Zobrazeno 1 - 10
of 316
pro vyhledávání: '"Ding-Geng Chen"'
Publikováno v:
AppliedMath, Vol 4, Iss 1, Pp 394-426 (2024)
The reliability of the multicomponent stress–strength system was investigated under the two-parameter Burr X distribution model. Based on the structure of the system, the type II censored sample of strength and random sample of stress were obtained
Externí odkaz:
https://doaj.org/article/57cf3fe18b7742e6a840348126030f3e
Autor:
Marjan Nouri-Keshtkar, Mohadeseh Shojaei Shahrokhabadi, Azadeh Ghaheri, Roya Hosseini, Hassan Ketabi, Mojtaba Farjam, Ding-Geng Chen, Mehdi Rezaeian, Reza Homayounfar, Yaser Tahamtani, Mehdi Totonchi
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-12 (2023)
Abstract Many factors can lead to an increase in the prevalence of metabolic syndrome (MetS) in different populations. Using an advanced structural equation model (SEM), this study is aimed to determine the most important risk factors of MetS, as a c
Externí odkaz:
https://doaj.org/article/921c1b451c774290b1dc1a90650e1aec
Publikováno v:
Journal of Imaging, Vol 10, Iss 7, p 151 (2024)
Nonmydriatic retinal fundus images often suffer from quality issues and artifacts due to ocular or systemic comorbidities, leading to potential inaccuracies in clinical diagnoses. In recent times, deep learning methods have been widely employed to im
Externí odkaz:
https://doaj.org/article/44f8a24970544b10b2bd248fde098ca1
Autor:
Yesuf Abdela Mustefa, Ding-Geng Chen
Publikováno v:
Archives of Public Health, Vol 79, Iss 1, Pp 1-7 (2021)
Abstract Background Survival analysis is the most appropriate method of analysis for time-to-event data. The classical accelerated failure-time model is a more powerful and interpretable model than the Cox proportional hazards model, provided that mo
Externí odkaz:
https://doaj.org/article/b31cd37bc90f406eb85fbca64ab33cb7
Publikováno v:
Global Health Research and Policy, Vol 5, Iss 1, Pp 1-7 (2020)
Abstract Background Many studies have modeled and predicted the spread of COVID-19 (coronavirus disease 2019) in the U.S. using data that begins with the first reported cases. However, the shortage of testing services to detect infected persons makes
Externí odkaz:
https://doaj.org/article/7cbce080c09b48c0b8b1ea8915a52e16
Publikováno v:
Brain and Behavior, Vol 11, Iss 12, Pp n/a-n/a (2021)
Abstract Introduction Kessler Psychological Distress Scale (K10) is a 10‐item screening tool designed for nonspecific psychological distress. The current study aims to identify a best‐fitting factor structure of the K10, and to test its cross‐g
Externí odkaz:
https://doaj.org/article/c8b3c958987f4c7e8f5cafccd73a6926
Autor:
Mahdi Salehi, Mohammad Arashi, Andriette Bekker, Johan Ferreira, Ding-Geng Chen, Foad Esmaeili, Motala Frances
Publikováno v:
Frontiers in Public Health, Vol 8 (2021)
The purpose of this paper is to introduce a useful online interactive dashboard (https://mahdisalehi.shinyapps.io/Covid19Dashboard/) that visualize and follow confirmed cases of COVID-19 in real-time. The dashboard was made publicly available on 6 Ap
Externí odkaz:
https://doaj.org/article/3906c743484547e7b306c7fc51baf1ef
Publikováno v:
General Psychiatry, Vol 33, Iss 5 (2020)
In studies on psychiatry and neurodegenerative diseases, it is common to have data that are correlated due to the hierarchical structure in data collection or to repeated measures on the subject longitudinally. However, the feedback effect created du
Externí odkaz:
https://doaj.org/article/30c657a17aa04ea0a6f4ee9a709220c8
Publikováno v:
Mathematics, Vol 9, Iss 24, p 3245 (2021)
The purpose of this paper is to develop a data augmentation technique for statistical inference concerning stochastic cusp catastrophe model subject to missing data and partially observed observations. We propose a Bayesian inference solution that na
Externí odkaz:
https://doaj.org/article/7329900abdfd4b0b9786df1ba3ac59d0
Autor:
Ding-Geng Chen
Publikováno v:
Preventive Medicine Reports, Vol 7, Iss C, Pp 116-118 (2017)
To quantify the HIV epidemic, the classical population-based prevalence and incidence rates (P rates) are the two most commonly used measures used for policy interventions. However, these P rates ignore the heterogeneity of the size of geographic reg
Externí odkaz:
https://doaj.org/article/348cef2be9f04c078cd899522de420e7