Composite GDP nowcasting using macroeconomic variables and electricity data. by Zhiqiang Lan, Zidi Liu, Guoyao Wu

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Two-stage multiple imputation with a longitudinal composite variable by Xuzhi Wang, Martin G. Larson, Chunyu Liu

“…Results In simulation studies, the imputation models within two-stage MI, assuming appropriate ignorability assumptions, exhibited the smallest bias and achieved optimal coverage probabilities for the means, slopes across different time points, and hazard ratios for mortality related to the composite variable. …”
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A Unique Bifuzzy Manufacturing Service Composition Model Using an Extended Teaching-Learning-Based Optimization Algorithm by Yushu Yang, Jie Lin, Zijuan Hu

“…Based on bifuzzy theory, we put forward a unique bifuzzy manufacturing service portfolio model. Through the application of the fuzzy variable to express quality of service (QoS) value of manufacturing services, this model also accounts for the preferences of manufacturing firms by allocating various weights to different sub-tasks. …”
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A Forecast Model for COVID-19 Spread Trends Using Blog and GPS Data from Smartphones by Ryosuke Susuta, Kenta Yamada, Hideki Takayasu, Misako Takayasu

“…By employing time series’ trend decomposition and Spearman’s rank correlation, we identify and select a set of significant variables from the GPS and blog data to construct two models: a fixed-period model and a sequential adaptive model that updates with each new wave of infections. …”
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Prediction of the monthly river water level by using ensemble decomposition modeling by Chaitanya Baliram Pande, Lariyah Mohd Sidek, Bijay Halder, Okan Mert Katipoğlu, Jitendra Rajput, Fahad Alshehri, Rabin Chakrabortty, Subodh Chandra Pal, Norlida Mohd Dom, Miklas Scholz

“…Similarly, in the testing phase, the best two models performances are very well as a CEEMDAN-RF (R2:0.94) and CEEMDAN-RS (R2:0.90) in second combination variables, and the first combination variables based SVM- Linear (R2:0.93) and RF (R2:0.89) models are performance higher compared with other models. …”
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Process variables in mixture experimental design applied to wood plastic composites by Javier Cruz-Salgado, Sergio Alonso-Romero, Edgar Augusto Ruelas-Santoyo, José Alfredo Jiménez-García, Israel Miguel-Andrés, Roxana Zaricell Bautista-López

“…The highest tensile strength is achieved at maximum levels of these variables, indicating a synergistic effect. Response variable optimization identifies the optimal mixture composition as 90% PET, 10% wood, and no coupling agent. …”
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Capacity allocation method for a hybrid energy storage system participating in secondary frequency regulation based on variational mode decomposition by Taiying Zheng, Minghao Ye, Qinghua Wu

“…This paper introduces a method for configuring the capacity of a HESS engaged in the secondary frequency regulation, utilizing Variable Mode Decomposition (VMD). An economic model for a HESS, considering lifecycle costs and frequency regulation benefits, is constructed to maximize net income. …”
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Eigen‐Swarm: Swarm's Thermospheric Mass Density Modeling via Eigen‐Decomposition by Charles Owolabi, Hyunju Connor, Don Hampton, Denny M. Oliveira, Andres Calabia, V. Sai Gowtam, Eftyhia Zesta

“…We employ the Eigen‐Decomposition technique to extract dominant spatio‐temporal modes, with the first three capturing 99.12% of the variance, forming the basis of the Swarm‐based Eigen‐Decomposition model. …”
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Advances and applications of empirical mode decomposition and its variants in hydrology: A review by CHEN Yunfei, LIU Zuyu, LIU Xiuhua, HE Junqi, ZHENG Ce, MA Yandong

“…We provide a comprehensive overview of the fundamental theory, methodological characteristics, and current challenges of EMD, covering five EMD-based methods: Hilbert-Huang Transform (HHT), Ensemble Empirical Mode Decomposition (EEMD), Multivariate Empirical Mode Decomposition (MEMD), Extreme Point Symmetric Mode Decomposition (ESMD), and Variational Mode Decomposition (VMD). …”
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A Deformation Prediction Model for Concrete Dams Based on RSA-VMD-AttLSTM by Pei Liu, Hao Gu, Chongshi Gu, Yanbo Wang

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AI-Enhanced Photovoltaic Power Prediction Under Cross-Continental Dust Events and Air Composition Variability in the Mediterranean Region by Pavlos Nikolaidis

“…Using machine learning methods, particularly regression trees, the proposed approach evaluates the impact of key environmental variables on PV performance, with an emphasis on atmospheric dust transport and air composition variability. …”
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Investigating reduced-dimensional variability in aircraft-observed aerosol–cloud parameters by Kayley M. Butler, Sam J. Silva, Armin Sorooshian, Richard H. Moore, Glenn S. Diskin, John B. Nowak, Luke Ziemba, Ewan Crosbie, Michael A. Shook, Joshua DiGangi, Edward Winstead, Claire Robinson, Yonghoon Choi

