Objective: The aim of this study was to evaluate the effect of mineralized plasmatic matrix (MPM), comprising a combination of synthetic graft and platelet concentrates, on bone regeneration. Methods: Critical size defects of 6-mm diameter were created on the tibias of 6 male sheep, with the animals subsequently assigned into 2 groups. Of the 5 bone defects generated per animal, 4 were randomly filled with MPM, beta-tricalcium phosphate graft (β-TCP), platelet-rich fibrin (PRF) + β-TCP, and autogenous graft. One defect was left empty as a control group. Animals were killed at 3 weeks (early healing group) and 6 weeks (late healing group). The specimens underwent histologic and histomorphometric analysis to evaluate new bone formation. Results: In both healing periods, new bone formation from autogenous bone was observed significantly more often than from biomaterials or the empty defect. The degree of new bone formation for MPM was significantly higher than that of the control group at all healing periods. In addition, it was significantly higher in both healing periods than that of β-TCP albeit only in the late healing period than that of the PRF + β-TCP combination. In all biomaterial groups, residual graft ratios decreased from early to late healing periods. Conclusion: The results indicated that MPM, representing growth factors in a fibrin network, increases new bone formation in surgically created defects in sheep tibia as confirmed by histologic assessment.
A pandemic was declared in 2020 due to COVID-19. The most important way to deal with the virus is mass vaccination which is a complex task in terms of fast transportation and process management. Hospitals and other health centers can be used for vaccination. In addition, in order to separate other diseases from COVID-19 and provide rapid access to vaccines, mobile vaccination clinics can also be considered. In this study, the location assignments of mobile vaccination clinics that can serve some regions of three cities in Turkey are examined. The linear formulation of the problem is given, and the multi-facility location problem for COVID-19 vaccination is investigated with Lagrange relaxation and modified saving heuristic algorithm. For the proposed fuzzy MCDM integrated saving heuristic, the importance of candidate locations is calculated with the aid of decision makers who give their views in spherical bipolar fuzzy information. The results of different approaches are compared, and it is intended to guide future studies.
In a supply chain management, supplier selection is an important step to determine the structure of the model. Multi-criteria decision making methods are appropriate tools for dealing with the selection of suitable suppliers. In addition, fuzzy multi-criteria decision making approaches are helpful to include different and uncertain views of decision makers.In this study, a new combined fuzzy methodology is proposed to handle green supplier selection problem. The proposed model consists of a spherical fuzzy-SWARA method, which is used to calculate the criteria weights, and MARCOS method, which is applied to rank the alternatives. In the case study, green supplier selection problem of a textile company located in Turkey is discussed. Six alternative suppliers are evaluated against twelve green criteria, and alternatives are ranked. Finally, a sensitivity analysis is performed to compare the results of different scenarios.
Due to the increase in energy demand, many countries suffer from energy poverty because of insufficient and expensive energy supply. Plans to use alternative power like nuclear power for electricity generation are being revived among developing countries. Decisions for installation of power plants need to be based on careful assessment of future energy supply and demand, economic and financial implications and requirements for technology transfer. Since the problem involves many vague parameters, a fuzzy model should be an appropriate approach for dealing with this problem. This study develops a Fuzzy Multi-Objective Linear Programming (FMOLP) model for solving the nuclear power plant installation problem in fuzzy environment. FMOLP approach is recommended for cases where the objective functions are imprecise and can only be stated within a certain threshold level. The proposed model attempts to minimize total duration time, total cost and maximize the total crash time of the installation project. By using FMOLP, the weighted additive technique can also be applied in order to transform the model into Fuzzy Multiple Weighted-Objective Linear Programming (FMWOLP) to control the objective values such that all decision makers target on each criterion can be met. The optimum solution with the achievement level for both of the models (FMOLP and FMWOLP) are compared with each other. FMWOLP results in better performance as the overall degree of satisfaction depends on the weight given to the objective functions. A numerical example demonstrates the feasibility of applying the proposed models to nuclear power plant installation problem.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.