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Your search keyword '"Liang, Rui"' showing total 325 results
Start Over Author "Liang, Rui" Publisher elsevier b.v.
325 results on '"Liang, Rui"'

49. Pyrolytic homogeneity enhancement of municipal solid waste using a clustering-based sorting strategy.

Author

Guo, Zilin, Liang, Rui, Tao, Junyu, Li, Ning, Cheng, Zhanjun, Yan, Beibei, and Chen, Guanyi

Subjects
*SOLID waste, *HOMOGENEITY, *K-means clustering, *HIERARCHICAL clustering (Cluster analysis), *WASTE products as fuel, *PYROLYTIC graphite
Abstract

[Display omitted] • A sorting strategy was proposed to improve the pyrolytic homogeneity of MSW. • An index (C) was established to quantify the sorting performance for Ea and logA. • The strategy outperformed traditional ones in pyrolytic homogeneity enhancement. • M3 model showed optimal C_Ea (1578.30 kJ/mol) and C_logA (93.11 -log min). Heterogeneity of pyrolytic parameters in municipal solid waste (MSW) significantly hinders its waste-to-energy efficiency. So far, hardly any light has been shed on current pyrolytic heterogeneity conditions or feasible pyrolytic homogeneity enhancement approaches of MSW. Accordingly, pyrolytic properties (E a and logA) of 130 MSW samples in 6 categories were collected from literature. A kinetic parameters clustering-based sorting strategy for MSW was proposed. A so-called C index was established to compare their sorting performance for E a and logA against two traditional sorting strategies (substance categorization and density clustering). Results showed that the proposed sorting strategies outperformed the traditional ones in pyrolytic homogeneity enhancement, where the optimal C_E a and C_logA reached 1578.30 kJ/mol and 93.11 -log min. Among investigated clustering methods, k-means clustering outperformed hierarchical clustering, which could be attributed to its adaptability to the sample structure. Future perspectives involving data set expansion, model framework development, and downstream technologies matching were also discussed. The index C established in this study can be used to evaluate other clustering models. [ABSTRACT FROM AUTHOR]

Published
2024
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50. MRI-Based Radiomics and Delta-Radiomics Models of the Patella Predict the Radiographic Progression of Osteoarthritis: Data From the FNIH OA Biomarkers Consortium.

Author

Jiang, Hai, Peng, Yi, Qin, Si-Yu, Chen, Chao, Pu, Yu, Liang, Rui, Chen, Yong, Zhang, Xiao-Ming, Sun, Yang-bai, and Zuo, Hou-dong

Abstract

To analyse the MRI-based radiomics and delta-radiomics features to establish radiomics models for predicting the radiographic progression of osteoarthritis (OA). The data used in this research come from the dataset of the FNIH Biomarker Consortium Project within the Osteoarthritis Initiative (OAI). 565 participants randomly divided into training and validation groups at a 7:3 ratio. The training cohort consisted of 395 participants and included 202 cases. The validation cohort consisted of 170 participants and included 87 cases. Least absolute shrinkage and selection operator (LASSO) was used for feature selection. Support vector machine (SVM) was used to establish radiomics models and clinical and biomarker models for predicting the radiographic progression of OA. The predictive ability of the model was evaluated by the area under the curve (AUC). The baseline, 24 M, Delta, and two combination radiomics models (Baseline and Delta, 24 M and Delta) all showed good predictive performance in the training and validation cohorts, with the combination model exhibiting the best performance. In the training cohort, the AUCs were 0.851 (95% CI: 0.812–0.890), 0.825 (95% CI: 0.784–0.865), 0.804 (95% CI: 0.761–0.847), 0.892 (95% CI: 0.860–0.924) and 0.884 (95% CI: 0.851–0.917), respectively. The AUCs in the validation cohort were 0.741 (95% CI: 0.667–0.814), 0.786 (95% CI: 0.716–0.856), 0.745 (95% CI: 0.671–0.819), 0.781 (95% CI: 0.711–0.851) and 0.802 (95% CI: 0.736–0.869), respectively. As compared, the clinical and biomarker models have AUC < 0.74. The DeLong test showed that the predictive performance of the radiomics models in the training and validation cohorts was significantly better than that of the clinical and biomarker models (P < 0.001). The MRI-based radiomics models of the patella all showed good predictive performance performed better than the clinical and biomarker models in predicting the radiographic progression of OA. Delta radiomics can improve the predictive performance of the single time model, the combined model of 24 M and Delta provided the best predictive performance. [ABSTRACT FROM AUTHOR]

Published
2024
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