Abstract: How to better evaluate the value of urban real estate is a major issue in the reform of real estate tax system. So the establishment of an accurate and efficient housing batch evaluation model is crucial in evaluating the value of housing. In this paper the second-hand housing transaction data of Zhengzhou City from 2010 to 2019 was used to model housing prices and explanatory variables by using models of Ordinary Least Square (OLS), Spatial Error Model (SEM), Geographically Weighted Regression (GWR), Geographically and Temporally Weighted Regression (GTWR), and Multiscale Geographically Weighted Regression (MGWR). And a correction method of Barrier…Line and Access Point (BLAAP) was constructed, and compared with three correction methods previously studied: Buffer Area (BA), Euclidean Distance (ED), and Non-Euclidean Distance, Travel Distance (ND, TT). The results showed: The fitting degree of GWR, MGWR and GTWR by BLAAP was 0.03–0.07 higher than by ND. The fitting degree of MGWR was the highest (0.883) by BLAAP but the smallest by Akaike Information Criterion (AIC), and 88.3% of second-hand housing data could be well interpreted by the model.
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Keywords: Housing price, big data, MGWR, GTWR, BLAAP, batch evaluation model
Abstract: BACKGROUND: In malignant tumours of the female reproductive system, cervical cancer is second only to breast cancer, seriously threatening the health and safety of most women. OBJECTIVE: To evaluate the clinical value of 3.0 T multimodal nuclear magnetic resonance imaging (MRI) in the International Federation of Gynecology and Obstetrics’ (FIGO) staging of cervical cancer. METHODS: The clinical data of 30 patients with pathologically diagnosed cervical cancer admitted to our hospital from January 2018 to August 2022 were analysed retrospectively. Before treatment, all patients were examined with conventional MRI, diffusion-weighted imaging and multi-directional contrast-enhanced imaging. RESULTS: The accuracy of multimodal MRI…in the FIGO staging of cervical cancer (29/30, 96.7%) was significantly higher than the accuracy obtained in a control group (21/30, 70.0%), with a statistically significant difference (p = 0.013). In addition, there was good agreement between two observers applying multimodal imaging (kappa = 0.881) and moderate agreement between two observers in the control group (kappa = 0.538). CONCLUSION: Multimodal MRI can evaluate cervical cancer comprehensively and accurately to enable accurate FIGO staging, providing significant evidence for clinical operation planning and subsequent combined therapy.
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Keywords: Multimodal imaging, uterine cervical neoplasms, magnetic resonance imaging