Introducing the MESMER-M-TPv0.1.0 module: spatially explicit Earth system model emulation for monthly precipitation and temperature by S. Schöngart, S. Schöngart, L. Gudmundsson, M. Hauser, P. Pfleiderer, Q. Lejeune, S. Nath, S. I. Seneviratne, C.-F. Schleussner, C.-F. Schleussner, C.-F. Schleussner

“…Owing to their runtime efficiency, emulators are especially useful when large amounts of data are required, for example, for in-depth exploration of the emission space, for investigating high-impact low-probability events, or for estimating uncertainties and variability. …”
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Analysis of Heat Transfer Performance in a Brayton Cycle Recuperator by Elvis Falcão de Araújo, Guilherme Borges Ribeiro, Lamartine Nogueira Frutuoso Guimarães

“…The characteristic curves of heat transfer rate, effectiveness, convection coefficient and Colburn factor are built for each of the studied geometries in function of the Reynolds number. …”
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Influence of structural surface roughness on the strength of layered fill bodies based on PFC2D by Jinxing WANG, Zongsheng HU, Huazhe JIAO, Xiaolin YANG, Qi ZHANG, Xiaohui LIU, Ping XU, Junqiang XU, Xun CHEN

“…The relationship between cemented surface roughness and backfill strength is examined by analyzing variables such as cemented surface roughness, mass fraction of slurry, cement-to-sand ratio, and filling interval time. …”
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Estimation of Fuzzy Measures Using Covariance Matrices in Gaussian Mixtures by Nishchal K. Verma

“…The main contribution of this paper is the estimation of fuzzy measures efficiently and directly from covariance matrices found in GMM, avoiding the computational burden greatly while learning them iteratively and solving polynomial equations of order of the number of input-output variables.…”
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Inference of Impulse Responses via Bayesian Graphical Structural VAR Models by Daniel Felix Ahelegbey

“…Impulse response functions (IRFs) are crucial for analyzing the dynamic interactions of macroeconomic variables in vector autoregressive (VAR) models. …”
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Dimensions-Reduced Volterra Digital Pre-Distortion Based On Orthogonal Basis for Band-Limited Nonlinear Opto-Electronic Components by Hananel Faig, Yaron Yoffe, Eyal Wohlgemuth, Dan Sadot

“…However, naive implementation of the Volterra polynomial model usually introduces significant complexity due to the large number of model coefficients. Here, we propose the use of orthogonal polynomial basis functions for efficient DPD implementation. …”
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On the development of a practical Bayesian optimization algorithm for expensive experiments and simulations with changing environmental conditions by Mike Diessner, Kevin J. Wilson, Richard D. Whalley

“…The method is validated on two synthetic test functions, and the effects of the noise level, the number of environmental parameters, the parameter fluctuation, the variability of the uncontrollable parameters, and the effective domain size are investigated. …”
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Cost-effective process design for methanol synthesis from carbon dioxide hydrogenation by Sheng-Zhong Huang, Chih-Yao Lin, Chianghui Wang, Akhmat Fauzan Saputra, Henggar Yudha Hananto, Nicolas Justin Sutanto, Anggit Raksajati, Vincentius Surya Kurnia Adi

“…Accordingly, this research introduces an optimization framework capable of handling diverse process configurations, operating variables, and their interactions. This approach enables effective optimization using total annual cost (TAC) as the objective function and significantly enhances process design efficiency. …”
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Multi-Objective Optimal Design of 200 kW Permanent Magnet Synchronous Motor Based on NSGA-II by Chengxu Sun, Qi Li, Tao Fan, Xuhui Wen, Ye Li, Hongyang Li

“…Interior permanent magnet synchronous motors (IPMSMs) are widely applied as drive motors in electric vehicles because they have the advantages of high power density, high efficiency, and excellent dynamic performance. This paper introduces a framework for multi-objective optimization, tailored for the demands of V-Shaped IPMSMs, which involves high-dimensional variables. …”
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COVID-19 recovery: benefits of multidisciplinary respiratory rehabilitation by Wim Janssens, Greet Hermans, Rik Gosselink, Daniel Langer, Thierry Troosters, Hilde Beyens, Stephanie Everaerts, Arne Heyns, Natalie Lorent

“…Impressive results on physical recovery were determined after 6 weeks and 3 months, with significant improvement of lung function, muscle force and exercise capacity variables. …”
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Application of machine learning algorithms for predicting the life-long physiological effects of zinc oxide Micro/Nano particles on Carum copticum by Maryam Mazaheri-Tirani, Soleyman Dayani, Majid Iranpour Mobarakeh

