A novel grey prognostic model based on Markov process and grey incidence analysis for energy conversion equipment degradation

Purpose The purpose of this paper is to study the modelling mechanisms of several grey incidence analysis models with great influence, including Deng’s grey incidence model, absolute degree of grey incidence model, slope degree of incidence model, similitude degree of incidence model and closeness degree of incidence model; then analyse the problems to be solved in grey incidence analysis models; and clarify the applicable ranges of commonly used grey incidence models. Design/methodology/approach The paper comes to conclusions by means of comparable analysis. The authors compare several commonly used grey incidence analysis models, including Deng’s grey incidence model, absolute degree of grey incidence model, slope degree of incidence model, similitude degree of incidence model and closeness degree of incidence model and give several examples to clarify the reasons why quantitative analysis results of different models are not exactly the same. Findings As the intension of each kind of incidence model is clear and the extension is relatively obscure, grey incidence orders calculated by different incidence models are often different. When making actual decisions, incompatible results may appear. According to different characteristics of extraction, grey incidence analysis models can be divided into three types: incidence model based on closeness perspective, incidence model based on similarity perspective and incidence model based on comprehensive perspective. Practical implications The conclusions obtained in this paper can help people avoid some defects in the process of actual selection and choose the better incidence analysis model. Originality/value The conclusions can be used as a reference and basis for the selection of grey incidence analysis models, it can help to overcome the defects and shortcomings of models caused by themselves and screen out more excellent analytical models.

Download Full-text

Research on influencing factors of industry transfer based on grey incidence analysis

Green Communications and Networks ◽

10.2495/gcn130211 ◽

2014 ◽

Author(s):

Han Nan

Keyword(s):

Influencing Factors ◽

Grey Incidence Analysis ◽

Incidence Analysis

Download Full-text

Industry 4.0 and Higher Education: An Evaluation of Barriers Affecting Master’s in Business Administration Enrolments Using a Grey Incidence Analysis

IEEE Access ◽

10.1109/access.2021.3082144 ◽

2021 ◽

pp. 1-1

Author(s):

Asmaa Fahim ◽

Bismark Appiah Addae ◽

Jeffrey Ofosu-Adarkwa ◽

Tan Qingmei ◽

Uzair Aslam Bhatti

Keyword(s):

Higher Education ◽

Industry 4.0 ◽

Business Administration ◽

Grey Incidence Analysis ◽

Incidence Analysis

Download Full-text

An Innovative Prognostic Model Based on Four Genes in Asian Patient with Gastric Cancer

SSRN Electronic Journal ◽

10.2139/ssrn.3391354 ◽

2019 ◽

Author(s):

Jiahui Chen ◽

Anqiang Wang ◽

Guoqing Lyu ◽

Kai Zhou ◽

Ke Ji ◽

...

Keyword(s):

Gastric Cancer ◽

Prognostic Model ◽

Asian Patient ◽

Model Based

Download Full-text

Identification of Multi-omics Biomarkers and Construction of the Novel Prognostic Model for Hepatocellular Carcinoma

10.21203/rs.3.rs-452644/v1 ◽

2021 ◽

Author(s):

Xiaokai Yan ◽

Chiying Xiao ◽

Kunyan Yue ◽

Min Chen ◽

Hang Zhou

Keyword(s):

Hepatocellular Carcinoma ◽

Survival Analysis ◽

Prognostic Model ◽

Prognostic Models ◽

Prognostic Indicators ◽

Omics Data ◽

Training Set ◽

Test Set ◽

Model Based ◽

Cox Analysis

Abstract Background: Change in the genome plays a crucial role in cancerogenesis and many biomarkers can be used as effective prognostic indicators in diverse tumors. Currently, although many studies have constructed some predictive models for hepatocellular carcinoma (HCC) based on molecular signatures, the performance of which is unsatisfactory. To fill this shortcoming, we hope to construct a novel and accurate prognostic model with multi-omics data to guide prognostic assessments of HCC. Methods: The TCGA training set was used to identify crucial biomarkers and construct single-omic prognostic models through difference analysis, univariate Cox, and LASSO/stepwise Cox analysis. Then the performances of single-omic models were evaluated and validated through survival analysis, Harrell’s concordance index (C-index), and receiver operating characteristic (ROC) curve, in the TCGA test set and external cohorts. Besides, a comprehensive model based on multi-omics data was constructed via multiple Cox analysis, and the performance of which was evaluated in the TCGA training set and TCGA test set. Results: We identified 16 key mRNAs, 20 key lncRNAs, 5 key miRNAs, 5 key CNV genes, and 7 key SNPs which were significantly associated with the prognosis of HCC, and constructed 5 single-omic models which showed relatively good performance in prognostic prediction with c-index ranged from 0.63 to 0.75 in the TCGA training set and test set. Besides, we validated the mRNA model and the SNP model in two independent external datasets respectively, and good discriminating abilities were observed through survival analysis (P < 0.05). Moreover, the multi-omics model based on mRNA, lncRNA, miRNA, CNV, and SNP information presented a quite strong predictive ability with c-index over 0.80 and all AUC values at 1,3,5-years more than 0.84.Conclusion: In this study, we identified many biomarkers that may help study underlying carcinogenesis mechanisms in HCC, and constructed five single-omic models and an integrated multi-omics model that may provide effective and reliable guides for prognosis assessment and treatment decision-making.

