The soft sets were known since 1999, and because of their wide applications and their great flexibility to solve the problems, we used these concepts to define new types of soft limit points, that we called soft turning points.Finally, we used these points to define new types of soft separation axioms and we study their properties.
In this paper,there are new considerations about the dual of a modular spaces and weak convergence. Two common fixed point theorems for a -non-expansive mapping defined on a star-shaped weakly compact subset are proved, Here the conditions of affineness, demi-closedness and Opial's property play an active role in the proving our results.
The idea of ech fuzzy soft bi-closure space ( bicsp) is a new one, and its basic features are defined and studied in [1]. In this paper, separation axioms, namely pairwise, , pairwise semi-(respectively, pairwise pseudo and pairwise Uryshon) - fs bicsp's are introduced and studied in both ech fuzzy soft bi-closure space and their induced fuzzy soft bitopological spaces. It is shown that hereditary property is satisfied for , with respect to ech fuzzy soft bi-closure space but for other mentioned types of separations axioms, hereditary property satisfies for closed subspaces of ech fuzzy soft bi-closure space.
In this article, the partially ordered relation is constructed in geodesic spaces by betweeness property, A monotone sequence is generated in the domain of monotone inward mapping, a monotone inward contraction mapping is a monotone Caristi inward mapping is proved, the general fixed points for such mapping is discussed and A mutlivalued version of these results is also introduced.
Background: Disinfection of denture and soft denture liners became among the priorities for cross contamination control as well as patient's health. All the trials aimed to have maximum infection control with minimal adverse changes in the materials properties.
Materials and methods: Discs of 30x2mm were made from Coe Super Soft and Coe Soft denture liners. Every 5 specimens were immersed separately and daily in CHX, Sodium hypochlorite and chlorine dioxide, control group specimens were immersed in the distilled water. Hardness property of the experimental and control groups was evaluated by using Shore A durometer after 1, 7, 30 days.
Results: Statistical analysis indicated non significant difference
Feed Forward Back Propagation artificial neural network (ANN) model utilizing the MATLAB Neural Network Toolbox is designed for the prediction of surface roughness of Duplex Stainless Steel during orthogonal turning with uncoated carbide insert tool. Turning experiments were performed at various process conditions (feed rate, cutting speed, and cutting depth). Utilizing the Taguchi experimental design method, an optimum ANN architecture with the Levenberg-Marquardt training algorithm was obtained. Parametric research was performed with the optimized ANN architecture to report the impact of every turning parameter on the roughness of the surface. The results suggested that machining at a cutting speed of 355 rpm with a feed rate of 0.07 m
... Show MoreIn the present study, the cluster concept was adopted to find points parallel to the cumulative points of any subset in topology cluster proximity spaces. The takeoff set term was given by the researcher to the set of all points. Also, an opposite definition was found for it, which is the follower set. The relation between them was found and their most important properties were highlighted. Through these two sets, new sets were built that are called, f_σ-set ,f_tσ-set ,t_fσ-set ,bushy set, scant set .
In this paper, the concept of contraction mapping on a -metric space is extended with a consideration on local contraction. As a result, two fixed point theorems were proved for contraction on a closed ball in a complete -metric space.
Cutting forces are important factors for determining machine serviceability and product quality. Factors such as speed feed, depth of cut and tool noise radius affect on surface roughness and cutting forces in turning operation. The artificial neural network model was used to predict cutting forces with related to inputs including cutting speed (m/min), feed rate (mm/rev), depth of cut (mm) and work piece hardness (Map). The outputs of the ANN model are the machined cutting force parameters, the neural network showed that all (outputs) of all components of the processing force cutting force FT (N), feed force FA (N) and radial force FR (N) perfect accordance with the experimental data. Twenty-five samp
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