Demand prediction of public bike sharing services using machine learning algorithms

Machine learning algorithms are used in the transportation field to successfully identify patterns and predict user?s behavior. This study aims to predict the demand of bike sharing services by means of this type of algorithms. The necessary input data and best parameters leading to optimal results will be identified, and a case study based in the bicing system in Barcelona, is going to be presented

STABILITY OF Z-STRINGS IN STRONG MAGNETIC FIELDS

We show that the Z-strings of the standard electroweak theory can be stabilized by strong external magnetic fields, provided that $\beta^{1/2} \equiv M_H/M_Z\leq 1$, where $M_H$ and $M_Z$ are the Higgs and Z masses. The magnetic fields needed are larger than $\beta^{1/2} B_c$ and smaller than $B_c$, where $B_c\equiv M^2_W/e$ is the critical magnetic field which causes W-condensation in the usual broken phase vacuum. If such magnetic fields were present after the electroweak transition, they would stabilize strings for a period comparable to the inverse Hubble rate at that time. Pair creation of monopoles and antimonopoles linked by segments of string is briefly considered.Comment: 12 pages, 3 uuencoded figs ,LaTeX (RevTeX). A postcript version can be obtained via anonymous ftp at ftp://ftp.ifae.es/preprint.f