Decomposition Mechanisms of Lignin-Related Aromatic Monomers in Solution Plasma by Takaki Miyamoto, Jeanielle Amurao, Eiji Minami, Haruo Kawamoto

“…In this study, an aqueous solution plasma treatment was investigated for the catalyst-free production of valuable chemicals from lignin. To elucidate the decomposition mechanism, the aqueous solution plasma treatment was applied to the fundamental lignin aromatic model compounds—phenol, guaiacol, and syringol. …”
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Prediction of dam deformation using adaptive noise CEEMDAN and BiGRU time series modeling by WANG Zixuan, OU Bin, CHEN Dehui, YANG Shiyong, ZHAO Dingzhu, FU Shuyan

“…【Method】The model uses sample entropy reconstruction and the K-means clustering algorithm to optimize the adaptive noise complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) process, generating multiple intrinsic mode functions (IMF). …”
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Order assignment mechanism based on hierarchical decomposition and element matching by ZHANG Xinyue, LYU Weimin, WEI Dingjiang, YANG Dongsheng

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Interpretable Supervised Muscle Network Decomposition by Multifactorial ANOVA-ICA by Jun-Ichiro Hirayama

“…Multifactorial ANOVA modeling and ICA both effectively improve the interpretability of the decomposition, relative to other baseline approaches. …”
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Personalized prediction of gait freezing using dynamic mode decomposition by Zhiwen Fu, Congping Lin, Yiwei Zhang

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A Method of Sample Models of Program Construction in Terms of Petri Nets by D. I. Kharitonov, E. A. Golenkov, G. V. Tarasov, D. V. Leontyev

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Application of decomposition to hyperbolic, parabolic, and elliptic partial differential equations by G. Adomian

“…The decomposition method is applied to examples of hyperbolic, parabolic, and elliptic partial differential equations without use of linearizatlon techniques. …”
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Behind-the-Fence Generation Forecasting: A Batched Decomposition Framework by Gideon Egharevba, Arne Dankers, Hamidreza Zareipour

“…The batched decomposition method was shown to outperform the benchmarks for both test cases.…”
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Implementation of Principal Component Analysis (PCA)/Singular Value Decomposition (SVD) and Neural Networks in Constructing a Reduced-Order Model for Virtual Sensing of Mechanical... by M. A. Melgarejo, A. Pérez, D. Ruiz, A. Casas, F. González, V. González de Lena Alonso

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Assessing Vertical Equity in Defined Benefit Pension Plans: An Application to Switzerland by Tanja Kirn, Gijs Dekkers

“…We use an aligned dynamic microsimulation model to apply this method to the first pillar of the Swiss pension system and highlight the following three key effects: (1) the impact of the accrual rate on vertical equity; (2) the assessment of actuarial neutrality through the comparison of migrants with the non-migrant population; and (3) vertical equity across marital statuses. …”
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Electricity pinch analysis method for flexibility supply-demand matching in power systems by Yaling Mao, Tiejiang Yuan, Xueqin Tian, Yue Teng

“…Leveraging the theoretical framework of pinch technology from process engineering, this paper proposes an Electricity Pinch Analysis (EPA) method for flexibility assessment. First, the net-load profile is decomposed by successive variational mode decomposition (SVMD) optimized with the Red-billed Blue Magpie Optimization (RBMO) algorithm to construct a flexibility demand model. …”
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Nighttime foggy image generation algorithm based on semi-analytic model by GUO Fan, LIU Wentao, YANG Jianan, TANG Jin

“…Then, an intrinsic image decomposition method was used to decompose the night image into illuminance map and reflection map to obtain the relevant parameters of the semi-analytical model. …”
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Effects of Medicare predictors in health disparities in the risk of Alzheimer's disease by Igor Akushevich, Arseniy Yashkin, Julia Kravchenko

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A survey of model compression techniques: past, present, and future by Defu Liu, Yixiao Zhu, Zhe Liu, Yi Liu, Changlin Han, Jinkai Tian, Ruihao Li, Wei Yi

“…To meet the urgent demand for efficient deployment, we delve into several compression methods—such as quantization, pruning, low-rank decomposition, and knowledge distillation—emphasizing their fundamental principles, recent advancements, and innovative strategies. …”
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PCL-RC: a parallel cloud resource load prediction model based on feature optimization by Guoxiu Zhang, Xinyi He, Xiaofeng Wang

“…To address the problem of nonlinear load data feature extraction, a feature extraction optimization method that is based on combining an improved random forest method and complete ensemble empirical modal decomposition with adaptive noise is proposed to realize regular decomposition and feature extraction from fluctuating data. …”
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A decomposition of Fisher’s information to inform sample size for developing or updating fair and precise clinical prediction models for individual risk—part 1: binary outcomes by Richard D. Riley, Gary S. Collins, Rebecca Whittle, Lucinda Archer, Kym I. E. Snell, Paula Dhiman, Laura Kirton, Amardeep Legha, Xiaoxuan Liu, Alastair K. Denniston, Frank E. Harrell, Laure Wynants, Glen P. Martin, Joie Ensor

“…However, more guidance is needed for targeting precise and fair individual-level risk estimates. Methods We propose a decomposition of Fisher’s information matrix to help examine sample sizes required for developing or updating a model, aiming for precise and fair individual-level risk estimates. …”
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Key agreement method based on multi-dimensional advantage distillation over mmWave MIMO channels by MAO Dandan, WANG Ning, ZHEN Jina, ZHANG Ning, HUANG Kaizhi

“…The mmWave MIMO channel was modeled as a high-dimensional tensor spanning the space, time, and frequency domains. …”
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Transformer Oil Acid Value Prediction Method Based on Infrared Spectroscopy and Deep Neural Network by Linjie Fang, Chuanshuai Zong, Zhenguo Pang, Ye Tian, Xuezeng Huang, Yining Zhang, Xiaolong Wang, Shiji Zhang

“…In comparison with the traditional infrared spectral preprocessing method and regression model, the proposed prediction model has a coefficient of determination (R²) of 97.12% and 95.99% for the prediction set and validation set, respectively, which is 4.96% higher than that of the traditional model. …”
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Gearbox fault diagnosis method based on optimized VMD-NLM with 1DDRSN by WAN Zhiguo, ZHAO Wei, WANG Zhiguo, DOU Yihua

“…ObjectiveAiming at the problem of poor accuracy of gearbox fault diagnosis under noise interference, a new fault diagnosis method for gearboxes based on the denoising methods of optimized variational modal decomposition (VMD）and non-local means (NLM) was constructed, combined with a one-dimensional deep residual shrinkage network (1DDRSN).MethodsFirstly, the parameters in the VMD were automatically optimized using the subtractive average-based optimization (SABO); secondly, each intrinsic mode function (IMF） after the decomposition of the VMD was filtered using sample entropy, and the noise-containing components were subjected to the NLM denoising and reconstruction; then, a residual network that combines the attention mechanism with soft thresholding was introduced to model 1DDRSN; finally, the denoised and reconstructed signals were inputted into the 1DDRSN for fault diagnosis and identification. …”
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Spectrum processing method for measuring low levels of specific activity of 137Cs with a NaI(Tl) detector in the presence of natural radionuclides by V. S. Repin, K. A. Sednev

“…The method involves the modeling the spectral regions for each natural radionuclide in the area of 137mBa peak and subtracting the simulated regions and the background spectrum from the total spectrum under the peak of 137mBa. …”
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