Sequential learning on a tensor network Born machine with trainable token embedding by Wanda Hou, Miao Li, Yi-Zhuang You

“…This approach maximizes the utilization of operator space and enhances the model’s expressiveness. Empirical results on RNA data demonstrate that the proposed method significantly reduces negative log-likelihood compared to one-hot embeddings, with higher physical dimensions further enhancing single-site probabilities and multi-site correlations. …”
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Disturbance Robust Generalized Predictive Control Applied to an EV Charger Grid Converter by Jefferson S. Costa, Angelo Lunard, Luis F. Normandia Lourenco, Lucas Rodrigues, Alfeu J. Sguarezi Filho

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Total factor carbon emission productivity in China’s manufacturing industry: a study considering indirect carbon emissions by Zhijie Hao, Xiaotian Wang

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Dynamic Response of a Single-Rotor Wind Turbine with Planetary Speed Increaser and Counter-Rotating Electric Generator in Starting Transient State by Radu Saulescu, Mircea Neagoe

“…The proposed analytical dynamic algorithm involves the decomposition of the wind system into its component rigid bodies, followed by the description of their dynamic equations using the Newton–Euler method. …”
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Comparative analysis of wellbore electrical heating, low-frequency heating, and steam injection for in-situ conversion in continental shale oil reservoirs by Zhaobin Zhang, Maryelin Josefina Briceño Montilla, Zhuoran Xie, Shouding Li, Yanzhi Hu, Xiao Li

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Analysis of approaches to identification of trend in the structure of the time series by U. S. Mokhnatkina, D. V. Parfenov, D. A. Petrusevich

“…Trend modeling using Fourier series decomposition leads to quite accurate results for time series of different natures. …”
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Development of a Software Package Architecture for Simulation and Prototyping of Radar Systems and Complexes by I. S. Serdiukov

“…However, these software packages are either versatile, thus being incapable of taking the specifics of radar operation into account and requiring hand-made implementation of mathematical models for simulating radar signals, or are aimed at a narrow range of prototyping problems and algorithm development for processing radar information for a strictly defined radar type (or even a specific model). …”
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Hydroformer: Frequency Domain Enhanced Multi‐Attention Transformer for Monthly Lake Level Reconstruction With Low Data Input Requirements by Minglei Hou, Jiahua Wei, Yang Shi, Shengling Hou, Wenqian Zhang, Jiaqi Xu, Yue Wu, He Wang

“…Seasonal and trend patterns of catchment meteorological factors and lake level are initially identified by a time series decomposition block, then independently learned and refined within the model. …”
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Analysis of the healthy food market in the Russian Federation and the Republic of Adygea by G. V. Karamushko, Z. N. Khatko, N. Kh. Navasardyan

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Overview of Tensor-Based Cooperative MIMO Communication Systems—Part 2: Semi-Blind Receivers by Gérard Favier, Danilo Sousa Rocha

“…After a reminder of some tensor prerequisites, we present an overview of tensor models, with a detailed, unified, and original description of two classes of tensor decomposition frequently used in the design of relay systems, namely nested CPD/PARAFAC and nested Tucker decomposition (TD). …”
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Nonlinear time domain and multi-scale frequency domain feature fusion for time series forecasting by Kejiang Xiao, Yefeng Li, Yaning Dong, Wenqi Yang, Binting Yao, Liang Chen

“…Nevertheless, existing methods face challenges such as insufficient nonlinear modeling, incomplete multi-scale feature separation, and ineffective time-frequency domain fusion. …”
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Spectral Entropic Radiomics Feature Extraction (SERFE): an adaptive approach for glioblastoma disease classification by V. L. Sowmya, A. Bharathi Malakreddy, Santhi Natarajan, N. Prathik, I. S. Rajesh

