In this paper several nonlinear fitting algorithms without matrix inversion are proposed and investigated. The fitting algorithms represent an integral part of the analysis procedure, allowing us to simultaneously process large numbers of peaks in large blocks of data. The algorithms were applied to the analysis of both one-dimensional as well as two-fold coincidence gamma-ray spectra. The properties of the proposed methods and the suitability of employing the appropriate algorithms to different kinds of gamma-ray spectra were studied.
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http://dx.doi.org/10.1366/000370203322102825 | DOI Listing |
Sci Rep
January 2025
School of Architecture and Urban Planning, Beijing University of Civil Engineering and Architecture, Beijing, 100055, China.
Air pollution is a critical global environmental issue, further exacerbated by rapid industrialization and urbanization. Accurate prediction of air pollutant concentrations is essential for effective pollution prevention and control measures. The complex nature of pollutant data is influenced by fluctuating meteorological conditions, diverse pollution sources, and propagation processes, underscores the crucial importance of the spatial and temporal feature extraction for accurately predicting air pollutant concentrations.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom.
Many machine learning techniques have been used to construct gene regulatory networks (GRNs) through precision matrix that considers conditional independence among genes, and finally produces sparse version of GRNs. This construction can be improved using the auxiliary information like gene expression profile of the related species or gene markers. To reach out this goal, we apply a generalized linear model (GLM) in first step and later a penalized maximum likelihood to construct the gene regulatory network using Glasso technique for the residuals of a multi-level multivariate GLM among the gene expressions of one species as a multi-levels response variable and the gene expression of related species as a multivariate covariates.
View Article and Find Full Text PDFRheumatology (Oxford)
January 2025
Department of Rheumatology, Rheumazentrum Ruhrgebiet, Herne, Germany.
Objectives: To compare the utility values of Spondyloarthritis (SpA)-specific ASAS Health Index (U-ASAS-HI) to generic utilities and to understand the contribution of health outcomes, personal- and country-level factors to the U-ASAS-HI.
Methods: Ancillary analysis of the ASAS-HI international validation study. SpA patients who completed the ASAS-HI, 5-level EuroQol-5D (EQ-5D-5L) and Short Form-36 (SF-36) questionnaires were selected, and utilities calculated.
Front Med (Lausanne)
January 2025
Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
Introduction: Fatty liver disease is potentially linked to chronic kidney disease (CKD), yet the association between the Framingham Steatosis Index (FSI) and CKD remains uncharted. Our study thoroughly investigated the correlation between FSI and CKD, aiming to elucidate the underlying links between these two conditions.
Methods: The relationship between FSI and CKD was evaluated using a weighted multivariate logistic regression model, and the curvilinear relationship between FSI and CKD was explored through smooth curve fitting.
Sci Rep
January 2025
Department of Orthopedics, Shanghai Changhai Hospital, Shanghai, 200433, China.
With the emergence of numerous classifications, surgical treatment for adolescent idiopathic scoliosis (AIS) can be guided more effectively. However, surgical decision-making and optimal strategies still lack standardization and personalized customization. Our study aims to devise proper deep learning (DL) models that incorporate key factors influencing surgical outcomes on the coronal plane in AIS patients to facilitate surgical decision-making and predict surgical results for AIS patients.
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