Computationally Effective Modeling of Cold Rolling: Application to Al Alloys by János György Bátorfi, Jurij J. Sidor

“…The distortion of meshes predicted by both FEM and FLM follow the same evolutionary pattern.…”
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Automatic location of surface-monitored microseismicity with deep learning by Zhaolong Gan, Xiao Tian, Xiong Zhang, Mengxue Dai

“…In this study, we propose an approach to locate microseismic events using a deep learning algorithm with surface data. A fully convolutional network is designed to predict source locations. …”
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Machine tool FEM model correction assisted by dynamic evolution sequence by Weihao Lin, Peng Zhong, Xindi Wei, Li Zhu, Xuanlong Wu

“…Moreover, replacing the random sequence with a dynamic evolution sequence enhances the search space coverage of the heterogeneous comprehensive learning particle swarm optimization (HCLPSO) algorithm. The results of numerical examples demonstrate that the finite element model, corrected using the proposed method, accurately predicts the true displacement responses of the machine tool. …”
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On Evaluation and Comparison of Blast Loading Methods Used In Numerical Simulations by Vahdet Uçar, Atıl Erdik

“…Mesh resolution study of ALE formulation is conducted todetermine the influence of element mesh size on predicting blast loadingeffects that is converted through fluid structure interaction algorithm fromEulerian to Lagrangian type of elements. …”
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Thermoelectric Modeling and Online SOC Estimation of Li-Ion Battery for Plug-In Hybrid Electric Vehicles by Aishwarya Panday, Hari Om Bansal, Pramod Srinivasan

“…Therefore, an accurate battery model is required to predict the I-V characteristic and dynamic behavior of a battery. …”
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Novel insights of disulfidptosis-mediated immune microenvironment regulation in atherosclerosis based on bioinformatics analyses by Huanyi Zhao, Zheng Jin, Junlong Li, Junfeng Fang, Wei Wu, J. F. Fang

“…Furthermore, a nomogram including hub genes was established that accurately predicted the occurrence of AS. The consensus clustering algorithm was used to separate patients with early atherosclerotic plaques and patients with advanced atherosclerotic plaques into two disulfidptosis subtypes. …”
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Development and Analysis of Patient-Based Complete Conducting Airways Models. by Rafel Bordas, Christophe Lefevre, Bart Veeckmans, Joe Pitt-Francis, Catalin Fetita, Christopher E Brightling, David Kay, Salman Siddiqui, Kelly S Burrowes

“…A number of global clinical lung function measures are compared to resistance predictions in the models to assess their suitability for use in a patient-specific setting. …”
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A deep learning software tool for automated sleep staging in rats via single channel EEG by Andrew Smith, Snezana Milosavljevic, Courtney J. Wright, Charlie A. Grant, Ana Pocivavsek, Homayoun Valafar

“…The algorithm was able to predict key parameters of sleep architecture, including total bout duration, average bout duration, and number of bouts, with significant accuracy. …”
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Block Markov superposition transmission of LDPC codes by Qianfan Wang, Kongjing Guo, Xiao Ma

“…To analyze the error-floor performance of BMST-LDPC codes, we employ the genie-aided (GA) lower bound, which can efficiently predict the error-correcting performance in error-floor region. …”
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Exothermic thermosolutal convection in a nanofluid-filled square cavity with a rotating Z-Fin: ISPH and AI integration by Kuiyu Cheng, Abdelraheem M. Aly, Nghia Nguyen Ho, Sang-Wook Lee, Andaç Batur Çolak, Weaam Alhejaili

“…The ANN model, employing the Levenberg–Marquardt algorithm, achieved a coefficient of determination $ R = 0.99994 $ and a mean squared error $ \text{MSE} = 4.21\times {10}^{-6} $, demonstrating its reliability in predicting thermal performance. …”
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VAS value set still has its own application value compare with TTO value set of EQ-5D-3L in Chinese population by Lin Zhuo, Siyuan Gao, Rui Jin, Lang Zhuo, Xiuying Wang

“…Five categories of indices, including quality of original data, distribution of rescaled values, goodness of fit of models, distribution of predicted values, and dimensions order were adopted to compare between Chinese VAS and TTO value sets. …”
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Evaluating soil erosion zones in the Kangsabati River basin using a stacking framework and SHAP model: a comparative study of machine learning approaches by Javed Mallick, Saeed Alqadhi, Swapan Talukdar, Md Nawaj Sarif, Tania Nasrin, Hazem Ghassan Abdo

