POLAND AND UKRAINE IN THE LIGHT OF PARADYSZ'S PERIOD FERTILITY MODEL by J. Paradysz, K. Paradysz

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Cross-Attention U-Net for Elastic Wavefield Decomposition in Anisotropic Media by Youngjae Shin

“…Elastic wavefield separation in anisotropic media is essential for seismic imaging but remains challenging due to complex interactions among multiple wave modes. Traditional methods often rely on solving the Christoffel equation, which is computationally expensive, particularly in heterogeneous models. …”
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Transformer network for time series prediction via wavelet packet decomposition by Zhichao Wu, Aiye Shi, Yan Ping Tao

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FedSVD: Asynchronous Federated Learning With Stale Weight Vector Decomposition by Giwon Sur, Hyejin Kim, Seunghyun Yoon, Hyuk Lim

“…These outdated updates hinder the convergence of the global model during aggregation. To address this staleness problem, we propose FedSVD, a method that leverages vector decomposition of stale weights. …”
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Managing Soil Biota-Mediated Decomposition and Nutrient Mineralization in Sustainable Agroecosystems by Joann K. Whalen

“…Next, it explores experimental approaches to measure the physical, chemical, and biological barriers to decomposition and nutrient mineralization. Methods are proposed to determine the rates of decomposition and nutrient release from organic residues. …”
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Intelligent hybrid method to predict generated power of solar PV system by Prashant Singh, Navneet Kumar Singh, Asheesh Kumar Singh

“…<p>This paper presents a brand-new hybrid solar photovoltaic (PV) power forecasting model called empirical mode decomposition (EMD)-particle swarm optimisation (PSO)-adaptive neuro-fuzzy inference system (ANFIS). …”
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A New Prediction Model of Dam Deformation and Successful Application by Shuangping Li, Bin Zhang, Meng Yang, Senlin Li, Zuqiang Liu

“…In view of the poor accuracy of the monitoring data, which reflect the overall deformation response in the current dam monitoring practices, this paper proposes an innovative solution of ensemble empirical mode decomposition and a wavelet noise reduction method. A high-precision prediction model considering spatial correlation is constructed. …”
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Monthly Runoff Prediction Based on STL-CEEMDAN-LSTM Model by WANG Hai, SHEN Yanqing, QI Shansheng, PAN Hongzhong, HUO Jianzhen, WANG Zhance

“…According to the nonlinear and non-stationary characteristics of monthly runoff sequences, the quadratic decomposition method was combined with machine learning to construct a model for predicting monthly runoff. …”
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Combined wideband speech enhancement method based on statistical model and EMD by Xuan ZHOU, Chang-chun BAO, Bing-yin XIA

“…A combined wideband speech enhancement method based on statistical model and empirical mode decomposition (EMD) was proposed.First,statistical model was used to eliminate the main noise component in noisy speech.Then,the residual noise was further suppressed by a post-processing module which is a speech enhancement algorithm with voice activity detection (VAD) based on EMD.The advantages of the two methods were combined effectively.The performance of the proposed method was evaluated under the standard of ITU-T G160.The experimental results indicate that the algorithm is more effective for improving the SNR in the different noise environments than classical statistical model approach.Meanwhile,in low SNR conditions,musical noise is reduced effectively,and the speech sounds more comfortable.…”
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Multiscale Image Representation and Texture Extraction Using Hierarchical Variational Decomposition by Liming Tang, Chuanjiang He

“…In order to achieve a mutiscale representation and texture extraction for textured image, a hierarchical (BV,Gp,L2) decomposition model is proposed in this paper. We firstly introduce the proposed model which is obtained by replacing the fixed scale parameter of the original (BV,Gp,L2) decomposition with a varying sequence. …”
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Progressive Domain Decomposition for Efficient Training of Physics-Informed Neural Network by Dawei Luo, Soo-Ho Jo, Taejin Kim

“…This study proposes a strategy for decomposing the computational domain to solve differential equations using physics-informed neural networks (PINNs) and progressively saving the trained model in each subdomain. The proposed progressive domain decomposition (PDD) method segments the domain based on the dynamics of residual loss, thereby indicating the complexity of different sections within the entire domain. …”
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PCCNN: A CNN classification model integrating EEG time-frequency features for stroke classification by Teng Wang, Fenglian Li, Jia Yang, Wenhui Jia, Fengyun Hu

