Celal Bayar University

About: Celal Bayar University is a education organization based out in Magnesia ad Sipylum, Turkey. It is known for research contribution in the topics: Population & Heat transfer. The organization has 2960 authors who have published 6024 publications receiving 100646 citations.

The use of neural networks for CPT-based liquefaction screening

Abstract: This study deals with development of two different artificial neural network (ANN) models: one for predicting cone penetration resistance and the other for predicting liquefaction resistance. For this purpose, cone penetration numerical simulations and cyclic triaxial tests conducted on Ottawa sand–silt mixes at different fines content were used. Results obtained from ANN models were compared with simulation and experimental results and found close to them. In addition, the performance indices such as coefficient of determination, root mean square error, mean absolute error, and variance were used to check the prediction capacity of the ANN models developed. Both ANN models have shown a high prediction performance based on the performance indices. It has been demonstrated that the ANN models developed in this study can be employed for predicting cone penetration and liquefaction resistances of sand–silt mixes quite efficiently.

Measurement of CP violation in B-s(0) ->Phi Phi decays

Abstract: A measurement of the decay time-dependent CP-violating asymmetry in B-s(0) -> phi phi decays is presented, along with measurements of the T-odd triple-product asymmetries. In this decay channel, the CP-violating weak phase arises from the interference between B-s(0) -(B) over bar (0)(s) mixing and the loop-induced decay amplitude. Using a sample of proton-proton collision data corresponding to an integrated luminosity of 3.0 fb(-1) collected with the LHCb detector, a signal yield of approximately 4000 B-s(0) -> phi phi decays is obtained. The CP-violating phase is measured to be phi(s) = -0.17 +/- 0.15(stat) +/- 0.03(syst) rad. The triple-product asymmetries are measured to be A(U) = -0.003 +/- 0.017(stat) +/- 0.006(syst) and A(V) = -0.017 +/- 0.017(stat) +/- 0.006(syst). Results are consistent with the hypothesis of CP conservation.

Genetic algorithm–based sensor deployment with area priority

Abstract: We are introducing a new design goal called area priority to determine optimal sensor node distribution. The environment in which the wireless sensor network WSN will be placed is divided into parts and priorities are attached to these parts. Priorities make the deployment problem adaptable to nonhomogeneous environments with regions that have different importance levels such as forests. Various tree/animal types and densities, residential in the forest can be classified by the area priority concept that we propose. We also develop a genetic algorithm–based method to optimize the total importance in a fully connected WSN. Experimental results obtained for different priorities are presented and discussed.