Physiological serum uric acid concentrations correlate with arterial stiffness in a sex-dependent manner by Oliver Thews, Thomas Schmid, Alexander Kluttig, Andreas Wienke, Melanie Zinkhan, Wolfgang Ahrens, Till Bärnighausen, Hermann Brenner, Stefanie Castell, Berit Lange, Wolfgang Lieb, Karin Halina Greiser, Marcus Dörr, Lilian Krist, Stefan N. Willich, Volker Harth, Nadia Obi, Michael Leitzmann, Annette Peters, Börge Schmidt, Matthias B. Schulze, Henry Völzke, Matthias Nauck, Stephanie Zylla, Anke Hannemann, Tobias Pischon, Ilais Moreno Velásquez, Matthias Girndt, Claudia Grossmann, Michael Gekle

“…In parallel, we applied a machine learning approach to identify and quantify factors associated with PWV in a hypothesis-free manner. …”
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Design of Centrifugal Fan with Flexible Blades to Extend the Effective Operating Range in Various Speeds and Mass Flows Based on Numerical Analysis and Statistical Computation by Thai Hiep, Le Long, Vu Thang, Hong Thong

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Construction of gas content model based on KPCA-SVR for Southern Sichuan shale gas by Zhong-yuan Liu, Di-Quan Li, Jing Jia, Yun-Qi Zhu, Zhong-Le Wang, Xue-Song Xie

“…However, the relationship between gas content (Vg) and well logging parameters (e.g., porosity (POR), density (DEN), natural gamma (GR)) and geochemical parameters (e.g., total organic carbon (TOC), potassium-uranium-thorium ratio (U), organic matter maturity (Ro), resistivity (ρ)) remains poorly understood. …”
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Dimensional Accuracy Analysis of Splined Shafts and Hubs Obtained by Fused-Deposition Modeling 3D Printing Using a Genetic Algorithm and Artificial Neural Network by Alin-Daniel Rizea, Cristina-Florena Banică, Tatiana Georgescu, Alexandru Sover, Daniel-Constantin Anghel

“…Optimized printing parameters improve dimensional accuracy and performance. …”
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Optimization Method of High-Speed Train Composite Material Workshop Planning and Scheduling by Yanliang Jie, Lei Hao, Shujun Yan, Xueli Zhang

“…Each layer of coding is divided into different machine segments to represent the batch processing sequence on different machines. …”
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Investigating factors affecting the quality of water resources by multivariate analysis and soft computing approaches by Bing Cheng, Xinyu Liu, Keke Guo, Ahmad Rastegarnia

“…The primary factor responsible for approximately half of the impact on water quality, accounting for 55.12% of the total variance, includes the EC, Ca2+, SAR, SO4 −, Na+, CO3 −, %Na, Cl−, and TDS parameters. These parameters are directly related to water quality and are influenced by the natural characteristics of the region. …”
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Neural Network Models for Ionospheric Electron Density Prediction at a Fixed Altitude Using Neural Architecture Search by Yang Pan, Mingwu Jin, Shun‐Rong Zhang, Simon Wing, Yue Deng

“…Abstract Specification and forecast of ionospheric parameters, such as ionospheric electron density (Ne), have been an important topic in space weather and ionospheric research. …”
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Polymer Materials for U-Shaped Optic Fiber Sensors: A Review by Patryk Sokołowski, Jacek Łubiński, Paweł Wierzba, Jakub Czubek, Piotr Miluski, Filip Janiak, Shanyue Guan, Małgorzata Szczerska

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Prediction of cardiac remodeling and myocardial fibrosis in athletes based on IVIM-DWI images by Yujiao Deng, Min Tang, Qian Liu, Xinrong Fan, Jian Shu, Jing Chen, Meining Chen, Lu Yang

“…Athletes with CR and/or MF had lower myocardial slow apparent diffusion coefficient (ADCslow) values than those without (p < 0.05). A gradient boosting machine (GBM) effectively predicted CR and/or MF based on these hypoperfusion parameters, with an area under the receiver operating characteristic curve of 0.947 in the training set and 0.841 in the test set. …”
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Inverse design of photonic surfaces via multi fidelity ensemble framework and femtosecond laser processing by Luka Grbčić, Minok Park, Mahmoud Elzouka, Ravi Prasher, Juliane Müller, Costas P. Grigoropoulos, Sean D. Lubner, Vassilia Zorba, Wibe Albert de Jong

