A critical issue when selecting an ordered weighted aggregation (OWA) operator is the determination of the associated weights. For this reason, numerous weight generating methods have appeared in the literature. In this paper, a generalization of the binomial OWA operator on the basis of the Stancu polynomial is proposed and analyzed. We propose a weight function in the parametric form using the Stancu polynomial by which the weights of OWA operators can be generated easily. The proposed Stancu OWA operator provides infinitely many sets of weight vectors for a given level of the orness value. An important property of this kind of OWA operator is its orness, which remains constant, irrespective of the number of objectives aggregated and always equal to one of its parameters. This approach provides a significant advantage for generating the OWA operators' weights over existing methods. One can choose a set of weight vectors based on his/her own preference. This class of OWA operators can utilize a prejudiced preference to determine the corresponding weight vector. The maximum entropy (Shannon) OWA operator's weights for a given level of orness is calculated by the purposed weight function and compared with the existing maximum entropy OWA operator.Index Terms-Maximum entropy, ordered weighted aggregation (OWA) operators, orness, Stancu polynomial. 1063-6706

In this paper we have studied separation axioms T i , i = 0, 1, 2 in an intuitionistic fuzzy topological space introduced by Coker. We also show the existence of functors B : IF-Top → BF-Top and D : BF-Top → IF-Top and observe that D is left adjoint to B. Proposition 22. Let (X, τ 1 , τ 2 ) be a BFTS andProof. Clearly members of τ τ1,τ2 are intuitionistic fuzzy sets, and 0 ∼ and 1 ∼ belong to it. Now let ( . Thus (X, τ τ1,τ2 ) is an IFTS. Now let U ∈ τ 1 then (U, φ) ∈ τ τ1,τ2 . Therefore τ 1 ⊆ (τ τ1,τ2 ) 1 . Conversely let U ∈ (τ τ1,τ2 ) 1 then ∃V ∈ I X such that (U, V ) ∈ τ τ1,τ2 ⇒ U ∈ τ 1 , so (τ τ1,τ2 ) 1 ⊆ τ 1 . Thus (τ τ1,τ2 ) 1 = τ 1 . Similarly we can show that (τ τ1,τ2 ) 2 = τ 2 .The IFTS (X, τ τ1,τ2

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Reckoning the height of a release in the source term estimation is important since a ground‐level approximation of the release leads to errors in capturing the actual extent of a plume. A least‐squares inversion technique, free from initial guess, is adapted here for the reconstruction of an elevated point release in a discretized space. Primarily, this involves estimation of the effective height of the release above the ground along with its location and strength from a limited set of noisy concentration measurements. The methodology is evaluated here with the nine runs from the Idaho diffusion experiment (1974) corresponding to low wind, stable conditions. Both real and model‐generated synthetic data are used to test the method. With synthetic data, the methodology can exactly reproduce the input source terms. With real data, the average release height is estimated as 3.1 m, which is very close to the effective release height (3 m) reported in the Idaho data. The release location is retrieved with an average error of 30 m, whereas the minimum distance between source and detectors is 100 m; strength is retrieved within a factor of two in all the runs. The deviations in the source parameters from their prescribed values are explained in the context of model representativeness, wind variability and available monitoring network. The sensitivity of the source term estimation is evaluated against several parameters: (i) receptors' height either neglected or duly taken as 0.76 m, (ii) measurement noises and (iii) number of receptors utilized in the inversion. In addition, the limitations and meteorological issues related to the inversion of an elevated release are highlighted.