“…Abstract Soil erosion is a major concern in the Kangsabati River basin, necessitating a comprehensive scientific approach for effective soil erosion management. This study aimed to predict soil erosion susceptibility zones in the basin using integrated soil erosion and deep learning (DL) based stacking framework. …”
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Uncertainty analysis of clinically relevant distances derived using photogrammetric intersection for the assessment of motor-speech-control in children by L. Boyle, P. Helmholz, R. Ward, R. Palmer, D. Lichti

“…In contrast, photogrammetry can predict facial inter-landmark distances and their uncertainties through intersection and variance propagation. …”
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An Intelligent Crow Search Optimization and Bi-GRU for Forest Fire Detection System Using Internet of Things by Syed Abdul Moeed, Bellam Surendra Babu, M. Sreevani, B. V. Devendra Rao, R. Raja Kumar and Gouse Baig Mohammed

“…This neural network architecture is capable of processing sequential data from multiple directions, enhancing the system’s ability to predict the spread and intensity of fires. Crow Search Optimization (CSO) and fractional calculus are used to create an optimal solution in the proposed crow search fractional calculus optimization (CSFCO) algorithm for deep learning. …”
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Modelos univariados de séries temporais para previsão das temperaturas médias mensais de Erechim, RS Univariate time series methods for forecasting the monthly mean air temperature... by Leonardo Chechi, Fábio M. Bayer

“…In the class of ARIMA models using criteria information, SARIMA type models that consider the seasonal characteristics of air temperature were selected, whereas for exponential smoothing models Holt-Winters additive algorithm were used. Smoothing constants are determined to minimize the mean square error between observed and predicted values. …”
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MAP17 is a Novel NASH Progression Biomarker Associated with Macrophage Infiltration, Immunotherapy Response, and Oxidative Stress by Huang Z, Chen J, Liu S, Xiang X, Long Y, Tan P, Fu W

“…Our further analyses included differential expression evaluation, protein-protein interaction (PPI) network analysis, and Venn diagram analysis to discover novel targets. The CIBERSORT algorithm assessed the correlation between MAP17 and immune cell infiltration within the tumor microenvironment (TME), while the TIDE algorithm predicted responses to immunotherapy. …”
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Theoretical investigation of the free fall of a spherical particle in a viscous fluid using continuous piecewise linearization method by Akuro Big-Alabo, Joseph Chukwuka Ofodu

“…The CPLM algorithm was shown to predict published experimental results accurately and compared very well with numerical solutions and existing analytical solutions. …”
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A Multi-Input Neural Network Model for Accurate MicroRNA Target Site Detection by Mohammad Mohebbi, Amirhossein Manzourolajdad, Ethan Bennett, Phillip Williams

“…These images are processed in parallel by the MINN algorithm, allowing it to learn a comprehensive and precise representation of the underlying biological mechanisms. (3) Results: Our method, on an experimentally validated test set, detects target sites with an AUPRC of 0.9373, Precision of 0.8725, and Recall of 0.8703 and outperforms several commonly used computational methods of microRNA target-site predictions. (4) Conclusions: Incorporating diverse biologically explainable features, such as duplex structure, substructures, their MFEs, and binding probabilities, enables our model to perform well on experimentally validated test data. …”
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Radiographic Measurements and Diagnosis of Hip Dysplasia in Infants Using Computer Vision and a Rule-Based System by Ernesto Pérez Pérez, Roberto Díaz Amador, Karina Leonor Fernández Sánchez, José Julio Requeiro Molina, José Julio Requeiro Morejón

“…<strong><br />Conclusion</strong>: there was a high coincidence between the measures calculated by the algorithm and those calculated manually. The correspondence between the diagnosis predicted by the system and that issued by specialist doctors was also high.…”
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Artificial Intelligence for Fault Detection of Automotive Electric Motors by Federico Soresini, Dario Barri, Ivan Cazzaniga, Federico Maria Ballo, Gianpiero Mastinu, Massimiliano Gobbi

“…This study focuses on durability testing of Permanent Magnet Synchronous Motors for automotive applications, using Autoencoders (AEs) to predict and prevent failures. This AI-based fault detection strategy employs acceleration signals coming from electric motors tested under challenging conditions with significant variations in torque and speed. …”
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