“…All ML algorithms showed varied efficiencies in predicting the nonlinear relationships among parameters, with higher efficiency in predicting the behavior of root and shoot dry mass, root fresh weight and number of flowers according to R2 index. …”
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Codesign of Transmit Waveform and Receive Filter with Similarity Constraints for FDA-MIMO Radar by Qiping Zhang, Jinfeng Hu, Xin Tai, Yongfeng Zuo, Huiyong Li, Kai Zhong, Chaohai Li

“…Finally, the Riemannian limited-memory Broyden–Fletcher–Goldfarb–Shanno (RL-BFGS) algorithm is employed to optimize the variables in parallel. Simulation results demonstrate that our method achieves a 0.6 dB improvement in SINR compared to existing methods while maintaining competitive computational efficiency. …”
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Robust Optimization Research of Cyber–Physical Power System Considering Wind Power Uncertainty and Coupled Relationship by Jiuling Dong, Zilong Song, Yuanshuo Zheng, Jingtang Luo, Min Zhang, Xiaolong Yang, Hongbing Ma

“…Furthermore, the deterministic power balance constraints are relaxed into inequality constraints that account for wind power forecasting errors through fuzzy variables. The lower-level model focuses on minimizing traffic load shedding by establishing a topology–function-constrained information network traffic model based on the maximum flow principle in graph theory, thereby improving the efficiency of network flow transmission. …”
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Research on Robust Adaptive Model Predictive Control Based on Vehicle State Uncertainty by Yinping Li, Li Liu

“…To overcome these limitations, three key innovations are introduced: a three-degree-of-freedom vehicle dynamic model integrated with recursive least squares-based online estimation of tire slip stiffness for real-time lateral force compensation; an adaptive weight adjustment mechanism that dynamically balances control energy consumption and tracking accuracy by tuning cost function weights based on real-time state errors; and a dynamic constraint relaxation strategy using slack variables with variable penalty terms to resolve infeasibility while suppressing excessive constraint violations. …”
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Retrieval of carbon and inorganic phosphorus during hydrothermal carbonization: ANN and RSM modeling by Abolfazl Shokri, Mohammad Amin Larki, Ahad Ghaemi

“…Next, Multilayer Perceptron (MLP) and Radial Basis Function (RBF) were used to compare the results and improve the model fit. …”
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Support vector regression model for the prediction of buildings’ maximum seismic response based on real monitoring data by Dongwang Tao, Shizhe Fang, Haixu Liu, Jianqi Lu, Jiang Wang, Qiang Ma

“…Our results demonstrate that SVR-MDR model outperform other machine learning models such as kernel ridge regression and decision tree models, and SVR-MDR and RSVR-MDR models outperform conventional loglinear regression and multinomial models, because SVR can map the complex nonlinear function of multiple variables and consider the available information of buildings especially the fundamental frequency. …”
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IDENTIFICATION OF NOMINAL DYNAMIC CHARACTERISTICS OF AIRCRAFT GAS TEMPERATURE SENSORS by A. F. Sabitov, I. A. Safina

“…The heat transfer coefficient is calculated based on the expected operating conditions of the gas temperature sensors.The result of the study was that the parameters of the hyperbolic function of the nominal variables of the time constants for the dynamic model of the second order gas temperature sensors were found. …”
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Production of Cyanobacterial Microscale Adsorbent by Hydrothermal Method for Pre-concentration of Cadmium from Water by T. Mohammadi Arian, G. Rahimi, R. Khavari Farid

“…Solid-phase extraction using carbon adsorbents is the most efficient and common method of pre-concentration of heavy metals from environmental samples. …”
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Prox-STA-LSTM: A Sparse Representation for the Attention-Based LSTM Networks for Industrial Soft Sensor Development by Yurun Wang, Yi Huang, Dongsheng Chen, Longyan Wang, Lingjian Ye, Feifan Shen

“…For deep learning based soft sensors, the spatiotemporal attention (STA)-LSTM is a newly emerged technique which provides efficient predictions for quality variables of industrial processes. …”
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Active RIS-NOMA Uplink in URLLC, Jamming Mitigation via Surrogate and Deep Learning by Ghazal Asemian, Mohammadreza Amini, Burak Kantarci

“…The complexity of the optimization problem, involving numerous interacting variables, leads us to develop a deep regression model to predict optimal network configurations, providing a computationally efficient approach as well as reducing the signaling overhead. …”
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