In this paper, crosstalk sensitivity analysis of a microwave coupled-line structure due to the fabrication imperfections is investigated using Least Square-Support Vector Machine (LS-SVM) method. Since LS-SVM uses a set of linear equations instead of a convex quadratic programming problem, the computational cost is extremely reduced compared to that of the well-known Monte Carlo (MC) analysis or even Support Vector Machine (SVM) without decreasing the accuracy. Using this method, the geometrical parameters of the coupled-line are assumed to be randomly distributed using the Latin Hypercube function and the variation range of each parameter is set to ± 50% around its central value. The frequency response of the coupled-line is estimated and compared with those of the measured and simulation ones for a few well-known practical case studies. The results show that the LS-SVM procedure quickly predicts the worst-case crosstalk expectation values and accurately anticipates the probability of obtaining various outcomes of the coupled-line for the specified parameter variation over a wide frequency range.
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http://dx.doi.org/10.1038/s41598-023-42728-4 | DOI Listing |
Int Urogynecol J
January 2025
School of Nursing, Binzhou Medical University, Bincheng District, No. 522, Huanghe Third Road, Binzhou, Shandong, China.
Introduction And Hypothesis: This study aims to develop a postpartum stress urinary incontinence (PPSUI) risk prediction model based on an updated definition of PPSUI, using machine learning algorithms. The goal is to identify the best model for early clinical screening to improve screening accuracy and optimize clinical management strategies.
Methods: This prospective study collected data from 1208 postpartum women, with the dataset randomly divided into training and testing sets (8:2).
Front Public Health
January 2025
Triveni Rai Kisan Mahila Mahavidyalaya, D. D. U. Gorakhpur University, Kushinagar, India.
Background And Objective: This study delves into the parenting cognition perspectives on COVID-19 in children, exploring symptoms, transmission modes, and protective measures. It aims to correlate these perspectives with sociodemographic factors and employ advanced machine-learning techniques for comprehensive analysis.
Method: Data collection involved a semi-structured questionnaire covering parental knowledge and attitude on COVID-19 symptoms, transmission, protective measures, and government satisfaction.
Front Genet
January 2025
School of Mathematics and Physics, The University of Queensland, Brisbane, QLD, Australia.
Introduction: Due to its favorable traits-such as lower lignin content, higher oil concentration, and increased protein levels-the genetic improvement of yellow-seeded rapeseed has attracted more attention than other rapeseed color variations. Traditionally, yellow-seeded rapeseed has been identified visually, but the complex variability in the seed coat color of has made manual identification challenging and often inaccurate. Another method, using the RGB color system, is frequently employed but is sensitive to photographic conditions, including lighting and camera settings.
View Article and Find Full Text PDFJ Multidiscip Healthc
January 2025
Department of Nuclear Medicine, The First People's Hospital of Yunnan Province, the Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, 650032, People's Republic of China.
Objective: This study aimed to explore the value of a radiomic nomogram based on contrast-enhanced computed tomography (CECT) for differentiating benign and malignant solid-containing renal masses.
Materials And Methods: A total of 122 patients with pathologically confirmed benign (n=47) or malignant (n=75) solid-containing renal masses were enrolled in this study. Radiomic features were extracted from the arterial, venous and delayed phases and further analysed by dimensionality reduction and selection.
Front Plant Sci
January 2025
Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
The Leaf Area Index (LAI) is an essential parameter that affects the exchange of energy and materials between the vegetative canopy and the surrounding environment. Estimating LAI using machine learning models with remote sensing data has become a prevalent method for large-scale LAI estimation. However, existing machine learning models have exhibited various flaws, hindering the accurate estimation of LAI.
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