“…Conventional tools such as PyRadiomics and CaPTk rely on extensive handcrafted feature sets, which often result in redundancy and necessitate further optimization steps.MethodsThis study proposes a novel framework, Spectral Entropic Radiomics Feature Extraction (SERFE), which integrates spectral frequency decomposition, entropy-driven feature selection, and graph-based spatial encoding. …”
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Environmental Data Analytics for Smart Cities: A Machine Learning and Statistical Approach by Ali Suliman AlSalehy, Mike Bailey

“…Spatiotemporal analysis highlighted persistent hotspots in industrial areas and unexpectedly high levels in some residential zones. A range of models was tested, with ensemble methods (Extreme Gradient Boosting (XGBoost) and Categorical Boosting (CatBoost)) achieving the best performance (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup><mo>></mo><mn>0.95</mn></mrow></semantics></math></inline-formula>) and XGBoost producing the lowest Root Mean Squared Error (RMSE) of 0.0371 ppm. …”
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Alignment Error Estimation of the Conductive Pattern of 3D-Printed Circuit Boards by O. N. Smirnova, A. A. Aleksandrov, Yu. S. Bobrova, K. M. Moiseev

“…Interlayer alignment errors are estimated by microsection analysis and X-ray inspection, as well as using the misalignment decomposition method described by Yu.B. Tsvetkov for electronics.Results. …”
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Chronic Kidney Disease Prediction Based On Machine Learning Algorithms by Kethineni Likitha., Nithinchandra, Kumar Narendra, Sk Sajida Sultana.

“…Several recommendation methods were used: KNN Basic, Nonnegative Matrix Factorization (NMF), Co-Clustering, and Singular Value Decomposition (SVD). …”
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Virtual measurement system for UHF-transistor amplifiers by Alexander D. Tupitsyn

“…VMS development for measuring of amplifiers parameters by means of simulation modeling based on amplifier topology.Methods and materials. …”
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A comprehensive review of machine learning applications in forecasting solar PV and wind turbine power output by Ian B. Benitez, Jai Govind Singh

“…Key features for SPVPO forecasting include solar irradiance, ambient temperature, and prior SPVPO, while wind speed, turbine speed, and prior wind power output are crucial for WTPO forecasting. Moreover, ensemble models, support vector machines, Gaussian processes, hybrid artificial neural networks, and decomposition-based hybrid models exhibit promising forecasting accuracy and reliability. …”
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The Metrics for Promising R&D Early Forecast by Sergey Petrovich Kovalev

“…The key questions are described in details for source data formation to calculate more complex functional-based metrics using some lexical-graph R&D text models, to solve decomposition tasks and path search on graphs of terms collocations and co-words with the purpose of terminology evolution investigations, tautological definitions localization, and texts structure quality estimation. …”
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RS-SpecSDF: Reflection-supervised surface reconstruction and material estimation for specular indoor scenes by Dong-Yu Chen, Hao-Xiang Chen, Qun-Ce Xu, Tai-Jiang Mu

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“Optimizing sEMG Gesture Recognition with Stacked Autoencoder Neural Network for Bionic Hand” by Mr. Amol Pandurang Yadav, Dr. Sandip.R. Patil

“…This study presents a novel deep learning approach for surface electromyography (sEMG) gesture recognition using stacked autoencoder neural network (SAE)s. The method leverages hierarchical representation learning to extract meaningful features from raw sEMG signals, enhancing the precision and robustness of gesture classification. • Feature Extraction and Classification MODWT Decomposition: The sEMG signals were decomposed using the MODWT DECOMPOSITION(Maximal Overlap Discrete Wavelet Transform) to capture various frequency components. • Time Domain Parameters: A total of 28 features per subject were extracted from the time domain, including statistical and spectral features. • Classifier Evaluation: Initial evaluations involved Autoencoder and LDA (Linear Discriminant Analysis) classifiers, with Autoencoder achieving an average accuracy of 77.96 % ± 1.24, outperforming LDA's 65.36 % ± 1.09.Advanced Neural Network Approach: Stacked Autoencoder Neural Network: To address challenges in distinguishing similar gestures within grasp groups, a Stacked Autoencoder Neural Network was employed. …”
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