“…This paper proposes a stroke classification method using multi-channel electroencephalography (EEG) data. …”
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Modification of Adomian decomposition technique in multiplicative calculus and application for nonlinear equations by Farooq Ahmed Shah, Muhammad Waseem, Alexey Mikhaylov, Gabor Pinter

“…The primary objective of this work is to modify and implement the Adomian decomposition method within the multiplicative calculus framework and to develop an effective class of multiplicative numerical algorithms for obtaining the best approximation of the solution of nonlinear equations. …”
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Theoretical and empirical validation of software trustworthiness measure based on the decomposition of attributes by Hongwei Tao, Yixiang Chen, Hengyang Wu

“…From the perspective of attribute decomposition, there are a variety of software trustworthiness metric models. …”
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An analysis of the decomposition and driving force of carbon emissions in transport sector in China by Jing Gu, Shuhua Jiang, Jing Zhang, Jinde Jiang

“…It first calculates the China’s transport carbon emissions by IPCC carbon emission factor method, and then applies the Logarithmic Mean Divisia Index (LMDI) model for decomposition analysis. …”
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Research on a Joint Extraction Method of Track Circuit Entities and Relations Integrating Global Pointer and Tensor Learning by Yanrui Chen, Guangwu Chen, Peng Li

“…Next, the Tucker decomposition method is utilized to capture the semantic correlations between relations, and an Efficient Global Pointer is employed to globally predict the start and end positions of subject and object entities, incorporating relative position information through rotary position embedding (RoPE). …”
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Synergistic and Antagonistic Effects of Mixed-Leaf Litter Decomposition on Nutrient Cycling by Vestine Mukamparirwa, Salim M. S. Maliondo, Canisius Patrick Mugunga

“…Specifically, we expected mixtures containing nutrient-rich species to exhibit synergistic effects, resulting in faster decay rates and sustained nutrient release, while mixtures with nutrient-poor species would demonstrate antagonistic effects, slowing decomposition. We conducted a mesocosm experiment using a custom wooden setup filled with soil, and the litterbag method was used to test various leaf litter mixtures. …”
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Overhead Transmission Line Modeling Strategies for EMT-Based Traveling-Wave Analysis and Fault Location by Adrian Wilmer Diaz Sarmiento, Jaimis Sajid Leon Colqui, Jose Pissolato Filho

“…The results show that: 1) uniform soil resistivity assumptions introduce negligible errors in TW arrival times despite minor amplitude variations; 2) shield wires significantly affect modal structure, compromising the effectiveness of Clarke transformation for ground quasi-mode decoupling while preserving aerial quasi-mode reliability; 3) exact eigenvector-based decomposition improves ground mode identification but remains impractical for field applications; 4) the classical two-terminal fault location method maintains high accuracy across all modeling configurations; and 5) simplified OHTL modeling uniformly distributed sections at both terminals achieves an optimal balance between accuracy and computational efficiency for simulating TWs. …”
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A Quality Control Method based on Combination Deep Learning for Measurement Data of Complex Mountain Wind Farm by Runjin YAO, Shuaibing CHENG, Qianqian ZHAO, Wenlong LI, Dong QIAN

“…Mountainous winds exhibit strong intermittent， fluctuating， and non-stationary characteristics due to the influence of terrain， resulting in poor observation quality， which makes conventional quality control methods unable to effectively improve their observation quality.To address this issue， a quality control method （VCG） based on variational mode decomposition， convolutional neural networks， and deep learning of gated cyclic units is constructed， and a particle swarm optimization strategy and wind power reconstruction model are introduced to comprehensively improve the quality of observation data.To verify the effectiveness of this method， 10 minute wind speed and direction data of target wind turbines in six complex mountainous wind farms in Jiangxi Ganzhou， Sichuan Guangyuan， Anhui Wuhu， Hubei Huangshi， Henan Pingdingshan， and Guangxi Hezhou in 2016 was quality controlled by VCG and compared with single machine learning method， spatial regression method （SRT）， and inverse distance weighting method （IDW）.The results indicate that VCG method is suitable for quality control of observed wind data in mountainous wind farms， and has a higher error detection rate for suspicious data compared to conventional methods； The controlled data can better restore the observed background field and have a lower error rate when applied to the power generation evaluation business of wind farms； And it has the characteristics of strong terrain adaptability.…”
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Research on short-term precipitation forecasting method based on CEEMDAN-GRU algorithm by Hua Xu, Zongkai Guo, Yu Cao, Xu Cheng, Qiong Zhang, Dan Chen

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