MODELING THE TRANSFER EFFECT OF EXCHANGE RATE ON PRICES IN RUSSIA by M. G. Tiunova

“…The structural identifcation of the model is carried out by the recursive ranking of variables and decomposition of structural shocks elaborated by André-Louis Cholesky. …”
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Interpretable Machine Learning Predictions of Bruch’s Membrane Opening-Minimum Rim Width Using Retinal Nerve Fiber Layer Values and Visual Field Global Indexes by Sat Byul Seo, Hyun-kyung Cho

“…The aim of this study was to predict Bruch’s membrane opening-minimum rim Width (BMO-MRW), a relatively new parameter using conventional optical coherence tomography (OCT) parameter, using retinal nerve fibre layer (RNFL) thickness and visual field (VF) global indexes (MD, PSD, and VFI). We developed an interpretable machine learning model that integrates structural and functional parameters to predict BMO-MRW. …”
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Stroh–Hamiltonian Framework for Modeling Incompressible Poroelastic Materials by Kinza Arshad, Vincenzo Tibullo

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Analyzing the maxillary sinuses using 3D-modeling by O. V. Zeleva, A. V. Kolsanov, P. M. Zel'ter, E. A. Sidorov

“…The most developing technique that allows you to move from planar images to a picture that clearly represents the shape of the anatomical structure and topographic-anatomical relationships is 3D-modeling.Purpose. …”
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Modeling and Testing Landslide Hazard Using Decision Tree by Mutasem Sh. Alkhasawneh, Umi Kalthum Ngah, Lea Tien Tay, Nor Ashidi Mat Isa, Mohammad Subhi Al-Batah

“…These factors are vegetation cover, distance from the fault line, slope angle, cross curvature, slope aspect, distance from road, geology, diagonal length, longitude curvature, rugosity, plan curvature, elevation, rain perception, soil texture, surface area, distance from drainage, roughness, land cover, general curvature, tangent curvature, and profile curvature. Decision tree models are used for prediction, classification, and factors importance and are usually represented by an easy to interpret tree like structure. …”
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Evaluating Diverse Meta-modeling Approaches for Predicting Performance Characteristics of a Twin Air Intake Based on Experimental Data by Human AMIRI, U. C. Kucuk, O. Kucukoglu, Y. F. Kuscu, O. V. Ozdemır

“…The performance of each model is rigorously evaluated based on goodness of fit, precision, accuracy, monotonicity, and interpretability. …”
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Three-Component Forward Modeling for Transient Electromagnetic Method by Bin Xiong

“…To improve the integrated interpretation precision of TEM, it is necessary to study the three-component forward modeling and inversion. …”
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Anti-Butterfly Effect in Ribavirin Studied by Combined Experiment (PXRD/¹H-¹⁴N NQR Cross-Relaxation Spectroscopy), Quantum Chemical Calculations, Molecular Do... by Jolanta Natalia Latosińska, Magdalena Latosińska, Janez Seliger, Veselko Žagar, Tomaž Apih

“…The influence of the structural alteration on the NQR parameters was modeled using GGA/RPBE. …”
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Clues to seismic data interpretation within the zones of the sand injections in so called "anomalous section" of the Bazhenovo Black Shales formation on the examples of the Potochn... by Gatina Nadezhda, Sarieva Marina, Mukhutdinova Oksana, Popkov Andrey, Gavrilov Sergey

“…The listed zones were formed at different geological times, belong to different regional clinoforms, but have common patterns of structure, which is reflected in the seismogeological model of the Ob river region GSIC. …”
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Unsupervised Machine Learning‐Derived Anion‐Exchange Membrane Polymers Map: A Guideline for Polymers Exploration and Design by Yin Kan Phua, Nana Terasoba, Prof. Manabu Tanaka, Prof. Tsuyohiko Fujigaya, Prof. Koichiro Kato

“…Here, we address these challenges by proposing an innovative approach for the efficient design and screening of AEM polymers using unsupervised machine learning. Our model, which combines principal component analysis with uniform manifold approximation and projection, generates an intuitive map that clusters AEM polymers based on structural similarities without any predefined knowledge regarding anion conductivity or other experimentally derived variables. …”
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Assessing the impact of multi-source environmental variables on soil organic carbon in different land use types of China using an interpretable high-precision machine learning meth... by Feng Wang, Ruilin Liang, Shuyue Li, Meiyan Xiang, Weihao Yang, Miao Lu, Yingqiang Song

