Sustainability assessment in construction projects: a sustainable earned value management model under uncertain and unreliable conditions.

All three pillars of sustainability must be met for a construction project to be considered sustainable. Although several studies have been carried out on sustainability in the construction industry under uncertain conditions, there has been a lack of a comprehensive model to assess all three pillars of sustainability during the execution of a construction project with consideration of the uncertain and unreliable nature of the gathered data. In this article, a fuzzy inference system model was developed to fill this research gap. The Cost Performance Index from earned value management, an effective tool for cost control of projects, is used to assess the economic sustainability status during the project execution. Besides, from the expert opinions and literature review, sustainability-related attributes are gathered to identify the dimensions and enablers of a construction project. This research aimed not only to improve the previous social sustainability Indexes for construction projects by using Z-numbers but also to develop and introduce a comprehensive Z Construction Environmental Sustainability Index alongside the other two pillars. The previous hierarchal method to calculate the Fuzzy indexes is also improved to overcome possible complexity problems. Calculating the three pillars of sustainability and using them as inputs into the Mamdani FIS model to obtain the overall sustainability status, the proposed model, which is calculated with the Z-numbers, is compared with the results of conventional Fuzzy numbers and deterministic approach. The results of the comparison show that the proposed model is more rigorous and effective than the previous methods in the numerical case.

Predictors of Death in the Liver Transplantation Adult Candidates: An Artificial Neural Networks and Support Vector Machine Hybrid-Based Cohort Study.

Background: Model for end-stage liver disease (MELD) is currently used for liver transplantation (LT) allocation, however, it is not a sufficient criterion. Objective: This current study aims to perform a hybrid neural network analysis of different data, make a decision tree and finally design a decision support system for improving LT prioritization. Material and Methods: In this cohort follow-up-based study, baseline characteristics of 1947 adult patients, who were candidates for LT in Shiraz Organ Transplant Center, Iran, were assessed and followed for two years and those who died before LT due to the end-stage liver disease were considered as dead cases, while others considered as alive cases. A well-organized checklist was filled for each patient. Analysis of the data was performed using artificial neural networks (ANN) and support vector machines (SVM). Finally, a decision tree was illustrated and a user friendly decision support system was designed to assist physicians in LT prioritization. Results: Between all MELD types, MELD-Na was a stronger determinant of LT candidates' survival. Both ANN and SVM showed that besides MELD-Na, age and ALP (alkaline phosphatase) are the most important factors, resulting in death in LT candidates. It was cleared that MELD-Na <23, age <53 and ALP <257 IU/L were the best predictors of survival in LT candidates. An applicable decision support system was designed in this study using the above three factors. Conclusion: Therefore, Meld-Na, age and ALP should be used for LT allocation. The presented decision support system in this study will be helpful in LT prioritization by LT allocators.

Quality of Life and Its Determinants in Liver Transplantation Candidates: A Missed Link in Liver Care Program during the Waiting Time for Liver Transplantation.

Background: The health-related quality of life (HRQOL) in the before liver transplantation (LT) stage has not been studied as much as that after the LT stage. We aimed to assess HRQOL and its determinants before the LT stage. Methods: As a cross-sectional study, HRQOL of all adult patients (n=632) referred to the LT center of Shiraz, Iran in 2018-2019 were assessed. Demographic, socioeconomic, medical, and paraclinical data were requested. Physical (PCS) and mental (MCS) aspects of HRQOL were assessed using the SF36 questionnaire. Univariable, multivariable (linear regression), and confirmatory factor analysis were performed utilizing SPSS 20 and Mplus 6.1 software. P<0.05 was considered to be significant. Results: The mean age of the patients was 47.6±12.3 years, while 414 (65.6%) were men, and the mean, score of the model for end-stage liver disease (MELD) was 18.36±5.58. The mean score of QOL, PCS, and MCS was 50.01±21.73, 46.23±23.23, and 53.78±23.91 (out of 100), respectively. Vitality had the most association with HRQOL, while role limitations had the lowest. The multivariable analysis revealed that unemployment (P<0.001), anemia (P=0.005), weight loss (P=0.005), diabetes mellitus (DM) (P=0.009), low MELD score (P=0.027), and drug use (P=0.03) were the significant determinants of HRQOL, respectively. Conclusion: The present study showed that HRQOL in the LT candidates was at the intermediate level, while their PCS and MCS are at the low and moderate levels, respectively. Furthermore, physical performance, job status, anemia, weight loss, MELD score, DM, and drug use should be considered as the significant determinants of HRQOL in the LT candidates.

Analyzing the Status of Sustainable Development in the Manufacturing Sector Using Multi-Expert Multi-Criteria Fuzzy Decision-Making and Integrated Triple Bottom Lines.

A sustainable manufacturing company depends on the developments in three aspects in order to minimize harmful impacts on the environment, improve the social relations, and simultaneously maximize the economic benefits. Despite the increasing types of investigations that researchers have carried out in environmental and economic aspects, the minimum attention has been paid to social relations. In response to this deficiency, this paper proposes a new framework to obtain the overall sustainability index in manufacturing companies by encapsulating the sustainability criteria/sub-criteria. This article collected 33 sub-criteria for five pillars of sustainability as social, environment, economic, technological advancement, and performance management. The key contributions of this paper are highlighted as the hierarchical method that obtains the status of sustainability in uncertain conditions, the ability to identify the weak points, and a new framework for gathering the data about sustainability performance in manufacturing companies. The findings of this paper will aid both policymakers and decision-makers to assess the sustainability status of manufacturing systems and improve the performances of them.