in this work, we define a fuzzy soft set theory and its related properties. we then define fuzzy soft aggregation operator that allows constructing more efficient decision making method. finally, we give an example which shows that the method can be successfully applied to many problems that contain uncertainties.

‎let $x$ be a real normed  space, then  $c(subseteq x)$  is  functionally  convex  (briefly, $f$-convex), if  $t(c)subseteq bbb r $ is  convex for all bounded linear transformations $tin b(x,r)$; and $k(subseteq x)$  is  functionally   closed (briefly, $f$-closed), if  $t(k)subseteq bbb r $ is  closed  for all bounded linear transformations $tin b(x,r)$. we improve the    krein-milman theorem  ...

The traditional set stability of Boolean networks (BNs) refers to whether all the states can converge a given state subset. Different from existing results, investigated in this paper is initial destination set. This studies and stabilization avoiding undesirable sets BNs control (BCNs), respectively. First, by virtue semi-tensor product (STP) matrices, dynamics are established. Then, reachabil...

and Applied Analysis 3 It is worth noting that if α 0, then the definitions of α-well-posedness, αwell-posedness in the generalized sense, L-α-well-posedness, and L-α-well-posedness in the generalized sense for MQVLI , respectively, reduce to those of the well-posedness, well-posedness in the generalized sense, L-well-posedness, and L-well-posedness in the generalized sense for MQVLI in 20 . We...