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Modelling and Analysis of a Pest-Control Pollution Model with Integrated Control Tactics by Yiping Chen, Zhijun Liu, Wenjie Qin

“…A hybrid impulsive pest control model with stage structure for pest and Holling II functional response is proposed and investigated, in which the effects of impulsive pesticide input in the environment and in the organism are considered. …”
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Manifesto for transparent mathematical modeling: from ecology to general science by Vyacheslav L. Kalmykov, Lev V. Kalmykov

“… Mathematical black-box models, which hide the structure and behavior of the subsystems, currently dominate science. …”
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MODELING AS A METHOD FOR SCIENTIFIC COGNITION OF COMPLEX MEAT SYSTEMS by Marina A. Nikitina, Aleksandr N. Zakharov, Victoria V. Nasonova, Andrey B. Lisitsyn

“…Using the one-way analysis of variance, which is one of the system modules, a comprehensive amount of statistical data for interpretation of the results was obtained. The program modules (correlation and regression analysis) allow establishing the model structure and parameters that link quantitative resulting and factorial variables, as well as assessing a degree of their correspondence with the experimental data. …”
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Developing an alternative data-driven model to resemble geomorphologic rainfall-runoff models by Pin-Chun Huang, Kwan Tun Lee

“…In contrast, they may require more expertise and complicated numerical operations compared to data-driven models. The present study aims to improve the predictive capability of data-driven models by including the advantages of physics-based models in the model’s structure and preprocessing input features. …”
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From situated knowledges to situated modelling: a relational framework for simulation modelling by Anja Klein, Krystin Unverzagt, Rossella Alba, Jonathan F. Donges, Tilman Hertz, Tobias Krueger, Emilie Lindkvist, Romina Martin, Jörg Niewöhner, Hannah Prawitz, Maja Schlüter, Luana Schwarz, Nanda Wijermans

“…Moreover, current methodological discussions tend to focus on integrating social and ecological dynamics or diverse knowledges and data within a model. Yet choices regarding types of modelling, model structure, data handling, interpretation of results and model validation are not purely epistemic. …”
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Normalized impulse response testing in underground constructions monitoring by Aleksei A. Churkin, Vladimir V. Kapustin, Mikhail S. Pleshko

“…Impulse Response testing is a widespread geophysical technique of monolithic plate-like structures (foundation slabs, tunnel lining, and supports for vertical, inclined and horizontal mine shafts, retaining walls) contact state and grouting quality evaluation. …”
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MINN: A metabolic-informed neural network for integrating omics data into genome-scale metabolic modeling by Gabriele Tazza, Francesco Moro, Dario Ruggeri, Bas Teusink, László Vidács

“…On the other hand, mathematical models, such as Genome-Scale Metabolic Models (GEMs), offer a structured framework for analyzing the organization and dynamics of specific cellular mechanisms. …”
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Network-Based Modeling of Lean Implementation Strategies and Planning in Prefabricated Construction by Pei Dang, Linna Geng, Zhanwen Niu, Shan Jiang, Chao Sun

“…Thus, this paper aims to propose a quantitative network-based model by integrating Interpretive Structural Modeling (ISM) and Matrix Impact Cross-Reference Multiplication Applied to a Classification (MICMAC) under complex network theory to develop a Lean implementation framework for effective strategy formulation. …”
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Urban informal settlements interpretation via a novel multi-modal Kolmogorov–Arnold fusion network by exploring hierarchical features from remote sensing and street view images by Hongyang Niu, Runyu Fan, Jiajun Chen, Zijian Xu, Ruyi Feng

“…The proposed KANFusion model employs the Kolmogorov–Arnold Network (KAN) instead of the conventional MLP structure to enhance the model-fitting capability of heterogeneous modality-specific features and uses a novel Multi-level Feature Fusion Module with KAN block (MFF) to fuse the hierarchical modality-specific and modality-fusion features from remote sensing and street view images for better UIS interpretation performance. …”
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