Wireless sensor networks (WSNs) represent one of the key technologies in internet of things (IoTs) networks. Since WSNs have finite energy sources, there is ongoing research work to develop new strategies for minimizing power consumption or enhancing traditional techniques. In this paper, a novel Gaussian mixture models (GMMs) algorithm is proposed for mobile wireless sensor networks (MWSNs) for energy saving. Performance evaluation of the clustering process with the GMM algorithm shows a remarkable energy saving in the network of up to 92%. In addition, a comparison with another clustering strategy that uses the K-means algorithm has been made, and the developed method has outperformed K-means with superior performance, saving ener

In this study, concentrations of radon and uranium were measured for twenty six samples of soil. The radon concentrations in soil samples measured by registrant alpha-emitting radon (²²²Rn) by using CR-39 track detector. The uranium concentrations in soil samples measured by using registrar fission fragments tracks in CR-39 track detector that caused by the bombardment of U with thermal neutrons from ²⁴¹ Am-Be neutron source that has flux of 5 ×10³n cm^-2 s^-1.
The concentrations values were calculated by a comparison with standard samples The results show that the radon concentrations are between (91.931-30.645Bq/m³).
The results show that also the uranium concentrat

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      In the current study, the observations depended on some nuclear properties of Germanium isotopes that are used for multiple purposes by studying transverse sections when interacting with charged particles such as alpha and proton particles and their interaction with gamma rays of conjugal isotopes relative to the stability of the nucleus with other nuclei. By calculating the cross sections of the  (α, )   ,  (γ,x)0-NN-1, (γ,2n)  , (α,p)  reactions of  isotope. Nuclear reactions in the newer global libraries (EXFOR, ENDF, JEF, JEFF, GENDL) have been published to identify appropriate energies in calculating the inverse nuclear re

The nuclear structure of some cobalt (Co) isotopes with mass number A=56-60 has been studied depending on the effect of some physical properties such as the electromagnetic properties effects, such as, elastic longitudinal form factors, electric quadrupole moments, and magnetic dipole moments. The fp model space is used to present calculations using GXFP1 interaction by adopting the single particle wave functions of the harmonic oscillator. For all isotopes under consideration, the ⁴⁰Ca nucleus is regarded as an inert core in fp model-space, while valence nucleons are moving through 1f_7/2, 2p_3/2, 1f_5/2, and 2p_1/2 orbits. The effects of core-polarization are obtained by the first orde

The nuclear structure of ³⁸Ar, ⁵⁹Co, ¹²⁴Sn, ¹⁴⁶Nd, ¹⁵³Eu and ²⁰³Tl target nuclei used in technology for nuclear batteries have been investigation, in order that, these nuclei are very interesting for radioisotope thermo-electric generator (RTG) space studies and for betavoltaic battery microelectronic systems. The single particle radial density distribution, the corresponding root mean square radii (rms), neutron skin thicknesses and binding energies have been investigated within the framework of Hartree-Fock Approximation with Skyrme interaction. The bremsstrahlung spectrums produced by absorption of beta particles in betavoltaic process and backscattered p

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

       Using orbit- motion limited theory, as the exact theory in calculating the ion and electron current in dusty plasma, the variations of charge number on a dust grain in Ar-plasma are studied by changing various charging parameters. Most of dependences of charge number on plasma parameters in this paper take into account the close packed effect.