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Decision-making under uncertainty refers to a dilemma when a decision-maker is aware of a variety of potential natural states but lacks adequate information to assign any probabilities of occurrence to them. The uncertainty related to the input parameters is one of the main issues in the majority of decision-making situations. Uncertainty may produce some irrational results, which could make the decision-making process even more challenging. To overcome this challenge, a fuzzy extension of Best-Worst Method (BWM) has been proposed, using trapezoidal fuzzy sets, to combine the advantages of a reduced number of pair-wise comparisons and easy handling of ambiguity. The criteria and alternatives have been evaluated by the proposed Trapezoidal Fuzzy Best-Worst Method (TrFBWM), where the weight of each element is represented by a Trapezoidal Fuzzy Number (TrFN). To verify the coherence of judgment, the consistency ratio is evaluated for TrFBWM. The proposed method is then applied to the location selection of a water treatment plant along the bank of the Brahmaputra river in Assam. The obtained results are compared to one previous work and found that the outcomes of the proposed method indicate a good agreement with that. The outcomes of the study provide useful insights for selecting a suitable location for a surface water treatment plant which can also be extended to other service facilities. [ABSTRACT FROM AUTHOR]

The aim of this treatise is to introduce the concept of strongly almost convergence in complex uncertain sequences. Also, we investigate the strongly almost convergence in almost surely (in shortly a.s.), convergence in measure, convergence in mean, convergence in distribution and convergence in uniformly almost surely of complex uncertain sequences and study the relationships among them. [ABSTRACT FROM AUTHOR]

The main aim of this article is to introduce the notion of statistical convergence of a complex uncertain triple sequence. Four types of statistically convergent complex uncertain triple sequences are presented, namely statistical convergence in measure, in mean, in distribution and with respect to almost surely. The existence of such statistically convergent triple sequences are shown along with the interrelationship between all the concepts. [ABSTRACT FROM AUTHOR]

The concept of statistical convergence plays a very prominent role in the study of sequence spaces. In this treatise, we extend the research on different types of statistical convergence viz., statistical convergence in mean, in measure, in distribution, in almost surely and with respect to uniformly almost surely of complex uncertain triple sequences in a given uncertainty space. We emphasize to focus on characterizing statistical convergence of complex uncertain triple sequence in some extent. Moreover, we initiate the notion of complex uncertain Cauchy triple sequence and establish the interconnection of it with a statistically convergent triple sequence. [ABSTRACT FROM AUTHOR]

We introduce the notions of soft relative bitopology. We investigated some properties on the separation axioms in soft bitopological spaces, i.e. soft Ti -spaces for i=1, 2, 3, and 4. Also we have established results on soft regular spaces and soft normal spaces. [ABSTRACT FROM AUTHOR]