Study of the energy spectrum of primary cosmic rays: EAS size fluctuations at a fixed primary energy

During the initial period of the Samarkand EAS array operations the showers were selected on the basis of charged-particle flux density, and during the subsequent periods the showers were selected on the basis of Cerenkov light flux density. This procedure made it possible to measure the shower energy, to estimate the EAS size fluctuations at a fixed primary energy, and to experimentally obtain the scaling factor K(Ne, Eo) from the EAS size spectrum to the primary energy spectrum. Six scintillators of area S = 2 sq m each were added to the array. The fluctuations of EAS sizes in the showers of fixed primary energies and the scaling factors K(Ne, Eo) were inferred from the data obtained. The showers with zenith angles 30 deg were selected. The EAS axis positions were inferred from the amplitude data of the scintillators. The primary energy Eo was determined by the method of least squares for the known EAS axis position using the data of the Cerenkov detector located at 80 to 150 m EAS axis. It is shown that the Cerenkov light flux fluctuations at 100 m from EAS axis, q sub 100, do not exceed 10% at a fixed EAS energy, so the parameter q sub 100 may be used to estimate the EAS-generating primary particle-energy

On the determination of the depth of EAS development maximum using the lateral distribution of Cerenkov light at distances 150 m from EAS axis

The Samarkand extensive air showers (EAS) array was used to measure the mean and individual lateral distribution functions (LDF) of EAS Cerenkov light. The analysis of the individual parameters b showed that the mean depth of EAS maximum and the variance of the depth distribution of maxima of EAS with energies of approx. 2x10 to the 15th power eV can properly be described in terms of Kaidalov-Martirosyan quark-gluon string model (QGSM)

Study of the shower maximum depth by the method of detection of the EAS Cerenkov light pulse shape

The results of processing the data on the shape of the EAS Cerenkov light pulses recorded by the extensive air showers (EAS) array are presented. The pulse FWHM is used to find the mean depth of EAS maximum

Use of the industrial property system in Colombia (2018): A supervised learning application

The purpose of this paper is to establish ways to predict the spatial distribution of the use of the intellectual property system from information on industrial property applications and grants (distinctive signs and new creations) and copyright registrations in 2018. This will be done using supervised learning algorithms applied to information on industrial property applications and grants (trademarks and new creations) and copyright registrations in 2018. Within the findings, 4 algorithms were identified with a level of explanation higher than 80%: (i) Linear Regression, with an elastic network regularization; (ii) Stochastic Gradient Descent, with Hinge loss function, Ringe regularization (L2) and a constant learning rate; (iii) Neural Networks, with 1,000 layers, with Adam’s solution algorithm and 2,000 iterations; (iv) Random Forest, with 10 tree