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1 – 2 of 2Kaiying Kang, Jialiang Xie, Xiaohui Liu and Jianxiang Qiu
Experts may adjust their assessments through communication and mutual influence, and this dynamic evolution relies on the spread of internal trust relationships. Due to…
Abstract
Purpose
Experts may adjust their assessments through communication and mutual influence, and this dynamic evolution relies on the spread of internal trust relationships. Due to differences in educational backgrounds and knowledge experiences, trust relationships among experts are often incomplete. To address such issues and reduce decision biases, this paper proposes a probabilistic linguistic multi-attribute group decision consensus model based on an incomplete social trust network (InSTN).
Design/methodology/approach
In this paper, we first define the new trust propagation operators based on the operations of Probability Language Term Set (PLTS) with algebraic t-conorm and t-norm, which are combined with trust aggregation operators to estimate InSTN. The adjustment coefficients are then determined through trust relations to quantify their impact on expert evaluation. Finally, the particle swarm algorithm (PSO) is used to optimize the expert evaluation to meet the consensus threshold.
Findings
This study demonstrates the feasibility of the method through the selection of treatment plans for complex cases. The proposed consensus model exhibits greater robustness and effectiveness compared to traditional methods, mainly due to the effective regulation of trust relations in the decision-making process, which reduces decision bias and inconsistencies.
Originality/value
This paper introduces a novel probabilistic linguistic multi-attribute swarm decision consensus model based on an InSTN. It proposes a redefined trust propagation and aggregation approach to estimate the InSTN. Moreover, the computational efficiency and decision consensus accuracy of the proposed model are enhanced by using PSO optimization.
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Maryam Nilghaz, Mohammadreza Shahparvari, Azita Hekmatdoost, Saeede Saadati, Moloud Ghorbani, Amir Sadeghi and Zahra Yari
Dietary components have been mentioned as modifiable risk factors in the development of gallstone disease (GSD), but it has been less addressed. The present study aimed to…
Abstract
Purpose
Dietary components have been mentioned as modifiable risk factors in the development of gallstone disease (GSD), but it has been less addressed. The present study aimed to investigate the potential association between different types of dietary carbohydrate and the risk of gallstone.
Design/methodology/approach
In this case-control study 189 patients diagnosed with GSD as a case group and 342 people as a control group were enrolled. Dietary intake of the participants was collected through a 168-item semi-quantitative food frequency questionnaire. Total intakes of calories, macronutrients and different types of carbohydrate were estimated. Crude and multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to assess the association between carbohydrate intake and GSD.
Findings
Patients with gallstone consumed significantly more fructose and sucrose and less fiber. After fully adjustment, the logistic regression indicated significant association between GSD with dietary intake of total carbohydrate (OR = 1.65, 95% CI: 1.1–2.4, p = 0.009), sugar (OR = 1.23, 95% CI: 0.8–1.7, p = 0.014), fructose (OR = 2.5, 95% CI: 1.7–3.9, p < 0.001), glucose (OR = 1.9, 95% CI: 1.3–2.9, p = 0.002) and sucrose (OR = 1.37, 95% CI: 0.9–1.6, p = 0.042). Also, increasing intakes of lactose, galactose and maltose were associated with a decrease in the risk of GSD, but not statistically significant, although lactose was close to significance (OR = 0.71, 95% CI: 0.48–1, p = 0.051).
Originality/value
There is a positive and significant relationship between total carbohydrate, sugar, fructose, glucose and sucrose intake and the occurrence of gallstone.
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