Download Full-text

Identification and Validation of a Prognostic Model Based on Three MVI-Related Genes in Hepatocellular Carcinoma

International Journal of Biological Sciences ◽

10.7150/ijbs.66536 ◽

2022 ◽

Vol 18 (1) ◽

pp. 261-275

Author(s):

Yongchang Tang ◽

Lei Xu ◽

Yupeng Ren ◽

Yuxuan Li ◽

Feng Yuan ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Prognostic Model ◽

Model Based

Download Full-text

Predicting mortality after traumatic brain injury: Prognostic model based on admission characteristics

Journal of Critical Care ◽

10.1016/j.jcrc.2017.09.154 ◽

2017 ◽

Vol 42 ◽

pp. 418

Author(s):

Jose Daniel Charry Cuellar ◽

Lisseth Paola Lopez Narvaez ◽

Juan Felipe Cáceres Sepúlveda ◽

Andrea Catherine Salazar Trujillo

Keyword(s):

Traumatic Brain Injury ◽

Brain Injury ◽

Prognostic Model ◽

Model Based

Download Full-text

Identifying key industry factors of remanufacturing industry using grey incidence analysis

Grey Systems Theory and Application ◽

10.1108/gs-08-2016-0016 ◽

2016 ◽

Vol 6 (3) ◽

pp. 398-414 ◽

Cited By ~ 1

Author(s):

Wenjie Liu ◽

Jing Zhang ◽

Chenfan Wu ◽

Xiangyun Chang

Keyword(s):

Grey System Theory ◽

Grey System ◽

Annual Sale ◽

Content Type ◽

Building Area ◽

Industry Characteristic ◽

Grey Incidence Analysis ◽

Incidence Analysis ◽

Practical Implications ◽

Key Industry

Purpose The purpose of this paper is to identify most favorable (or quasi-preferred) industry characteristics of remanufacturing industry and most favorable (or quasi-preferred) industry factors which have an effect on these characteristics so as to improve these factors. Design/methodology/approach Grey system theory has prominent advantage of using few data and uncertainty information to analyze many factors. Therefore, it is more suited for system analysis than traditional statistical analysis methods like regression analysis, variance analysis and principal component analysis, which require massive data, certain probability distribution in the data and few variant factors. So in this paper, grey incidence analysis method, which is an important part of grey system theory, is used to identify industry characteristics and key industry factor of remanufacturing industry in China and then put forward appropriate industrial policies and countermeasures to improve these industry factors. Findings According to the results of this study, it reveals that there are no most favorable industry characteristics and no most favorable industry factors in remanufacturing industry of China. “Annual sale of remanufacturing industry” is identified as quasi-preferred industry characteristic, and “total number of employees with master degree or above in remanufacturing enterprise” is identified as the quasi-preferred industry factor. “Total building area of remanufacturing enterprise” is referred as the most unfavorable industry factors. Practical implications Judging from the findings of this study, four practical implications are summarized as follows: “annual sale of remanufacturing industry” should be given great importance because it is a quasi-preferred industry characteristic. “Total number of employees with master degree or above in remanufacturing enterprise” and “total number of research institution and university participated in remanufacturing” should be further strengthened by establishing an industry-university-research institute collaboration network, due to the fact that they are the top two quasi-preferred industry factors. “Total investment of remanufacturing industry” and “total annual R&D expenditures” have not played their due role in improving remanufacturing industry, so they should be moderately controlled so as to reduce waste of investment. “Total building area of remanufacturing enterprise” must be strictly controlled because of its little impact on remanufacturing industry. Originality/value In this research, grey incidence analysis is applied to identify key industry factors of remanufacturing industry for the first time. It helps in finding industry factors which are in urgent need of improvement and assists in making appropriate industrial policies and countermeasures to improve them by studying relationships between industry characteristic and industry factors.

Download Full-text