The impact of Fed rate cut on CNY exchange rate: Empirical analysis based on vector autoregressive model by Wang Xulin

“…The core variables are sorted out, global macroeconomic data from 2015 to 2024 are integrated, and a VAR model including CPI, Fed interest rate, EX and M2 is constructed. …”
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Analyzing and Predicting the Agronomic Effectiveness of Fertilizers Derived from Food Waste Using Data-Driven Models by Ksawery Kuligowski, Quoc Ba Tran, Chinh Chien Nguyen, Piotr Kaczyński, Izabela Konkol, Lesław Świerczek, Adam Cenian, Xuan Cuong Nguyen

“…Plant yield and IENU exhibited substantial variability, averaging 2268 ± 3099 kg/ha and 32.3 ± 92.5 kg N/ha, respectively. …”
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FOREIGN EXCHANGE RATE PREDICTION OF INDONESIA'S LARGEST TRADING PARTNER BASED ON VECTOR ERROR CORRECTION MODEL by M. Fariz Fadillah Mardianto, Muhammad Fikry Al Farizi, Made Riyo Ary Permana, Alfian Iqbal Zah, Elly Pusporani

“…In this study, the Vector Error Correction Model (VECM) was used to predict the foreign exchange rate of Indonesia's largest trading partners. …”
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MODEL OF GEOMEDIA CONTAINING DEFECTS: COLLECTIVE EFFECTS OF DEFECTS EVOLUTION DURING FORMATION OF POTENTIAL EARTHQUAKE FOCI by I. A. Panteleev, O. A. Plekhov, O. B. Naimark

“…The authors introduce ‘tensor structural’ variables associated with two specific types of defects, fractures and localized shear faults (Fig. 1). …”
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Empirical Data-Driven Linear Model of a Swimming Robot Using the Complex Delay-Embedding DMD Technique by Mostafa Sayahkarajy, Hartmut Witte

“…The collected data were analyzed offline, proposing a new complex variable delay-embedding dynamic mode decomposition (CDE DMD) algorithm that combines complex state filtering and time embedding to extract a linear approximate model. …”
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Global, regional, and national trends in drug use disorder mortality rates across 73 countries from 1990 to 2021, with projections up to 2040: a global time-series analysis and mod... by Soeun Kim, Hayeon Lee, Selin Woo, Hyeri Lee, Jaeyu Park, Tae Kim, Guillaume Fond, Laurent Boyer, Masoud Rahmati, Lee Smith, Guillermo F. López Sánchez, Elena Dragioti, Christa J. Nehs, Jinseok Lee, Hyeon Jin Kim, Jiseung Kang, Dong Keon Yon

“…Methods: In this time-series analysis and modelling study, we investigated the global trends in DUD mortality rates from 1990 to 2021 using the WHO Mortality Database and forecasted future trends through 2040. …”
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Travel Patterns Analysis Using Tensor-Based Model from Large-Scale License Plate Recognition Data by Zhaoxin Liu, Xiaolu Wang, Yufeng Bi, Jun Kong, Run Xu, Yuanpei Chen, Jinjun Tang

“…As travel patterns are influenced by many variables, a method framework based on the tensor model is proposed to explore the influence of variables on travel characteristics. …”
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A dengue fever predicting model based on Baidu search index data and climate data in South China. by Dan Liu, Songjing Guo, Mingjun Zou, Cong Chen, Fei Deng, Zhong Xie, Sheng Hu, Liang Wu

“…Firstly, the time series dengue fever data were decomposed into seasonal, trend and remainder components by the seasonal-trend decomposition procedure based on loess (STL). Secondly, the time lag of variables was determined in cross-correlation analysis and the order of autocorrelation was estimated using autocorrelation (ACF) and partial autocorrelation functions (PACF). …”
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Development of a river dissolved oxygen prediction model integrating spatial effects and multiple deep learning algorithm by Yubo Zhao, Mo Chen

“…The model is benchmarked against several baselines and evaluated using RMSE, MAE, R2, and PICP, which are recorded as 0.2935, 0.2053, 0.9852, and 98.9 %, respectively—outperforming all competing models. …”
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Evaluation of long-term carbon dynamics in a drained forested peatland using the ForSAFE-Peat model by D. Escobar, D. Escobar, S. Manzoni, J. Tapasco, P. Vestin, S. Belyazid

