In this paper, we investigate two stress-strength models (Bounded and Series) in systems reliability based on Generalized Inverse Rayleigh distribution. To obtain some estimates of shrinkage estimators, Bayesian methods under informative and non-informative assumptions are used. For comparison of the presented methods, Monte Carlo simulations based on the Mean squared Error criteria are applied.
Wireless Sensor Networks (WSNs) are promoting the spread of the Internet for devices in all areas of
life, which makes it is a promising technology in the future. In the coming days, as attack technologies become
more improved, security will have an important role in WSN. Currently, quantum computers pose a significant
risk to current encryption technologies that work in tandem with intrusion detection systems because it is
difficult to implement quantum properties on sensors due to the resource limitations. In this paper, quantum
computing is used to develop a future-proof, robust, lightweight and resource-conscious approach to sensor
networks. Great emphasis is placed on the concepts of using the BB8
Background: The aim of this study was to evaluate the push-out bond strength of four different obturation materials to intraradicular dentin and to determine the failure mode. Materials and method: forty straight palatal roots of the maxillary first molars teeth were used in this study, the roots were instrumented using crown down technique and rotary EndoSequence system, the roots were randomly divided into four groups according to the materials used for obturation (n=10).Group (1): AH Plus sealer and gutta-percha. Group (2): Activ GP glass ionomer sealer and Activ GP gutta-percha (Activ GP system). Group (3): Bioceramic sealer and Bioceramic gutta-percha. Group (4): GuttaFlow2 sealer and gutta-percha. For all groups single cone obturatio
... Show MoreWater absorbent polymers (WAP) are new component in producing building materials. They provide internal curing which reduces autogenous cracking, eliminates autogenous shrinkage, mortar strength increased, enhance early age strength to withstand strain, improve the durability, introduce higher early age compressive strength, have higher performance and reduce the effect of insufficient external curing. This research used different percent of polymer balls to choose the percent that provides good development in compressive strength with time for both water and air curing. The water absorption polymer balls in this research have the ability to absorb water and after usage in concrete they spill out the water (internal curing) and shri
... Show MoreTest results of six half-scale reinforced concrete flat plates connections with an opening in the vicinity of the column are reported. The test specimens represent a portion of a slab bounded by the lines of contraflexure around the column. The tests were designed to study the effect of openings on the punching shear behavior of the slab-column connections. The test parameters were the location and the size of the openings. One specimen had no opening and the remaining five had various arrangements of openings around the column. All specimens were cast with normal density concrete of approximately 30 MPa compressive strength. The openings in the specimens were square, with the sides parallel to the sides of the column. Three sizes of ope
... Show MoreThis study aims to evaluate the influence of the air abrasion of dentin on the shear bond strength of lithium disilicate using three different types of luting cements. Sixty cylindrical specimens were milled from lithium disilicate CAD/CAM blocks (IPSe.max CAD). Sixty sound human maxillary premolar teeth were decoronated to the level of peripheral dentin, then randomly divided into three groups according to the type of luting cement used for the cementation of the lithium disilicate specimens (n = 20); Group A: Glass ionomer cement (Riva Self- Cure); Group B: Adhesive resin cement (Rely X Ultimate); Group C: Self-adhesive resin cement (Rely X U200). Each group was then further subdivided into two subgroups (n=10); Subgroups AI, BI, and CI,
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