Energy-saving building program evaluation with an integrated method under linguistic environment

In the context of sustainable development, building energy conservation has become the development trend of the construction industry. The selection of energy-saving building program, as a multi-criteria decision-making (MCDM) problem, has a direct influence on the actual energy-saving effect. In this paper, an integrated MCDM method combining the extended best worst method (BWM) and Weighted Aggregated Sum Product Assessment (WASPAS) method is proposed to solve the energy-saving building program selection problem under the linguistic Pythagorean fuzzy environment. The Linguistic Pythagorean fuzzy sets (LPFSs) are used to model the uncertain evaluation information of experts. The extended BWM is developed to determine the weights of criteria, while the extended WASPAS method is proposed to determine the ranking of alternatives. To validate the applicability and reliability of the proposed method, this paper presents a numerical example of the selection problem for energy-saving building programs. Some managerial insights are also given for practitioners to use the proposed method

Sustainable construction supplier selection by a multiple criteria decision-making method with hesitant linguistic information

Within the context of resource constraints and ecological environment imbalance, the adoption of green suppliers can help construction enterprises achieve sustainable development and improve their competitiveness. The selection of sustainable construction suppliers is a multi-criteria decision-making problem since multiple factors should be considered. The increasingly complex decision-making environment makes it difficult for evaluators to give accurate evaluation values. In this regard, the hesitant fuzzy linguistic term set is a qualitative evaluation tool to represent the comprehensive linguistic evaluation values of experts by considering the hesitancy behaviors of experts. In this paper, a scientific multi-criteria decision-making model based on the improved Stepwise Weight Assessment Ratio Analysis (SWARA) method and the double normalizationbased multi-aggregation (DNMA) method in the hesitant fuzzy linguistic environment is proposed. A new distance measure is proposed to measure the differences between hesitant fuzzy linguistic term sets with different lengths without changing the original evaluation information of experts. The proposed distance measure is applied to the proposed multi-criteria decision-making model. After improving the calculation steps of the traditional SWARA method, we can determine the weights of criteria effectively through our proposed model. To verify the applicability of the proposed method, we implement it to select sustainable building suppliers. The effectiveness of the method is verified by sensitivity analysis. We also compare the results obtained by our method and those derived by the Weight Aggregated Sum Product ASsessment (WASPAS) method and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. The proposed method have a strong applicability to solve the sustainability-related decision problems given that it can effectively determine the weights of criteria and flexibly meet the needs of decision-makers by adjusting the coefficient

Cold Chain Logistics Management of Medicine with an Integrated Multi-Criteria Decision-Making Method

Medicine is the main means to reduce cancer mortality. However, some medicines face various risks during transportation and storage due to the particularity of medicines, which must be kept at a low temperature to ensure their quality. In this regard, it is of great significance to evaluate and select drug cold chain logistics suppliers from different perspectives to ensure the quality of medicines and reduce the risks of transportation and storage. To solve such a multiple criteria decision-making (MCDM) problem, this paper proposes an integrated model based on the combination of the SWARA (stepwise weight assessment ratio analysis) and CoCoSo (combined compromise solution) methods under the probabilistic linguistic environment. An adjustment coefficient is introduced to the SWARA method to derive criteria weights, and an improved CoCoSo method is proposed to determine the ranking of alternatives. The two methods are extended to the probabilistic linguistic environment to enhance the applicability of the two methods. A case study on the selection of drug cold chain logistics suppliers is presented to demonstrate the applicability of the proposed integrated MCDM model. The advantages of the proposed methods are highlighted through comparative analyses.This article belongs to the Special Issue Artificial Intelligence in Health CareThe research was funded by the National Natural Science Foundation of China under Grant 71771156, 71971145 and the APC was funded by the National Natural Science Foundation of China under Grant 71971145

Cold Chain Logistics Management of Medicine with an Integrated Multi-Criteria Decision-Making Method

Medicine is the main means to reduce cancer mortality. However, some medicines face various risks during transportation and storage due to the particularity of medicines, which must be kept at a low temperature to ensure their quality. In this regard, it is of great significance to evaluate and select drug cold chain logistics suppliers from different perspectives to ensure the quality of medicines and reduce the risks of transportation and storage. To solve such a multiple criteria decision-making (MCDM) problem, this paper proposes an integrated model based on the combination of the SWARA (stepwise weight assessment ratio analysis) and CoCoSo (combined compromise solution) methods under the probabilistic linguistic environment. An adjustment coefficient is introduced to the SWARA method to derive criteria weights, and an improved CoCoSo method is proposed to determine the ranking of alternatives. The two methods are extended to the probabilistic linguistic environment to enhance the applicability of the two methods. A case study on the selection of drug cold chain logistics suppliers is presented to demonstrate the applicability of the proposed integrated MCDM model. The advantages of the proposed methods are highlighted through comparative analyses

Valproic acid exhibits anti-tumor activity selectively against EGFR/ErbB2/ErbB3-coexpressing pancreatic cancer via induction of ErbB family members-targeting microRNAs

Abstract Background Deregulated ErbB signaling plays an important role in tumorigenesis of pancreatic cancer. However, patients with pancreatic cancer benefit little from current existed therapies targeting the ErbB signaling. Here, we explore the potential anti-tumor activity of Valproic acid against pancreatic cancer via targeting ErbB family members. Methods Cell viability assay and apoptosis evaluation were carried out to determine the efficacy of VPA on pancreatic cancer cells. Western blot analyses were performed to determine the expression and activation of proteins. Apoptosis enzyme-linked immunosorbent assay was used to quantify cytoplasmic histone associated DNA fragments. Lentiviral expression system was used to introduce overexpression of exogeneous genes or gene-targeting short hairpin RNAs (shRNAs). qRT-PCR was carried out to analyze the mRNAs and miRNAs expression levels. Tumor xenograft model was established to evaluate the in vivo anti-pancreatic cancer activity of VPA. Results VPA preferentially inhibited cell proliferation/survival of, and induced apoptosis in EGFR/ErbB2/ErbB3-coexpressing pancreatic cancer cells within its clinically achievable range [40~100 mg/L (0.24~0.6 mmol/L)]. Mechanistic investigations revealed that VPA treatment resulted in simultaneous significant down-regulation of EGFR, ErbB2, and ErbB3 in pancreatic cancer cells likely via induction of ErbB family members-targeting microRNAs. Moreover, the anti-pancreatic cancer activity of VPA was further validated in tumor xenograft model. Conclusions Our data strongly suggest that VPA may be added to the treatment regimens for pancreatic cancer patients with co-overexpression of the ErbB family members