“…Model simulations aligned well with observed groundwater levels (<span class="inline-formula"><i>R</i>²=0.78</span>) and soil temperatures (<span class="inline-formula"><i>R</i>²≥0.76</span>) and captured seasonal and annual net ecosystem production patterns, although daily variability was not always well represented. …”
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A Two-Stage Deep Fusion Integration Framework Based on Feature Fusion and Residual Correction for Gold Price Forecasting by Cihai Qiu, Yitian Zhang, Xunrui Qian, Chuhang Wu, Jiacheng Lou, Yang Chen, Yansong Xi, Weijie Zhang, Zhenxi Gong

“…Nonetheless, traditional single prediction models usually suffer from limited predictive performance and fail to capture complex variability of market behavior. …”
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Transforming Building Energy Management: Sparse, Interpretable, and Transparent Hybrid Machine Learning for Probabilistic Classification and Predictive Energy Modelling by Yiping Meng, Yiming Sun, Sergio Rodriguez, Binxia Xue

“…The framework clusters occupancy-driven energy usage patterns using K-means and Gaussian Mixture Models, identifying three distinct household profiles: high-energy frequent occupancy, moderate-energy variable occupancy, and low-energy irregular occupancy. …”
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POD-DEIM Based Model Order Reduction for the Spherical Shallow Water Equations with Turkel-Zwas Finite Difference Discretization by Pengfei Zhao, Cai Liu, Xuan Feng

“…To reduce the dimension of the SWE model, we use a well-known model order reduction method, a proper orthogonal decomposition (POD). …”
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A New Maintenance Optimization Model Based on Three-Stage Time Delay for Series Intelligent System with Intermediate Buffer by Xiaolei Lv, Qinming Liu, Zhinan Li, Yifan Dong, Tangbin Xia, Xiang Chen

“…Firstly, the intelligent series system is decomposed into n − 1 virtual series systems by using approximate decomposition method. The impact factor is introduced to establish the failure rate and maintenance rate model of each virtual machine. …”
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A first-of-its-kind two-body statistical shape model of the arthropathic shoulder: enhancing biomechanics and surgical planning by Justin Blackman, Joshua W. Giles

“…Abstract Background Statistical Shape Models are machine learning tools in computational orthopedics that enable the study of anatomical variability and the creation of synthetic models for pathogenetic analysis and surgical planning. …”
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Deep VMD-attention network for arrhythmia signal classification based on Hodgkin-Huxley model and multi-objective crayfish optimization algorithm. by Hang Zhao, Xiongfei Yin

“…Two types of arrhythmia characterized by significant anomalies in the variables of the HH model were simulated, and corresponding synthetic ECG signals were generated. …”
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Financial Dynamics of Cooperatives of the Popular and Solidarity Economy of Ecuador: An Analysis Using the Tucker3 Model and Projection with Multidimensional Neutrosophic Regressio... by Gabriela Valeria Bustos–Chiliquinga, Purificación Galindo-Villardón, Cristian Cornejo Gaete

“…The purpose of the tensor decomposition is to acknowledge significant associative patterns over time across entities and variables assessed at this level, leading to three clear temporal moments: growth, crisis and recovery. …”
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Inequality in opportunities for utilizing community-based care services among the elderly in China – an analysis based on the “circumstance-effort” perspective by He Jiang, Yanshuo Huang

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Assessment of Large‐Scale Reservoirs' Impact on the Local Precipitation by Han Zhou, Jun Qiu, Mengjia Li, Houliang Lu, Fangfang Li

“…Instead of assuming that the natural variability of the contrast region and the study region is identical, this study develops an interpretable machine learning model to investigate relationships between precipitation‐influencing factors and precipitation itself, including both stable components (sum of trend and seasonality from STL decomposition) and random components (residuals after removing trend and seasonality), which is then used to forecast natural precipitation in the absence of reservoir operation. …”
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Crop Model Ensemble Averaging: A Large But Underappreciated Uncertainty Source for Global Crop Yield Projections Under Climate Change by Xiaomeng Yin, Guoyong Leng, Jiali Qiu, Xiaoyong Liao, Shengzhi Huang, Jian Peng

“…Further uncertainty decomposition analysis shows that ensemble averaging methods contributes to 39%–87% of total uncertainties for global yield projections, which is even higher than climate models. …”
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