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Interannual variability of moisture sources and isotopic composition of Meiyu-Baiu rainfall in southwestern Japan: Importance of Asian monsoon moisture for extreme rainfall events by Xiaoyang Li, Ryuichi Kawamura, Kimpei Ichiyanagi, Kei Yoshimura

“…However, the contributions and thermodynamic processes of major moisture sources, along with their interannual variability, remain unclear. To better understand the underlying atmospheric processes responsible for interannual variability of Meiy-Baiu rainfall, we utilized an isotopic regional spectral model to investigate the moisture sources and isotopic composition of Meiyu-Baiu rainfall in southwestern Japan from 2004 to 2023. …”
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Evaluation of Eight Decomposition-Hybrid Models for Short-Term Daily Reference Evapotranspiration Prediction by Yunfei Chen, Zuyu Liu, Ting Long, Xiuhua Liu, Yaowei Gao, Sibo Wang

“…Given this, in this study we evaluate eight decomposition-hybrid models that integrate various decomposition techniques with a long short-term memory (LSTM) network to enhance short-term (5-day, 7-day, and 10-day) ET_o forecasting. …”
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Daily Runoff Prediction Model Based on Multivariate Variational Mode Decomposition and Correlation Reconstruction by DING Jie, TU Peng-fei, FENG Yu, ZENG Huai-en

“…Finally, the integrated prediction combining fluctuation and random terms under condition 5 yielded R2 of 0.87 and 0.93 for the overall prediction at Ankang and Baihe stations, respectively, demonstrating excellent model performance. [Conclusions](1) The MVMD decomposition method can control the number of decomposition layers, ensuring complete signal feature extraction without overfitting while improving processing speed.(2) Pearson correlation coefficient method enhances prediction accuracy through decomposed data classification.(3) The MEA-BP can improve signal-to-noise ratio, adapt to complex environments, enhance learning efficiency and generalization ability, and reduce computational complexity.(4) The GWO-ELM algorithm integrates grey wolf optimizer with extreme learning machine, providing a fast and adaptive solution for time-series prediction with reduced overfitting and improved efficiency.(5) The overall combined model can efficiently and stably process large amount of data while ensuring high accuracy.…”
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Numerical benchmarking of dual decomposition-based optimization algorithms for distributed model predictive control by Vassilios Yfantis, Achim Wagner, Martin Ruskowski

“…This paper presents a benchmark study of dual decomposition-based distributed optimization algorithms applied to constraint-coupled model predictive control problems. …”
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A Hybrid Model for Carbon Price Forecasting Based on Secondary Decomposition and Weight Optimization by Yongfa Chen, Yingjie Zhu, Jie Wang, Meng Li

“…Secondly, a two-stage feature-selection method is employed, in which the partial autocorrelation function (PACF) is used to select relevant lagged features, while the maximal information coefficient (MIC) is applied to identify key variables from both historical and external data. Finally, this paper introduces a dynamic integration module based on sliding windows and sequential least squares programming (SLSQP), which can not only adaptively adjust the weights of four base learners but can also effectively leverage the complementary advantages of each model and track the dynamic trends of carbon prices. …”
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The Influences of Canopy Species and Topographic Variables on Understory Species Diversity and Composition in Coniferous Forests by Hong Huo, Qi Feng, Yong-hong Su

“…Using constrained ordination and the variation partitioning model, we quantitatively assessed the relative effects of two sets of explanatory variables on understory species composition. …”
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Evaluation of tensile strength variability in fiber reinforced composite rods using statistical distributions by Hao Qin, Thierno Aliou Ka, Xiang Li, Kangxin Sun, Kaiqiang Qin, Sarkar Noor E Khuda, T. Tafsirojjaman

“…However, the inherent variability of composite materials poses a critical challenge, particularly in tensile strength, which directly impacts the safety and durability of structures. …”
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Woody and Herbaceous Species Diversity Respond Differently to Environmental Variables in Semiarid Areas in Ghana by Reginald T. Guuroh, Bertrand F. Nero, Fousseni Folega, Kwame A. Oduro, Fred Kalanzi, Gloria K. Adeyiga, Adu‐Gyamfi Asamoah, Mark Appiah, Miracle Obeng, E. Amponsah

“…Species richness and the Shannon–Weiner Index are the response variables. Two‐way ANOVA was performed to test the interaction effects of climate and land‐use on species diversity; linear mixed‐effect models were used to test the relationships between multiple environmental variables, and structural equation modelling was used to determine the direct and indirect effects of climate and land‐use on species diversity. …”
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