Correction: A dynamic generalized fuzzy multi-criteria group decision making approach for green supplier segmentation.

[This corrects the article DOI: 10.1371/journal.pone.0245187.].

A dynamic generalized fuzzy multi-criteria croup decision making approach for green supplier segmentation.

Supplier selection and segmentation are crucial tasks of companies in order to reduce costs and increase the competitiveness of their goods. To handle uncertainty and dynamicity in the supplier segmentation problem, this research thus proposes a new dynamic generalized fuzzy multi-criteria group decision making (MCGDM) approach from the aspects of capability and willingness and with respect to environmental issues. The proposed approach defines the aggregated ratings of alternatives, the aggregated weights of criteria, and the weighted ratings by using generalized fuzzy numbers with the effect of time weight. Next, we determine the ranking order of alternatives via a popular centroid-index ranking approach. Finally, two case studies demonstrate the efficiency of the proposed dynamic approach. The applications show that the proposed appoach is effective in solving the MCGDM in vague environment.

Prediction Model for Antimalarial Activities of Hemozoin Inhibitors by Using Physicochemical Properties.

The rapid spread of strains of malaria parasites that are resistant to several drugs has threatened global malaria control. Hence, the aim of this study was to predict the antimalarial activity of chemical compounds that possess anti-hemozoin-formation activity as a new means of antimalarial drug discovery. After the initial in vitro anti-hemozoin-formation high-throughput screening (HTS) of 9,600 compounds, a total of 224 hit compounds were identified as hemozoin inhibitors. These 224 compounds were tested for in vitro erythrocytic antimalarial activity at 10 µM by using chloroquine-mefloquine-sensitive Plasmodium falciparum strain 3D7A. Two independent experiments were conducted. The physicochemical properties of the active compounds were extracted from the ChemSpider and SciFinder databases. We analyzed the extracted data by using Bayesian model averaging (BMA). Our findings revealed that lower numbers of S atoms; lower distribution coefficient (log D) values at pH 3, 4, and 5; and higher predicted distribution coefficient (ACD log D) values at pH 7.4 had significant associations with antimalarial activity among compounds that possess anti-hemozoin-formation activity. The BMA model revealed an accuracy of 91.23%. We report new prediction models containing physicochemical properties that shed light on effective chemical groups for synthetic antimalarial compounds and help with in silico screening for novel antimalarial drugs.