangtao Ran, Tengyu Ma, Juntao Pan

“…Subsequently, the BiTCN captures bidirectional temporal dependencies from vibration signals, while the attention mechanism dynamically filters critical fault features, constructing an end-to-end diagnostic model. Experiments on the CWRU dataset demonstrate that the proposed method achieves an average accuracy of 99.44% across 10 fault categories, outperforming state-of-the-art models (e.g., VMD-TCN: 97.5%, CNN-BiLSTM: 84.72%).…”
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Segmented Frequency-Domain Correlation Prediction Model for Long-Term Time Series Forecasting Using Transformer by Haozhuo Tong, Lingyun Kong, Jie Liu, Shiyan Gao, Yilu Xu, Yuezhe Chen

“…In addition, we incorporate a normalization module and a desmoothing factor into the model. These components address the problem of oversmoothing that arises in sequence decomposition methods. …”
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Short-term forecasting of the main indicators of the COVID-19 epidemic in Ukraine based on the seasonal cycle model by Alexei Alyokhin, Anna Brutman, Alexandr Grabovoy, Tetiana Shabelnyk

“…This method, which has no direct analogies, provides the decomposition of a general forecasting task into several simpler tasks, such as the tasks of building a model of the seasonal cycle of a time series, aggregating the original time series, taking into account the duration of the seasonal cycle, forecasting an aggregated time series, developing an aggregated forecast into a forecast in the original time scale, using the seasonal cycle model. …”
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Constrained reduced-order modeling using bounded Gaussian processes for physically consistent reacting flow predictions by Muhammad Azam Hafeez, Alberto Procacci, Axel Coussement, Alessandro Parente

“…As a linear decomposition method, POD complicates the enforcement of constraints in the reduced space, while GPR lacks inherent provisions to ensure physical consistency. …”
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DRHT: A Hybrid Mathematical Model for Accurate Ultrasound Probe Calibration and Efficient 3D Reconstruction by Xuquan Ji, Yonghong Zhang, Huaqing Shang, Lei Hu, Xiaozhi Qi, Wenyong Liu

“…The homography transformation, which can be estimated using the singular value decomposition method, reduces the dimensionality of the calibration data and could significantly accelerate the computation of image points in ultrasonic three-dimensional reconstruction. …”
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Comprehensive approach to modeling and simulation of dynamic soft-sensing design for real-time building energy consumption by Kai Liu, Ting-Zhang Liu, Ping Fang, Zhan-Pei Li

“…Through frequency-domain decomposition, the heat balance equation is used to validate the autoregressive and particle swarm optimization neural network model. …”
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A New Radar Echo Generation Model for Ultra-Low Altitude Targets in Far-Field Conditions by Zhao Long Wang, Chuang Ming Tong, Tong Wang, Yi Jin Wang, Qing Kuan Wang

“…By using the method of temporal decomposition, the wide-time pulse is decomposed into a plurality of narrow pulse signals, and the narrow pulses act on scattering elements with different distances and orientations in space. …”
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Numerical solutions of a fractional order SEIR epidemic model of measles under Caputo fractional derivative. by Nawa A Alshammari, N S Alharthi, Abdulkafi Mohammed Saeed, Adnan Khan, Abdul Hamid Ganie

“…The Homotopy perturbation transform method (HPTM) and Yang transform decomposition methodology (YTDM) have been employed to obtain the numerical solution of the time fractional model. …”
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Application of a Bi-Mamba Model for Railway Subgrade Settlement Prediction During Pipe-Jacking Tunneling by Yipu Peng, Ning Zhou, Bin Wang, Hongjun Gan

“…To explore a more accurate prediction method for subgrade settlement induced by underpass construction, this study takes the existing railway project of Ningbo Yuanyi Road underpass as a case to construct a subgrade settlement prediction model based on the Mamba neural network. …”
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Intent-Based Multi-Cloud Storage Management Powered by a Fine-Tuned Large Language Model by Jingya Zheng, Gaofeng Tao, Shuxin Qin, Dan Wang, Zhongjun Ma

“…Additionally, the system constructs a collaborative intent decomposition method, which guarantees the integrity of intents. …”
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Long-time dynamics of a random higher-order Kirchhoff model with variable coefficient rotational inertia by Penghui Lv, Yuxiao Cun, Guoguang Lin

“…The equation is solved using the Galerkin method, and a stochastic dynamical system is established on this basis. …”
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