“…Abstract We demonstrate a multi-fidelity (MF) machine learning ensemble framework for the inverse design of photonic surfaces, trained on a dataset of 11,759 samples that we fabricate using high throughput femtosecond laser processing. …”
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Comprehensive analysis of drilling responses in additively manufactured PLA using a regression—based statistical learning approach by Vishwadarshan, Gauthami Shetty, Raviraj Shetty, Supriya J P, Balaji V, Adithya Hegde

“…Chip analysis revealed three distinct types: long chips (5–6 cm), medium chips (1–2 cm), and fine chips (0.01–0.1 cm), correlated with specific parameter settings. The study highlights the significance of optimizing machining parameters, identifying an optimal combination of 30% in-fill density, 0.4 mm nozzle diameter, 300 μm layer height, 1800 RPM spindle speed, 10 mm min ^−1 feed rate, and 4 mm drill diameter to minimize delamination. …”
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Smart prediction of rock crack opening displacement from noisy data recorded by distributed fiber optic sensing by Shuai Zhao, Shao-Qun Lin, Dao-Yuan Tan, Hong-Hu Zhu, Zhen-Yu Yin, Jian-Hua Yin

“…The Bayesian optimization is used via the Hyperopt Python library to determine the appropriate hyper-parameters of four ML models. To ensure that the best hyper-parameters will not be missing, the configuration space in Hyperopt is specified by probability distribution. …”
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Application of Categorical Boosting to Modelling the Friction Behaviour of DC05 Steel Sheets in Strip Drawing Test by Marek Szewczyk, Krzysztof Szwajka, Sherwan Mohammed Najm, Salwa O Mohammed

“…This work utilises the Categoric Boosting (CatBoost) machine learning algorithm created by Yandex to estimate the COF and surface roughness, intending to conduct a comprehensive investigation of process parameters. …”
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Evaluation of Gas Hydrate Saturation Based on Joint Acoustic–Electrical Properties and Neural Network Ensemble by Donghui Xing, Hongfeng Lu, Lanchang Xing, Chenlu Xu, Jinwen Du, Xinmin Ge, Qiang Chen

“…In this paper, experiments regarding methane hydrate formation and dissociation in clay-bearing sediments were carried out based on the Ultrasound Combined with Electrical Impedance (UCEI) system, and the measurements of the joint electrical and acoustic parameters were collected. A machine learning (ML)-based model for evaluating hydrate saturation was established based on electrical–acoustic properties and a neural network ensemble. …”
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Correlating pore space morphology with numerically computed soil gas diffusion for structured loam and sand, including stochastic 3D microstructure modeling by Benedikt Prifling, Matthias Weber, Maximilian Rötzer, Nadja Ray, Alexander Prechtel, Maxime Phalempin, Steffen Schlüter, Doris Vetterlein, Volker Schmidt

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Sustainable optimization of concrete strength properties using artificial neural networks: a focus on mechanical performance by Aneel Manan, Zhang Pu, Ali Majdi, Wael Alattyih, S K Elagan, Jawad Ahmad

“…An Artificial Neural Network was used machine to predict mechanical properties of RCA concrete. …”
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Modelling current and future forest fire susceptibility in north-eastern Germany by K. H. Horn, K. H. Horn, S. Vulova, S. Vulova, H. Li, B. Kleinschmit

“…The model results underscore the importance of anthropogenic parameters and vegetation parameters in modelling FFS on a regional level. …”
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Effects of Heating and Liquid Nitrogen on Mechanical Characteristics and Rockburst Characteristics of Granite by Xue Li, Yi Xue, Fa-ning Dang, Zhihao Zhang, Lin Zhu, Shanjie Su, Shengcheng Wang, Ruifu Liu

“…The RA-AF diagram of AE parameters can characterize two failure modes of rock, tensile failure and shear failure. …”
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Study on the Mechanical Behavior and Acoustic Emission Properties of Granite under Triaxial Compression by Jiaqi Guo, Pengfei Liu, Junqi Fan, Hengyuan Zhang

“…The failure mode of granite samples judged by acoustic emission parameters according to the distribution of characteristic values of AE parameters RA and AF is consistent with the reality. …”
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Electromagnetic design, sensitivity analysis, optimization and Multiphysics capability of rare-earth-free synchronous reluctance motor for electric trike vehicle by V Rajini, VS Nagarajan, Karunya Harikrishnan, Mohan Lal Kolhe

“…Furthermore, an Extreme Learning Machine (ELM)-based interpolation technique is employed for estimating the performance parameters during each step of the optimization routine. …”
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