Scaffolds made of chitosan nanofibers are often too mechanically weak for their application and often their manufacturing processes involve the use of harmful and flammable organic solvents. In the attempt to improve the mechanical properties of nanofibrous scaffolds made of chitosan without the use of harmful chemicals, pectin, an anionic polymer was blended with chitosan, a cationic polymer, to form a polyelectrolyte complex and electrospun into nanofibers for the first time. The electrospun chitosan-pectin scaffolds, when compared to electrospun chitosan scaffolds, had a 58% larger diameter, a 21% higher Young's modulus, a 162% larger strain at break, and a 104% higher ultimate tensile strength. Compared to the chitosan scaffolds, the chitosan-pectin scaffolds' swelling ratios decreased by 55% after 60 min in a saline solution and more quickly released the preloaded tetracycline HCl. The L929 fibroblast cells proliferated slightly slower on the chitosan-pectin scaffolds than on the chitosan scaffolds. Nonetheless, cells on both materials deposited similar levels of extracellular type I collagen on a per DNA basis. In conclusion, a novel chitosan-pectin nanofibrous scaffold with superior mechanical properties than a chitosan nanofibrous scaffold was successfully made without the use of harmful solvents.

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This paper studies the decentralized bi-level multiobjective programming (DBLMOP) problem with one decisionmaker (DM) at the higher level and more than one DM at the lower level. The number of objective functions to be optimized by each DM may be different. The fuzzy relation technique is first incorporated into fuzzy goal programming (FGP) to solve the hierarchical optimization problem. After characterizing the fuzzy goals of the objective functions and the higher-level DM's decision vector by the corresponding membership functions, the concept of fuzzy binary relation is introduced to define the imprecise cooperation relations between the two DMs located at the different levels. An FGP model is formulated to reach the satisfactory values of all the fuzzy goals as close to the optimums as possible by minimizing their deviation variables and thereby finding a candidate solution. The proposed solution algorithm involving an interactive procedure with two orientations enables the higher-level DM to adjust the decision powers of the DMs through changing the cooperation relations, if the solution needs to be re-evaluated. A numerical example is presented to illustrate and demonstrate the proposed method.

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